energy systems optimization of a shopping mall

1

Energy systems optimization of a Shopping mall

Aristotelis Giannopoulos

26/09/08

Supervised by:

Prof. David Fisk (Civil and Environmental Engineering)

Prof. Stratos Pistikopoulos (Chemical Engineering)

A thesis submitted to Imperial College London in partial fulfilment of the

requirements for the degree of Master of Science in Sustainable Energy Futures and

for the Diploma of Imperial College

Faculty of Engineering

Imperial College London

London SW7 2AZ, UK

3

Table of Contents

Table of Contents ...................................................................................................................... 3

List of Figures and Tables ......................................................................................................... 6

Glossary .................................................................................................................................. 11

Abstract ................................................................................................................................... 12

1. Introduction

1.1 Global Energy Consumption & Buildings contribution ........................................... 13

1.2 Decentralized energy systems ................................................................................... 14

1.3 Short plan and explanation of the model ................................................................... 16

2. Literature review

2.1 Energy Consumption in a Shopping Mall ................................................................. 19

2.2 Alternative Technologies and Energy sustainability in SM ...................................... 27

2.2.1 Description of the different technical alternatives ........................................... 27

2.2.2 Photovoltaic’s ................................................................................................. 28

2.2.3 Co-generation ................................................................................................... 28

2.2.4 Tri-generation model........................................................................................ 29

2.2.5 Gas boiler ......................................................................................................... 30

2.2.6 Grid Electricity and other parameters .............................................................. 30

2.2.7 Electric chiller .................................................................................................. 31

2.2.8 Absorption chiller ........................................................................................... 32

2.3 Distributed Energy Resources in SM and other Commercial Buildings ................... 32

3. Model inputs

3.1 Technology database ................................................................................................. 36

3.2 Shopping mall description ........................................................................................ 41

3.3 Tariffs inputs

3.3.1 Natural gas prices ....................................................................................... 46

3.3.2 Electricity prices (Grid) ............................................................................. 47

4. Mathematical Model

4.1 Introduction ............................................................................................................... 50

4.2 Mathematical Programming ...................................................................................... 50

4

4.3 General Algebraic Modeling System (GAMS) ........................................................ 51

4.4 Model Description .................................................................................................... 52

4.5 Mathematical Formulation ........................................................................................ 54

5. Results

5.1 Scenarios and Sensitivities .............................................................................................. 59

5.2 Outline of results .............................................................................................................. 61

5.3 Overview of spot market prices results scenario .............................................................. 62

5.4 Assessment of specific cases

5.4.1 Case 1: Grid plus boiler ................................................................................... 67

5.4.2 Case 2: Without CHP/CCHP ........................................................................... 69

5.4.3 Case 3: Without CCHP .................................................................................... 71

5.4.4 Case 4: Final case ............................................................................................. 73

5.4.5 Case 5: PV plus Grid plus Boiler ..................................................................... 78

5.4.6 Case 6: At least seven PV ................................................................................ 81

5.4.7 Case 7: High carbon price ................................................................................ 88

5.4.8 Case 8: High carbon price with a 20% PV capital reduction ........................... 88

5.4.8 Case 9: 50% PV capital reduction .................................................................... 89

5.4.9 Case 10, 11: 50 % cheaper electricity prices, 50% more expensive NG ......... 89

5.5 Fixed electricity price scenario ........................................................................................ 90

5.5.1 Electricity price up to 0.08 $/KWh .................................................................. 91

5.5.2 Electricity price from 0.09 to 0.12 $/KWh ....................................................... 91

5.5.3 Electricity price 0.13 $/KWh ........................................................................... 92

5.5.4 Electricity price 0.14$/KWh ............................................................................ 93

5.5.5 Electricity price from 0.15 to 0.49 $/KWh ....................................................... 94

5.5.6 Electricity price from 0.5 to 0.57 $/KWh ......................................................... 96

5.5.7 Electricity price from 0.58 $/KWh ........................................................ 98

6. Conclusions ................................................................................................................. 99

5

Bibliography ............................................................................................................... 104

Appendix .................................................................................................................... 106

6

List of Figures

Figure 1, World Population distribution in urban and rural place……………………………..….13

Figure 2, Total London Energy use breakdown……………………………………………….….13

Figure 3, Electricity generation by fuel in US (IEA, World Energy Outlook, 2004)……………..14

Figure 4, graphic representation of the DGT-SM…………………………………………………17

Figure 5, technical alternatives used in this model………………………………………………..18

Figure 6, monthly electricity consumption profiles for the four shopping malls

(Joseph C.Lam D. H., 2003)……………………………………………………………………...21

Figure 7, breakdown of the major end uses in the four shopping malls

(Joseph C.Lam D. H., 2003)…………………………………………………………………..….22

Figure 8, measured hourly electrical load profiles for Building A………………………….…....23

Figure 9, measured hourly electrical load profiles for Building B…………………………..…....23

Figure 10, measured hourly electrical load profiles for Building C………………………………23

Figure 11, measured hourly electrical load profiles for Building D………………………………23

Figure 12, January Peak Load for Mall……………………………………………………………25

Figure 13, August Peak Load for Mall……………………………………………………………..25

Figure 14, Mall Week Load Shape………………………………………………………………...25

Figure 15, Mall Peak Load Shape………………………………………………………………….25

Figure 16, Mall Weekend Load Shape……………………………………………………………..26

Figure 17, Superstructure with the most important technical alternatives meeting the electricity and

heat demand in a SM………………………………………………………………………………..27

Figure 18, Average costs and productivity of PV’s………………………………………………...28

Figure 19, Efficiencies of the overall system, (Nan Zhou a *. C., 2006)……………………………33

Figure 20, carbon emissions comparing base and optimal solution for all the buildings, (Nan Zhou a *.

C., 2006)…………………………………………………………………………………………….34

Figure 21, Annual savings, (Nan Zhou a *. C., 2006)………………………………………………34

Figure 22, Technology database (Firestone, 2004)………………………………………………….40

Figure 23, SM Electrical load (F. Javier Rubio, 2001)………………………………………...……44

Figure 24, SM Electrical-only demand……………………………………………………………...44

Figure 25, SM cooling demand……………………………………………………………………..45

Figure 26, SM Heating demand…………………………………………………………………….45

Figure 27, monthly natural gas prices in $ per MMBTU for the calendar years 2007, 2008……...46

7

Figure 28, graph representation for natural gas prices in $ per MMBTU for 2008……………..….47

Figure 29, Contribution of distribution costs to electricity bill (Williams P. a., 2001)……………..48

Figure 30, Spot market electricity prices……………………………………………………………49

Figure 31, Grid electricity price with the distribution company revenue…………………………..49

Figure 32 Bill savings over grid + boiler basic scenario……………………………………………63

Figure 33, Carbon savings over basis grid + boiler scenario………………………………………..64

Figure 34, Energy payments to the grid…………………………………………………………….65

Figure 35, Capital investment cost (includes installation and fixed costs) (section results overview).65

Figure 36, Energy sales back to the grid (section results overview)………………………………..66

Figure 37, Net present value (all included) (section results overview)……………………………..66

Figure 38, Carbon Taxes (all included) (section results overview)…………………………………67

Figure 39, Natural gas payments (all included) (section results overview)…………………………67

Figure 40, Energy balance and economic result for the grid plus boiler case……………………….68

Figure 41, NG purchases for meeting the SM heating load (Grid plus boiler case)……………...…69

Figure 42, total electricity purchases from grid, for all months and hours (grid plus boiler case)….69

Figure 43, Energy balance and economic results for without CHP/CCHP case…………………….70

Figure 44, Total electricity purchases from the grid (without CHP/CCHP case)……………….…71

Figure 45, Sales back to the grid (without CHP/CCHP case)………………………………………71

Figure 46, energy balance results (without CCHP case)……………………………………………73

Figure 47, economic results (without CCHP case)…………………………………………………73

Figure 48, energy balance results (final case)………………………………………………………75

Figure 49, economic results (final case)……………………………………………………………75

Figure 50, NG-1000CCHP power generation for electrical-only end use loads (final case)………76

Figure 51, NG-1000CCHP power generation for cooling end use loads (final case)………………76

Figure 52, NG-1000CCHP Recovered heat going to meet cooling demand (final case)…………..77

Figure 53, NG-1000CCHP Recovered heat going to meet heating demand (final case)…………..77

Figure 54, NG-1000CCHP Energy sales back to the grid (final case)………………………………78

Figure 55, energy balance results (PV plus grid plus boiler case)……………………………………79

Figure 56, economic results (PV plus grid plus boiler case)…………………………………………79

Figure 57, Total electricity purchases from grid (PV plus grid plus boiler case)……………………80

Figure 58, PV power generation for electrical-only end use loads (PV plus grid plus boiler case)…80

Figure 59, PV power generation for cooling end use loads (PV plus grid plus boiler case)……….81

8

Figure 60, energy sales back to the grid (PV plus grid plus boiler case)………………………..…81

Figure 61, energy balance results (at least 7 PV case)…………………………………………..…82

Figure 62, economic results (at least 7 PV case)……………………………………………………83

Figure 63, NG-1000CCHP power generation for electrical-only end use load (at least 7 PV case)..83

Figure 64, NG-1000CCHP power generation for cooling end use loads (at least 7 PV case)………84

Figure 65, 7 PV-100 power generations for electrical-only end use loads (at least 7 PV case)…….84

Figure 66, 7 PV-100 power generations for cooling end use loads (at least 7 PV case)…………….85

Figure 67, NG-1000CCHP recovered heat going to meet heating demand (at least 7 PV case)…….86

Figure 68, NG-1000CCHP recovered heat going to meet cooling demand (at least 7 PV case)…...86

Figure 69, NG purchased for meeting heating demand by direct-fire burning (at least 7 PV case)...87

Figure 70, Energy sales back to the grid by power generated from PV’s (at least 7 PV case)…….87

Figure 71, Energy sales back to the grid by power generated from NG-1000CCHP (at least 7 PV

case)…………………………………………………………………………………………………88

Figure 72 (appendix), energy balance and economic results (high carbon price scenario)……….106

Figure 73, energy balance results (High carbon price with a 20% PV capital reduction case)……107

Figure 74, economic results (High carbon price with a 20% PV capital reduction case)………….107

Figure 75, NG-1000CCHP power generation for electrical-only end use (High carbon price with a 20%

PV capital reduction case)………………………………………………………………………….108

Figure 76, NG-1000CCHP power generation for cooling end use (High carbon price with a 20% PV

capital reduction case)……………………………………………………………………………..108

Figure 77, PV’s power generation for electrical-only end use (High carbon price with a 20% PV

capital reduction case)…………………………………………………………………………….109

Figure 78, PV’s power generation for cooling end use (High carbon price with a 20% PV capital

reduction case)…………………………………………………………………………………….109

Figure 79, NG-1000CCHP recovered heat going to meet heating demand (High carbon price with a

20% PV capital reduction case)……………………………………………………………………110

Figure 80, NG-1000CCHP recovered heat going to meet cooling demand (High carbon price with a

20% PV capital reduction case)…………………………………………………………………….110

Figure 81, NG purchased to meet heating demand in a boiler (High carbon price with a 20% PV capital

reduction case)……………………………………………………………………………………111

Figure 82, NG-1000CCHP power generation for selling back to the grid (High carbon price with a

20% PV capital reduction case)……………………………………………………………………111

Figure 83, PV-100 power generation for selling back to the grid (High carbon price with a 20% PV

capital reduction case)………………………………………………………………………………112

Figure 84, energy balance results (50% PV capital reduction)……………………………………..112

Figure 85, economic results (50% PV capital reduction)……………………………………………113

Figure 86, NG-1000CCHP power generation for electrical-only end use loads (50% PV capital

reduction)……………………………………………………………………………………………113

9

Figure 87, NG-1000CCHP power generation for cooling end use loads (50% PV capital

reduction)………………………………………………………………………………………….114

Figure 88, NG-1000CCHP power generation for selling back to the grid (50% PV capital

reduction)………………………………………………………………………………………….114

Figure 89, PV power generation for electrical-only end use loads (50% PV capital reduction)…115

Figure 90, PV power generation for cooling end use loads (50% PV capital reduction)…………115

Figure 91, NG-1000CCHP recovered heat going to meet cooling demand (50% PV capital

reduction)…………………………………………………………………………………………116

Figure 92, NG purchased to meet heating demand by direct burning in boiler (50% PV capital

reduction)…………………………………………………………………………………………116

Figure 93, recovered heat going to meet heating demand (50% PV capital reduction)…………117

Figure 94, PV power generation for selling back to the grid (50% PV capital reduction)………117

Figure 95, energy balance results (50% cheaper electricity prices case)…………………………118

Figure 96, economic results (50% cheaper electricity prices case)………………………………118

Figure 97, energy balance results (50% more expensive NG price case) ………………………..119

Figure 98, economic results (50% more expensive NG price case)………………………………119

Figure 99, graph representation of the model results for different electricity prices……………..90

Figure 100, economic results for electricity price less than 9p/KWh (Fixed electricity price

scenario)……………………………………………………………………………………………119

Figure 101, NG-100CHP total electrical production (Electricity price from 0.09 to 0.12 $/KWh

case)………………………………………………………………………………………………92

Figure 102, Heating demand met by NG-100CHP (Electricity price from 0.09 to 0.12 $/KWh

case)………………………………………………………………………………………………92

Figure 103, energy balance and economic results (Electricity price from 0.09 to 0.12 $/KWh

case)………………………………………………………………………………………………120

Figure 103, energy balance and economic results (Electricity price 0.13 $/KWh)………………121

Figure 104, NG-60 CHP total electrical production (Electricity price 0.13 $/KWh)……………122

Figure 105, Heating demand met by NG-60CHP (Electricity price 0.13 $/KWh)………………122

Figure 106, Purchased NG to meet heating demand (Electricity price 0.13 $/KWh)……………93

Figure 107, total electricity purchases from grid (Electricity price 0.14$/KWh case)……………123

Figure 108, NG-300CCHP total electricity production (Electricity price 0.14$/KWh case)……94

Figure 109, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh

case)………………………………………………………………………………………………94

Figure 110, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh

case)………………………………………………………………………………………………123

Figure 111, energy balance and economic results (Electricity price 0.14$/KWh case)…………124

Figure 112, energy balance and economic results (Electricity price from 0.15 to 0.49 $/KWh)…125

10

Picture 114, NG-1000CCHP power generation for electrical-only end use loads (Electricity price from

0.15 to 0.49 $/KWh)………………………………………………………………………………95

Picture 115, recovered heat going to meet cooling demand (Electricity price from 0.15 to 0.49

$/KWh)……………………………………………………………………………………………96

Figure 116, energy balance and economic results (Electricity price from 0.5 to 0.57 $/KWh)…126

Figure 117, recovered heat going to meet cooling demand (Electricity price from 0.5 to 0.57

$/KWh)…………………………………………………………………………………………97

Figure 118, energy sales back to the grid (Electricity price from 0.5 to 0.57 $/KWh)…………97

Figure 119, energy balance and economic results (Electricity price from 0.58 $/KWh)…………98

List of Tables

Table 1, summary of the building envelops and HVAC designs, (Joseph C.Lam D. H., 2003)………21

Table 2, summary of annual electricity per unit floor area (Joseph C.Lam D. H., 2003)……………..22

Table 3, summary of the buildings envelops and HVAC designs, (Joseph C.Lam D. H., 2003)……...22

Table 4, Characteristics of cogeneration technologies available for use at the scale of individual

large buildings (micro turbines, fuel cells, reciprocating engines) and district heating networks

(simple- and combined-cycle turbines) (Lemar,

2001)…………………………………………………………………………………………………....29

Table 5, Costs (electricity, gas, and biomass) and also CO2 trading factor, (SEA/RENUE, 2006)……30

Table 6, Proportion of electricity supplied to the national grid from different sources, and associated

CO2 emission factors, 2005……………………………………………………………………….……31

Table 7, CO2 factors (grid, boilers, natural gas, and renewables) and other parameters (inflation,

discount factor etc), (SEA/RENUE, 2006)…………………………………………………………….31

Table 8, CO2 equivalents of electricity and fuels (1998 data), (F, 2005)……………………………..33

Table 9, Underlying Assumptions……………………………………………………………………..39

Table 10, β and γ values………………………………………………………………………………..39

Table 11, Scenarios examined………………………………………………………………………….59

Table 12, Examined sensitivities……………………………………………………………………….60

11

Glossary

DGT-SM: Distributed Generation Technologies Selection Model

DGT: Distributed Generation Technologies

SM: Shopping Mall

DG: Distributed generation

PV: Photovoltaic’s

CHP: Combined Heat and Power

CCHP: Combined Cooling Heat and Power

BIPV: Building-integrated photovoltaic’s

LCA: Life Cycle Analysis

NG: Natural Gas

GHG: Green House Gases

HVAC: heating, ventilation and air-conditioning

NPI: normalized performance indicators

COP: Coefficient of performance

OTTV: overall thermal transfer value

GAMS: Generic Algebraic Modeling System

FC: fuel cell

MAISY: market analysis and information system

O&M: operation and maintenance

MMBTU: Million British thermal unit

NPV: Net Present Value

12

Abstract

The usage of distributed generation technologies (DGT) for on-site electricity,

heating and cooling production gives great opportunities to commercial consumers to

evade all the transmission, distribution, supply and other non-energy delivery costs.

Additionally, the usage of DGT technologies close to the thermal load gives the

prospect to utilize the waste heat (for heating and cooling purposes) from the

electricity production and finally reach higher efficiencies of burning the fuel from

the conventional centralized power station. Despite the previous two very important

facts, the usage of DGT and especially CHP/CCHP in commercial level is nearly not

existed. This denial for installing these distributed technologies is the bad economic

results of some bad installed systems. In order one system like CHP to meet the

customer demand cheaper than the mature centralized power stations, a very careful

planning of the system needed in order to be utilized most of the waste heat which

will compensate for the lower electrical output compared to conventional power

station. Until now very few tools existed, which are able to make a careful planning

of these small scale generation systems. In this thesis, a mathematical model

developed in GAMS, which is able to address these decisions and planning problems

commercial consumers face to install DGT. The models name is Distributed

Generation Technologies Selection Model (DGT-SM), and it is a mixed-integer linear

program. Given the customer load (electricity, cooling, and heating), market

information (natural gas prices, electricity prices), technologies database (capital cost,

lifetime etc) DGT-SM is able to find the optimum combination of DGT that minimize

the annual customer energy bill while at the same time the model decides their

capacities and operation schedule throughout the year. The model was tested in a

commercial shopping mall under many different scenarios and sensitivities and the

results indicate substantial economic savings for all the cases (over the already

existing grid and boiler case). Most of them (except one) had also enormous carbon

savings. For the final scenario, where all the technologies was available for

installation, the technology chosen by the model was one MW combined cooling,

heating and power (CCHP) natural gas engine. The results for this scenario were 51%

annual energy bill savings and 17% carbon savings over the grid plus boiler basic

scenario.

13

1. Introduction

1.1 Global Energy Consumption & Buildings contribution

According to world energy outlook the world’s primary energy needs in the

Reference Scenario are projected to grow by 55% between 2005 and 2030, at an

average annual rate of 1.8% per year (IEA, World Energy Outlook:China and India

insights, 2007). The population will exceed the 9 billion (now 6 billion) until 2050

(IEA, World Energy Outlook, 2004), and more than 80% of global population will

live in cities (Figure 1). As we can see and from the Figure 2 below, buildings

(domestic & commercial) account for the biggest

amount of energy consumed in a city, almost 60%

of the total. As becomes obvious, in the future the

energy consumption in the buildings will

dramatically increase comparing with the present.

Urbanization of China and India is a representative

example of the above fact.

Between the buildings commercial sector seems to have a growing interest. Several

numbers of existing cities going from industrialism to service oriented economies.

The last gives clear signal for the

upcoming big growth of the commercial

sector. One existing example of this

change is Hong-Kong which economy

shifted from being manufacturing based

to more service oriented financial

structure (Joseph C.Lam D. H., 2003).

As a result, there has been rapid development in many large scale commercial

building projects. It becomes obvious that the result of this transition is more energy

consumption in commercial buildings. Out of all the buildings, Shopping Malls, is a

rapidly growing sector which until now very little research has been done and it is

very interesting area because of the high electricity consumption per 𝑚2 compared to

the other commercial buildings (Joseph C.Lam D. H., 2003).

Figure 1, World Population distribution in urban and rural places

Figure 2, Total London Energy use breakdown

14

Until now building demand met in a very inefficient way, both electricity supply

(which consumed in buildings) and electricity demand. Electricity for the cities

produced in power plants with a mean efficiency 30% (Tester W. Jefferson, 2005).

More over this electricity consumed in the buildings in inefficient appliances such as

light bulbs (3-5% efficiency), badly designed air-conditioned systems etc (Tester W.

Jefferson, 2005).

1.2 Decentralized energy systems

According to Lovins and Gumerman there is great potential for benefits from moving

our economy from the centralized to a more distributed power generation model

(Gumerman, 2003) (Lovins, 2002). Some concepts of decentralizing which are

common and used systematically are micro-grids or distributed generation

technologies (DGT) etc. All these concepts have differences between of them, but at

the same time all of them agree that is a great need for our economies to decouple

themselves as much as is possible from fossil fuels (e.g. renewable) or if this is not

feasible for the near future at least to try to minimize the losses (e.g. unutilized heat).

As we discussed in the previous section the greatest energy consumers of our

economies in total are cities, where the greatest needs associated with the electricity

consumption (e.g. cooling, lighting). The losses in electricity production (when fossil

fuels are used) are mainly heat loses, heat losses which are growing if we consider the

continuing increase of the electricity consumption worldwide. A characteristic

example of this inefficiency is the case of USA, as can be easily noted in figure 3.

Figure 3, (IEA, World Energy Outlook, 2004)

15

As can become obvious from this graph the losses are huge and going hand in hand

with the electricity needs. The common logic says that these inefficiencies are a very

good starting point for our economies to start moving to a more ‘’Sustainable Energy

Future’’. For a more sustainable and green future except the renewable energy

technologies key role can and must play the Combined Heat and Power (CHP)

technologies. The biggest advantage of CHP (commercial use) is that can utilize the

waste heat due to the fact that is close to the customer load, compared with the

common power stations which are far from the end-user and cannot use this heat. The

last happen due to the fact that the low-grade heat cannot travel like the electricity

without significant loses.

These technologies are common in industrial places but in order to make a big

difference worldwide this technology must be applied and penetrate successfully in a

commercial level (shopping malls, hotels, houses etc). Successful penetration of CHP

in commercial level needs the acceptance of the people which means that must have a

better economic result (also take into account the environmental effect) compared to

the current conventional way of power production. The greatest challenge a CHP

faces in a commercial level is the need to utilize a high amount of waste heat in order

to reach high efficiencies and be economically feasible compared to the state of the

art centralized power stations (economies of scale). This power and heat match

becomes even more difficult if we thing the high volatility in buildings requirements

driven by the working hours, electricity tariffs, fuel cost and weather. The last great

challenges for scheduling and control in the commercial use of CHP was the spark for

this project.

Self-generation big advantage except the advantage of utilization of heat (if exist) is

the avoidance of transmission and distribution of the electricity which typical account

almost for the 50% of the final energy bill (Williams P. a., 2001). For most of the

commercial buildings the electricity cost is much higher than the heating cost and the

potential energy bill savings will come from the provision of the electricity and not

from the heat. Due to the fact as mentioned before that the centralized power stations

have bigger efficiencies for electricity production (less waste heat in analogy) it

becomes obvious that the high utilization of heat for heating or cooling purposes is a

16

must. In order this to happen the commercial building must have except from

electricity needs and high heating or cooling (use absorption chillers) loads.

Bearing in mind the two previous facts shopping mall (SM) seems an ideal solution

for many reasons. First of all SM are in particular consuming more energy than the

other buildings and appear increasing across the world. Moreover, due to the variety

of different stores and the nature of a SM (great cooling and lighting demand) there is

a good ratio of electrical and heating loads, if we consider that the cooling demand

can be covered with absorption chillers driven by heat. Another great advantage of a

shopping mall is that during the working hours of year have an almost flat electrical

load profile and a relatively high load profile all the off-working hours (e.g. high

refrigeration demand during night).

Until now very few methods are available for optimizing operation of commercial

scale CHP, especially under variable fuel prices, and with the burden of small-scale

diseconomies. Taken into account the grade importance of CHP (and generally the

distributed generation technologies) in commercial scale in this report will developed

a method for jointly optimizing heat and electricity production and use within a cost-

minimizing framework while taking into account the carbon emissions.

1.3 Short plan and explanation of the model

The present study focuses on the development of a general mathematical optimization

model, with name Distributed Generation Technology Selection Model (DGT-SM),

in GAMS (General Algebraic Modeling System) which will be able to minimize the

energy payments of a shopping mall while minimize the environmental effect (CO2).

In other words DGT-SM is able to make a Shopping Mall more ‘’Sustainable’’ while

at the same time don’t compromise any comfort and meeting all the cooling, heating

and electrical demand.

In order DGT-SM to achieve this goal we must provide as data: Technologies (figure

2) information, market information and finally customer information. After the

optimization the model will give as outputs: optimal technology combination,

17

operating schedule as well as and some other outputs (e.g. energy bill cost, CO2 etc).

Figure 4 gives a graphic representation of the DGT-SM and figure 5 gives the

technical alternatives which will be used in this version of the model.

In chapter 3 will be explained in more detail the inputs of the model, in chapter 4 will

be given and explained thoroughly the mathematical model while in chapters 5 and 6

will discussed the results and some conclusions on them.

Figure 4, graphic representation of the DGT-SM

18

Sources

Generation Technologies

Conversion Technologies

Demand

GRID Electricity

Natural Gas

PV

CHP

Boiler

VC air cooled VC water cooled

Absorption Cooling

Electricity-only

Cooling

Heating

Waste Heat

Figure 5, technical alternatives used in this model

19

2. Literature Review

Previous works have been selected and reviewed based on the relevance to Energy

Consumption in a Shopping Mall, Distributed Energy resources in Shopping Malls

and big Commercial Buildings in General, Alternative Technologies and Energy

sustainability in SM. The related journals have been summarized with the problem,

method used, and how successful the work was. Also, some of the definitions and

introduction of basic principles of urban energy Buildings modeling and Optimization

are presented.

2.1 Energy Consumption in a Shopping Mall

In order to be able to see and compare the different options for meeting and

decreasing the demand in SM it is important to understand and become familiar with

the actual needs of this type of building first. Despite the fact that SM penetrating the

building market in a very fast pace, few studies have been done as regards the

electricity characteristics in shopping malls. According to energy audits and surveys

which have been made for commercial air-conditioned buildings by the University of

Canberra (Lam JC, 1995), air-condition account for 40-60% of the total electricity

consumption with lighting in the second place accounting for the 20-30%. For a

shopping mall these two factors become even more important if we consider the

population density and the larger lighting load and, hence, the higher air-conditioning

needs compared with the common commercial buildings. Until now the needs of

commercial buildings covered from grid as regards the electricity and from boilers

(natural gas, diesel) as regards the heating. As becomes obvious from now on this

scenario will be the common or base case. Below, will be exhibited, some previous

works as regards the demand and the loads in shopping malls and other with similar

needs commercial buildings.

One good approach in analyzing the consumption characteristics in shopping malls in

subtropical climates was made in China by the City University of Hong Kong (Joseph

C.Lam D. H., 2003). The objective of this study was to investigate the electricity use

characteristics in shopping centers in subtropical Hong Kong. The four buildings

20

examined in this study are fully air-conditioned and was made during the 1990s. The

table 1 below summarizes the main characteristics of the buildings envelope. Twelve

months electricity consumption data were gathered for each of the four shopping

centers. The monthly electricity consumption for the different shopping mall’s

showed in Figure 6. As was presumable the electricity consumption peaks during the

summer period due to the hot summer months and the air-conditioning needs. During

the mid-season the electricity consumption is also high due to the high internal loads,

such as people, office but mainly the thermal loads from the artificial light.

The next information was takes was the breakdown of the four major electricity end

uses in percentages (Figure 7). In order to breakdown this total electricity

consumption the following method was followed. For lighting consumption, the

number of light fixtures and their corresponding power ratings in both the landlord

and tenants areas were surveyed and estimated wherever appropriate. Then taking

into account the daily operating hours, the electricity consumption for lighting was

determined. A similar approach was adopted for the electrical appliances

consumption. For the escalators and the lifts were used energy analyzers (DRANETZ

8000-2) in order to measure the electricity consumption. The HVAC consumption

was obtained by subtracting the total electricity consumption from the other three.

The biggest consumer was the HVAC system, with percentages 47 to 54 of the total

consumption. Lighting accounted for the 33-38% and with average lighting load

densities for the landlord and tenants areas 15 and 55𝑊 𝑚2 , respectively. On

average, HVAC and lighting accounted for about 85% of the total building electricity

use. Finally in table 2 showed the normalized performance indicators (NPI), which

defined as the electricity use per unit floor area. For the landlord and tenants the

consumption were 485–795 𝑊 𝑚2 (landlord area only) and 294–327 𝑊 𝑚2

(tenants

area only), respectively. The total annual electricity use per unit gross floor area was

from 391–454𝑊 𝑚2 , with a mean NPI of 430𝑊 𝑚2

.

In conclusion, we can say that this report gave a good indication of the electricity

consumption characteristics of SM in subtropical climates. Of course the number of

the shopping mall was limited; the sub-metering wasn’t 100% accurate due to the

lack of all tenants’ data. The breakdown of major electricity end uses was estimated

21

using only the non-weather sensitive loads (lighting, appliances etc) and finally, they

didn’t give more specific data for the electrical loads (cooling, lighting etc) during the

days of a normal week and for different seasons of a year (summer, winter etc).

Table 1, summary of the building envelop and HVAC designs, (Joseph C.Lam D. H., 2003)

Figure 6, monthly electricity consumption profiles for the four shopping malls, (Joseph C.Lam D. H., 2003)

22

Figure 7, breakdown of the major end uses in the four shopping malls (Joseph C.Lam D. H., 2003).

Table 2, summary of annual electricity per unit floor area (Joseph C.Lam D. H., 2003)

Office building 1 Office building 2 Office building

3 Office building

4 Number of storeys 18 Multi-tenant 22 18 Total gross floor area (𝒎𝟐) 22.000 10.000 29.000 9.000 Building envelope Window-to-wall ratio

(WWR)

Inserted windows

50% Curtain walling

60%

Inserted windows

20%

RC structure 50%

Glazing type Shading Coefficient

Single tinted glass 0.7

single reflective glass

0.3

Single clear glass

0.9

Single tinted glass

0.6 HVAC plant/equipment Air side system

Chiller type

PAU/Fan-coil unit

Hermetic centrifugal

PAU/Fan-coil unit

Variable air volume (VAV)

Ceiling-mounted fan coil

Constant air-volume

Fan-coil unit

VAV

Heat rejection method Air-cooled Air-cooled Sea water-cooled

Air-cooled

Chiller COP (kWr output/kWe input)

3 3 5 3

Table 3, summary of the buildings envelop and HVAC designs, (Joseph C.Lam D. H., 2003)

23

In a second study made in air-

conditioned commercial/office

buildings (Joseph C.Lam D. H.,

2003), almost the same results were

takes as before. The buildings

characteristics are given in the

above table 3. In this study the

hourly load profiles was monitored

during the hot months of July and August

and the results for the four

buildings (A, B, C, D) showed in

the figures 8, 9, 10, 11. The results

show that HVAC was the larger

electricity end user, accounting for

30-60% of the total electrical

demand during the office hours.

Lighting came in the second place

accounting for the 20-35% of the total

electrical demand. Small power for a

15-25% with lifts in the last place with

only few percentages mainly in peak

hours. During the office hours (08:00 –

18:00) the variation was up to 10%,

which occurred mainly between 12:00-

15:00 when peak demand was reached. The

major consumer between the HVAC

systems was the chiller which

consumes the 70% of the HVAC

consumption (or 40% of the total

electric load). In this study was

suggested a chiller load shifting in the

night using thermal chilled store if it is

Figure 8, measured hourly electrical load profiles for Building A

Figure 9, measured hourly electrical load profiles for Building B

Figure 10, measured hourly electrical load profiles for Building C

Figure 11, measured hourly electrical load profiles for Building D

24

economically feasible.

Concluding, from the previous study we noticed that the electrical needs for big

commercial office buildings don’t defer that much with the shopping mall demand.

Both have the same marginal needs in HVAC and lighting (in summer) and of course

they have almost the same electrical load profiles. This derives from the fact that both

have many commons. They have same working hours, almost the same building

envelop, and finally are in the same climate. Of course they have and some

differences such as lighting loads and people densities. In a shopping mall the

lighting loads are much higher (20-50 W/𝑚2) than in an office (12-25 W/𝑚2) which

not only cause a higher electrical need but also cause and higher thermal loads, which

means higher cooling loads. Moreover the higher occupancy density causes the need

of higher cooling loads and in humid climates we have the humidity more easily in

the building (also cooling problem). Another important difference is that in the night

the shopping mall has bigger electrical loads, comparing with the peak demand, due

to the refrigeration needs from the food stores.

25

In the CERTS Customer

Adoption Model paper (F. Javier

Rubio, 2001) examine the use of

distributed energy sources in a

Mall and give the electrical loads

of them. According to these data

the ratio of minimum to maximum

load is smaller in January than it is

in August (0.31 in January and

0.53 in August). This implies that the

difference between minimum load

and the peak is more evident in

January (Figure 12) than in August

(Figure 13). The seasonal

differences in the shape of the

profiles are obvious in the two

figures (12, 13). In January (Figure 12) there is a high level of load demand from

approximately 9:00 to 22:00, and then the demand drops dramatically to the low level

(these are the mall working hours). On the other hand, August (Figure 13) has a peak

in the profile at around 15:00 (during the hottest part of the day). In all other hours,

the load declines to or rises from the level that is maintained from around 22:00 to

10:00. The load factor for this customer is 0.36, pretty low, showing that the peaks

are well above the average load demanded (686 kW). At the below figures 14, 15, 16

we can see the week, peak, and weekend loads for the different months of the year

during the day.

Figure 12, January Peak Load for Mall

Figure 13, August Peak Load for Mall

Figure 15, Mall Peak Load Shape Figure 14, Mall Week Load Shape

26

Other papers attempts to analyze the Electricity consumption of Commercial

buildings are the: Electricity use characteristics of

purpose-built office buildings in subtropical

(Joseph C. Lam *, 2004), a study of energy

performance of hotel buildings in Hong Kong

(Deng Shi-Ming, 2000)

For a specific site, the source of end use energy

load estimates is typically building energy simulation

using a model based on the DOE-2 engine, such as eQUEST, or the more advanced

but less user-friendly EnergyPlus. These tools can calculate the hourly energy loads

and costs of several types of commercial buildings given information about: building

location, construction, operation, utility rate schedule, heating, ventilating, air-

conditioning (HVAC) equipment, and finally distributed generation unit performance

parameters and operation strategy.

Concluding, Shopping Malls are large energy consumers, with energy consumption

per 𝑚2larger than the majority of the commercial buildings. The main energy need in

a SM is electricity for cooling and lighting. Especially the cooling requirements are

large due to the high density of people during the working hours and the high thermal

loads from the artificial lighting inside the building. Also and the light requirements

are high due to the special needs of a SM. Until now very little work has been done in

SM as regards the energy optimization and sustainability in adverse with the large

amount of papers existing for other commercial buildings. In the next section will be

introduced the different alternative technologies can be used in SM.

What are the future challenges?

As becomes obvious from the existing analysis buildings due to their increasing

contribution in the global energy consumption and their inefficient way they meet

their demand until now there is a lot of potential to both decrease and meet the

demand in a different more efficient way. In other terms, the objective is the

Sustainable Development of the Buildings and especially in this case Shopping Malls

while the comfort level of these buildings remains constant.

Figure 16, Mall Weekend Load Shape

27

2.2 Alternative Technologies and Energy sustainability in SM

2.2.1 Description of the different technical alternatives

In the next figure 17, we can see some of the most important technical alternatives

can be used in a SM to meet the electricity and heat demand. As we can see from the

figure for electricity the alternatives are: Grid, Photovoltaic’s (PV), Combined Heat

and Power (CHP, natural gas). For heat the alternatives are: CHP and boiler. For

cooling we can use both electricity or/and heat in a VC cooled air condition and in an

Absorption cooling system respectively. A short introduction and description of the

above technologies are listed below.

Figure 17, Superstructure with the most important technical alternatives meeting the electricity and heat demand in a SM

Sources

Generation

Technologies

Conversion

Technologies

Demand

GRID

Electricity

Natural Gas

PV

CHP

Boile

r

VC air cooled

VC water cooled

Absorption

Cooling

Electricity-

only

Cooling

Heating

Waste Heat

28

2.2.2 Photovoltaic’s

Solar radiation can be converted directly into electricity using photovoltaic (PV)

cells. The electrical efficiency of PV is between 5-15%, and the energy output of such

a system depends from the solar radiation, for UK the radiation range between 800-

1000 kW h (Northern to Southern England). According to the common

technologies the installed cost of a BIPV is about 500 pounds per for roof tile and

900 pounds per for the most expensive facades (F, 2005). One squared meter of

mono-crystalline array will produce roughly 150 kW h per year, and also for each

kW installed will produced about 700 kW h per year. The maintenance and

operation cost of a PV system is too low that is not included, and the lifetime is in

average about 30 years. Finally, PV is almost ‘emission-free’, because there is no

need for fuel or cooling water; it operates silently and is believed to fit in urban

development. One kW panel can save 0.1 to 1 tonne of emitted per year.

However, the manufacture of PV requires a lot of energy and is embodied some

(F, 2005). The above prices summerized in the 18 Figure.

Capacity Cost per in

pounds

Energy output kWh

p a

kWh per year for

each kW installed

1>kW 500 - 900 120 - 150 560 - 700

2.2.3 Co-generation

Co-generation is also called combined heat and power (CHP). CHP in contrast with

conventional power plants uses heat that is normally discarded to produce thermal

energy, which can be provided to district heating systems, with result to reduce𝐶𝑂2

emissions and running costs. The efficiency depends from the type, scale and

operation of the CHP with an average of 70-80% (25-35% electricity and 45-55%

high grade or useful heat 71-82 c) (F, 2005). The different types of CHP are: Micro

turbines, Fuel cells, Reciprocating engines, Gas turbines (simple-cycle cogeneration),

Gas and steam turbines (combined-cycle cogeneration) and gas engines. Some data

about those (capital cost, efficiency, power to-heat ratio, emissions etc) are

represented in the table 4. Interesting issue is the operation of CHP’s, because in

cogeneration it is important to optimize the balance of heat and electricity generation.

This balance depends on the customer loads (electrical and thermal) and is possible

Figure 18, Average costs and productivity of PV’s

29

the CHP to follow the thermal or the electrical load. Other option is to produce more

electricity and/or heat and sell it back to the grid/customer in order to have some

profit. One other option is the fuel, natural gas or biomass. All the above options and

other must be taken into account and optimized in the CHP installation to meet the

same demand with less cost and emissions.

Table 4, Characteristics of cogeneration technologies available for use at the scale of individual large buildings (micro turbines, fuel cells, reciprocating engines) and district heating networks (simple- and combined-cycle turbines) (Lemar, 2001)

2.2.4 Tri-generation model

Tri-generation is also known as combined heating, cooling and power generation or

CHCP. CHCP uses the waste heat from CHP not only to meet the heat but also the

cooling demand by applying the heat to absorption chillers. This chiller utilizes the

heat to increase the pressure of refrigerant instead of using compressors which highly

consume electricity. All the facts from co-generation also existing here, with more

complexity because the optimization problem now extended further more. The

advantage of the CHCP compared to co-generation becomes clear in buildings with

high cooling demand like in this case in a Shopping Mall.

30

2.2.5 Gas boiler

A boiler is a device for generating steam for power, processing, or heating purposes

or for producing hot water for heating purposes or hot water supply (used until now

for the majority of the buildings). It provides the building with heating and hot water

with efficiencies between 80-90% (Tester W. Jefferson, 2005) and can burn natural

gas or biomass. A great disadvantage of the boiler is the ``bad`` use or degradation of

high quality fuels like natural gas for the production of low grade heat for heating

needs comparing with the CHP which use the same fuel to produce some high quality

energy source (electricity) and some low grade heat.

2.2.6 Grid Electricity and other parameters

The cost of electricity, gas and biomass are given in the table 5 for domestic

commercial and wholesale use and also the 𝐶𝑂2 trading factor. In table 6 depicted the

average 𝐶𝑂2 emissions factor for the total UK grid mix (g/kWh) and in table 7 are

given values about the kg 𝐶𝑂2 emitted per kWh produced for the natural gas, boilers,

renewables, and grid. Also in the table 7 are given prices about the inflation, discount

rate etc.

Table 5, Costs (electricity, gas, and biomass) and also 𝑪𝑶𝟐 trading factor, (SEA/RENUE, 2006).

31

Table 6, Proportion of electricity supplied to the national grid from different sources, and associated 𝑪𝑶𝟐 emission factors, 2005.

Table 7, 𝑪𝑶𝟐 factors (grid, boilers, natural gas, and renewables) and other parameters (inflation, discount factor etc), (SEA/RENUE, 2006)

2.2.7 Electric chiller

The majority of the shopping malls use Vapor compression (VC, base scenario) with

air-cooled chiller for air conditioning. The electric chiller is defined by its efficiency

which expressed by the coefficient of performance (COP). The bigger is the COP the

more efficient is the electric chiller with result the decrease of the electricity used (for

the same comfort) and consequently the reduction of the fuel used (to produce

electricity) and the emissions going into the environment. Most of the HVAC systems

used in the shopping malls until now have a COP 3, but there are already existing

vapor compressions with water-cooled chiller systems in the market with COP 5.

32

2.2.8 Absorption chiller

The alternative choice of the VC is the absorption cooling (AC) with absorption

chiller (COP 1.2, heating) (Tester W. Jefferson, 2005). Absorption chillers use heat

instead of mechanical energy to provide cooling. A thermal compressor consists of an

absorber, a generator, a pump, and a throttling device, and replaces the mechanical

vapor compressor. The basic cooling cycle is the same for the absorption and electric

chillers, but the basic difference between the electric chillers and absorption chillers

is that an electric chiller uses an electric motor for operating a compressor used for

raising the pressure of refrigerant vapors and an absorption chiller uses heat for

compressing refrigerant vapors to a high-pressure. The rejected heat from the power-

generation equipment (e.g. turbines, micro turbines, and engines) may be used with

an absorption chiller to provide the cooling in a CHP (Combined Heat and Power)

system. The interesting part is to see through the optimization if it is more economic

and environmentally feasible to operate a CHP with higher electric to thermal ratio in

order to produce more electricity which will be used by an electric chiller in order to

meet the cooling demand or is better to operate the CHP in a higher thermal to

electric ratio in order to drive the heat through an absorption chiller and produce in

this way the cooling demand.

2.3 Distributed Energy Resources in Shopping Malls and

Commercial Buildings

Many researchers have been conducted until now as regards the passive design of the

building and the potential for reducing the demand (electricity, heating), but very few

have been done as regards the different ways to meet this demand (e.g. renewable,

CHP etc) in a Commercial building and especially for Shopping Mall less than five.

As regards the Shopping Mall until now there is no paper which use a simulation or

model optimization tool to integrate different distributed energy resources (more than

one e.g. PV & CHP) in it. For other Commercial Building like hospital, big offices

etc, there are studies with the majority of them examine only one energy source (e.g.

33

PV) and not a combination of them, and in the case they examine more than one

usually they do an exhaustive case by case simulation (no global optimum guarantee).

One other fact is that most of the studies are not develop an energy optimization

model but they use the existing commercial tools to examine different buildings.

Furthermore from the energy optimization models existing, most of them focused

only in some technologies (e.g. only in photovoltaic’s, or only in micro-turbine CHP,

or only efficiency techniques etc) and in some aspects (e.g. only economic benefits or

only environmental benefits examined but not both etc) of using decentralized energy

resources in buildings. Until now no research has been done in which will examined

different ways of meeting the demand and decreasing at the same time the demand of

a building (without take into account the passive design of the building), with final

objective not only the economic but also and the environmental benefit.

One of the interesting studies was conducted in Japan by Nan Zhou (Nan Zhou a *.

C., 2006). The objective was to find the best distributed energy resource system for

different types of commercial buildings (hospital, big office, hotel sport facilities and

retail) with constraint to meet the energy demands. In order this to be achieved was

used an information base with different distributed technologies, Japanese energy

tariffs and fuel prices, and the buildings needs which have been developed. Three

scenarios were taken for each building type. The first scenario was to take no action

in order to take the baseline (grid, NG boiler) costs, consumption and emissions. The

second scenario made available to purchase a generation technology only for

electricity production (without heat recovery and absorption cooling), and the third

scenario was included everything (generation, recovery and with waste heat cooling).

The results show a significant increase in the efficiency (Figure 19), decrease in

carbon emissions (Figure 20) and finally decrease in annual energy cost (Figure 21).

The results show a great potential and a very promising payoff (between 3 - 6.8

years).

Figure 19, Efficiencies of the overall system, (Nan Zhou a *. C., 2006).

34

Figure 20, carbon emissions comparing base and optimal solution for all the buildings, (Nan Zhou a *. C., 2006)

Figure 21, Annual savings, (Nan Zhou a *. C., 2006)

In the next paper Medrano (M. Medrano, 2008) try to investigate the economic,

energy-efficiency, and environmental impacts of the integration of distributed

technologies (high-temperature fuel cells, micro-turbines, and photovoltaic solar

panels) into four representative generic commercial buildings (office building,

medium office building, hospital, and college/school), using as simulation tool the

DOE-2.2- derived user-interface eQUEST program. This tool can calculate the hourly

energy loads and costs of several types of commercial buildings given information

about: building location, construction, operation, utility rate schedule, heating,

ventilating, air-conditioning (HVAC) equipment, and finally distributed generation

unit performance parameters and operation strategy.

The methodology Medrano follow have four steps. First, is the base case where n DG

are included and during this step the electric and gas hourly profiles for days

corresponding to peak electric and gas consumption are analyzed. In the second step

are introduced and implemented different cost effective energy efficiency measures

35

(e.g., day lighting, exterior shading, and improved HVAC performance) according to

energy use intensity with objective to reduce energy consumption and emissions. In

the third case different DG technologies integrated in the buildings with the constraint

that the waste heat utilized only for hot water and/or space heating. In the last

approach, the traditional HVAC systems were replaced by heat driven absorption

chillers alternatives, systems which works with hot water loops. In this way the

thermal loads are utilized with result the increase of the overall efficiency of the DG

system. Finally, the influences of utility gas and electric tariffs and weather

conditions are illustrated, comparing the DG economic viability of the same office

building in two U.S. locations.

According to this paper the results gave a promising potential of the DG in these

types of buildings. But I won’t stay in these results but in the methodology and the

tools Medrano used in this report. Using this kind of simulation tools like eQUEST

he investigates case by case combinations of DG in buildings, with the result not to

find the optimum solution for cost reduction and environmental benefits and

efficiency maximization.

36

3. Model Inputs

In this section will be presented and explained all the different inputs to the model.

First will be explained the technology database, then the shopping mall loads and

finally the market inputs.

3.1 Technology database

In this section will be presented and explained the technology database that was used

as input to our model. These dada depicted in figure 22 was initially produced by the

National Renewable Energy Laboratory (NREL) in the study ‘’Gas-Fired Distribution

Energy Resource Technology Characterizations’’ (Goldstein, 2003), and then further

developed by Ernest Orlando Lawrence Berkeley National Laboratory in 2004 report

Distributed Energy Resources Customer Adoption Model Technology Data

(Firestone, 2004).

This technology database contain information for the technologies: fuel cells (FC),

gas turbines (GT), micro-turbines (MC), natural gas engines (NG), and photovoltaic’s

(PV). Each technology described by a number of parameters, parameters which are

inputs to the model and are explained below:

Capacity (maxp): This represents the maximum electrical output of the

machine in KW.

Lifetime (years): is the average life of the machine in years.

Capital cost (capcost): includes the machines cost, the system design and

finally the installation cost. This parameter defined as the cost per KW

electrical output capacity ($/KW). These machines can be purchased:

a) Without heat recovery potential (no CHP)

b) With heat recovery for heating purposes (CHP)

c) With heat recovery for both heating and cooling (CCHP)

37

Operation and Maintenance Fixed Costs (OMFix): OMFix includes all the

fixed annual operation and maintenance costs ($/KW per annum) (excludes

fuel costs)

Operation and Maintenance Variable Costs (OMVar): OMVar includes all

variable operation and maintenance costs ($/KWh) (excludes fuel costs)

Heat rate (HeatR): is the equipment heat rate (kJ fuel/KWh). Heat rate is

linked to electrical efficiency, E by the equation:

HeatR = 3600 𝑘𝐽

𝐾𝑊ℎ

𝐸

HeatR in expressed with esteem to the higher heating value (HHV) of natural

gas, due to the fact that the purchase of NG is with respect to the HHV

Heat to power Ratio (α): α is the ratio of recoverable heat per KWh electrical

produced (maxp to maxp).

According to Firestone, α value is based on the waste heat energy content

prior to conversion via a heat exchanger, and here referred as recoverable heat

(e.g. 1 KWh recoverable heat doesn’t cover 1 KWh heating demand but

1KWh x heat exchanger efficiency).

Conversion Efficiency for Recoverable Heat to Load Displacement (γ): γ

value is an estimate of the portion of the recoverable heat that is useful and

can displace real heating or/and cooling loads.

γ value for heating is 0.8 and is actually the heat exchanger efficiency.

Cooling loads according to Firestone are defined as the amount of electricity

required to give the amount of cooling needed (assuming a specified value for

electric chiller efficiency). γ for absorption cooling is consequently the ratio

of electrical cooling load displacement to recoverable heat. This must take

into account the heat exchanger efficiency in addition to the relative

performance of electric and absorption chillers as described in the below

38

equation (where the COPabs is the coefficient of performance of an absorption

chiller and COPelectric is the coefficient of performance of an electric chiller).

γabs = EfficiencyHeatExanger * COP abs

COP electric

COPabs has value 0.65 for single-stage hot-water fired absorption chillers and

COPelectric has value 4 for electric compression driven chillers. Thus, γabs has a

value of 0.13 for CCHP (Firestone, 2004). The γ values for different end-uses

are shown in table 10.

Conversion Efficiency for Fuel to Load Displacement (β): β is an estimate

of the portion of the fuel energy content that is useful for displacing heat loads

with the use of heat exchanger or/and cooling by the use of absorption

chillers. β value for heating is 0.8 (boiler efficiency) and for cooling 0.13 as

before. The lower value for cooling is due to the fact that cooling loads are

expressed as the amount of electricity requested to provide the wanted amount

of cooling and cooling data is invariably expressed as electricity used by the

air conditioner. Thus, β for absorption chillers must incorporate the ratio of

fuel energy to useful heat as well as the relative performance of electric and

absorption chillers as discussed before (Firestone, 2004). The β values are

depicted in table 10, while the table 9 summarizes the assumptions used for

the β and γ values.

39

Table 9, Underlying Assumptions (Firestone, 2004)

Table 10, β and γ values (Firestone, 2004)

40

Figure 22. Technology database (Firestone, 2004)

41

3.2 Shopping mall description

In this section, we are going to describe the shopping mall load profiles (electrical-

only, cooling and heating). The most difficult part through this study was to find real

24 hour load profiles for SM’s due to the fact that these profiles either must

calculated from a company (in response to a customer) or to produced by simulation

tools like EnergyPlus or DOE-2, tools that wasn’t available in this MSc course

boundaries.

For that reason, ready electrical loads profiles were taken from the CERTS Customer

Adoption Model paper (F. Javier Rubio, 2001). This shopping mall is located in

southern California and the profiles were extracted from Maisy from the year 1998

data for the state of California. These data were reproduced and depicted in figure 23.

Someone can claim that the SM in California has many differences with a SM in UK

and thus the existed load profiles can’t be input to this report. But here this isn’t

actually the case for two reasons:

First, SM’s are a very specific consumer with especially large energy demand for

cooling and lighting. From the previous two, only the cooling could have great

differences between a building from California to London (due to climate

differences), but actually in the SM this is not happening because the thermal loads

that must be removed from a SM usually come not that much from the outside

thermal mass transfer but mainly from the high density of people during the working

hours and the high thermal loads from the artificial lighting inside the building.

Second, in this report the most important is not actually the results as numbers but

actually the model and the accuracy of the thermodynamic equations it uses in order

to produce the results.

This electrical load profile is described in a more detail in the literature review

chapter in the section energy consumption in a SM. The problem with these data is

that these load profiles are the total electrical load profiles (aren’t separated) and are

42

not fitted to our model which takes as input for every month the 24 hour electrical-

only, cooling and heating loads separately.

For that reason these profiles were separated manually, without great detail but

following a constant logic. From the research in energy consumption in shopping

malls the energy breakdown was:

40-60% HVAC

20-30% LIGHTING

5-10% other appliances

3-4% lifts

The heating demand in a SM due to the great thermal loads from the lights and the

high people densities during the working hours is mainly in early morning or late

afternoon hours with bigger needs during the winter months. On the other hand

cooling demand for the same reasons is peaked during the hours 12:00 to 15:00 with

greater effect on summer months, when and the outside temperature comes to be

added in the high internal thermal loads. Finally the electrical-only loads are almost

stable during the 24 hours and the 12 months.

Mainly for the previous reasons the breakdown of the total electrical load to the

electrical-only, cooling and heating follow the below separation rules (For each hour

of a day, every month and season the sum of the electrical-only, cooling and heating

percentages must have sum the 100% of the total electrical load ):

Summer months:

1) Electrical-only loads (percentages to the total):

From the hours 22:00 to 6:00, 50%

All the rest hours of the day, 40%

2) Cooling loads:



43



3) Heating loads:





Winter months:

4) Electrical-only loads (percentages to the total):

All the hours, 40%

5) Cooling loads:





6) Heating loads:





By following the previous rules the SM detailed profiles are depicted in figures 24,

25, and 26.

44

Figure 23, SM Electrical load (F. Javier Rubio, 2001)

Figure 24, SM Electrical-only demand

0

200

400

600

800

1000

1200

1400

0 5 10 15 20 25 30

End

-use

load

(K

W)

Hours

SM Electrical Load

January

February

March

April

May

June

July

August

September

October

November

December

0

100

200

300

400

500

600

0 5 10 15 20 25 30

End

-use

load

(K

W)

Hours

SM Electrical-only demand

January

February

March

April

May

June

July

August

September

October

November

December

45

Figure 25, SM Cooling demand

Figure 26, SM heating demand

0

100

200

300

400

500

600

0 5 10 15 20 25 30

End

-use

load

(K

W)

Hours

SM Cooling demand January

February

March

April

May

June

July

August

September

October

November

December

0

50

100

150

200

250

300

350

400

450

0 5 10 15 20 25 30

End

-use

load

(K

W)

Hours

SM Heating demandJanuary

February

March

April

May

June

July

August

September

October

November

December

46

3.3 Tariffs inputs

Tariffs are a key input to our mathematical model. The two market inputs that will be

explained in the next two sub-sections in detail are the natural gas prices and the grid

electricity prices.

3.3.1 Natural gas prices

Natural gas prices are a commodity that don’t change price so often during the

month, has small volatility, and thus we take average monthly prices in contrast with

electricity prices which change in a few minutes basis. The natural gas prices in $ per

MMBTU were taken from the Energy Information Administration website

(http://www.eia.doe.gov/) which is the official energy statistics from the U.S.

government. For the case of the SM the commercial prices of 2008 were used

(Release Date: 8/29/2008) and represented in the below figure 27, 28. Due to the fact

that the NG prices for 2008 are not completed for this year (data are up to June 2008),

but also it wasn’t wise to use the 2007 (this year prices are lower) for the rest of the

year (July to December) an analogy was used in this way: we calculated the

percentage that 2008 prices (up to June) are higher from 2007 prices and we added

this to the 2007 prices for the rest of the year.

Figure 27, monthly natural gas prices in $ per MMBTU for the calendar years 2007, 2008

NG price in $ per MMBTU 2008 2007

January 11.07 11.14

february 11.37 11.24

March 11.76 11.82

April 12.45 11.51

May 13.23 11.51

June 14.41 11.87

July 12.74 11.63

August 12.24 11.18

September 11.94 10.9

Octomber 11.83 10.8

November 12.09 11.04

December 12.07 11.02

http://www.eia.doe.gov/

47

Figure 28, graph representation for natural gas prices in $ per MMBTU for 2008

3.3.2 Electricity prices (Grid)

After the explanation of the NG prices the next important tariff input to the model is

electricity price purchased from the grid to the customer.

For this model the electricity price is calculated as shown in the figure 29. According

to Peter Williams and Goran Strbac, in their book costing and pricing of Electricity

distribution services the consumer final electricity price consisted by 51% from the

electricity generation cost and the rest 49% from the transmission, distribution, and

supply cost. The generation cost will be assumed to be the spot market electricity

prices for this year (2008). These spot market prices will be taken from the Elexon

BSC website (http://www.elexon.co.uk/), and more specifically in the section Pricing

data the market index data for the year 2008

(http://www.elexon.co.uk/marketdata/PricingData/MarketIndexData/default.aspx).

Like with the natural gas and here because of the fact that this year is not ended yet,

the real 2008 data will be from January to June and the rest months July to December

will be calculated as before: find the percentage that 2008 prices (up to June) are

higher from 2007 prices, then add this to the 2007 prices for the rest of the year and

finally keep these new prices as the rest 2008 values. These market spot prices are

depicted in figure 30.

0

2

4

6

8

10

12

14

16$

pe

r M

MB

TUNatural gas Price in $ per MMBTU

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

http://www.elexon.co.uk/

http://www.elexon.co.uk/marketdata/PricingData/MarketIndexData/default.aspx

48

As soon as the spot market prices calculated, then the annual average price is

calculated and this value is multiplied by 49/51 in order to find the distribution,

transmission and supply payment. This parameter is called DistrPay in the GAMS

and its value is 0.13936531 $/KWh (for 2008). So each time the customer purchases

one KWh, the price will be payed back to the grid consisted of the spot market price

for the exact time of the purchase and the constant DistrPay. Using this method the

final electricity prices for the whole year depicted in figure 31.

Figure29. Contribution of distribution costs to electricity bill (Williams P. a., 2001)

49

Figure 30, Spot market electricity prices

Figure 31, Grid electricity price with the distribution company revenue

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 5 10 15 20 25 30

$ p

er

KW

h

Hour

Spot market electricity priceJanuary

february

March

April

May

June

July

August

September

Octomber

November

December

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25 30

$ p

er

KW

h

Hour

Grid electricity price with the distribution company revenue January

february

March

April

May

June

July

August

September

Octomber

November

December

50

4. Mathematical Model

4.1 Introduction

In this part, the mathematical model will be presented and explained but also and the

reasons behind this venture. The results which are presented are intended more to

show: the great usage of GAMS in solving difficult optimization problems, the

possible savings that can be achieved by the optimization of the energy systems

(combination of DG and grid) in a shopping mall (extended in a microgrid) and not

the actual numbers of the final energy cost of a shopping mall and the actual carbon

savings. Improvements must be undertaken in the tariffs in order the model to use an

accurate electricity tariff system, in the load profiles which are key input to the model

(must be monitored a real SM for a year and calculated the accurate electricity,

heating and cooling profiles), and also the technology information (more accurate

costs and a more accurate thermodynamic model which will take into account the

efficiency drops etc). Finally we can say, that given all the foresaid inputs (customer

demand, electricity/NG tariffs and technology information) the model can give back

some strategic results about the way DG technologies and Grid must be combined

(which DG must installed) and work (when this capacity will operate during the year)

in order to have some energy, money and carbon savings while we meet the constant

customer demand.

4.2 Mathematical Programming

We use mathematical programming in order to build the energy models. H. Paul

Williams gives a definition for the mathematical programming (Williams H. , 1999):

Mathematical programming has a sense of planning for the purpose of optimization,

it is a mathematical problem regarding to maximizing or minimizing something

which is known as objective function and it has to satisfy the conditions called

constraints. The mathematical programming models are able to be classified as linear

programming models, non-linear programming models and integer programming

models.

Linear Programming Model (LP): Linear programming is the optimization

problem in which the objective functions and the constraints are all linear.

51

Non-linear Programming (NLP): Non-linear programming is the optimization

problem in which at least one of the objective functions or the constraints is a

non-linear function.

Mixed-integer Programming (MIP): Mixed-integer programming is the

optimization problem that has both continuous variables together with integer

variables. It can be mixed-integer linear programming (MILP) or mixed-

integer non-linear programming (MINLP).

Mixed-integer linear Programming (MILP): Mixed-Integer Programming

(MIP) methods (L.T. Biegler, 1997) are suitable for modeling and analyzing

buildings energy systems towards design, investment planning and

optimization: this established algorithmic framework fulfills the requirements

and captures the complexities of an investment planning procedure, by

considering the superstructure of all alternatives, representing all possible

choices for a system by binary (0–1) variables, while all the physical and

economic quantities are expressed as continuous variables. All logical and

physical relations are translated into equality or inequality constraints. The

best plan is derived by conducting an optimization for a specific objective

function (Liu Pei, 2007).

4.3 General Algebraic Modeling System (GAMS)

General Algebraic Modeling System (GAMS) is multipurpose optimization software

which is particularly designed for modeling linear, non-linear and mixed integer

optimization (MIP) problems. Most of the researchers use GAMS for solving large

and complex mixed integer linear programming (MILP) problems. Of course GAMS

can do much more than these but is not in the current needs of this subject.

The basic reasons GAMS selected for this optimization are:

52

Offer a high level language, for the illustration of large, difficult and

complex models.

Provide easy and safe changes in the specification of the model.

Allows unambiguous statements of algebraic relationships

Allows comments in the model which are independent to the model

solutions.

4.4 Model Description

In this current model, there are two input fuels: natural gas and electricity from the

grid. At the other end of the model there are three end uses that can be met: electrical-

only, cooling and heating loads. The models objective function is to minimize the

cost of meeting the Shopping malls energy demand for one year (while taking into

account the carbon emissions) by optimizing the usage of different distributed

generation technologies and the power from grid. In order to reach this objective the

model must answer the following questions:

If it is economically feasible, which DG technologies must be adopted?

The chosen DG technologies in which capacity will be installed?

How this capacity must be operated during day/year in order to minimize the

energy cost while meeting at all times the customer demand?

It is more economically for the customer to disconnect for the grid or there are

profit opportunities by selling electricity back to the grid (especially the times

of the high demand / high price)?

The model inputs are:

The SM electricity-only, cooling and heating load profiles,

The hourly spot electricity prices for the year 2008/2007 2 (with the payment

of distribution company) and the monthly spot natural gas prices for the same

year,

53

DG technology information which includes: capital (include installation),

operation and maintenance, and fuel costs (taken into account the lifetime and

annualized for one year), basic technology physical characteristics, and finally

some general thermodynamic parameters for the efficiency of the combined

heat and power.

Rate of carbon emissions from the microgrid and from the on-site onsite

generation by the usage of natural gas

Carbon taxes by using the price from ETS

The model outputs will be:

The mixture of the DG technologies that will installed to the shopping mall

The capacity level in which these technologies will be installed

Hourly operating schedule of the installed capacity (not 15 minute basis that is

common in spot markets due to the computational constraints but also and the

difficulty to find the load profiles in this study)

The final energy cost to meet the shopping mall demand and also the carbon

emissions through the usage of the DG equipment and/or grid

Some important model’s assumptions:

The optimization based clearly on economic criteria. At the same time while

there is a try to capture the externality of the carbon emissions by taxation

there is no consideration of a detailed environmental model (e.g. maybe there

is a reduction on carbon emission but not local pollution)

The customer can buy and sell electricity back to the grid at any time. Despite

the fact that the spot market electricity prices are real, the distribution

payment (is constant for the year) and the price the customer sells back to the

grid (half of the spot market price and if we take into account the payment for

the distributor is almost the ¼ of the price we buy from the grid) are not

accurate but based more in an average base.

Are not taken into any deterioration in output efficiency of the equipment

during its lifetime, and also there is no penalty in the efficiency for part load

operation (start-up also).

54

In this economic report is not taken account the reliability of these equipment

(e.g. if the CHP broke for one reason the power purchase from grid are not in

the same tariffs as usual)

At the same way, CHP/CCHP benefits, power quality and reliability also are

not taken into account.

Equipment price and performance are accepted without question.

2Will be used the prices until July for the 2008, and the prices for the rest of the year from 2007.

4.5 Mathematical Formulation

Customer Data

Name Description

Cdemand m,h,u Customer demand in kW for end-use u during hour h, and month

m (end-use are electric-only, cooling, heating).

Market Data

Name Description

ElectrPricem,h Spot market electricity price during month m and hour h

($/KWh)

CarbonTax Taxes on Carbon emissions ($/Kg)

MCRate Carbon emission rate from the grid (Kg/KWh)

NGCRateu Carbon emission rate from burning onsite natural gas to meet the

end use u (Kg/KWh)

NGpricem Spot market natural gas price during month m ($/Kj)

Technology Information

Name Description

Name Description

DGmaxi Capacity rating of technology i ( kW)

DGlifetimei

Expected lifetime of technology i (a)

DGcapcosti Capital and installation cost of technology i ( $/kW)

DGOMfixi Fixed annual operation and maintenance costs of technology i

($/kW)

DGOMvari Variable operation and maintenance costs of technology i

($/kWh)

DGCostKWhi,m

Generation cost of technology i during month m ($/kWh)

55

CarbonRatei Carbon emissions rate from technology i (kg/kWh)

DFCap

Capacity of direct-fired absorption chiller (kW)

DFPrice Capital and installation cost of direct-fired absorption chiller

($/KW)

E(i) Set of end-uses that can be met by technology i

Aditional Parameters

Name Description

InterestRate

Interest rate on DG investments (%)

SolarInsm,h

Average portion of maximum solar insolation received (%)

during hour h and month m and used from photovoltaic cells

DistRev Distribution company revenue1

NGHR

Natural gas heat rate (kJ/kWh)

1This price is added to the spot electricity market when the customer buys from the grid (wholesale market) and pay the cost of

the distribution of the electricity from the producer to the customer. According to Williams and the paper Costing and pricing of electricity distribution services (Williams P. a., 2001) this value is almost the 50% of the total electricity cost. For the simplicity

here the average annual electricity spot price multiplied by 49/51 and this is the value for the whole year.

Variables

Name Description

InvDGi Integer variable which shows if one technology will be installed

and in which quantity

DF

Binary variable which shoes if a direct-fired absorption chiller

will be installed

ai

The amount of heat (in kWh) that can be recovered from every

kWh of electricity is produced by using DG technology i (a has

value 0 for all technologies that are not CHP or CCHP)

bu The amount of heat (in kWh) generated from unit kWh of natural

gas obtained for end-use u (the corresponding value of bu for

electricity-only load is zero due to the fact that never uses NG)

gi,u The quantity of valuable heat (in kWh) that can be allocated to

end-use u from unit kWh of recovered heat from technology i

(given that the electricity-only loads never use recovered heat,the

gi,u equals to zero)

56

GenOni,m,u,h Generated electricity by technology i during month m, hour h to

meet the onsite demand u (kWh)

GenSeli,m,h Generated electricity by technology i during month m and hour h

to sell back to the grid (kWh)

PurNGm,u,h Obtained natural gas during month m hour h for end use u (kWh)

(for direct burning)

GridElectrm,u,h

Purchased electricity from the grid by the customer during month

m, hour h to meet the customer demand u (kWh)

RecHeati,m,u,h Recovered heat from technology i that is used to meet the

customer demand u during month m and hour h (kWh)

Objective Function

The mathematical formulation of the problem is:

Min

InvDGi

GenOni,m,u,h

GenSeli,m,h

PurNGm,u,h

RecHeati,m,u,h

DF

333

(1)

𝑚 (ℎ 𝑢 GridElectrm,u,h) ∙ ( ElectrPricem,h+ DistRev)

+ 𝑚 𝑢 ℎ (GridElectrm,u,h ∙ CarbonTax ∙ MCRate)

+ 𝑖 𝑚 𝑢 ℎ (GenOni,m,u,h ∙ DGCostKWhi,m )

+ 𝑖 𝑚 ℎ (GenSeli,m,h ∙ DGCostKWhi,m )

+ 𝑖 𝑚 𝑢 ℎ (GenOni,m,u,h ∙ DGOMvari)

+ 𝑖 𝑚 ℎ (GenSeli,m,h ∙ DGOMvari)

+ 𝑖 𝑚 𝑢 ℎ (GenOni,m,u,h ∙ CarbonTax ∙ CarbonRatei )

+ 𝑖 𝑚 ℎ (GenSeli,m,h ∙ CarbonTax ∙ CarbonRatei)

+ 𝑖 InvDGi ∙ DGmaxi ∙ (DGcapcosti ∙ Annuityi + DGOMfixi)

+ DF ∙ DFPrice ∙ DFCap ∙ Annuity

+ 𝑚 𝑢 ℎ (PurNGm,u,h ∙ NGHR ∙ NGpricem)

+ 𝑚 𝑢 ℎ (PurNGm,u,h ∙ CarbonTax ∙ NGCRateu)

- 𝑖 𝑚 ℎ (GenSeli,m,h ∙ (ElectrPricem,h /2))

57

Constraints:

Cdemand m,h,u = 𝑖 GenOni,m,u,h + GridElectrm,u,h + bu ∙ PurNGm,u,h + 𝑖 (gi,u ∙

RecHeati,m,u,h) ∀ m,h,u (2)

𝑢 GenOni,m,u,h + GenSeli,m,h ≤ InvDGi ∙ DGmaxi ∀ i, m, h (3)

Annuityi = InterestRate

(1− 1

1+InterestRate 𝐷𝐺𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒𝑖 ) ∀ i (4)

𝑢 GenOnj,m,u,h + GenSelj,m,h ≤ InvDGj ∙ DGmaxj ∙ SolarInsm,h ∀ m, h

if j ∈ {PV} (5)

𝑢 RecHeati,m,u,h ≤ ai ∙ 𝑢 (GenOni,m,u,h ) + ai ∙ GenSeli,m,h)

∀ i, m, h (6)

RecHeati,m,u,h = 0 ∀ i, m, u, h if u ∉ E(i) (7)

GenOni,m,u,h = 0 ∀ i, m, h if u ∈ {heating} (8)

GridElectrm,u,h = 0 ∀ m, h if u ∈ {heating} (9)

PurNGm,u,h ≤ DFCap ∙ DF ∀ m, h if u ∈ {cooling} (10)

Explanation of the equations:

(1) This is the objective function of the model which will try to minimize the total

demand cost / maximize the net present value for the whole year while at the

same time will take into account the carbon emission externality. This cost

consisted from : the cost of buying electricity from the grid (included the

payment to the distribution company), the carbon taxes we have to pay due to

this purchase (internalize the externality), the fuel cost (natural gas) for

producing electricity using the DG technologies for the purpose to meet our

own demand or to sell electricity back to the grid. The variable operation and

58

maintenance cost for producing electricity onsite (meet the demand or sell

back to the grid), the carbon taxes we have to pay for the onsite production,

the fixed operation and maintenance cost for producing electricity onsite, the

annualized capital (included installation) costs for the technologies that will

installed, the Natural gas cost for the direct burning application with also the

equivalent carbon taxes. All these costs will be added and will subtracted

from the revenue we make by selling back electricity to the grid.

(2) The second equation/constraint will make sure that will covered all the

customer demand during the year and also specify the means through which

the load for energy end use u may be satisfied (the demand will be covered

through on-site production and/or purchase from the grid and/or direct

burning of natural gas and/or the recovered heat).

(3) The third equation make sure that the on-site production is limited by the

capacity we initially invest (this balance is for every hour for every day the

whole year).

(4) The fourth equation annualizes the cost of capital investment of DG

technologies.

(5) Fifth equation is responsible to enforce the constraint on how much power

PV’s can produce during the day and this production is in proportion to the

solar insolation.

(6) This equation limits how much heat can be recovered from each type of DG

equipment/technology.

(7) This equation avert the usage of recovered heat by end uses that can not

satisfied by specific DG technology.

(8) This equation not allow the usage of electricity for meeting heating loads

(electrical heaters are extremely un-efficient)

(9) The same with the previous equation, now for the grid

(10) Finally this equation is responsible to prevent the direct burning of

natural gas for meeting cooling demands without the existence of absorption

chiller.

59

5. Results

In this part of the report will be represented the results of the model for different

working scenarios, and different sensitivities analysis (change of the variables by

alteration of the input parameters). The first target of using different scenarios and

sensitivity analysis is the verification of the model. Secondly, by giving different

input values we can see in the lump the great opportunities DG and renewables are

giving for the future for money saving and carbon emissions reductions (regardless

the absence of some details in the model, like extremely accurate tariff system).

5.1 Scenarios and Sensitivities

In this report will be examined two different tariff scenarios (ways SM buy electricity

from the grid). One of the two scenarios will be used furthermore for a detailed

sensitivity analysis. The different scenarios depicted and explained in Table 11.

Table 11, Scenarios examined

The different sensitivities that will be examined are described in Table 12.

Scenarios Description

Constant electricity price In this case the model will take as

electricity tariff input constant prices for

the whole year. That means that the

shopping mall will purchase electricity in

different hours, different months at a fixed

price. The goal behind this scenario is the

verification of the model through the

different selection of the technologies

(grid, CHP etc) under different average

electricity prices.

Spot market prices In this case the shopping mall buys

electricity from the grid in the real spot

market prices (2008) plus the distribution

revenue for each KWh purchased (for

using the lines, for the operator etc). This

scenario will be the bases for the sensitivity

analysis due to the fact that is closer to the

reality.

60

Table 12, Examined sensitivities

Sensitivities Description

Grid plus Boiler In this case the model cannot use any of

the technology to meet the shopping mall

demand. This will be the base for the

comparison with the other results (Net

present value, carbon emissions).

Without CHP/CCHP In this case the model will be constraint

and won’t be able to invest in a CHP

and/or a CCHP system, but will be able to

invest in PV and on-site power generation

only Without CCHP

The shopping mall can invest in

everything except to CCHP (not

absorption cooling). Final case

Here the model will be able to take all the

inputs without any constraint and find the

final solution (include all the

technologies, detailed tariff system,

detailed customer demand). Only PV, Grid and Boiler In this case the model examine the

optimum solution if the only technology

existed from DG is PV (Included boiler

and grid).

At least 7 PV Here will be put in the model constraint to

invest at least 7 PV panels. Then the

model will find the optimum solution

starting from this point

High carbon price with a

20% PV capital reduction Here will be examined a friendly to PV

case with a capital cost reduction for PV

20% and 100$ per tone of CO2 emitted. 50 % cheaper electricity

prices The spot market electricity prices (and the

distribution payment) will be reduced by

50% (all the rest remain the same).

50% more expensive NG The customer will buy natural gas 50%

more expensive prices (without change

the grid electricity prices). High carbon price High price per tone of CO2 emission will

be examined (100$ per tone compared

with the 40$ existing now).

61

5.2 Outline of results

For the bases scenario (spot market prices) and all sensitivities, the below data given

as output:

Technology adopted (name and capacity in KW)

SM total power demand (KWh)

SM electricity-only demand (KWh)

SM cooling demand (KWh)

SM heating demand (KWh)

Electricity met by the grid (KWh)

Electricity me by on-site generation (KWh)

Electricity sales back to the grid (KWh)

Cooling demand met by the grid (electric chiller) (KWh)

Cooling demand met by on-site power generation (electric chiller) (KWh)

Cooling demand met by on-site recovered heat (if any CCHP) by driving

absorption chiller (KWh)

Cooling demand met by on-site direct fire burning of NG (boiler plus

absorption chiller, not CCHP) (KWh)

Heating demand met by recovered heat (CHP or/and CCHP) (KWh)

Heating demand me by direct burning of NG (boiler) (KWh)

NG purchases (KWh)

NG purchases (without those are targeted for selling back to grid) (KWh)

And the annual economic results:

Energy payments to the grid ($)

Carbon taxes for grid purchases ($)

Fuel cost for on-site generation (include the fuel for sales back to the grid) ($)

Variable cost for on-site generation ($)

Carbon taxes for on-site generation ($)

Capital investment costs (included installation) ($)

62

NG purchases for direct burning (boiler, boiler plus absorption chiller) ($)

Carbon taxes for direct burning of NG ($)

Carbon emissions for meeting total SM demand (tones CO2)

Carbon savings over basic grid plus boiler scenario (%)

Energy sales back to the grid ($)

Net Present Value (NPV) ($)

Money savings over basic grid plus boiler scenario (%)

For the constant electricity price scenario, will be given some of the above due to the

fact that the goal of this scenario is the verification of the model and not the actual

results.

5.3 Overview of spot market prices results scenario

The usage of distributed generation technologies (DGT) in a shopping mall and

generally in a microgrid can substantially reduce the annual energy costs. The figure

32 below represents the percentage of annual bill savings of some examined DGT

scenarios over the basic grid + boiler scenario. It becomes clear that all the DGT

cases (some are independent to the grid and some not) have considerable savings with

the final case to have 51% savings over grid + boiler, the case without CCHP 48%,

the case without CHP/CCHP 44%, the case at least 7 PV 38%, and finally the PV+

Grid + Boiler 16% (all these cases/sensitivities will examined analytically at the sub-

section each case detailed results). The reduction of the final cost/NPV is greater in

the final case, where all the technologies are accessible, and is available not only the

CHP but also the CCHP (invest on CCHP and take the advantage of absorption

cooling). The second cheapest solution (without CCHP) takes the advantage of using

CHP (to meet the SM heating demand for ‘free’) but has a bit worse results than the

final case due to the absence of the absorption cooling (lose the ability compared to

the previous scenario to use some ‘spare/waste’ heat to drive the absorption cooling

and meet some cooling demand for free). The third case in the row analogically can

63

take the advantage of all the DGT technologies except the ones with CHP or CCHP.

Also here existed very promising result, and the deference with the previous cases is

that here we don’t utilize at all the waste heat. The next case allow to the model to

use only PV, grid and boiler (PV + Grid + boiler). Also here the model invests on

PV’s and gives considerable savings (16%). The final case was the one in which the

model had the constraint that must invest at least 7 PV, and find the optimum

combination with this constraint (note that in the model you can put any constraint

and start the optimization from this point). In all the above cases, with exception the

PV + Grid + Boiler, the power purchases from the grid were negligible and almost all

the demand were met on-site by a DGT combination. The last shows that the model

find cheaper to buy, install, and run DGT than buy the power from the grid. Note that

the model ensures that every time is chosen the combination of technologies

(including grid) that minimizes the cost to meet the customer demand (internalize

also the carbon emissions externality).

Figure 32 Bill savings over grid + boiler basic scenario

At the figure 33 below we can observe for each of the above cases the carbon results

compared to the basic case. All the cases have considerable carbon savings except the

first case which is without CHP/CCHP. The last result was expected due to the fact

that in this case the DG doesn’t take the advantage of the recovered heat, which is

one substantial difference between DG and the big power stations (as regards the

efficiency of burning the fuel). The big power stations due to the economies of scale

work like in this first case (without utilizing in most cases the waste heat) but with

64

higher efficiencies and for that reason emit less carbon per KWh produced (but big

power station are substantially more expensive due to the need of transmission,

distribution and supply). On the other hand, the other two cases (without CCHP, and

final case) which are using natural gas for power, heat and cooling (CCHP) have less

emissions because they utilize the waste heat and finally reach higher efficiencies of

burning the natural gas. Finally the two last cases have even better carbon results (but

worse financial results see above) because of the utilization of PV which are ‘carbon

free technologies’ (not considered the carbon from manufacturing).

Figure 33, Carbon savings over basis grid + boiler scenario

Finally at the figures 34 to 39, represented the net cost breakdown of the above five

cases. At the figure 34, we can observe that only the cases PV + Grid + Boiler and the

basic case have important grid purchases. The first two cases (without CHP/CCHP)

have extremely small purchases and the final scenario and the remaining at least 7 PV

are completely independent.

-10

0

10

20

30

40

50

60

Pe

rce

nta

ge o

f ca

rbo

n s

avin

gs o

ver

grid

p

lus

bo

iler

bas

ic c

ase

(%

)

case

Percentage of carbon savings over grid +boiler basic scenario (%)

DG without CHP/CCHP

Without CCHP

Final Case

PV+Grid+Boiler

At least 7 PV

65

Figure 34, Energy payments to the grid

The next figure 35 depicts the capital investment costs. The cases without PV’s have

smaller investments (all annualized) compared to the ones with (the basic case have

zero investment). Figure 36 represents the power sales back to the grid, sales which

are small compared with the NPV because of the small prices that a microgrid can

sell back to the grid and the high running costs of the DGT (not PV). Exception is

only the case with at least 7 PV, and this because of the existence of the PV’s (once

installed they use all the spare capacity after meeting the demand to sell back to the

grid no matter what the price is). The same is not happened and in the case of PV+

Grid + Boiler due to the fact that all the PV’s capacity is used for the on-site demand

(less economically to sell from PV’s and buys from the grid).

Figure 35, Capital investment cost (includes installation and fixed costs)

0

200

400

600

800

1000

1200

1400

1600

case

Tho

usa

nd

s $

Energy payments to the Grid (K$)

Without CHP/CCHP

Without CCHP

Final case

PV+Grid+Boiler

At least 7 PV

grid+boiler

0

100

200

300

400

500

600

700

case

Tho

usa

nd

s$

Capital investment cost (include installation and fixed costs) (K$)

Without CHP/CCHP

Without CCHP

Final case

PV+Grid+Boiler

At least 7 PV

Grid + Boiler

66

Figure 36, Energy sales back to the grid

The next three figures 37, 38, 39 show the Net present value, the carbon taxes and the

fuel costs respectively. It is worthwhile to notice in the figure 39 the different cost for

natural gas purchases. For the first three cases these costs are high because the SM

meets its demand by burning NG. For the PV + Grid + boiler and the Grid + Boiler

that purchases are narrowed just to the fuel needed to be burned in a boiler for

meeting the heating demand. Finally for the case at least 7 PV there is a reduction of

fuel usage compared to the three first cases because of the power produced from

PV’s.

Figure 37, Net present value (all included)

0

10

20

30

40

50

60

70

80

case

Tho

usa

nd

s$

Energy sales back to the grid

Without CHP/CCHP

Without CCHP

Final case

PV+Grid+Boiler

At least 7 PV

Grid + Boiler

0

200

400

600

800

1000

1200

1400

1600

1800

case

Tho

usa

nd

s$

Net present Value (NPV)

Without CHP/CCHP

Without CCHP

Final case

PV+Grid+Boiler

At least 7 PV

Grid + Boiler

67

Figure 38, Carbon Taxes (all included)

Figure 39, Natural gas payments (all included)

5.4 Assessment of specific cases

In this section will be examined all the different sensitivities for the spot market

prices scenario in detail. Note that for some cases the graphs will be transferred to the

Appendix.

5.4.1 Case 1: Grid plus boiler

For the grid plus boiler case the model results breakdown are depicted in the Figure

40. This case and the results are simple due to the fact that there is no other

technology and the whole electricity-only and cooling demand met by the grid while

0

20

40

60

80

100

120

140

case

Tho

usa

nd

s$

Carbon taxes

Without CHP/CCHP

Without CCHP

Final case

PV+Grid+Boiler

At least 7 PV

Grid + Boiler

0

100

200

300

400

500

600

700

800

case

Tho

usa

nd

s$

Fuel costs (NG)

Without CHP/CCHP

Without CCHP

Final case

PV+Grid+Boiler

At least 7 PV

Grid + Boiler

68

the heating demand is met by burning natural gas in boilers with efficiency 80%. The

NG purchases during the different months and the different hours of the day are

represented in figure 41 and the total electricity demand in figure 42 (coincide with

the SM demand). Two factors that make this scenario extremely expensive are:

Firstly the high 2008 electricity prices, and secondly the facts that the SM purchases

electricity during periods when the spot market prices are extremely high like 12:00

August/July (0.45$), 18:00 November (0.55$) and 19:00-20:00 October (55$) (of

course and the added costs for transmission and distribution). On the other hand when

DGT existing in SM these peak hours are beneficial to sell back to the grid and make

a profit.

Figure 40, Energy balance and economic result for the grid plus boiler case

Electricity-only demand(KWh) 81470

Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 81470 Cooling demand met by the grid (KWh) 68432 Heating demand met direct NG burning(KWh) 59298 NG purchases (KWh) 74123 Energy payments to the grid ($) 1360706 Carbon taxes for grid purchases ($) 96149 NG purchase for direct fire ($) 74331 carbon taxes for direct burning of NG ($) 17507 Carbon emissions for meeting on-site total demand (tonnes) 2841 NPV 1548694

69

Figure 41, NG purchases for meeting the SM heating load (Grid plus boiler case).

Figure 42, total electricity purchases from grid, for all months and hours (grid plus boiler case)

8.4.2 Case 2: Without CHP/CCHP

At this case, the SM is able to meet its electrical demand (electrical-only, cooling) by

purchasing electricity from the grid, invest in PV’s, invest in power production by

burning NG (no CHP/CCHP) or a combination of the three above. The heating

demand can only be met by burning NG in boiler.

The results of this case are represented on figure 43. For this scenario the technology

adopted was one MW Natural gas engine (electrical). It is obvious from the results all

the electrical-only demand met by self-generation. The cooling demand also met by

70

DG except only 20 KWh that met by the grid during the 15:00 of the August (figure

44) and that due to the fact that the DG capacity wasn’t enough to cover all the SM

cooling needs . The heating demand met all by boilers (not recovered heat existed).

Figure 43, Energy balance and economic results for without CHP/CCHP case

As regards the economic results of this case, the savings over the basic grid plus

boiler are significant with a 44% reduction in the final energy bills of the SM. We can

also identify some sales back to the grid (figure 45). These sales have been made

during the peak hours when the spot market price were too high and could cover the

production cost and the reduced price that SM is able to sell back to the grid (also

there was spare capacity after meeting the on-site demand). Finally, there is an

Energy Balance results Column1

Technology Adopted (name,capacity) NG-1000E


Cooling demand (KWh) 68432

Heating demand (KWh) 47439

Total demand(KWh) 197340

Electricity met by Grid (KWh) 0

Electricity met by on-site generation (KWh) 81470

Electricity sales back to the grid (KWh) 1523

Cooling demand met by the grid (KWh) 20

Cooling demand met by on-site power generation (KWh) 68412

Cooling demand met by on-site recovered heat (KWh) 0

Cooling demand met by direct burning of NG(KWh) 0

Heating demand met by recovered heat(KWh) 0

Heating demand met by direct burning of NG(KWh) 47439

NG purchases (KWh) 518074

NG purchases without those for selling to grid(KWh) 513608

Economic Results Column1

Energy payments to the grid ($) 237

Carbon taxes for grid purchases ($) 13

Fuel cost for on-site generation (with selling to the grid)($) 568719

Variable cost for onsite genaration ($) 41475

Carbon taxes for onsite generation ($) 104859

DG annualized capital cost(include installation) 73384

NG purchase for direct fire ($) 74331

carbon taxes for direct burning of NG ($) 17507 Carbon emissions for meeting on-site total demand

(tonnes) 3033

Energy sales back to the grid ($) 9323

NPV 871203

Savings over basic grid plus boiles scenario (%) 44

Carbon savings over basic grid plus boiles scenario (%) -7

71

increase of carbon emissions to the environment in this scenario with a negative

carbon savings over the basic scenario. This result could be predictable if we take

into account the bigger efficiency a power plant can reach due to the bigger capacity

compared with the one MW installed in the SM. The same is not happening and with

the electricity price because of the transmission, distribution and control needed for

the electricity produced in the power station. For this case the overall efficiency of

burning NG to meet the onsite demand is 40%.

Figure 44, Total electricity purchases from the grid (without CHP/CCHP case)

Figure 45, Sales back to the grid (without CHP/CCHP case)

8.4.3 Case 3: Without CCHP

This case is very familiar with the previous one, with the only difference that now the

customer is able to invest also in CHP technologies but still isn’t available the CCHP

options. CHP technologies give the opportunity through the usage of heat exchangers

72

to recover some ‘waste heat’ and utilize it in order to meet some or all the heating

demand of the SM while producing power to meet the electrical-only and cooling

demand of the SM. This feature of this technology is also the main advantage of the

DG over the centralized power plant due to the fact that the power production is near

the heating load and can be utilized, unlikely with a power station which usually is far

from the heating loads (e.g. cities), and as it is widely known the heating is not

economically travelling long distances (electricity does).

In this specific SM’s case the model decide to invest in a one MW CHP Natural gas

engine, and the energy balance results are represented in figure 46. Similarly with the

case without CHP/CCHP almost all the SM’s electrical demand met by on-site

generation (exception was 20KWh cooling load met by the grid), and the schedule of

the self-generation output and the schedules (month, hour) are exactly the same as the

SM’s demand profile (see in customer description section). As regards the heating, on

the contrary with the previous case now the SM meet this entire load by the recovered

heat with a 12% reduction of NG usage in this scenario compared to the previous one.

At the same time this heat utilization improves not only the economics results (figure

47) of this case compared to the grid plus boiler scenario from 44% to 48% but also

invert the negative carbon results from -7% to +9% and makes this case more

sustainable. Finally we can see clearly from the results an overall burning efficiency

of the NG for meeting onsite demand of 45% (much bigger compared to the previous

case)

73

Figure 46, energy balance results for without CCHP case

Figure 47, economic results for without CCHP case

5.4.4 Case 4: Final case

Final case is the most cost effective solution for meeting the SM’s demand by

allowing the model to choose between all the technologies without constraint. Due to

the fact that in this economic model we internalize the carbon externality, by turning

the carbon emissions into cost, we could say that this case is the more sustainable

Energy balance results Column1

Technology Adopted (name, capacity) NG-1000CHP

Electricity-only demand (KWh) 81470



Total demand (KWh) 197340








Heating demand met by recovered heat (KWh) 47439

Heating demand met direct NG burning(KWh) 0



Economic results Column1



Fuel cost for on-site generation (with selling to the

grid)($) 568719

Variable cost for onsite generation ($) 41475




carbon taxes for direct burning of NG ($) 0

Carbon emissions for meeting on-site total demand

(tonnes) 2595


NPV 802297

Savings over basic grid plus boiler scenario (%) 48

Carbon savings over basic grid plus boiler scenario (%) 9

74

ones. This case also don’t differ a lot from the previous two ones, because the

technology choice remain the one MW natural gas engine with the difference that

hear the model decide to invest in CCHP (combined cooling heating and power).

Obviously the model find that the utilization of the recovered heat for not only

heating (part or all of them) but also cooling (part or all of them) loads overcome all

the other costs (capital, variable/fixed costs, carbon taxes etc) and makes this case the

most attractive of all. Indeed the economic results (figure 49) of this case are stunning

with an annual energy bill saving reaching 51% (compared to the basic grid plus

boiler scenario) while at the same time the carbon savings to be near 20%. The

energy sales back to the grid increased almost 300% (compared the two previous

cases 2, 3) (figure 52) and simultaneously the microgrid is completely autonomous

from the grid.

As regards the self generation output and schedule for the electricity-only end use

(figure 50) and the recovered heat which is going to meet the heating demand (figure

53) are coincide with the corresponded electricity-only SM demand and the SM

heating demand (actually figure 53 give the recovered heat need to cover the SM

heating demand, recovered is the 120% of the heating demand due to the fact that his

heat passes from heat exchangers that have efficiency 80%).

The interesting part in this final case is how the CCHP is going to meet SM’s cooling

demand by utilizing the whole spare recovered heat (figure 52) (after meeting the

heating demand) to drive absorption coolers and then meet the rest of the cooling

demand driving electrical chillers (usual air-conditioning systems) and using by self-

generated power (figure 51). Actually, if we notice in more detail the figures 52 and

53, becomes clear that these stunning results came naturally because of the different

hours the heating demand and the cooling demand peaks and thus there is no

antagonism of the two different demands (cooling, heating) for the recovered heat.

For the numbers, the cooling that has been met from the recovered heat is 21% (out

of the total cooling demand, due to low conversion of recovered heat by absorption

cooling), and the overall efficiency for burning on-site NG reached the 50% (much

higher from the usual conventional centralized power stations). Finally this case

meets the same demand with 20 % and 10 % less NG compared to the two previous

scenarios respectively.

75

Figure 48, energy balance results (final case)

Figure 49, economic results (final case)


Technology Adopted (name,capacity) NG-1000CCHP




















grid)($) 525969







(tonnes) 2345


NPV 757988


Carbon savings over basic grid plus boiles scenario (%) 17

76

Figure 50, NG-1000CCHP power generation for electrical-only end use loads (final case)

Figure 51, NG-1000CCHP power generation for cooling end use loads (final case)

77

Figure 52, NG-1000CCHP Recovered heat going to meet cooling demand (final case)

Figure 53, NG-1000CCHP Recovered heat going to meet heating demand (final case)

0

100

200

300

400

500

600

0 5 10 15 20 25 30

KW

Hour

Recovered heat going to meet heating demand

January

February

March

April

May

June

July

August

September

October

November

December

78

Figure 54, NG-1000CCHP Energy sales back to the grid (final case)

5.4.5 Case 5: PV plus Grid plus Boiler

PV plus grid and boiler is one interesting scenario to examine, especially for places

where the NG is either unreachable or even extremely expensive. In this case the

model is able to find the optimum solution by taking into account only the grid, PV’s

and direct fired boiler. Due to the fact that the PV cannot produce heating loads (no

solar thermal as option), and also can produce electricity only some hours of the day

(there are no storage options), this scenario cannot be independent from the grid and

the boiler. For the existing SM the combination that minimize the annual energy bills

are seven 100 KW PV panels combined with the grid and the boiler. The energy

balance results and the economic results for this scenario are shown in the figures 55,

56. As becomes clear from the results almost half of the electrical-only and cooling

demands are met by self-generation and the rest purchased from the grid. Figures 57,

58 and 59 show how the Electrical-only and cooling loads are going to be met.

These results was expected if we consider that the hours the PV produce electricity

coincide with the hours when the electricity from the grid is expensive (look grid

electricity price diagram). So, the 700 MW PV can not only cover almost the sunny

79

hour’s electrical demand of the SM but at the same time can sell back to the grid and

make a considerable profit (figure 60) (without any subsidies included). As regards

the economic results this case has 16% savings over the basic grid plus boiler

scenario, and finally 41% carbon savings which is and the highest amongst all the

previous results.

Figure 55, energy balance results (PV plus grid plus boiler case)

Figure 56, economic results (PV plus grid plus boiler case)


Technology Adopted (name,capacity) 7 PV-100




















grid)($) 0







(tonnes) 1664


NPV 1294081



80

Figure 57, Total electricity purchases from grid (PV plus grid plus boiler case)

Figure 58, PV power generation for electrical-only end use loads (PV plus grid plus boiler case)

81

Figure 59, PV power generation for cooling end use loads (PV plus grid plus boiler case)

Figure 60, energy sales back to the grid (PV plus grid plus boiler case)

5.4.6 Case 6: At least seven PV

This case is also a very interesting scenario for many reasons. First of all the

economic results indicates an annual bill saving of 38% over the basic scenario and

an amazing 53% of annual carbon savings, results that makes this scenario after the

82

final scenario the most interesting because of the high green profile and the

considerable money savings. Secondly, some customers maybe have some initial

requirements (like in this case PV) and the model can take these requirements as

initial constraints and find the optimum solution with them. This scenario wasn’t

chosen accidentally but comes as continuity from the previous PV plus grid plus

boiler scenario. Due to the fact that before the optimum scenario indices 7 100 KW

PV panels, now in this scenario this was the constraint in order to see if the model

finds cheaper to use the grid for the off peak hours or a DGT.

Figure 61, energy balance results (at least 7 PV case)


Technology Adopted (name,capacity) NG-1000CCHP , 7 PV-100
















83

Figure 62, economic results (at least 7 PV case)

The results for this scenario shown in figures 61, 62 and indicates except the 7 PV

panels the installation and one MW CCHP natural gas engine. The system is

completely independent from the grid and the sunny hours the electrical-only and

cooling demand met by PV’s (figure 65, 66) while the rest hours the electrical loads

met by the natural gas engines (figures 63, 64). That indicates that even for the off

peak hours the system find cheaper to self-generate the rest recommended power than

purchasing from the grid.

Figure 63, NG-1000CCHP power generation for electrical-only end use load (at least 7 PV case)





grid)($) 302115

Variable cost for onsite generation ($) 22036






(tonnes) 1349


NPV 958137

Savings over basic grid plus boiler scenario (%) 38

Carbon savings over basic grid plus boiler scenario (%) 53

84

Figure 64, NG-1000CCHP power generation for cooling end use loads (at least 7 PV case)

Figure 65, 7 PV-100 power generations for electrical-only end use loads (at least 7 PV case)

85

Figure 66, 7 PV-100 power generations for cooling end use loads (at least 7 PV case)

At the same time CCHP with the power production, recover some useful heat that

covers 12% of cooling demand (figure 68) and more than 60% of the total heating

demand (figure 67). The rest of the heating demand is going to be met by the usage of

the boiler (figure 69), hours that coincide with the PV working hours (these hours

CCHP don’t produce power and there is a lack of recovered heat to meet heating

demand and drive absorption coolers for cooling). Finally at the figures 70 and 71

represented the energy sales to the grid from the PV and the CCHP respectively.

Concluding for this case we not only identify some very promising economic and

environmental results but also diversity in the technology combination which makes

the system more flexible in cases of one technology failure, high fuel/electricity

prices and make the system more secure, in high volatility years.

86

Figure 67, NG-1000CCHP recovered heat going to meet heating demand (at least 7 PV case)

Figure 68, NG-1000CCHP recovered heat going to meet cooling demand (at least 7 PV case)

87

Figure 69, NG purchased for meeting heating demand by direct-fire burning (at least 7 PV case)

Figure 70, Energy sales back to the grid by power generated from PV’s (at least 7 PV case)

88

Figure 71, Energy sales back to the grid by power generated from NG-1000CCHP (at least 7 PV case)

5.4.7 Case 7: High carbon price

This is a hypothetical case in which the price per tone of CO2 emitted cost 100$

compared to the current 40$ which exist in ETS now. The energy balance and

economics results are depicted at figure 72 (appendix), and the optimization still

indicates that the optimum combination of technologies and the output schedules

remaining the same as in the final scenario.

5.4.8 Case 8: High carbon price with a 20% PV capital reduction

A more realistic future case than the previous examined could be a high carbon price

(100$ per tone CO2 emitted) combined with a 20% PV capital reduction. The

technologies adopted in this analysis was one MW CCHP natural gas engine and two

100 KW PV panels. The interesting thing in this scenario compared to the previous,

which is exactly the same with the final case with the only difference the carbon

price, is that here the model due to the PV capital reduction find cheaper to meet the

SM peak electrical demand (coincide with the sunny hours) by using PV panels and

not by using CCHP. The last is very important and have a hidden meaning, and this is

89

that in the peak hour demands (10:00 to 16:00) the power loads don’t coincide with

the heating loads which remain low (look the initial electrical loads compared to the

heating loads). That means that the spare recovered heat (if we didn’t have the PV’s,

like in the final case) would go to drive an absorption cooling with a very low

efficiency which is almost equivalent to ‘dump the heat’ and thus running the CCHP

in an extremely low efficiency which in this high carbon cost case means high

energy bill. In contrast here, with the usage of PV’s for the peak demand the CCHP

run with a better analogy of the electricity to heat ratio which means higher efficiency

of burning the fuel (look the figures 44 to 48, and think that for this NG for every

KWh e produced and 1.36 KWh th). The energy balance results, economic results,

DG power generation for electrical-only/cooling (CCHP, PV), recovered heat for

cooling/heating, NG purchased for heating, and finally energy sales back to the grid

(PV, CCHP) are depicted in figures 73 to 83 in appendix.

5.4.8 Case 9: 50% PV capital reduction

At this case we assume that the capital cost of PV’s reduced by 50%, by a future

subsidy or by innovation in the PV construction or both two. Under this assumption

the model invest in six PV-100 panels and in one MW natural gas engine CCHP. The

results of this combination are very similar to the at least 7 PV case and the results

and graphs are depicted in Appendix in figures 84 to 94.

5.4.9 Case 10, 11: 50 % cheaper electricity prices, 50% more expensive NG

Here the natural gas price is 50% more expensive than the current market prices.

Despite the fact that this increase in the NG prices is extremely high, the model still

find more cost effective to meet all the SM demand by installing and running a

natural gas engine (one MW CCHP). This fact indicates the great opportunities these

technologies can give to large consumers with a balanced electrical, cooling and heat

profile but also and the favorable circumstances with high electricity prices and the

upcoming carbon emissions taxing (which are favorable for DG with high waste heat

utilization).

90

The same happen in the case of decreasing the current electricity prices by 50% on

average. The energy balance and economic results for both cases are depicted in

Figures 95 to 98 in Appendix.

5.5 Fixed electricity price scenario

In this section will be described the fixed electricity scenario for different grid

electricity prices. The scope of this unrealistic scenario (not existed fixed prices for

all the hours of the day and the different months) is to verify once again that the

model ‘’thinks’’ and respond in a reasonable way. For different electricity prices the

model results are represented in figure 99 and explained in the below sub-sections.

Just to make clear the grid fixed electricity price includes in this scenario the

distribution, transmission and supply costs (also all the other inputs on the model

remain exactly the same). Note that each gap in figure 99 represents and the below

sub-sections.

Figure 99, graphic representation of the model results for different electricity prices

Heatin

g

load

Coolin

g

load

Electri-

only

load

Grid

Boiler

Grid

Grid

CHP rec heat

CHP power

Electri-

only

load

Boiler

Electri-

only

load

Grid

CHP power

Grid

CHP power

Grid

Grid

CHP recovered heat

Boiler

Electri-

only

load

CHP power

Electri-

only

load

Grid

Grid

CCHP power

CCHP recovered

heat

Grid

CCHP abs.cooling

Grid

CCHP power

CCHP recovered

heat

Sales

to grid

No sales No sales No sales

CCHP power

Grid

CCHP abs.cooling

Grid

No sales

CCHP power

CCHP power

CCHP

recovered heat

CCHP power

CCHP

abs.cooling

Grid

No sales

Boiler

CCHP power

CCHP power

Turn SM to a

power station

Use the entire

spare cap. For

sales back to

grid

Electricity price

91

5.5.1 Electricity price up to 0.08 $/KWh

When the fixed electricity price is less than 9p/KWh the model decides that the

‘’sustainable’’ combination of technologies that minimize the energy bill while takes

into account the carbon emissions is the basic scenario grid plus boiler. For this case

the whole electrical load met by the grid while the heating demand met by direct

burning of NG in the boiler. The economic results for fixed grid electricity price at

0.08 $/KWh represented in figure 100 (appendix).

5.5.2 Electricity price from 0.09 to 0.12 $/KWh

For electricity price from 9p/KWh and up to 12p/KWh the model except from grid

and boiler invest in one 100 KW CHP natural gas engine. In this case the model find

economic feasible to use the NG-100 engine and follow the SM base load heating

demand and cover at the same time some electric base load.

In other words the model find cheaper to produce 1 KWh electrical plus the displaced

heating demand (from the recovered heat, displaced heating demand = recovered heat

(α) * EfficiencyheatExchanger) in CHP unit than meet the correspondent electricity and

heating demand separately by using grid and boiler. At the same time the capacity

which invest (100KW) is small (not follow all the heating demand), due to the fact

that in order CHP to be more economical compared to the grid plus boiler

combination must have high load factor (work almost 100% throughout the year in

order to cover the investment and fixed annual costs).

The above become much clearer if we look the CHP electrical and heating production

in figures 101 and 102 respectively. Finally the energy balance and economic results

are represented in figure 103 (appendix).

92

Figure 101, NG-100CHP total electrical production (Electricity price from 0.09 to 0.12 $/KWh case)

Figure 102, Heating demand met by NG-100CHP (Electricity price from 0.09 to 0.12 $/KWh case)

5.5.3 Electricity price 0.13 $/KWh

For 13 p/KWh electricity price the model decides to further invest in one 60 KW

CHP natural gas engine. The same things existing as discussed before with the

difference that now the model almost covers all SM heating demand by CHP, and this

can be clearly seen in figure 106 were the represented the NG purchases for direct

burn in boiler. The electrical and heating outputs of the NG-60 CHP are depicted in

93

figures 104 and 105 respectively (appendix). We can understand from that case that

the one p/KWh difference in electricity price from the grid can compensate for the

lower load factor of the small natural gas engine, which also follows strictly the

heating loads. Finally the energy balance and economic results depicted in figure 103

(appendix).

Figure 106, Purchased NG to meet heating demand (Electricity price 0.13 $/KWh)

5.5.4 Electricity price 0.14$/KWh

In this case the model decides that the more sustainable combination is a CCHP NG-

300 engine combined with the grid. CCHP will cover the whole heating demand

(figure 110 appendix) and the base electrical load (electrical-only and cooling) while

grid will meet the rest electrical demand (figure 107, appendix) which usually is peak

load demand. Figure 108 show the electrical output of the CCHP, while figure 109

depict the cooling load me by the recovered heat remained after meeting the heating

demand. We can notice that this system works with a very high load factor (CCHP

works 100% most of the time) and utilize all the ‘’waste heat’’ and thus we can say

that this system has very high efficiency. All the energy balance and economic

results are represented in figure 111 (appendix).

94

Figure 108, NG-300CCHP total electricity production (Electricity price 0.14$/KWh case)

Figure 109, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case)


This case is the same with the final case of the spot market electricity price scenario.

Here the model decides that the sustainable technology solution is to invest on a NG-

1000CCHP engine and meet the whole demand on-site (not use boiler and grid). The

energy balance and the economic results are represented in figure 112 (appendix).

The electrical-only loads are met by on-site power generation while the heating loads

95

are met only by the recovered heat from the power generation. Finally, cooling loads

are going to be met both by self-generated power but also and from recovered heat

which driven through absorption chiller. The graphs representing the electrical-only

outputs and heating output are exactly the same as the final case explained in the

previous sections and thus skipped. The only graphs that are different from the final

case are the ones have to do with the cooling demand. The on-site power production

for meeting cooling loads depicted in figure 114 and the recovered heat which going

to meet cooling loads depicted in figure 115.

Picture 114, NG-1000CCHP power generation for electrical-only end use loads (Electricity price from 0.15 to 0.49 $/KWh)

0

50

100

150

200

250

300

350

400

450

500

0 5 10 15 20 25 30

KW

Hour

DG power generation for cooling end use loads

January

February

March

April

May

June

July

August

September

October

November

December

96

Picture 115, recovered heat going to meet cooling demand (Electricity price from 0.15 to 0.49 $/KWh)


For electricity prices 0.5 $/KWh to 0.57 $/KWh the model finds optimum not only to

use the NG-1000CCHP for meeting the onsite demand but also to use and the spare

capacity (capacity not used for the on-site demand) for selling electricity back to the

grid (figure 118). What we can conclude from these results is that the fuel (plus

variable) cost for producing one KWh e is lower than the price that we can sell back

to the grid and thus is profitable (the investment and fixed costs are already in place).

Here due to the high power generation production for selling back to the grid we have

a great portion of recovered heat which is used to meet almost half of the cooling

demand through the absorption chillers (figure 117, note that this is the recovered

heat which will pass through absorption coolers to produce cooling and not the

displaced cooling demand), unlike with the previous case where the biggest quantity

of the cooling demand was met by on-site power generation. The economic and

energy balance results depicted in figure 116 (appendix).

0

200

400

600

800

1000

1200

0 5 10 15 20 25 30

KW

Hour

Recovered heat going to meet cooling demand

January

February

March

April

May

June

July

August

September

October

November

December

97

Figure 117, recovered heat going to meet cooling demand (Electricity price from 0.5 to 0.57 $/KWh)

Figure 118, energy sales back to the grid (Electricity price from 0.5 to 0.57 $/KWh)

0

200

400

600

800

1000

1200

1400

1600

0 5 10 15 20 25 30

KW

Hour

Recovered heat going to meet cooling demand

January

February

March

April

May

June

July

August

September

October

November

December

0

100

200

300

400

500

600

700

800

900

0 5 10 15 20 25 30

KW

Hour

Energy sales back to the grid

January

February

March

April

May

June

July

August

September

October

November

December

98

5.5.7 Electricity price from 0.58 $/KWh

From this electricity price and above the SM makes huge profits by turning into a

power station (invest in large number gas turbines). Obviously the cost of just

producing electricity (included the investment, OMVar, OMFic costs etc) without

utilizing almost any of the recovered heat is lower than the selling price back to the

grid and thus is economically profitable to transform the SM into a power station.

The results for this case are depicted in figure 119 (appendix).

Figure 119, energy balance and economic results (Electricity price from 0.58 $/KWh)


Technology Adopted (name,capacity) 100 GT-40000E
















Economic results









Carbon emissions for meeting on-site total demand (tonnes) 15274925


NPV -39357324

99

6.Conclusions

Buildings are a growing sector of energy consumption and shopping malls are in

particular consuming more energy than the other buildings and appear increasingly

across the world. The potential of DG and Energy efficiencies measures in the

building sector and especially in commercial sector has been discussed as part of the

energy agenda in the last decade.

On-site generation offers to commercial customers the opportunities to increase the

electricity production efficiency decrease the energy bills costs and finally decrease

the carbon emissions compared to energy from the conventional centralized power

stations. These above results achieved through the utilization of the ‘’waste heat’’

from CHP/CCHP schemes and the absence of transmission, distribution and control

of electrical energy. Moreover self-generation gives advantages of autonomy and

reliability in energy purchases.

Many research projects led by academics and engineers have been steered towards

decreasing and meeting the basic demand in buildings in different ways. Most of

these projects are using long analytic tradition methods for getting an efficient

selection and operation of distributed generation technologies in large centralized

power stations or large scale community heating CHP’s. In contrast the methods for

optimizing in commercial level are very rare and usually the ones existed are very

limited.

At this thesis was developed an optimization mathematical model, model which is

able to minimize the annual energy bills (also takes into account the carbon

emissions) of a shopping mall (a general building or Micro-grid) by finding the

optimal combination of distributed generation technologies (included grid), the

capacity of those technologies and finally the operating schedule of them during the

year. The model takes as inputs the customer load (electrical, cooling and heating),

the different technologies (their economics and characteristics), market prices

(electricity and natural gas prices) and gives as outputs the combination of

technologies, their capacities, their operating schedule, their energy balance and

100

economic results. The given name of this mathematical model is Distributed

Generation Technology Selection Model (DGT-SM) and was developed as a GAMS

model.

The model was tested under many different scenarios and sensitivities. The two

different scenarios was diverged in the way customer buy’s electricity from the grid,

and the one incorporates the actual spot market prices for the whole year (hourly and

monthly changes), while the other scenario use fixed electricity prices throughout the

year. As regards the different sensitivities, for the spot market price scenario was

examined thoroughly a variety of different cases in order both to validate the model

but also to scan some real life cases (different places, different needs, and different

constraints).

The results were really encouraging, and actually all the examined cases invested in

distributed generation and gave astonishing annual energy bills and carbon savings.

Firstly was examined the common now case of grid plus boiler in order to use these

results as a guide to the other cases. Then the first case with DG availability was the

one without CHP/CCHP. In this case the model was able to invest in every

technology but without the ability to purchase CHP or CCHP systems (only electrical

production), which means in other words that there is no heat utilization. The results

of this case were the expected ones with annual energy bills saving (44% over grid

plus boiler case) but with worse environmental impact (-9% over grid plus boiler

case). We refer the outcome as expected because as regards the economic results the

absence of the transmission costs makes the on-site power generation cheaper, but

worse environmentally due to the lower efficiency (not utilization of heat) compared

with the big centralized power stations.

When the CHP option unlocked in the model (the model invest in a NG-1000CHP),

case without CCHP, except the economic results (48%) and the environmental results

(always compared to the grid plus boiler case) turned to be positive (9% annual

savings). This outcome is logical due to the utilization of the waste heat, waste heat

which usually dumped in the conventional power stations. Finally, in the final

scenario the adoption of the combined cooling, heating and power (NG-1000CCHP)

gave the opportunity to the model to utilize almost all the waste heat, reach very high

101

efficiencies of burning the fuel on-site (50% burning on-site natural gas) and gave the

astonishing annual economic savings of 51% and carbon savings 17%. Further,

examined the case were the only DGT option except the basic grid and boiler was the

PV’s. Also in this case the model found more economic to invest in 700 KW PV

panels and meet almost half of the SM total electrical demand onsite. Concerning the

results of this case, there was a 16% annual bill savings and 41% carbon savings. The

reason for carbon savings is obvious, but the most interesting is the economic savings

despite the high capital PV costs and these are existed due to the fact that the

productive sunny hours for PV’s coincide with the Peak SM’s and spot market

(during lunch time observed the higher electricity prices) electrical demand which

means that the PV’s displace the higher and most expensive SM’s electrical loads.

Astonishing results accomplished also in the case at least seven PV’s. The constraint

in this case was that the model has to invest at least on seven PV panels. This case

seems somehow unrealistic but it’s not if we consider that many customers are

interested more for their ‘’Green profile’’ no matter what is the cost. The interesting

in this case compared with the previous one is that the model finds optimum to also

invest in a NG-1000CCHP engine in order to meet the electrical demand the hours

that PV’s are not producing (and not purchase from the grid like before) and at the

same time meet almost all the heating demand by recovered heat. This scenario has

38% energy bill savings and the highest carbon savings over all the cases (53%).

Except the very good results of this case this combination gives and a good security

(NG/electricity prices, CCHP out of work for sometime) over the volatile period of

time.

Model, also experience very high NG prices (50% more expensive) and extremely

low grid electricity prices (50% less cost from grid), in two cases in order to see what

will happen if a very sharp change happen to the market. The results didn’t change

from the final scenario and the SM find under these sever circumstances that is more

economic feasible to be independent from the grid and produce all the electricity and

heat on-site by using the NG-1000CCHP engine. Finally, the last interesting case was

the one where the PV panels prices reduced by 20% and the carbon price set to 100$

per tone CO2 emitted. This case is also very likely to happen in the near future due to

the continuing innovation on PV panels, and the increasing environmental problem.

102

At this scenario the model invests not only in a NG-1000CCHP but also and in two

PV-100 KW panels. By this way the model cover the peak hour demand by PV’s and

uses the CCHP in the base load in order to keep utilizing almost the whole waste heat

and reach high burning efficiencies (reduce the CO2 emissions which now are

computable).

In the fixed price scenario (see figure 99) we examined the adequacy of the model for

different electricity prices from the grid and the results indicates that the model

decides logically. Up to 9p/KWh the model decides not to invest in DGT and meet

the demand by using grid and boiler. For electricity price from 9p/KWh and up to

12p/KWh the model except from grid and boiler invest in one 100 KW CHP natural

gas engine. In this case the model find economic feasible to use the NG-100 engine

and follow the SM base load heating demand and cover at the same time some

electric base load. It is clear in this case that in order the CHP to be cheaper from the

grid must work almost 100% all the time. For 13p/KWh the model except from the

previous 100KW invest in 60KW more and now meets the biggest part for heating by

recovered heat (also meets a small portion of the electrical load). After this point and

for price at 14p/KWh the model invest in 300KW CCHP and meet the entire heating

load (recovered heat) but also a great part of the electrical load also. Here the model

stops to invest in CHP and started to invest to CCHP because finds more economic

feasible to pay more for the absorption coolers and use the rest recovered heat (after

meeting heating load) to meet some cooling demand than just buy it from the grid.

The next gap is from 15p/KWh to 49p/KWh. Here the case is the same with the final

case from the spot market price scenario and the model invest in a NG-1000CCHP.

Now the SM is in depended from the grid and meet the whole demand onsite but is

not selling back to the grid. From 50p/KWh to 57p/KWh the model finds

economically feasible to use the spare capacity and sell back to the grid and make

some profit. The final case after this is when the electricity price is so high to be

profitable to turn the SM into a power station.

Besides the fact that the model seems to be able to find accurately results as regards

the selection of technologies, capacities and operating schedule improvements must

be undertaken in the inputs of the model as regards both the validity but also the

detail and the more sophisticated structure of them. Moreover changes needed to the

103

thermodynamic model for more accuracy and finally more options (e.g. technologies)

and a graphic environment is under way for the next version of this model.

104

Bibliography Administration, N. O. (2007, February). Global trends in major greenhouse gasses. Retrieved from Homepage of U.S. federal goverment: http://www.cmdl.noaa.gov/albums/cmdl_overview/Slide11.sized.png Administration, N. O. (2007, February). Global trends in major greenhouse gasses. Retrieved from Homepage of U.S. federal goverment. Anandalingam, G. B. (1996). Sustainable urban energy-environment management with multiple objectives. Oxford: Pergamon Press. Bailey Owen, C. C. (2003). Distributed Energy Resources in Practice:A case study analysis and Validation of LBNL's Customer Adoption Model. Berkeley: Environmental Energy Technologies Division, Ernesto orlando Lawrence Berkeley National Lab. C.Lam, J. (1998). Energy analysis of commercial buildings in subtropical climates. Elsevier, Direct Science . C.Lam, J. (2000). Energy analysis of commercial buildings in subtropical climates. Building and Environment 35 , 19- 26. Consultants, P. (2000). The Eco-indicator 99, A damage oriented method for the life cycle impact assessment. Amersfoort,Netherlands. Deng Shi-Ming, B. J. (2000). A study of energy performance of hotel buildings in Hong Kong. Energy and Buildings , 7-12. F, C. G. (2005). Energy efficiency in buildings. London: CIBSE. F. Javier Rubio, A. S. (2001). CERTS Customer Adoption Model. California: Lawrence Berkeley National Laboratory. Firestone, R. (2004). Distributed Energy Resources Customer Adoption Model Technology Data. California: Ernest Orlando Lawrence Berkeley National Laboratory. Goldstein, L. B. (2003). Gas-Fired Distributed Energy Resource Technology Characterizations. California: National Renewable Energy Laboratory. Gumerman, E. Z. (2003). Evaluation framework and tools for distributed energy resources. CA, USA: LBNL. Hassan, R. (2008). A systematic methodology for optimising the energy performance of buildings in Bahrain. Energy and Buildings . Heijungs R, S. S. (2002). The computational structure of life cycle assessment. Dordrecht, Netherlands: Kluwer Academic Publishers. IEA. (2004). World Energy Outlook. IEA. IEA. (2007). World Energy Outlook:China and India insights. IEA. Joseph C. Lam *, R. Y. (2004). Electricity use characteristics of purpose-built office. Energy Conversion and Management 45 , 829–844. Joseph C.Lam, D. H. (2003). An analysis of electricity end-use in air-conditioned office buildings in Hong Kong. Building and Environment 38 , 493 – 498. Joseph C.Lam, D. H. (2003). Electricity consumption characteristics in shopping malls in subtropical climates. Energy Conversion and Management 44 , 1391–1398. Jun-hyung Ryu*, †. a. (2007). Multiperiod Planning of Enterprise-wide Supply Chains Using an Operation. Ind. Eng. Chem. Res., 46 , 8058-8065. L.T. Biegler, I. G. (1997). Systematic Methods of Chemical Process Design. Englewood Cliffs. Lam JC, C. A. (1995). Energy audits and surveys of air conditioned buildings. Australia: University of Cnbera. Lemar, P. (2001). The potential impact of policies to promote Combined Heat and Power in the S Industry,. Liu Pei, D. I. (2007). Modeling and optimization of polygeneration energy systems. Catalysis Today 127 , 347–359. Lovins, A. B. (2002). Small is profitable: the hidden economic benefits of making. CO, USA: Rocky Mountain Institute, Snowmass. M. Medrano, J. B. (2008). Integration of distributed generation systems into generic types of commercial buildings in California. Energy and Buildings 40 , 537–548. Maribua Karl Magnus, F. R. (2007). Distributed energy resources market diffusion model. Energy Policy 35 , 4471–4484. Marnay Chris, G. V. (2007). Optimal Technology Selection and Operation of Microgrids in Commercial Buildings. Environmental Energy Technologies Division . Marnay Chris, J. S. (2001). Modeling of customer adoption of distributed energy resources. California: Lawrence Berkeley National laboratory.

105

Marnay Chris, R. M. (2007). Microgrids for Commercial Building Combined Heat and Power and Power and Heterogeneous Power Quility and Reliability. Berkeley: Environmental Energy Technologies Division, Ernesto Orlando Lawerence Berkeley National Laboratory. Michael Stadler, R. F. (2006). On-site Generation Simulation with EnergyPlus for Commercial Buildings. Ernesto Orlando Berkeley National Laboratory . Nan Zhou a, *. C. (2006). An analysis of the DER adoption climate in Japan using optimization results for prototype buildings with U.S. comparisons. Energy and Buildings 38 , 1423–1433. Nan Zhou a, ,. C. (2006). An analysis of the DER adoption climate in Japan using. Energy and Buildings , 1423–1433. Pan Yiqun, Y. R. (2007). Energy modeling of two office buildings with data center. Energy and Buildings . Partnership, L. E. (2006). London Carbon Scenarios to 2026. London: London Energy Partnership. Ryan, M. C. (2007). Microgrids: An emerging paradigm for meeting building electricity and heat requirements efficiently and with appropriate energy quality. Environmental Energy Technologies Division. SEA/RENUE. (2006). London Carbon Scenarios to 2026. London: partnership, London Energy. Sezgen Osman, K. J. (2000). Interactions between lighting and space conditioning energy use in US commercial buildings. Energy , 793-805. Tester W. Jefferson, D. M. (2005). Sustainable Energy: Choosing among options. Cambridge: Massachusetts Institute of Technology. Wall, P. Centralized versus Decentralized Information Systems in Organizations. Ireland: Waterford Institute of Technology Department of P&Q. Wang Weimin, Z. R. (2005). Applying multi-objective genetic algorithmsin green building design optimization. Building and Environment , 1512-1525. Williams, H. (1999). Model Building in Mathematical Programming, 4th edition. Chichester: John Wiley and Sons Ltd. Williams, P. a. (2001). Costing and pricing of electricity distribution services. (15(3)). Yang Z. X.H. Li, Y. H. (2006). Study on solar radiation and energy efficiency of building glass system. Applied Thermal Engineering , 956-961. Yiqun Pan, R. Y. (2007). Energy modeling of two office buildings with data center for green building design. Shanghai: Elsevier. Yongwen Yang, W. G. (2006). Optimal Combination of Distributed Energy System in an Eco-Campus of Japan. Environmental Energy Technologies Division.

106

Appendix

Figure 72, energy balance and economic results (high carbon price scenario)




























(tonnes) 5879


NPV 902257

107

Figure 73, energy balance results (High carbon price with a 20% PV capital reduction case)

Figure 74, economic results (High carbon price with a 20% PV capital reduction case)


Technology Adopted (name,capacity) NG-1000CCHP, 2 PV-100




















grid)($) 441068







(tonnes) 2005


NPV 900381

108

Figure 75, NG-1000CCHP power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case)

Figure 76, NG-1000CCHP power generation for cooling end use (High carbon price with a 20% PV capital reduction case)

109

Figure 77, PV’s power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case)

Figure 78, PV’s power generation for cooling end use (High carbon price with a 20% PV capital reduction case)

110

Figure 79, NG-1000CCHP recovered heat going to meet heating demand (High carbon price with a 20% PV capital reduction case)

Figure 80, NG-1000CCHP recovered heat going to meet cooling demand (High carbon price with a 20% PV capital reduction case)

111

Figure 81, NG purchased to meet heating demand in a boiler (High carbon price with a 20% PV capital reduction case)

Figure 82, NG-1000CCHP power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case)

112

Figure 83, PV-100 power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case)

Figure 84, energy balance results (50% PV capital reduction)

Technology Adopted (name,capacity) NG-1000CCHP, 6 PV-100
















Final efficiency of burning the natural gas (%) 0

113

Figure 85, economic results (50% PV capital reduction)

Figure 86, NG-1000CCHP power generation for electrical-only end use loads (50% PV capital reduction)

Column1 Column2

Economic results Energy payments to the grid ($) 0










NPV 712945



114

Figure 87, NG-1000CCHP power generation for cooling end use loads (50% PV capital reduction)

Figure 88, NG-1000CCHP power generation for selling back to the grid (50% PV capital reduction)

115

Figure 89, PV power generation for electrical-only end use loads (50% PV capital reduction)

Figure 90, PV power generation for cooling end use loads (50% PV capital reduction)

116

Figure 91, NG-1000CCHP recovered heat going to meet cooling demand (50% PV capital reduction)

Figure 92, NG purchased to meet heating demand by direct burning in boiler (50% PV capital reduction)

117

Figure 93, recovered heat going to meet heating demand (50% PV capital reduction)

Figure 94, PV power generation for selling back to the grid (50% PV capital reduction)

118

Figure 95, energy balance results (50% cheaper electricity prices case)

Figure 96, economic results (50% cheaper electricity prices case)



















Economic results Energy payments to the grid ($) 24124










NPV 761435

119

Figure 97, energy balance results (50% more expensive NG price case)

Figure 98, economic results (50% more expensive NG price case)

Figure 100, economic results for electricity price less than ninep/KWh (Fixed electricity price scenario)





























NPV 1017570







(tonnes) 2841

NPV 552998

120

Figure 103, energy balance and economic results (Electricity price from 0.09 to 0.12 $/KWh case)


Technology Adopted (name,capacity) NG-0100CHP
















Economic results











NPV 589572

121

Figure 104, energy balance and economic results (Electricity price 0.13 $/KWh)


Technology Adopted (name,capacity) NG-0100CHP, NG-060CHP
















Economic results











NPV 741554

122

Figure 104, NG-60 CHP total electrical production (Electricity price 0.13 $/KWh)

Figure 105, Heating demand met by NG-60CHP (Electricity price 0.13 $/KWh)

123

Figure 107, total electricity purchases from grid (Electricity price 0.14$/KWh case)

Figure 110, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case)

124

Figure 111, energy balance and economic results (Electricity price 0.14$/KWh case)

















Economic results











NPV 762986

125

Figure 122, energy balance and economic results (Electricity price from 0.15 to 0.49 $/KWh)


















Economic results











NPV 763162

126

Figure 116, energy balance and economic results (Electricity price from 0.5 to 0.57 $/KWh)


















Economic results











NPV 730421

energy systems optimization of a shopping mall

Technology

final case

energy sustainability

model description

mathematical model

energy systems optimization

decentralized energy

sustainable energy futures

distributed energy resources