d4.4 draft method for holistic energy system design

Grant Agreement No: 680447 Project acronym: MODER Project title: Mobilization of innovative design tools for refurbishing of buildings at district level Funding scheme: Innovation Action 0

Starting date of project: 1st September 2015 Duration: 36 months

D4.4 – Draft method for holistic energy system design

Due date of deliverable: Aug 31, 2017 Actual submission date: Aug 01, 2017

WP 4 Leader: Siemens AG Task 4.4 Leader: Siemens AG

Dissemination Level

PU/CO Public / Confidential, only for members of the consortium (including the Commission Services)

PU

This project has received funding from the European Union’s Horizon 2020 research and innovation

programme under grant agreement No 680447

D4.4 – Draft method for holistic energy system design 1

Table of Contents Table of Contents............................................................................................................................ 1 1 Introduction ............................................................................................................................. 3

1.1 Publishable summary ........................................................................................................ 3 1.2 Purpose and target group ................................................................................................. 3 1.3 Contribution of partners .................................................................................................... 3 1.4 Relation to other tasks/deliverables .................................................................................. 4 1.5 Terminology and definitions .............................................................................................. 4

2 Draft (one-node) method for holistic energy system design ..................................................... 8 2.1 Description of the energy system design problem ............................................................. 8 2.2 Schematic illustration of the algorithm ............................................................................... 9 2.3 Mathematical formulation ................................................................................................ 11

2.3.1 Assumptions ............................................................................................................ 11 2.3.2 Variables and parameters ........................................................................................ 11 2.3.3 Optimization problem ............................................................................................... 12

3 Example case study for Suonenjoki ....................................................................................... 17 3.1 Input Data ....................................................................................................................... 17

3.1.1 General data ............................................................................................................ 17 3.1.2 Climate data ............................................................................................................ 22 3.1.3 Load profiles ............................................................................................................ 23 3.1.4 Energy technologies ................................................................................................ 27 3.1.5 Grid connections and commodity prices................................................................... 39

3.2 Superstructure Development .......................................................................................... 44 3.2.1 Scenarios................................................................................................................. 44 3.2.2 Sensitivity Analysis .................................................................................................. 45 3.2.3 City level superstructure .......................................................................................... 47

3.3 Results............................................................................................................................ 50 3.3.1 Scenario analysis of Greenfield Base cases (Greenfield Base) ................................ 51 3.3.2 Scenario analysis of Brownfield Base cases (Brownfield Base) ............................... 53 3.3.3 Sensitivity analysis of Greenfield reference cases (Greenfield Ref) ......................... 55 3.3.4 Sensitivity analysis of Brownfield reference cases (Brownfield Ref) ......................... 57 3.3.5 Sensitivity analysis of cost optimized Greenfield cases (Greenfield OptimCO2_0) ... 59 3.3.6 Sensitivity analysis of cost optimized Brownfield cases (Brownfield OptimCO2_0) .. 62

4 Conclusion ............................................................................................................................ 65 5 References ............................................................................................................................ 66


Acknowledgements The work presented in this document has been conducted in the context of Horizon 2020 programme of the European community project MODER (n° 680447). MODER is a 36-month project that started in September 2015 and is funded by the European Commission as well as by the industrial and research partners. Their support is gratefully appreciated. The partners in the project are:

• Sweco Finland Ltd. (Finland)

• VTT Technical Research Centre of Finland Ltd.(Finland)

• Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung EV - Fraunhofer

Institute for Building Physics IBP (Germany)

• Siemens AG (Germany)

• REM PRO SIA (Latvia)

• Stichtung W/E Adviseurs Duurzaam Bouwen - W/E Consultants Sustainable Building (The

Netherlands)

• Ertex Solartechnik GmbH (Austria)

• Gradbeni Institut, ZRMK DOO – GI ZRMK (Slovenia)

• Finnenergia Oy (Finland)

• Lokalna Energetska Agencija Gorenske Javni Zavod - LEAG (Slovenia).


1 Introduction

1.1 Publishable summary The objective of Task 4.4 was to develop a draft method for how to select an optimal combination of energy conversion and energy storage technologies for holistic, multi-modal energy systems. In particular, the following targets were proposed: ▪ Calculating the impact of energy systems on energy cost and CO2 foot print: The presented

draft method is shown to calculate both, total expenditures and the carbon footprint. Moreover, both can be used as objective function for the optimization of the energy system.

▪ Reflect smart operation strategies of single energy conversion and storage units: Economic dispatch (optimal control) is included for all energy conversion and storage units.

▪ Account for new links between energy supply systems for electricity, heat and cold: The energy system design method was formulated to optimize an energy system described by a technical superstructure. This superstructure was developed in the previous deliverable D4.3 [1] and contains a multitude of links between different energy carriers.

▪ Optimize the capacities of the relevant energy conversion units in the system: The optimization problem of the draft method was formulated in a way to allow the optimization of both energy conversion and energy storage unit capacities.

The draft method for holistic energy system design was formulated for a one-node energy system, i.e., transport processes between the different energy technologies can be neglected. For validation and testing, the draft method was applied to different airport sites around the world [2] as well as to a specific area in Suonenjoki (Finland). The essential input parameters for the development of a multi modal energy system superstructure concerning the investigated area of Suonenjoki were prepared. The relevant input data contain general data, climate data, load profiles, energy technology parameters, grid connections and commodity prices. Some of these parameters were provided by VTT and Sweco, others had to be assumed by Siemens. Based on these data the optimizations of different cases were performed. These varied due to different scenarios like the optimization of costs or CO2 - emissions. Also alternating sensitivities were tested like for example higher or lower electricity prices. All different scenarios and sensitivities were considered for the two superstructure types “Brownfield” and “Greenfield”. Concerning the Brownfield cases it was assumed that the current energy system is installed in Suonenjoki. The superstructure type “Greenfield” was characterized by the fact that no energy unit had been installed yet. The gained results are discussed and compared to the current energy system of Suonenjoki.

1.2 Purpose and target group The purpose of this study is to provide a draft method for holistic energy system design in city districts and building scale. The target group of Task 4.4 was the MODER consortium.

1.3 Contribution of partners The work was organized and reported by Siemens AG. However, data from MODER project partners VTT and Sweco was used for the Suonenjoki example case study. The following experts contributed to developing the draft method for holistic energy system design:


▪ Vladimir Danov ▪ Jochen Schäfer ▪ Ludwig Bär ▪ Florian Reißner ▪ Martin Kautz ▪ Alexander Zillich ▪ Sebastian Thiem

1.4 Relation to other tasks/deliverables This work was based on the energy system component unit model library previously developed in the MODER project [1]. Furthermore, the method will be further extended to multi-node use cases within the MODER project.

1.5 Terminology and definitions Table 1.1 shows the abbreviations used in this text. The symbols were tabulated in Table 1.2 and Table 1.3 below.

Table 1.1 – Abbreviations.

Abbreviation Description

AC Absorption chiller

AWHP Air-Water heat pump

Bat Battery

CAPEX Capital expenditures

CC Compression chiller

CHP Combined heat and power plant

COP Coefficient of performance

Cplg Coupling of energy forms of same type

CU Conversion unit

Dem Energy demand

EB Electric boiler

ESD Energy system design

GB Gas boiler

GCin Input grid connection

GCout Output grid connection

GSHP Ground source heat pump

HFO Heavy fuel oil

HO Heating oil

HP Heat pump

HWS Hot water storage

ICE Internal combustion engine

Kuo Kuopiontie (heat generation plant)

LFO Light fuel oil

LIB Lithium-ion battery

LP Linear programming problem

LPG Liquid petroleum gas

MILP Mixed-integer linear programming

MMES Multi-modal energy systems

MODER Mobilization of innovative design tools for refurbishing of buildings at district level


Abbreviation Description

O&M Operation and maintenance

OB Oil boiler

OPEX Operating expenditures

PV Solar photovoltaic panels

RE Renewables

Ref Reference

ST Solar thermal heating

Energy storage

TOTEX Total expenditures

VTT Technical Research centre of Finland

WACC Weighted average costs of capital

WB Wood boiler

Table 1.2 – Symbols (Latin).

Symbol Unit Description

𝑎𝑛𝑛 [1/a] Annuity factor

𝐴 [m²] Area

𝑐 [specific] Coefficient

𝐶𝑎𝑝 [specific, e.g., W]

Capacity of equipment

E [specific, e.g., J]

Storage energy content

𝑓 [$/a] (Objective) function

𝑔 [−] Time weightingfactors

𝐺𝐻𝐼 [W/m²] Global horizontal irradiance

𝑖 [specific] Invest parameters

𝐼𝑛𝑠𝑡 [−] Installation of equipment

𝑙𝑜𝑠𝑠 [%/h] Self-discharge rate

𝑁 [−] Number, quantity

𝑛 Asset depreciation range (economical life time)

𝑂𝑀 [specific] Operation and maintenance cost parameters

𝑂𝑛 [−] Online status of energy conversion units

𝑃 [specific, e.g., W]

Power

𝑝 [Pa] Pressure

[specific] Commodity price

𝑟 [%/h] Ramping constraint parameter [−] Interest rate

𝑆𝑂𝐶 [−] State of charge

𝑆𝑈 [specific] Specific start-up costs

𝑇 [K] Temperature

[−] Time span (number of time steps)

u [−] Part-load ratio; relative power output

𝑤 [−] Weighting factor

𝑥, 𝑦 [specific] Variables, placeholders

𝑧 [−] Boolean variable


Table 1.3 – Symbols (Greek).

Symbol Unit Description

𝛼 [−] Boolean variable indicating whether a specific power flow is relevant for a certain energy form

𝛽 [1/K] PV temperature coefficient

𝜂 [−] Efficiency

𝜑 [−] Relative humidity

Table 1.4 – Subscripts and superscripts.

- Negative direction

+ Positive direction

0 Initial

a Year

AC Alternating current

amb Ambient conditions

avg Average

CAPEX Capital expenditures

ch Charge of an energy storage

C/O CAPEX over OPEX

Comm Commodity/Energy purchase and sale

Cplg Coupling of similar energy forms

CU Energy conversion unit

DC Direct current

dch Discharge of an energy storage

Dem Energy demand

dem Demand charge

e Energy-related charge

𝑒 Energy form

el Electricity

fix Fixed costs; i.e., fixed annual connection charge

GCin Inlet grid connection

GCout Outlet grid connection

𝑖 Technology

𝑑 Day

ICE Internal combustion engine

𝑗 Input or output number

𝑘 Discrete time step

m Month

max Maximum

min Minimum

O&M Operation and maintenance costs

p Period

PV Photovoltaic

RE Renewable

ref Reference

ST Energy storage

Solar thermal heating

SU Start-up costs

T Final

Total Total


TOTEX Total expenditures

Use Usable

Waste Waste of energy


2 Draft (one-node) method for holistic energy system design The following description of the draft energy system design method and its mathematical formulation is based on [2].

2.1 Description of the energy system design problem With an energy system design method, the optimal design and operation of energy systems can be determined. In this report, energy systems are described as multi-modal energy systems (MMES). With MMES, different types of energy are considered in a holistic approach. Hence, synergies between different energy forms can be employed and problems in energy transport or storage in one energy form can be optimally solved by conversion in one more different other energy forms (sector coupling). The draft method is described for a technical superstructure that describes the solution space. Out of this solution space, the optimal solution can be determined. Employing MMES, the coupling of different types of energy, e.g., electricity and heat, could be analyzed (e.g., by including heat pumps in the solution space). The overall energy system design (ESD) process is illustrated in Figure 2.1 in more detail. The types of ESD problems can be distinguished into Greenfield projects (sites to be developed newly) and Brownfield projects (existing sites that could potentially benefit from expansion of the on-site energy systems). Installation of new equipment in the future is a part of both project types. Hence, actual (future) energy demands may not be known apriori. However, for this method, deterministic climate, price and load predictions are assumed. Climate data can be determined from analysis of historic data. Information on current commodity pricing is publically available. Energy demands have to be estimated based on historic data or simulated using designated simulation software.


Figure 2.1: Schematic illustration of the energy system design (ESD) process (LPG: Load profile generation; EM: Energy

management) [2].

For the optimization problem, a superstructure of all available energy conversion and storage technologies, as well as their feasible interconnections is defined. This superstructure defines the solution space. The optimal set of technologies, their capacities and operation schedule is determined by the ESD method. Different objectives, such as total expenditures, carbon dioxide emissions, or combinations of these can be used as a reference for determining the optimality of a certain energy system. The result of the ESD method is preliminary and must be post-processed and its feasibility must be examined: For example, quotes for specific components have to be obtained from manufacturers. The installation of the new components has to be planned. Also, legal and other constraints must be considered. Once the energy system has been installed, an energy management (EM) system can help to operate the system. The method proposes modelling the energy system in ‘energy-only’ terms. Hence, hourly resolution for time-dependent data was sufficient. Frequency control use cases, however, cannot be considered with this resolution.

2.2 Schematic illustration of the algorithm The schematic illustration in Figure 2.2 shows the ESD algorithm.


Figure 2.2: Schematic illustration of the ESD algorithm [2].

The energy system design method consists of four layers that were nested within each other. The layers and their respective steps can be utilized – if needed – to reduce the complexity of the optimization problem. First, the problem is solved with constant part-load efficiencies (Layer 0). In a number of iterations,

a sufficiently good value the CAPEX-OPEX weight (𝑤C/O, described in more detail in Section 2.3) was determined. For the second step of Layer 0), the determined set of technologies was fixed, but their capacity constraints were weakened once again. In the second step, the ESD problem was optimized using non-constant part-load efficiencies. Layer 1) was also split into two parts. First, both the capacity of the technologies and their operation are optimized. In the second step, the capacities are fixed and only the operation of the system is optimized. The reason, why Layer 1) was split into two steps, is because of the time-scale separation strategy proposed for Layer 2). The individual time steps of the ESD problem are coupled because of the existence of energy storages, and due to ramping constraints. Hence, the ESD problem becomes mathematically quite


complex to handle (see Section 2.3). The decomposition method was developed based on a time-scale separation approach. First, all constraints (climatic conditions, commodity prices, energy demand profiles) were averaged for the entire year. Optimal capacities that could be identified were treated as minimum capacities for the subsequent subproblems. Then, the year can be optimized with monthly time steps, averaging the constraints on a monthly basis. Afterwards, each month is considered subsequently. The time step is now reduced to one hour. Boundary conditions for the energy storages are set according to the result of the previous subproblem (with monthly time steps). Layer 3) is executed for each of these iterations. First, the constraints are averaged, as well as invest coefficients and part-load efficiencies are determined. Then, the optimization problem is built and solved. The solution of the optimization problem is then analyzed, verified and the component capacities are updated. The optimization of the sum of smaller problems is much faster than the optimization of the entire problem at once. It shall be considered that the solution, however, is suboptimal (e.g., see the introduction of the CAPEX-OPEX weight). Therefore, the decomposition method in Layer 2) shall only be employed if really needed (e.g., if non-constant part-load efficiencies are considered). As mentioned above, the individual functions can be combined to handle the complexity of the problem if needed.

2.3 Mathematical formulation

2.3.1 Assumptions The main assumptions for the ESD method are as follows: ▪ The superstructure includes all feasible energy technologies. Hence, a selection among these

technologies delivers the best design of the energy system; ▪ All energy technologies are arranged correctly and the temperature levels of thermal energy

carriers are adequate; ▪ Quasi-steady one-hour time steps describe the energy system accurately enough.

2.3.2 Variables and parameters The main optimization variables are:

▪ The installation of equipment (𝐼𝑛𝑠𝑡 ∈ {0,1}); ▪ The capacity of equipment (𝐶𝑎𝑝 ∈ ℝ≥0); ▪ Energy flows (𝑃 ∈ ℝ≥0); ▪ Storage energy contents (𝐸 ∈ ℝ≥0); ▪ Online status of energy conversion units (𝑂𝑛 ∈ {0,1}). The above mentioned variables are used in the following equations. Furthermore, the index (𝑖) indicates the particular technology, the index (𝑗) identifies the input or output in multiple input-

output units,the index (𝑘) is the discrete time step, and the index (𝑒) the energy form. For energy system components, the following groups are proposed: ▪ Conversion units (CU); ▪ Energy storages (ST);


▪ Renewables (RE); ▪ Inlet and outlet grid connections (GCin and GCout), respectively; ▪ Energy demands (Dem); ▪ Couplings of similar energy forms (Cplg), e.g., the flow of high to medium temperature heat; ▪ Waste of energy (Waste), i.e., energy that is not used for satisfying any demands. Each conversion unit (CU) is described by its first output (e.g., the electric power output for combined heat and power plants). Hence, also the capacity determines the maximum allowed first output. Climatic conditions can be described by the ambient dry-bulb temperature (𝑇amb), ambient pressure

(𝑝amb), relative humidity (𝜑amb), global horizontal irradiance (𝐺𝐻𝐼), and wind speed (𝑣w).

Table 2.1: Energy conversion units and storages model parameters [2].

Parameter Variable Units Function of

Energy conversion units and storages

Minimum and maximum capacity 𝐶𝑎𝑝min,

𝐶𝑎𝑝max

[kW] [m3/h] -

[kWh] [m3]

Fixed and variable invest (including transportation and installation)

𝑖 [$] [$/kWh]

[$] [$/m3]

𝐶𝑎𝑝

[$] [$/kWh]

[$] [$/m3]

Fixed and variable operation and maintenance costs

𝑂𝑀 [$/a] [$/kW/a]

[$/a] [$/(m3/h)/a]

-

[$/a] [$/kWh/a]

[$/a] [$/m3/a]

Asset depreciation range (economical life time)

𝑛 [a] -

Negative and positive ramping constraint

𝑟−,

𝑟+

[%/h] -

Energy conversion units

Start-up costs 𝑆𝑈 [$/kW] [$/(m3/h)] -

Minimum and maximum part-load ratio

𝑢min,

𝑢max

[-] 𝑇amb, 𝑝amb, 𝜑amb, 𝐶𝑎𝑝

Efficiencies (electrical, thermal, …) 𝜂 [-] 𝑇amb, 𝑝amb, 𝜑amb, 𝐶𝑎𝑝, 𝑢

Energy storages

Charge and discharge efficiency 𝜂ch,

𝜂dch

[-] -

Self-discharge rate 𝑙𝑜𝑠𝑠 [%/h] -

Energy conversion and storage technologies were modeled by the parameters summarized in Table 2.1. The functional relationships of the parameters are also shown in this table.

2.3.3 Optimization problem


The optimization problem of the ESD method was formulated as a mixed-integer linear programming (MILP) problem. The objective function was defined as the minimization of total

expenditures (𝑓TOTEX) and carbon dioxide emissions (𝑓CO2),

𝑓 = 𝑤TOTEX ∙ 𝑓TOTEX +𝑤CO2 ∙ 𝑓CO2, (1)

with 𝑓TOTEX described by:

𝑓TOTEX = 𝑓CAPEX + 𝑓O&M + 𝑓Comm + 𝑓SU. (2)

𝑤TOTEX and 𝑤CO2 are weighting factors for parts of the objective function.

The total expenditures (𝑓TOTEX) are the sum of weighted capital expenditures (𝑓CAPEX),

𝑓CAPEX = 𝑔CAPEX∑[𝑎𝑛𝑛𝑥𝑖 ( 𝐼𝑛𝑠𝑡𝑥𝑖𝑖1,𝑥𝑖⏟ Fixed invest costs

+ 𝐶𝑎𝑝𝑥𝑖𝑖2,𝑥𝑖⏟ Variable invest costs

)]

𝑁𝑥

𝑖=1

, ∀𝑥 ∈ {RE, CU, ST}, (3)

weighted operation and maintenance costs (𝑓O&M),

𝑓O&M = 𝑔O&M∑(𝐼𝑛𝑠𝑡𝑥𝑖𝑂𝑀1,𝑥𝑖⏟ Fixed O&M costs

+ 𝐶𝑎𝑝𝑥𝑖𝑂𝑀2,𝑥𝑖⏟ Variable O&M costs

)

𝑁𝑥

𝑖=1

, ∀𝑥 ∈ {RE, CU, ST}, (4)

weighted commodity/energy purchases and sales (𝑓Comm),

𝑓Comm =∑

[

𝑔fix𝐼𝑛𝑠𝑡𝑦𝑖𝑝fix,𝑦𝑖⏟ Fixed charge

+ 𝑔dem𝐶𝑎𝑝𝑦𝑖𝑝dem,𝑦𝑖⏟ Demand charge

+∑(∆𝑡𝑘𝑃𝑦𝑖,𝑘𝑝e,𝑦𝑖,𝑘)

𝑇

𝑘=1⏟ Energy charge ]

𝑁𝑥

𝑖=1

, ∀𝑦 ∈ {𝐺𝐶𝑖𝑛, 𝐺𝐶𝑜𝑢𝑡}, (5)

and start-up costs (𝑓SU),

𝑓SU =∑∑(𝐶SU,CU𝑖,𝑘)

𝑇

𝑘=1

𝑁CU

𝑖=1

. (6)

The annuity factor (𝑎𝑛𝑛) is as follows:

𝑎𝑛𝑛 =(1 + 𝑟)𝑛𝑟

(1 + 𝑟)𝑛 − 1. (7)


Here, 𝑟 is the interest rate, i.e., weighted average cost of capital (WACC). Time weights (𝑔CAPEX,

𝑔O&M, 𝑔fix and 𝑔dem) were introduced for the decomposition method introduced in Section 2.2, time weights were introduced:

𝑔CAPEX = 𝑔O&M =∆𝑡𝑝

∆𝑡𝑎+ (1 −

∆𝑡𝑝

∆𝑡𝑎)𝑤C/O, (8)

𝑔fix =∆𝑡𝑝

∆𝑡𝑎, (9)

𝑔dem = ⌈∆𝑡𝑝

∆𝑡𝑚⌉, (10)

The time span of the current period (∆𝑡𝑝), a month (∆𝑡𝑚 = 720 h) and a year (∆𝑡𝑎 = 8760 h) are

given. The CAPEX-OPEX weight (𝑤C/O ∈ ℝ, 0 ≤ 𝑤C/O ≤ 1) over-weighs the CAPEX and O&M related cost terms in the objective function. These cost terms were magnified, so that certain periods in the decomposition approach would not lead into investment of overall suboptimal technologies. Besides the objective function, another part of the optimization problems are constraints. Energy of all considered energy forms must be conserved at every instance in time. Furthermore, at every

time step (𝑘), the energy demands have to be met. The superstructure can be rewritten into a mathematical form:

∑(𝛼GCin𝑖,𝑒𝑃GCin𝑖,𝑘)

𝑁GCin

𝑖=1

− ∑ (𝛼GCout𝑖,𝑒𝑃GCout𝑖,𝑘)

𝑁GCout

𝑖=1

+∑(𝛼RE𝑖,𝑒𝑃RE𝑖,𝑘)

𝑁RE

𝑖=1

+∑[ ∑ (𝛼CU𝑖,out𝑗,𝑒𝑃CU𝑖,out𝑗,𝑘)

𝑁CU𝑖,out

𝑗=1

− ∑ (𝛼CU𝑖,inj,𝑒𝑃CU𝑖,in𝑗 ,𝑘)

𝑁CU𝑖,in

𝑗=1

]

𝑁CU

𝑖=1

+∑(𝛼ST𝑖,dch,𝑒𝑃ST𝑖,dch,𝑘 − 𝛼ST𝑖,ch,𝑒𝑃ST𝑖,ch,𝑘)

𝑁ST

𝑖=1

+ ∑(𝛼Cplg𝑖,in,𝑒𝑃Cplg𝑖,𝑘 − 𝛼Cplg𝑖,out,𝑒𝑃Cplg𝑖,𝑘)

𝑁Cplg

𝑖=1

= ∑(𝛼Dem𝑖,𝑒𝑃Dem𝑖,𝑘)

𝑁Dem

𝑖=1

+ ∑ (𝛼Waste𝑖,𝑒𝑃Waste𝑖 ,𝑘)

𝑁Waste

𝑖=1

,

(11)

with 𝑁 ∈ ℕ≥0 describing the number of units per category. In this equation, the new variable (𝛼 ∈{0,1}) determines whether a certain power flow (𝑃𝑖) is relevant for a certain energy form (𝑒). Conversion units were modeled by the following two equations [2]:

𝑃CU𝑖,in𝑗,𝑘 = (𝑐1,CU𝑖,in𝑗 ,𝑘𝑃CU𝑖,out1,𝑘 + 𝑐2,CU𝑖,in𝑗 ,𝑘𝐶𝑎𝑝CU𝑖)𝑂𝑛CU𝑖,𝑘 , (12)


𝑃CU𝑖,out𝑗,𝑘 = (𝑐1,CU𝑖,out𝑗,𝑘𝑃CU𝑖,out1,𝑘 + 𝑐2,CU𝑖,out𝑗,𝑘𝐶𝑎𝑝CU𝑖)𝑂𝑛CU𝑖,𝑘 , (13)

𝑐1 and 𝑐2 are constant coefficients. Over a time step, the change of storage energy content could be described as:

𝐸ST𝑖,𝑘+1 − 𝐸ST𝑖,𝑘 = (𝑃ST𝑖,ch,𝑘𝜂ST𝑖,ch −𝑃ST𝑖,dch,𝑘

𝜂ST𝑖,dch− 𝐶𝑎𝑝ST𝑖𝑙𝑜𝑠𝑠ST𝑖) (𝑡𝑘+1 − 𝑡𝑘 ). (14)

Initial and final boundary conditions are needed for the energy storage content:

𝐸ST𝑖,𝑘=0 = {𝐶𝑎𝑝ST𝑖𝑆𝑂𝐶0 , if period at the beginning of the year

𝐸ST𝑖,0, otherwise, (15)

𝐸ST𝑖,𝑘=𝑇 = {𝐶𝑎𝑝ST𝑖𝑆𝑂𝐶𝑇 , if period at the end of the year

𝐸ST𝑖,𝑇, otherwise. (16)

The parameters (𝑆𝑂𝐶0 and 𝑆𝑂𝐶𝑇) were constant (e.g., here 0.5) and defined beforehand. 𝐸ST𝑖,0 and

𝐸ST𝑖,𝑇 were also constant, but determined from the previous time scale (compare Figure 2.2).

A lower (𝐶𝑎𝑝min) and upper bound (𝐶𝑎𝑝max) constrained the installation of energy system components:

𝐼𝑛𝑠𝑡𝑥𝑖𝐶𝑎𝑝min,𝑥𝑖 ≤ 𝐶𝑎𝑝𝑥𝑖 ≤ 𝐼𝑛𝑠𝑡𝑥𝑖𝐶𝑎𝑝max,𝑥𝑖 , ∀𝑥 ∈ {GCin, GCout, RE, CU, ST} (17)

The capacity (and the maximum part-load ratio) limits the power flows:

0 ≤ 𝑃𝑥𝑖,𝑘 ≤ 𝐶𝑎𝑝𝑥𝑖 , ∀𝑥 ∈ {GCin, GCout}, (18)

0 ≤ 𝑃CUi,out1,𝑘 ≤ 𝐶𝑎𝑝CU𝑖𝑢max,CU𝑖,𝑘 , (19)

0 ≤ 𝐸ST𝑖,𝑘 ≤ 𝐶𝑎𝑝ST𝑖 . (20)

Ramping constraints can further constraint the dispatch possibilities:

𝐶𝑎𝑝𝑥𝑖𝑟𝑥𝑖− ≤

𝑃𝑥𝑖,𝑘+1 − 𝑃𝑥𝑖,𝑘

𝑡𝑘+1 − 𝑡𝑘≤ 𝐶𝑎𝑝𝑥𝑖𝑟𝑥𝑖

+, ∀𝑥 ∈ {GCin, GCout}, (21)

𝐶𝑎𝑝CU𝑖𝑟CU𝑖− ≤

𝑃CU𝑖,out1,𝑘+1 − 𝑃CU𝑖,out1,𝑘

𝑡𝑘+1 − 𝑡𝑘≤ 𝐶𝑎𝑝CU𝑖𝑟CU𝑖

+ , (22)

𝐶𝑎𝑝ST𝑖𝑟ST𝑖− ≤

𝐸ST𝑖,𝑘+1 − 𝐸ST𝑖,𝑘

𝑡𝑘+1 − 𝑡𝑘≤ 𝐶𝑎𝑝ST𝑖𝑟ST𝑖

+ . (23)


The online status of conversion units (𝑂𝑛CU) is constrained by its installation (𝐼𝑛𝑠𝑡CU):

𝑂𝑛CU𝑖,𝑘 ≤ 𝐼𝑛𝑠𝑡CU𝑖 . (24)

With a helper variable (𝑧 ∈ {0,1}),,

𝑂𝑛CU𝑖,𝑘 −𝑂𝑛CU𝑖,𝑘−1 ≤ 𝑧CU𝑖,𝑘 , (25)

start-up costs can be included as follows:

𝐶SU,CU𝑖,𝑘 = 𝑧CU𝑖,𝑘𝐶𝑎𝑝CU𝑖𝑆𝑈CU𝑖 . (26)

The dispatch of energy conversion units is further constrained by minimum (𝑢min) and maximum

(𝑢max) part-load ratios:

𝐶𝑎𝑝CU𝑖𝑢min,CU𝑖,𝑘𝑂𝑛CU𝑖,𝑘 ≤ 𝑃CU𝑖,out1,𝑘 ≤ 𝐶𝑎𝑝CU𝑖𝑢max,CU𝑖,𝑘𝑂𝑛CU𝑖,𝑘 . (27)

Some equations contain products of binary and continuous variables. These products can be rewritten to mixed-integer linear programming formulations using standard methods [3].


3 Example case study for Suonenjoki In the following, the previously described draft method for holistic energy system design is applied to a part of the city Suonenjoki located in Finland.

3.1 Input Data The first step to create an optimal multi-modal energy system for a city or a certain part of a city is to prepare the input data. Owing to the fact that not all necessary input parameters were available in this case, also assumptions had to be made and noted. The mandatory data for energy system design studies can be separated into the following five categories: ▪ General data ▪ Climate data ▪ Load profiles ▪ Energy technologies ▪ Grid connections and commodity prices Concerning the Suonenjoki case, several mandatory data were available due to measured and simulated parameters, further research and information from the local utility. In the following chapters, the available and assumed input data will be presented.

3.1.1 General data General data includes basic information about the case that is investigated. As part of the project MODER, the energy system setup of a defined area in Suonenjoki (Finland) had to be optimized. Essential for the optimization process are specific parameters of the buildings inside the investigated area, shown in Figure 3.1, such as building numbers, addresses, buildings types, floor areas, electricity load profiles, heat load profiles and heat sources of the buildings. Due to the fact, that not every parameter of each building was provided, further assumptions had to be made by Siemens. To get an overview of the different data that were available for each building, the mentioned parameters were collocated according to Table 3.1.


Table 3.1 - Scheme for the collocation of building specific parameters.

Building

numberAddress

Building

type

Floor

area

Heat

source

Electricity

load

profile

Heat load

profile

1 x x x x x x

2 - - - - - -

… … … … … … …

42 x x x x - -

… … … … … … …


Figure 3.1 - Investigated area of Suonenjoki [4].

For most of the 163 buildings inside this area, the energy demands for the year 2016 had been measured or calculated. To characterize a certain building, they were divided into different building types and assigned to a number from 1 to 163, illustrated in Figure 3.2. The different building types are: Apartment building, Arena ice, Commercial building, Detached house, Gas station, Hospital, Industrial building, Library, Maintenance building, Office building, Outbuilding, Power plant, Public school, Public swimming pool, Restaurant, Retirement home, Row house, Sauna, Semi-detached house, Sheltered home, Squash hall, Storage building, Unknown.


Figure 3.2 - Building numbers of the investigated area [5].

The buildings which were assigned to the numbers 11, 35, 44, 47, 59, 78, 89, 116, 131 and 134 contain more than one building. For example, number 44 includes three different apartment buildings. To consider this in the calculation of the different load profiles, building number 44 was split into the buildings 44a, 44b and 44c. Many building specific parameters such as addresses, building types, floor areas and heat sources were provided by the Technical Research Centre of Finland (VTT).


Not available building data were assumed with the following methods. Missing addresses and floor areas were looked up or calculated in Google® Maps with the function “measure distance”, respectively. The floor areas had to be measured for 31 buildings, which building types were mostly unknown. An exemplary result of the area calculation with Google® Maps is illustrated in Figure 3.3.

Figure 3.3 - Floor area calculation with Google® Maps [4].

In this example, the floor area of building 85, a commercial building was measured, although the value had already been provided by VTT. The given floor area was 773 square meters and the measured one was 776 square meters. In order to the small difference between the two values the floor area calculation with Google® Maps was proofed to be a good method for defining the missing floor areas. Only in the case of the industrial building with the number 27, the missing area was not calculated with Google® Maps, but it was assumed that the area of this building is as large as the area of industrial building 42. Concerning the heat sources of the different buildings, there are six different types [5]: ▪ District heating


▪ Electric heating ▪ Oil heating ▪ Wood heating ▪ Geothermal heating ▪ No heating For 30 buildings there was no information about the heat source, so the following assumptions were made. Nearly every building of the type “Outbuilding” has no heat source. So buildings which look like Outbuildings were assumed to have no heat source. This affects the buildings with the numbers 3, 4, 7, 8, 10, 30, 45, 48, 82, 126, 128, 136, 160. Also for the buildings 2 and 162 no heat load was assumed, because they are heat plants. Building number 27 was assigned to the district heating network, because in Figure 3.11 a connection to the district heating network can be recognized. The heat source for the buildings 74, 145 and 147 was assumed to be wood, because in surrounded buildings also wood heating is used as a heat source. Oil heating is probably used by the inhabitants of building 153, because the heat source of nearby buildings is also oil. The remaining buildings 17, 46, 60, 75, 76, 97, 111, 159, 161 and 163 were assigned to electric heating, because they probably have a heat demand and no further information was given.

3.1.2 Climate data Information about climate data is important to calculate the performance of several energy conversion units (e.g. internal combustion engine, gas turbine) and renewable energy systems (e.g. photovoltaic, wind turbine). Depending on which conversion units are chosen for the development of the energy system superstructure, different climate parameters are necessary. For this case the mandatory climate data are: ▪ Dry-bulb temperature ▪ Global Horizontal Irradiance To get the mandatory hourly climate data profiles for the year 2016, Meteonorm V7.1.11.24422 is used [6]. This is a program with access to climate databases of 8350 sites over the whole earth. Additionally the ambient temperature from Kimpankatu (Suonenjoki) between Jan 1, 2016 and Dec 31, 2016 was provided by VTT. A graphical comparison of the two available temperature profiles received from VTT and Meteonorm is illustrated in Figure 3.4.


Figure 3.4 - Dry bulb temperature profiles of Suonenjoki (blue: Meteonorm; red: VTT).

There are some differences between the two temperature profiles for the year 2016. Due to the fact that the district heat load profile fits better to the temperature parameters of VTT, this temperature profile was used for the energy system optimization. The hourly profile of the global horizontal irradiance was taken from Meteonorm V7.1.11.24422.

3.1.3 Load profiles Concerning the Suonenjoki case, the heat consumption and the electricity consumption of the 163 buildings were considered. Each building had a different hourly heat and electricity consumption for the year 2016. To create the most suitable multi modal energy system for the investigated area, the load profiles of every building have to be known. For this reason, a load profile for every building was measured, simulated or assumed. In this chapter the steps to get the heat and the electricity load profile of each building will be presented. Electricity consumption At first, the electricity consumption will be investigated. The most reliable consumption data are measured data. For buildings, whose electricity load profiles were measured and provided by VTT, these profiles were used for the energy system design. Additionally VTT also simulated the electricity consumption of several buildings. These


calculated data were used for buildings, whose electricity consumption was not measured. For the remaining buildings the electricity load profiles were assumed. As already mentioned in Chapter 3.1.1, there was a quantity of buildings that include more than only one building. For these, special assumptions had to be made, because only one electricity load profile was measured or simulated. For the building numbers 11, 35, 44 and 47, it was assumed that the given electricity consumption is a summarized consumption for the included buildings. So to calculate, for example, the electricity demand of the buildings 11a and 11b, the summarized consumption of 11 was counted down on the floor area of building 11a and 11b. For building number 59, it was assumed that the given profile is assigned to building 59a. The consumption of building 59b was supposed to be proportional to building 59a because both of them are Apartment buildings. The proportional factor is the quotient between the floor areas of building 59b and 59a. Concerning the building numbers 78, 89, 116, 131 and 134, the assumption was made, that the given profiles belong to the buildings 78a, 89a, 116a, 131a and 134a. The profiles of the buildings 78b, 89b, 116b, 131b, 131c and 134a were not available. When these assumptions are taken into account, the different buildings can be separated into three groups. One of them consists out of buildings that electricity consumptions were measured. Another group contains all buildings with simulated electricity consumptions and the third group was assigned to buildings with no data so further assumptions had to be made for them. In Table 3.2 each building and the group it belongs to is visualized.


Table 3.2 - Buildings with measured, simulated and no available electricity consumption.

At this point, the electricity load profile of the buildings with no available data had to be estimated. For building 59b, it was already mentioned that the profile was assumed to be proportional to the profile of building 59a because they are the same type of building. Similar to this case, the load profile of the buildings 27 and 42 were assumed. Both of them are Industrial buildings and there is also the Industrial building 155 whose hourly electricity consumption was simulated. In consequence the electricity consumption of buildings 27 and 42 were supposed to be proportional to the electricity demand of building 155. The proportional factor is again the quotient between the floor area of building 27 or rather building 42 and building 155. After these assumptions, the electricity load profiles of 122 out of 175 buildings were available. To get an hourly load profile for the 53 remaining buildings, the consideration was made, that the profile of these buildings is proportional to the summarized load profile of the 122 buildings. The proportional factor is the quotient of the floor area of each building and the summarized floor area of the 122 buildings. Heat consumption Concerning the heat load profiles, at first, the buildings were separated into groups of different heat sources. These are district heating, electric heating, wood heating, oil heating, geothermal heating and no heating. Of course for non-heated-buildings there was no heat load profile needed.

GroupMeasured

electricity consumption

Simulated


No available


Buildings 1,6,9,11a,11b,12,14,33,

35a,35b,44a,44b,44c,

47a,47b,50,52,53,55,

59a,146,148,150

∑ 23

5,13,15,16,18,19,20,21,

22,23,25,26,28,29,31,32,

34,37,38,39,40,41,43,51,

54,56,57,58,61,62,63,64,

65,66,67,68,69,70,71,72,

73,77,78a,79,80,81,83,

84,85,86,87,88,89a,90,

91,92,93,94,95,96,98,99,

100,101,102,103,104,

105,106,107,109,113,

114,116a,119,120,121,

122,125,129,130,131a,

132,133,134a,135,138,

139,143,144,149,151,

155,156,157,158

∑ 96

2,3,4,7,8,10,17,24,27,30,

36,42,45,46,48,49,59b,

60,74,75,76,78b,82,89b,

97,108,110,111,112,115,

116b,117,118,123,124,

126,127,128,131b,131c,

134b,136,137,140,141,

142,145,147,152,153,

154,159,160,161,162,

163

∑ 56

Marked:

special assumption

Normal:

average assumption


The heat consumption of the building numbers that include more than one building were treated nearly the same way as it was done calculating the electricity consumption. For the building numbers 11, 35, 44 and 47, the assumption was made, that the available heat load profiles were summarized profiles, so they were counted down on the floor area of each building. The profile of building number 59 was estimated to be the profile of building 59a, so the heat consumption of building 59b was again supposed to be proportional to the heat demand of building 59a. Regarding the building numbers 78, 89, 116, 131 and 134, the given profiles were assigned to the buildings 78a, 89a, 116a, 131a and 134a, because the buildings 78b, 89b, 116b, 131b and 134b don’t have any heat source. Building 131c uses electric heating, but it was assumed that there is no available heating profile. By taking these assumptions into account, the different buildings can be separated into four groups. These four groups include buildings whose heat consumption was either measured, simulated, assumed or no heating source was installed at all. An overview of the four groups and the buildings that belong to these groups is represented in Table 3.3.

Table 3.3 - Buildings with measured, simulated, no available and no heat consumption.

GroupMeasured

heat consumption

Simulated

heat consumption

No available

heat consumptionNo heating

Buildings 6,9,11a,11b,12,14,

33,35a,35b,47a,

47b,50,52,59a,

146,148,150

∑ 17

1,5,13,15,16,18,

19,20,21,22,23,25,

26,28,29,31,32,34,

37,38,39,40,41,43,

44a,44b,44c,51,

53,54,55,56,57,58,

61,62,63,64,65,66,

67,68,69,70,71,72,

73,77,78a,79,80,

81,83,84,85,86,87,

88,89a,90,91,92,

93,94,95,96,98,99,

100,101,102,103,

104,105,106,107,

109,113,114,116a,

119,120,121,122,

125,129,130,131a,

132,133,134a,135,

138,139,143,144,

149,151,155,156,

157,158

∑ 102

17,27,42,46,49,

59b,60,74,75,76,

97,108,111,131c,

145,147,153,159,

161,163

∑ 20

Marked:

special

assumption

Normal:

average

assumption

2,3,4,7,8,10,24,30,

36,45,48,78b,82,

89b,110,112,115,

116b,117,118,123,

124,126,127,128,

131b,134b,136,

137,140,141,142,

152,154,160,162

∑ 36


Concerning the buildings with no available heat load profile, further assumptions had to be made. The heat load profiles of the buildings 27, 42 and 59b were calculated in the same way as the electricity load profile of these buildings, so there were only 17 buildings left with no available heating profile. To get these demands, it was assumed that the heat consumption of each of these buildings is proportional to the summarized measured and simulated heat load profiles. The proportional factor is again the quotient of the floor area of each building and the summarized floor area of the 122 buildings with measured and simulated data.

3.1.4 Energy technologies The energy system component unit model library developed in D4.3 within the MODER project [1], was partially extended and updated, see below. Currently, there are five heat production plants located in Suonenjoki that provide the heat demand of the district heating system. The heat for buildings with no connection to the local district heating system is produced by electric boilers, the burning of wood or heating oil or the use of geothermal heat. The demand of electricity is at the moment covered by an external power grid [7]. Before creating a new energy system superstructure, the scenarios, which energy technologies could be installed into the energy system of Suonenjoki, have to be set up. The technologies that were investigated are the following: ▪ Existing heat production plants and energy conversion units ▪ Photovoltaic plants ▪ Solar thermal district heating ▪ Combined heat and power units ▪ Heat pumps ▪ Electric boilers ▪ Hot water storages ▪ Lithium-ion batteries Existing heat production plants and energy conversion units The name of the existing heat production plants as well as the necessary parameters, to create an energy superstructure in contemplation of different scenarios, can be taken from Table 3.4. The economic and technical lifetimes of all energy units were assumed to be the same.

Table 3.4 - Mandatory input data of the existing heat plants.

Plant name

LK 25 Main

Production Kimpankatu

[7]

LK 25 Peak

Production Kimpankatu

[7]

LK 14 Herralantie

[7]

LK 15 Koulukatu

[7]

Kuopiontie [7]

Input commodity Wood, peat

[7] Light fuel oil

[7] Light fuel oil

[7] Heavy fuel

oil [7]

Liquid petroleum

gas [7]

Output commodity District heat

[7] District heat

[7] District heat

[7] District heat

[7] District heat

[7]


Capacity [MW] 10 [7] 8 [7] 8 [7] 8 [7] 3 [7]

Specific investment

costs [€/kW] 500 [8] 225 [8] 225 [8] 225 [8] 225 [8]

Specific operation and maintenance

costs [€/(kW ∙ a)] 10 [8] 3.4 [8] 3.4 [8] 3.4 [8] 3.4 [8]

Specific operation and maintenance

costs [€/kWh] 0.002 [8] 0.0007 [8] 0.0007 [8] 0.0007 [8] 0.0007 [8]

Lifetime [a] 20 [9] 20 [9] 20 [9] 20 [9] 20 [9]

Efficiency [%] 100 [9] 100 [9] 100 [9] 100 [9] 100 [9]

As already explained before, the heat consumption of the buildings is supplied by different heat sources. The heat demand of buildings with district heating is covered by the 5 heat plants. For buildings with electric heating it was assumed that there is an electric boiler installed inside these buildings which converts electricity into heat. Similarly inside buildings with oil or wood heating, an oil or wood boiler is used to transform the different commodities into heat. Concerning the building with geothermal heating, the assumption was made, that a ground source heat pump is used to cover the heat demand. The mandatory input parameters of these existing energy conversion units are depicted in Table 3.5.

Table 3.5 - Mandatory input data of the existing energy conversion units.

Conversion unit Electric Boiler Oil Boiler Wood Boiler Ground Source

Heat pump private

Input commodity Electricity Heating oil Wood Electricity

Output commodity Heat Heat Heat Heat

Capacity [kW] 1281.13 [9] 1262.75 [9] 178.69 [9] 4.64 [9]

Specific investment

costs [€/kW] 300 [9] 500 [9] 500 [9] 1800 [9]

Specific operation and maintenance costs [€/(kW ∙ a)]

1.35 [9] 10 [9] 10 [9] 47 [9]


Lifetime [a] 20 [9] 20 [9] 20 [9] 20 [9]

Efficiency / COP [%] 100 [9] 97.4 [9] 99 [9] 277 [9]

Photovoltaic plants In order to reduce the dependency on the external power grid, there is the possibility to put up fixed photovoltaic systems in Suonenjoki. These could be located either on nearby fields or on roofs of the buildings inside the investigated area. For photovoltaic systems on nearby fields, firstly, potential areas had to be selected and the total size of these had to be measured. The location and number of the chosen fields are illustrated in Figure 3.5.

Figure 3.5 - Selected photovoltaic fields of Suonenjoki [4].


To calculate the total size of these fields, the function “measure distance” of Google® Maps was used [4]. For the example of field “P1” the use of this function is depicted in Figure 3.6. The measured total size of “P1” is about 10100 square meters.

Figure 3.6 - Size calculation of field P1 [4].

For evaluating photovoltaic systems, a required parameter is the maximum power of the installed photovoltaic modules. The maximum power can be specified either as a DC-Power or as an AC-Power. To take the inverter losses into account, which occur by feeding the produced power of the photovoltaic modules into the electricity grid, the AC-Power values were used in this case.

The AC-Power per total area for fixed, small photovoltaic systems (> 1 MW,< 20 MW) can be

assumed to 32 MWAC km2⁄ . With the conversion factor between AC-Power and DC-Power of 0.85,

also the DC-Power of a photovoltaic system can be calculated according to formula (3.1) [10].


𝑃DC =

𝑃AC𝐴Tota𝑙

∙ 𝐴Total

0.85

(3.1)

The results for the different photovoltaic fields are presented in Table 3.6.

Table 3.6 - Power of photovoltaic systems on different fields.

Number 𝑨𝐓𝐨𝐭𝐚𝐥 [m2] 𝑷𝐀𝐂 𝑨𝐓𝐨𝐭𝐚𝐥⁄ [MWAC km

2⁄ ] 𝑷𝐀𝐂 [kWAC] 𝑷𝐃𝐂 [kWp]

P1 10100 32 323.2 380.2

P2 5700 32 182.4 214.6

P3 15200 32 486.4 572.2

P4 28400 32 908.8 1069.2

P5 6100 32 195.2 229.6

P6 22300 32 713.6 839.5

P7 32100 32 1027.2 1208.5

P8 24500 32 784.0 922.4

P9 19500 32 624.0 734.1

P10 22600 32 723.2 850.8

P11 9300 32 297.6 350.1

P12 23700 32 758.4 892.2

P13 30200 32 966.4 1136.9

P14 33200 32 1062.4 1249.9

P15 7100 32 227.2 267.3

P16 25700 32 822.4 967.5

P17 27000 32 864.0 1016.5

P18 34800 32 1113.6 1310.1

P19 24330 32 778.6 916.0

P20 19285 32 617.1 726.0

P21 3651 32 116.8 137.4

P22 15140 32 484.5 570.0


Number 𝑨𝐓𝐨𝐭𝐚𝐥 [m2] 𝑷𝐀𝐂 𝑨𝐓𝐨𝐭𝐚𝐥⁄ [MWAC km

2⁄ ] 𝑷𝐀𝐂 [kWAC] 𝑷𝐃𝐂 [kWp]

P23 9848 32 315.1 370.7

Sum 449754 32 14392.1 16931.9

For the implementation of photovoltaic systems on buildings, the sizes of suitable roof areas had to be measured. To avoid the size measurement of every single roof, a part of the investigated area was selected and the results were assumed to be representative and projected on the whole project area. Figure 3.7 shows the part of Suonenjoki, whose roof areas were evaluated.

Figure 3.7 - Area of Suonenjoki with evaluated roof sizes [4].


The different sizes were measured once again with the function “measure distance” of Google® Maps [4]. While for pitched roof shapes only the roof area towards the sun was measured, for flat roof shapes the whole roof area was taken into account. The results are presented in Table 3.7.

Table 3.7 - Roof sizes of a part of the investigated area.

Building number 𝑨𝐔𝐬𝐞 [m2]

Roof shape

14 103 Pitched

15 153 Pitched

16 138 Pitched

20 69 Pitched

21 50 Pitched

22 180 Pitched

26 380 Flat

18 132 Pitched

17 52 Flat

19 149 Pitched

23 50 Pitched

25 54 Pitched

24 76 Flat

The different roof areas were summarized. With an assumed AC-Power per useable roof area of

25 MWAC km2⁄ , the DC-Power can be calculated by using formula (3.1). The evaluated values are

shown in Table 3.8.

Table 3.8 - Roof photovoltaic Power of a small urban area.

Plant name 𝑨𝐓𝐨𝐭𝐚𝐥 [𝐦𝟐]

𝑨𝐔𝐬𝐞 [𝐦𝟐]

𝑷𝐀𝐂 𝑨𝐔𝐬𝐞⁄

[𝐌𝐖𝐀𝐂 𝐤𝐦𝟐⁄ ]

𝑷𝐀𝐂 [𝐤𝐖𝐀𝐂]

𝑷𝐃𝐂 [𝐤𝐖𝐩]

Small urban area

21900 1586 25 39.7 46.6

The quotient between total area and useable roof area of the small urban area was needed to define the useable roof area of the whole investigated area. The size of the whole investigated area can be seen in Figure 3.1. The DC-Power can once again be calculated with formula (3.1). The results of the calculations can be seen in Table 3.9.


Table 3.9 - Roof photovoltaic Power of the whole investigated area.

Plant name 𝑨𝐓𝐨𝐭𝐚𝐥 [𝐦𝟐]

𝑨𝐔𝐬𝐞 [𝐦𝟐]

𝑷𝐀𝐂 𝑨𝐔𝐬𝐞⁄

[𝐌𝐖𝐀𝐂 𝐤𝐦𝟐⁄ ]

𝑷𝐀𝐂 [𝐤𝐖𝐀𝐂]

𝑷𝐃𝐂 [𝐤𝐖𝐩]

Investigated area

563305 40795 25 1019.9 1199.7

The whole photovoltaic AC-capacity that could be installed in Suonenjoki is calculated by summarizing the roof photovoltaic capacity of the investigated area and the photovoltaic capacity

of the 23 fields. The result of this calculation is 15,412 kWAC.

The potential photovoltaic power output 𝑃PV is computed by multiplying the relative power output 𝑢PV with the capacity 𝐶𝑎𝑝PV shown in formula (3.2).

𝑃PV = 𝑢PV ∙ 𝐶𝑎𝑝PV. (3.2)

The relative power output describes the potential of the photovoltaic panels and is modelled according to formula (3.3):

𝑢PV,𝑘 = [1 − 𝛽ref ∙ (𝑇amb,𝑘 − 𝑇ref)] ∙𝐺𝐻𝐼𝑘𝐺𝐻𝐼ref

, (3.3)

with the parameters (𝛽ref = 0.0045 1 K ⁄ ; 𝑇ref = 25°C ; 𝐺𝐻𝐼ref = 1000 W m2⁄ ). 𝑇amb,𝑘 denotes the

ambient temperature and 𝐺𝐻𝐼𝑘 the Global Horizontal Irradiance at a time step (𝑘) [1]. Regarding further parameters of photovoltaic plants, the assumptions depicted in Table 3.10 were made.

Table 3.10 - Cost parameters of photovoltaic plants.

Renewable energy unit Photovoltaic

Output commodity Electricity

Maximum Capacity [kWAC] 15412

Specific investment costs [€/kWAC] 1200 [1]

Specific operation and maintenance costs

[€/(kWAC ∙ a)] 16.2 [1]

Lifetime [a] 20 [1]


Primary Energy Factor [kW/kW] 0.1 [5]

Specific CO2 Emissions [kWCO2/kW] 0.025 [5]

Solar thermal district heating For evaluating the use of solar thermal district heating in Scandinavian countries, an implementation of this energy technology unit should also be tested for the Suonenjoki case.

The maximum area that could be covered with solar thermal collectors is 1000 m2 large [5]. Per

square meter, a solar thermal power of 0.7 kWth can be installed. Hence, for the Suonenjoki case, the maximum solar thermal capacity was calculated to 700 kWth [11].

The potential solar thermal power output 𝑃ST is computed by multiplying the relative power output

𝑢ST with the capacity 𝐶𝑎𝑝ST shown in formula (3.4):

𝑃ST = 𝑢ST ∙ 𝐶𝑎𝑝ST. (3.4)

The relative power output describes the potential of the solar thermal collectors. It depends on the

solar thermal collector efficiency 𝜂ST. This efficiency is not constant, but decreases with a higher

difference between the collector temperature 𝑇c and ambient temperature 𝑇amb. A model of the collector efficiency in dependence of the temperature difference at a global horizontal irradiance of

1000 W/(m2 ∙ K) is shown in Figure 3.8.

Figure 3.8 - Collector efficiency in dependence of the temperature difference [12].

In consequence, the relative power output of the solar thermal panel 𝑢ST can be modelled according to formula (3.5):


𝑢ST,𝑘 = (−0.0047 ∙ (𝑇c − 𝑇amb) + 0.8577) ∙𝐺𝐻𝐼𝑘𝐺𝐻𝐼ref

∙ 1

𝜂ref (3.5)

The reference global horizontal irradiance is 𝐺𝐻𝐼ref = 1000 W m2⁄ . The reference solar thermal collector efficiency is 𝜂𝑟𝑒𝑓 = 0.7. 𝐺𝐻𝐼𝑘 denotes the global horizontal irradiance at a time step (k).

The collector temperature 𝑇c has to be the same as the temperature of the hot water inside the

district heating system, which was on average approximately 80°C. Regarding further parameters of solar thermal plants, the assumptions depicted in Table 3.11 were made.

Table 3.11 - Cost parameters of solar thermal plants.

Renewable energy unit Solar thermal energy

Output commodity Heat

Maximum Capacity [kWth] 700

Specific investment costs

[€/kW] 1078 [13], [8]

Specific operation and maintenance costs [€/(kW ∙ a)]

7.69 [8], [13], [14]

Lifetime [a] 20 [9]

Primary Energy Factor

[kW/kW] 0.1 [5]

Specific CO2 Emissions

[kWCO2/kW] 0.025 [5]

Combined heat and power Combined heat and power units offer the possibility to supply part of the district heat demand as well as part of the electricity consumption of the different buildings in Suonenjoki. In this case combined heat and power units fired with liquid petroleum gas and wood are considered. All parameters that were used to implement these technologies are presented in Table 3.12.


Table 3.12 - Parameters of the combined heat and power units.

Energy conversion unit CHP (LPG) CHP (Wood)

Input commodity Liquid petroleum gas Wood

Output commodity Heat, Electricity Heat, Electricity

Maximum Capacity [kW] 10000 50000


[€/kW] 900 [1] 1200 [9], [15]

Specific operation costs

[€/(kWh)] 0.025 [9] 0.05 [15]

Lifetime [a] 20 [9] 20 [9]

Efficiency liquid petroleum

gas to electricity [−] 0.44 [9] 0.42 [9]

Efficiency liquid petroleum gas to heat [−]

0.45 [9] 0.47 [9]

Heat pumps Furthermore, the implementation of heat pumps to convert electricity into heat was tested for the Suonenjoki case. Two different sorts of heat pumps were considered. One of them is a ground-source heat pump and the other one an air-source heat pump. The heat produced by these technologies is also supplied into the district heating network. The required parameters of both applications are illustrated in Table 3.13.

Table 3.13 - Parameters of the heat pumps.

Energy conversion unit Ground-source heat pump

plant Air-Water heat pump

Input commodity Electricity Electricity

Output commodity Heat Heat

Maximum Capacity [kW] 10000 10000


Specific investment costs [€/kW]

850 [9] 500 [1]

Specific operation and

maintenance costs [€/(kW ∙ a)] 20 [9] 13.5 [1]

Lifetime [a] 20 [9] 20 [1]

COP [−] 3.28 [9] Unit model [1]

The assumed efficiency of the ground-source heat pump is higher than the one of the air-source heat pump. This results from the higher inlet temperature of the ground-source heat pump, compared to the air-source heat pump. Electric boilers Beside the installed electric boilers in private buildings, also the implementation of electric boilers to supply the district heat system was tested. The required parameters for the implementation are illustrated in Table 3.14.

Table 3.14 - Parameters of the electric boilers.

Energy conversion unit Electric boiler

Input commodity Electricity

Output commodity District heat

Maximum Capacity [kW] 50000

Specific investment costs [€/kW]

117 [9]


maintenance costs [€/(kW ∙ a)] 1.35 [9]

Lifetime [a] 20 [9]

Efficiency [%] 100 [9]


Storage units Concerning storages that could be included into the energy system of Suonenjoki, hot water storages and lithium-ion batteries were under investigation. The data used for these storage technologies can be taken from Table 3.15.

Table 3.15 - Parameters of the storage units.

Energy storage unit Hot water storages Lithium-ion batteries

Input commodity Heat Electricity

Output commodity Heat Electricity

Maximum Capacity [kWh] 50000 100000


[€/kWh] 25 [1] 1000 [1]


maintenance costs [€/(kWh ∙a)]

0.114 [1] 0.544 [1]

Lifetime [a] 40 [1] 15 [1]

Charging time [%/h] 20 [1] 100 [1]

Maximum Charging Rate [%/h] - 20 [1] - 100 [1]

Maximum Discharging Rate

[%/h] 98 [1] 93.5 [1]

Discharge efficiency [%] 98 [1] 93.5 [1]

Losses [%/h] 0.5 [1] 0.00102 [1]

3.1.5 Grid connections and commodity prices The different commodities that are part of the energy system of Suonenjoki are the following:


▪ Electricity ▪ Liquid petroleum gas ▪ Wood ▪ Peat ▪ Light fuel oil ▪ Heavy fuel oil ▪ Heat oil The electricity demand of the investigated area can be supplied by an external power grid. As illustrated in Figure 3.9, the electricity prices of Finland were depending on the consumption of a certain building. For the year 2016, the electricity prices are read off and depicted in Table 3.16.

Figure 3.9 - Price of electricity by type of consumer [5].

Table 3.16 - Electricity price based on different electricity consumptions in Finland for the year 2016.

Electricity consumption

[MWh/a] Electricity price

[ct/kWh]

2 18

5 15

18 12,5

2000 8

With these four data points, a mathematical function of the electricity price in dependence on the electricity consumption is created and demonstrated in Figure 3.10.


Figure 3.10 – Electricity price in dependence on the electricity consumption in Finland for the year 2016. Applying the formula for the electricity price in dependence on the electricity consumption, the price of electricity supplied from the power grid could be calculated for every building. As already mentioned in Chapter 2.2.1, typical optimization objectives are minimum total expenditures, minimum CO2 emissions, minimum (non-renewable) primary energy consumption, or combinations of these. To analyze the carbon footprint, as well as the primary energy demand, not only the price, but also the primary energy factor and specific CO2 emissions of the different commodities were required. An overview of the used parameters is shown in Table 3.17.

The CO2 tax of Finland amounts to 48 $ 𝑡𝐶𝑂2⁄ respectively 43.2 € 𝑡𝐶𝑂2⁄ [16]. It had also to be included into the different commodity prices by multiplying it with the specific CO2 emissions of the commodity and adding this value to the commodity price without the CO2 tax. Concerning the CO2 taxation in Finland the special case had to be considered, that the burning of wood is excluded from the CO2 taxation [17]. This is because wood is accounted to be a renewable energy source due to the fact that the burning of wood is supposed to be CO2 neutral. Another important aspect regarding the CO2 taxation is that they were reduced to 50% for the operation of CHP units [17].


Table 3.17 - Commodity prices, primary energy factors and specific CO2 emissions.

Commodity Primary

energy factor [kW kW⁄ ]

Specific CO2 Emissions [kgCO2/kWh]

Price without CO2 tax [€/kWh]

Price with CO2 tax [€/kWh]

Electricity 1.7 [5] Hourly

calculated by VTT

Different for every building

-

Liquid petroleum gas

for CHP 1.12 [5] 0.231 [5] 0.083 [18], [19] 0.0929792

Liquid petroleum gas for heat plants

1.12 [5] 0.231 [5] 0.083 [18], [19] 0.0879896

Wood 1.1 [9] 0 [17] 0.021 [20] 0.021

Peat 1.1 [9] 0.38 [21] 0.015 [20] 0.031416

Light fuel oil 1.1 [5] 0.317 [5] 0.0761 [22] 0.0897944

Heavy fuel oil 1.1 [5] 0.329 [5] 0.0225 [23],

[24] 0.0367128

Heating oil 1.1 [9] 0.245 [25] 0.0798 [26],

[27] 0.090384

Photovoltaic 0.1 [5] 0.025 [5] - -

Solarthermics 0.1 [5] 0.025 [5] - -

If the produced electricity exceeds the demanded electricity, it can be sold on the Elspot market. The hourly Elspot market price of the year 2016 was taken from Nord Pool [28]. During some hours of the year 2016, the Elspot market price was higher than the calculated price of electricity provided by the external power grid. For avoiding electricity to be bought and sold at higher prices, the relevant Elspot market prices were adjusted to be 0.0001 €/kWh lower than the electricity price of the external power grid. If the thermal losses of the district heating network imaged in Figure 3.11 are calculated, further information about the length of the pipes will be necessary. For this purpose the distance of the pipes between the existing heating plants and the middle of every cluster was measured with the function “measure distance” of Google® Maps [4]. The average of the distance between one plant


and the four clusters was assumed as the distance between the plant and the middle of the city. The consequential results are depicted in Table 3.18. However, since this study uses the draft method for holistic energy system design, which is described for a one-node multi-modal energy system, transport losses can be neglected.

Figure 3.11 - District heating network of Suonenjoki [7].


Table 3.18 - District heating pipe distances between plants and clusters.

Power plant number Plant name Distance between plant and

middle of City [m]

LK14 (LFO) Herralantie 800

LK15 (HFO) Koulukatu 825

(LPG) Kuopiontie 1225

LK25 (wood,peat / LFO) Kimpankatu 2487.5

3.2 Superstructure Development

3.2.1 Scenarios Before developing the superstructure of the energy system, the different scenarios that should be investigated had to be set up. These were partially based on the technologies that could be installed into the energy system of Suonenjoki. For example, it may be interesting to see, how the energy system will be structured and how much it will cost if a minimum power of photovoltaic plants is installed. Other considered scenarios include the influences of a rising weighted CO2 factor for the optimization objective. This means that the main aim of the optimization is no more to reduce the expenditures, but to decrease both costs and CO2 emissions. To make statements whether the existing energy system setup of Suonenjoki is the best, it is also interesting to consider the different scenarios for the case, that there are no energy technologies installed in Suonenjoki so far (Greenfield). Of course the scenarios were also tested for the case that the heating plants, private electric boilers, oil boilers, wood boilers and the private ground source heat pump have already been installed (Brownfield). The different scenarios that were tested for the two types Brownfield and Greenfield are summarized in Table 3.19

Table 3.19 - Tested Scenarios with Explanation.

Scenario Explanation

Ref The current energy system is installed (Brownfield) respectively only energy technologies of the current

system can be installed (Greenfield)

OptimCO2_0 Weighted factor costs = 1 Weighted factor CO2 = 0




OptimCO2_100 Weighted factor costs = 1

Weighted factor CO2 = 100

OptimCO2_1000 Weighted factor costs = 1

Weighted factor CO2 = 1000

InstGSHP GSHP minimum capacity = 3000 kW

InstPV PV minimum capacity = 5000 kW

InstST ST minimum capacity = 300 kW

InstCHP CHP (LPG) minimum capacity = 3000 kW

InstBat Battery minimum capacity = 3000 kW

InstHWS Hot water storage minimum capacity = 10000 kW

Concerning the two reference scenarios for Greenfield and Brownfield, no other energy technologies apart from those that exist in reality can be installed. For the other scenarios the upper limits for the implementation of the different energy units, which were considered in Chapter 3.1.4, are their maximum capacities.

3.2.2 Sensitivity Analysis To make statements about how an adjustment of different parameters influences the optimized structure of the energy system, a sensitivity analysis was established. The change of the different parameters was tested for every scenario. Table 3.20 presents the considered sensitivities and their meaning.

Table 3.20 - Tested sensitivities with Explanation.

Sensitivities Explanation

Base1 Base case without sensitivities

InvBat250 Specific investment costs of Batteries: 250 €/kWh

InvBat500 Specific investment costs of Batteries: 500 €/kWh

CO2_70 CO2 tax: 70 $ 𝑡CO2⁄

CO2_100 CO2 tax: 100 $ 𝑡CO2⁄

CO2_150 CO2 tax: 150 $ 𝑡CO2⁄

El-50% Electricity price and Elspot price 50 % lower

1 Base: Specific investment costs of Batteries: 1000 [€/kWh] CO2 tax: 0.048 $ 𝑘𝑔𝐶𝑂2⁄ Weighted average costs of capital (WACC): 7%


El-25% Electricity price and Elspot price 25 % lower

El+25% Electricity price and Elspot price 25 % higher

El+50% Electricity price and Elspot price 50 % higher

InvHP-50% Specific investment costs of heat pumps 50 % lower

InvHP-80% Specific investment costs of heat pumps 80 % lower

WACC3% Weighted average costs of capital: 3 %

WACC12% Weighted average costs of capital: 12 %

Sensitivities Explanation

Wood+25% Wood price 25 % higher



With the 2 types Greenfield and Brownfield, the 12 scenarios and the 17 sensitivities, there are 408 cases that were optimized. The schematic in Figure 3.12 gives an overview of all cases.

Figure 3.12 - Matrix of the different cases that are optimized.

For this report, the in Figure 3.12 highlighted cases of the sensitivity “Base” and of the scenarios “Ref” and “OptimCO2_0” will be shown. These results were supposed to be the most interesting concerning the Suonenjoki case.

Scenarios (12)

Sensitiv

ities (1

7)

Ref

Optim

CO

2_0

Base

…

…


3.2.3 City level superstructure After the different cases that should be optimized are determined, the city level superstructure could be developed. Regarding the city level model, it was assumed that every part of the energy system (e.g. conversion units, consumers, storages) was located at the same place. Therefore the model was only based on one node, so the connections and losses between energy suppliers and consumers were neglected. Also the electricity consumption of the different buildings was summed up. The electricity price was assumed to be the weighted average electricity price of all buildings. This means that the electricity price multiplied with the yearly electricity consumption of every building was summed up and divided by the overall electricity consumption. The resulting electricity

price for the whole investigated area was 0.1083 € kWh⁄ . The heat load profiles were added up as well, but they had to be separated into heat load profiles of the different heat sources (district heating, electric heating, oil heating, wood heating and geothermal heating). In order to avoid long optimization times, the model was calculated as an LP-model, so part-load characteristics and size-dependent specific capital costs were neglected. To illustrate the different energy units of a certain case and how they are connected to each other, a schematic overview of the reference cases is depicted in Figure 3.13. In these, only the current energy system of Suonenjoki was considered. Figure 3.14 represents an overview of the optimized cases, in which the energy units were integrated that could be installed additionally to the existing energy system of Suonenjoki.


Figure 3.13 - Schematic overview of the reference cases.

Po

wer g

rid

LP

G

LF

O

Pe

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Wo

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HO

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LK

14 H

eat

pla

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FO

)

Ku

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eat

pla

nt (L

PG

)


Figure 3.14 - Schematic overview of the optimized cases.

To get a deeper understanding of the investigated case, also hourly power balances were plotted. For the district heating system of the two mentioned Base cases, the hourly power balance is

Po

we

r grid

LP

G

LF

O

Peat

Wo

od

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O

HO

Ele

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ity

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)

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ST

AW

HP

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ctric

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P (W

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illustrated in Figure 3.15. One important point to consider is that the heat consumption in summer is very low compared to the other seasons, because of the higher ambient temperatures in these months. Furthermore, two plants as well as combined heat and power units fired with wood, electric boilers and hot water storages had to be installed in the cost optimized case.

Figure 3.15 - Power balance of the district heating system concerning the Greenfield Base Reference and OptimCO2_0

case.

3.3 Results In the next pages, the results of the different cases are illustrated. For these the following objectives were considered: ▪ Capacities of the installed energy units ▪ Total costs ▪ CO2 emissions


▪ Use of primary energy

3.3.1 Scenario analysis of Greenfield Base cases (Greenfield Base) The Greenfield cases were characterized by the fact that no energy unit had been installed yet. So one interesting point is to consider the differences between the reference case, where only energy technologies of the current energy system are allowed to be installed, and the optimized cases, where this restriction is not taken into account. The energy unit capacities of the Greenfield Base cases are presented in Figure 3.16.

Figure 3.16 - Energy unit capacities of the Greenfield Base cases.

The total costs as well as the CO2 emissions and the use of primary energy are illustrated in Figure 3.17.


Figure 3.17 - Total costs, CO2 emissions and primary energy use of the Greenfield Base cases.

The optimized scenarios differentiate significantly from the reference case. A main aspect is that concerning the optimized cases, combined heat and power units fired with wood as well as electric boilers for the district heating system were built up. Due to this, the costs, CO2 emissions and primary energy use are reduced in the optimized cases compared to the reference case. Air-water heat pumps and combined heat and power units fired with liquid petroleum gas were not installed to reduce the total expenditures and CO2 emissions, owing to the special situation that there is no CO2 tax assigned to the burning of wood in Finland and the assumption that the burning of wood is CO2 neutral. Also the low wood price in Finland supports the use of wood in energy systems. Another result of this special situation is that the peat and wood heat plant LK25 was implemented and fired with wood in nearly every case. In the cases OptimCO2_100 and OptimCO2_1000 there are even no CO2 emissions, because the electricity is not supplied by the external power grid, but produced by the wood fired combined heat and power units. Concerning these cases, the electric boilers convert only electricity provided by the combined heat and power units to supply the district heating system.

(a) Costs

(b) CO2 Emissions (c) Primary Energy


At this point, it has to be mentioned that for the input of wood no limit was assumed. Compared to the Brownfield Base Reference case, the maximum wood input is twice as high and amounts to 30GWh. If there is a restriction to the input of wood and the burning of wood is assumed to emit CO2, maybe heat pumps and more electric boilers will be installed to reduce costs and CO2 emissions. Another interesting point is that in every optimized case, hot water storages were implemented, especially to store the district heat produced by heat generation plants, combined heat and power plants and electric boilers. The fact that the installation of wood-fired combined heat and power plants as well as electric boilers is both more ecological and economical compared to the reference system is also shown in Figure 3.18. An imaginary curve through the points of the optimized cases with increasing weighted CO2 factors would be a hyperbola. The higher the CO2 factor, the lower the total CO2 emissions and the higher the total expenditures.

Figure 3.18 - Total CO2 vs. TOTEX and Primary Energy vs. TOTEX of different Greenfield Base cases. If the target is to install a specific energy unit, the total expenditures will not decline, which validates the draft method for holistic energy system design.

3.3.2 Scenario analysis of Brownfield Base cases (Brownfield Base) The Brownfield cases were characterized by the fact that the current energy system of Suonenjoki had already been installed. Concerning the Base cases, no sensitivities are considered. The energy unit capacities of the Brownfield Base cases are depicted in Figure 3.19.


Figure 3.19 - Energy unit capacities of the Brownfield Base cases.

Furthermore, the total costs as well as the CO2 emissions and the use of primary energy are visualized in Figure 3.20.


Figure 3.20 - Total costs, CO2 emissions and primary energy use of the Brownfield Base cases.

The most important aspect to consider, when looking at the results of the Brownfield Base cases, is that the structure of the Ref and the OptimCO2_0 cases differentiate, due to the installation of wood fired combined heat and power units in the optimized case. As a result of this, the costs and the use of primary energy of the current energy system installed in Suonenjoki can be reduced and the total CO2 emissions can be halved. The reasons for this effect were already mentioned in Chapter 3.3.1.

3.3.3 Sensitivity analysis of Greenfield reference cases (Greenfield Ref) The Greenfield reference cases were characterized by the fact that only energy technologies of the current system installed in Suonenjoki were allowed to be implemented. It has to be considered, how the change of the different sensitivities influences the energy system setup. The energy unit capacities of the Greenfield Ref cases are visualized in Figure 3.21.

(a) Costs



Figure 3.21 - Energy unit capacities of the Greenfield Ref cases.

The total costs as well as the CO2 emissions and the use of primary energy are presented in Figure 3.22.


Figure 3.22 - Total costs, CO2 emissions and primary energy use of the Greenfield Ref cases.

In every case only the peat and wood as well as the heavy fuel oil fired heat production plants were installed to supply the district heat. In order to a higher CO2 tax, more wood is burned, because wood is not affected by changes of the CO2 taxes. A higher wood price leads to the firing of peat and heavy fuel oil inside the two heat generation plants, which effects the CO2 emissions significantly.

3.3.4 Sensitivity analysis of Brownfield reference cases (Brownfield Ref) The Brownfield reference cases were characterized by the fact that the current energy system of Suonenjoki was already installed. It has to be considered, how the change of the different sensitivities influences the operation and properties of the current energy system set up. The energy unit capacities of the Brownfield Ref cases are depicted in Figure 3.23.

(a) Costs



Figure 3.23 - Energy unit capacities of the Brownfield Ref cases.

The total costs as well as the CO2 emissions and the use of primary energy are demonstrated in Figure 3.24.


Figure 3.24 - Total costs, CO2 emissions and primary energy use of the Brownfield Ref cases.

The results show that the change of most of the sensitivities does not influence the energy system of the Base case. Only if the wood price is 50% higher or even more, less wood and more peat will get burned. This influences the CO2 emissions negatively, because of the higher CO2 emission factor of peat compared to wood. Also it can be determined that lower electricity prices lead to lower operation costs and therefore lower total expenditures.

3.3.5 Sensitivity analysis of cost optimized Greenfield cases (Greenfield OptimCO2_0) By considering the Greenfield OptimCO2_0 cases, it is interesting to investigate, which energy units would be implemented in Suonenjoki to achieve minimum total expenditures under different circumstances if there were no energy technologies installed yet. Also the influences on the CO2 emissions and the use of primary energy of the different cases have to be considered.

(a) Costs



The energy unit capacities of the Greenfield OptimCO2_0 cases are visualized in Figure 3.25.

Figure 3.25 - Energy unit capacities of the Greenfield OptimCO2_0 cases.

The total costs as well as the CO2 emissions and the use of primary energy are illustrated in Figure 3.26.


Figure 3.26 - Total costs, CO2 emissions and primary energy use of the Greenfield OptimCO2_0 cases.

As shown in Figure 3.26, only the two heat generation plants fired with wood, peat and heavy fuel oil were implemented into the energy system in most of the different Greenfield OptimCO2_0 cases. At this point it has to be mentioned that the five conventional heat plants supply also buildings with district heat which are outside the investigated area, so the installation of all plants may be necessary. Additionally, the redundancy of the energy system is not considered in the optimized cases. Again wood fired combined heat and power plants as well as electric boilers were implemented in nearly every case. Only if the electricity price is 50% lower or the costs of wood are 100 % higher, it is more economical to take more electricity from the external power grid than to build combined heat and power units.

(a) Costs



For the two cases characterized by a 50% lower electricity price and 80% lower heat pump investment costs, the implementation of ground-source heat pumps and air-water heat pumps is economical. Furthermore, it is important to note that concerning every case hot water storages were implemented into the energy system. Especially when the CO2 tax rises, an interaction between the burning of wood and the storing of the produced heat inside hot water storages is useful and environmentally friendly due to lower CO2 emissions. The highest CO2 emissions were reached for the cases of high wood prices, since large amounts of heavy fuel oil are burned instead of wood.

3.3.6 Sensitivity analysis of cost optimized Brownfield cases (Brownfield OptimCO2_0) When analyzing the Brownfield OptimCO2_0 cases, it should be considered under which circumstances other energy technologies had to be added to the current energy system to achieve less total expenditures and how this influences the CO2 emissions and the use of primary energy. To consider these aspects, the energy unit capacities of the Brownfield OptimCO2_0 cases are shown in Figure 3.27.


Figure 3.27 - Energy unit capacities of the Brownfield OptimCO2_0 cases.

The total costs as well as the CO2 emissions and the use of primary energy are depicted in Figure 3.28.


Figure 3.28 - Total costs, CO2 emissions and primary energy use of the Brownfield OptimCO2_0 cases.

It can be recognized that in most of the cases combined heat and power units fired with wood are added to the current energy system of Suonenjoki. Only if the electricity price falls by at least 25% or the wood price rises by 100%, it is more economical to take more electricity from the external power grid than to install combined heat and power plants. This will lead to higher CO2 emissions, since the burning of wood is assumed to be CO2 neutral. Also peat will get burned if the wood price rises by at least 50%.

(a) Costs


2,5


4 Conclusion In the first part of Task 4.4, a draft method for holistic energy system design was developed. The objective of this energy system design method is to determine the optimal capacities of energy conversion and storage units placed in multi-modal energy systems. The method was schematically introduced and the optimization problem was mathematically formulated. Furthermore, the draft method was applied to part of the city Suonenjoki (Finland). At first, mandatory input data for the development of an energy system superstructure was prepared. Some of the necessary parameters were provided by Sweco and VTT, others were assumed by Siemens AG as a result of detailed research. With the acquired data, an energy system superstructure of the investigated area was created. The optimization was then executed by taking 12 scenarios, 17 sensitivities and 2 types into account. The energy system structure of important cases was later on discussed and compared to the current energy system set up of the investigated area. The discussion of the results was based on economic and environmental aspects. The key findings show that: ▪ If there were no energy units installed in Suonenjoki, the total expenditures, CO2 emissions

and the use of primary energy could be reduced for the investigated area by installing:

• Combined heat and power plants fired with wood

• Electric boilers

• Hot water storages

▪ The total expenditures, CO2 emissions and the use of primary energy of the current energy

system of Suonenjoki can be reduced by installing wood fired combined heat and power units. Reasons for this are:

• No CO2 taxation on the burning of wood in Finland

• Low wood prices in Finland

• Burning of wood is assumed to be CO2 neutral

▪ If there were no energy units installed in Suonenjoki, the energy system would change a lot

compared to the existing one considering the tested sensitivities. Maximal two heat generation plants as well as hot water storages would be installed in every case. 50% lower electricity prices or 80 % lower investment costs for heat pumps would enable the installation of ground-source heat pumps and air-water heat pumps to decrease the total expenditures.

The target group of this study was the MODER consortium. The draft method for holistic energy system design, as described in this deliverable, was formulated as one-node model and will be further extended for multi-node energy systems (→ deliverable D4.5).


5 References

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d4.4 draft method for holistic energy system design

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