modelling of multi-energy systems in buildings - insa de...

Order number 2008-ISAL-0042 Year 2008

PhD Dissertation

Modelling of multi-energy systems in

buildings

submitted to

Politecnico di Torino

and

Institut National des Sciences Appliquées de Lyon

for the

degree of doctor

Doctorate Schools : SCUDO and MEGA

Doctorate Course : Energetica and Génie Civil

by

Enrico FABRIZIO

discussed on July 2nd

2008

Board of Examiners

FILIPPI Marco Professor Supervisor

VIRGONE Joseph Associate professor Supervisor BECCALI Marco Associate professor Examiner ROUX Jean-Jacques Professor Examiner

SCORLETTI Gérard Professor Referee

ZECCHIN Roberto Professor Referee

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Abstract

Multi-energy systems are hybrid energy systems that can supply the thermal, cooling and

electric loads of a building by means of different energy converters fed by a mix of

energy sources, both primary and secondary, renewable and non renewable. These

systems are receiving increasing attention because of the opportunity to exploit renewable

energy sources, the potential of increase in the energy efficiency and in the heating and

cooling water production that lies in these systems, if correctly designed and managed. A

good operation strategy is of the foremost importance for these systems since they run at

full load for a very limited period of time.

Many examples of multi-energy systems can be found in the literature combining solar

collectors for heating and cooling, PV panels, biomass plants, fuel cells, wind turbines,

etc.

The design of a multi-energy system, both in terms of definition of the power and

operating strategies, consists in defining the energy demand profiles and in the

optimization between the energy demand, the energy supply, the energy converters, the

storages and the backup components. In the literature, this problem is addressed with

reference to specific system configurations, but not yet with reference to an integrated

tool allowing for comparison between different choices. This work mainly intends to

provide a synthetic view to cover this topic. When the number of energy converters

increases, the traditional analysis methods become inadequate.

The aim of the research carried out was to develop a specific knowledge on the definition

of criteria for the selection of energy converters to be installed, energy sources to be

adopted, operation strategies and technical solutions pursuing a greater efficiency in the

use of renewable and non renewable energy and a reduction of CO2 emissions due to the

operation of buildings, whose sector represents the 40% of the primary energy

requirements in Europe.

On this subject, this thesis provides an original both theoretical and applied contribution

based on the methodology of the energy hub that allows the coupling between the energy

demand and the energy supply in a building to be modelled in a synthetic way. This

methodology is able to take into account the quality of the building energy demand, the

variability of the conversion efficiencies as a function of boundary conditions and of the

part load, and the variability of operating conditions.

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For some categories of energy converters, a set of performance and economical data was

created. This set has to be used when examining the performance of a specific multi-

energy system in relation to the building energy demand profile. From this set it is

possible to determine the numerical coefficients of the equations that express the

performance of a energy converters category. Theoretical and applied investigations on

the decision criteria characterization and on the selection procedures were made.

Compared to the currently available procedures, the proposed research aims at providing

a multi-energy systems modelling tool able to take into account all energy fluxes of a

building and referred to an open configuration, not related to a particular plant

technology.

The potential applications of the outcomes of this research are of considerable interest,

since the number of multi-energy systems, in the near future, will grow as a consequence

of a constantly increasing spread of renewable energy technologies, and thanks to the

legislative requirements. Theoretical and applied knowledge gained through this research

will also support administrative governments, installation companies, private end users

and energy converters producers to promote a more conscious use of (renewable and non

renewable) energy in the built environment.

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Sommario

Con il termine sistemi multienergia si intendono quei sistemi energetici ibridi in grado di

coprire i carichi termici, frigoriferi ed elettrici di un edificio attraverso l'attivazione di

convertitori di energia diversi, alimentati da fonti energetiche, primarie e secondarie,

rinnovabili e non. Tali sistemi sono caratterizzati da un ampio potenziale di

miglioramento dell'efficienza nella trasformazione delle energie e nella produzione dei

fluidi energetici se correttamente progettati e gestiti anche in funzionamento ai carichi

parziali (condizione in cui si trovano a dover operare per la maggior parte del tempo per

la caratteristica della domanda di energia degli edifici).

Molteplici esempi di sistemi multienergia sono ricavabili dalla letteratura, e

comprendono, variamente associati, i convertitori per lo sfruttamento dell'energia solare a

fini termici, frigoriferi ed elettrici, impianti che sfruttano le biomasse, i microcogeneratori

convenzionali, le pompe di calore geotermiche, le celle a combustibile, le turbine eoliche,

ecc.

La progettazione di un sistema multienergia, in termini di dimensionamento e di logica di

esercizio, consiste nella definizione delle dinamiche della domanda di energia e nella

ottimizzazione dell'offerta di energia attraverso l'impiego dei diversi convertitori, degli

accumuli e dei componenti di back-up. In letteratura tale problema progettuale risulta

trattato con riferimento a specifiche configurazioni impiantistiche, di cui si forniscono

alcuni esempi, ma non attraverso strumenti integrati che consentano il confronto tra

molteplici configurazioni. Tale lavoro si configura perciò primariamente come un lavoro

di sintesi atto a colmare tale lacuna. Per di più, quando il numero di convertitori energetici

aumenta, gli strumenti tradizionali di analisi divengono inadeguati.

La tesi si propone l'avanzamento della conoscenza in merito alla definizione dei criteri per

la selezione dei convertitori energetici da installare, delle fonti energetiche da utilizzare,

delle logiche di funzionamento e delle soluzioni tecnologiche da adottare al fine di

perseguire gli obiettivi di una maggiore efficienza nell'uso delle energie rinnovabili e non

rinnovabili e di una riduzione delle emissioni di CO2 nel settore civile, il cui consumo

rappresenta ben il 40% dei consumi di energia primaria in Italia e in Europa.

Alla base della ricerca vi è la definizione di una metodologia originale per la

modellazione delle configurazioni di sistemi multienergia basata sullo strumento

dell‟energy hub, che consente di esprimere in maniera sintetica l‟accoppiamento tra la

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domanda e l‟offerta di energia di un edificio. Tale metodologia permette di prendere in

considerazione la qualità delle energie richieste dall'edificio, la variabilità delle efficienze

di conversione in funzione delle condizioni al contorno e di funzionamento a carico

parziale e la variabilità delle condizioni di funzionamento.

Per alcune categorie di convertitori energetici si è creato un set di dati prestazionali ed

economici di riferimento da utilizzare nel momento in cui si verifichino le prestazioni di

un intero sistema multienergia in relazione al profilo di domanda energetica che lo

interessa. Dal set di dati di riferimento è stato possibile determinare i coefficienti numerici

delle funzioni matematiche che descrivono il comportamento di una famiglia di

convertitori energetici. Approfondimenti teorici ed applicativi sono destinati

all‟individuazione dei criteri e alla determinazione delle procedure per la selezione dei

sistemi multienergia a servizio degli edifici, con particolare riguardo alle specificità di tali

sistemi.

Rispetto alle procedure correntemente disponibili, la presente ricerca si propone di

configurare uno strumento di modellazione dei sistemi multienergia a servizio degli

edifici che prenda in considerazione tutti i flussi energetici in gioco e che si riferisca ad

una configurazione aperta, non riferibile ad una singola tipologia impiantistica in

particolare.

Le potenzialità applicative dei risultati di tale ricerca appaiono notevoli in quanto il

numero dei sistemi multienergia a servizio degli edifici è necessariamente destinato a

crescere nel prossimo futuro, in considerazione della costante diffusione di tecnologie

impiantistiche per lo sfruttamento di energie rinnovabili, in Italia anche in forza di

obblighi legislativi. Le conoscenze teoriche ed applicative maturate saranno anche di

supporto ad amministrazioni pubbliche, installatori, utenti finali e produttori di sistemi di

conversione dell'energia, in quanto concorrono alla maturazione di una maggiore

consapevolezza energetica ed ambientale nell'accoppiamento di diverse fonti energetiche,

convertitori e sistemi impiantistici a servizio dell'edificio.

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Résumé

Avec le terme systèmes multi énergies on entend les systèmes énergétiques hybrides qui

sont à même de faire face aux charges thermiques, frigorifiques et électriques d‟un

bâtiment par la mise en service de convertisseurs d‟énergie divers, activés par des sources

d‟énergie primaires et secondaires, renouvelables ou non. Ces systèmes sont caractérisés

par un grand potentiel d‟amélioration de l‟efficacité énergétique dans la transformation

des énergies et dans la production des fluides énergétiques lorsqu‟ils sont correctement

conçus et gérés même lorsqu‟ils fonctionnent à charge partielle (une condition dans

laquelle ils se trouvent opérer la plupart du temps à cause de la variation de la demande

d‟énergie des bâtiments).

Plusieurs exemples de système multi énergies peuvent être tirés de la littérature, et

comprennent, diversement associés, les convertisseurs pour l‟exploitation de l‟énergie

solaire à des fins thermiques, frigorifiques et électriques, les systèmes à biomasses, les

micro-cogénerateurs, les pompes à chaleur géothermiques, les piles à combustible, les

éoliennes, etc.

Le projet d‟un système multi énergies, en terme de dimensionnement et de gestion,

consiste à définir les dynamiques de la demande d‟énergie et à optimiser l‟offre d‟énergie

par l‟emploi de convertisseurs divers, des stockages, des systèmes de back-up. Dans la

littérature ce problème est traité en se référant à des configurations spécifiques, dont on

fournit des exemples, mais non à travers des outils intégrés qui permettent la comparaison

entre plusieurs configurations. Ce travail est donc principalement un travail de synthèse

qui comble cette lacune.

La thèse propose l‟avancement des connaissances relatives aux critères de sélection des

convertisseurs d‟énergie à utiliser, des sources d‟énergie à exploiter, des logiques de

fonctionnement et des systèmes techniques à utiliser afin de poursuivre les objectifs

d‟une meilleure efficacité dans l‟usage des énergies renouvelables ou non, et de réduire

les émissions de CO2 du secteur du bâtiment, dont la consommation représente 40% de la

consommation en énergie primaire en Europe.

A la base de la recherche c‟est la définition d‟une méthodologie originale pour la

modélisation des configurations des systèmes multi énergies basée sur la méthode

d‟analyse du energy hub qui permet de prendre en compte, d‟une manière synthétique, le

couplage entre demande et offre d‟énergie dans un bâtiment. Cette méthode permet aussi

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de prendre en compte la qualité des énergies, la variabilité des rendements de conversion

en fonction des conditions de fonctionnement des systèmes et la variabilité des conditions

de fonctionnement.

Pour quelques catégories de convertisseurs d‟énergie, un ensemble de données de

référence sur la performance et les coûts a été ressemblé qu‟il convient d‟utiliser

lorsqu‟on vérifie la performance d‟un système multi énergie complet par rapport au profil

de demande d‟énergie qui le concerne. A partir de cette base de données, nous avons

déterminé les coefficients numériques des fonctions qui décrivent la performance de

chaque famille de convertisseurs d‟énergie.

Les approfondissements qui en découlent, soit théoriques, soit applicatifs, concernent la

définition des critères d‟évaluation et les procédures de sélection de ces systèmes, en

prenant particulièrement en compte toutes les spécificités de ces systèmes.

Par rapport aux procédures couramment disponibles, cette recherche a visé à configurer

un outil de modélisation des systèmes multi énergies pour les bâtiments qui prenne en

compte tous les flux d‟énergies dans le bâtiment et qui puisse se référer à une

configuration ouverte et non pas à une unique typologie de système en particulier.

Les potentiels des applications de cette recherche apparaissent nombreux, étant donné le

nombre de systèmes multi énergies dans les bâtiments et qui va certainement augmenter

dans un futur proche, en considération de la constante diffusion de systèmes exploitant les

énergies renouvelables. Les connaissances théoriques et applicatives que cette thèse

apporte, pourront aussi servir de support aux administrations, usagers, installateurs et

producteurs de systèmes, du moment où elles concourent au mûrissement d‟une plus

grande conscience énergétique et environnementale dans le couplage de différentes

sources d‟énergies, convertisseurs et systèmes pour le bâtiment.

9

Table of contents

1 INTRODUCTION 14 1.1 Examples 15 1.2 Discussion 20 1.3 Position of the problem and objectives of the thesis 20 1.4 Outline of the thesis 21

2 MODELLING TECHNIQUES AND SOFTWARE TOOLS FOR MULTI-ENERGY SYSTEMS ANALYSIS 24 2.1 Introduction 24 2.2 Multi-energy systems modelling techniques 24 2.2.1 Time series models 24 2.2.2 Statistical models 25 2.2.2.1 LPSP (Loss of Power Supply Probability) 25 2.3 Multi-energy systems optimization techniques 26 2.4 Software tools to simulate multi-energy systems 27 2.4.1 EnergyPlus 27 2.4.2 HOMER 29 2.4.3 RETScreen International 30 2.4.4 Other tools 30 2.4.5 Discussion 32 2.5 Conclusions 34

3 THE ASSESSMENT OF THE BUILDING ENERGY DEMAND 35 3.1 Introduction 35 3.2 Parameters 35 3.2.1 Load profiling 39 3.3 Factors of influence 39 3.3.1 System boundary 39 3.3.2 Fluids and temperatures 41 3.3.3 Indoor environmental quality 41 3.4 Methods 43 3.4.1 Measurements 43 3.4.2 Simulation 43 3.4.2.1 Simplified procedures of the European Standards 43 3.4.2.2 The monthly steady-state method (TNO) 44 3.4.2.3 The simplified hourly method (CSTB) 46 3.4.2.4 Dynamic simulation 47 3.4.2.5 Discussion 47 3.4.3 Literature 49 3.5 Conclusions 49

4 THE ASSESSMENT OF THE ENERGY SUPPLY 50 4.1 Introduction 50 4.2 Generalities 51 4.1 Natural gas 51 4.2 Electricity 52 4.3 Hydrogen 53 4.4 Hydropower 53 4.5 Solar energy 54 4.6 Geothermal energy 55 4.7 Biomass and biofuels 56 4.8 Wind power 57

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4.9 Conclusions 58

5 THE ASSESSMENT OF THE ENERGY CONVERTERS 59 5.1 Introduction 59 5.2 The energy performance characterization 59 5.2.1 Boilers and condensing boilers 60 5.2.1.1 Full load efficiency 60 5.2.1.2 Part load efficiency 61 5.2.2 Chillers 62 5.2.2.1 The modelling approach 63 5.2.2.2 Part load curves 65 5.2.3 Absorption chillers 66 5.2.3.1 Single-stage absorption chillers 66 5.2.3.2 Double-stage absorption chillers 66 5.2.4 Cogeneration equipment 67 5.2.5 Fuel cells 68 5.2.6 Wind turbines 68 5.3 The economic characterization 69 5.3.1 On the selection of the specific capital cost function 69 5.3.2 Specific capital cost functions of multi-energy systems components 72 5.3.2.1 Boilers and heat exchangers 72 5.3.2.2 Condensing boilers 73 5.3.2.3 Wood and biomass boilers 74 5.3.2.4 Chillers and cooling towers 75 5.3.2.5 Absorption chillers 76 5.3.2.6 Cogeneration equipment 78 5.3.2.7 Fuel cells 78 5.3.2.8 Wind turbines 79

6 ENERGY HUB MODELLING 80 6.1 The energy hub concept 80 6.2 The coupling algorithm 82 6.2.1 The determination of the coupling matrix entries 83 6.2.1.1 The connection between fluxes 83 6.2.1.2 The energy converters 85 6.2.1.3 The energy storage 88 6.3 The applications of the coupling algorithm to the multi-energy system analysis 91 6.3.1 Design of the multi-energy system 92 6.3.1.1 The position of the problem 93 6.3.1.2 The resolution process 94 6.3.1.3 The characteristics of the solver 94 6.3.1.4 Forms of hubs 95 6.3.2 Operational optimization of the multi-energy system 95 6.3.3 Simulation of the multi-energy system 96 6.3.4 On the selection procedures 97 6.4 Selection criteria and parameters of the objective functions 97 6.4.1 Selection criteria 97 6.4.2 Objective functions 98 6.4.2.1 Economy objective functions 98 6.4.2.2 Energy objective functions 103 6.4.2.3 Environment objective functions 107

7 ENERGY HUB APPLICATIONS AND CASE STUDIES 112 7.1 The applications of the coupling algorithm 112 7.2 The seasonal steady-state method 112

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7.2.1 Background and scope of the method 112 7.2.2 Model specifications 113 7.2.3 Input data 115 7.2.4 Output data and results 115 7.2.5 Maison Mozart 116 7.2.5.1 Case study description 116 7.2.5.2 The energy hub description 116 7.2.5.3 The objective functions 120 7.2.5.4 Renewable sources constraints 120 7.2.5.5 Class of optimization problem 121 7.2.5.6 System design 121 7.2.6 Block of flats 123 7.2.6.1 Case study description 123 7.2.6.2 The energy hub description 123 7.2.6.3 The objective functions 126 7.2.6.4 Constraints on renewable sources and on the heat pump 127 7.2.6.5 System design 127 7.2.7 Discussion 129 7.3 The hourly method 130 7.3.1 Scope of the method and model specifications 130 7.3.2 Input data 131 7.3.3 Output data and results 132 7.3.4 Hotel 132 7.3.4.1 Case study description 132 7.3.4.2 The energy hub description 133 7.3.4.3 The energy converters characteristics 135 7.3.4.4 The objective functions and performance indicators 136 7.3.4.5 Class of optimization problem 137 7.3.4.6 System design 138 7.3.5 Discussion 142

8 CONCLUSIONS AND FUTURE WORK 144

9 REFERENCES 148

10 APPENDIX 157 10.1 Estimation of performance curves for chillers 157 10.1.1 Water-cooled reciprocating chillers 157 10.1.2 Water-cooled scroll chillers 158 10.1.3 Water-cooled screw chillers 160 10.1.4 Water-cooled centrifugal chillers 162

11 PUBLICATIONS LIST 168

NOMENCLATURE

Nomenclature

cK specific cost of the hub component K €/kW

c specific cost of the energy-ware €/kWh

CK cost of the hub component K €

COPK coefficient of performance of the converter K

COPhub coefficient of performance of the energy hub

CUE cost for one unit of energy €/kWh

d yearly discount rate

dnm backward coupling matrix entry

D backward coupling matrix of the hub (n × m)

DHW domestic hot water

Ein vector of hub energy input (n × 1)

Eout vector of hub energy output (m × 1)

E energy-wares/energy sources set

e emission factor kg/kWh

Ex exergy J

f function

g global warming potential factor kgCO2/kWh

H hub

H heating value of fuel kWh/kg

Isol solar radiation W

K hub converters set

L building loads set

m number of building loads

n number of energy-wares/energy sources

N maximum life time of a component/an hub year (y)

NPC net present cost €

NPV net present value €

P

in power of the energy-ware/energy source at the input port of the hub kW

Pin vector of hub energy flow input (n × 1)

PK power of the hub converter K kW

PK,in input power of the hub converter K kW

PK,out output power of the hub converter K kW

Paout power of the building load a at the output port of the hub kW

Pout vector of hub energy flow output (m × 1)

P

sto energy flow entering or leaving a storage kW

p non-renewable primary energy emission factor

pT total primary energy emission factor

PLF part load factor

PLR part load ratio

r yearly rate of increase

REF renewable energy fraction

T period of time (generally one year) year (y)

NOMENCLATURE

13

T absolute temperature K

T0 absolute temperature of the exterior environment K

tco entering condenser fluid temperature °C

tev leaving chilled water temperature °C

TNPC total net present cost €

yK life time of a component year (y)

scaling exponent a

a ratio between the load a covered by the energy-ware and the load a

a

K1 ratio between the load a covered by the converter K1 and the load a

efficiency

K (conversion) efficiency of the converter K b

K

(conversion) efficiency from energy carriers to a of the converter K

time s

storage factor of energy

Subscripts

cool cooling season

d design

ec economy based

en energy based

ev environment based

heat heating season

K hub component/converter

s seasonal/annual

Superscripts

ex exergetic

K hub component/converter

energy carrier

1 INTRODUCTION

Latterly there has been a great development in small scale new and renewable energy

systems such as photovoltaic, microturbines, ducted wind turbines, micro-hydros, fuel

cells, geothermal heat pumps. These systems have given rise to the new micro-power

approach whereby renewable energy technologies are embedded within the built

environment. This has led to a progressive integration of various energy sources, not only

renewable ones but also non-renewable ones.

First examples of multi-energy systems were the isolated hybrid energy systems designed

to be an alternative to power line construction in remote locations or power line upgrade

in cases when a new load is added. Since the 1990‟s multi-energy systems are also used

as distributed generation applications, adding power generation in the distribution system

of one utility.

Generally, the term “multi-energy system” refers to the combination of two or more

energy conversion devices and/or two or more fuels for the same device, that when

integrated, overcomes limitations that may be inherent in either [1].

These systems, frequently called combined or hybrid energy systems, are referred to in

this document as multi-energy systems to emphasize the fact that in these applications

multiple energy converters are used together to supply one or more energy requirement

and to distinguish these systems from conventional systems that typically are based on a

single fossil fuel source.

Frequently, but not necessarily, at least one of the energy converters is powered by a

renewable energy source (RES). The multi-energy system may be isolated or grid-

connected; in this second case energy carriers between the system and the net may flow in

both directions. A considerable interest was recently put also on those multi-energy

systems that are grid-connected but that can exchange power flows only on the direction

from the grid to the system (as in many applications in buildings).

In any case, the multi-energy system approach, especially when RES are integrated,

requires the matching of local energy supply potentials to the energy demand [2]. An

integrated simulation approach must be set out in order to identify the optimum mix of

sources, systems and demand reduction measure, that is one of the objectives of this

work.

INTRODUCTION

15

1.1 Examples

A plant designed in order to allow the operator‟s choice between multiple energy sources

is referred to a multi-energy system (or hybrid energy system). These systems are

receiving increasing attention as valuable mean to exploit renewable energy sources and

options to facility owners.

There are various types of multi-energy systems. They encompass various combinations

of thermal and electric equipment such as cogenerators, electric chillers, gas or steam

absorption chillers, fuel cells, traditional boilers, wood boilers, thermal solar collectors,

photovoltaic collectors, thermal and photovoltaic collectors, etc…). A multi-energy

system is fed by a combination of various energy sources, both renewable and non

renewable, to cover the thermal and electric loads of a building with the maximum

efficiency.

Several examples of multi-energy source building systems can be found in the literature;

in the following figures, schematics of multi-energy systems are presented. They exploit

solar energy or wind power, geothermal heat pumps, fuel cells, in combination with

plants powered by conventional fossil fuels, that are still integrative systems and back up

sources.

Figure 1.1 outlines a system that integrates the exploitation of wind energy, solar thermal

and photovoltaic with a cogeneration plant for the production of heat and electricity [3].

This system is a complex of 40 residential housing units (annual thermal requirements

325 MWh; annual electricity requirements 157 MWh) and it is made of a 55 kW CHP

unit, a thermal solar collectors field of 200 m2, a 210 kWp photovoltaic array and a wind

turbine with a maximum power of 200 kW. A long-term thermal storage is also provided

and is used to store the energy output of solar collectors and of the CHP. Two backup

boilers ensure that the system will meet the load for space heating and hot water.

Figure 1.1 - CHP plant integrated to solar thermal, solar photovoltaic and wind energy

with thermal storage (from [3])

INTRODUCTION

16

As regards the operation, the electrical load is covered, as a priority, by renewable sources

(wind and photovoltaic) and, only if they are unable to meet he energy demand, by the

CHP powered by natural gas. The CHP operates as a function of the electrical load. To

ensure the heat supply, a backup boiler and a seasonal heat storage are installed. The solar

collectors operate according to a usual hot water demand pattern, with a storage tank of

1500 litres. In the absence of an hot water demand, the thermal output of the solar

collectors charges the seasonal storage. Similarly, the thermal output of the CHP, in the

absence of an instantaneous thermal load, charges the seasonal storage.

Solar heating and cooling plants are also multi-energy systems [4]. In winter season solar

energy can be used for heating, as shown in the schematic of a system installed at the

University of Freiburg reported in figure 1.2. This system, whose two main components

are a 70 kW absorption chiller and an evacuated tube solar collector field of 170 m2 [5],

albeit simple, can highlight the typical problems of a multi-energy system. The mismatch

between the energy demand and the energy supply in summer season requires the

installation of a thermal storage for both hot and chilled water. It is to be noted that the

temperature of the thermal input of the absorption chiller not only affects the chiller

coefficient of performance (COP), but also the performance of the solar collectors. The

optimal fluid temperature should be selected balancing the need to keep the absorber

coefficient of performance (COP) sufficiently high and the collector efficiency not too

low.

The use of fuel cells and hydrogen goes hand in hand with the proliferation of multi-

energy systems. Fuel cells can be used as CHP plants powered by natural gas or hydrogen

produced by electricity from renewable source.

A hybrid wind/photovoltaic fuel cells system for a residential building is reported in

figure 1.3 [6]. Instead of a battery bank storage, the system uses a fuel cell associated with

an electrolyser that accumulates the excess energy produced from renewable sources in

the form of hydrogen. The system is designed to cover the electrical load of an off grid

residential building. When there is an excess of electricity produced by the wind turbine

or the PV panels, the surplus electricity activates the electrolyser to produce hydrogen that

is stored in a tank; conversely, when the electricity demand is higher than the production

from renewable sources, the load is covered by fuel cells fed by the hydrogen in the tank.

Figure 1.2 – Schematic of a solar heating and cooling plant (from [5])

INTRODUCTION

17

Figure 1.3 – Schematic of a generation electric hybrid wind and photovoltaic with accumulation in the form of hydrogen through a system formed by electrolyser and fuel cells (from [6])

The efficiency of fuel cells and of the electrolyser are equal respectively to 50% and 74%.

Hydrogen is stored at ambient temperature and at 13.6 atm pressure in a volume of about

1200 litres. The peak power of photovoltaic modules installed is equal to 2.4 kWp. With

such a PV array, the energy storage amounts to 44 kWh. Having fixed the power of the

wind turbine (1 kW), the increase in the number of installed PV panels decreases the

amount of energy stored in the form of hydrogen. Of all the infinite options, the number

of installed PV panels and energy to be stored are sought on the basis of the minimum

cost of investment and operation. As the cost of PV panels is definitely higher than the

cost of the hydrogen storage, the best configuration is one that minimizes the number of

installed PV panels while requiring the greater storage. An equally energy-efficient

configuration would be the one that minimises the hydrogen storage.

One of the key problems that a multi-energy system is intended to give an answer is the

mismatch between the production (supply) and the consumption (demand) of energy. This

issue, even more emphasized in the case of exploitation of renewable energy, whose

driving forces are randomly variables, is usually resolved by the integration of an energy

storage (heating energy, cooling energy or chemical energy) within the system. The

material of the storage can be water (in the form of hot water, chilled water or ice),

ground, phase-change materials (PCM), hydrogen, chemical batteries.

Also the energy storage can be hybridized: it is possible to adopt traditional chemical

batteries alongside reversible fuel cells or super-accumulators (that compared to

traditional ones can be charged and discharged instantly). An example of hybrid energy

storage is presented in figure 1.4. In this case, the system includes a wind turbine, a

micro-hydropower, a solar PV field and a diesel generator to cover a DC and AC electric

load. There are two storage systems: one is based on traditional batteries, the other on a

electrolyser-hydrogen tank-fuel cells system. In a system of this complexity (12 variables

describe its behaviour) it is particularly important the choice of optimal operation

strategies of the system and of charging/discharging schedules [7].

INTRODUCTION

18

Figure 1.4 – Schematic of a hybrid wind electricity generation, hydropower and photovoltaic with accumulation in the form of hydrogen and through batteries (from [7])

Another innovative technology can be found in the solar thermal and photovoltaic panels

(PVT), a sort of solar cogenerators (electricity produced by photovoltaic conversion and

heat at low temperature according to the traditional principle of a solar water or air

collector). This not only increases the efficiency of conversion of solar energy incident on

the panel but also allows a more efficient operation of the photovoltaic panel itself (up to

30% more) thanks to the cooling of the photovoltaic cell by a thermal fluid (air or water).

In the figure 1.5 a PVT solar collectors field coupled with a geothermal heat pump for

space heating and domestic hot water production [8] is reported. This system operates

similarly to the ones that couple the energy technologies to ground-source heat pumps

(GSHP). The GSHP works, in winter season, subtracting the heat from the ground, while

solar collectors are used for the production of hot water and space heating through a

radiant floor. In summer, the heat pump, if reversible, can operate for cooling purposes

injecting into the ground the heat, while solar collectors are used for the production of the

hot water. The surplus thermal output of solar collectors in summer is used to recharge the

ground, preventing it from the thermal drift (possible when the annual requirements for

heating are much greater than those for cooling). The heat pumps performance (in terms

of COP) is maintained constant. In the case of a GSHP coupled to solar PVT collectors

field, there is the added benefit to feed the heat pump with electricity from a renewable

source produced at a high conversion efficiency.

The system of figure 1.5 serves a single new built house. The thermal energy requirement

for heating is equal to 19 kWh/m2 per year and the thermal energy requirement for hot

water production is equal to 22 kWh/m2 per year. The thermal energy produced by solar

panels PVT, whose surface equals 25 m2, is used to pre-heat a 200 litres water storage.

This fluid is then heated to a temperature of 55 °C by the heat pump (increasing the solar

collectors performances).

INTRODUCTION

19

Figure 1.5 – Diagram of a geothermal heat pump coupled to solar thermal collectors and photovoltaic (PVT) (from [8])

This allows the heat pump to work at a constant leaving fluid temperature of 55 °C. In

addition, another thermal resistance is used, each week, to increase up to 65 °C the water

in the tank in order to avoid the risks of legionella. Simulations performed over a period

of ten years show that solar collectors PVT cover 95% of the electricity needs of the

system (including the operation of auxiliaries).

The schematic in figure 1.6 is a trigeneration system for a multi-family building of

20,000 m2. The CHP system consists of two internal combustion engines of 143 kW each,

that are fuelled by natural gas and that produce electricity for the dwellings and thermal

energy used for air conditioning, both in winter and in summer seasons [9]. The CHP was

designed to meet the maximum heating/cooling demand for air conditioning. All

electricity produced is consumed inside the building or sold to the grid.

The heat produced by the CHP is used in winter for space heating, while in summer feeds

an absorption chiller and a regenerator of a chemical dehumidification plant. The

distinction between the latent load, covered by the chemical dehumidification plant, and

Figure 1.6 – Schematic of a trigeneration system for a residential building (from [9])

INTRODUCTION

20

the sensible load covered by the absorption chiller, allows the chilled water to flow at a

temperature range of 15 – 18 °C, thus raising the coefficient of performance of the

absorption chiller. There are also a thermal storage tank and a desiccant storage tank. This

means that in a particularly humid hot day, the heat produced by the CHP plant feeds only

the chemical dehumidification plant, and the sensible load is met by the thermal storage

discharge.

1.2 Discussion

The objective of the previously described multi-energy systems is twofold: decreasing the

consumption of primary energy from non-renewable sources and generating energy at the

same site where it is consumed. It is out of doubt that in the near future they will be

applied not only in new constructions but also for interventions of repair in existing

systems. This is also towards the trend of the zero energy buildings (ZEB), since only by

means of a multi-energy system it is possible to reach a zero net energy consumption over

a year.

Renewable energy sources are going to cohabit with conventional and non-renewable

energy sources, and research has to be directed to the searching for technical solutions to

optimise the integration between the various energy sources in order to fully exploit their

potential.

Given this picture, the exploitation of renewable energy should be considered not only in

terms of the building energy demand of a single dwelling, but also in relationship to the

overall energy infrastructures.

It is believed that electricity networks will evolve towards a non-hierarchical and

decentralized generation and distribution systems, made of a series of nodes at the same

time producers and consumers of energy in the form of electricity and others (chemical,

thermal, hydrogen). In this scenario, since the producers of the network will be small

plants with an intermittent energy production, bi-directional energy trade shall be enabled

between the energy system of the building and the network and vice versa, to respond to

variations in the energy supply and in the energy demand. In this context of multi-energy

systems (connected to the energy infrastructures) it will not be easy for the owner to

identify the system configurations and operation strategies that maximize the profits,

reduce energy consumption and improve the efficiency, that exploit utility rates profiles

and economic incentives, where they exist, on the investment cost or on the operation cost

to amortize the investment in a reasonable time.

1.3 Position of the problem and objectives of the thesis

Since there are many configurations that can be adopted, the study of the optimization

between the energy demand, the energy supply, the converters, the storage and the back-

up sources characteristics, has to be at the foremost when designing and operating a

multi-energy system.

This integration problem can be solved by determining all the relations between the

different quantities that affect the performance of the system and then finding the values

INTRODUCTION

21

of the design parameters by optimising the system once an objective function has been

established, or by simulating a great number of different cases and subsequently ranking

them in function of one, or of a combination, of performance parameters.

In this work, the first choice, which implies a synthesis problem, was adopted.

The objective of the thesis is implementing a procedure of analysis and selection of multi-

energy systems for various types of buildings being able to take into account:

the quality, the quantity and the differentiation of the building loads;

the variability of the energy conversion efficiencies of the energy converters as a

function of the installed capacity, which is the power that the converter must meet

at design condition;


function of the part load at which the converter works;


function of the climatic boundary conditions and operating conditions;

the variability of the operating strategies.

Preliminarily, the following issues are treated:

the definition of the multi-energy systems, typical configurations and coupling

with zone equipment systems;

the multi-energy systems design techniques;

the software tools to design and simulate the performance of the multi-energy

systems in buildings;

then, a particular and original coupling algorithm between the energy demand and the

energy supply in buildings is set out and developed into two degrees of detail (seasonal

method, hourly method). To fully exploit the potentials of this algorithm, a consistent

work is done regarding:

the characterization of the building energy demand by means of design values,

yearly energy requirements;

the characterization of the energy supply sources in terms of spatial and temporal

availability (power density values, energy values, profiles), costs;

the performance characterization of the energy converters consistent with the

degree of detail of the model adopted (definition of the efficiencies at both full and

part load as a function of operating conditions);

the economic characterization of the energy converters by means of appropriate

cost functions for each class of converter;

the criteria that can be used to select a multi-energy system and the parameters to

specify the objective functions.

To test and show benefits and drawbacks of the methodology, applications at the different

degrees of detail to various case studies representative of different buildings types are

performed

1.4 Outline of the thesis

In the first chapter the subject of multi-energy systems in building was presented with

reference to the state of the art and some examples, then the main objects of the thesis are

summarized and discussed.

Chapter 2 first deals with the theoretical peculiarities of the multi-energy systems

INTRODUCTION

22

modelling through an extensive review on the modelling techniques adopted in the field

of hybrid energy systems, that gained wide interest in the last few decades (almost from

the mid ‟80). It appears that the technical literature concentrates on hybrid energy

systems, which are not properly multi-energy systems, and that they are frequently used

in isolated applications for specific purposes. The review points out that there is generally

a lack of information on the relation between the building and the system, and that studies

were frequently concentrated on the electricity generation.

The discussion on some of the software tools that can be used to model and select multi-

energy systems (Chapter 2) suggests the same conclusions. Some of these tools were

designed to carry out the thermal design and performance of buildings and can simulate a

large amount of conversion equipment but cannot perform an optimization; others were

specially designed to carry out the hybrid systems optimization, but do not considers the

variety of energy converters that are used in buildings (for example various types of

vapour compression chillers and absorption chillers). In other cases, the underlying

equations of some component models seem too simple to account for the performance of

the energy converters of a building, whose performance is characterized by an almost

continuous part load condition.

As a result, the problem of the analysis of multi-energy systems as stated in Chapter 1 still

remains unresolved.

This is the reason why a new modelling approach is set out in the following chapters.

Initially, three chapters (3,4 and 5) are dedicated to each of the basic entities of a multi-

energy system: the energy demand, the energy supply and the energy converters.

Chapter 3 treats the issues related to the heating, cooling and electricity energy demand

assessment, from the parameters that can be adopted (design values, time series,

cumulative curves) to the factors of influence; a substantial care is spent on the subject of

the calculation methods of heating and cooling energy needs of a building.

Like the previous one, Chapter 4 discusses the characterization of the energy sources that

can feed a multi-energy system: their spatial and temporal variation, their costs and

availability.

Passing on to the components of the system, in Chapter 5 the characterization of the

energy converters is addressed both in terms of energy performance and costs. This

chapter is intended to cover the lack of information on the performance and economic

data. First of all, a general modelling framework of the component consistent with the

modelling framework of the system – that will be introduced in Chapter 6 – is set out,

then the energy performance is characterized by means of design efficiencies and part

load curves, derived from technical or scientific literature, for some of the most common

converters used in buildings. An example of a detailed characterization of the energy

performance is provided in the Appendix in case of chillers. For other converters only

some reference data are provided. The same approach is used for the economic

characterization, where a market research was carried out in order to set out original cost

curves. For the other converters, some reference data from handbooks are provided.

In Chapter 6 the energy hub concept is presented and then applied to derive the coupling

algorithm between energy demand and energy supply in buildings. Then, the selection

procedures and criteria are investigated and the parameters that allow the objective

functions to be expressed are provided.

INTRODUCTION

23

Chapter 7 is dedicated to the applications of the methodology presented in Chapter 6,

which is specified into two different methods (a steady-state method and an hourly

method). These applications are carried out on some case studies of various types of

buildings (a single-family house, a multi-family building, an hospital, an hotel).

Finally, future works are suggested in Chapter 8.

2 MODELLING TECHNIQUES AND

SOFTWARE TOOLS FOR MULTI-

ENERGY SYSTEMS ANALYSIS

2.1 Introduction

In the previous sections 1.1 and 1.2 the characteristics that combine to complicate the

process of designing and operating a multi-energy system were outlined. Some of these

are:

the intermittent nature of renewable sources and their inherited uncertainty;

the large number of possible system configurations and equipment characteristics;

the need to use accurate economic modelling to account for the life-cycle cost of

the system.

Therefore, models have to be set out to simulate the complex behaviour of these

composite systems and to evaluate the set of all different possible combinations of

components, both in terms of types (necessarily an integer variables) and sizes (integer or

continuous variables).

Contrarily to the simulation, in the design of these systems an optimization technique may

also be used, which will be related to the modelling framework adopted.

Generally, in the field of building systems, especially in the technical literature, the term

optimization may be used both to denote a mathematical optimization problem solved

with a proper optimization algorithm, and a choice between different scenarios based on a

decision criterion. To avoid any ambiguity, in this work the term optimization will be

used only for the first kind of applications, while the term selection will be used for the

second kind of applications.

2.2 Multi-energy systems modelling techniques

There are basically two types of multi-energy systems modelling techniques: the time

series models and the statistical models [10].

2.2.1 Time series models

In the time series models the time of analysis is divided into discrete time steps and the

performance of the system is simulated at each time step. Usually the dynamic behaviour

MODELLING TECHNIQUES AND SOFTWARE TOOLS FOR MULTI-ENERGY SYSTEMS

25

of the system is not actually modelled but considered as a succession of steady state

conditions. Most models use a 1-hour time step since it balances the trade off between the

accuracy and both the computation time and the unavailability of resources and loads data

at a finest time step. The core of a time series model is the energy balance on the energy

flows entering/leaving the system components and storage. One or more operating

strategies must be set out in order to proceed from one time step to another and make

dispatch decisions (for oversupply, loss of load, etc.). Time series models sometimes

require algorithms to create hourly values of loads, and resources (solar radiation, wind

speed) from more readily available average data and other few parameters. Similarly,

algorithms to summarize the hourly output values of the model are used to allow a more

comprehensible interpretation of the results.

2.2.2 Statistical models

In the statistical models the system performance is calculated for each month of a year,

and to account for the effect of short-term variability (shorter than the month for example)

some form of statistical manipulation is used. The amount of input required is limited to

the monthly or annual average load and resource data and some performance parameters

of the components. Other parameters such as the degree to which a load is correlated to a

source may be required to be inputted if not calculated by the same model.

Finally, statistical models are simpler and faster than time series models at the cost of

accuracy and flexibility. It is in fact not so easy to implement complex system

configurations (multiple renewable sources, multiple generators, sophisticated control

strategies, etc.) in these models. An example of a statistical model application is given in

section 2.2.2.1.

2.2.2.1 LPSP (Loss of Power Supply Probability)

The loss of power supply probability is a probabilistic technique introduced by Abouzahr

and Ramakumar in the field of wind electric conversion systems [11] and photovoltaic

[12], and later widely used in the design and optimization of stand-alone wind-

photovoltaic systems [13], [14] until recently [15]. It is based on the concept of LPSP

which is the probability that the system will encounter a supply shortage at a time during

one period of analysis, that is to say a condition in which the system would not be able to

supply the load. From the information about the resource variability and its correlation to

the load variability, the LPSP can be calculated. This method is particularly useful when

evaluating the behaviour of an energy storage.

As an example (from [13]), in case of a wind-PV system the power input to the storage

system S(t) is time dependent and can be expressed, as S(t) = P(t) – L(t), by the difference

between the power supply P(t), which is the sum of the wind turbine output and the PV

output, and the load L(t). It is to be noted that S(t) cannot fall below a minimum value – at

which the system would not be able to supply the load – or exceed a maximum value.

Assuming that P(t) and L(t) are statistically independent and that the probability density

functions fP(p) and fL(l) are known, the probability density function of the power input to

the storage fS(s) is given by convolving the probability density functions of P and L, and

the loss of power supply probability is


26

1

maxmin

)(LPSL

s

LP

S dssf (2.1)

For design purpose, values of LPSL can be assumed equal to 1% or one day in ten years,

and the storage capacity is varied to find the appropriate value of LPSL.

Similarly to the LPSL, the Loss Of Load Probability (LOLP) can be used as a criteria for

the hybrid renewable energy system selection [16]. It is worth to be noted that the LPSL

and LOLP are selection criteria that can be used not only within a statistical model, but

also when a time series model is used: in this case the LPSL or LOLP is numerically

calculated from the results and not determined from the cumulative probability density

function as in Eq. (2.1), that may be difficult to determine analytically in case of complex

systems.

2.3 Multi-energy systems optimization techniques

There are many numerical optimization techniques that can be used to search for the

optimal system configuration. The method selected depends on the nature of the decision

variables (both integer and continuous), of the objective function, and of the constraints.

They are therefore related to the nature of the model adopted for the system simulation.

Even if there is not an unique categorization of optimization methods, in the following

pages a not exhaustive summary is provided.

Linear programming. In case of linear objective functions and constraints, linear

optimization algorithms have been widely used in the field of hybrid energy systems, for

both stand-alone renewable systems and conventional cogeneration and trigeneration

systems. Many algorithms are available to solve linear programming optimization

problems with more than two variables. Some of the most commonly used algorithms are

the simplex method and the revised simplex method, but other algorithms can be derived

for special purposes that solves a particular model much more efficiently than the

traditional sparse simplex codes [17].

Nonlinear programming with constraints. Nonlinear objective functions and

constraints are most commonly encountered in thermal design optimization problems,

since many properties of the energy converters (e.g. efficiency) show a quadratic or cubic

variation with respect to converter operation variables (e.g. capacity). Among the

approaches to solve this type of problem, the Lagrangian multiplier methods, the iterative

quadratic programming methods, the iterative linearization methods are the most

common.

Genetic algorithms. Genetic algorithms also have gained wider acceptance in the design

and optimization of large energy systems, and have been recently used for building and

district-scale systems [18]. This is due to the capability of genetic algorithms:

to handle objective functions of any complexity, with both integer and continuous

decision variables;

to perform the optimization only on the basis of the results of the simulation model


27

of the system.

Genetic algorithms, originally developed to simulate the evolution of population in

natural systems, have the advantage that they do not require any particular property

(continuity, existence of the derivatives) of the quantity to be minimized and that they are

also efficient when multi-objective optimization problems are concerned.

2.4 Software tools to simulate multi-energy systems

In this section some of the software tools used to model and optimise multi-energy

systems in buildings are presented. Most of them are based on time series models.

2.4.1 EnergyPlus

EnergyPlus is an energy analysis and thermal loads simulation program that has its roots

in the two U.S. simulation software DOE-2 and BLAST, produced respectively by the

U.S. Department of Energy and the U.S. Department of Defence. Over twenty years of

updates of these two programs had in fact led to hundreds of subroutines difficult to

manage in implementations of new algorithms. It was therefore decided, in 1996, to build

from scratch a modular simulation program by completely rewriting the code in Fortran

90 language, but keeping some of the most advanced routines of DOE-2 and BLAST.

The team that made the program comprises, in addition to DOE and CERL (US Army

Construction Engineering Research Laboratories), the University of Illinois, the Lawrence

Berkeley National Laboratory, the Oklahoma State University, and GARD Analytics .

The result is a program that combines the more detailed algorithms of DOE-2 and

BLAST, but that is a new software [19] based on integrated simulation of building,

systems and plants. This is unlike the previous programs where information on specific

thermal loads was sent, in a cascade, to the plant components (building → load

distribution system → plant).

The main feature of the program is that it is a simulator that fully couples building

envelope, systems and plants, since the information on the load that the system is actually

able to balance is used to determine the indoor air temperature, according to an iterative

process. In this integrated approach, system and plant output directly impact the building

thermal response, thus allowing a more accurate investigation of air temperature

fluctuations and of the thermal comfort, that can be assessed by means of the most

common parameters (such as Fanger PMV, Pierce TSV, Standard Effective Temperature

ET, Corrected Effective Temperature ET*).

In fact, in EnergyPlus a time step for the analysis of the interaction between thermal zones

and the exterior environment as well as a time step for the analysis of the interaction

between the indoor air and the air conditioning systems and plants can be defined. These

two time step may differ, thus responding to the needs of a more realistic modelling of

systems control and operation.

The main simulation engine of the program consists of two basic modules, the heat and

mass balance simulation engine that solves the balance through the simultaneous

simulation of radiant and convective heat flows, and the building system simulation

engine for the simulation of systems and plants components. These two main modules


28

interact with all the secondary modules (for example, as regards the heat and mass

balance, those that determine the position of the sun, the shading coefficients, the

properties of transparent components, the heat conduction flow transmitted through the

walls, the heat balance on the wall surface, etc…).

Input file and output files are ASCII text files; for the creation of the input file an editor is

available. Some of the output files are converted by the software into CSV (comma

separated value) files readable in a common spreadsheet.

In particular, the heat balance engine comes from IBLAST (research version of BLAST)

and differs from the method of room weighting factors of the DOE-2 because it

implements the Air Heat Balance (AHB) on the zone air (and therefore it is also defined

exact method).

The principal assumptions of the method are:

uniform zone air temperature (perfect mixing);

uniform temperature of each zone surface;

uniform irradiation at high and short wavelength;

Lambertian surfaces (uniformly diffusive);

one-dimensional heat transfer conduction.

The air heat balance can be written, ignoring the heat flow dispersed for infiltration and

ventilation between neighbour zones, as

Nzpvzis,

N

1i

ii

N

1i

ci,z

z

sup

QttcmttAhQd

dtC

(2.2)

where N is the number of internal convective heat sourcesci,Q , zis,ii ttAh is the

convective heat flow between each zone surface at a temperature ts and the zone air,

zpv ttcm is the ventilation heat flow and N

Q is the system input. The heat capacity

Cz takes into account the thermal capacity of the zone air and the masses in thermal

equilibrium with the zone air.

The conduction heat flows through the walls (both envelope and interzone walls) are

determined using the conduction transfer function method, based on the concept of a

transfer function, which is an algorithm that relates the time dependent output of a

particular physical system (in this case the wall) with the time dependent input. The

numerical coefficients of the transfer function, which is linear, are called CTF

coefficients. At each time step the conduction heat flow on the internal and external

surfaces of a wall is linearly correlated to the values of temperature on the two sides of the

wall at the time step considered and at earlier time steps, and to the values of the surface

outside heat flow at earlier time steps.

The CTF coefficients are constant and therefore must be calculated only once, their

number grows when the thickness and weight of the wall increases and varies depending

on the time step considered (1h, 0.25 h, …). No information on the temperature inside the

wall can be provided by this method.

The determination of these conduction transfer functions can be done in various ways:

EnergyPlus adopts the space method procedure, already developed in IBLAST.

Alternatively to the conduction transfer function method, other algorithms such as the

moisture transfer functions, the conduction finite difference and the moisture finite

difference can be used.


29

Compared to the AHB implemented in BLAST, the AHB of EnergyPlus can also

simulate mass transport and radiant heating and cooling systems. From DOE-2 were

inherited the modules to determine natural lighting conditions and the module to perform

an electric lighting control based on illuminance values.

The building system simulation engine can simulate the most common plant components.

The first version of the software was released in 2001. Update versions are released twice

a year and are constantly adding new features and modules, e.g. those for the

displacement ventilation, the PV systems, the cogeneration equipment, fuel cells and

distributed generation electric managers, ground-source heat pumps, ground heat

exchangers for condenser equipments, desiccant dehumidifiers, water use systems,

economics and environmental reports.

As a general rule, the number and the accuracy of input data required for all these models

are so high than they cannot be at the disposal of the designer.

2.4.2 HOMER

HOMER is a micropower and distributed generation optimization model developed by

the National Renewable Energy Laboratory (NREL). It can evaluate a range of equipment

options for off-grid and grid-connected power systems [20]. HOMER is particularly

focused on the distributed generation, and can be used to size each component of a

system. The model must be provided with the resource availability, technology options,

component costs and loads to be met by the system.

HOMER is a time series model and performs an hourly energy balance over one year for

each system configuration entered by the user and then displays the list of the system

configurations sorted by the net present cost (that combines capital cost – annualised by

amortizing it over component lifetimes using the real discount rate – replacement,

operation and maintenance costs).

Loads – primary and deferrable loads, thermal load, hydrogen load – can be entered as

daily load profiles or imported from an hourly data file. At any rate, hourly values of the

loads must be defined for each of the 8760 hours of a year.

Components that can be modelled include PV modules, wind turbines, generators,

batteries, AC/DC converters, electrolysers, reformers, hydrogen tanks. A key element of

the model are the two AC and DC buses, to whom each component and load has to be

linked.

As regards the components, linear cost functions are assumed and sizes to be considered

have to be specified in order to perform the optimization. The number of sizes multiplied

by the number of components gives the number of system configurations simulated by the

program.

Sensibility analyses can also be performed for some variables, whose range of variability

can be entered by the user, showing when a particular system configuration is more cost-

effective than another as a function of two variables (e.g. fuel cost, annual wind speed).

Finally, a reliability constraint analysis can also be performed by means of a maximum

annual capacity shortage constraint parameter: if this parameter is set to 0%, then the

system must meet all the load all of the time, whereas if it is set to values from 1% to 5%

considerable savings in peak power and different optimised scenarios are obtained [21].


30

HOMER is widely used and accepted as a distributed simulation tool both at national and

international level. Many hybrid energy system analyses were performed by means of this

tool and were published as peer reviewed articles, especially on the “Renewable Energy”

review: see for example works by Iqbal [22] for applications in Newfoundland, Shaahid

and Elhadidy [23] in the field of hybrid photovoltaic-diesel-battery systems, Beccali et al.

[24] in the field of RET-hydrogen systems.

2.4.3 RETScreen International

RETScreen International is a statistical model for renewable power system design made

of a collection of spreadsheet-based tools to assess renewable energy technologies (RET)

projects developed by the Minister of Natural Resources Canada. Many applications were

available as single spreadsheets models – Wind energy, Small hydro, Photovoltaics,

Combined Heat & Power, Biomass heating, Solar air heating, Solar water heating,

Passive solar heating, Ground-source heat pumps – until a unique version of the software

was published that includes and updates all the previous models.

Each module is organized into 5 spreadsheets: the energy model, the cost analysis, the

greenhouse gas emissions analysis, the financial summary (optional), and the sensitivity

and risk analysis [25].

The main feature that distinguishes RETScreen from other tools is the stress put on the

financial accuracy of the analyses. The risk analysis module is based on a Monte Carlo

Simlation techniques: the distribution of the financial input values is generated by input

parameters randomly selected, within a predetermined range.

This software also has gained wider acceptance in the scientific community (see for

example the references [26] and [27]).

2.4.4 Other tools

DER-CAM (Distributed Energy Resources-Customer Adoption Model) is an

optimization tool for distributed energy resources selection developed by the Ernest

Orlando Lawrence Berkeley National Laboratory (LBNL). It is inputted with the hourly

heat and electricity loads profiles, market information on tariffs and fuel prices, DER

Technology information on generation, (CHP, solar collectors) and provides both the

optimal technology choices and the optimal operating schedule for provision of electricity

and heat as well as other outputs such as costs, energy and emissions. The optimization is

performed by DER-CAM minimizing the annual energy cost for a given customer. The

tool was used by Zhou et al. [28] to select optimum distributed energy technologies in

various types of buildings (hotel, hospital, school, retail, restaurant).

HYBRIDS is a commercially available spreadsheet-based renewable energy sources

assessment application by Solaris Homes (Queenskand, Australia). It requires daily

average loads (average daily energy consumption in kWh per month) and resources data

estimated for each month. Unlike other tools, HYBRIDS can simulate only one system

configuration at a time, and therefore optimization can only be performed off-line by

simulating a set of configurations and sort them as a function of an optimization function.

It requires the specification by the user of many efficiency factors and properties of the


31

energy converters that are not taken into account, or assumed in underlying equations, in

other tools. It implies an high level of knowledge on renewable energy systems

configurations and, as a spreadsheet-based application, all formulations are visible for the

user.

In a feasibility analysis of a stand-alone renewable energy system for a large hotel [29]

the model has proved to produce results similar to the ones of HOMER.

Hybrid2, developed by the Renewable Energy Research Laboratory of the University of

Massachussets is a software package to perform long-term performance and economics

analysis of hybrid energy systems which include three types of electrical loads, wind

turbines, PV, diesel generators, battery storages, and other devices. It is based on time

series input of resources, but does not consider short-time systems fluctuations caused by

system dynamics or component transients. The tool has a Graphical User Interface and a

library of equipment commercially available with manufacturer‟ specifications.

FACES (Forecasts of Air Conditioning system‟s Energy environmental and economical

performance by Simulation) is a software tool for the selection of the most appropriate

heat source in the early building design stage [30]. It is based on simulation of the energy,

environmental and economical performance of an air conditioning system. The gap

between the insufficient data available at the early design stage and the numerous input

data required to perform full scale programs for heat loads calculations and systems

simulation is covered by the design of appropriate algorithms and default data that are

built into FACES.

The software requires a minimum amount of input data very small (e.g. building location,

total floor area, building function, number of stories, type of system) and provides

cooling, heating and electricity loads, appropriate system configurations, energy,

environmental and economical evaluation of the alternatives. Some sub-modules are

called sequentially: these are the heat load calculator, the hot water and electrical load

generator, the automatic system designer, the system evaluator and the economical

evaluator. The simulation tool, developed by Nikken Sekkei Ltd., the Chubu University

and Tokyo, Chubu and the Kansai Electric Power Companies in close cooperation with

many other Electric Power Companies of Japanese cities (Hokkaido, Tohoku, Hokuriku,

Chugoku, Shikoku, Okinawa) has proved to be simple and accurate.

MESSAGE (Model for Energy Supply Strategy Alternatives and their General

Environmental impact) is a systems optimization model used in medium and long term

energy system planning, and scenarios development. It was develop in 1995 (currently

updated to the version IV) by the International Institute for Applied Systems Analysis

(IIASA, www.iiasa.ac.at), an international non-governmental research organization

devoted to studies on the environmental, economic, technological and social

developments. MESSAGE is a tool created for decision makers and the scientific

community intended to provide the installed capacity of various technologies, energy

outputs and inputs, costs, emissions on some energy systems scenarios. In its framework,

all the interdependencies between resource extraction, imports and exports, conversion of

energy, transport, distribution and provision of energy are considered. The model, which

http://www.iiasa.ac.at/


32

is a UNIX based system, develops scenarios through the minimization of the total system

cost under the constraints imposed on the energy system.

The degree of technological detail in the energy systems modelling is flexible, thus

allowing various modelling as a function of the scope of the problem.

Energy demand input consistent with MESSAGE can be generated by a second IIASA

tool called SG (Scenarios Generator) that provides scenarios of economic and energy

development with the aid of historical economic and energy time series and of regression

equations that represent key relations between economic and energy development.

Many relations useful to model and optimise multi-energy systems in buildings that are

used by the tools can be found in the reference manual of the mathematical formulations

of MESSAGE [31].

For the reasons stated before, this tool is used to model and optimise energy systems at a

district and regional level.

2.4.5 Discussion

The tools presented encompass a wide range of applications, from those related to the

building simulation (as EnergyPlus) to those related to the system simulation (as

HOMER), but they all can be used to simulate multi-energy systems provided that

appropriate assumptions are made.

As regards the purpose, some of the tools were designed particularly for the hybrid

systems simulation and selection (e.g. HOMER, RETScreen, MESSAGE, HIBRIDS),

while others were originally designed to model the building and its air conditioning

systems and plants but were then enlarged to cover the possibility of simulate electricity

generators and other renewable energy technologies (that is the case of EnergyPlus).

As regards the use of tool, some of them are intended to be used as a simulation tool,

which means that they can verify a scenario but not perform an optimization or a selection

(at least not in the tool itself), others are intended to be used as a decision support tool and

gives, as a result, a particular optimised system configuration (e.g. HOMER). In the first

case, the software can be coupled with an optimization tool (which is, for example,

GenOpt in case of EnergyPlus).

It is to be noted that for those software tools that provide optimal scenarios (e.g.

HOMER), the optimizaion does not refer to the use of a mathematical optimization

algorithm, but to a choice between different scenarios on the basis of one or more

decision criteria.

Generally, the number of input data required to perform a simulation is not small, except

for some particularly designed cases (FACES).

In the table 2.1 a comparison between the three most important software tools cited before

is carried out. It can be seen that the problem of the selection of the converter sizes and of

the system lay-out is treated differently among them. As regards the energy converters,

not all the components are included into each software. A software especially designed to

select hybrid energy systems like HOMER does not consider all the variety of the energy

converters that are used in buildings (for example various types of vapour compression

chillers and absorption chillers). RETScreen shows the most comprehensive list of energy

converter, but at the same time it is based on a statistical model which prevent the use of


33

the software when the time domain is considered. When contrasting the capabilities of the

software tools, also the accuracy of the underlying equations that model the converter

performance should be considered. In some tools, for example, these equations seems too

simple to account for the variable performance of the energy converters that are used in

buildings, which are characterized by an almost continuous part load condition.

Table 2.1 – Comparison between EnergyPlus, HOMER and RETScreen

EnergyPlus HOMER RETScreen Int.

Modelling technique Time series Time series Statistical

Time step of the calculations Sub-hourly 1 Hourly Hourly

Building simulation yes no no

Design

optimization

Converters size yes 2 no

3 no

System lay-out no yes 4 yes

4

Sensitivity analysis no 5 yes yes

System scale any any any

Converters Photovoltaics x x x Boilers x x x Wood boilers x x x Generators x x x Fuel cells x x x Solar collectors x x Wind turbine x x Hydro turbine x Batteries x

AC/DC converter x

Chillers x x Heat pumps x x

Integrated analysis 6 yes yes no

Input data Efficiencies Variables 7 Variables

7 Variables

7

One year hourly

loads profiles

x x _

Representative

days hourly load

profiles

_ x x

Weather data TRY 8 Mean values/TRY

8 Mean values

Output

data

Primary energy x x x Pollutants emissions x x x

Financial indicators x x x 1 Up to 1 minute for the systems and plants calculations, up to 15 minutes for the thermal zone calculations.

2 Only for some variables that can be autosized on the basis of a calculation performed on design conditions.

3 Sizes of the converters must be entered by the user; the software gives the optimal size between those

entered. 4 As for the converters sizes as stated in footnote 2, the system lay-out optimization does not refer to a

mathematical optimization problem, but to a choice between different scenarios using a decision criterion. 5 Sensitivity analyses can be performed off-line.

6 The possibility to relate the impact of the variation of characteristics of one system onto the related ones.

7 The underlying equations that models the converters energy performance vary between the tools.

8 Test Reference Year of the location (time series of 8760 values of climate data).


34

2.5 Conclusions

In this chapter different aspects of the simulation and optimization of multi-energy

systems are brought together.

First of all, a general outline of the modelling technique is done. This allows the two great

families of methods, the time series methods and the statistical methods, to be identified.

Then, some of the optimization algorithms that can be used to select the converters sizes

or the system lay-out of a multi-energy system are presented. It is to be pointed out that

mathematical optimization procedures are almost only used for research purposes in

original papers, and are applied to specific problems, but not in software tools.

On the contrary, the software tools that are said to perform an optimization of the system

(for example HOMER is called “the micropower optimization system”), do not really

perform a mathematical optimization, but rather a ranking between a finite number of

design alternatives that originate from the amount of sizes inputted by the user.

At the end of the chapter, some of the software tools that can be used to perform

simulations of multi-energy systems in buildings are synthetically reviewed. A main

distinction can be made between those software that are designed to assess the thermal

performance of a building and that can also simulate multi-energy systems and those

software that are specially designed to perform the optimization/selection of hybrid

energy systems, but are not particularly focused on building systems peculiarities.

The tools that are most advanced in the hybrid systems simulation do not particularly

consider the variety of energy converters that are used in a building, but tend to

concentrate on distributed electricity generation applications.

Ultimately, this chapter is also a review on multi-energy system analyses. This review

points out that there is a lack of information on the relation between the building and the

system, and that studies were concentrated on the electricity generation, rather than on the

heating and cooling energy generation (see the references cited in sections 2.2 and 2.4).

Therefore, having reviewed the modelling techniques and software tools available, it can

be stated that the problem of the multi-energy systems design and performance simulation

as formulated in Chapter 1, still remains unresolved, and is the reason of the original

proposal of this thesis.

3 THE ASSESSMENT OF THE

BUILDING ENERGY DEMAND

3.1 Introduction

In this chapter the crucial issue of the assessment of the energy demand of a building is

addressed. The reason of including chapter on the energy demand in a multi-energy

system dissertation is that the assessment of the building energy demand is of the

foremost importance since it affects both the energy converters performance and the

energy sources exploitation.

The energy demand of a building varies continuously along the operation period, and this

affects the behaviour of the energy system. The time dependency of the building energy

demand is in fact one of the features that distinguishes the energy systems in the civil

sector and that should be taken into account when assessing the energy demand over a

period of time.

Moreover, there are many reasons that combine to complicate the assessment of the

building energy demand. First of all, there are many parameters that can be used to

express the building energy demand, and those that are more suitable to be used in the

multi-energy system analysis applications are outlined. Another thorny issue is the

influence of the system boundary that should be used to identify the building energy

demand. The influence of fluids, temperatures and the indoor environmental quality are

also treated, and should be taken into account when estimating the building energy

demand.

Among the numerous methods that can be used to assess the building energy demand,

three methods are discussed and compared.

3.2 Parameters

The energy demand of a building of whatever type and size can be subsumed into three

loads: a heating load, a cooling load and an electricity load. The first two loads can be

further divided into different loads as a function of the temperature at which the heat is

provided and also into a sensible load and a latent load.

The building energy demand is assessed in terms of:

THE ASSESSMENT OF THE BUILDING ENERGY DEMAND

36

design values (e.g. design heating load, design cooling load, design electricity

load);

monthly or annual values (e.g. heating energy, cooling energy and electricity).

Both parameters can be absolute values or specific values parameterised as a function of

one – or a combination – of variables such as floor area, space volume, beds (for hotels

and hospitals). An example of annual energy demand values is given in table 3.5.

A second degree of information on the building energy demand is characterized be the

presence of:

time series (or load profiles) of the heating energy, cooling energy and electricity;

a statistical manipulation of the heating energy, cooling energy and electricity time

series.

In the first case, it is possible to identify a full set of time series values of load profiles

(that is to say a time series of 8760 hourly values) or a set of daily load profiles that refers

to a particular condition (e.g. winter day load profile, summer day load profile, daily load

profile for January,…).

In the second case, the statistical manipulation usually is used only in case of hourly

values over a period of one year.

As stated before, when the time domain is considered, regardless of the calculation or

measurement time period, a time period of reporting of 1 h is adopted, since it balances

the needs of both accuracy and simplicity.

The graphical representation of these time series poses however some problems,

especially in case of highly variable loads: alternatively to the load profile plot (figure

3.1) of a given quantity it is possible to plot the cumulative frequency curve (figure 3.2)

of this quantity.

As a reference for the entire work, an example of the three representation of the energy

demand of a building, that will be used throughout this work, is presented hereinafter.

In figure 3.1 the space heating and cooling load profiles of the case study of section

7.2.5.1 are represented. This information goes with the one provided in the table 3.1,

where the peak loads are reported. In the second column of table 3.1 the design loads,

calculated assuming design boundary conditions (e.g. outdoor air temperature and solar

radiation) are reported. As can be seen from the third and fourth columns of table 3.1, as

well as from the load profiles of figure 3.1, the heating and cooling loads are always

lower than the design heating and cooling loads during the reference year of analysis.

This is due to the fact that the design of the air-conditioning equipment and of the energy

conversion technologies is made in the worst possible conditions, that certainly will not

be reached during a typical year, as it is the reference year adopted when a yearly

simulation is performed. It remains to be noted that in the cooling mode the loads are

generally much closer to the design load than in heating mode.

To this point, it is remarkable to note that in summer the difference between the design

load and the maximum operating load is reduced, that is the maximum cooling load

merely equals the design cooling load, instead in winter the maximum heating load during

operating condition is much lower than the design heating load. This involves the fact that

energy converter of multi-energy systems in building are, almost always, working at part

load conditions and, for most of the time, at part load factors even smaller than 0.5. To

quantify this peculiarity, it is possible to represent heating, cooling and electricity loads


37

not in terms of time series, but by means of cumulative frequency curves, as in figure 3.2,

where the relation between the maximum load and the loads at the various operating

conditions is outlined.

This information goes with the one provided in the table 3.2 where mean seasonal load

factors are reported in the third and fourth columns. A cumulative curve is usually

characterized by a general decreasing slope; one or more steps in the curve are possible

for those customers that have a large difference between day and night power demand, or

week and weekend power demand. In case of the curves of figure 3.2 no steps are present

since there is a general time uniformity in space heating and cooling energy demand.

In many applications there may be some interest in providing also monthly values of

heating energy, cooling energy and electricity, as shown in figure 3.3.

Heating and cooling load profiles, as shown in figure 3.1, can be used as a graphical

representation of the time series of input data and allow different seasons (for example

heating season, cooling season, heating and cooling season) to be identified. The

cumulative curves of loads, as shown in figure 3.2, are on the contrary used when the part

load performance of energy converters has to be assessed: they provide a synthetic

representation of the variability of the energy demand as a function of the maximum

required power. The monthly energy demand profiles, as the ones of figure 3.3, are used

when the yearly performance of a system is studied.

Table 3.1 – The assessment of the building energy demand in terms of loads and energy of a case study (see section 7.2.5.1) for the Rome location

Peak loads [kW] Design Heating season Cooling season

Heating load 4,152 1,890 0

Cooling load 3,500 0 3,001

Electricity 3,000 3 3

Energy demand [kWh] Annual Heating season Cooling season

Heating energy 1419 (14 kWht/m²) 1419 0

Cooling energy 2637 (26 kWhf/m²) 0 2637

Electricity 3328 (33 kWhe/m²) 1525 1803

Table 3.2 – Seasonal loads and seasonal load factors of a case study (see section 7.2.5.1) for the Rome location

Seasonal mean load

[kW]

Seasonal load factor [-]

(calculated on the

design power)


(calculated on the peak

power)

Heat 0,358 0,09 0,20

Cool 0,563 0,16 0,19

Electricity 0,380 0,13 0,13


38

-3

-2

-1

0

1

2

3

1 1096 2191 3286 4381 5476 6571 7666 8761

LO

AD

[kW

]

Cooling Season15/4 31/10

1419 kWht/y

2637 kWhf/y

Figure 3.1 – Heating and cooling loads profiles of the case study of section 7.2.5.1 for the Rome location

0

1095

2190

3285

4380

5475

6570

7665

8760

0 0.5 1 1.5 2 2.5 3

LOAD (kW)

NU

MB

ER

OF

HO

UR

S

A = COOLING

B = HEATING

A

B

Figure 3.2 – Cumulative frequency curves of heating and cooling loads curves of the case study of section 7.2.5.1 for the Rome location

-800

-600

-400

-200

0

200

400

600

800

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

months

[kW

h]

Heating Energy

Coling Energy

Electricity

Figure 3.3 – Monthly heating energy, cooling energy and electricity of the case study of section 7.2.5.1 for the Rome location


39

3.2.1 Load profiling

Load profiling techniques refer to the formation and use of profiles of quantities related to

an energy use and were particularly developed in the electricity sector. After the

progressive liberalization of the electricity market was carried out, electricity and utility

companies had the necessity to dispose of representative load profiles for class of

customers in order to assess the impact of new tariffs. Typical load profiles for class of

customers are identified and summed up to give a total load profile that can be used to

design, optimise and operate the electricity network and carry out economical analyses.

To generate profiles that typify a particular set of customers, in the presence of an

abundant number of data (measurements, historical time series) clustering algorithms are

used. These algorithms compute the distance between two elements of the principal initial

set and are able to identify, on a given set of data, a number of homogeneous sub-sets (the

clusters). In the load profiling applications, the objective of a clustering method is

generating the best representative and distinct load profiles [32].

Hierarchical clustering algorithms are sequential techniques that at each step aggregate

two elements until all elements are grouped into one or more cluster. Partitioning

clustering algorithms are techniques that, starting from a set of possible clusters, vary the

clustering to find the sub-sets that maximize the separation between the clusters.

As regards aggregate profiles, many loads profiles are summed up and give profiles that

are smoother than the originals due to the contemporanity factor.

A set of normalized cumulative curves (load duration curves) for electricity consumers,

expressed in a synthetic mathematical representation, were proposed and validated by

Poulin et al. [33] from Hydro Québec metered data. The class of customer analysed were

bank, education and food and beverage customers with different rate structures, a factor

that influences the shape of the load duration curve. The six parameters of the synthetic

load duration curve equation along with the maximum power demand can be used to fully

characterize the electricity consumer.

3.3 Factors of influence

Among the many factors that influence the assessment of the building energy demand, in

the following paragraphs the system boundaries, the fluid properties and thermal levels,

and the indoor environmental quality are addressed.

3.3.1 System boundary

When assessing the building energy demand, it is of the greatest importance to specify the

boundaries of the system studied. An enlargement of the system boundaries usually

involves an increase in system losses, and therefore a larger value of the energy demand.

A comparison between two different energy demands can be made only if the same

system boundaries are defined.

A first choice of the system is considering the zone air, whose boundaries are the building

envelope elements. It is commonly used to assess the energy need for heating and cooling


40

(zone air energy demand) when the energy performance of the building as a passive

system is investigated and when the sizing of zone mechanical systems components is

performed. Many heat balance methods (e.g. the air heat balance) are based on the

assumption that only the zone air is the portion of system considered.

A second step of analysis is enlarging the system boundary to account for the system

losses of distribution of energy into the building (zone equipment losses, heat pipe

losses). In this case, the zone system energy demand takes into account also the

characteristics of the zone equipment installed and the control strategies of the air

conditioning system.

To give an example, the difference between the zone air load and the system load for the

same air temperature set point is reported in figures 3.4 and 3.5. It refers to the case study

of a single family house located in Torino equipped with a radiant floor for both heating

and cooling whose assumption and boundary conditions are specified in the work [34]. As

can be seen from both figures, the profile of the zone air load, which is the purely

convective heating/cooling load that must be supplied to maintain the air temperature set

point, is smoother than the profile of the heating/cooling energy that has to be supplied to

the zone equipment, the radiant floor, and that is calculated from the values of

temperature of the water leaving and entering the radiant floor and the water flow rate of

the radiant floor. This is due to the nature of the radiant floor, which is both a convective

and radiative component, and to the intermittent nature of the control strategies of the

radiant floor (whose regulation acts on the flow rate). Also the time constant of the radiant

floor – in this case of medium thermal inertia – affects its performance. For both heating

and cooling, the system energy is greater than the zone air energy, and this is due to the

regulation which cannot perfectly maintain the set point temperature as in the case of the

ideal system associated with the zone air energy.

The zone system energy demand is the energy demand that must be supplied by a multi-

energy system in a building, and the latter is supplied with the energy carriers from the

utility networks.

0.0

1.0

2.0

3.0

4.0

5.0

0:00

2:00

4:00

6:00

8:00

10:0

0

12:0

0

14:0

0

16:0

0

18:0

0

20:0

0

22:0

0

Hea

ting

lo

ad

[kW

]

Zone air heating load System load (radiant floor)

0.0

1.0

2.0

3.0

4.0

5.0

0:00

2:00

4:00

6:00

8:00

10:0

0

12:0

0

14:0

0

16:0

0

18:0

0

20:0

0

22:0

0

Coo

ling

load

[k

W]

Zone air cooling load System load (radiant floor)

Figure 3.4 – Comparison between the zone air heating load and the radiant floor load in a single-family house for a typical day in January

Figure 3.5 – Comparison between the zone air cooling load and the radiant floor load in a single-family house for a typical day in July


41

3.3.2 Fluids and temperatures

Air and/or water are the fluids that are used in buildings to carry heating and cooling

energy. Air is used as an energy carrier fluid only in case of fully air conditioned

buildings, and most of the times a water loop is necessary to the conditioning of the zone

air (except for air heaters, direct expansion chillers, variable volume of refrigerant

packaged chillers).

As regards water, different temperatures of the water loops can be used and the choice of

the appropriate design temperature for the hot water loop and for the chilled water loop

must be consistent with both the zone equipment adopted (radiator, radiant floor, etc…)

and the heating/cooling plant characteristics (standard boiler, condensing boiler, ground

source heat pump, solar collectors, etc…).

Basically two design temperature ranges (between supply water and return water) can be

adopted for hot water and chilled water:

a standard temperature range, typically equal to 95 – 85 °C for the hot water and 7

– 12 °C for the chilled water;

a moderate temperature range, typically equal to 55 – 45 °C for the hot water and

17 – 21 °C for the chilled water.

In this second case it is realized what is called a low temperature heating and an high

temperature cooling [35]. When hot water and the chilled water are produced by the

plants at these temperatures, a greater conversion efficiency can generally be reached

(increase in condensing boilers efficiency and in chillers and heat pumps coefficient of

performance). Since these temperatures are much closer to the indoor comfort

temperature, systems that adopt such temperatures of the hot and chilled water are also

called low exergy systems, to stress the fact that low temperature heating and high

temperature cooling reduce the exergy losses of these processes.

As regards the high temperature cooling it is to be noted that not all the cooling energy

can be provided at a temperature of 17 °C. For fresh air dehumidification purposes, it is in

fact necessary to reach the dew point temperature of the air to be conditioned, that implies

a temperature of the chilled water entering the cooling coils of about 7 – 10 °C. However,

if the latent cooling load is decoupled from the sensible cooling load, for example using

dedicated outdoor air systems, (see to the point the ASHRAE Green Tip 6, [36]) the

chilled water for all secondary equipment (fan coils, radiant floors) but air conditioning

can be delivered at temperature above 7 °C.

As an example, in case of the use of a chemical dehumidification system, there is no more

the need to produce any chilled water at low temperature for air conditioning purposes,

then the principle of an high temperature cooling can be effectively used. Another benefit

of chemical dehumidification is the possibility to use waste heat for the desiccant

regeneration.

3.3.3 Indoor environmental quality

In recent years, there has been a growing interest in the evaluation of the energy demand

for building heating and cooling, that is due to the many research activities that arose after

European Directive 2002/91/CE, concerning the energy performance of buildings, was

issued. As the Directive underlines, the assessment of the energy demand for the climatic


42

control of a building can only be dealt with if the level of the indoor environmental

comfort is clearly defined: the building energy performance index must be shown

together with an indoor environment comfort quality index. This is due to the fact that a

reduction in the energy demand can also lead to a decease in the comfort level; on the

contrary, it is clear that a higher energy demand should be foreseen for an increase in the

comfort level demand, as the system and plant technologies are equal [37] . Some studies

have been carried out in order to associate the concept of building energy performance to

the concept of building comfort level [38],[39],[40], because both these concepts have to

be expressed to globally qualify the performance of a building. The required level of

microclimatic quality in fact has a direct effect on the building energy consumption

[41],[42].

In general, indoor environmental quality and its relationship with energy consumption can

be analysed by focusing on the possible use of mechanical, natural or hybrid strategies for

microclimatic control. The possible strategies depend on both the expectations and the

behaviour of the users (e.g. opening or closing the windows by occupants for natural

ventilation or free cooling). They also depend on the availability of „„natural energy

sources‟‟ to be used for the microclimatic control (e.g. the number of hours during a year

when the outdoor air temperature is suitable for free cooling). In a sustainable

environmental oriented approach, once the obtained thermal comfort level has been fixed,

the use of natural resources has to be maximised in order to keep the energy demand for

the microclimatic control at the lowest value.

Recent studies ([43], [44]) have pointed out that, in not fully mechanically controlled

buildings, the expectations of the users concerning the thermal environment allow the

interval of acceptable temperatures to be wider than that obtained from Fanger‟s theory

[45] based on the PMV index and centred on slightly different values. These studies

belong to the results of the research carried out by de Dear and Brager [43] that are

referred to as the „„adaptive comfort theory‟‟, which takes into account adaptive

adjustment mechanisms (physiological, psychological or behavioural adjustment) induced

by outside weather conditions that people can activate to modify their perception of

thermal comfort. In this sense, it should be pointed out that the relationship between

environmental quality and energy consumption is greatly influenced by „„microclimatic

control‟‟ strategies, that is, the HVAC control system, and the occupants‟ use of space.

To quantity the effect of the thermal comfort on the energy demand, results of a

parametric study concerning the impact of indoor thermal conditions, system controls and

building types on the building energy demand [46] can be used as a reference.

Despite the significant energy reductions that can be achieved in not fully mechanically

controlled buildings, in reality the decision whether to adopt an adaptive thermal comfort

approach or a static thermal comfort approach depends on the building destination and air

conditioning equipment and also on the possibility of the occupants being able to control

the indoor thermal environment and to change their way of dressing. This is why the

client and the designers (the mechanical engineer and the architect) should carefully

consider the potential of energy savings that can be obtained from the selected comfort

approach. For a correct interpretation of the required building energy demand, it is of the

utmost importance to associate the thermal comfort approach that has been adopted to the

energy demand.


43

It is out of doubt that not only thermal comfort, but also visual comfort has a direct effect

on the energy demand since it affects the electricity consumption for electric lighting.

3.4 Methods

There are basically three ways to assess a building energy demand. The choice of one of

them depends not only on the type of information available (e.g. measurements, thermal

features of the building, users data) but also on the scope of the assessment (e.g. systems

sizing, asset rating, operational rating, resource consumption estimation).

3.4.1 Measurements

Measured energy requirements for space heating, cooling and electricity are the best way

to provide information on the building energy demand. However, seldom such data are

available because of the high costs of the installation of a measurement apparatus for

heating and cooling energy, and of the necessity to process a large amount of data to

obtain mean values. In the process, there is also the need to replace and correct unrealistic

or missing data. Another peculiarity of measured data is that they are referred to particular

weather conditions, building features, users characteristics and therefore they are not

representative of standard conditions.

This is why measurements are commonly used to assess the building energy demand only

by utilities services companies (electricity, gas, district heating), that always measure

complete profiles of all its customers and, for research purposes, they can measure

profiles of single customers.

Measured energy consumption is a way to assess the energy demand that can be

performed when funded by financial aids. Two examples worth to be cited are the

European projects Eureco and the work of the Cabinet Enertech Sidler.

3.4.2 Simulation

3.4.2.1 Simplified procedures of the European Standards

The implementation of Directive 2002/91/EC on the energy performance of buildings, the

EPBD, requires a general framework for a calculation methodology of the total energy

performance of buildings. This methodology should include, among other aspects, the

thermal features of buildings, heating and air-conditioning installations.

The European Commission entrusted CEN with the elaboration and adoption of standards

for a methodology to calculate the integrated energy performance of buildings and to

estimate the environmental impact, in accordance with the terms set forth in the Directive.

Among the work items created to apply the Directive, an important one regards „„the

assumptions, boundary conditions and validation tests for a calculation procedure for the

annual energy use for space heating and cooling‟‟. Another work item, developed in

collaboration with ISO (International Organization for Standardization), deals with the

„„calculation methodology of energy use for space heating and cooling‟‟. This standard,


44

the EN ISO 13790:2008 covers different levels of complexity, and three calculation

method of the energy demand for heating and cooling are presented:

1) a monthly quasi-steady state calculation method;

2) a simple hourly calculation method;

3) detailed simulation methods.

Consistency between these three types of methods is ensured by common procedures and

descriptions, boundary conditions and input data.

All these models, like previous standards on energy assessment, provide the sensible

energy needs for heating, QNH, and for cooling, QNC as an output. When air humidity is

mechanically controlled, it is therefore necessary to determine the latent load through

different models. The delivered energy can then be calculated from the building net

energy need accounting for system losses by means of standards issued by CEN TC 228

Heating systems in buildings and TC 156 Ventilation for buildings.

In the EN ISO 13790:2008, the first method is analogous to the calculation method

previously proposed by the standards related to the calculation of the energy need for

heating (UNI 10344; UNI EN 832; UNI EN ISO 13790:2005), and is a quasi-steady state

model developed by the TNO (Netherlands Organization for Applied Scientific Research)

and based on a monthly balance. The second method is an hurly simplified dynamic

method proposed by the CSTB (Centre Scientifique et Technique du Bâtiment), based on

an equivalent resistance-capacitance model.

3.4.2.2 The monthly steady-state method (TNO)

As regards the first method, it is based on a monthly balance of heat losses and heat gains

determined in steady-state conditions. The dynamic effects on the energy need for heating

and cooling are taken into account by introducing the following dynamic parameters:

an utilization factor for the mismatch between transmission+ventilation heat losses

and solar+internal heat gains leading to heating/cooling loads;

an adjustment of the set point temperature for intermittent heating/cooling or set-

back.

The previous parameters depend on the thermal inertia of the building, on the ratio of heat

gain to heat loss and on the occupancy/system management schedules.

The energy need for space heating of a zone, for each month, is calculated as:

HG,HG,HL,NHη QQQ (3.1)

whereas the energy need for space cooling is calculated as:

CL,CL,CG,NCη QQQ (3.2)

where QL,H and QL,C are the total heat losses for the heating mode and for the cooling

mode respectively; QG,H and QG,C represent the total heat gains for the heating mode and

for the cooling mode respectively, all calculated in steady-state conditions and ηG,H and

ηL,C are the dynamic parameters called utilization factors.

There is a direct correlation between the dynamic parameters of the model (utilization

factors ηG,H and ηL,C) and the heating/cooling load profiles. The shape of the hourly load

profile not only depends on the thermal dynamic properties of the building and on the

average values of the boundary conditions, but also on the hourly profiles of the boundary

conditions (internal set point schedule, external climate). The dynamic effect is


45

particularly significant when hourly profiles of the heat losses and heat gains tend to

intersect, i.e. in summer conditions.

The EN ISO 13790 standard proposal gives similar expressions to determine the gain

utilization factor for heating:

1

1

1η

H

H

a

H

a

HH,G

(3.3)

and the loss utilization factor for cooling:

1,

λ1

λ1η

C

C

a

C

a

CCL

(3.4)

where H = QG,H/QL,H and C = QL,C/QG,C.

Coefficients aH and aC are linearly correlated with the time constant of the building ,

according to coefficients that depend on the building category, in terms of occupancy

profile.

The concept of time constant, referring to a mono capacitive model of the building, is the

time needed for the internal-external temperature difference to decrease by a factor of a

1/e in the absence of heat gains and considering a constant external temperature. This

parameter, usually expressed in hours, allows the attitude of the building not to vary its

internal temperature when submitted to an internal dynamic solicitation (e.g. solar

radiation entering through windows, internal heat gains, system intermittency) to be

quantified.

According to the standard, the time constant of the building is determined as the ratio of

the internal heat capacity to the heat transfer coefficient.

The following correlation is proposed for residential buildings, both for heating and for

cooling:

aH = aC = 1 + τ / 15 (3.5)

Values of the loss utilization factor for cooling are plotted in figure 3.7. This factor

increases for high values of the time constant of the building and for low values of the

loss/gain ratio. In the case of a negative loss/gain ratio, which means that the average

external temperature exceeds the internal temperature, the loss utilization factor is equal

to 1 while QL,C in equation (3.2) becomes negative.

0,0

0,2

0,4

0,6

0,8

1,0

1,2

-1,0 0,0 1,0 2,0 3,0 4,0

C

L,C

= 168

= 8

= 24= 48

=

Figure 3.7 – Loss utilization factor vs. loss/gain ratio for different values of the time constant of the building, as proposed by correlation of equations (3.4) and (3.5).


46

From a large number of dynamic simulations, the following simple correlation of the loss

utilization factor for cooling was obtained by Corrado and Fabrizio [47]. It is based on the

time constant of the building and the ratio of the window area to floor area

aC = 8,1 – 13 + τ / 17 (3.6)

which shows the same slope as correlation (3.5).

As far as corrections for intermittency are concerned, EN ISO 13790 asserts that “due to

the diurnal pattern of the weather, and the effect of thermal inertia, an evening/night

thermostat setback or switch-off has, in general, a relatively much smaller effect on the

energy need for cooling than on the heating energy need”.

3.4.2.3 The simplified hourly method (CSTB)

The method is based on an equivalent 5 resistances – 1 capacitance model (5R1C). The

schematic of the simplified heat transfer modelling is reported in figure 3.8. The

distinction between the internal air temperature and the average temperature of the

internal surfaces, allows the ventilation heat losses and the heat losses through the

building envelope components to be quantified separately. Envelope components are also

subdivided into those that are purely resistive, such as the transparent ones, and those that

possess a thermal resistance and a thermal capacitance.

The heat transfer coefficient Hw relates the external operating temperature to the surface

internal temperature. The heat transfer coefficient through opaque components Hop is

subdivided into the two coupling conductance Hem e Hms linked to the node m which

represents the thermal mass of the building, at a temperature tm . On this node the internal

heat capacity of the building Cm is inserted. It is characterized by an effective mass area

Am equal to

2

j

2

m

m

jA

CA (3.7)

where Aj and χj are respectively the area and the internal heat capacity per area of the

building element that are exposed to the air of the thermal zone. Solar gains and internal

gains (Qs and Qi respectively) act on each of the internal node i, s, m. In particular, the

convective fraction of the internal gains (fixed by the standard equal to 50%) acts on the

node i, whereas the radiant fraction of internal gains and solar gains are divided between

the nodes s and m depending on the ratio between the effective area Am and the total area,

and on the coefficient Hes, ratio between the transmission coefficient of transparent

components Hw and the floor area.

The actual heating or cooling need QNHC to be delivered to the zone by the system is

considered as purely convective and it is then located on node i. The time step considered

is equal to one hour and the calculation is performed in two successive steps. The air

temperature calculated assuming QNHC equal to zero is compared to the set point

temperatures: if it is found to fall outside the acceptability band, the heating or cooling

need QNHC is calculated as the value that makes the air temperature reach the required set

point temperature.

By setting QNHC equal to zero, the model can be used to determine the free running

internal temperature of a zone: the standard EN ISO 13792 on the summer internal


47

temperature calculation in the absence of a mechanical cooling system, also proposes an

RC model similar to the one in question.

Moreover, since the model distinguishes the internal air temperature from the mean

average temperature of internal surfaces, it allows the values of internal operative

temperature to be calculated and to perform an assessment, albeit simplified, on the global

thermal comfort of the zone.

Figure 3.8 – Schematic of the heat transfer in the simplified hourly model

3.4.2.4 Dynamic simulation

Dynamic simulation can be used to assess the energy demand of a particular building, and

will be used throughout this work for many case studies. Dynamic simulation can be

implemented by means of a software tool (e.g. EnergyPlus, TRNSYS, ESP-R, DOE-2,

BLAST, etc…). Many of these software tools are programmed to solve the air heat

balance – see Eq. (2.1) – of the thermal zone and can reach a considerable detail in input

data and boundary conditions. This is why they require a computational time and cost that

may not always be affordable in practice. For a detailed description of one of these

software tool and the underlying calculation assumptions see section 2.4.1.

3.4.2.5 Discussion

A comparison between the two previously described models and a dynamic simulation

programme (EnergyPlus) is provided below in tables 3.3 and 3.4. It can be noted that to

implement the CSTB model, it is necessary to dispose of a test reference year as regards

the outdoor air temperature and global solar radiation on each wall (since the algorithms

to calculate the solar radiation on a given surface are not implemented). Hourly input

schedules allows the occupancy, the operation of shading devices, the set points

temperatures and the controls strategies of the systems to be modelled in greater detail.

On the contrary, the TNO method is characterized by a greater simplicity, but determining

the heating and cooling season duration is a critical issue that involves a great amount of

uncertainty in the energy needs of intermediate months.


48

Table 3.3 – Input and output of three models to assess the building energy demand

Parameter TNO CSTB EnergyPlus In

pu

t

Wea

ther

dat

a Outdoor dry bulb X X X

Sky temperature - (1)

- X

Wind velocity - - X

Site latitude, longitude and elevation - - X

Direct normal radiation - - X

Diffuse radiation - - X

Global radiation on each surface of the building

envelope X X

computed

by the tool

Co

nst

ruct

ion

dat

a

Full set of material thermal properties (s, , ,

c, s) - - X

Thermal transmittance X X -

Internal heat capacity per unit area X X -

Full set of glass material properties - - for each

glass pane

SHGC window / window+shade X X -

Shading thermal and geometrical characteristics - - X

Oth

er p

aram

eter

s

Interior/exterior convection coefficients X X computed

by the tool

Interior/exterior radiative coefficients X X -

Shading coefficients X X computed

by the tool

Internal gains X X X

Occupants, activity schedules - X X

Ou

tpu

t

Heating load, cooling load - X X

Heating energy, cooling energy X X X

Electricity - - X

Zone air temperature - X X

Zone mean radiant temperature - X X

Comfort indexes - - X

(1) Radiative heat transfer to the sky is taken into account by means of a correction factor in the solar equivalent area calculation.

Table 3.4 – Time step in input and output data of three models to assess the building energy demand

Parameter TNO CSTB EnergyPlus

Time step of the calculations monthly hourly sub-hourly

Inp

ut Weather data monthly hourly hourly

Internal gains monthly hourly hourly

Occupants, activity schedules - hourly hourly

Ou

tpu

t

Heating load, cooling load - hourly time step

Heating energy, cooling energy monthly monthly whatever

Electricity - - whatever

Zone air temperature - hourly time step

Zone mean radiant temperature - hourly time step

Comfort indexes - - time step


49

3.4.3 Literature

A third way to assess the building energy demand is the use of literature data on heating

and cooling loads and energy requirements for heating, cooling and electricity. There is

generally a lack of information on those data, but some sources are provided at

international level, see for example the BSRIA (The Building Services Research and

Information Association) Rules of thumb [48].

Literature data should be carefully selected: it is necessary to consider climatic conditions,

building features and users data.

As an example, typical values of the energy demand of various buildings located in the

North of Italy are reported in the table 3.5 from the Italian source [49].

It is obvious that those values should only be used in the absence of more detailed

information and when a simulation cannot be performed for insufficient data.

Table 3.5 – Energy demand values for some typical North-Italian buildings (from [49])

Type of building Heating energy Cooling energy Electricity Design

electricity load

[kWht/(m3year)] [kWhf/(m

3year)] [kWhe/(m

3year)] [We/m

3]

Residential 40.9 7.75 9.42 9.43

Commercial 21.7 19.2 79.2 14.00

Office 37.5 13.9 24.7 6.47

Sport 133.3 14.0 35.1 11.10

Hotel 55.8 16.7 10.7 2.44

Hospital 52.2 14.4 19.0 4.52

3.5 Conclusions

This chapter introduced the problems that must be dealt with when assessing the energy

demand to perform a multi-energy system analysis. The parameters, the estimating tools

and the assessment methodology that will be used later in this work were presented.

From the information provided it can be seen that the assessment of the building energy

demand is a complex task for both the necessity to collect various data (building features,

users data, etc…) and the complexity of the energy estimation modelling.

In this field, in order to attain to parameterized values of energy demand (in terms of heat,

cool, electricity) for representative new and existing building constructions, there is a

need of further research activities that can be identified in the following topics:

the study of the influence of the climatic variables on the energy demand,

especially as regards the cooling energy need;

the study of the quantities that can be used to parameterize the building energy

demand and the related indicators;

the determination of representative values as a function of the building features,

destinations and design specifications.

So far, no detailed and wide studies on these subjects have been carried out at the Italian

level.


ENERGY SUPPLY

4.1 Introduction

The main reason of this chapter is to provide reference data for the assessment of the

energy sources that supply a multi-energy system. This is in fact of the foremost

importance when designing and predicting the behaviour of such systems. It concerns the

resource availability with reference to space and time and the resource economics.

Aspects related to availability and reliability are crucial issues in case of renewable

energy resources, that usually are available at no cost but are characterized by an

intermittent nature and uncertainty factors. Non-renewable energy resources, especially

those provided by public utilities, do not have a theoretical limit on the energy supply but

are available at a cost which is fixed or time variable.

In the following paragraphs these aspects will be addressed for those sources that are

commonly used in buildings.

To the satisfaction of the final energy uses, both primary and secondary energy sources

can be used: primary sources are available without any type of conversion (coal, oil,

natural gas, uranium, thorium, solar radiation, wind, geothermic, tidal), while secondary

sources result from the conversion processes of primary sources and are the energy

carriers (oil end products, electricity, district heating, hydrogen).

Among primary energy sources, a distinction can be made between those sources hat are

not renewable, and are usually extracted from the soil where they exist in limited

quantities (at least to the time scale of practical interest) and those that are renewable and

that can be continuously exploited, if this is done within their regeneration limits. In order

to perform analyses of the building systems, it is crucial to get an estimate of these

quantities, and this chapter provides a preliminary introduction to this issue. Other

specifications will be introduced later (see for example the renewable energy sources

constraints of sections 7.2.5.4 and 7.2.6.4).

However, evaluating the global energy potential of a renewable resource and evaluating

its annual producibility of energy is not, except in a few cases, straightforward [50]. All

renewable energy resources, although in a different measure, are subject to annual,

seasonal or daily variations and thus usually require an integrative back-up system. A

THE ASSESSMENT OF THE ENERGY SUPPLY

51

combination of several different renewable resources can increase the system reliability

and is a good prospect to reduce the dependence on non-renewable resources.

4.2 Generalities

The formation of fossil non-renewable energy sources(coal, oil, natural gas) dates from

the end of the primary age (250 to 300 million years ago), while nuclear energy goes back

to four billion years ago, for uranium and thorium, or to the Earth origin for deuterium

and lithium [50]. The difference between the current rate of extraction of these sources

and time needed to their regeneration (of the order of thousands of times longer than the

extraction and exploitation) implies that they are considered non-renewable.

For these sources, it is more significant to analyze the relation between reserve and

production but the R/P ratio of reserves remaining at the end of a year and the production

for that year. At the worldwide current consumption rates (worldwide production and

consumption in fact coincide), the R/P ratio indicates that the oil tends to run out in about

40 years, natural gas in 65 years and coal in 155 years [51]. These numbers are to be seen

as purely indicative, as it should be taken into account that from one year to another both

the numerator and denominator of this ratio vary.

The most adopted unit of measure for the evaluation of non-renewable sources is a ton of

oil equivalent, toe (toe in English) corresponding energy of a ton of oil and is equal to 107

kcal, or 42 GJ or alternatively 11,630 kWh. It also used the barrel of oil, a unit of volume

equal to 159 litres, which corresponds to an energy of about 1700 kWh.

The secondary energy sources results from the conversion of primary energy sources and

represent the energy carriers. Approximately 30% of secondary sources resulting from the

processing of primary sources is due to electricity, while the remaining 35%, 24% and 6%

are attributable respectively to thermal energy, fuel and non-energy uses.

Renewable energy resources are the resources that can constantly be collected from the

outside environment if their exploitation occurs in a time comparable with the one

required for their regeneration. They come in large part (with the sole exceptions of

geothermal and tidal energy) from the conversion processes of the solar energy provided

to the Earth from the Sun [52].

4.1 Natural gas

Natural gas is a mixture of saturated hydrocarbons and inert gases in various

concentrations and it is often associated with the production of oil. The origin of natural

gas may be a biochemical or heat. In the first case, the gas comes from anaerobic

degradation in the environment of organic remains accumulated by metanogeni bacteria

and is found on the surface (as in the marshes) or in depth, at levels no higher than 2000

metres below the ground. The natural gas extracted from superior depth comes from

thermal cracking of large hydrocarbon molecules into smaller molecules as a result of

temperature oil wells. Depending on the progress of the process of formation of

hydrocarbons, which depends on the weather and temperature, varies the proportion of

gas relative to oil in the reservoir [50].


52

Natural gas is made up of fuel elements and inert elements. It is composed of various

gaseous hydrocarbons, mostly methane, and for the rest, ethane, propane, butane, pentane.

In addition to these components that can be burned, there are, in varying proportions

depending on the gas mine, carbon dioxide, hydrogen sulphide, nitrogen. For example,

the natural gas extracted from the reservoir of Frigg, in the North Sea, is composed of

methane to 95.7%, from 3.6% to ethane and the remaining 0.7% inert, rare gases and

liquefied natural gas , while that extracted from Lacq (France) is composed of methane

only for the 69%, from ethane to 3%, from sulphur compounds for 15% and the

remainder from inert. Natural gas does not contain carbon monoxide and, contrary to the

town gas, is not toxic. Its calorific value is approx 11 kWh per normal cubic meter, but

may vary in time depending on the composition of the mixture of natural gas supplied by

the distributor [50].

Reserves of natural gas, which amounted to 180 trillion cubic meters per 2005, for more

than a quarter are placed in the Russian Federation; other large reserves are those of Iran

(15% of world reserves) and Saudi Arabia (14%). The major producers of natural gas are

the Russian Federation (22% of total production) and the USA (19%), followed by the

Canada (6%) and a number of countries (Norway, United Kingdom, Iran and Algeria)

whose respective Production is about 3% of the world total (from [53]).

One of the biggest consumers of natural gas are again the USA (23% of world

consumption) and the Russian Federation (14%), followed by the UK, Canada, Germany,

Italy, Japan, each with a consumption equal to about 3 % of the world total. The ratio

between reserves and consumption is currently equal to about 65 years, but according to

some authors, considering the reserves of methane at high depths (15 to 30 km) up to now

not exploitable with current technologies, natural gas reserves would be sufficient to

cover the current consumption for the next 200 years (from [53]).

On the other hand, analysts say that over the next 30 years the natural gas will become the

most important energy source in the world, both for the capability in responding to the

increasing demands of governments and communities in the field of CO2 emissions

reduction, and for the progressive liberalisation of the gas market and technology

developments that enhance the benefits of investments in this field.

4.2 Electricity

Electricity is produced in Italy mainly through thermoelectric plants, where the primary

energy is transformed into heat and used to drive steam turbines or gas turbines. The

performance efficiency of these plants ranges from 25% in case of smaller installations to

50% of large combined-cycle gas turbine plants. More than half of Italian thermal power

is supplied by natural gas (59% in 2005), 17% from coal, 14% from petroleum

derivatives, while the remaining is powered by other fuels including some type of

renewable energy such as biomass. Hydropower supplies 14.6% of the total production

wind and photovoltaic 0.8%. (data from Terna, 2006). Recently, the biggest growth in the

field of electricity generation from renewable sources is due to waste and biomass fuelled

plants.


53

4.3 Hydrogen

The story of energy is marked by switching to fuels characterized by a higher content of

hydrogen: from wood, to coal, oil and finally natural gas, all characterized by a higher

calorific value for the majority presence of carbon and hydrogen. The higher calorific

value between chemical fuels is the one of hydrogen (120 MJ/kg). The name hydrogen

derives from the fundamental combustion reaction that does not produce CO2 but only

vapour. This explains why hydrogen is seen as a clean energy carrier. However, hydrogen

production takes place, for now, largely from non-renewable sources such as natural gas

(reforming), coal (gasification), petroleum products (for thermolysis). Only in the event of

a production from renewable sources a closed “green” hydrogen energy cycle may be

created [54]. Hydrogen can be produced through electrolytic conversion (one gets about

15 grams of hydrogen per kWh of electricity), and vice versa it is possible to produce

electricity in fuel cells (see section 0).

In the near future, the possibility that the hydrogen will assist or replace electricity as the

main energy carrier is likely for many reasons, not only because it has the highest

calorific value and because it is able to meet all end-use energy (utilities fixed for

production of electricity and heat, utilities furniture traction, utilities portable), but also

because, contrarily to electricity, it can be stored. It allows to store the "clean" energy

produced from renewable sources and could become the main energy carrier of the

energy grid of the future. The economist Jeremy Rifkin has even written of a third

industrial revolution which should be driven from hydrogen.

4.4 Hydropower

The water cycle absorbs about 38% of solar energy intercepted by the Earth and about

half of this energy produces evaporation of ocean and surface water. The water

evaporated takes part of the water cycle that can be seen as a thermal machine whose heat

source is the solar input and whose heat sink is the interplanetary space.

To exploit water in the form of hydropower it is necessary that a certain body of water is

in motion (owns kinetic energy) or can be put in motion (has potential energy) and this is

possible only in favourable morphological and orographical conditions. The hydraulic

energy is a form of energy of high quality because it is mechanical energy that can be

converted, with high efficiency, in electricity [50].

The evaluation of the potential hydropower, on the basis of which assessing the

exploitation that every country makes of this form of energy, however, is a particularly

complex problem, because it is certainly not possible to determine it from the total

volume of surface water flowing toward the oceans and from an average altitude gradient.

A global estimate (from [50]) of this potential is equal to 40,000 TWh per year, but taking

into consideration the technological limitations and then the profitability of exploitation,

we can determine a technically exploitable hydropower potential (technically exploitable

capability) and an economically exploitable hydropower potential (economically

exploitable capability) much lower, amounting to about 14,500 TWh per year the first and

about 8,100 TWh the second, most of which is situated in Asia, Africa and Latin

America. Just this last value can be taken as a reference of the upper limit, to evaluate the


54

use of this form of energy, that although is firmly the first renewable energy sorce

worldwide, covering about 17% of electricity needs worldwide, it is used , on average,

only for 30% of its potential (from [50]). But there are strong differences in the world: in

Europe and North America the percentage of potential hydropower is high (equal to 75%

and 69%), whereas in Africa, Asia and South America the data (equal respectively to 7%,

22% and 33%) indicates that there is space for further investments in this technology [50].

A discharge of 1 m3/s that exploits a jump of 1 m, produces a power equal to 9.81 kW,

assuming an unitary efficiency.

The plants for the exploitation of hydropower can be flowing water plants, consequently

working with small local relieves but high discharges, incorporating in an effluent weir of

the watercourse the hydraulic turbines or, alternatively, deriving from the watercourse the

necessary discharge by a channel.

Another type of flowing water plant is the one that provides for the inclusion of a

regulation tank to ensure a constant discharge to the turbines. The plants established by a

dam, on the contrary, allow to exploit local relieves much higher, (even if a dam can

never be completely emptied to avoid the risk of formation of vortices in the intake) and

to keep constant the discharge if the dam is a daily, weekly or annually accumulation.

Finally, the pumping generation plants have two benefits, one upstream and one

downstream; the volume of water is pumped from the downstream tank to the upstream

one during the night when electricity has a low cost, or however, is in excess of demand,

whereas in the day the upstream tank volume is used to produce electricity. Depending on

the duration of production and pumping periods, pumping power can be less than the

power of production, allowing to the cover demand peaks. The role of a plant of this kind

is in fact precisely to rebalance the national electric network using the nocturnal

production of thermal or nuclear power stations. In addition, a hydroelectric system with a

tank may also be coupled to a system of wind generation to secure coverage of the energy

demand that the only wind system, because of the random nature of the phenomenon, can

not guarantee.

The electricity produced in Italy from hydroelectric sources in 2005 was equal to 42,360

GWh (approximately 72% of the economically exploitable potential), a value equal to

15% of the electricity generated at the national level (data from Terna, 2005).

4.5 Solar energy

Solar energy originates from thermonuclear fusion reactions within the sun, which has a

total power of 3.85·1026

W, that is a heat flux density (power per area unit) of 63 MW/m2.

The flux density received by the earth, outside of the atmosphere, called solar constant,

represents the theoretical maximum limit o the irradiance at the ground, and is equal to

1368 W/m2 (the value is subject to variation around a mean value because of the elliptical

trajectory of the Earth around the Sun). Solar energy in the strict sense is that radiant

energy that can be captured directly to be converted into heat or electricity. Of all the

radiant energy emitted from the Sun, the one intercepted from the Earth, assuming the

solar constant equal to 1368, is equal to 5,5·1012

TJ per year; nonetheless, because of the

attenuation due to the atmosphere, only a part of this reaches the ground, and because the


55

lands represent about 29% of the earth's surface, one can estimate equal to 7.5·1011

TJ per

year the value of solar energy. For area unit, this gives rise to 5 GJ/m2 (1390 kWh/m

2)

that is a flux density on average equal to 160 W/m2. In any case this value is subject to

wide variations: the annual radiation in the sunniest regions of the planet (Sahara, Arabian

Peninsula, Californian and Australian deserts, West Coast of the Latin America) can

exceed 2200 kWh/m2, whereas in the most disadvantaged regions (northern Canada,

Scandinavia, Siberia) is below 800 kWh/m2 (from [50]).

Even if the integral value of solar energy received from the lands is much higher than the

annual consumption of the worldwide primary energy (approximately 2000 times higher),

one should take into account that solar energy is subject to wide variations (both seasonal

and daily) , which is made up of a percentage that is a vector quantity (direct radiation),

and so must be correctly intercepted and a percentage that is a scalar quantity (diffuse

radiation), that is an energy characterized by a low specific power (it can reach maximum

values, in clear sky, of the order of 1000 W/m2) and that it is not possible to fully exploit

it because of the low efficiency of reception systems and the obvious inability to cover the

whole territory.

There are two major types of radiant solar energy exploitation: the photovoltaic

conversion for the conversion into electricity and the thermal conversion (passive or

active). In Italy were installed, at the end of 2004, 30 MW of peak of photovoltaic solar

energy, and 458,000 m2 of solar panels (from [55]).

4.6 Geothermal energy

Geothermal energy is thermal energy generated within the earth by radioactive decay of

isotopes of some natural elements, emitting particles or , as uranium, thorium,

potassium. The thermal gradient of the Earth, for the first km of depth, those of practical

interest, is on the average equal to 3 °C of temperature increase each 100 meters of depth.

Overall, the geothermal energy that is dissipated from the core of the Earth to the crust is

equal to more than 100 millions of GWh per year (the 99% of the mass of the Earth is at

temperatures above 1000 °C). This is nonetheless a form of energy that provides on the

average an amount of energy per unit of time very low; in fact the areal heat flow

generated from thermal gradient varies between 0.04 and 0.08 W/m2 (significantly lower

than the average solar flux equal to 160 W/m2) [50]. In some places, placed by the faults

between tectonic plates, more substantial amounts of this form of energy reach the surface

in the form of hot water, vapour (borax fumaroles and geysers), high-temperature gases,

volcanic eruptions, which can therefore be exploited for direct thermal uses or for the

production of electricity [56].

The technologies adopted depend on the enthalpy level to which the geothermal source is

(usually distinguished between low and high enthalpy, adopting a discrimination at

130 °C). The geothermal resources can be used for the production of electricity by means

of many types of plants or, more efficiently, for direct thermal uses. Even in the absence

of specific geothermal phenomena, a geothermal heat pump (GSHP in English) can be

used to subtract heat from the ground at 20 °C in winter and to provide it to the

conditioned space or working in reverse cycle in the summer disposing of the heat


56

subtracted to the internal environment in the soil.

Geothermal energy, available continuously and not variable as many other renewable

energy resources, is not a renewable source in the strict sense of the word, but it is to the

extent that the rate of extraction is compatible with the rate of natural or artificial recharge

of the land.

Worldwide geothermal energy is used (WEC data of the end of 2002, [51]) to produce

electricity with an installed capacity of 8220 MW and an annual production of 51,000

GWh, and for direct thermal uses with power of 17,000 MW and a production of 49,360

GWht. Italy is one of the countries that possesses the largest geothermal resources in the

world, which are exploited (from 1913, the first country in the world) both for the

electricity generation (at Lardarello, Castelnuovo di Val di Cecina, Pomarance,

Radicondoli, Monte Amiata) and for the space heating by district heating networks, and

for the heating of agricultural greenhouses and fish farms. The electricity from

geothermal energy is estimated at 862 MWe of installed power for an annual production

of 4,660 GWh. Direct thermal uses excluding the spa and the balneary ones, are estimated

at about 680 MWt with an output of 2,476 GWh per year. On the whole geothermal

power covers about 0.8% of primary Italian energy needs.

4.7 Biomass and biofuels

Biomasses are all the organic materials that originate from biological processes and that

can be exploited to produce energy. They are a renewable source because solar energy by

the process of photosynthesis is stored in the vegetables in the form of chemical energy of

the ties between the organic molecules which are formed of. This is an exploitation of

solar energy, that on the one hand has an efficiency very low compared with a

photovoltaic panel (the average efficiency of the process of photosynthesis is of the order

of 0.6%) [50], on the other has a very low cost and allows to overcome the intermittent

nature of solar radiation because the energy is stored in the vegetables.

Biomasses can be grouped into [57]:

wooden resources;

residues and waste from agriculture;

crops for energy purposes.

To the first category belongs wood in all its forms and the energy content resides in

cellulose and especially in lignin. But its calorific value, around 5 ÷ 6 kWh/kg, strictly

depends on humidity, since, to extract water from fibers, it is necessary to provide the

heat of vaporization which goes to the detriment of calorific value. The wood can contain

humidity up to 70%, and once dried up until 25% (depending on humidity and

temperature of the air in which is kept).

To the second category belong all wastes from agricultural crops and zootechny that that

are used in all those areas where firewood is rare (China, northern India, Pakistan,

Bangladesh), and which nevertheless must be exploited to the extent that ensure

reproduction of biomasses.

To the third category belong all those crops specifically dedicated to the exploitation of

the energy potential of biomass (such as rape for the production of bio-diesel).


57

There are three modes of energetic exploitation of biomass:

1) combustion (characteristic mode of wooden resources);

2) fermentation that, in the absence of oxygen, gives rise to ethanol, methanol or

biogas (mixture of CH4 and CO2);

3) extraction by mechanical process or pyrolysis of vegetable oils that are at the basis

of liquid biofuels.

Among the crops specifically dedicated to energy exploitation there are, in addition to

rape, sunflower, sweet potatoes, sugar cane, corn, wheat, but also aquatic plants such as

water hyacinth.

The biomasses cover around 15% of world energy consumption, even though they escape

much of the marketing, and they are now the main source of energy for more than two

billion people in the countries of the southern world [50]. In developed countries there

have been attempts to develop the use of bio-ethanol to replace gasoline.

The production of biogas can also come from natural methanation from household waste

in landfills and from treatment of sludge in purification plants. At European level the

nation that is the largest user of biogas is Great Britain (with a specific production

amounted to 30 tep every thousand inhabitants), followed by Germany, Denmark and

Sweden. In Italy, for example, the total production of biogas is equal to 334 ktep, 89% of

which comes from landfill; the exploitation of biogas is essentially electricity (17% of

which also in cogeneration), with a production of over 1,300 GWh per year.

4.8 Wind power

Wind power is a further form of solar energy, since the wind is a stream of air that moves

along a gradient of pressure generated by the different absorption by the soil and the air of

the sun energy. At a global scale, atmospheric currents (and ocean currents) tend to

reduce the temperature differentials that originate between warmer and cooler zones on

the planet.

Wind power is widespread worldwide, although the most windy areas tend to be scantily

populated (such as Patagonia). The assessment of wind energy potential is not easily

practicable, and therefore the values proposed to 0.6 – 3 EJ per year worldwide and

130 TWh per year for the European Union should be taken as purely indicative (from

[50]). Great Britain, Ireland, Denmark, Greece, southern France enjoy favourable

conditions, with average wind speeds between 7 and 8 m/s (measured at 50 meters above

the ground). In Italy windy areas of interest for wind turbine installations are located

mainly in the Centre-South and the islands. Only in some coastal areas in Sardinia and on

the border between Campania and Puglia, winds reach average speeds exceeding 5 m/s.

Campania, Molise, Puglia, Sicily and Sardinia are the windiest regions (with average

annual speeds measured 25 meters above the ground between 4 and 5 m/s). In the north,

only in mountainous areas at high altitudes are observed average speeds of interest

(between 3 and 4 m/s) [57].

The total installed wind power in the world (from [51]) is equal to more than 31,000 MWe

mainly in Germany (38% of the total installed power), USA (15%), Spain (15%),

Denmark (9%), India (5%) . In Italy 790 MWe are installed. The annual global production


58

of 58,000 GWh per year is divided between Germany (29% of the total production), USA

(21%), Spain (17%), Denmark (8%), India (6%), Italy (3% ). The difference between the

percentage in power and energy is due to the different operating conditions of the turbines

(those installed in the United States have on average, for example, a load factor higher

than those installed in Germany).

One of the main recent developments of wind energy are offshore installations,

representing a small fraction of the power installed, but steadily growing.

4.9 Conclusions

As can be seen from the R/P ratios of the conventional sources, the main challenge of the

building energy systems of the future is to exploit various forms of renewable energy and

to proceed towards the limit of a zero energy building. If this goal is hard to reach, it is

out of doubt that in the near future, the energy systems of the buildings will integrate

many forms of renewable energy. These sources, however, as highlighted in the previous

sections, are characterized by a great random variability, and only a carefully designed

multi-energy system can overcome the problems and the limitations of this integration.

This is another reason that explain the interest in the subject of multi-energy systems in

buildings.


ENERGY CONVERTERS

5.1 Introduction

In this chapter the last of the three entities of a multi-energy system, the energy converters

are characterized both in terms of energy performance and costs. This chapter is mainly

intended to cover the lack of information on the performance and economic data about the

energy converters.

In this work, a great effort was spent to collect specific information on the energy

converters and to make it available in a uniform manner.

First of all, a general modelling framework of the component/converter consistent with

the modelling framework of the system – that will be introduced in Chapter 6 – is set out,

then the energy performance is characterized by means of design efficiencies and part

load curves derived from technical or scientific literature, for some of the most common

converters used in buildings.

An example of a detailed characterization of the energy performance is provided in the

Appendix in case of chillers. For other converters only some reference data are provided.

The same approach is used for the economic characterization, where a market research

was carried out in order to set out original cost curves. For the other converters, some

reference data from handbooks are provided

5.2 The energy performance characterization

In the energy performance simulation of multi-energy systems conversion devices, an

essentially sequential approach is used. This means that each system component is

modelled by an equivalent input-output relationship. As a function of the level of detail

required by the simulation method, algorithms of each components may be simplified or

detailed. Generally, simplified algorithms based on manufacturers‟ data will be adopted in

the following sections instead of detailed algorithms based on physical models of the

components and actual geometry, materials and fluid properties [2].

The modelling of converters was kept, whenever possible, to the most simplified level.

Each energy converter is considered as a unit that is fed by one or more energy-wares (the

THE ASSESSMENT OF THE ENERGY CONVERTERS

60

inputs) and provides one or more energy carriers (the outputs). The parameters to model

the performance of the converters are efficiencies or coefficients of performance for those

systems for which an efficiency coefficient cannot be defined.

As a general rule, the design energy conversion efficiency of a converter K in steady-state

is the one rated at full load and may be a function of some parameters p

K,d , COPK,d = f (p1, p2, p3, …) (5.1)

where one of the parameters is the design power of the converter

p1 = PK,d (5.2)

The conversion efficiency at whatsoever working condition may be determined as a

function of the design efficiency, the part load ratio and some other parameters

K , COPK = f (K,d /COPK,d , PLR, p1, p2, p3, …) (5.3)

where the part load ratio PLR is the ratio between the actual power provided by the

converter and the design power of the converter

][PLR,

dK

K

P

P (5.4)

The ratio between the actual efficiency and the design efficiency is called part load factor

PLF, a function of the part load ratio

][)PLR(PLFCOP

COP,

η

η

dK,

K

dK,

K f (5.5)

The definition of appropriate equations like (5.1) and (5.3) for each converter was made

by means of scientific (ASHRAE Handbooks [59], [60], Reference Manuals of simulation

programs [61], [62]) or technical literature available.

5.2.1 Boilers and condensing boilers

5.2.1.1 Full load efficiency

Default values for generator efficiency at full load and intermediate load (defined as the

30% of the full load, depicted in dashed line) are reported in the figure 5.1a from EN

15316-4-1 [63]. A standard atmospheric gas boiler has an intermediate load efficiency

between 97 and 99 % of the full load efficiency; a low temperature atmospheric gas boiler

has an intermediate load efficiency nearly equal to the full load efficiency; a condensing

boiler has an intermediate load efficiency approximately 9% greater than the full load

efficiency.

In the same figure, the full load generator efficiency as requested by the Italian legislative

rules D.Lgs. 192/2005 and D.Lgs. 311/2006 and the full load and intermediate load

generator efficiency of the Energy and Environment Attachment to the building

regulations of the city of Turin are reported.

All formulations (reported in [63]) are of the type

100

hPLogBAd

(5.6)

with different values of the coefficients A and B (as an example A = 90 and B = 2 for a

standard boiler, from [63]).


61

For a condensing boiler the efficiency is a function of the inlet and outlet water

temperatures.

5.2.1.2 Part load efficiency

The part load efficiency of a boiler is evaluated as follows

= d*PLF (5.7)

where the part load factor (PLF) curve for a standard boiler can be reformulated as

3

8

2

765

3

4

2

321

PLR*EPLR*EPLR*EE

PLR*EPLR*EPLR*EEPLF

(5.8)

where the coefficients are

E1 -7.5573460E-02

E2 1.3973111E+00

E3 -7.0013017E+00

E4 2.1753776E+01

E5 3.4867123E-02

E6 6.7774347E-01

E7 -5.3419591E+00

E8 2.0666463E+01

and were determined by a best fitting on the data of a generic boiler part load performance

proposed by the handbook [64], representative of a generic standard boiler. The

mathematical formulation (5.8) and the parameters E were determined by the author on

the basis of the data of [64]. The reason of the use of a rational function in Eq. (5.8) is due

to the necessity to model both the sharp drop in the PLF for small part load ratios and the

asymptotic behaviour at PLF greater than 0.4.

The part load efficiency of a condensing boiler is evaluated similarly to the generic boiler,

Eq. (5.7), where the part load factor (PLF) curve, for a boiler operating with a temperature

range between inlet and outlet water of 90 – 70 °C is given by

4

53

42

321 PLR*EPLR*EPLR*EPLR*EEPLF (5.9)

where the coefficients are

E1 1.1028780E+00

E2 4.0960402E-01

E3 -1.8956168E+00

E4 2.1626771E+00

E5 -7.7911853E-01

and were determined by a best fitting on the data provided by a manufacturer [65].

Similarly to the Eq. (5.8), also the formulation (5.9) was determined by the author on

the basis of the data of [65].

The part load factor curves (5.8) and (5.9) for both standard and condensing boilers are

reported in figure 5.1b.


62

0.80

0.85

0.90

0.95

1.00

1.05

1.10

0 100 200 300 400 500 600

Ph [kW]

R [

-]

Standard atmospheric gas boiler Low temperature atmospheric gas boilerCondensing boiler Alto rendimento (All EA Com TO)Standard boiler (D.Lgs. 192/06 s.m.i)

Figure 5.1a – Efficiency at full lad (unbroken lines) and at 30% load (dashed lines) for various boilers (from [63])

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

R [-

]

Standard boiler Condensing boiler

Figure 5.1b – Part load efficiencies of a standard boiler and of a condensing boiler (data from [64] and [65], curves by the author)

5.2.2 Chillers

The term usually refers to the vapour-compression refrigeration chillers. Among the

fundamental components of these converters (evaporator, compressor, condenser,

expansion valve), the main factor of influence is the type of compressor used. When

assessing the performance of chillers it is of the foremost importance to ascertain the

influence of the compressor. There are mainly 4 compressors types that are used in air-

conditioning applications (reciprocating compressors, screw compressors and scroll

compressors, which are positive displacement compressors; centrifugal compressors

which are dynamic compressors) that will be briefly presented.

Reciprocating compressors use pistons driven by a crankshaft to compress the gas into a

compression cylinder; the gas is then discharged at high pressure with compression ratios*

that can reach 9:1. Due to the reciprocating motion of the pistons, they are larger and

noisier than other compressors. Reciprocating compressors, driven by electricity motors

or engines, represent a mature technology, and they tend to be replaced by other types of

compressors.

* The ratio between the absolute pressure of the gas discharged to the absolute pressure of the gas entering

the suction manifold


63

Rotary screw compressors use two meshed rotating helical screws to force the gas into a

smaller chamber created by the two screws that progressively move from the intake to the

delivery. The mechanism is simpler than the one of reciprocating compressors and, since

the motion is continuous, there are minor mechanical stresses. The compression ratios

(4:1) are lower than those of reciprocating compressors while the efficiency is higher.

Rotary screw chillers produce high noise which has typically a tonal component that can

be annoying.

Rotary scroll compressors use two interleaved spiral vanes (scrolls) of different geometry

(archimedean spiral, involute or hybrid curves); usually one scroll is fixed and the other

orbits eccentrically compressing the gas between the scrolls. Scroll compressors became

commercially available for air conditioning not until the 1980‟s. Since they have fewer

moving parts than reciprocating compressors and since vibrations can be minimized by

masses that perfectly counterbalance the orbiting scroll mass, they operate more quietly

and reliably than conventional compressors.

Centrifugal compressors use a impeller (a vaned rotating disc) to convert the dynamic

energy into pressure energy to compress the gas. The gas, forced into the rim of the

impeller, increases its velocity that is converted into pressure energy by a divergent duct.

Reciprocating compressors are merely obsolete nowadays; the most used compressors are

screw, scroll and centrifugal ones. The first two have similar characteristics: the head

pressure is constant and is independent from the speed compressor. On the contrary, in

centrifugal compressors the head pressure is proportional to the square of the speed

compressor: the lower the speed is, the lower the pressure difference between the gas

discharged and the gas entering the compressor is. The refrigerant flow rate is linearly

correlated to the speed compressors in both rotary and centrifugal compressors.

Another variable of analysis is the efficiency of compression: while for centrifugal

compressors the efficiency of compression is always constantly high, at values of about

0.75 for all compression ratios, for rotary compressors the efficiency of compression is

maximized for a specific compression ratio (about 3.5 – 4 for screw compressors, with an

efficiency of 0.7; about 2.5-3 for scroll compressors, with an efficiency of 0.75). Different

types of liquid chillers used for air conditioning are summarized in the figure 5.2 as a

function of the cooling capacity.

There are different unloading mechanisms that can be used to control the chiller output at

part load ratio: in case of a centrifugal chiller inlet vanes and VSD (variable speed drives)

are used; in case of a screw chiller slide valve is used. In the variable speed drives case

the compressor runs at a lower speed reducing the flow of refrigerant through the

compressor. The energy savings can be significant if the chiller operates many hours at

part load conditions

5.2.2.1 The modelling approach

The quantities that account for the performance of a chiller are:

CC cooling capacity kW

tev temperature of the chilled water leaving the evaporator °C

tco temperature of the fluid (water or air) entering the condenser °C

COP coefficient of performance -

Pin energy input kW


64

All these quantities, except the cooling capacity (that can be unknown and then autosized)

and the energy input that can be determined from the cooling capacity and the coefficient

of performance, must be defined at a reference point (subscript R).

The actual performance of the chiller during operating conditions, that is to say at chilled

water temperatures and condenser water temperatures different from the values set at the

reference point, can be determined by use of three performance curves.

1) The variation of the cooling capacity (CC) as a function of the leaving chilled water

temperature (tev) and the entering condenser fluid temperature (tco) can be parameterized

by means of a biquadratic curve as a function of the leaving chilled water temperature and

of the condenser fluid temperature. The actual operating cooling capacity can be

determined from the cooling capacity at reference conditions CCR as

CC = CCR (C1 + C2*tev + C3* tev² + C4* tco + C5* tco ² + C6* tev * tco ) (5.10)

from [61], where C1, .... C6 are coefficients that can be determined for every single chiller

from manufacturer or measured data.

2) The variation of the coefficient of performance (COP) as a function of the leaving

chilled water temperature (tev) and the entering condenser fluid temperature (tco) can be

parameterised by means of a biquadratic curve similar to the previous one. The actual

operating coefficient of performance can be determined from the coefficient of

performance at reference conditions COPR as

coev6

2

co5co4

2

ev3ev21

R t* t*T t*T t*T t*T t*T T

1COPCOP

(5.11)

from [61].

Both these biquadratic performance curves are valid for a range of temperatures that must

be specified.

3) The variation of the coefficient of performance (COP) as a function of the part load

ratio (PLR) can be parameterized by use of a part load function curve

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

10 100 1000 10000

Cooling Capacity [kW]

SCROLL

RECIPROCATING

SCREW

CENTRIFUGAL

Figure 5.2 – Types of liquid vapour-compression chillers used in air-conditioning applications


65

COP = COPR*PLF (5.12)

The part-load function determination can be made either by a direct quadratic equation as

PLF = P1 + P2*PLR + P3*PLF2 (5.13)

from [61], or using DOE-2 performance curves (electric input to cooling output ratio

function of part load factor, EIR-FPLR) as

2

321 PLF*EPLF*EEPFPLR-EIR (5.14)

by the relation (from [64])

2

321 PLR*EPLR*EE

PLR

FPLR-EIR

PLRPLF

(5.15)

Examples of performance curves that account for the variations of cooling capacity and

coefficient of performance and that will be used in the applications are described in detail

for different types of chillers in the Appendix.

5.2.2.2 Part load curves

It is of interest to report a summary of the different part load curves of various water-

cooled chillers specified in the Appendix. These are represented in the figure 5.3, where

the variation of the coefficient of performance as a function of the part load ratio is

plotted: both the reciprocating chiller and the screw chiller show a similar curve, where

the COP decreases at low PLR; the centrifugal compressors instead have a maximum

COP at a part load ratio of about 0.7 – 0.8 and show similar curves, where the variable

speed drive (VSD) unloading mechanism (instead of the inside vanes) guarantees a better

performance at part load.

The chiller that has the most performing behaviour at part load conditions, even at very

low loads is the one equipped with the scroll compressor. Those chillers are however

available in a low range of cooling capacity (see figure 5.2) which limits their application

to small plants.

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

Reciprocating Scroll

Screw Centrifugal (1023 kW)

Centrifugal (5465 kW) Centrifugal VSD (1407 kW)

Figure 5.3 – Variation of the coefficient of performance (COP) of various water-cooled chillers as a function of the part load ratio (PLR) – see Appendix


66

5.2.3 Absorption chillers

The characteristic of absorption refrigeration equipment is that they are activated by heat

and no mechanical compression of vapour is needed to operate the thermodynamic

refrigeration cycle [60]. They can be direct-fired by combustion of a fuel (usually natural

gas) or activated by waste heat (steam or hot water). Two fluids operate in absorption

equipment: a solute and a solvent: they must form an homogeneous liquid mixture. There

are generally two pairs of fluids that are used: ammonia (refrigerant) and water (solvent),

water (refrigerant) and lithium bromide (absorber). Commercial absorption chillers

usually use this second working pair; they have a lower limit in chilled water temperature

(4 °C) since the refrigerant (water) freezes at 0 °C. In the following paragraphs only

water-lithium bromide absorption chillers will be addressed.

An electric power form 3 to 11 W (with a lower limit of 1 W for smaller machines) per

kilowatt of refrigeration capacity is usually required.

5.2.3.1 Single-stage absorption chillers

The principle of a single stage absorption chiller is briefly discussed [60]. A dilute

solution (high refrigerant content and low lithium bromide content) is pumped into the

generator at the high pressure where is boiled by the steam or hot water releasing

refrigerant vapour that enters the condenser. The generator can be indirect-fired or direct-

fired. The now concentrated solution (low refrigerant content and high lithium bromide

content) flows by gravity through a solution heat exchanger into the absorber.

In the condenser, the refrigerant vapour changes to a liquid and the condensation heat is

rejected. The refrigerant liquid passes through an expansion valve and enters the

evaporators where it vaporizes and produces the cold of the equipment.

The refrigerant vapour flows in the absorber, where it flows also the concentrated lithium

bromide (the absorber) solution from the generator. The refrigerant vapour is then

absorbed into the absorbent solution releasing the heat of dilution and condensation that is

removed by a water flow. A now dilute solution is then pumped through the solution heat

exchanger and the generator.

In a solution heat exchanger, the dilute solution from the absorber is preheated by the

concentrated solution being returned to the absorber: this increases the efficiency of the

machine because the energy required to induce the boiling in the generator is reduced.

The load of the cooling tower is also reduced since the temperature of the solution to the

absorber is decreased.

The four major components of the machine (generator and condenser, evaporator and

absorber) can be contained in two vessels.

5.2.3.2 Double-stage absorption chillers

In the double-stage (also double-effect) absorption cycle an intermediate solution can be

identified which is a mixture of diluted and concentrated solution [60]. The components

are similar to those of a single-stage absorption chiller except for an added generator,

condenser and heat exchanger. In the high temperature generator, it enters the

intermediate solution, while the dilute solution enters in the low temperature generator.


67

Different types of absorption chillers used for air conditioning are summarized in the

figure 5.4a as a function of the cooling capacity. In the figure 5.4b the part load factor

curve for an absorption chiller is reported from [67]. The formulation of this curve is

similar to the (5.15) and reads

2PLR*8333.0PLR*0833.024999.0

PLRPLF

(5.15b)

5.2.4 Cogeneration equipment

There are mainly four equipment types that can be used as cogenerators in buildings

applications [68], [69]: internal combustion engines, microturbines are widely used;

Stirling engines and fuel cells (in the following section) are emerging technologies.

The size of internal combustion engines ranges from 1 kWe to 60 MWe, and have an

electrical efficiency at about 30%, and a total efficiency at 80%. Recently, internal

combustion engines of small sizes are being created and marketed. These have a lower

electrical efficiency but a higher total efficiency. In any case, internal combustion engines

thermal output is directly correlated to the energy production (only one degree of

freedom).

Microturbines are an alternative to internal combustion engines, because they have a

similar electrical efficiency (even if decreased at part load) but less pollutants emissions.

0

0.5

1

1.5

2

2.5

3

3.5

10 100 1000 10000


Single-effec indirect-fired

Double-effect indirect-fired

Double-effect direct-fired

Figure 5.4a – Type of absorption chillers used in air-conditioning applications (data from [60])

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

Figure 5.4b – Variation of the coefficient of performance (COP) of an absorption chiller as a function of the part load ratio (PLR) – from [67]


68

5.2.5 Fuel cells

The fuel cells are electrochemical generators that convert continuously the chemical

energy of a fuel (reducing gas) into electricity and can be fed, as a reducing gas, by

hydrogen (the ideal fuel for fuel cells), natural gas (a near term alternative to hydrogen),

methanol (from biomass or natural gas), desulphurised diesel oil. The heat that is

produced during the electrochemical reaction is recovered as a direct use or even where

temperatures reach close to 1000 ° C, to fuel a gas turbine for the production of

electricity.

Fuel cells are an emerging technology that in the last few decades has been used in a

number of demonstration systems - usually supported by large governmentally funded

development programs – and that is likely to be used, in the following years for those

reasons [70]:

the ability to produce electricity at constant conversion efficiency (or even

increased at part load);

the ability to respond almost instantaneously to changes in the power demand.

5.2.6 Wind turbines

The technology used for the exploitation of wind energy is the wind turbine, a driving

machine whose outgoing mechanical power is the power to the shaft following a given

couple, and a certain angular velocity of rotation of the horizontal or vertical axis ( in the

latter case perpendicular to the ground and thus to the wind direction). The wind turbines

with horizontal axis of bigger dimension are upwind type, whereas for the smaller powers

you use those of downwind type, that put themselves with the prevailing wind direction

like a flag (as all the vertical axis turbines). A part of the total aerodynamic force exerted

by the wind is the driving component which is recuperated in the shaft, the remaining part

is the push component that must be discharged to the ground from the base of the tower

and is therefore wasted power. The maximum power theoretically extractable by a current

of wind through a wind turbine, schematizing its working by the disk actuator theory, is

equal to 60% of the kinetic power of the wind (Betz limit, equal to 16/27). The losses due

to the frictions and to the real working conditions make the power extractable from the

fluid current significantly lower than the Betz theoretical limit, and not higher than values

equal to 30% of the kinetic power of the current [50].

The rotors of the usual wind turbines are composed of 3 blades (between whose back and

loop originates the pressure difference) that can change their inclination as the optimal

position for maximum exploitation of the current depends on the wind speed. In standby

position (i.e. in the absence of wind) they must be placed parallel to the ground.

The electric power of the turbines has been growing over the years [51], going from a

value of 25 - 75 kW of turbines built in the 80s to the typical value of current turbines

equal to 1 - 2 MW. The technological limits make impossible to build turbines with a

diameter exceeding 100 m and axis height from the ground of 80 m. With a diameter of

100 m, the minimum distance between a turbine and the other must be at least 300 m.


69

5.3 The economic characterization

In order to carry out economic analyses of multi-energy systems, the estimation of the

cost of each component of the system must be set out.

Even if the best estimation of the cost of a component, especially for the large ones, can

be made by seeking vendors quotations for each system analysed, in the following section

some data are provided by means of a market research on some of the most common

components.

Attention should be paid to the nature of the cost estimate: it may comprise the

investment cost associated to a component or only the purchase cost of the component.

A cost estimation can be made by means of estimating charts obtained through the

correlation of a large number of cost and design data. Among many elements, such as

size, materials, features, temperature, pressure, that affect this cost, one parameter – or a

combination of parameters – is chosen as the main variable, usually the component size,

and the others parameters can be taken into account by factors that must be multiplied by

the bare cost obtained through the chart. At the design stage, the component size is

generally not known and so the effect of the component size must be taken into account

as one of the independent variables in the cost equations. The cost of the component

divided by the size of the component will be called specific cost and written cK.

Appropriate data for the component purchasing and investment cost were determined

from a series of price lists drawn up by the ASSISTAL ([Italian] National Association of

the Building Services Companies) [71], the Piedmont Region [72], the Lombardy Region

[73], the Umbria Region [74], the Bologna public work superintendency [75].

5.3.1 On the selection of the specific capital cost function

A preliminary study on various specific cost functions was carried out on a set of market

costs referring to steel boilers from the aforementioned price lists. These costs are

reported in figure 5.5 as a function of the heating capacity in kilowatt, showing that there

is a strong correlation between these two quantities.

Assuming a linear correlation between cost and heating capacity is a first, simple, option.

This gives a line equation which, in this case, is equal to

76.1338223.8C PmPn (5.16)

The specific cost function, the cost per unit size, which is the function that will be used in

the economic optimizations, can be calculated for all P > 0, from Eq. (5.16) as

PP

mncK 76.1338

223.8 (5.17)

Values of specific cost obtained by this hyperbola are plotted in figure 5.6 as a function of

the heating capacity over market values. This function shows a good behaviour but a

considerable overestimation of the specific cost near the lower boundary of the domain.

A much more appropriate correlation can be expressed by the generic cost function

Rp

pRCC (5.18)


70

C = 8.2225 P + 1338.7616

R2 = 0.9414

C = 77.63374 P0.69185

R2 = 0.9486

0

2000

4000

6000

8000

10000

12000

14000

16000

0 200 400 600 800 1000 1200 1400 1600

Heating Capacity P [kW]

Co

st C

[€

]

Figure 5.5 – Market cost of various steel boilers as a function of the heating capacity (sources [71],[72],[74],[75])

reported in [76] and adopted in the economics calculations of the DOE-2 simulation

program [62], where CR is the cost rated at a value of the sizing parameter p equal to pR.

This equation allows the cost at a given size to be calculated when the cost of the same

component at a different size is known; p may be only one sizing variable, for example

the capacity, or a combination of various sizing variables. The exponent is called the

scaling exponent and it is usually less than unity, which means that the percentage

increase in cost is smaller than the percentage increase in component size. In the absence

of detailed information, Bejan [76] suggests to use a value of 0.6 (which gives the so

called six-tenths rule).

Typical values of the scaling exponent of Eq. (5.18) for thermal system equipment items

can be found in [76] and range from values near the unity (blowers, cooling towers,

condensing steam turbines) to values near 0.40 (dryers, flat plate heat exchangers, small

power pumps). Two key considerations are to be made: a size range of applicability of the

exponent is specified, and, for the same component, different values of the scaling

exponent are provided for different size ranges.

Eq. (5.18) can be rewritten, assuming the heating capacity P as the sizing factor p, as

PkP

PR

RCC (5.19)

where the parameters k and can be determined form a best fit on market data. In the

case of the set of market data reported in figure 5.5 it is

6918.0634.77C PPk

(5.20)

and this curve is plotted in the same figure.

From Eq. (5.19) the specific cost – cost per unit size – function, reads

)1( Pkc K

(5.21)

which, in case of (5.20) becomes

3082.0)1( 634.77 PPkc K

(5.22)


71

for all P > 0. Values of the specific cost of steel boilers obtained trough the Eq. (5.22) are

plotted in the same figure 5.6 as a function of the heating capacity over market values of

the same specific cost.

From the data of figure 5.6, a third specific cost function can be derived considering the

cK a logarithmic decreasing function of the heating capacity

Pbac K ln (5.23)

for all P > 0, where a > 0 and b > 0 are coefficients to be determined from the best fitting

procedure over market data. The a value, expressed in a specific monetary unit, is the

specific cost when the parameter P equals 1

1 PKca (5.24)

Such a curve can be used in a range of the parameter P, which in any case must be

b

a

eP (5.25)

to give a positive specific cost.

The best fitting procedure carried out on the same set of market values steel boiler costs

gives the following third specific cost function

Pc K ln995.4762.42 (5.26)

To judge whether specific cost function to adopt, a comparison between the three

equations can be made from the data reported in figure 5.6.

Considerations have already been made regarding the hyperbola. The logarithmic

decreasing function shows a good behaviour, especially at low values of the sizing

parameter, but tends to underestimate the specific cost at high values of the sizing

parameter, which is caused by

PbacP

K

Plnlim lim (5.27)

while, for the power function, it is

0 lim lim )1(Pkc

P

K

P (5.28)

0

10

20

30

40

50

60

70

0 200 400 600 800 1000 1200 1400 1600Heating Capacity P [kW]

Sp

ecif

ic c

ost

[€

/kW

]

Market data

Power function

Hyperbola

Logaritmic decreasing function

Figure 5.6 – Comparison between market specific costs (sources [71],[72],[74],[75]) and specific costs determined by the three functions (5.17), (5.22) and (5.26) of steel boilers


72

which gives values of the specific cost always positive, without the necessity to impose a

upper limit to the size range, as required by the decreasing logarithmic function.

The best cK function seems then to be the power function of Eq. (5.22), based on the

scaling exponent, which shows a good behaviour at high values (and usually also at low

values) of the sizing parameter. This function is further adopted for all the cost estimates.

In case of very small values of the sizing parameter, since it is

)1(

00 lim lim Pkc

P

K

P (5.29)

values provided by this power function may be exceedingly high and overestimate the

specific cost. To overcome this drawback it may be assumed that the specific cost cannot

exceed a maximum specific cost, which may be fixed as the maximum market specific

cost.

Finally, the generic specific cost function further adopted is

max1

max1

max

)1(

,0

,0

00

K)(α

K)(α

K

K

cPkifP

cPkifP

Pif

c

Pkc

(5.30)

5.3.2 Specific capital cost functions of multi-energy systems components

In the following section, specific cost power functions for many components used in the

multi-energy systems will be determined following the procedure outlined in 5.3.1. Since

the market analysis was not performed for all the components that will be discussed later,

in the absence of specific information, representative values of purchase and investment

cost are provided.

5.3.2.1 Boilers and heat exchangers

Market costs of two types of boilers and of flat plate heat exchanger (to be used when

there is a connection to a district heating infrastructure) collected from various sources

([71], [72], [74], [75]) are reported in figure 5.7. The scaling exponent of the cost

equation obtained (0.78 and 0.69 for boilers, 0.36 for heat exchangers) are similar to those

reported in the literature ([76]). The resulting specific cost equations are:

2176.0)1( 089.73 PPkc K

(5.31.a)

3082.0)1( 634.77 PPkc K

(5.31.b)

6375.0)1( 91.413 PPkc K

(5.31.c)

respectively for cast iron boilers, steel boilers and flat plate heat exchangers. Values of

specific costs calculated with Eqs. (5.31) are plotted over the market data in the figure 5.8.


73

0

10

20

30

40

50

60

70

0 200 400 600 800 1000 1200 1400 1600 1800


Sp

ecif

ic C

ost

[€

/kW

]

cast iron (Umbria Region) cast iron (Bologna Superint)cast iron (ASSISTAL) steel (Umbria Region)steel (Piedm Region) steel (Bologna Superint)steel (ASSISTAL) steel (ASSISTAL)flat plate heat exchangers cast ironsteel flat plate heat exchangers

C = 73.089 P0.7824

R2 = 0.9213

C = 77.634 P0.6918

R2 = 0.9486

C = 413.91 P0.3625

R2 = 0.8095

0

5000

10000

15000

20000

25000

30000

35000

0 200 400 600 800 1000 1200 1400 1600


Co

st C

[€

]

Figure 5.7 – Market cost of various boilers as a function of the heating capacity (sources: ASSISTAL [72], Piedm Region [74],Umbria Region [75], Bologna Superint [75])

0

10

20

30

40

50

60

70

0 200 400 600 800 1000 1200 1400 1600 1800


Sp

ecif

ic C

ost

[€

/kW

]

cast iron (Umbria Region) cast iron (Bologna Superint)cast iron (ASSISTAL) steel (Umbria Region)steel (Piedm Region) steel (Bologna Superint)steel (ASSISTAL) steel (ASSISTAL)flat plate heat exchangers cast ironsteel flat plate heat exchangers

Figure 5.8 – Comparison between market specific costs and specific costs determined by the three functions (5.31.a), (5.31.b), and (5.31.c) of various boilers

5.3.2.2 Condensing boilers

Market costs of condensing boilers collected from various sources ([71],[74]) are reported

in figure 5.9. The scaling exponent of the cost equation obtained, equal to 0.6 is consistent

with the values reported in the technical literature ([76]). The resulting specific cost

equation is:

3904.0)1( 35.510 PPkc K

(5.32)


74

which is reported in figure 5.10 over market data.

y = 510.35x0.6096

R2 = 0.8506

0

10000

20000

30000

40000

50000

0 200 400 600 800 1000 1200 1400 1600


Co

st C

[€

]

Umbria Region ASSISTAL

Figure 5.9 – Market cost of various condensing boilers as a function of the heating capacity (sources Umbria Region [74] and ASSISTAL [71])

0

50

100

150

200

250

0 200 400 600 800 1000 1200 1400 1600


Sp

ecif

ic C

ost

[€

/kW

]

Umbria Region ASSISTAL Power function

Figure 5.10 – Comparison between market specific costs and specific costs determined by the power function (5.32) in the case of condensing boiler

5.3.2.3 Wood and biomass boilers

Market costs for wood and biomass boilers are reported in figure 5.11 as a function of the

heating capacity. The scaling exponent of the cost equation obtained are 0.29 for wood

boilers, 0.33 for pellets boilers, 0.64 for chips boilers.

The resulting specific cost equations are:

712.0)1(

3325

CCC

K PPkc (5.33.a)

6618.0)1(

5.4999

CCC

K PPkc (5.33.b)


75

3612.0)1(

4.1523

CTCTCT

K PPkc (5.33.c)

respectively for wood boilers, pellets boilers and chips boilers.

Values of specific costs calculated with Eqs. (5.33) are plotted over the market data in the

figure 5.12.

C = 4999.5 P0.3312

R2 = 0.877

C = 3325 P0.288

R2 = 0.9322

C = 1523.4 P0.6388

R2 = 0.9924

0

20000

40000

60000

80000

100000

120000

140000

10 100 1000


Co

st C

[€

]

Wood Pellets Chips

Figure 5.11 – Market cost of various wood boilers as a function of the heating capacity (source [73])

0

200

400

600

800

1000

1200

10 100 1000 10000


Sp

ecif

ic C

ost

[€

/kW

]

Wood Pellets Chips

Wood power function Pelets power function Chips power function

Figure 5.12 – Comparison between market specific costs and specific costs determined by the functions (5.33.a), (5.33.b) and (5.33.c) of various wood boilers

5.3.2.4 Chillers and cooling towers

Market costs of air-cooled chillers, water-cooled chillers and cooling towers collected

from various sources ([71], [72], [74], [75]) are reported in figure 5.13 as a function of the


76

cooling capacity. The scaling exponent of the cost equation obtained are 0.79 for air-

cooled chillers, 0.81 for water-cooled chillers and 0.61 for cooling towers.

The resulting specific cost equations are:

2057.0)1(

11.579

CCC

K PPkc (5.34.a)

191.0)1(

57.383

CCC

K PPkc (5.34.b)

3915.0)1(

37.264

CTCTCT

K PPkc (5.34.c)

respectively for air-cooled chillers, water-cooled chillers and cooling towers.

First, it must be noted that the significance of the sizing parameter PCT in the cooling

tower specific cost equation is not the same as the one of the parameter PC in the chillers

specific cost equations. A cooling tower is rated as a function of the energy that can be

rejected in the ambient (cooling capacity of a cooling tower PC), while a chiller is rated as

a function of the cooling energy that can be produced at the evaporator (cooling capacity

of a chiller P). Values of specific costs calculated with Eqs. (5.34) are plotted over the

market data in the figure 5.14.

In case of a closed condenser fluid loop linked to a cooling tower, the specific cost of the

cooling tower must be summed to the specific cost of the water-cooled chiller. In this last

case it is

)1()1(

CTCTCC

K PkPkc (5.35)

For the relation between the cooling capacity of the chiller and the cooling capacity of the

cooling tower, a specific cost of the set of chiller and coling tower (per unit of cooling

capacity) can be written as

C

CC

CTCC

K

P

PP

kPkc

C)1( COP

(5.36)

and is plotted in the same figure 5.14 for a chiller COP equal to 5.5.

Curves of figure 5.14 show that the specific/unit cost of water-cooled chillers is low than

the one of air-cooled chillers, but this cost, in case of the absence of groundwater or

lake/pond water, has to be summed to the cost of a cooling tower as specified

hereinbefore. The cost of the set of the water-cooled chiller and cooling tower is generally

lower than the cost of the air-cooled chiller, especially for a large cooling capacity.

5.3.2.5 Absorption chillers

Market costs of absorption chillers collected from various sources ([74] and [75]) are

reported in figure 5.15. The resulting specific cost equation is:

4836.0)1( 2.5221 PPkc K

(5.37)

which is reported in figure 5.16 a over market data. As can be seen from figure 5.16 the

values are much higher than those of a conventional chiller for low sizes machines; at

high cooling capacities the difference in the specific costs between the two converters is

less important.


77

C = 579.11P0.7943

R2 = 0.9884

C = 383.57P0.809

R2 = 0.9758

C = 264.37P0.6085

R2 = 0.9584

0

50000

100000

150000

200000

250000

0 200 400 600 800 1000 1200 1400

Cooling Capacity P [kW]

Co

st C

[€

]

Air-cooled chiller (ASSISTAL) Water-cooled chiller (ASSISTAL)

Cooling tower (ASSISTAL) Cooling tower (Bologna Pub.Work Sup)

Cooling tower (Piedm. Reg.) Water-cooled chiller (Piedm. Reg.)

Air-cooled chiller (Piedm. Reg.) Cooling tower (Umbria Reg.)

Water-cooled chiller (Umbria Reg.) Air-cooled chiller (Umbria Reg.)

Figure 5.13 – Market costs of various chillers and cooling towers as a function of the cooling capacity (sources ASSISTAL [71], Piedm. Region [72], Umbria Region [74], Bologna Pub. Work Sup [75])

0

50

100

150

200

250

300

350

400

10 100 1000 10000Cooling Capacity P [kW]

Sp

ecif

ic C

ost

[€

/kW

]

Air-cooled chiller (ASSISTAL) Water-cooled chiller (ASSISTAL)Cooling tower (ASSISTAL) Cooling tower (Bologna Pub.Work Sup)Cooling tower (Piedm. Reg.) Water-cooled chiller (Piedm. Reg.)Air-cooled chiller (Piedm. Reg.) Cooling tower (Umbria Reg.)Water-cooled chiller (Umbria Reg.) Air-cooled chiller (Umbria Reg.)Air-cooled chiller Water-cooled chillerCooling tower Water-cooled chiller+Cooling tower

Figure 5.14 – Comparison between market specific costs and specific costs determined by the functions (5.34.a), (5.34.b) and (5.34.c) of various chillers and cooling towers

.


78

C = 5221.2 P0.5164

R2 = 0.798

0

50000

100000

150000

200000

250000

300000

0 500 1000 1500 2000 2500


Co

st [

€]

Absorption chiller (H2O, NH4) Direct-fired gas absorption chilllerAbsorption chiller

Figure 5.15 – Market cost of various absorption chillers as a function of the cooling capacity (sources [74] and [75])

0

100

200

300

400

500

600

700

800

0 500 1000 1500 2000 2500 3000


Sp

ecif

ic C

ost

[€

/kW

]

Absorption chiller (H2O, NH4) Direct-fired gas absorption chilller

Absorption chiller

Figure 5.16 – Comparison between market specific costs and specific costs determined by the power function (4.) for absorption chillers

5.3.2.6 Cogeneration equipment

The specific capital cost of internal combustion engines ranges from 1100 €/kWe to

950 €/kWe. Microturbines have a specific cost generally slightly higher than internal

combustion engines, at around 1100 €/kWe. Stirling engines have a greater cost, up to

3000 €/kWe but are available only in small capacities.

5.3.2.7 Fuel cells

The specific capital cost of a fuel cells stack depends on the fuel cell technology but is


79

considerably high. In fact, costs from [49] range from 6000 €/kWe for PEM (proton

exchange membrane fuel cells) and SOFC (solid oxide fuel cells) to 5500 €/kWe for

MCFC (molten carbonate fuel cells) to 4000 €/kWe for PAFC (phosphoric acid fuel cells).

5.3.2.8 Wind turbines

The specific capital cost of the wind generator ranges from 2000 €/kW for small

capacities to 1000 €/kW and less for capacities over 20 kW. The rated capacity of a wind

turbine is the capacity at maximum wind speed. The total investment cost (generator,

mast, foundation, etc.) ranges from 4000 €/kW for a 10 kW wind power plant to 2600

€/kW for a 20 kW wind power plant.

6 ENERGY HUB MODELLING

A procedure to model and select multi-energy systems in buildings is developed in the

following sections. It is based on the concept of the hybrid energy hub and was

customized to be used at different design stages, thus leading to two different methods.

First, the basic equations of the model are presented together with its applications, then

selection criteria and objective function are specified. In the following chapter, the model

is implemented into two different methods characterized by a different level of

complexity.

6.1 The energy hub concept

The energy hub [77] (or hybrid energy hub) was introduced by a research team of the

Power Systems and High Voltage Laboratories at the ETH Zurich in the framework of a

project named Vision of Future Energy Networks. This project – summarised in [78] –

aims at defining the structure of energy networks in the long term horizon. Two major

key aspects mark out the project: the network is supposed to adapt to the need of

consumers and producers (following an approach currently widely adopted by

international researchers, see for example [79], [80], [81]) and not only electricity, but

also other needs (heating, cooling, chemical power, etc…) are taken into account.

In the frame of this project, two key elements were defined [78]:

a centralized unit that provides transformation, conversion and storage of various

forms of energy called energy hub

a combined electrical, chemical and thermal energy conductor called energy

interconnector.

The energy hub is an abstract model of the interface between power producers, consumers

and transportation infrastructure (energy interconnectors). Many examples of energy hubs

can be found, e.g. power stations, industrial plants, districts, island power systems and

also buildings. An example of an energy system including four energy hubs is represented

in figure 6.1 left. In figure 6.1 right an energy hub including conversion and storage of

various form of energy is reported (from [82]).

In the first phase, the project focused on the definition of a tool of analysis of multiple

ENERGY HUB MODELLING

81

energy carrier systems and on the development of a modelling and analysis framework.

Then, using the developed tool, it was possible to determine optimal system structures

and operation strategies. A review on the main project topics (hybrid energy hubs, hybrid

transmission concept, general system view) and of the most significant project results

(cost-emissions analysis, interactions between energy carriers, evaluation of investment)

can be found in [83].

The concept of the energy hub was specified with the definition of a set of matrix

formulations relating the power flow at the input port and at the output port of an hub.

This can be found in the report [84] written by Geidl, where the port-to-port power flow

coupling and the hub continuity equation are determined and explained on some cases.

This modelling approach was used by Geidl and Andersson to perform a topological (or

structural) optimization of a single energy hub in [85], [86] and to perform an operational

optimization of a system of interconnected hubs in [85], [87]. With the same modelling

approach, Koeppel and Andersson assessed the reliability of supply in [88].

A comprehensive description of the energy hub concept and of the possible applications

in the field of energy systems can be found in the PhD thesis of Geidl at the ETH Zurich

[89]. The application of the energy hub concept to assess reliability conditions in multi-

carrier energy systems and the effects of storages is the topic of the PhD thesis of Koeppel

[90].

The energy hub concept and the simplified energy flow theory developed by Andersson,

Frölich, Geidl, Koeppel et al. set a general theoretical framework to understand the

behaviour of complex, highly interlinked combinations of various energy supply systems.

As stated by its authors in [83], this theory also covers the lack in literature about the

general integration of different modelling methods into one theory for hybrid energy

systems, since hybrid energy systems have been addressed in the past 20 years as single

systems.

In this work, the energy hub was used as a valuable tool to develop the model of the

multi-energy systems performance.

Figure 6.1 – System of energy hubs (left) and an example of energy hub (right)


82

6.2 The coupling algorithm

To determine a modelling procedure of a generic energy system of a building, the energy

system is partitioned into three entities: the energy supply, the energy demand and the

energy conversion, storage and regulation. The energy supply is intended as the set of the

energy-wares that are supplied to the multi-energy system of the building to fed the

energy converters. Every quantity (power P, energy E) referring to the energy supply side

of the system is identified by the subscript in. Given E the set {} of the n energy-

wares, the power inputs supplied to the energy system by the n energy-wares are

n

inininin PPPP ...,, (6.1)

where the superscript refers to the energy-ware (e.g. refers to natural gas, to district

heating, to electricity from the grid). The set of n energy-wares power inputs can be

expressed in a (n × 1) vector as

Tn

ininininin PPPP ...,, P (6.2)

Omitting for the moment the energy conversion, storage and regulation that will be

discussed later, the energy demand is the set of the building loads to be covered by the

energy converters of the multi-energy system of the building. Every quantity referring to

the energy demand side of the system is identified by the subscript out. Given L the set

{a,b,c...} of the m building loads typology, the m building loads covered by the system

are

m

out

c

out

b

out

a

out PPPP ...,, (6.3)

where the superscript refers to the nature of energy (e.g. a refers to heating energy at

75°C, b refers to heating energy at 45° C, c refers to cooling energy at 7°C, d refers to

electricity at a voltage of 230 V). The set of m building loads can be expressed in a

(m × 1) vector as

Tm

out

c

out

b

out

a

outout PPPP ...,,P (6.4)

Once the vectors of building loads Pout and energy-wares Pin are defined, the coupling

between the energy demand and the energy supply of an energy system of a building can

be written as

outin PDP (6.5)

provided that a suitable (n × m) coupling matrix D is defined. This formulation is adopted

since the vector Pout is supposed to be known. The matrix D is called backward coupling

matrix as to distinguish it from the forward coupling matrix that relates the outputs as a

function of the inputs and follows the direction of the main physical energy flows. For a

given system, the determination of the forward coupling matrix from the backward

coupling matrix is not so easy: since generally n ≠ m, the matrix D is not a square matrix.

Even if square, the matrix may be singular and therefore not invertible.

In the following chapters, only backward coupling matrixes will be used to model multi-

energy systems, assuming that the building loads at the output port are known and that the

unknowns of the formulations are the energy sources at the input port.


83

6.2.1 The determination of the coupling matrix entries

The problem of the determination of suitable values of the dij coupling matrix entries can

be dealt with rewriting Eq. (6.5)

m

out

c

out

b

out

a

out

nmncnbna

mcba

mcba

mcba

n

in

in

in

in

P

P

P

P

dddd

dddd

dddd

dddd

P

P

P

P

...

...

...............

...

...

...

...

(6.6)

in explicit form which gives, for the first energy-ware

m

outm

c

outc

b

outb

a

outain PdPdPdPdP 1...

(6.7)

There are basically three aspects that must be taken into account when deriving each entry

dij:

1) the connections between the fluxes of the hub;

2) the conversion losses of the hub energy converters;

3) the energy stored in some hub components.

The first aspect deals with the dispatch of fluxes (the hub lay-out); the second aspect deals

with the energy converters: they can in fact not only change the form of energy which

passes through them (aspect theoretically yet taken into account at point 1) but also

change the amount of energy that passes through them due to the energy losses of the

components of the converter; the third aspect deals with the storages, that affect the

energy flow between input and output when a time-domain simulation of the hub is

performed. To begin with, each aspect will be discussed separately.

6.2.1.1 The connection between fluxes

Assuming that there are no energy converters or that all energy converters are ideal (that

is to say there are no conversion losses), and assuming that no energy storage is present,

the entries have to represent only the hub lay-out, that is to say the net of the hub, the flow

coupling between input and output. As an example, the hub represented in figure 6.2 left

can be described by the following equation

m

out

c

out

b

out

a

out

n

in

in

in

in

P

P

P

P

P

P

P

P

...

0...000

...............

0...001

0...000

0...110

...

(6.8)

where the entries of the matrix D can only assume the values of 1 (connection between

the two fluxes) or 0 (no connection). In this case, one energy source is used to more than

one load; on the contrary, if one building load is fed by more than one source, the

backward coupling matrix entries can assume any value between 0 and 1 (whereas in a

forward coupling matrix these conditions are inverted). It is therefore necessary to

introduce the parameter representing the ratio between the power flow on a line and the

total power flow at the output. As an example, the hub represented in figure 6.2 right can


84

be described by the following equation

m

out

c

out

b

out

a

out

a

a

a

a

n

in

in

in

in

P

P

P

P

P

P

P

P

...

0...000

...............

0...00

0...100

0...01

...

(6.9)

where a

out

a

aa

aP

P

(6.10)

is the ratio between the building load a covered by the energy-ware and the building

load a.

Similarly, it is a

out

a

aa

aP

P

, and as a

out

a

a

a

a PPP , it follows 1

a

a

a

a .

In case of sole connection (no converter losses and storage) it is d = . The relation

between input and output in the generic hub of figure 6.3, where all possible connections

are taken into account but converters and storages are not (yet) present, becomes

mout

cout

bout

aout

mmn

ccn

bbn

aan

mc

cb

ba

a

mm

cc

bb

aa

mm

cc

bb

aa

nin

in

in

in

P

...

P

P

P

...

...............

...

...

...

P

...

P

P

P

(6.11)

where ii

ik 10 L = {a, b, c, ... }, k E = {, ... } (6.12)

and

in

k

i

ik 1

L = {a, b, c, ... } (6.13)

Since has the physical meaning of a factor, it must be comprised between 0 and 1 as

stated in (6.12). The sum of all factors for each building load must be equal to 1 as

stated in (6.13), which gives, in this particular case of sole connection between fluxes,

that the sum of each column of entries of the backward coupling matrix in Eq. 6.11 is

equal to 1.

Figure 6.2 – Two examples of energy hub with sole connection between fluxes


85

Figure 6.3 – A generic energy hub with sole connection between fluxes

6.2.1.2 The energy converters

With the object of the multi-energy systems modelling, which in one way tries to simplify

the complexity of such systems, energy converters are entities that can convert one form

of energy into another with a given conversion efficiency. In case of perfect converters

(no energy losses) we fall into the previous case modelled by Eq. (6.11). If the energy

conversion efficiency of a converter is ≠ 1 there are some energy losses or gains that must

be taken into account in Eq. (6.11).

The simplest way to take into account the energy converters is to introduce in the

coupling matrix appropriated energy conversion efficiencies. Energy converters are

considered as single units fed by one or more energy inputs PK,in and providing one or

more energy outputs PK,out. The conversion efficiency of the generic converter K

represented in figure 6.4 is the ratio between the energy output PK,out and the energy input

PK,in

inK

outK

KP

P

,

,η (6.14)

PK,ls in figure 6.4 are the conversion losses. In case of more than one energy output, more

than one energy conversion efficiency may be necessary to assess the converter

performance (for example a cogenerator unit has a thermal efficiency and an electric

efficiency). The modelling of each converter has to be done as a consequence of its

operation characteristics and is discussed in Chapter 5. It encompasses many level of

complexity (from simplified models to the most accurate ones) which leads to various

levels of complexity of the same energy hub model.

The introduction of some energy converters in the energy hub of figure 6.2 right is

represented in figure 6.5. The matrix D is in this case a function of both factors and

energy efficiencies

D = f () (6.15)

In an hub with energy converters, to the sake of a simpler and more clarifying notation,

the subscript of a factor provides the name of the converter instead of the flow from one

energy carrier to another. It is, for example,

a

a

a

K 1 and a

a

a

K 4


86

Being K the set of the energy converters, the resulting equation that models the hub of

figure 6.2 is

m

out

c

out

b

out

a

out

K

a

K

K

KK

a

K

n

in

in

in

in

P

P

P

P

P

P

P

P

...

0...000

...............

0...00η

0...η

100

0...0η

1

η

...4

4

3

21

1

(6.16a)

with the constraints

ii

K 10 L = {a, b, c, ... }, K K = {K1, K2, K3, ... Kn } (6.16b)

iKn

KK

i

K 11

L = {a, b, c, ... } (6.16c)

KK0 K = {K1, K2, K3, ... Kn} (6.16d)

Converters that have multiple energy input can be modelled with the same approach. As

an example, the energy hub of figure 6.6 left can be modelled by the matrix equation

m

out

c

out

b

out

a

out

an

K

a

K

a

K

aα

K

n

in

in

in

in

P

P

P

P

P

P

P

P

...

0...00η

1...............

0...00η

1

0...00η

1

0...00η

1

...

(6.17)

where each efficiency factor must be 10 K .

A superscript reporting the source and the output can be added to the efficiency factors to

clarify the significance of each term.

Figure 6.4 – Schematic representation of a generic energy converters

Figure 6.5 – Energy hub of equation 6.16


87

Attention must be paid in case of converters that have multiple energy output. Usually

these outputs are related by some relations (e.g. the relation between the thermal output

and the electric output of cogenerator) and therefore they are not all independent variables

but some of them may be dependent variables. Since in this backward coupling

formulations the outputs are considered as a known term, and so independent, relations

other than Eq. 6.16 must be set out. In case of a converter with m multiple outputs (see

figure 6.6 right) the hub matrix equation is

m

out

c

out

b

out

a

outm

K

c

K

b

K

aα

K

n

in

in

in

in

P

P

P

Pmmmm

P

P

P

P

...

0...000

...............

0...000

0...000

η

1...

η

1

η

1

η

1

...

(6.18)

where each efficiency factor must be 10 K . Usually only one of the outputs of the

converter is independent and the other ones are dependent, so m–1 relations between the

outputs have to be added to Eq. 6.18 to simulate the hub performance. Assuming the a

load as the independent output, these relations are:

aα

K

b

Ka

out

b

out PP

η

η

(6.19a)

aα

K

c

Ka

out

c

out PP

η

η

(6.19b)

…

aα

K

m

Ka

out

m

out PP

η

η

(6.19c)

The matrix equation (6.18) must be solved with the constraints of Eqs. (6.19).

A list of the most commonly encountered energy converters of multi-energy systems in

buildings, grouped for energy input, is provided below in table 6.1. For each converter the

ideogram adopted in the hub representations, the initials, the energy inputs and the energy

outputs are summarized.

Figure 6.6 – Energy hubs with a converter with multiple inputs (on the left, equation 6.17) and with a converter with multiple outputs (on the right, equation 6.18)


88

The schematic representation of some energy converters – a gas boiler GB, an absorption

chiller AC, an internal combustion engine ICE – are reported in figure 6.7. As a clarifying

convention, the actual energy carrier of the input and output power is reported in the

superscript: g for natural gas, e for electricity (both at the input and at the output), t for

thermal energy, c for cooling energy. The efficiency of a gas boiler is

g

inGB

t

outGB

GBP

P

,

,η (6.20)

The efficiency of an absorption chiller, expressed in terms of coefficient of performance

(COP), is

k

inAC

c

outAC

ACP

P

,

,COP (6.21)

where k = g for a gas driven absorption chiller and k = t for a thermal driven absorption

chiller.

For an internal combustion engine two efficiencies (electric, superscript e and thermal,

superscript t) are defined

g

inICE

t

outICEt

ICEg

inICE

e

outICEe

ICEP

P

P

P

,

,

,

,η;η (6.22)

Figure 6.7 – Schematic representation of a gas boiler, an absorption chiller and an internal combustion engine in a generic hub

6.2.1.3 The energy storage

One of the main problems of a multi-energy system is the mismatch between energy

supply and energy demand [91]. This is especially true in case of renewable sources. This

problem, as for the plants that exploit solar energy to produce cold [92], is usually dealt

with the integration of a storage. Typical storage mediums are water (refrigerated or ice),

ground, PCM or hydrogen.

In an hub, energy can be stored upstream of a converter, as an energy-ware at the input

port of the hub, downstream of a converter, as heating energy, cooling energy or

electricity at the output port of the hub, or between two converters. The effect on the hub

performance of these three types of energy storage (at the input port, at the output port,

between two converters) are quite different and will be analysed later.


89

Table 6.1 – List of energy converters used in multi-energy systems in buildings

Converter Energy input * Energy output *

GBGB GB Generic boiler Natural gas/oil Heating energy

CBCBCB

CB Condensing boiler Natural gas Heating energy

WBWB

WB Wood boiler Wood Heating energy

WG Wood gasifier Wood Natural gas

ICEICE

ICE Internal combustion

engine (CHP)

Natural gas/oil/biofuel Electricity

Heating energy

MT Micro-turbine (CHCP) Natural gas Electricity

Heating energy

MTT Micro-turbine with

trigeneration (CHCP)

Natural gas Electricity

Heating energy

Cooling energy

ST Steam turbine Natural gas Electricity

Heating energy

AC AC Absorption chiller Natural gas/Heating

energy

Cooling energy

FC FC Fuel cells Hydrogen/Natural gas Electricity

Heating energy

EZ

EZ Electrolizer Electricity, Water

Electric heater Electricity Heating energy

- Appliances Electricity Desired result

C C Chiller Electricity Cooling energy

HP

HP Heat pump Electricity,

Air/Water/Groundwater/

Ground

Heating energy

HP/C

HP/C Reversible heat pump Electricity,

Air/Water/Groundwater/

Ground

Heating energy,

Cooling energy

GHP

GHP Gas absorption heat

pump

Natural gas Heating energy,

Cooling energy

HE

HE Heat exchanger Heating energy (district

heating)

Heating energy

SC

SC (Thermal) Solar collector Solar radiation Heating energy

PV

PV PV (Photovoltaics) Solar radiation Electricity

PVT

PVT PV/T Solar radiation Electricity

Heating energy

WT

WT Wind turbine Wind energy Electricity

HTHT

HT Hydro-turbine Hydraulic energy Electricity

* In the energy input and output fields a comma means “and” and a stroke means “or”


90

In the modelling approach adopted, storage is an energy sink/source that is fed by a

power P

sto where represents the energy carrier (that may differ from the actual nature

of energy stored in the storage medium – water, ground, PCM, hydrogen). An example of

hub with two storages, one put at the input port, the other put at the output port, is

reported below (figure 6.8). It is assumed that:

- if P

sto > 0 energy enters the storage: the storage is charging;

- if P

sto < 0 energy leaves the storage: the storage is discharging;

- if P

sto = 0 no charging or discharging of the storage.

To take into account energy storages in the backward coupling matrix formulation of the

hub, and to maintain unaltered vectors of energy input and energy output, a new

parameter is introduced. It is the factor , defined as

out/in

sto

P

P E U L (6.23)

that can relate the energy flow – entering or leaving the storage – to the energy flow at the

input or at the output of the hub. This allows to introduce the term P

sto neither in the

unknowns nor in the known quantities. It is:

- for > 0 the storage is charging;

- for > 0 the storage is discharging;

- for = 0 no charging or discharging of the storage.

Thus, for a storage at the input port, the energy flow downstream of the storage can be

expressed as

inP1 (6.24)

that in the case of the hub of figure 6.8 equals 2KP .

For a storage at the output port, the energy flow upstream of the storage can be expressed

as

a

out

a P1 (6.24)

that in this case equals a

K

a

K

a

K PPP 321 .

The introduction of some energy storage in the energy hub of figure 6.5 right is

represented in the figure 6.9. The matrix D is in this case a function of factors , energy

efficiencies and storage factors

D = f () (6.25)

Pin

Pin

Pin

Pin

Pout

aPout

a

K1

K2

K3

STa

ST Psto

aPsto

a

Psto

Psto

Figure 6.8 – Energy hub with two storages, one at the input port (energy-ware ) and one at the output port (load a)


91

The resulting matrix equation is

m

out

c

out

b

out

a

out

a

K

a

K

K

K

a

K

a

K

n

in

in

in

in

P

P

P

P

P

P

P

P

...

0...000

...............

0...00)1(η

0...η

100

0...0)1(η

1

)1(

)1(

η

...3

3

2

41

1

(6.26)

where 131 a

K

a

K and KK 10 K = {K1, K2, K3, ... }.

Constraints for the storage parameters , that theoretically may assume any value in [–;

+ ], are introduced since, at a time step the energy stored E is equal to the one stored at

the previous time step plus the charged or discharged energy at this time step:

11101 t,out,sto,sto PEE E U L

So, the limit at the discharge of the storage can be written as a function of the storage

parameter as

nt

n,out

n,ston

P

E

1

1

The hourly method is the only application where the introduction of such energy storage

modelling is appropriate.

Figure 6.9 – Introduction of two energy storages in the energy hub of figure 6.5

6.3 The applications of the coupling algorithm to the multi-energy system analysis

A procedure of analysis of multi-energy systems in buildings is developed. It is based on

the matrix formulation to express the coupling between the energy demand and the energy

supply in buildings presented hereinbefore.

In the most general formulation (Eq. 6.26) of this coupling algorithm, there are basically

five sets of parameters:


92

parameters related to the energy demand side of the hub (building loads Pin);

parameters related to the dispatch of fluxes in the hub (factors );

parameters related to the performance of the energy converters (efficiencies );

parameters related to the performance of the energy storages (factors );

parameters related to the energy supply side of the hub (energy-wares Pout).

The determination of the parameters of the first set involves the assessment of the

building energy demand that was discussed previously. In the following sections this set

of parameters is supposed to be known.

The parameters of the other sets may be either known or unknown quantities. Depending

on number and type of the unknown parameters, the energy hub formulation – as

expressed in Eqs. 6.16 or 6.26 – can be used to perform three types of analyses:

1) a design of the multi-energy system;

2) an operational optimization of the multi-energy system;

3) a simulation of the multi-energy system.

Each analysis is outlined in the following paragraphs. The differences between the three

types and the relations with hubs forms are also summarized in figure 6.10.

Operational optimization

Specification of system

operation strategies

Design optimization

Specification of the converters to be installed, of the

distribution of energy fluxes between the converters and

of operation strategies

The most general one hub Only conversions of practical interest are taken into account.

Converters are defined

Optimization

Simulation

Generic Hub Tailored Hub

Converters and operation

strategies are defined

Figure 6.10 – Relations between types of analyses and energy hub forms

6.3.1 Design of the multi-energy system

In this case the multi-energy system a serving a building must be designed. This means

to:

specify the set of energy-wares to be consumed at the input port of the hub;

specify the set of energy converters to be used in the hub;

specify the values of the design power of the energy converters;

specify the values of the energy consumed for each energy-ware.

The selection of the hub consists in finding the set of values of factors , the decision

variables, that best minimize an objective function selected on the basis of one or more

decision criteria

{aK1,

aK2,…

bK1,

bK2

cK1,

cK2,…

mK1,

mK2,…} : min f (6.27)

Unknowns of the selection problem of Eq. (6.27) are not all the factors since, as stated

by Eq. (6.13), for each building load the sum of factors must be equal to 1, resulting in

one less unknown for each building load.

This is the core of the problem however the method will be applied. The decision

variables, factors , have the physical meaning of the distribution of energy fluxes

between the energy converters of the hub: they determine the hub layout. Moreover, the


93

knowledge of values of these factors allows all other unknowns of the problem (design

power of the converters and power of the energy-wares at the input port of the hub) to be

determined under certain assumptions.

Since the scope of the procedure is to design the system, factors refer to the design

condition. There are however many problems to be dealt with when performing a design

selection. First, it has to be specified what “design condition” means for a multi-energy

system serving a building, whether it involves single values or profiles of the input

quantities. A second peculiarity of the application refers to the design of the system, that

may be based not only on the performance of the system at design condition, but also on

the performance of the system during a period of time of operation. As regards time,

different period of analysis may be adopted: one day, one month, one season or one year.

Moreover, depending on the time period of the analysis and on the level of complexity of

the model, the input data may differ. As a result, the boundary conditions and the time of

analysis adopted in the selection may greatly affect the results.

Usually, values of Eq. (6.27) are assumed constant throughout the period of analysis,

otherwise the number of unknowns would be exceedingly high (the number of unknowns

is in fact multiplied by the number of time steps) and it would be extremely difficult to

assign a physical meaning to values of factors that were free to fluctuate over time.

A design of a multi-energy system may or not define also the operation strategies of the

system during a period of time of analysis. As a general rule, plant design can be

optimised without performing a preliminary optimization of its operation only in energy

systems operating in steady-state conditions. In systems characterized by a variable

demand – as multi-energy systems in buildings – the optimization of design and of

operation are strongly interrelated and complex [93].

6.3.1.1 The position of the problem

The selection of the hub components at the design stage is outlined as follows for an

energy hub without energy storage. The inclusion of the energy storage – only possible

when the time domain is considered – leads to another set of unknowns values of i.

Data

Objective function f (Pin, PK)

Hub equation Pin = D Pout

Constraints on factors i, other constraints

Unknowns

- values of Pout

- parameters of PK (e.g. cost in € /kW installed)

- parameters of Pin (e.g. cost in €/kWh consumed, emission factors, …)

- matrix D = f () with parameters known

- values of factors iK

- design power PK

- power Pin

The determination of the unknowns from the data is done by minimizing one objective

function subjected to the hub equation (6.16a) and to the constraints on factors (6.12),

(6.13) and (6.16b-6.16c). Other constraints such as (6.19) may be necessary for the energy

converters that have multiple outputs.


94

6.3.1.2 The resolution process

A schematic representation of the resolution process of the design selection is given in

figure 6.11. The physical outputs of the hub Pout, that is the building loads, are one of the

inputs of the model together with the parameters and equations (values of efficiency, part-

load curves, etc…) that can account for the performance of the energy converters at

different working conditions. These last entries depend on the level of complexity of the

model adopted and will be discussed later on. The model calculates the values of the

power of the energy converters PK = {PK1, PK2, PK3,… PKn} and the values of energy-

wares power Pin at the input port of the hub. These output values of the model (the

physical inputs of the hub) are the argument of an objective function to perform the

selection. The definition of the objective function is made by means of one or more

selection criteria (e.g. minimal cost, maximum return of the investment, minimal

emissions of pollutants, etc.) and parameters that can numerically express the selection

criterion into a certain objective function. A solver has the goal to iteratively search for

the set of iK that best minimize the objective function. In the following sections, a

commercially available reduced gradient method algorithm will be adopted.

Objective

function

f (Pin , PK)Optimization

critera and

parameters

Constraints

Constraints

Model

Pin=D Pout

Pin

PK

Pout

Converters

parameters and equations

Solver

ii

K 10 KL = {a, b, c, ... }, K = {K1, K2, K3,..., Kn}

iKn

KK

i

K 11

L = {a, b, c, ... }

i

K

Figure 6.11 – Schematic representation of the resolution process

6.3.1.3 The characteristics of the solver

The commercially available Microsoft Excel 2000® Solver, one of the most used general-

purpose optimization modelling systems, is used in the following applications (Chapter

7). It is based on the Generalized Reduced Gradient (GRG2) algorithm developed by

Lasdon and Waren [94], to optimize nonlinear problems. The generalized reduced

gradient algorithm used is a nonlinear extension of the simplex method for linear


95

programming and according to the authors can be used for solving efficiently small to

medium size problems. The full specification of the algorithm and the program list is

reported in [94]. It is based on the reduction of the original optimization problem to a

simpler reduced problem. The gradient of the reduced objective function of the problem is

used to search for the minimization of the objective.

The Solver extracts and builds the optimization problem from spreadsheet formulas

inputted by the user. The partial derivatives of the problems functions (objectives and

constraints) with respect to the decision variables that form the Jacobian matrix are

evaluated by a finite difference approximation (the symbolic differentiation being not

applicable due to the user-written functions in the spreadsheet). Either a forward and a

central difference approximations are possible, but the default is forward difference.

The solver perturbs each variable in turns, recalculates the active spreadsheet, and obtains

values for the Jacobian matrix [95].

To save time, there is the possibility to specify whether the model is linear: in this case

the Solver implements the simplex method with bounded variables. In case of a nonlinear

problem, the Solver uses the generalized reduced gradient method as implemented in the

code of [94].

The finding of a local optimum is guaranteed only on problems with continuously

differentiable functions and in the absence of numerical difficulties.

In order to judge whether the local optimum found by the solver satisfying all constraints

and optimality conditions is a global optimum of the problem, external knowledge of the

problem must be applied to determine the region in which the global optimum lies and to

start from several different initial points.

A graphical user interface is available to select various options (finite difference

approximation scheme, maximum number of iterations, convergence). For a complete

discussion on the capabilities of the Microsoft Excel Solver see [95].

6.3.1.4 Forms of hubs

As can be seen from the schematic representation of figure 6.10, two different forms of

hub can be selected as a starting layout:

a generic hub which takes into account all possible conversions (and the relative

components) that energy sources can undergo before covering the loads: it is the

most general one multi-energy system;

a tailored hub which takes into account only the conversions (and the relative

components) that are of practical application and of interest to the building owner

in the specific analysis context.

The first approach is more systematic, but the second is more practical. This also reflects

different uses: a first appraisal about the energy resources and about the technical and

economical feasibility of some converters can be done preliminarily to pass directly on

the tailored hub. It is as if there were two selection levels: a banal selection made by the

decision maker and an instructed selection done through the energy hub modelling.

6.3.2 Operational optimization of the multi-energy system

This is the case of an existing system. It may be actually manufactured or just designed

but the hub layout and the design power of the energy converters are known. The problem


96

to be solved reduces to:

specify the system operation strategies;

specify the values of the power consumed for each energy-ware.

It has to be remarked that this type of optimization, contrary to the design selection, can

only be performed in a time domain analysis. Theoretically, it involves the determination

of the set of values of factors that best minimize an objective function as in (6.27),

where the solution is not a single set of factors , but a series of sets each one of them

referred to a particular time subdomain.

The selection of the operation of a system is of particular interest in case of multi-product

converters, namely cogeneraors and trigenerators and of utility rate profiles. In these cases

the two alternative management strategies are the electricity tracking and the heat

tracking, but many hybrid criterion that result from the combination of the two strategies

are possible. Further to electricity tracking, more operating strategies can be applied for

the electricity produced by on-site generators: a baseload strategy corresponds to an

electric generator constantly operating at a fixed output to cover a baseload of the

electricity demand, a demand limiting strategy corresponds to an electric generator

operating to limit the amount of purchased electricity from the utility grid.

Others operational optimization strategies can be used in case of multiple converters

meeting together the same load not in an alternative way (as it is supposed in the design

of the energy hub). This offers a series of operational optimization strategies: multiple

converters in various sizes, converters working in series, in parallel, etc…)

The operational optimization application process is outlined as follows for an energy hub

without energy storage.

Data

Objective function f (Pin)

Hub equation Pin = D Pout

Constraints on factors i ; on maximum powers of the

converters; other constraints

Unknowns

- values of Pout

- values of PK

- parameters of Pin (e.g. cost in €/kWh consumed, emission factors, …)

- matrix D = f () with parameters known

- values of factors i

- power Pin

Similarly to the design selection, unknowns are determined from the data by imposing the

minimization of an objective function of the energy-wares consumed at the input port of

the hub. The resolution process is the same of the design selection reported in figure 6.11,

with the only difference of values of design powers of the converters PK known. This

involves a third set of input data in the model, and an objective function whose argument

is only the power of energy-wares consumed at the input port of the hub.

6.3.3 Simulation of the multi-energy system

The energy hub coupling formulations (6.16) and (6.26) are also a valuable mean to

perform energy simulation of a multi-energy system. Given a set of factors , the model

can be used to simulate different hub layout/scenarios of converters to be compared with

the selected scenarios. This application also can be used to select a system, following an

approach commonly used in building systems simulation software tools.


97

In this case the selection procedure is then quite different: contrary to the two previous

ones, this one is based on a finite set of scenarios that must be selected by the user.

For an existing system, it is possible to determine the energy requirements under its actual

operating conditions.

The largest interest as a simulation tools lies in the detailed hourly method, that is the

most similar to the building system simulation tools. The seasonal method is on the

contrary the most innovative tool if used for the design selection.

6.3.4 On the selection procedures

Finally, it is to be remarked that the design selection is not based on a finite set of

scenarios to be evaluated and compared to find the optimal solution , as it is traditionally

done in the field of energy systems for buildings [97] but it sets out a selection procedure

where some choices are based on a continuous domain. In fact, energy converters design

powers can assume any positive value between 0 and ; factors – namely the

distribution of energy fluxes of the hub between the converters – can assume any value

between 0 and 1.

The relation between factors and the capacities of the energy converters can be

explicated form the equations recalling that it is

a

out

a

Ka

KP

P 11

and, assuming the building load known, the converter capacity is only a function of the

factor as

a

out

a

K

a

K PP 11

This is why the problem of the system design, which implies the selection of the energy

converters sizes, can be solved by use of only the factors as decision variables.

The procedure presented, once the original hub is set out, do not require the selection of

converters aggregations or benchmarks to be adopted as a reference. When the number of

possible converters is high, as in multi-energy systems for the definition, the number of

converters aggregations that can be worth to be simulated considerably increases, making

more convenient to perform a selection with the energy hub method as described in

section 6.3.1)

6.4 Selection criteria and parameters of the objective functions

6.4.1 Selection criteria

There are several criteria that can be adopted as a decision criteria to select the

components of a multi-energy system in a building. Some of them are the followings:

reducing the operation costs (RC);

reducing the investment costs (IC);

reducing the amount of energy consumed in a period of time (EC);

maximizing the efficiency of the system (EE, both in terms of energy and of


98

exergy);

minimizing the pay-back period of the investment (PBP);

reducing the environmental impact of the building and services (EI);

maximizing the use of natural resources that are available at no cost (NR);

optimizing the utility rates profiles structures (UP);

optimizing the use of incentives during construction phase (CI);

optimizing the use of incentives during operation phase (OI).

Basically, they can be grouped in three categories: economy, energy and environment

based criteria.

One of the above criterion, or a combination of criteria, can be adopted to perform the

selection of the multi-energy system. The decision on whether criterion should be

selected/adopted belongs to the building owner or administrator.

Each criterion must then be specified into objective function through numerical

parameters suitable to specify an objective function.

Table 6.2 – List of decision criteria to be adopted in the selection procedures

Economy Energy Energy-Economy Environment

RC EC EC EI

IC EE EE NR

PBP NR RC

UP, CI, OI IC

PBP

UP, CI, OI

: maximization; : minimization; optimization

6.4.2 Objective functions

Some of the objective functions adopted are addressed below, ordered as a function of the

group of criteria.

6.4.2.1 Economy objective functions

As a general rule, energy saving measures involve an expenditure of capital resources (a

greater investment cost) at a given time with the expectation of benefits in the form of

reduced operation costs due to the energy saved, that is not purchased from the utilities.

An economic selection tries to find the solution that best maximizes the cost-benefit ratio.

With reference to the multi-energy systems analysis as outlined in the previous sections,

two key considerations are to be made:

in the operational optimization the existing system can be selected as a reference

to define an economic optimal scenario;

in the design problem, on the contrary, a procedure to search for the economical

optimal scenario must be set out even in the absence of any system to be selected

as a reference.

The utility of defining a reference scenario R is the possibility to have benefits evaluated

as savings with reference to the scenario R. If no reference scenario is defined, from an

end-user standpoint there will be basically only costs and no (or very small) benefits.

Profitability indexes used in the energy economics of renewable and efficiency power


99

systems [99] such as:

simple pay-back period;

initial rate of return;

discounted pay-back period;

internal rate of return;

are relevant when there is the expectation of a balance between cash outflows (usually

concentrated in the first period of time) and future cash inflows. These indexes are of use,

as an example, for the grid-connected PV systems evaluation of investment – see [100] –

but they cannot be applied at the complete energy hub modelling and evaluation.

Even if a cash inflow may be the case of some particular energy converters as a

consequence of some financial incentives, the energy hub will be, during its life, a

money-consumer, and the present net value of cash flows from the end-user standpoint

will always be negative.

On the contrary, in case of a user selling the energy outputs of the hub on the market, the

net present value of the cash flow should be positive and it is possible to calculate the

price at which a unit of energy must be sold so that the investor gets the required rate of

interest [101].

Given this picture, many economy objective functions adapted to the energy hub and

useful from an end-user standpoint, can be derived. A first economy objective function is

the total investment cost. It reads

€K

1 K

KK

ec Pcff P (6.28)

where K K and cK are the specific investment costs (in €/kW) of the hub components –

basically energy converters and energy storages since the interconnections within the hub

are omitted (the respective costs are assumed to be equal between different scenarios). In

this function as in the followings, the investment cost reads for only the purchased cost

and the investment cost of the hub equipment (direct costs). The capital needed to

purchase the land and build all necessary facilities (indirect costs) is not included since it

is assumed that it has a similar impact on all the different design options. The

minimization of this function corresponds to the minimization of the investment cost

criterion that can be of interest by investors merely interested in the construction phase

but not in the building life-cycle. Unfortunately, this is still the case of many non

institutional investors in Italy that, since they construct and sell to private customers, they

are not inclined to evaluate building costs on a long period (for the relation between

sustainability issues and the Italian real estate market see the full description in [102]).

The objective function of Eq. (6.28) can correctly be used to compare two different

scenarios if all components of the hub have the same life time. If not, a first possibility is

dividing each investment cost by the expected life time of each component yH, thus

€/y1

1

Kn

KK K

K

K

K

ec

y

Pcff P (6.29)

where K K . A more general formulation of the total installed cost is represented by the

net present cost NPC, the present value of investment and operation costs over the life

time of the system [24]. Maintenance cost may be either or both:


100

fixed, as in case of PV systems, fuel cells, and proportional to the installed

capacity;

variable as a function of the energy produced by a specific energy converter, as in

the case of microturbine, engines.

The NPC is the sum of all costs paid for the system over its life time N, formulated as

€)1()1(

NPC1

1 0

,

1

1

1

1

N

ii

Kn

KK

T

outKN

ii

Kn

KK

K

K

Kn

KK

K

K

K

ec

d

dPm

d

Pm

Pcff

P (6.30)

where K K and E , having introduced fixed maintenance costs mK and variable

maintenance costs m dependent on the energy supplied by the converter. In Eq. (6.30) the

PK is the design power of the converter K, which is time independent, and PK,out is the

output power of the converter K, which is time dependent, N is the life time in years and d

is the yearly net discount rate (above inflation) to take into account the time value of the

money. The time integral is evaluated on one year (T equals one year). Values of

maintenance and operation costs are discounted to be summed to the first investment cost.

Again, Eq. (6.30) can correctly be used to compare two different scenarios only if the life

time N is the same for each hub component. If it is not so, the greatest life time yK should

be fixed as the period of time of analysis N, that is the life time of the hub

Nyyy KnKK ,...,max 21

and investment costs for replacement should be added for all the components that have a

life time yK lower than N. The replacement cost is equal to the initial cost of the

component minus the residual worth of the component at the end of the life time of the

hub. This can be seen in figure 6.12, where a comparison between two converters is

represented as a function of years of life time. A converter that has a life time X lower

than N will have to be bought again after X years, and if its life time will exceed N, the

residual worth for the years (2X – N) should be subtracted from the second investment

cost. So Eq. (6.30), omitting for simplicity maintenance and operation costs, becomes

€NPCKKK yyy1

Ny

NNK

K

y

K

K

K

ec

KK

RPcPcff P (6.31)

where the residual worth R can be evaluated as the amortization cost of the component

that has a life time X for the years exceeding the life time of the hub N

€)1(

2)1(

1

X

XNI

K

K

XX

IXPcR (6.32)

Both Eqs. (6.31) and (6.32) are written under the assumptions that yearly inflation rate

equals the discount rate (if not a net yearly discount rate should be introduced) and that

5.0,...,min 21

N

yyy KnKK (6.33)

which means that components must be replaced no more than once during the life time of

the hub, otherwise Eq. (6.31) can easily be rewritten taking into account more than one

replacement of hub component for which X < N.


101

Figure 6.12 – Schematic representation of two hub components with different life times

A second category of economy objective functions takes into account the energy cost over

a period of time T. In the basic form it reads

€/y0

inin2

n T

ec dPcff

P (6.34)

where E, it is measured in €/y if the time integral is evaluated on one year. Also in this

case, a further term due to the maintenance cost may be added to Eq. (6.34) that is the

basic function of the energy services managers who tend to minimize the total energy cost

of the energy-wares purchased.

It may be of interest to investigate operational costs, mainly caused by energy purchased

by the utilities, over a period of time greater than one year to take into account the yearly

net increase of energy cost that may be different from one source to another (for example

the future increase in the cost of oil is expected to be much higher than the increase of

natural gas). This can be done by computing the present value of the energy cost on a

period of years comparable to the expected serviceable lifetime of the hub N, for example

20 years. This function can be written as

€1

1

1 0

2

n N

i

T

ini

i

inec dP

)d(

)r(cff P (6.35)

where r is the yearly increase rate of energy-wares cost, for each energy-ware , d is the

discount rate, the time period of index i is the year and N is, as usual, the life time of the

hub in years (that is to say the maximum component life time). Assuming as constant

values the energy costs c and the time integral of the energy consumed, these two

quantities can be extracted from the summation and, for the series properties, Eq. (6.35)

can be rearranged in:

€

1

11

1

11

1

1

0

2

n T

in

N

N

inec dP

)d(

)r(

)d(

)r(

)d(

)r(

cff P (6.36)

Yearly increase rate of energy-wares can take into account one of the key advantages of

renewable energy systems, which is the increase with time of the money saved by using

renewable energy due to the energy-ware escalation (when the increase rate is greater

than the inflation rate). Systems exploiting one renewable energy source are, on the

contrary, independent from the uncertainties associated with the future fuel costs.

In the economy objective functions based on operation costs (6.34), (6.35), (6.36), a

dominant role is played by energy that is available at no cost, basically all renewable


102

energy sources. These “zero-cost” sources tend to be extremely valorised when this type

of selection is performed by means of Eqs. (3.34), (6.35) or (6.36). However, since

converters to exploit these energy sources have a cost generally greater than the one of

converters fed by non-renewable energy sources (and lower efficiency respectively to the

latters) there is a need to combine in some manner capital cost of equipment, operation

and maintenance cost and energy-wares cost, into a third category of objective functions.

On a yearly basis, it is possible to sum one year operation cost for energy-wares to the

investment cost divided by the years of serviceable life time of each hub component that

is, combining Eqs. (6.34) and (6.29),

€/y,1

K

0

3

Kn

KKK

Kn T

υ

inKin

ec

y

PcdPcff

PP (6.37)

or, combining Eqs. (6.34) and (6.31),

€/yNPC

,0

3N

dPcffn T

υ

inKin

ec

PP (6.38)

On a hub life time basis, it is possible to sum total present operation cost for energy-wares

purchased to the total present investment cost. It yields, combining Eqs. (6.35) and (6.31),

€NPC1

1TNPC

1 0

3

n N

i

T

ini

i

Kinec dP

)d(

)r(c,ff PP (6.39)

which represents the total net (cash inflows are considered negative values) present cost

of the hub over its life time. This objective function, as all the previous ones, has to be

minimized to search for the optimal solution.

When performing an operational optimization or a simulation (see sections 6.3.2 and

6.3.3), as in retrofitting actions [97], [98], a reference system is present, so the net present

value calculation procedure [101], [103], can be use to perform the comparison of the

various or retrofitted scenarios with the so called “do nothing” alternative. The procedure

steps of the application of the net present value calculation are, in these cases, the

followings:

1) setting a reference configuration R (converters, allowable conversions, power

flows, design powers of the converters) of the system;

2) setting the period (number of years N) for the investment analysis (not necessarily

equal to the serviceable life time of the hub);

3) determining the initial cost difference IR – I, and the operation cost (operation,

maintenance and energy-wares purchased) difference Ci,R – Ci, between

alternative scenarios and the reference case for each year i;

4) calculating the net present value NPV for each scenario based on capital and

operation cost differences over the fixed period.

€)1(

NPV1

R,

R4

N

ii

iiec

d

CCIIf (6.40)

As a general rule, in design alternatives, after a greater capital cost in the first year,

usually savings on operation costs are expected. The optimal scenario is the one that has


103

the greater net present value (this comparison can be done if all net present values are

calculated assuming the same period of time). This function has to be maximized in order

to search for the optimal solution: this explains the minus before the value of NPV to be

used when applying Eq. (6.40). The determination of the internal rate of return (IIR) of

the investment may be of interest; this index also has to be maximized.

A further objective function can be formulated with reference to the cost for one unit of

energy supplied by the hub. This approach well suits energy systems that produce one

form of energy (electricity, heat) sold to the utilities, the market, or end-users, and it is

generally not used in multi-energy systems in buildings (the only energy sold to the utility

may be the electricity produced by a PV panel or the electricity surplus of a cogenerator).

In any case, a theoretical cost of the unit of energy produced may be calculated as

€/yTNPC

CUE

0

5

m

a

T

υ

out

outKin

ec

dPN

,,ff PPP (6.41)

and the formulation (6.41) minimized to perform the optimization. It is worth noting that

the cost for one unit of energy supplied is not only a function of hub energy inputs and

hub energy components, but also of hub energy outputs. The formulation of the CUE may

so be viewed as an indicator of the energy efficiency of the hub once the energy output is

fixed – as in case of buildings: the lower the cost for one unit of energy is, the more the

system is efficient.

Since it is not always possible, and not usually significant, to sum electricity heat and

cool, as the summation of the denominator in Eq. (6.41) may suggest, a cost for one unit

of energy for each load may be provided. In this case, attention should be paid in

evaluating the correct total net present cost involving only components and energy wares

related to the specific energy output, whose formulation will not be general any more and

will depend on the hub lay out.

6.4.2.2 Energy objective functions

To evaluate the energy efficiency of a converter of a multi-energy system the first law of

thermodynamics is traditionally employed, even when there are different form of energy.

The same modelling approach of the converters presented in 6.2.1.2 is based on the first

law. Studies to relate the efficiency of converters to the exergy (the portion of energy

useful to do mechanical work) have been made from the ‟70 in the field of large energy

systems (see for example the comprehensive textbook by Lucien Borel, [105]) but the

application of the exergy analysis to the converters and processes encountered in HVAC

applications [106], [107] and generally building energy systems [108] is not yet generally

used (one of the reason may be the extremely low values that exergy efficiency reaches in

building uses).

At a system level, it is less frequent to encounter energy efficiency indicators of the whole

system. An example worth to be cited is the coefficient of performance of the entire

HVAC system and the second law efficiency of the whole system proposed by Wei and

Zmeureanu [109]. The first one is defined as the ratio between the useful thermal energy

for heating, cooling and ventilation and the total energy input for all components (for a


104

VAV system serving an office building ranges between 1 and 1.4). The second one is

defined as the ratio between the total exergy content of and the total exergy demand for

sensible and latent heating, cooling and ventilation energy requirements (for the same

system it ranges between 0.06 and 0.08).

As can be seen, there are many energy objective functions that can be adopted.

As regards the energy system design, it may be adopted the minimization of the installed

capacity of the components of the hub (basically the energy converters), whose equation

reads

kW1

1

Kn

KK

KK

en Pff P (6.42)

or the minimization of the power energy input of the hub

kW1

n

inin

en Pff

P (6.43)

Both Eqs. (6.42) and (6.43) lead to the same value if the energy converters capacity is

rated as a function of the energy input (which is not the case of the cogenerators for

example) and if no energy sources or sinks are present in the hub. An evaluation of a

system based on these functions can be of considerable interest (and also very simple) in

case of energy systems operating nearly at steady-state conditions, as in some industrial

applications. However, building energy systems are particularly variable-demand systems

(due to the variability of the building loads) and so the minimization of Eqs. (6.42) and

(6.43) may lead to poor significance results.

However, an objective function based on power values can be of interest in case of

operational optimization, using an equation like (6.43) to search for the optimality

working condition of the system at each time step [104].

The total hub energy input implies on the contrary the sum of the energy hub input over a

period of time. This can be done in more than one way, since a substantial distinction has

to be made between on-site energy (or simply site energy) that is the energy used when

measured at the hub site, and off-site energy (also called source energy) that is the energy

content at the energy source of the energy consumed by the hub. Torcellini and Crawley

discussed this distinction in case of Zero Energy Buildings [110]: there is a particular

difference between “net zero site energy buildings” and “net zero source energy

buildings”, since in the first case the system boundary is the building, whereas in the

second case the system boundary is drawn around the building to transmission and

generation of energy (the full sequence of the fuel cycle). While source energy best

accounts for the energy impact of a system, difficulties in finding site-to-source

conversion factors may be a limitation. Spatial differences in these factors may lead, for

the same system, to a low energy impact in an area with a large percentage of

hydroelectricity and to an high energy impact in an area with a large percentage of fossil

fuel electricity generation plants. Also temporal variations of these factors may occur.

Instead of acquiring the correct (in term of space, time, boundary system, etc…) value of

site-to-source energy conversion factor, it is of possible interest to compute the primary

energy by the primary energy conversion factors, which have the same meaning of site-


105

to-source factors and are fixed at a national level. This approach is widely used in Europe

and particularly in Italy.

As to the formulations of the objective functions, the sum of all energy-wares/energy

sources (site energy) metered at the input port of the hub reads

kWh/y0

inin2

n T

en dPff

P (6.44)

measured in kWh/y when the integration time period equals one year, but this does not

take into account any difference between the various energy-wares.

The sum of the energy consumed done in terms of primary energy gives

/ykWh t

0

inin3

n T

en dPpff

P (6.45)

where factors p are the primary energy factors for each energy carrier . It is worth

noting that in Eq. (6.45) exported energy carriers (e.g. electricity produced by PV) may be

incorporated with a negative value so as to decrease the total value of the function.

According to the European draft standard EN 15603 Energy performance of buildings –

Overall energy use, CO2 emissions and definition of energy ratings [111], two different

types of primary energy factors p may be used:

total primary energy factors pT that represent all the energy overheads of the

delivery, a factor always exceeding the unity;

non-renewable (also non-regenerative) primary energy factors p that represent the

energy overheads of the delivery but exclude the renewable energy component, a

factor that is less than the unity for renewable energy sources.

In any case, primary energy factors include:

energy to extract the primary energy carrier;

energy to transport the energy carrier from the production site to the utilization

site;

energy to process, store, generate, transmit and distribute the energy carrier that is

delivered to the building.

Other components such as:

energy to build the transformation units;

energy to built the transportation system;

energy to dispose the waste of the production;

may be added to primary energy factors. As an example, total and non-renewable primary

energy factors that include the energy to built the transformation and transportation

systems are reported from [111] and other sources in table 6.3. Even renewable energy

sources as biomass, have a primary energy factor greater than zero, and the total primary

energy factor is the sum of the non-renewable energy factor plus the unity (energy

provided by the carrier).

Usually, in the following energy based objective functions, non-renewable primary

energy factors will be used, since they can give a better interpretation of the use of natural

energy sources. It may however be of interest to determine the weight of renewable

energy on the energy hub performance: this may be done by computing the renewable


106

energy fraction REF as

-

)(

)(REF

0

in

0

in

3

3

n T

T

n T

T

T

en

en

dPp

dPpp

pf

pf (6.46)

If adopted to perform the hub selection, Eq. (6.46) has to be maximized.

Other energy efficiency indicators may be formulated as the ratio between the hub energy

input and the hub energy output. Similarly to the system COP of Wei and Zmeureanu

[109] a coefficient of performance of the hub may be computed as

-COP

0

in

0

out

2

0

out

n T

m

a

T

en

m

a

T

hub

dP

dP

f

dP

(6.47)

that may be greater than unity if cooling energy is provided by the hub. Again, this

formulation does not take into account the differences between various forms of energy,

so it may be rewritten as

-PCO

0

in

0

out

3

0

out

n T

m

a

T

en

m

a

T

hub

dPp

dP

f

dP

(6.48)

using the primary energy of Eq. (6.45) instead of the sum of energy inputs of Eq. (6.44).

Table 6.3 – Total and non-renewable primary energy factors from [111] and other sources

Energy carrier Primary energy factor

Non-renewable Total

Fuel oil 1.35 1.35

Gas 1.36 1.36

Anthracite 1.19 1.19

Lignite 1.40 1.40

Coke 1.53 1.53

Wood shavings 0.06 1.06

Log 0.09 1.09

Beech log 0.07 1.07

Fir log 0.10 1.10

Hydroelectricity 0.50 1.50

Electricity form nuclear power plants 2.80 2.80

Electricity from coal power plant 4.05 4.05

Electricity mix UCPTE 3.14 3.31

Electricity mix in Italy * 2.76 n.f.

n.f.: not found; * from CTI R3/03 [112]


107

A further efficiency indicator can be determined from the exergy theory as the ratio

between the useful exergy delivered to the building and the exergy content of energy-

wares/energy sources. Even if in the following sections the exergy efficiency of the

converters will not be addressed, it is always possible to determine the exergy content of

the energy delivered to the building from the power flow P

out, the temperature T of the

energy delivered and the ambient temperature T0. Detailed formulation to estimate the

chemical exergy of various fuels based on the mass fraction for each element are reported

by Hepbasli in [113], but simply the exergy content of the energy-wares inputed, on the

purpose of this simplified analysis, can be calculated from the calorific value of fuels H.

This gives the expression of Eq. (6.49). When computing Eq. (6.49), the Carnot factor in

the numerator may be assumed constant if an average annual outdoor dry bulb

temperature is adopted as the ambient temperature, or variable and so dependent on time

and to be integrated. Also the temperature T of the energy delivered may change over the

time period.

-

1

0

in

0

out0

n T

m

a

T

exhub

dPH

dPT

T

(6.49)

In the determination of the exergy input, attention should be paid to the assessment of the

exergy content of renewable energy. As an example, the exergy received by a solar

collector is calculated in [114] as

dPT

TEx

T

s

s

0

in

01 (6.50)

where Ts is the solar temperature which changes with respect to the hour of the day and

Psin is the solar radiation.

Finally, it is to be remarked that the exergy efficiency of the hub calculated as in Eq.

(6.49) takes into account only the exergy efficiency of the hub and not the exergy

efficiency of the systems installed (e.g. radiators, radiant heating and cooling, etc.) with

reference to the thermal levels required in the building. An high value of ex

hub can be

determined not only by a low value of exergy input but also by a high value of the exergy

output, which in case of a building is not at all desirable. Attention should then be paid to

the use of this index.

No combination of Eqs. (6.42) or (6.43) and (6.44) or (6.45) seems applicable.

6.4.2.3 Environment objective functions

It is well known that no energy source or technology is entirely benign. Every form of

energy conversion implies the conservation not only of the energy (for the first law of

thermodynamics) but also of the chemicals of the energy source in altered form.

Residuals such as carbon monoxide, carbon dioxide, water, sulphur dioxide, nitrogen

oxides, are to be dissipated. So, energy conversion and use, in whatever form, affects the


108

environment in different ways. Associated impacts, both direct and indirect (or

secondary), can be identified only if the complete sequence of activities of the fuel cycle

is assessed, bearing in mind that not only pollutants but also other burdens on the

environment can be associated with it.

The comparison of the environmental impact of different energy conversion technologies

can be made by evaluating pollutants emissions but also by changing the estimate of

impacts into a different unit of measurement, usually monetary one, and estimate the

economic value of the impacts [115]. Since this second step is much more complicated to

carry out than the first one (the individuals willingness to pay to avoid an incremental

damaging impact has to be estimated), a procedure of environmental impact estimation

based on pollutants emissions is assumed throughout this study.

Environment objective functions are based, as many energy objective functions, on the

energy consumed by the hub on a period of time. The assessment of the environmental

impact of a building and its energy system is done by the evaluation of the emissions

caused by operation. While more than one hundred of pollutants are emitted from fossil

fuel combustion or electricity generation, a large proportion of pollutants can be ascribed

to the followings [61]:

carbon dioxide CO2

carbon monoxide CO

methane CH4

nitrogen oxides NOX

nitrous oxide N2O

sulphur dioxide SO2

particulate matter PM

particulate matter 2.5 < PM10 < 10 m

particulate matter PM10 < 2.5

ammonia NH3

non-methane volatile organic compounds NMVOC

mercury Hg

lead Pb

of which carbon dioxide, methane and nitrous oxide are the major greenhouse gases that

Kyoto Protocol requires each country to report together with ozone-depleting substances.

The general environment objective formulation is a system of q objective functions (as

the number of pollutants q) where the pollutants emissions are evaluated from the energy

consumed at the input port of the hub by use of emission factors e

XX of pollutant XX for

the fuel

..........

/yg

/yg

/ykg

222

XXX

222

SO

0

inSOinSO,1

NO

0

inNOinNO,1

CO

0

inCOinCO,1

n T

ev

n T

ev

n T

ev

dPeff

dPeff

dPeff

P

P

P

(6.51)


109

A complete definition of emission factor can be found in [116]:

“an emission factor is a representative value that attempts to relate the quantity of a

pollutant released to the atmosphere with an associated activity. It is usually

expressed as the weight of pollutant divided by a unit weight, volume, distance or

duration of the activity emitting the pollutant. […] In most cases, these factors are

simply averages of all available data of acceptable quality, and are generally

assumed to be representative of long-term averages for all facilities in the source

category”.

Since emissions factors generally depend not only on the fuel type but also on combustion

technology, in the application of Eqs. (6.51) to the hub, it is to be remarked that the same

energy carrier may be consumed in different converters leading to different emissions.

Assigning the same emissions factor to an energy-ware that is used in various converters

may lead to some small errors in the pollutants evaluation.

Source for appropriate emissions factors may be found in various reference, both at

international [116], [117], [118], and national [119] level. The Intergovernmental Panel

on Climate Change (IPCC) developed methodologies to estimate carbon dioxide

emissions in various sectors and to provide values of carbon dioxide emission factors

together with the lower and upper value of the 95% confidence interval for fuel

combustion in the energy sector for 53 fuel types [117]. The Decision of the European

Commission of 18 July 2007 [118] reports the fuel emission factors related to the net

calorific value from the 2006 IPCC guidelines. Biomass is considered CO2 neutral: an

emission factor of 0 tCO2/TJ is assigned to wood, wood waste, charcoal, biogasoline,

biodisel, bioethanol, landfill gas, sludge gas, other biogas, plants and parts of plants,

biomass wastes, products and by-products.

Emission factors for non-CO2 gases from fuel combustion are strongly dependent on the

energy conversion technology used, so they vary considerably between different sectors.

Repertories of emission factors for the various pollutants as a function of combustion

technology such as [116] and [119] should then be used carefully, since they are

representative of an average range of emission rates, that may be over or less source-

specific permit limits or regulations and not representative of the best available

technology on the market. If this is the case, the best source of emission factors

information is actual test data (or emission performance guarantee) on specific equipment

similar to the one to be evaluated .

Also in case of emission factors for electricity generation, a distinction between on-site

emission factors and off-site emission factors has to be introduced. The same arguments

expressed in case of energy are applicable to pollutants. The determination of emission

factors must take into account how the electricity is generated and what is the mix of

generation within a spatial area. Transmission and distribution losses of electricity should

also be evaluated. Greenhouse gas and pollutants emission factors for electricity

generation may be provided at a national level or argued from analogy on other sources

such as the complete list of pollutants emission factors for US summarized in [61] from

various EPA sources. Values of 0.59 kgCO2/kWhe for Italy and of 0.09 kgCO2/kWhe for

France can be assumed [121].

A comparison between emission factors for some fuel types and conversion technologies

is reported in table 6.4 from various sources. Attention should be paid to the metrics of


110

the emission factors, especially to the unit of energy, whether they are based on gross

calorific value or net calorific value. Conversions between factors can be made assuming

net calorific values 5, 10 and 20% lower than gross calorific values respectively for coal

or oil, natural gas, and wood.

Once acquired emission factors, the application of Eq. (6.51) has to be discussed. Every

objective equation of the system can be minimized, even if emissions are measured in

different units of metrics, which affects the result. One way is to retain only the equation

of the most important greenhouse gas, the carbon dioxide, and minimize it. A powerful

alternative is to make use of the concept of the carbon dioxide equivalent to translate

emissions of gases other than CO2 into equivalents using global warming potentials

(GWP). This index estimates the relative global warming contribution due to atmospheric

emission of a kg of a particular greenhouse gas compared to the emission of a kg of

carbon dioxide. Using this concept, one objective function taking into account carbon

dioxide, methane and nitrous oxide can be derived as

/ykg222

442

CO

0

inON

0

inCH

0

inCOin2

n T

ON

n T

CH

n T

ev

dPeg

dPegdPeff

P

(6.52)

Values of global warming potentials g for methane and nitrous oxide proposed by EPA

[120] in 2005 are 21 and 310 respectively for a time horizon of 100 years, which is

usually adopted. Other GWP values may be provided with other time horizons (usually

the GWP index increases for shorter periods and decreases for longer periods).

Obviously, the g factor for carbon dioxide is the unity in any case.

Table 6.4 – Fossil fuels emission factors

Pollutant CO2 CO CH4 NOX N2O SO2 PM PM10 PM2.5 NMVOC Hg Pb

Fuel [kg/MWh] [kg/MWh] [kg/MWh] [kg/MWh] [kg/MWh] [kg/MWh] [kg/MWh] [kg/MWh] [kg/MWh] [kg/MWh] [g/MWh] [g/MWh]

Natural gas (IPCC) 202.0

(195.5-

210)

Natural gas (EPA) 180.8 0.127 0.0035 0.151 00033

0.0010

0.00090 0.0115 0.0086 0.0029 0.00829 0.000392 0.000754

Natural gas (15603) 277.0

Natural gas (APAT) 199.5 0.090 0.180 - 0.024 0.018 -

LPG – butane (EPA) 237.7 0.055 0.0024 0.221 0.0012 1.570 0.0211 0.0119 0.0092 0.0038 0.0125 0.0167

LPG (IPCC) 227.2

(221.8-

236.2)

LPG (APAT) 224.4 0.036 0.180 - 0.007 0.007 -

Residential Oil (EPA) 246.5 0.055 0.197 0.199 0.001 1.570 0.077 0.057 0.021 0.0079 0.0125 0.0167

Diesel Oil (EPA) 253.8 1.470 - 6.825 - 0.449 - 0.480 - 0.542 - -

Gas/Diesel Oil (IPCC) 266.8

Fuel oil (15603) 330.0

Gasolio/Oil (APAT) 263.6 0.072 0.180 0.338 0.013 0.011

Gasoline (APAT) 246.8 96.93 2.518 0.083 0.158 3.025

Wood (IPCC) 403.2

(342.0-

475.2)

Wood shavings

(15603)

4.0

Wood (APAT) 340.3 26.98 0.288 - 0.939 2.158

Municipal Waste no

biomass fract. (IPCC)

330.1

(263.88-

435.6)

Municipal Waste

biomass fract. (IPCC)

360.0

(304.9-

475.2)

Municipal Waste

(APAT)

112.9 0.029 0.450 0.015 0.018 0.176

For low NOX burner

7 ENERGY HUB APPLICATIONS AND

CASE STUDIES

7.1 The applications of the coupling algorithm

Two different methods were identified to simulate a multi-energy system as a function of

inputs detail and of the design phase of the project concerned. A considerable care was

spent on this subject, since it was deemed really important to develop simulation and

selection tools to be used at all the design phases. This is especially true for buildings

where, not only at the conceptual design phase, but also at the development design phase,

many times the variability of thermal, cooling and electricity loads are far from being

calculated, even in cases of large commercial building projects.

These are the reasons why the model of multi-energy systems presented in section 6.2

was applied into two different methods tailored to the nature of input data and results. As

can be seen in table 7.1, not all methods can be used for all the applications, it depends on

the time domain of analysis.

Table 7.1 – Relation between the model, the methods and the applications

1 model

of multi-energy

systems

2 methods

of analysis

3 applications

Design Operation Simulation

Energy hub Seasonal method × ×

Hourly method × × ×

7.2 The seasonal steady-state method

7.2.1 Background and scope of the method

Since multi-energy systems, for the definition given in section 1.1, usually adopt non

convectional energy converters, new aggregation of components, unusual system layouts,

and are also particularly sensitive to boundary conditions of whatever type – energy,

ENERGY HUB APPLICATIONS AND CASE STUDIES

113

economic, environmental – there is the need to made available, from the design concept

stage (also called pre-design, schematic design and conceptual design) an energy-

economy-environmental feasibility analysis procedure of these systems. This is the

answer that the seasonal steady-state application of the coupling algorithm presented in

the previous chapter is intended to give.

This application is also towards the recent integrated design process theories, based on the

whole building concept, where “all the design variables that affect one another are

considered together and resolved in an optimal fashion” [122]. The seasonal hub

modelling procedure provides a powerful tool to implement the sustainable design

sequence based on:

the reduction of the building loads to the minimum;

the increase of system efficiency;

the use of regenerative systems;

use of renewable sources as systems driving inputs;

Even if the procedure may seem quite simplified, there are many reasons that explain this

use.

On one hand, it is well known, as discussed by Lewis in [122], that potential benefits of

design inputs taken at the design concept stage are much greater than the benefits of

design choices taken at the design development and construction document phase. At the

earliest stages also the cost of implementing concepts to improve the energy performance

of the building is lower.

On the other hand, modelling and evaluation methods of multi-energy systems currently

available, based on detailed simulation models, can be applied only with a great number

of input data, boundary conditions and users profiles (that are of the greatest importance

in building energy assessment, [123]) that usually are not known in design concept phase.

These modelling methods are therefore useful during the phase of advanced design to

evaluate a finite set of alternatives in accordance with a top-down approach that may be

called a design-evaluation approach.

To the lack of quantitative evaluation methods suitable to the first phase of the design,

when it is neither advantageous nor possible to carry out detailed simulations, and when it

is of the utmost importance to evaluate a large amount of different design alternatives, an

answer was given in the field of building design through the elaboration of architectural

conception design aiding tools that are able to select, at the design concept phase, the

building shape, window size, orientation, building height, etc…[124]. Still there is a need

of simplified procedures to model and select the energy system of the building.

Being conscious that the degree of the design effort is greater during the program pre-

design and schematic design phases [122], it is of a great importance to concentrate the

research activities on the elaboration of a methodology to model and optimize the

coupling between energy demand and energy supply in a building at the design concept

phase, taking into account all the constraints that arise in real-life building design.

7.2.2 Model specifications

The coupling algorithm of section 6.2 is used here in the formulation of Eqs. 6.16 – 6.20.

These equations are applied both with design powers (subscript d) and annual or seasonal


114

energy. This gives

d,dd, outin PDP (7.1)

and outin EDE s (7.2)

It is usually Tn

ininininin EEEE ...,, E and Tm

out

c

out

b

out

a

outout EEEE ...,,E

with

dPE

T

outinoutin

0

// E U L (7.3)

so E

in is the energy of energy-ware consumed at the input port of the hub in the period

of time T, Eaout is the energy a required at the output port of the hub in the period of time

T.

Matrixes D d and D s, whose subscripts refer to design and seasonal conditions, are

determined in accordance to 6.2.1.1 and 6.2.1.2. The only difference between them,

concerns the values of efficiencies to be adopted:

design efficiencies (usually full load efficiencies) of the converters in the matrix

D d;

mean seasonal/annual efficiencies of the converters in the matrix D s.

The same principle applies when more than one energy efficiency is needed in case of

converters with multiple outputs.

As regards factors , the same factors are considered in both matrixes at design and at

mean seasonal conditions. Besides that, more than one season may be analysed over a

year (e.g. heating season and cooling season) assigning to each season one hub lay-out

and so one set of decision variables . Usually a distinction between heating season and

cooling season is always necessary in buildings systems, because of the variability of the

energy demand over the year.

This distinction may be ignored and a unique set of decision variables may be set out

when no interconnections between cooling energy and heating energy are present in the

hub.

If more than one season is considered, the design capacities of the energy converters of

the hub are the maximum values obtained over the seasons

KP,......P,PmaxP sasn,Ksas,Ksas,Ksas

K 21 K (7.4)

This has to be specified, since design capacities of the energy converters are used in many

objective functions.

In case of energy-wares or energy sources that are available at the input port of the hub

with some limitations, specific constraints must be added to Eqs. (7.1) and (7.2). Energy-

wares like natural gas, electricity, district heating may be considered as always available

whereas other energy sources like renewable ones can be collected from the environment

within its regeneration rate. These limitations are taken into account at this stage by

imposing a simple maximum value on all decision variables related to this particular

energy source. This may be done limiting the appropriate factors within a range

0 < < max (7.5)

where max depends on the properties (area, orientation) of the solar catching area of the

building. It must be pointed out that at this stage of development of the method, only the

limitation on design capacity of the converters and integral values of energy can be


115

enforced. No limitation can be enforced on the particular availability profile of an energy

source (see section 7.3.1 for the overcoming of this drawback).

No energy storage device can directly be taken into account in this method since the

simulation is not performed in the time domain. The performance of an integrated energy

storage can however be simulated by the use of an appropriate value of mean seasonal

efficiency. This is the case of the thermal solar systems, that are always used with the

integration of a water storage. In these cases, a preliminary parametric study of the

performance of the integrated system converter+storage must be performed. As an

example, appropriate values of the mean annual efficiencies of a solar system for a

residential unit determined from sensitiveness analysis carried out with dynamic

simulation are reported in [34], [125], and can be used in this method.

7.2.3 Input data

Consistently with the design stage at which this method has to be applied, the number of

input data is very small.

As regards the building energy demand there is the necessity of only:

design value for each building load;

annual/seasonal energy requirement for each building load.

These values refers to the energy that must be supplied by the energy system of the

building, so they do not necessarily represent the building energy need, but take into

account all energy losses that may occur (e.g. distribution and regulation energy losses)

after the primary plant.

Even if theoretically many evaluation tools among those presented in chapter 3 can be

used, at this stage the energy demand is more likely determined through:

simplified standard methods (e.g. calculation procedure of EN 12831 for the

heating design load, ISO/DIS 13790 for the energy need for heating and cooling);

literature values (e.g. BSRIA Rules of thumb for cooling loads and energy

requirements [48]).

In any case, the number of input values for the energy demand equals 2ms where m is the

number of building loads and s is the number of seasons to be analysed.

The same rationale can be used when assessing the performance of the energy converters:

two values of conversion efficiencies must be provided for each converter, one at full

load, and the over at mean seasonal/annual condition. This second value is the most

difficult to be determined a priori, and it must be based on some existing literature,

results, or – at least – on expert judgments.

7.2.4 Output data and results

The time integral of Eq. (7.3) is only reported to clearly define the quantity E, but it is not

computed any time in this calculation procedure since only a value of seasonal/annual

energy is provides as an input. From Eqs. (7.1) and (7.2) the values of energy-wares

power inputs entering the hub, and the energy consumed by the hub can be determined. In

case of the system design, the application of one of the objective functions presented in

6.4.2 allows a set of decision variables to be determined under the constraints expressed

where the general procedure is described in section 6.3.1.


116

In the following paragraphs 7.2.5 and 7.2.6 the application of the steady-state method to

the problem of the selection and design of the energy system of a building is presented.

7.2.5 Maison Mozart

7.2.5.1 Case study description

The “Maison Mozart” is a 99.8 m² single-family house on an unique storey represented in

the figure 7.1. It was defined by EDF in cooperation with the CSTB and the GDF in 1994.

Glazed surfaces of the facades are respectively equal to the 16, 19, 39 e 26 % of the total

North, West, South and East surface area.

To perform the dynamic energy simulation in order to estimate heating and cooling loads,

the building was partitioned into five thermal zones (day, night, roof, garage, air space

over the ground) of whom day and night zones are conditioned.

Building features, schedules of the internal loads and ventilation flow rate, thermal

properties of the building constructions derive from the description document of the

Maison Mozart drawn up by EDF, CSTB and GDF. When defining the properties of the

building components, values of thickness of insulation materials greater than those

reported in the description document are assumed.

The building location is Torino. The building energy demand characterization is made in

tables 7.2 and 7.3. In figures 7.2 and 7.3 the space heating and cooling loads are reported

in terms of time series and cumulative frequency curves. In figure 7.4 the monthly heating

(for space heating and DHW preparation), cooling and electricity needs are reported.

Figure 7.1 – Maison Mozart floor

7.2.5.2 The energy hub description

The energy hub considered for this case study is reported in figure 7.5. Energy-wares at

the input port are wood (superscript w), natural gas (g), solar energy (s) and electricity

from the grid (e). The combination of components selected (tailored hub) provides the

possibility of meeting the thermal load (superscript t) – alternatively or in any

combination – through:

a wood boiler (WB);

a condensing boiler (CB);

an air-to-water reversible heat pump (HP);


117

a thermal solar combisystem (SC).

The cooling load can be met by:

an air-cooled split system (C);

an air-to-water reversible heat pump (HP);

The electricity can be met by:

electricity from the grid (e);

the output of a photovoltaic system (PV).

In this case, input power can be expressed as a function of output power and of

conversion efficiencies of the energy converters as

e

out

e

e

c

out

c

Cc

C

c

out

c

HPc

HP

t

out

t

HPt

HP

e

in

t

out

t

SC

SC

e

out

e

PV

PV

s

in

t

out

t

CB

CB

g

in

t

out

t

WB

WB

w

in

PPPPP

PPP

PP

PP

COP

1

COP

1

COP

1

11

1

1

(7.6)

with the usual significance of the factors discussed in sections 6.2.1.1 and 6.2.1.2. The

term COPtPC refers to the heating operation of the heat pump, while the term COP

cHP

refers to the cooling operation of the heat pump in reverse cycle.

Table 7.2 – The assessment of the building energy demand in terms of loads and energy of the Maison Mozart for the Torino location


Space heating load 5.025 2.957 0

Cooling load 3.192 0 2.892

Electricity 3.000 3 3

Energy demand [kWh] Annual Heating season Cooling season

Space heating energy 4174 (42 kWht/m²) 4174 0

DHW heating energy 2794 (28 kWht/m²) 1623 1171



Table 7.3 – Seasonal loads and seasonal load factors of the Maison Mozart for the Torino location

Seasonal mean load

[kW]


(calculated on the

desing power)


(calculated on the peak

power)

Heat 0,828 0,17 0,28

Cool 0,505 0,16 0,17

Electricity 0,380 0,13 0,13


118

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 8001 8501

LO

AD

[k

W]

1/5 30/9

Cooling season

1817 kWhf /y

4174 kWht /y

Figure 7.2 – Space heating and cooling loads profiles of the Maison Mozart (Torino location)

0

1000

2000

3000

4000

5000

6000

7000

8000

0 0.5 1 1.5 2 2.5 3

LOAD [kW]

NU

MB

ER

OF

HO

UR

S

A = HEATING

B = COOLING

8760

A

B

Figure 7.3 – Space heating and cooling loads cumulative curves of the Maison Mozart (Torino location)

-1500

-1000

-500

0

500

1000

1500

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Sep Oct

Nov

Dec

En

erg

y [

kW

h]

Heating Energy Cooling Energy Electricity

Figure 7.4 – Monthly heating (space+DHW), cooling and electricity energy demand of the Maison Mozart (Torino location)


119

Pin

e

Pin

s

Pin

g

Pin

w

Pin

ePin

e

Pin

sPin

s

Pin

gPin

g

Pin

wPin

w Pout

t

Pout

c

Pout

e

Pout

tPout

t

Pout

cPout

c

Pout

ePout

e

WB

CB

SC

PV

HP

C

WB

CB

SC

PV

HP

C

WB

t

SC

t

C

c

HP

cHP

t

CB

t

e

e

PV

e

WB

tWB

t

SC

tSC

t

C

c

C

c

HP

cHP

cHP

t

HP

t

CB

t

CB

t

e

ee

e

PV

ePV

e

Figure 7.5 – Schematic of the energy hub considered in the Maison Mozart and block of flats case studies

The set of equations (7.6) can be rewritten in a matrix form as

Pin = D Pout (7.7)

e

out

c

out

t

out

e

e

c

PCc

HP

c

Cc

C

t

PCt

HP

e

PV

PV

t

SC

SC

t

CB

CB

t

WB

WB

e

in

s

in

g

in

w

in

P

P

P

P

P

P

P

COP

1

COP

1

COP

1

10

1

001

001

(7.8)

As regards the decision variables, only 5 out of 8 factors are independent, since, for each

load, at the output port it is, as usual,

1,, ect

i

i

(7.9)

Design efficiencies and mean seasonal efficiencies of the energy converters are assumed

equal to the values reported in the table 7.4. As regards mean seasonal efficiencies of the

split system, it was adopted the value of the ESEER (European Seasonal Energy Efficiecy

Ratio) related to a EER equal to 2.5.

Table 7.4 – Design efficiencies and mean seasonal efficiencies of the hub energy converters

Conversion efficiency Design value [-] Mean seasonal

value [-]

WB 0.75 0.65

CB 1.05 0.90

COPC 2.50 2.90

COPtHP 3.10 2.70

COPfHP 3.30 3.80

PV 0.15 0.15

SC 0.70 0.30


120

7.2.5.3 The objective functions

Three objective functions were identified:

1) an economic function based on the energy consumed during heating and cooling

season and on the power installed, similar to the Eq. (6.37) that reads

KK

coolKheatK

y

PPcEEcf

,,

K

cool in,heat in,1

,max (7.10)

where subscripts “heat” and “cool” refer respectively to the heating and to the cooling

seasons. Fixed specific investment costs of energy converters cK are adopted and are

reported in table 7.5. The costs for energy wares c are equal to 0.025 €/kWh for the

wood, 0.06 €/kWh for the natural gas, 0 €/kWh for the solar energy and 0.15 €/kWh for

the electricity from the grid;

2) an energy objective function based on the energy consumed defined as Eq. (6.45),

whose weighting factors are the non-renewable primary energy factors of table 6.3;

3) an environmental objective function like Eq. (6.52), where only the carbon dioxide

emission factors are considered.

Table 7.5 – Specific investment costs and life times of energy converters of the hub

Converter cK y

K Converter c

K y

K

[€/kW] [y] [€/kW] [y]

WB 500 20 PV 6000 20

CB 150 15 SC 600 15

C 250 15 e 90 25

HP 250 15

7.2.5.4 Renewable sources constraints

To properly take into account the solar energy in Eqs. (7.1) and (7.2) and avoid an

overestimation of the energy input at both design and seasonal mean conditions, it is

necessary to introduce further constraints like (7.5).

Solar energy is in fact proportional to the suncatching area Ac, that – in the absence of

more detailed information – may be assume equal to half of the roof area. This quantity

multiplied by the total horizontal solar radiation Isol can be assumed as an indicator of the

upper limit of the solar energy. A choice can be made on the period of analysis to be

considered (one year, one season, one month).

For this case study, and for a period of one year, it is

Esin,max = AR · Isol = 50 · 1320 = 66000 kWh (7.11a)

which becomes, for a period of the heating season (as can be seen from the load profiles

of figure 7.1) of 5 months

Esin,max = AR · Isol = 50 · 512 = 25600 kWh (7.11b)

This is the upper value of the energy input to the solar collectors and the PV modules,

which gives the following inequality constraint on the factors of SC and PV components

e

PV

e

PV

t

SC

t

SCmax,

η

ε

η

ε e

out

t

outS

in

PPE (7.12)


121

7.2.5.5 Class of optimization problem

Mathematically, the problem can be stated as follows:

Minimize f (Pin, PK, cK, p, e)

Subject to outin EDE s

d,dd, outin PDP

ii

K 10 L = {a, b, c, ... }, K K = {K1, K2, K3, ... Kn }

iKn

KK

i

K 11

L = {a, b, c, ... }

iP i

in 0 E = {, ... }

KPK 0 K = {K1, K2, K3, ... Kn }

e

PV

e

PV

t

SC

t

SC

η

ε

η

ε25600

e

out

t

out PP (where refer to the cooling season)

The objective functions and the constraint are linear equations: the efficiencies in the

matrixes D are constant and also the parameters in the objective functions (specific costs,

primary energy factors and emissions factors are constant), so the optimization problem to

be solved is a linear constrained optimization problem. For details on the solver used see

section 6.3.1.3.

7.2.5.6 System design

In the table 7.6 the values of the factors that minimize each one of the three objective

functions adopted are reported. From these values it is possible to define the system

configurations. As an initial guess, a system where all the loads are uniformly distributed

onto the various energy converters is adopted.

A first design configuration can be derived from the minimum cost criterion: it is based

on a massive use of solar energy to cover the thermal loads both in heating and cooling

seasons, on the connection to the grid as regards all the electricity demand, and on the use

of the reverse cycle heat pump to cover the cooling load. This system is outlined in figure

7.6. A value of tSC equal to 1 should be regarded as only theoretical, since it cannot be

reached in practice, and a back-up heating should be provided: in this case the back-up

heating in winter can be made by means of the heat pump (this is the reason of the dashed

line in the hub schematic of figure 7.6). Even if there is a massive use of solar energy, the

constraint of equation (7.12) is at any rate satisfied, since the total solar energy need to

cover the thermal load is under the limits calculated by the (7.11) for both annual (23227

kWh < 66000 kWh) and seasonal (19323 kWh < 25600 kWh) conditions. This also

points out that the solar system – as it is – is oversized in summer, when the heating

demand is dramatically reduced.

The use of a split system instead of the reversible heat pump to cover the cooling load is

not preferred since it rises the annual cost to 908 €/y and it cannot be used as a back-up

heating source for the solar collectors in winter. It is to be noted that, compared with the

initial guess, this system configuration reduces not only the value of the economy

objective function, but also the values of the energy and environmental objective

functions.


122

A second design configuration can be determined from the minimization of the energy

objective function (case Energy n.1 in table 7.6 and 7.7). This is similar to the previous

one, but relies on the use of the PV to cover the building electricity load instead of the

electricity purchase from an utility network. As can be seen from table 7.7, this causes

however a sensible increase (44%) in the annual cost (the economy objective function),

and a dramatic decrease – of one order of magnitude – in the primary energy consumption

and carbon dioxide emissions.

Table 7.6 – Values of factors that minimize the objective functions

Selection

criteria

Heating season Cooling season

heat cool electr. heat cool electr.

WB

CB

HP

SC

C

HP

e PV

WB

CB

HP

SC

C

HP

e PV

Init guess ¼ ¼ ¼ ¼ 0.5 0.5 0.5 0.5 ¼ ¼ ¼ ¼ 0.5 0.5 0.5 0.5

Economy 0 0 0 1 - - 1 0 0 0 0 1 0 1 1 0

Energy n.1 0 0 0 1 - - 0 1 0 0 0 1 0 1 0 1

Energy n.2 .34 0 0 .66 - - 0 1 0 0 0 1 1 0 0 1

Environ. .34 0 0 .66 - - 0 1 0 0 0 1 1 0 0 1

Table 7.7 – Values of the objective functions for the scenarios of table 7.6

Selection

criteria

Value of the economy

objective function

[€/y]

Value of the energy

objective function

[kWh/y]

Value of the

environmental objective

function [kgCO2/y]

Initial guess 1189 10716 1892

Economy 885 10504 1751

Energy n.1 1275 1318 220

Energy n.2 1287 1532 266

Environmental 1287 1532 266

Pin

ePin

e

Pin

sPin

s

Pout

tPout

t

Pout

cPout

c

Pout

ePout

eSC

HP

SC

tSC

t

HP

cHP

c

e

ee

e=1

=1

=1

Pin

ePin

e

Pin

sPin

s

Pout

tPout

t

Pout

cPout

c

Pout

ePout

e

SC

PV

C

SC

tSC

t

C

cC

c

PV

ePV

e=1

=1

= 0.66

WBPin

wPin

wSC

tSC

t= 0.34

Figure 7.6 – Schematic of the energy hub converters selected as a function of the minimum annual cost

Figure 7.7– Schematic of the energy hub converters selected as a function of the minimum primary energy and emissions


123

This is due to the fact that the electricity consumption (3328 kWhe) accounts for approx

87% of the primary energy in the economy criterion and initial guess scenarios, since it is

weighted with a factor of 2.76.

Electricity from the grid is used only to produce cooling energy by means of the heat

pump, which again can be a valuable back-up source in heating season. In this case the

constraint of Eq. (7.12) is satisfied only on the basis of the year, while during the heating

season it is not satisfied, and the solar energy is out of the limit of eq. (7.11b). This

scenario (Energy n.1) is therefore not feasible, and should be substituted by another one.

Taking into account this limit, only 66% of the heating energy is provided by the solar

collectors in winter (case Energy n.2 in table 7.6), while all the electricity is provided by

the photovoltaics. The remaining heating energy has to be covered by the wood boiler.

The use of the condensing boiler would lead to a much greater primary energy

consumption – 4297 kWh compared to 1532 kWh of the wood boiler case – and also to a

greater annual cost.

The minimization of the environmental based function gives the same result of the

minimization of the energy objective function (case Energy n.2), as expected.

7.2.6 Block of flats


This building is a five-storey 1230 m² multi-family building with 10 apartments in total.

The building is built in concrete; the walls are made of two brick layers with an internal

glass wool layer. The roof is plane and not insulated. There are warehouses at the first

floor, which interfaces with the ground. A plan of the first floor and of the typical floor as

well as the South view of the building are reported in figure 7.8.

The building has been partitioned into three thermal zones, to account for the presence of

the stair-well and of the warehouses which both are unconditioned. Only the internal zone

of the apartments is a conditioned thermal zone. Reference on the building data to

perform the dynamic simulation are reported on the references [127] and [47].

The building location is the same of the previous case study, Torino, to perform a

meaningful comparison between the two buildings. The building energy demand

characterization is made in table 7.8. In figures 7.9 – 7.10 the space heating and cooling

loads are reported in terms of time series and cumulative frequency curves. In figure 7.11

the monthly heating (space and DHW), cooling end electricity demand are reported.


The energy hub considered for this case study is the same that was applied for the Maison

Mozart and that is reported in figure 7.5. The only differences are that the air-cooled split

system is replaced by a central cooling system equipped with a central water-cooled

chiller (C), and that the heat pump is of the water-to-water type. The matrix equation of

the hub remains the same as Eqs. (7.6), (7.8), with the constraint of (7.9).


124

Design efficiencies and mean seasonal efficiencies of the energy converters are assumed

equal to the values reported in the table 7.4 for all converters but the reverse cycle heat

pump and the air-cooled chiller, whose performance parameters are reported in table 7.9.

180

140

140

60

180

140

140

60120

240

240

120

120

140

140

180

140

120

140

180

140

180

240

120

140

120

140

60

240

120

300

270

240

120

140

60

260

365

440

260

260

440

330

260

260

230

220

260

160

260

210

260

220

260

270

260

100

290

290

100

290

100

290

100

270

260

260

270

260

270

240

200

290

100

290

100

290

100

0 2 6 10 20 m

180

140

140

60

180

140

140

60120

240

240

120

120

140

140

180

140

120

140

180

140

180

240

120

140

120

140

60

240

120

300

270

240

120

140

60

260

365

440

260

260

440

330

260

260

230

220

260

160

260

210

260

220

260

270

260

100

290

290

100

290

100

290

100

270

260

260

270

260

270

240

200

290

100

290

100

290

100

0 2 6 10 20 m

Figure 7.8 – Block of flats plans and south view

Table 7.8 – The assessment of the building energy demand in terms of loads and energy of the block of flats for the Torino location


Space heating load 65.19 43.46 0


Electricity 30 30 30

Energy demand [MWh] Annual Heating season Cooling season

Space heating energy 74.93 (61 kWht/m²) 74.93 0

DHW heating energy 29.52 (24 kWht/m²) 17.12 12.40

Cooling energy 29.42 (24 kWhf/m²) 0 29.42

Electricity 39.60 (30 kWhe/m²) 22.97 16.63

To select an appropriate value of the performance parameters for these last two

converters, certified data on coefficient of performance (COP, in heating mode), energy

efficiency ratio (EER, in cooling mode) and European seasonal energy efficiency ratio

(ESEER, in cooling mode) for various water-cooled liquid packaged chillers were collected

and reported in the figures 7.12 and 7.13. These data were determined under the Eurovent

certification procedure and are available at the certification web site [www.eurovent-

certification.com]. The converters whose data are reported in figure 7.12 are dedicated to

air-conditioning applications, while the others (figure 7.13) are dedicated to radiant

heating and cooling applications because of the high temperature of the chilled water,

produced at 18 °C and not at 7 °C, with the inherited increase in the machine efficiency,

as can be seen from the comparison between the data in the two figures. For the case

study, the values of air-conditioning machines are used.


125

-50

-40

-30

-20

-10

0

10

20

30

40

50

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 8001 8501

LO

AD

[k

W]

1/5 30/9

Cooling season

29.42 MWhf /y

74.93 MWht /y

Figure 7.9 – Space heating and cooling loads profiles of the block of flats (Torino location)

0

1000

2000

3000

4000

5000

6000

7000

8000

0 10 20 30 40 50

LOAD [kW]

NU

MB

ER

OF

HO

UR

S

A = HEATING

B = COOLING

8760

A

B

Figure 7.10 – Space heating and cooling loads cumulative curves of the block of flats (Torino location)

-15

-10

-5

0

5

10

15

20

25

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Sep Oct

Nov

Dec

En

erg

y [

MW

h]

Heating Energy Cooling Energy Electricity

Figure 7.11 – Monthly heating (space+DHW), cooling and electricity energy demand of

the block of flats (Torino location)


126

Liquid Chilling Packages water cooled / Reverse cycle / DT 12/7, DT 30/35

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60 70 80

Pc / Ph [kW]

EE

R, E

SE

ER

, C

OP

[-]

EER R407C ESEER R407C COP R407CEER R410A ESEER R410A COP R410A

Figure 7.12 – Values of three performance parameters for water-cooled liquid chiller

packages (rating conditions of the Eurovent certification program, chilled water at 12/7 °C

and condensing water at 30/35 °C) as a function of the heating/cooling capacity

Liquid Chilling Packages water cooled / Reverse cycle / DT 23/18, DT30/35

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60 70 80

Pc / Ph [kW]

EE

R, E

SE

ER

, C

OP

[-]

EER R407C ESEER R407C COP R407CEER R410A ESEER R410A COP R410A

Figure 7.13 – Values of three performance parameters for water-cooled liquid chiller packages (rating conditions of the Eurovent certification program, chilled water at 23/18 °C and condensing water at 30/35 °C) as a function of the heating/cooling capacity

Table 7.9 – Design efficiencies and mean seasonal efficiencies of the heat pumps and chillers

Conversion efficiency Design value [-] Mean seasonal

value [-]

COPC 3.50 4.00

COPtHP 4.30 4.30

COPfHP 3.50 4.00

7.2.6.3 The objective functions

To perform a comparison between this case study and the previous one, the same

objective functions of section 7.2.5.3 were adopted.

Fixed specific investment costs of energy converters cK adopted are reported in table 7.10.

Some of these values are lower than the ones adopted in the previous case study since the


127

converters capacities are much higher in this case (to this point, the value of the specific

investment cost appropriated to the converter size can be found from the cost curves

determined in section 5.3.2). The costs for energy wares remain unchanged.

Table 7.10 – Specific investment costs and life times of energy converters of the hub

Converter cK y

K Converter c

K y

K

[€/kW] [y] [€/kW] [y]

WB 250 20 PV 6000 20

CB 100 15 SC 600 15

C 250 15 e 90 25

HP 250 15

7.2.6.4 Constraints on renewable sources and on the heat pump

For this case study, the upper value of the yearly solar energy input of the hub, following

Eq. (7.11) is

Esin,max = AR · Isol = 123 · 1320 = 162.360 MWh (7.13a)

and the upper value of the solar energy input for the heating season is

Esin,max = AR · Isol = 123 · 512 = 63.0 MWh (7.13b)

Another constraint is necessary to the operation of the heat pump which, as reversible, can

operate also for cooling purposes, but not at the same time for heating and cooling. This

can be stated as:

if tHP,cool > 0, then

cHP,cool = 0 (7.14a)

if cHP,cool > 0, then

tHP,cool = 0 (7.14b)

Since there is no cooling energy demand in winter season, it is not necessary to impose an

analogous constraint on the heat pump operation in winter season.


The class of optimization problem to be solved is similar to the previous one (section

7.2.5.5). In the table 7.11 the values of the factors that minimize each one of the three

objective functions adopted are reported. Also in this case, the initial guess is the system

where all the loads are uniformly distributed onto the various energy converters.

The design configuration of components that can be derived from the minimum cost

criterion is reported in figure 7.14 and is based on the following components:

a thermal solar collectors system that provides 21% of the heating load in the

heating season and 56% of the heating load in the cooling season; the size of the

collectors is limited by the upper value of the solar energy input in the heating

season;

a 41 kW heat pump that covers part (63%) of the heating load in the heating

season and that in reverse cycle covers all the cooling load in the cooling season;

a 10.5 kW wood boiler that cover the remaining heating load (16%) in the heating

season and the 44% of the heating load in the cooling season;

the connection to the electricity grid to cover all the electricity demand in both

seasons.


128


Selection

criteria

Heating season Cooling season

heat cool electr. heat cool electr.

WB

CB

HP

SC

C

HP

e PV

WB

CB

HP

SC

C

HP

e PV

Init guess ¼ ¼ ¼ ¼ 0.5 0.5 0.5 0.5 ¼ ¼ ¼ ¼ 0.5 0.5 0.5 0.5

Economy .16 0 .63 .21 - - 1 0 .45 0 0 .56 0 1 1 0

Energy 1 0 0 0 - - .59 .41 1 0 0 0 1 0 .10 .90

Environ. 1 0 0 0 - - .59 .41 1 0 0 0 1 0 .10 .90


Selection

criteria

Value of the economy

objective function

[k€/y]

Value of the energy

objective function

[MWh/y]

Value of the

environmental objective

function [tCO2/y]

Initial guess 14.12 133.98 23.92

Economy 11.55 169.13 28.29

Energy 16.95 73.63 12.81

Environmental 16.95 73.63 12.81

Pout

tPout

t

Pout

ePout

e

SC

SC

tSC

t

HP

tHP

t

e

ee

e

= 0.63

= 0.21

= 1

HP

Pin

wPin

w

WBWB

tWB

t= 0.16

Pin

ePin

e

Pin

sPin

s

Pout

tPout

t

Pout

cPout

c

Pout

ePout

e

SC

SC

tSC

t

HP

cHP

c

e

ee

e

= 0.56

= 1

= 1

Pin

wPin

w

WBWB

tWB

t= 0.44

HP

Figure 7.14 – Schematic of the energy hub converters selected as a function of the minimum cost: heating season (left), cooling season (right)

In this way, the reversible heat pump is designed to cover the cooling load during the

cooling season and a great part of the heating load in winter. The remaining heat load is

covered by a small capacity wood boiler that in the winter covers only a minor part of the

load, but that in the summer is the only integration source to the solar collectors.

Even if there are three components to cover the heating load in winter, this configuration

has a minor cost of the one that sizes the heat pump to cover all the thermal load in winter

and provides the cooling energy by a chiller. The use of a condensing boiler instead of the

wood boiler leads to a greater annual cost (12.75 €/y), due to the high cost of the gas

compared to the wood, which pays back the greater investment cost of the component.

It remains to be noted that, compared to the initial guess system, this one reduces the

annual cost for investment and purchased energy wares, but increases the primary energy


129

consumption and the carbon dioxide emissions.

A second design configuration can be derived from the minimum primary energy

criterion and is reported in figure 7.15. It is based on the following components:

a 65 kW wood boiler that provides all the heating energy both in heating and

cooling seasons;

a 35 kW chiller – and not the heat pump in reverse cycle – that provides the

cooling energy;

a 26 kWp PV system that covers 89% of the electricity demand in the cooling

season and the 41% of the electricity demand in the heating season;

the connection to the electricity grid to meet the remaining electricity needs.

The constraints of Eq. (7.13) on the solar energy input limit the use of the PV panels to

cover only a part of the electricity demand.

The reason why in this scenario the use of PV is preferred to the use of solar collectors is

that, even if the conversion efficiency of the PV modules is lower than the one of solar

collectors, PV modules provide an energy output (the electricity) whose cost and primary

energy emissions are greater than the ones of the thermal energy provided by the solar

collectors.

All this results in a great decrease (– 56%) in the primary energy consumption and in an

increase of 45% in the annual cost.

Also in this case, a system selection based on the environmental objective function gives

the same result of a system selection based on the energy objective function, as expected.

Pin

wPin

w

WBWB

tWB

tPout

tPout

t

Pout

ePout

e

PV PV

ePV

e

= 1

= 0.41

e

ee

e= 0.59

Pin

wPin

w

WBWB

tWB

t

Pin

ePin

e

Pin

sPin

s

Pout

tPout

t

Pout

cPout

c

Pout

ePout

e

C

cC

c= 1

C

= 1

PVPV

ePV

e= 0.10

e

ee

e= 0.90

Figure 7.15 – Schematic of the energy hub converters selected as a function of the minimum primary energy and emissions: heating season (left), cooling season (right)

7.2.7 Discussion

The model presented is quite simple and allows analysis to be performed in presence of

only design power and annual or seasonal energy demand data. Such a model meets the

requirements of simplicity that characterize the design concept phase, but a factor of

uncertainty is represented by the choice of the values of the efficiencies.

Values of mean seasonal efficiencies greatly affect the results and appropriate values of


130

these properties are difficult to determine a priori and must be based on the consultant

experience.

Further research activity was carried out to overcome this drawback.

7.3 The hourly method

7.3.1 Scope of the method and model specifications

The scope of the hourly method is to perform an analysis of the multi-energy system able

to take into account:

the variability of a converter efficiency as a function of the working conditions

(fluid temperatures, outdoor air temperatures, etc.);

the variability of the converter efficiency as a function of the part load

performance;

the intermittent nature of renewable sources.

Only if the time domain is considered it is possible to model possible thermal energy

storages.

The coupling algorithm of the hub is always expressed by Eqs. (6.16) – (6.20), that are

applied at design conditions (design powers, subscript d) and at each time step i of the

runtime period of analysis (e.g. one reference year). This gives

d,dd, outin PDP (7.15)

and

ioutin PDP (7.16)

where i = 1, 8760 if the time step is the one of 1 hour for a year period of analysis.

Strictly speaking, the dynamic behaviour of the system components is not actually

modelled, but it is considered as a succession of steady state conditions variables over the

time step. This is the reason why this model is called “hourly” and not “dynamic”. The

smallest the time step is, the more accurately the dynamic behaviour of the system is

represented.

In the entries of the matrix D, the conversion efficiencies are variables as a function of the

working conditions and of the part load of the converter, and the factors are assumed

constant for a period of time (that may be all the time of analysis or a sub-interval), that

implies that they may change between design and operation. This is true for all sources

but the ones that have a load profile also on the energy supply side (that is the case of the

solar energy), for which constant factors cannot be adopted†.

As regards the limitations on energy sources at the input port of the hub, at this stage they

can be enforced by a factor that is multiplied to the design value (maximum irradiation,

mean wind speed).

As an example, the problem of the design and the estimation of the performance of a PV

system under an energy hub hourly method (e.g. the one in figure 7.20) is addressed

† As there is no sun during the night, the factor that represents the electricity produced by a PV array, if

there is a positive electricity demand, should be equal to zero. This implies that the factors of variable

sources must vary.


131

below. At design condition (subscript d) it is

e

dout

e

PV

e

dPV PP ,, (7.17)

but ePV is limited also by the relation

/kWpm

m2

2

,

sp

e

dPVA

AP (7.18)

where A is the sun-catchinging area and Asp is the specific area of a particular PV

technology, expressed as a function of the peak kilowatt (e.g. 8 – 10 m2/kWp). For this

reason, a maximum value of the electricity fraction covered by the PV system at design

condition ePV can be determined as

e

doutsp

e

PVPA

A

,

(7.19)

During the system operation, ePV cannot be kept constant, since solar energy has a

specific profile of the supply that can be accounted for in the hourly method by a

multiplier . It is therefore

0876 to1 fromie

d,PVe

,PV PPii

(7.20)

where the values of the multiplier can be determined by an off-line application. Its value

equals 1 for a 1000 W/m2 solar radiation, which is the design value at which PV panels

are rated and can be used to size the PV system. Such a treatment of this problem has the

advantage of not complicating the algorithms of the hourly method, keeping at the same

time a great accuracy in the solar energy converters performance simulation and

estimation.

The mean fraction of the electricity provided by the PV system e

PV becomes

8760

1 ,

,

i i

i

e

out

e

PVe

PVP

P (7.21)

that necessarily is

e

PV

e

PV (7.22)

Obviously it is also

ee

ee (7.23)

7.3.2 Input data

In case of the hourly method, it is necessary to dispose of both:

design values for each building load;

annual time series for each building load, at a time interval equal or smaller to the

time step of the model.

This always implies the use of a dynamic software simulation tool, or the presence of

convenient literature data.

In case of exploitation of solar energy, the multiplier is necessary.

Also tariffs profiles of energy-wares provided by the utility networks can be applied in the

cost functions of the energy-wares consumed at the input port of the hub.


132

7.3.3 Output data and results

In addiction to all the results that can be obtained by the previous type of model, the

hourly method can provide time series of the energy input of the hub.

In the following paragraph the application of the hourly method to the problem of the

selection of the converters of a multi-energy system for a case study (an hotel) is

presented.

7.3.4 Hotel


The hotel is located in San Donato Milanese (Milano) and has a complex shape in the

basement floor and in the ground floor, but from the first floor to the thirteen floor two

box-shaped blocks contains the hotel rooms. The ground floor plan is reported in figure

7.16. The two blocks are oriented respectively NW-SE the one, and North-South the

other. The total building height is equal to 45 meters.

The conditioned volume of the building equals 68,600 m3. The total conditioned area

equals 21,075 m2 and is divided into 1467 m

2 at the basement floor, 3358 m

2 at the

ground floor, 710 m2 for each typical floor of the first block, 540 m

2 for each typical floor

of the second block. The roof area equals 1250 m2.

The heating, cooling and electricity load calculation was performed by means of the

DOE-2 energy simulation tool by Corrado et al. [128] as a part of a research programme

on natural gas plants in the civil sectors. Milan, Rome and Trapani locations are available

on [128]. In this application, only the Rome location is considered. The energy demand

characterization is made in table 7.13. In figures 7.17-7-19 the heating, cooling and

electricity demand are reported in terms of time series, cumulative frequency curves and

monthly values. Contrarily to the previous figures, the DHW heating load is considered in

figure 7.19 as well as in figures 7.17 and 7.18.

In this case study, the electricity demand is greater than the one of the other two

residential case studies.

Table 7.13 – The assessment of the building energy demand in terms of loads and energy of the hotel for the Rome location

Peak loads [MW] Design Heating season H&C season

Heating load 1.61 1.29 0.89


Electricity 0.36 0.36 0.36

Energy demand [MWh] Annual Heating season H&C season

Space heating energy 1921 (92 kWht/m²) 1349 572




133

Figure 7.16 – Plan of the ground floor of the hotel


To apply the hourly method, a different energy hub was adopted and is represented in

figure 7.20. Energy-wares at the input port are natural gas (superscript g), district heating

(dh), solar energy (s), electricity from the grid (e). The combination of components

selected (tailored hub) provides the possibility to cover the thermal load (superscript t)

through:

a standard gas boiler (GB);

a condensing boiler (CB);

the connection to the district heating from cogeneration by an heat exchanger

(HE).

The cooling load can be met by:

a gas-fired absorption chiller (AC);

a centrifugal vapour compression chiller (CC);

a screw vapour compression chiller (SC).

The electricity load can be met by:

the electricity from the grid (e);

a photovoltaic system (PV).

In this case, the electricity produced by the PV system is supposed to be injected into the

net and sold to the electricity company at a price of 0.45 €/kWhe, under previsions of the

Italian law.


134

-1500

-1000

-500

0

500

1000

1500

2000

1 1096 2191 3286 4381 5476 6571 7666

LO

AD

[kW

]

Heating &Cooling Season20/3 21/11

1921 MWht/y

983 MWhf/y

8760

Figure 7.17 – Heating and cooling loads profiles of the hotel (Rome location)

A

B

C

0

1095

2190

3285

4380

5475

6570

7665

8760

0 200 400 600 800 1000 1200 1400 1600 1800

LOAD (kW)

NU

MB

ER

OF

HO

UR

S

A = COOLING

B = HEATING

C = ELECTRICITY

Figure 7.18 – Heating, cooling and electricity loads cumulative curves of the hotel (Rome location)

-400

-300

-200

-100

0

100

200

300

400

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

months

[MW

h]

Heating Energy

Cooling Energy

Elcetricity

Figure 7.19 – Monthly heating, cooling and electricity energy demand of the hotel (Rome location)


135

Input power can be expressed as a function of output power, dispatch factors and

efficiencies as

e

out

e

e

c

out

SC

c

SCc

out

CC

c

CCe

in

e

out

PV

e

PVs

in

t

out

HE

t

HEdh

in

t

out

AC

c

ACt

out

CB

t

CBt

out

GB

t

GBg

in

PPCOP

PCOP

P

PP

PP

PCOP

PPP

(7.24)

In the relations (7.24), that are calculated at design condition and at each time step, all the

factors but ePV are assumed as constant, while and COP are assumed variable at each

time step.

Pin

ePin

e

Pin

sPin

s

Pin

dhPin

dh

Pin

gPin

g Pout

tPout

t

Pout

cPout

c

Pout

ePout

e

GB

CB

PV

HE

AC

GB

tGB

t

SC

cSC

c

AC

cAC

cHE

tHE

t

CB

tCB

t

e

ee

e

PV

ePV

e

SC

CC

CC

cCC

c

Figure 7.20 – Schematic of the energy hub considered for the hotel case study

7.3.4.3 The energy converters characteristics

For the energy converters, the following parameters, performance curves and variable

cost functions were adopted.

Standard boiler

The design efficiency was assumed variable as a function of the heating capacity as in Eq.

(5.6), reported in figure 5.1a, and where the values of the parameters A and B are

respectively 90 and 2.

The part load factor curve is the one of Eq. (5.8) reported in figure 5.1b with the relative

coefficients reported at page 61.

The generic specific cost function of Eq. (5.30) is, from Eq. (5.31.b)

3082.0

634.77

GB

GB Pc (7.25)

Condensing boiler

The design efficiency was assumed variable as a function of the heating capacity as in Eq.

(5.6), where the values of the parameters A and B are respectively 94 and 1.

The part load factor curve is Eq. (5.9), reported in figure 5.1b, with the relative


136

coefficients reported at page 61.

The specific cost function, from Eq. (5.32) is

3904.0

35.510

CB

CB Pc (7.26)

Heat exchanger

A constant efficiency of 95% was adopted.

The specific cost function, from Eq. (5.31.c) is

6375.0

91.413

HE

HE Pc (7.27)

Absorption chiller

A COP rated at design conditions equal to 1 was adopted.

The part load factor curve is Eq. (5.15b) reported in figure 5.4b.


4836.0

2.5221

AC

AC Pc (7.28)

Centrifugal chiller

The COP of the centrifugal chiller is equal to 5.81 when rated at a leaving chilled water

temperature of 6.7 °C and at an entering condenser water temperature of 29.6 °C.

The actual cooling capacity (an unknown of the problem) and COP are calculated as a

function of the leaving chilled water temperature and of the entering condenser water

temperature by use of the curves of Eqs. (5.10) and (5.11) whose coefficients are reported

in the Appendix at section 10.1.4 (first step of cooling capacity). Fixed chilled water

temperature of 7 °C and condenser water temperature of 25 °C are adopted.

The actual COP as a consequence of the part load is calculated at each time step from the

part load factor curve of Eq. (5.15) whose coefficients are specified in the Appendix at

section 10.1.4 (first step of cooling capacity).


191.0, 57.383

C

SCCC Pc (7.29)

Screw chiller

The COP of the screw chiller is equal to 5.73 when rated at a leaving chilled water

temperature of 5.6 °C and at a entering condenser water temperature of 25.3 °C.

The actual cooling capacity and COP are calculated as a function of the leaving chilled

water temperature and of the entering condenser water temperature by use of the curves

of Eqs. (5.10) and (5.11) whose coefficients are reported in the Appendix at section

10.1.3. Fixed chilled water temperature at 7 °C and condenser water temperature at 25 °C

are adopted.

The part load factor of the COP curve is the Eq. (5.15) whose coefficients are specified in

the Appendix at section 10.1.3 for screw chillers.

The specific cost function is the same of the centrifugal chiller.

PV system

A module efficiency of 12%, a specific area Asp of 8 m2/kWp and a specific cost of

6500 €/kWp are assumed.

7.3.4.4 The objective functions and performance indicators

Various functions among those presented in section 6.4.2 were adopted to select the

multi-energy system of this case study. They are listed below and identified as fn, where


137

the subscript n refers to the number of this ordered list.

Economy objective functions

1) Investment cost computed by means of Eq. (6.28);

2) Investment cost computed by means of the NPC (Net Present Cost) as formulated

in Eq. (6.31), with a residual worth of the component determined by means of Eq.

(6.32); for the calculation of the NPC a life time horizon of 25 years was adopted

and life times of 15 years for all the components but the heat exchanger of the

district heating (20 years) and the PV system (25 years);

3) Operation cost computed by means of Eq. (6.34);

4) Life-cycle cost computed by means of Eq. (6.37) – which is a combination of f1

and f3;

5) Life-cycle cost computed by means of Eq. (6.38) – which is a combination of f2

and f3;

Energy objective functions

6) Power input of the hub computed by means of Eq. (6.43);

7) Non-renewable primary energy computed by means of Eq. (6.45) with the factors

p of table 6.3 (for the district heating from cogeneration, which is not included in

table 6.3, it is pT = 1 and p = 0.7);

8) REF (Renewable Energy Fraction) of the hub computed by Eq. (6.46), function to

be maximised;

9) COPhub of the hub computed by means of Eq. (6.48), function to be maximised;

Environmental objective functions

10) Carbon dioxide emissions computed by means of Eq. (6.52) where only CO2

emissions are considered;

11) Carbon dioxide equivalent emissions computed by means of Eq. (6.52)

This last function was not used since, assuming a CO2 emission factor for natural gas

equal to 277 kg/MWh, the natural gas carbon dioxide equivalent for CO, CH4 and

N2O is equal to 278.1 kg/MWh, giving a not at all appreciable difference between the

two values.

7.3.4.5 Class of optimization problem

Mathematically, the problem can be stated as follows:

Minimize f (Pin, PK, cK, p, e)

Subject to d,dd, outin PDP

ioutin PDP

ii

K 10 L = {a, b, c, ... }, K K = {K1, K2, K3, ... Kn }

iKn

KK

i

K 11

L = {a, b, c, ... }

iP i

in 0 E = {, ... }

KPK 0 K = {K1, K2, K3, ... Kn }

e

doutsp

e

PVi

e

PV

e

PVPA

Ai

,

, where


138

Both the objective functions and the constraint are nonlinear equations: the efficiencies in

the matrixes D are quadratic, cubic or rational functions of the design power and of the

load, some parameters in the objective functions (namely the specific costs cK) are

nonlinear functions of the design power, so the optimization problem to be solved is a

nonlinear constrained optimization problem. For details on the numerical solver used see

section 6.3.1.3. It should be remarked that in this class of problem, there is no guarantee

that the global optimum can be achieved.


In table 7.14 the factors that minimize (or maximize, in case of REF and COP) the

objective functions are reported. The relative values of the objective functions are

reported in table 7.15 for all the design scenarios.

A graphical representation of the annual investment and operation cost computed with

Eq. (6.37) for the different hubs (H) configurations is reported in figure 7.21. In this

figure, negative costs refers to the electricity sold to the electricity grid.

A further comparison between the hub configurations is made in figures 7.22 and 7.23 in

terms of energy supply – that is not reported in any table – (figure 7.22) and carbon

dioxide emissions – reported in the last column of table 7.15 – (figure 7.23). In figure

7.22 also the building energy demand is reported.

The hub configuration that reduces the investment cost (H1) computed by means of

Eq. (6.28), is the one that supply all the heating energy demand with the district heating,

has two chillers (a 830 kW screw chiller and a 330 kW centrifugal chiller) to cover the

cooling energy demand, and is connected to the electricity grid to cover all the electricity

demand. Reducing the investment cost also reduces the power input of the hub (function

f6).

The reduction of the Net Present Cost (H2), even if based on a function more complex

than the previous, leads to the same result as the minimum investment cost criterion.

On the contrary, in the minimum operation cost criterion more efficient energy

converters, or converters that can provide a reduction in running costs (like PV modules),

are preferred. The converters of this hub configuration (H3) are a 1540 kW condensing

boiler, a 1200 kW screw chiller, a 156 kWp PV system and the connection to the grid for

the remaining electricity demand. The PV system is sized at 156 kWp as a consequence of

the limitation of the roof size, similarly to the previous case studies and following the

Eq. (7.18). From the penultimate column of table 7.14 it can be noted that the 156 kWp

PV system, that at design conditions represents the 36% of the electricity demand,

provides on a yearly basis only 11% of the electricity demand due to the specific

availability profile of the solar energy. A reduction of 36% in the operation cost is

obtained, but the investment cost is four times higher.

The same results (H4, H5) are obtained by means of the life-cycle cost functions – Eqs.

(6.37) and (6.38) – since the operating cost has a larger influence on the life-cycle cost.


139


Selection criteria

heat cool electricity

tGB

tCB

tHE

cAC

cCC

cSC

ePV

e

PV ee

Initial guess 0.33 0.33 0.33 0.33 0.33 0.33 0.15 - 0.75

f1 (investment cost) 0 0 1 0 0.31 0.69 0 0 1

f2 (NPC) 0 0 1 0 0.31 0.69 0 0 1

f3 (operation cost) 0 1 0 0 0 1 0.36 0.11 0.64

f4 (life-cycle cost) 0 1 0 0 0 1 0.36 0.11 0.64

f5 (life-cycle cost) 0 1 0 0 0 1 0.36 0.11 0.64

f6 (power input) 0 1 0 0 1 0 0 0 1

f7 (primary energy) 0 0 1 0 0 1 0.36 0.11 0.64

f8 (REF) 0 0 1 0 0 1 0.36 0.11 0.64

f9 (COPhub) 0 0 1 0 0 1 0.36 0.11 0.64

f10 (environmental) 0 0 1 0 0 1 0.36 0.11 0.64


f1 f2

NPC

f3 f4 f5 f6 f7 f8

REF

f9

COP

f10

[k€] [k€] [k€/y] [k€/y] [k€/y] [MW] [GWh/y] [-] [-] [tCO2/y]

I.G. 33.1 877.2 494.4 527.4 529.4 3.07 10.27 0.073 0.441 1835

f1 8.98 252.6 411.1 420.0 421.2 2.26 6.79 0.082 0.667 1177

f2 8.98 252.6 411.1 420.0 421.2 2.26 6.79 0.082 0.667 1177

f3 51.5 1322 262.0 313.5 314.8 3.38 7.31 0.166 0.619 1303

f4 51.5 1322 262.0 313.5 314.8 3.38 7.31 0.166 0.619 1303

f5 51.5 1322 262.0 313.5 314.8 3.38 7.31 0.166 0.619 1303

f6 10.3 289. 402.2 412.5 413.8 2.21 7.90 0.000 0.573 1402

f7 48.8 1247 274.8 323.6 324.6 3.41 6.27 0.247 0.723 1091

f8 48.8 1247 274.8 323.6 324.6 3.41 6.27 0.247 0.723 1091

f9 48.8 1247 274.8 323.6 324.6 3.41 6.27 0.247 0.723 1091

f10 48.8 1247 274.8 323.6 324.6 3.41 6.27 0.247 0.723 1091

All the three configurations H3, H4 and H5 have a REF equal to 0.17, greater than that of

the other configurations, but a COPhub slightly lower.

The minimum power input gives an interesting configuration (H6), which is based on the

use of the condensing boiler (as in the H3, H4, H5 configurations), the centrifugal chiller

and on the connection to the electricity grid. This configuration is the one that, apart the

ones based on the reduction of the investment costs (H1 and H2), realizes the lower

investment cost: this is caused by the fact that a reduction of the input power of the hub

also leads to a reduction in the cost of converters installed.

Finally, for the last four selection criteria, the hub configuration is the same (H7–H10):

the use of the district heating, a 1200 kW screw chiller, a PV system of the maximum


140

possible capacity (156 kWp) and the connection to the grid. The same configuration:

reduces the primary energy use of the hub;

increases the REF (Renewable Energy Factor) of the hub (up to 0.25);

increases the COP of the hub (up to 0.723);

reduces the carbon dioxide emissions (as can be seen in figure 7.23).

Looking through all the results in figure 7.22, first it is to be noted that an energy supply

lower than the energy demand in H1 and H2 is due to the fact that the cooling energy is

reduced by a factor between 4 and 6 (the COP of the chillers). In the same figure, the

differences in the energy sources to be inputted to the hub can also be appreciated: in H6

for example, the energy consumed is merely the same of hubs H1 and H2 but the use of

the natural gas results in a worse environmental performance (higher emissions that can

be appreciated in the following figure 7.23).

If one configuration among the various scenarios should be selected, the best seems to be

the last one, since in addition to the environmental (low emissions) end energy benefits

(low primary energy use, high REF, COP), it has also an annual cost similar to the one of

the H3-H4 configurations (275 and 262 k€/y).

As regards the selection criteria and the objective functions, three groups of criteria can be

identified: the investment costs functions, the operation and life-cycle cost functions, the

energy and environmental functions. In fact there is not a great difference between the

investment cost as computed by the Eq. (6.28) and (6.31): both lead to the same result.

The same statement is true for the operating cost functions, that, when the energy systems

are larger, tend to be the prevailing cost in a life-cycle cost analysis. For the third group,

the use of other energy objective functions like REF and COPhub is interesting as they can

provide others description parameters of the hub performance, but they lead to the same

results of other energy objective functions.

The reason is clear for the function f8, as for its definition given in Eq. (6.46), an increase

in the REF can only be obtained by a reduction in the non renewable primary energy use

(which corresponds to a minimization of the f7). Similarly, an increase in the COP of the

hub, as defined in Eq. (6.48), can only be obtained by a reduction in the non renewable

primary energy use that is at the denominator of this fraction, since the numerator – the

sum of all the building energy demands – is a constant.

In case of the hub that reduces the energy use as the environmental impact, the variability

of the energy-wares to be inputted is reported in figure 7.24 for the month of February,

where the straight lines refer to the design power. On a yearly basis, such a graphical

representation is not more significant, and it is substituted by the cumulative frequency

curves of the same quantities and of the energy demand that are reported in figure 7.25.


141

-200 -100 0 100 200 300 400 500 600

Thousand €Annual cost

Gas boiler Condensing boiler District heating Absorption chiller

Centrifugal chiller Screw chiller PV Natural gas

District heating Electricity PV Electricity sold

H0 (initial guess)

-200 -100 0 100 200 300 400 500 600





H1 (minimum

investment cost),

H2 (minimum

NPC) -200 -100 0 100 200 300 400 500 600





H3 (minimum

operation cost), H4.

H5 (minimum life-

cycle cost) -200 -100 0 100 200 300 400 500 600





H6 (minimum

power input)

-200 -100 0 100 200 300 400 500 600





H7 (minimum

primary energy)

and H8, H9, H10 -200 -100 0 100 200 300 400 500 600





-200 -100 0 100 200 300 400 500 600





Figure 7.21 - Annual investment and operation cost computed with Eq. (6.37) for the different hubs (H) configurations reported in tables 7.14 and 7.15

0 1 2 3 4 5 6 7

H7, H8, H9, H10 Energy

Supply

H6 Energy Supply

H3, H4, H5 Energy

Supply

H1, H2 Energy Supply

H0 Energy Supply

Energy Demand

Energy [GWh/year]

Natural gas District heating Electricity Solar radiation Heat Cool Electricity

Figure 7.22 – Comparison between the building annual energy demand and the annual energy supply for the initial guess (H0) and the design scenarios (H1, …H10)


142

0 250 500 750 1000 1250 1500 1750 2000

H7, H8, H9, H10

H6

H3, H4, H5

H1, H2

H0

CO2 Emission [tCO2/year]

Natural gas District heating Electricity Solar radiation

Figure 7.23 – Comparison between the initial guess (H0) and the design scenarios (H1, …H10) in terms of carbon dioxide emission

7.3.5 Discussion

The hourly method presented along with the selection procedure, can be a valuable mean

to investigate the trade-off between economical and environmental objectives in multi-

energy systems in buildings. Among the economical objective functions, there are many

choices, but – except for the minimum investment cost criterion – all other criteria lead to

similar results. Also the different life times of the components do not have a significant

impact on the results. When a life-cycle economical selection is performed, in case of

large buildings the influence of the investment cost is very low and the annual operation

cost is prevailing.

A minor possibility of choice is available in case of environmental objective functions,

even if it is to be pointed out that there is a great random in the choice of emissions

factors since different sources may report very different values (see to this point table

6.4). Environmental objective functions must therefore be used with caution, especially

when different hubs are compared with each other.

Finally, it was proved that more complex objective functions – like Eqs. (6.31), (6.38) –

lead to hub configurations and results similar to the ones obtained by the use of simpler

objective functions, however they should be preferred when more refined results are

needed. To estimate appropriate investment and operation costs of an hub, and not only to

perform an selection of the converters of an hub, more detailed selection functions should

be preferred.

Overall, for design purposes the use of simpler objective functions seems justified for all

the selection criteria.


143

0

200

400

600

800

1000

1200

1400

1600

1800

744 844 944 1044 1144 1244 1344

HOURS [h]

LO

AD

[kW

]

Natural gas District heating Electricity Solar radiation

Figure 7.24 – Load profiles of the energy-wares to be inputted to the hubs H7, H8, H9, H10 for the month of February

0

200

400

600

800

1000

1200

1400

1600

1800

0 876 1752 2628 3504 4380 5256 6132 7008 7884 8760

HOURS

LO

AD

[kW

]

Natural gas District heating Electricity

Solar PV Electricity out Net Electricity in

Heating energy demand Cooling energy demand Electricity demand

Figure 7.25 – Cumulative frequency curves of the load profiles of the energy-wares to be inputted to the hubs H7, H8, H9, H10

8 CONCLUSIONS AND FUTURE

WORK

In the previous chapters, the reasons for the use of multi-energy systems in buildings,

their potentials, the characteristics that combine to complicate the design and operation of

such systems were outlined. Once that the current modelling techniques and software

tools were reviewed, the original modelling approach based on the concept of the hybrid

energy hub was developed and applied on some case studies. To the knowledge of the

writer, there are not building systems modelling techniques that may consider variable

design power of the energy converters and variable dispatch factors of the loads between

the energy converters. The power of the energy converters can assume any value as a

result of the factors that represent the distribution of energy fluxes between the hub

converters that can assume any value between 0 and 1. Some tools, like the EnergyPlus

system simulation manager (see section 2.4.1), allow the capacity of an energy converter

to be autosized, but this is done on the basis of the design day simulation, and not on the

year-round energy, environmental and economic performance of the converter and of the

system.

One of the main features of the presented procedure is that it is convenient when the

numbers of converters aggregations is high and therefore the number of converters

aggregations worth to be simulated dramatically increases.

The main contribution of the work is to provide a general modelling framework for

building systems not only from the theoretical point of view, but also with documentation

and information on how to carry out analyses as the ones of Chapter 7.

If the coupling algorithm to match the energy demand and the energy supply in buildings

has its roots in the hybrid energy hub framework of [77], it was re-written bearing in mind

the properties of building systems, and the differences between energy and power

analyses.

A model was developed into two degree of detail, a seasonal method and an hourly

method, and a particular care was spent on the subject of the objective functions to be

used in buildings systems optimization.

To fully exploit the potential of this model, also the issues of the energy demand

assessment, the energy performance and economic assessment of energy converters were

CONCLUSIONS AND FUTURE WORK

145

addressed and treated in detail.

This permitted to carry out specific and real life applications of the method, from which

some considerations can be drawn.

A first consideration has to be made on the energy efficiency parameters of the

converters: both the seasonal and the hourly methods are highly sensitive to these factors,

as to the environmental and economic boundary conditions. The selection of the most

appropriate design efficiencies, part load efficiencies and specific costs tailored to the

particular problem is of the foremost importance to assure a meaningful result in the

selection of a multi-energy system.

At the same time, these values are often difficult to find, because the technical literature

concentrates on the design values of the energy converters and frequently lacks in details

(as an example, only few performance curves may be found for use in a detailed systems

simulation software). It has to be remarked that the energy characterization has to be done

not only at full load, but also at part load.

Similar considerations can be made on the environmental and economic boundary

conditions. The specific emission factors of a fuel are strongly affected by the technology

of the energy converter and by its actual operating conditions. The scaling effect in the

specific costs and the life times of the energy converters also vary considerably. Besides,

different sources may provide respectively the performance characterization and the

economic characterization of an energy converter.

In the liberalized energy market that in Italy is going to be created, also the assessment of

the costs of the energy-wares purchased form the utility grids and of the buyback price for

the electricity fed into the grid by a producer, is a crucial issue.

Procedures of analysis for all these problems were set out in this work, in the general

framework of the energy hub modelling methods developed, but a considerable research

activity may be carried out for the characterization of the energy converters that are not

discussed in this thesis or that will appear in the near future.

Besides that, an intermediate level between the seasonal method and the hourly method

may be created by the use of a bin method. A bin method shall provide a better evaluation

of the efficiencies under operating conditions without the necessity to perform an hourly

calculation. In the seasonal steady-state model in fact, the mean seasonal (or annual)

operating condition greatly influence the results. When it is possible, it may be desirable

to provide more accurate evaluation of these efficiencies as a function of the actual

operating conditions of the energy converters (this is particularly true for those converters

that vary – decreasing or increasing – their outputs as a function of the load). In the

easiest way, this can be done by means of the cumulative frequency curves of loads, that,

regardless of the time variability of the quantity to be analysed, allow the part load

efficiency to be quantified.

A further research field is set out by the integration of the energy storages into the hub.

The hourly method may be used to assess the impact of different storages on the energy


146

supply reliability, taking into account that an energy storage can be placed upstream or

downstream of a converter, with a different effect on the overall hub energy performance.

The assessment of the potential increase in the hub energy efficiency that lies in the use of

energy storages is an interesting topic that may be developed.

A last further research topic lies in the use of the presented model as an operational

optimization tool, a promising application that still need to be specified if performed on

large time intervals.

In the end, the issue of the optimization, with reference to the solving technique and to the

selection of criteria is discussed.

The seasonal method produces linear optimization problems whose solution can be

considered as the global optimum.

Instead, in the case of the hourly method, the main nonlinearities lie in the efficiencies

dependent on the part load, and on the power specific cost functions. In these problems

there is no guarantee that the global optimum was reached, but the same solutions were

obtained starting the solver process from different, widely separated initial guesses.

In any case, instead of the commercially available simplified tool used in this work, a

further study on the best optimization algorithm, for simple and multi-objective

optimization, may be performed. Given the number of the objective functions presented

and the number of decision variables, a wide field of analysis opens on the choice of the

best optimization algorithms, that depends on the nature of the objective function, on the

decision variables and the equality and inequality constraints.

Other researchers we have discussed with the outcomes of this work, have also pointed

out the possibility to apply in these problems the advanced robust optimization

techniques.

The need to obtain the most accurate estimate of the global optimum solution should also

be considered together with other two considerations.

First, one of the main aspects of the applications is that the solution, both in case of

seasonal and hourly methods, are particularly sensitive to the boundary conditions and

calculation assumptions, especially the costs in case of economic optimizations and

primary energy factors in case of energy optimizations.

A second consideration, even more important than the first one, is that the outcomes of

different optimization criteria varies considerably: in other words, the selection criteria

are frequently conflicting. In fact, different objective functions lead to different results,

especially economy versus energy or environmental objective functions: the selection of a

energy objective function instead of a economy objective functions can results in a

completely different system lay-out and converters types (and not only sizes).

If this peculiarity can be of interest when studying the performance of the systems to

clarify all the relations between the energy-wares, the energy demand and the energy

converters use, in real life practice there is the need to make a decision. This can be done

by selecting one criterion, depending on the interests of the building owner, or by

combining in some manner more than one criterion into a multi-objective function.


147

As a general rule, a multi-objective function may be provided by a linear combination of

whatever function presented in the previous sections 6.4.2.1, 6.4.2.2 and 6.4.2.3, but a

further research activity may be carried out in order to apply multi-criteria decision

making (MCDM) tools as the Multi-Attribute Value Theory (MAVY, [130]), the Analytic

Network Process (ANP, [129], [131]) or similar. In the MAVT framework, the decision

problem is structured as a tree made of a top attribute (the overall decision objective) and

lower attributes to measure on a numerical scale to what extent the objectives are attained

(value tree analysis procedure).

The ANP framework, developed by Saaty, is similar to the MAVT but can be used in

non-hierarchical problems, when there are not dependent relations between the objectives:

instead of a tree the problem is structured as a series of nodes grouped into clusters.

Relations between nodes and between clusters can be established.

In one way or another, the weights on the attributes have to be specified: this can be done

by asking the decision-maker how many times one attribute is more important than

another. When applying multi-criteria optimization methods, the main challenge is to

obtain the information on the decision-makers‟ preferences that can be used to assign

weighting factors.

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10 APPENDIX

As an example of the energy performance characterization of the energy converters

discussed in Chapter 5, in this appendix performance curves that can account for the

operation of various types of chillers at any working conditions are reported. Some of

these curves are used in the case study of section 7.3.4. As a general overview on chillers

and on the modelling approach adopted, see section 5.2.2.

10.1 Estimation of performance curves for chillers

10.1.1 Water-cooled reciprocating chillers

For the water-cooled reciprocating chiller the performance curves adopted refer to the

generic reciprocating chiller of DOE-2. The variation of the cooling capacity and of the

coefficient of performance as a function of the temperature of the chilled water leaving

the evaporator and of the temperature of the fluid entering the condenser are reported.

CC = CCR (C1 + C2*tev + C3* tev² + C4* tco + C5* tco ² + C6* tev * tco )

0.60

0.70

0.80

0.90

1.00

1.10

1.20

15 20 25 30 35 40tco [°C]

CC

/CC

R

tev = 5 °C tev = 7 °C tev = 10 °C

C1 5.08E-01

C2 1.45E-01

C3 -6.26E-03

C4 -1.12E-03

C5 -1.30E-04

C6 -2.82E-04

tev,R = 6.7 °C

tco,R = 29.4 °C

APPENDIX

158

coev6

2

co5co4

2

ev3ev21


1COPCOP

0.60

0.70

0.80

0.90

1.00

1.10

1.20

15 20 25 30 35 40tco [°C]

CO

P/C

OP

R

tev = 5 °C tev = 7 °C tev = 10 °C

T1 1.03E+00

T2 -1.04E-01

T3 7.10E-03

T4 9.32E-03

T5 3.18E-04

T6 -1.04E-03

COPR = 3.67

tev,R = 6.7 °C

tco,R = 29.4 °C

The variation of the coefficient of performance as a function of part load ratio is

expressed by a quadratic DOE-2 performance curve of the EIR (the inverse of COP)

which results in the following variation of the part-load function.

2

321

RPLR*EPLR*EE

PLRCOPCOP

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

E1 8.81E-02

E2 1.14E+00

E3 -2.26E-01

COPR = 3.67

10.1.2 Water-cooled scroll chillers

For the water-cooled scroll chiller the performance curves adopted refer to a

commercially available chiller (Trane CGWD) of 207 kW. Since the cooling capacity of

scroll chillers are limited to a maximum value of 200 kW, this chiller can be used to

APPENDIX

159

account for the performance of all the possible scroll chillers installed.

The variation of the cooling capacity for this type of chiller, reported in the following

diagram, show a smaller slope than the same variation for the reciprocating chiller.


0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CC

/CC

R

tev = 5 °C tev = 7 °C tev = 10 °C

C1 9.44E-01

C2 3.37E-02

C3 9.76E-05

C4 -3.22E-03

C5 -4.92E-05

C6 -1.78E-04

tev,R = 6.7 °C

tco,R = 29.4 °C

coev6

2

co5co4

2

ev3ev21


1COPCOP

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CO

P/C

OP

R

tev = 5 °C tev = 7 °C tev = 10 °C

T1 7.27E-01

T2 -1.19E-02

T3 5.41E-04

T4 1.88E-03

T5 4.73E-04

T6 -7.11E-04

COPR = 3.99

tev,R = 6.7 °C

tco,R = 29.4 °C

The variation of the coefficient of performance as a function of part load ratio presents in

this case a maximum value at part load (about 0.4) instead of at full load, as it is reported

in the following diagram.

APPENDIX

160

2

321

RPLR*EPLR*EE

PLRCOPCOP

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

E1 4.15E-02

E2 6.54E-01

E3 3.04E-01

COPR = 3.99

10.1.3 Water-cooled screw chillers

Some data of commercially available electric screw chillers collected are reported in the

following table. The water-cooled screw chillers coefficients of performance are reported

in the figure 10.1. In this case, there is a slight dependence on the cooling capacity:

greater chiller size results in greater values of COP. In comparison with the centrifugal

chillers, screw chillers, for similar size and the same condensing temperature, have lower

COP values.

3

4

5

6

7

8

9

10

0 500 1000 1500 2000


CO

P [

-]

15.6 °C

23.9 °C

24.9 °C

25.3 °C

Leaving chilled

water temperature

5.6 °C

3

4

5

6

7

8

9

10

0 500 1000 1500 2000


CO

P [

-]

23.9 °C

29.4 °C

Leaving chilled

water temperature

6.7 °C

Figure 10.1 – COP of various water-cooled screw chillers

For the water-cooled screw chiller the performance curves adopted refer to a

commercially available chiller (Trane CGWD) of 1066 kW (refrigerant R-132a).

The variation of the cooling capacity for this type of chiller, reported in the following

diagram, shows a smaller slope than the same variation for chillers equipped with other

compressor types. On the contrary, the coefficient of performance of a screw chiller is

much more sensible to the variation of chilled water and condenser fluid temperatures.

To compare this chiller with the other water-cooled chillers, the cooling capacity and the

coefficient of performance at a chilled water temperature of 6.67 °C and at a condenser

APPENDIX

161

fluid temperature of 29.4 °C are respectively equal to 1042 kW and 5.06.

coev6

2

co5co4

2

ev3ev21


1COPCOP

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CO

P/C

OP

R

tev = 5 °C tev = 7 °C tev = 10 °C

T1 9.00E-01

T2 3.78E-03

T3 9.82E-04

T4 -4.05E-02

T5 2.04E-03

T6 -1.62E-03

COPR = 5.73

tev,R = 5.6 °C

tco,R = 25.3 °C


0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CC

/CC

R

tev = 5 °C tev = 7 °C tev = 10 °C

C1 1.06E+00

C2 4.58E-02

C3 2.80E-04

C4 -8.80E-03

C5 -3.13E-05

C6 -5.94E-04

tev,R = 5.6 °C

tco,R = 25.3 °C

APPENDIX

162

2

321

RPLR*EPLR*EE

PLRCOPCOP

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

E1 1.007344E-01

E2 1.119205E+00

E3 -2.173230E-01

COPR = 5.73

10.1.4 Water-cooled centrifugal chillers

Many data of actual electric chillers are analyzed in the following sections. These data are

summarized in the following figures.

The water-cooled centrifugal chillers coefficients of performance are reported in the

figure 10.2 for a leaving chilled water temperature of 5.6 °C and in the figure 10.3 for a

leaving chilled water temperature of 6.7 °C. Different water temperature entering the

condenser and two different unloading mechanisms (inlet vanes and VSD) are reported in

each diagram. It can be seen that both in case of condensing temperature of about 24 °C

and evaporator temperature of 5.6 °C, and in case of condensing temperature of about

26 °C and evaporator temperature of 6.7 °C, the chiller COP is between 6 and 7.

3

4

5

6

7

8

9

10

0 1000 2000 3000 4000 5000 6000


CO

P [-]

Inlet Vanes, 22.8 °C




VSD, 12.8 °C

VSD, 23.9 °C

VSD, 24.4 °C

Leaving chilled

water temperature

5.6 °C

Figure 10.2 – COP of various water-cooled centrifugal chillers

APPENDIX

163

3

4

5

6

7

8

9

10

0 1000 2000 3000 4000 5000 6000


CO

P [-]





VSD, 12.8 °C

VSD, 24.4 °C

Leaving chilled

water temperature

6.7 °C

Figure 10.3 – COP of various water-cooled centrifugal chillers

To account for the performance of the chiller at chilled water temperature and condenser

water temperature different from the values set at the reference, and at part load, two

different reference chillers were assumed, one representative of the performance of a

centrifugal chiller of a cooling capacity between 500 and 2000 kW, and the other

representative of a centrifugal chiller of a cooling capacity greater than 2000 kW.

For the first step of cooling capacity (500 < CC < 2000 kW) a commercially available

centrifugal chiller (York YT, R123) of 1023 kW of cooling capacity, with an inlet vanes

unloading mechanism, was selected.


0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CC

/CC

R

tev = 5 °C tev = 7 °C tev = 10 °C

C1 2.571195E-01

C2 -1.571421E-02

C3 -3.041761E-03

C4 8.106512E-02

C5 -2.568598E-03

C6 4.247073E-03

tev,R = 6.7 °C

tco,R = 29.6 °C

APPENDIX

164

coev6

2

co5co4

2

ev3ev21


1COPCOP

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CO

P/C

OP

R

tev = 5 °C tev = 7 °C tev = 10 °C

T1 5.254964E-01

T2 -1.972389E-02

T3 3.441072E-04

T4 1.651466E-02

T5 2.005198E-04

T6 -3.193246E-04

COPR = 5.81

tev,R = 6.7 °C

tco,R = 29.6 °C

For centrifugal chillers, the variation of the coefficient of performance as a function of

part load ratio usually presents a maximum value at part load of about 0.7 instead of at

full load. Depending on the chiller, the relative difference between the maximum COP

and the COP at full load can be estimated in a relative increase between 3%, as in this

case, and 8% (second case, York YK 5465 kW).

2

321

RPLR*EPLR*EE

PLRCOPCOP

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

E1 2.368399E-01

E2 3.286421E-01

E3 4.344939E-01

COPR = 5.81

For the second step of cooling capacity (CC > 2000 kW) a commercially available

centrifugal chiller (York YT, R134-a) of 5465 kW cooling capacity, with an inlet vanes

unloading mechanism was selected. This chiller is rated at a condenser fluid temperature

different from the previous chiller presented.

A comparison between this chiller and the other presented must be made having corrected

APPENDIX

165

the values of cooling capacity and coefficient of performance for the same value of

condenser fluid temperature as the other ones. With a condenser fluid temperature of 29.4

°C the cooling capacity is equal to 4300 kW and the COP is equal to 6.62.


0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CC

/CC

R

tev = 5 °C tev = 7 °C tev = 10 °C

C1 1.09E-01

C2 -1.23E-01

C3 -2.80E-03

C4 1.32E-01

C5 -4.71E-03

C6 9.21E-03

tev,R = 6.7 °C

tco,R = 26.1 °C

coev6

2

co5co4

2

ev3ev21


1COPCOP

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CO

P/C

OP

R

tev = 5 °C tev = 7 °C tev = 10 °C

T1 6.40E-01

T2 -5.95E-02

T3 5.88E-04

T4 3.17E-02

T5 -5.09E-04

T6 1.46E-03

COPR = 6.94

tev,R = 6.7 °C

tco,R = 26.1 °C

APPENDIX

166

2

321

RPLR*EPLR*EE

PLRCOPCOP

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

E1 2.80E-01

E2 7.87E-02

E3 6.42E-01

COPR = 6.94

A further centrifugal chiller with a VSD unloading mechanism is reported. In this case the

reference commercial chiller adopted (Carrier 19XR) has a cooling capacity of 1407 kW

and is rated at the usual values of chilled water temperature and condensing fluid

temperature.

With reference to the two previous centrifugal chiller, this one shows a smaller variation

of the cooling capacity as a function of chilled water and condensing fluid temperatures;

on the contrary it shows a greater variation of the coefficient of performance as a function

of chilled water and condenser fluid temperatures. The part load ratio curve shows a part

load performance similar to the one of the other centrifugal chillers.


0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CC

/CC

R

tev = 5 °C tev = 7 °C tev = 10 °C

C1 1.04E+00

C2 2.64E-03

C3 -1.47E-03

C4 1.37E-02

C5 -8.30E-04

C6 1.57E-03

tev,R = 6.7 °C

tco,R = 29.4 °C

APPENDIX

167

coev6

2

co5co4

2

ev3ev21


1COPCOP

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

15 20 25 30 35 40tco [°C]

CO

P/C

OP

R

tev = 5 °C tev = 7 °C tev = 10 °C

T1 1.03E+00

T2 -1.61E-02

T3 -1.09E-03

T4 -1.78E-02

T5 7.96E-04

T6 -9.59E-05

COPR = 6.04

tev,R = 6.7 °C

tco,R = 29.4 °C

2

321

RPLR*EPLR*EE

PLRCOPCOP

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 0.2 0.4 0.6 0.8 1 1.2

PLR [-]

CO

P/C

OP

R

E1 1.19E-01

E2 6.72E-01

E3 2.07E-01

COPR = 6.04

11 PUBLICATIONS LIST

The publications of the candidate that are related to this work are grouped into different

thematic areas referring to the PhD dissertation chapters.

The assessment of the general framework (Chapter 1)

FILIPPI M., CORGNATI S.P., FABRIZIO E. Impiantistica sostenibile: dai sistemi

monoenergia ai sistemi multienergia [Sustainable building services: from one energy to

multi-energy source systems]. Condizionamento dell‟aria, riscaldamento, refrigerazione,

2007, n° 2, pp. 10-15. ISSN 0373-7772 (in Italian).

VIRGONE J., FABRIZIO E., RAFFENEL Y., BLANCO E., THOMAS G.

Dimensionnement et contrôle des systèmes multi-énergies pour les bâtiments à haute

performance énergétique [Design and control for multi-energy systems in highly efficient

buildings]. La Revue 3EI, Paris, n° 2, mars 2008. ISSN 1252-770X (in French).

FABRIZIO E., FILIPPI M. La costruzione energeticamente sostenibile: dalla Passivhaus

ai Greentips [Sustainable building: from the Passivhaus to the Greentips]. RCI

(Riscaldamento, Climatizzazione, Idronica), n° 3, march 2008, pp. 76-96. ISSN 1120-

8457 (in Italian).

The assessment of the energy demand (Chapter 3)

CORGNATI S.P., FABRIZIO E., FILIPPI M. Energy and comfort: mutual relation

between thermal comfort and energy demand in office buildings. AICARR International

Congress “HVAC&R. Technologies, standards and maket”, Milano, 1-2 March 2006, pp.

587-597.

CORRADO V., FABRIZIO E., A simplified calculation method of the annual energy use

for space heating and cooling: assessment of the dynamic parameters for the Italian

building stock and climate. In: FAZIO P., GE H., RAO J., DESMARAIS G. (Eds.),

“Research in Building Physics and Building Engineering”. London: Taylor &

Francis/Balkema, 2006, pp. 645-653. ISBN 0-415-41675-2.

CORRADO V., FABRIZIO E., MARINO C., NUCARA A., PIETRAFESA M.,

Sperimentazione di procedure per la valutazione delle prestazioni energetiche

dell‟edificio: confronto tra modelli dettagliati e semplificati [Procedures for the

assessment of the building energy perforance: comparison between simplified and

PUBLICATIONS LIST

169

detailed models]. In: FILIPPI M., RIZZO G. (Eds.) “Certificazione energetica e verifica

ambientale degli edifici. Valutazioni delle prestazioni energetiche e della sostenibilità

delle scelte progettuali” [Energy and environmental certification of buildings]. Palermo:

Dario Flaccovio Editore, 2007, pp. 222-268. ISBN 8877586729 (in Italian).

CORRADO V., FABRIZIO E. Assessment of building cooling energy need through a

quasi-steady state model: Simplified correlation for gain-loss mismatch. Energy and

buildings, 2007, vol. 39, n° 5, pp. 569-579. ISSN 0378-7788.

CORRADO V., MECHRI H.E., FABRIZIO E. Building energy performance assessment

through simplified models: application of the ISO 13790 quasi-steady state method, BS

Building Simulation 2007, Beijing, 3-6 September 2007, pp. 79-86

CORGNATI S.P., FABRIZIO E., FILIPPI M. The impact of indoor thermal conditions,

system controls and building types on the building energy demand. Energy and buildings,

2008, vol. 40, n° 4, pp. 627-636. ISSN 0378-7788.

The assessment of the energy supply (Chapter 4)

FABRIZIO E., FILIPPI M. Le fonti energetiche non rinnovabili [Not renewable energy

sources]. In: STEFANUTTI L. (Ed.) Manuale degli impianti di climatizzazione [HVAC

Systems Handbook]. Milano: Tecniche Nuove, 2008. ISBN 978-88-481-1884-2 (in

Italian).

FABRIZIO E., FILIPPI M. Le fonti energetiche rinnovabili [Renewable energy sources].

In: STEFANUTTI L. (Ed.) Manuale degli impianti di climatizzazione [HVAC Systems

Handbook]. Milano: Tecniche Nuove, 2008. ISBN 978-88-481-1884-2 (in Italian).

The energy hub modelling and applications (Chpater 6 and 7)

FABRIZIO E., FILIPPI M., CORRADO V. Modeling and optimization of multi-energy

source building systems in the design concept phase. CLIMA 2007 Well Being Indoors,

Helsinki, 10-14 June 2007. ISBN 978-952-99898-3-6.

FABRIZIO E., FILIPPI M., VIRGONE J. Optimization of the energy performance of the

system: trade-off between environmental and economical objectives. COBEE The First

International Conference on Building Energy and Environment, Dalian (China), 14-16

July 2008.

FABRIZIO E., FILIPPI M., CORRADO V., VIRGONE J. La valutazione del sistema

multi-energia a servizio dell‟edificio: procedure di ottimizzazione e simulazione per un

edificio monofamiliare [The assessment of the building multi-energy system: simulation

and optimization procedures for a single-family house]. 62° Congresso Nazionale ATI,

Fisciano (Salerno), 11-14 settembre 2007, vol. I, pp. 497-508. ISBN 978-88-87998-77-1

(in Italian).

FABRIZIO E., FILIPPI M., VIRGONE J, CORGNATI S.P. La valutazione del sistema

multi-energia a servizio dell‟edificio: un caso di studio [The assessment of the building

multi-energy system: a case study]. 61° Congresso Nazionale ATI (Associazione

Termotecnica Italiana), Perugia, 12-15 September 2006, vol. 1, pp. 216-223 (in Italian).

170

FOLIO ADMINISTRATIF

THESE SOUTENUE DEVANT L'INSTITUT NATIONAL DES SCIENCES APPLIQUEES

DE LYON

NOM : FABRIZIO DATE de SOUTENANCE : 2 juillet 2008

Prénoms : Enrico

TITRE : Modelling of multi-énergy systems in buildings [Modélisation des systèmes multi énergies dans les bâtiments]

NATURE : Doctorat Numéro d'ordre : 2008-ISAL-0042

Ecole doctorale : MEGA (MECANIQUE, ENERGETIQUE, GENIE CIVIL, ACOUSTIQUE)

Spécialité : Génie Civil

Cote B.I.U. - Lyon : T 50/210/19 / et bis CLASSE :

RESUME :

Ce mémoire concerne la modélisation des systèmes multi énergies utilisés dans les bâtiments. Avec le terme

systèmes multi énergies on entend les systèmes énergétiques hybrides qui sont à même de faire face aux charges

thermiques, frigorifiques et électriques d’un bâtiment par la mise en service de convertisseurs d’énergie divers,

activés par des sources d’énergie primaires et secondaires, renouvelables ou non. Ces systèmes sont caractérisés

par un grand potentiel d’amélioration de l’efficacité énergétique dans la transformation des énergies et dans la

production des fluides énergétiques lorsqu’ils sont correctement conçus et gérés même lorsqu’ils fonctionnent à

charge partielle (une condition dans laquelle ils se trouvent opérer la plupart du temps à cause de la variation

de la demande d’énergie des bâtiments).

Plusieurs exemples de système multi énergies peuvent être tirés de la littérature, et comprennent, diversement

associés, les convertisseurs pour l’exploitation de l’énergie solaire à des fins thermiques, frigorifiques et

électriques, les systèmes à biomasses, les micro-cogénerateurs, les pompes à chaleur géothermiques, les piles à

combustible, les éoliennes, etc.

Le projet d’un système multi énergies, en terme de dimensionnement et de gestion, consiste à définir les

dynamiques de la demande d’énergie et à optimiser l’offre d’énergie par l’emploi de convertisseurs divers, des

stockages, des systèmes de back-up. Dans la littérature ce problème est traité en se référant à des configurations

spécifiques, dont on fournit des exemples, mais non à travers des outils intégrés qui permettent la comparaison

entre plusieurs configurations. Ce travail est donc principalement un travail de synthèse qui comble cette lacune.

La thèse propose l’avancement des connaissances relatives aux critères de sélection des convertisseurs

d’énergie à utiliser, des sources d’énergie à exploiter, des logiques de fonctionnement et des systèmes

techniques à utiliser afin de poursuivre les objectifs d’une meilleure efficacité dans l’usage des énergies

renouvelables ou non, et de réduire les émissions de CO2 du secteur du bâtiment, dont la consommation

représente 40% de la consommation en énergie primaire en Europe.

A la base de la recherche c’est la définition d’une méthodologie originale pour la modélisation des

configurations des systèmes multi énergies basée sur la méthode d’analyse du energy hub qui permet de prendre

en compte, d’une manière synthétique, le couplage entre demande et offre d’énergie dans un bâtiment. Cette

méthode permet aussi de prendre en compte la qualité des énergies, la variabilité des rendements de conversion

en fonction des conditions de fonctionnement des systèmes, les stockages de l’énergie et la variabilité des

conditions de fonctionnement.

Par rapport aux procédures couramment disponibles, cette recherche a visé à configurer un outil de modélisation

des systèmes multi énergies pour les bâtiments qui prenne en compte tous les flux d’énergies dans le bâtiment et

qui puisse se référer à une configuration ouverte et non pas à une unique typologie de système en particulier.

MOTS-CLES : systèmes multi-énergies, bâtiments baisse énergie,

Laboratoires de recherche : CETHIL Centre de Thermique de Lyon

DENER Dipartimento di Energetica, Politecnico di Torino

Directeurs de thèse: FILIPPI Marco et VIRGONE Joseph

Président de jury :

Composition du jury : FILIPPI Marco, VIRGONE Joseph, SCORLETTI Gérard, ZECCHIN Roberto, ROUX Jean-Jacques,

BECCALI Marco

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