Development of operating models through technology ...

Master thesis Analysis of explorative development paths for the German energy system -

Design of a unit commitment optimization model with a high share of renewable energy until 2050

Berlin, September 01, 2014

Presented by:

Supervised by:

Sebastian Rauner

Prof. Dr.-Ing. Andreas Gerhard Goldmann

Kandelstr. 43

Beuth Hochschule für Technik Berlin

79106 Freiburg

M. Eng. Wi.-Ing. Charlotte Senkpiel

Student number: 793423

Fraunhofer-Institut für Solare Energiesysteme (ISE), Freiburg

E-Mail: [email protected]

ABSTRACT The existing E2S model is analyzing the future energy system of Germany through the simulation of explorative development paths. It is based on the current and future development of the technological and economic framework. The goal is to derive guidance for different stakeholders of the energy system, ranging from political institutions and energy suppliers to industry. Through the expected ever higher penetration of renewable energy till 2050, it is very difficult to predict the development of the stock exchange energy price and market value factors of these technologies. Both are very significant parameters for the above mentioned model. Goal of the thesis The goal of the master thesis is therefore to forecast these parameters through a closely integrated unit commitment optimization model called German Energy System Optimization (GESOP) model. The steps to achieve this are as follows: 

Adapt the E2S model output to the optimization model



Investigate missing input through literature research



Development of an optimization model



Validation of the model with existing data of the past



Calculation of different scenarios

The main achievement hereby is the reasonable modelling concerning a wide range of features of the energy system. These include intertemporal restrictions of power plants, transmission capacity constraints as well as a reserve market.

Statutory declaration I hereby declare that I have authored this thesis independently, that I have not used other than the declared sources and that I have explicitly marked all material which has been quoted either literally or by content from the used sources.

Freiburg, 27.08.2014

………………………………………………. (signature)

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Contents ABSTRACT ................................................................................................................................................ II List of figures .......................................................................................................................................... VI List of Table s ........................................................................................................................................ VIII List of acronyms...................................................................................................................................... IX Nomenclature.......................................................................................................................................... X 1

2

Introduction ................................................................................................................................... 14 1.1

Motivation ............................................................................................................................. 14

1.2

Scope and purpose ................................................................................................................ 15

1.3

Structure ................................................................................................................................ 16

Energy system models ................................................................................................................... 16 2.1

2.1.1

Top-down ...................................................................................................................... 17

2.1.2

Bottom-up ..................................................................................................................... 17

2.1.3

Simulation...................................................................................................................... 17

2.1.4

Optimization .................................................................................................................. 17

2.2

3

4

Classification of energy system models ................................................................................ 16

Existing energy system models ............................................................................................. 18

2.2.1

The MARKet Allocation model (MARKAL) ..................................................................... 18

2.2.2

The TIMES model ........................................................................................................... 18

2.2.3

The PRIMES model ........................................................................................................ 18

Methodological approach ............................................................................................................. 19 3.1

Characteristic features .......................................................................................................... 19

3.2

Data and material of the model ............................................................................................ 20

3.2.1

Connected models ......................................................................................................... 20

3.2.2

Literature research ........................................................................................................ 21

Model structure............................................................................................................................. 36 4.1

Previous works ...................................................................................................................... 37

4.2

Model description ................................................................................................................. 38

-V-

5

4.2.1

R-program...................................................................................................................... 38

4.2.2

Optimization model ....................................................................................................... 42

Model implementation and validation.......................................................................................... 53 5.1

Implementation ..................................................................................................................... 53

5.2

Validation .............................................................................................................................. 55

6

Case study...................................................................................................................................... 64 6.1

Scenario description .............................................................................................................. 64

6.2

Scenario results ..................................................................................................................... 66

6.3

Sensitivity analysis ................................................................................................................. 78

7

Result interpretation ..................................................................................................................... 80

8

Discussion ...................................................................................................................................... 81

9

Conclusion and prospect ............................................................................................................... 83

10

Publication bibliography ............................................................................................................ 84

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List of figures

Figure 3-1 Input sources of the model .................................................................................................. 20 Figure 3-2 Development of electrical efficiency of conventional power plants ................................... 25 Figure 3-3 Development of the fuel prices of conventional power plants ........................................... 26 Figure 3-4 Part-load-efficiency of conventional power plants .............................................................. 32 Figure 3-5 Development of the heat credit of conventional power plants according to (Scholz 2010, p. 56).......................................................................................................................................................... 33 Figure 3-6 Development of the heat load ............................................................................................. 34 Figure 3-7 Annual reserve requirements in Germany ........................................................................... 35 Figure 3-8 Transmission capacity from state to state of Germany ....................................................... 36 Figure 4-1 GESOP model structure ........................................................................................................ 37 Figure 4-2 residual load of the scenario years ...................................................................................... 40 Figure 4-3 Step approach for part-load efficiency ................................................................................ 47 Figure 4-4 Part-load efficiency factor calculation ................................................................................. 48 Figure 4-5 Illustration of the reserve types (Amprion n.d.) .................................................................. 51 Figure 4-6 Illustration of the available ramp down rate of one power plant ....................................... 53 Figure 5-1 Rolling horizon method ........................................................................................................ 54 Figure 5-2 Residual load and total production of the reference weeks of the validation year 2012 ... 56 Figure 5-3 Residual load and total production of the first week of the validation year ....................... 56 Figure 5-4 Reference four week unit commitment pattern .................................................................. 57 Figure 5-5 Model results of the four weeks unit commitment pattern ................................................ 58 Figure 5-6 Reference one week unit commitment pattern .................................................................. 58 Figure 5-7 Model results of a one week unit commitment pattern...................................................... 59 Figure 5-8 Stock exchange price validation for the reference year 2012 ............................................. 60 Figure 5-9 Ordered annual price duration curve................................................................................... 61 Figure 5-10 Stock exchange price validation of the four reference weeks ........................................... 62 Figure 6-1 scenario C02 price variation ................................................................................................. 64 Figure 6-2 Conventional power plant park of the base scenario .......................................................... 65 Figure 6-3 Conventional power plant park of the CO2 50 and CO2 100 scenario ................................ 66 Figure 6-4 price pattern of the reference week for the base scenario ................................................. 67 Figure 6-5 Operation pattern of a reference week in 2030 for the base scenario ............................... 67 Figure 6-6 price pattern of the reference week 26 of the year 2012 for the CO2 50 scenario ............. 69 Figure 6-7 price pattern of the reference week 26 of the year 2012 for the CO2 100 scenario ........... 69

- VII Figure 6-8 Operation pattern of a reference winter week in 2030 for the CO2 100 scenario .............. 70 Figure 6-9 Operation pattern of a reference week in 2030 for the CO2 100 scenario .......................... 71 Figure 6-10 Operation pattern of a reference week in 2050 for the CO2 100 scenario ........................ 72 Figure 6-11 average stock exchange price of the four reference weeks of the scenarios .................... 73 Figure 6-12 Development of the market value factors for the scenarios based on the four reference weeks ..................................................................................................................................................... 74 Figure 6-13 CO2 emissions of the reference week 26 ........................................................................... 75 Figure 6-14 Hourly regional import-export difference of the four reference weeks of the base scenario ................................................................................................................................................. 76 Figure 6-15 Area specific installed capacity of solar power in the year 2030 (Senkpiel et al. 2010, p. 25)...................................................................................................................................................... 77 Figure 6-16 Development of the flexibility costs for the scenarios ...................................................... 78 Figure 6-17 sensitivity analysis of the fuel prices and the intertemporal constraints for the base scenario and the four reference weeks................................................................................................. 79 Figure 6-18 constitution of total operation costs of the four reference weeks of the base scenario of the year 2012 ........................................................................................................................................ 80 .

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List of Table s

Table 3-1 Average block size and block number of conventional power plants ................................... 23 Table 3-2 Variable cost of conventional power plants .......................................................................... 23 Table 3-3 Fixed cost of conventional power plants............................................................................... 24 Table 3-4 Availability of conventional power plants ............................................................................. 25 Table 3-5 CO2 emission factors of conventional power plants ............................................................. 27 Table 3-6 Start-up time of conventional power plants ......................................................................... 28 Table 3-7 Start-up cost of conventional power plants .......................................................................... 29 Table 3-8 Minimal load, up- and down time of conventional power plants ......................................... 29 Table 3-9 Ramping gradients of conventional power plants ................................................................ 30 Table 3-10 Ramping cost of conventional power plants ....................................................................... 31 Table 5-1 Computational complexity of validation ............................................................................... 55 Table 5-2 Unit commitment pattern model accuracy ........................................................................... 59 Table 5-3 Statistical evaluation of the reference weeks ....................................................................... 63 Table 5-4 Average market value factors of the reference weeks ......................................................... 63 .

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List of acronyms

(CCGT)

Combined cycle gas turbine

(CHP)

Combined Heat and Power

(UCTE)

Coordination of Transmission of Electricity

(DoD)

Depth of discharge

(EnergieStG)

Energiesteuergesetz

(EAF)

Equivalent Availability Factor

(EEG)

Erneuerbar Energien Gesetz

(EEX)

European Energy Exchange

(entsoe)

European Network of Transmission System Operators for Electricity

(GAMS)

General Algebraic Modeling System

(GESOP)

German Energy System Optimization

(GDP)

Gross domestic product

(ISO)

Independent System Operators

(TIMES)

Integrated MARKAL EFOM System model

(IEA)

International Energy Agency

(KernbrStG)

Kernbrennstoffsteuergesetz

(LCOE)

Levelized Costs of Electricity

(MARKAL)

MARKet Allocation model

(MVF)

Market value factor

(MAE)

Mean absolute error

(MPE)

Mean percentage error

(MCP)

Mixed complementarity problem

(MIP)

Mixed Integer Programming

(NUTS)

Nomenclature des unités territoriales statistiques

(OCGT)

Open cycle gas turbine

(PE)

Percentage error

(RoR)

Run-of-the-river

(TSO)

Transmission system operators

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Nomenclature

general form

Sets set of indexes of blocks set of indexes of transmission lines start-up type set of indexes of hour of the considered period set of indexes of temporal length of the considered period set of indexes of power plant blocks set of indexes of regions set of indexes of reserve types set of indexes of technology types

Parameters availability factor

[% of block size]

specific emission costs

[€/MWhel]

specific ramping costs

[€/MWhel]

specific start-up costs

[€/MWhel]

specific fuel price

[€/MWhel]

specific heat-credit

[€/MWhel]

specific fixed costs

[€/MWhel]

specific part-load penalty

[€/MWhel]

specific pumping costs

[€/MWhel]

specific fuel tax

[€/MWhel]

specific variable costs

[€/MWhel]

specific import-export costs

[€/MWhel]

- XI specific transmission costs

[€/MWhel]

self-discharge rate of the reservoir

[%]

fuel emission factor

[t/MWhel]

heat load

[MWhth/nbHours]

normalized heat load

[%/heat capacity]

fuel efficiency, efficiency regarding

[%]

the transformation of the energy content of the fuel to electric energy age related generation efficiency,

[%]

efficiency regarding efficiency losses and gains of past and future building dates of power plants normalized reserve requirements

[%/reference year]

transmission capacity

[MWhel/nbHours]

minimal commitment

[MWel]

residual load

[MWhel/nbHours]

total size

[MWel]

technical possible negative ramping

[MWel/nbHours]

technical possible positive ramping

[MWel/nbHours]

average block size

[MWel]

minimal up time

[h]

minimal down time

[h]

start-up time

[h]

lower limit for start-up types

[h]

upper limit for start-up types

[h]

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Variables specific fuel price

[€/MWhel]

specific fuel tax

[€/MWhel]

specific variable costs

[€/MWhel]

specific CO2 emission factor

[%]

total system costs

[€]

total variable costs

[€]

total fuel costs

[€]

total tax costs

[€]

total fixed costs

[€]

total transmission costs

[€]

total import-export costs

[€]

total emission costs

[€]

total ramping costs

[€]

total start-up costs

[€]

total storage water pump costs

[€]

total part-load costs

[€]

total heat credit costs

[€]

off-time counter variable

1…nbHours

up-time counter variable

1…nbHours

storage water reservoir level

[MWhel]

residual load

[MWhel/nbHours]

electricity load

[MWhel/nbHours]

renewable electricity production

[MWhel/nbHours]

- XIII solar electricity production

[MWhel/nbHours]

biomass electricity production

[MWhel/nbHours]

wind offshore electricity production

[MWhel/nbHours]

wind onshore electricity production

[MWhel/nbHours]

heat load

[MWhth/nbHours]

reserve requirements

[MWel]

commitment not considering

[MWel]

part-load efficiency losses transmission power

[MWel]

ramping power

[MWel/nbHours]

pumping power

[MWel/nbHours]

commitment considering

[MWel]

part-load efficiency losses transmission difference power

[MWel]

transmission import

[MWel]

transmission export

[MWel]

ramp down

[MWel/nbHours]

ramp up

[MWel/nbHours]

available positive spinning

[MWel/nbHours]

ramping gradient total used ramp up

[MWel/nbHours]

available negative spinning

[MWel/nbHours]

ramping gradient total used ramp down

[MWel/nbHours]

available positive

[MWel/nbHours]

non-spinning ramping gradient

- XIV available negative

[MWel/nbHours]

non-spinning ramping gradient

̌

block size

[MWel]

stock exchange price

[€/MWhel]

actual model result, variable to assess the quality of model result

̂

reference value, variable to assess the quality of model result

Binary Variables started-up

0/1

committed

0/1

shut-down

0/1

1 Introduction 1.1 Motivation The so called climate change is the most pressing challenge affecting the civilization on a global scale. This issued is widely perceived as being one affecting primarily the future generations, nevertheless there are already effects on all continents and across the oceans today. (IPCC 2014, p. 4) Although, even with a complete halt of CO2 emission, it is not possible to stop this problem the anticipated dramatic consequences require the moderation of its magnitude. The energy supply sector is globally the biggest single emitter of climate affecting gases with a share of 25.9 % in 2004 (IPCC 2007, p. 36). The government of Germany acknowledged this by implementing an ambitious energy policy called the Energiewende in 2011. This regulates the phase out of nuclear power by the end of 2022 and a power supply of at least 80 % renewable energy in 2050. (Dehmer 2013, p. 1) The therefore, among others, implemented policy of the Erneuerbar Energien Gesetz (EEG) proofed to

1 - Introduction

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be very successful in increasing the share of renewable energy already until today. Besides all the positive aspects this development adds some new challenges when analyzing the energy system. The nature of the most potent renewable energy technologies of being much more volatile and harder to predict than the conventional power plants makes especially the modelling of the future energy system a complex task. Nonetheless it is a relevant field of interest for a lot of stakeholders, from policy makers to the industry. Besides their interest, the cost effective realization of the Energiewende is a key factor for the approval of the people which have to carry the major share of the financial burden. A main parameter a lot of models are highly dependent on is the price of electricity at the stock exchange.1 This affects investment decision, the diffusion of innovation as well as the distribution of the share of different generation technologies, to name just a few. Another factor which starts to get scientific attention is the so called market value of renewable energies (Hirth 2013). For a sustainable growth, renewable energies need to prevail in the market without the subsidies of the state in the medium run. This requires the knowledge of the value of renewable energy in the market, reflected in the market value. Both are at the focus of this thesis.

1.2 Scope and purpose The aim of this thesis is to develop a unit commitment optimization model and connect it with two existing investment based models. The existing models are based on explorative decision making. The disadvantage of this approach is that it is not possible to take into consideration the intertemporal constraints of conventional power plants. With the changing energy system to a renewable energy based one these constraints gain importance. The optimization model will thus enhance the quality of the two existing models. The new model should optimize the total operation cost of the energy system. This will ensure a realistic power plant operation. The main focus lies on the time horizon of the years until 2030. It should further model the stock exchange price for the reasons stated above. Besides these reasons there are a lot of different market participants interested in the short and midterm development of the electricity price as an input for investment decision calculations. The model should therefore also be able to cover these application cases. To ensure this, the model should be implemented in a transparent and flexible way to facilitate the application on different study cases as well as the further development. Based on a comprehensive literature research the optimization model will be developed and implemented. The different models will then be connected and evaluated. The following validation and 1

The stock exchange price is defined as the day-ahead spot price of the European Energy Exchange (EEX)

2 - Energy system models

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parameterization will include a sensitivity analysis. In combination with the analysis of different scenarios a discussion of the properties is possible. The limit of the scope of the model is that the input data is not considering psychological factors. It is a techno-economic model taking into account technological and economic parameters only. Furthermore for the realistic modelling of the stock exchange price a mixed integer formulation is chosen, resulting in a computational complex problem. This limits the scope of the program, meaning that assumptions are made where they are relevant.

1.3 Structure The structure of this thesis is related to the steps of an energy system analysis according to (Möst 2009, pp. 13–14). After defining the problem and the scope of the model in chapter one and an general description of energy system model in chapter two the needed input parameters and the data research are described in chapter three. The model and its specific features are then described in detail in chapter four followed by the implementation and validation in chapter five. The calculation of case studies and the interpretation of results follow in chapter six and seven. Finally the thesis will finish with a discussion and a conclusion and prospect in chapter eight and nine.

2 Energy system models Energy system models are used to forecast the future energy supply and demand of continents, countries and regions. They are mainly used by research institutes, policy makers and private companies. Their main use is to explanatory describe the energy system under the assumption of the development of certain political, technological and economic conditions. Another use is to simulate and assess technological and political choices of stakeholders which will influence the energy market. Every model will use abstractions and generalizations in form for example of average figures and past trends aiming to represent the real energy system. The necessary losses of accuracy through assumptions need to be justifiable by an increase in performance or other enhancements. (Herbst et al. 2012, p. 112)

2.1 Classification of energy system models The wide range of tasks energy systems models are used for results in a variety of approaches to fulfill them. Therefore it is only possible to do a general classification. The two main classifications relevant for the developed model are described. They are the distinction between the top-down and bottom-up approach and the way of solving the problem.


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2.1.1 Top-down Top-down models are characterized by a high aggregation and the focus on a macroeconomic system, for example the national economy as whole or larger systems. There is no detailed consideration of specific technologies, rather the focus lies on the rules of substitution between the different sectors and commodities. The typical use for the top-down approach is economic equilibrium and national account systems models. (Möst 2009, pp. 16–17)

2.1.2 Bottom-up Bottom-up models in contrast try to draw conclusions from the techno economic perspective through the focus on process structures. This allows a detailed analysis of the development of the energy system under given boundary conditions. (Möst 2009, p. 18) Another difference is that no intersectional substitution will be considered why these models are also called partial models. The use of bottom-up models is the long run prognosis and scenario analysis. The strengths of this model approach made this kind of model an important tool for business consulting firms and research institutes. It allows a high level of detail and transparency as well as a flexible model structure not limiting it to a certain problem. According to (Möst 2009, p. 19) the typical perspective of bottom-up models is years to centuries. This long horizon is possible through an aggregation to representative years or shorter periods.

2.1.3 Simulation Non-optimizing models simulate explorativly possible development path of the energy system under a given framework through statistical procedures. (Möst 2009, p. 20) These statistical procedures are based an expert opinion or different scenarios. An example for simulating models is the agent based model. In this approach a lot of small agents are characterized by possible decision opportunities, representing different actors in the market. The result of the model is composed of all the different agent decisions combined. (Möst 2009, p. 23)

2.1.4 Optimization Optimization models try to optimize an objective function. The optimization can either be a minimization of cost or the maximization of profit. The feasible solution space is constraint by a set of equations characterizing the model. The algorithm is mostly implemented as a linear problem to be able to use linear solvers. The linear programming problem is mathematically described as follows:


Where

is the objective function and

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is the set of constraints. (Luenberger, Ye 2008, p. 20)

Goal of the models using this solution approach is to determine the optimal system under given conditions. A perfect competition market is implied because the individual market participants do not act strategically. The model has perfect foresight and investment decisions are made with perfect information to achieve the economic optimum. (Möst 2009, p. 20)

2.2 Existing energy system models 2.2.1 The MARKet Allocation model (MARKAL) The MARKet Allocation model (MARKAL) was developed in the 1970s after the oil crisis by the Brookhaven National Lab. It was then adopted and further developed by the International Energy Agency (IEA). Among the input are the structures of the energy system as well as the energy demands. MARKAL uses linear and MIP techniques to find the least cost set of technologies over time to satisfy the demand. Outputs contain the technological mix, total system cost, fuel demand and estimates of energy prices. The spatial resolution is country level and no transmission constraints are considered. Furthermore the time horizon is not variable and no age dependent parameters are included.(EPA, pp. 1–2)

2.2.2 The TIMES model The Integrated MARKAL EFOM System model (TIMES) is based on the MARKAL model, they share the same model approach and basic features. It was developed as part of the IEA Energy Technology Systems Analysis Program. The TIMES model was significantly extended in its flexibility compared to MARKAL. The model is capable of adjusting the time horizon, not possible in MARKAL. Further the model is considering storage possibilities and the dismantling process and cost is included in detail. Because of the aim of providing long horizon results TIMES is more used for scenario comparison rather than forecasts. (Loulou et al. 2005, pp. 7–8)

2.2.3 The PRIMES model The PRIMES model is developed by the E³M-Lab of the National Technical University of Athens. The construction of the model started in 1993 with the first major use in the preparation of the Kyoto conference. (Capros, p. 3) It is an agent based market equilibrium model formulated as a non-linear

3 - Methodological approach

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Mixed Complementarity Problem (MCP). This means the behavior of every agent is simulated separately, agents are defined on the sector level, e.g. household, steel sector. The behavior modelling includes economic theory but also other factors as habits, comfort and risk management. The model is structured modularly with several sub models for example for gas, CHP and renewable energies covering 35 European countries. (Technical University of Athens 2007, p. 7)

3 Methodological approach 3.1 Characteristic features The methodological approach of this thesis is to combine the explorativly generated input of the bottom-up E2S model with a unit commitment optimization model GESOP. This allows combining the strengths of both model approaches. The strength of the E2S model is the investment decision based modeling of the development of renewable energies and the conventional power plant park. In this way not the optimal energy system is built but, more realistically, the energy system resulting from individual decisions of different stockholders an individual political, economic and technical framework. This energy system is than operated in the optimal way through the bottom-up GESOP model. This allows a spatial and temporal highly detailed model, making it possible to investigate a wide range of questions arising for different stakeholders concerning the energy market, grid, import-export and power plant operation. For designing the model the task is to realistically model the price for electricity at the stock exchange in Germany as well as the market values of renewable energies. This requires a detailed list of power plants available and their technical and economical characteristics. They include the different operation costs as well as the intertemporal constraints. As being part of the European single market for electricity, it is not enough to focus on Germany alone. Therefore the surrounding countries and the connecting grid also need to be part of the model. The stock exchange price is the initial goal of the model, but due to its comprehensive design it is possible to integrate further details for other purposes. For the generation of the input and the result analysis the open source programming language R is used. The strength of R is the handling and manipulation of large data sets making it well suitable for this task. The actual model is written in the optimization language General Algebraic Modeling System (GAMS). The use of Mixed Integer Programming (MIP) enables the model the integration of binary constraints representing for example the on or off state of power plants. Latest improvements in the solution of MIPs made it possible to solve large scale problems, meaning a growing number of Independent System Operators (ISO) work with this kind of problem formulation. (Hedman et al. 2008, p. 1)


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3.2 Data and material of the model In this chapter the input data of the model is presented and described. The focus of the chapter lies especially on the sources of the data as well as the reasoning for assumptions which needed to be made. How this data is used in the model and why it is necessary for a realistic model however is in detail described in chapter 4.2. For all data the reference year 2012 is used, this is especially relevant for the costs. The model uses three sources of data, as illustrated in Figure 3-1. The two models RES_E_invest and KW_invest are both in-house developments of the Fraunhofer ISE and part of the E2S model. The RES_E_invest model is delivering the data of the stock of the renewable energies for all the year from the reference year 2012 until 2050, described in chapter 3.2.1.1. The KW_invest model is the source for the power plant list in these years, described in chapter 3.2.1.2. Finally the third part is a comprehensive literature research for the relevant parameters needed.

Figure 3-1 Input sources of the model

3.2.1 Connected models 3.2.1.1 RES_E_invest model The RES_E_invest model is an investment based model. The goal is to investigate how the expansion of renewable energies will take place until 2050, under certain economic and political conditions. In the model Germany is divided into the regional level of Nomenclature des unités territoriales statistiques (NUTS) 3 level to achieve a highly disaggregated result. The model approach is to determine the technical potential of every renewable energy technology. Through this potential the maximal yearly expansion is determined with the s-curve-approach, describing market diffusion of new technologies. These potentials are now distributed to different investor groups able to invest.


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To determine which potentials are used, the Levelized Costs of Electricity (LCOE) are calculated. They are based on the political, economic and technical conditions of every investor group. Among them are interest rates, efficiencies and expected developments of technologies. (Kost et al. 2013) With the known LCOE every investor is evaluating the investment individually. When the EEG refund and avoided electricity cost exceed the LCOE the investor will exploit its potential. (Senkpiel et al. 2014, pp. 49–50) This model is able to function as input for the stock of all renewable energies for Germany on NUTS-3 level until the year 2050. With the help of a normalized feed in pattern of 2012 and the future load it is possible to calculate the residual energy which needs to be covered by the conventional power plants. The procedure used is in detail described in chapter 4.2.1. 3.2.1.2 KW_invest model This model determines the power plant park and its regional distribution in Germany on the basis of investment decisions. The basis is the power plant park existing today. The model approach is to yearly determine the capacity of conventional power plants phased out due to their technical life span. The in this way reduced capacity is compared with the maximal yearly residual load. In the case of a gap the model decides if new power plants are built or old ones retrofitted. The decision which power plant is build is made by evaluating the economic aspects of the different power plant options. For this the full load hours are calculated with which it is possible to construct the merit order. The merit order enables the model to calculate the average yearly price under the assumption that the power plant is always running when the marginal cost are covered. In the case of multiple options, the one with the highest net present value is chosen (Santa 2014, pp. 21–22). The output of the model is a yearly list of conventional power plants. This list consist basic details like the type of power plant, the generation capacity and its NUTS-1 location. These lists are modified and then used as input for the optimization model as described in chapter 4.2.2.

3.2.2 Literature research For the economic and intertemporal input data of the model mainly the meta study “Current and Prospective Costs of Electricity Generation until 2050” is used. This study is explicitly designed to compile data for energy models to be used to make them more comparable. When there are different data available the most recent will be chosen. The advantage of a wide literature and study foundation of the data justifies the small errors due to not fully congruent definition of cost and the differing age of data looked at in this study. For data which is not available in this meta study or where other sources are for any reason more fitting the most relevant literature source will be chosen.


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3.2.2.1 Average block size of conventional power plants For a realistic model it is crucial to consider every block of a power plant separately. Especially for the intertemporal constraints in an environment of volatile residual load this is important. The power plant list from the KW_invest model used as input is only power plant specific. For the GESOP model the list therefore needs to be divided into blocks. The research for every power plant would be very time consuming and this information is not of special interest, which allows a division with average values. This average value is used for all the power plants of one type. To acquire realistic values for the average size of a block for the different types the power plant list of the Bundesnetzagentur of existing power plants is used. To enhance dynamics the results are rounded up.

(1) In the following description of the input sources the distinction is made between nine different conventional power plant technologies which are predefined through the KW_invest model. The two gas technologies are combined cycle gas turbine (CCGT) and open cycle gas turbine (OCGT) technologies. The next two are COAL and LIGNITE. COAL is defined as hard coal and lignite as brown coal burning power plants. The following two are WATER and STORAGE_WATER power plants. STORAGE_WATER is defined as a solely pumped storage water facility with a reservoir but without a feeder stream, these power plants are not considered. WATER on the other hand is defined as a water power plant using the water of a river to produce electricity, in literature they are often called run-of-the-river (RoR) power plants. The URANIUM technology is defined as a power plants using nuclear fuel, while OIL is a power plants using oil related fuel. The F_OTHER technology stands for all fossil fuel burning power plants that do not fit in the categories above. Among them are waste combustion and power plants which use a mix of fuels. Table 3-1 shows that the average block size varies considerably. On the upper end of the scale are obviously Uranium and Lignite power plants while Oil and OCGT power plants are considerably smaller. This reflects the anticipated role of the power plants regarding the type of load they are designed to cover. The bigger power plants are base load facilities designed for a relatively constant operation. The smaller power plants on the other hand are more flexible but also more expensive facilities covering the middle and peak load. An exception is storage water power, they are comparably potent power wise but also have a relatively big size. Costs in this study generally only include private cost and ignores other important costs like social costs inflicted by the energy system on the environment, called externalities. An example are the pollution of the air or soil, these costs are not included because they are very hard to estimate. Further the


- 23 -

transaction costs are not included which refer to the market, political and administrative transaction costs. (Schröder et al. 2013, p. 2) Technology

Technology number

CCGT COAL F_OTHER LIGNITE OCGT OIL STORAGE_WATER URANIUM WATER

1 2 3 4 5 6 7 8 9

Average block size [MW] 115.07 254.07 44.27 365.37 50.56 70.55 202.86 1340.89 25.66

Average number

1.45 1.21 1.19 1.13 0.99 0.85 0.63 1.00 1.05

block Average block number selected

2 2 2 2 1 1 1 1 1

Table 3-1 Average block size and block number of conventional power plants

3.2.2.2 Variable cost of conventional power plants The variable costs consist of the variable share of operation and management. They include additional cost for inspection and repair of the power plant as well as auxiliary materials and other consumables than fuel. All costs in this thesis are real values without the consideration of inflation. This requires a clearing of inflation of sources values when another year is used in different studies. Table 3-2 shows that the base power plants have about twice as high variable operation and management costs. Technology

Variable Source cost [EUR/MWh] CCGT 4.00 (Schröder et al. 2013, p. 78) COAL 6.00 (Schröder et al. 2013, p. 78) F_OTHER 3.00 (Schröder et al. 2013, p. 78) LIGNITE 7.00 (Schröder et al. 2013, p. 78) OCGT 3.00 (Schröder et al. 2013, p. 78) OIL 3.00 (Schröder et al. 2013, p. 78) STORAGE_WATER 0.00 URANIUM 10.00 (Schröder et al. 2013, p. 78) WATER 0.00 Table 3-2 Variable cost of conventional power plants

3.2.2.3 Fixed cost of conventional power plants The fixed costs are not relevant for modelling the stock exchange price which, through the merit order, only considers the variable cost. But for further use of the model fixed costs are included. They consist of fixed operation and management costs, primarily labor maintenance and also property tax as well as


- 24 -

insurance and network use of the system. (Schröder et al. 2013, p. 2) Table 3-3 points out that Water and Uranium power plants are the most expensive power plant types concerning fixed costs. Technology

fixed cost [€/MW installed and h] CCGT 54.79 (Schröder et al. 2013, p. 78) COAL 82.19 (Schröder et al. 2013, p. 78) F_OTHER 16.44 (Schröder et al. 2013, p. 78) LIGNITE 82.19 (Schröder et al. 2013, p. 78) OCGT 41.10 (Schröder et al. 2013, p. 78) OIL 16.44 (Schröder et al. 2013, p. 78) STORAGE_WATER 54.79 (Schröder et al. 2013, p. 78) URANIUM 187.84 (Wells 2013, p. 6) WATER 164.38 (Schröder et al. 2013, p. 78) Table 3-3 Fixed cost of conventional power plants

3.2.2.4

Efficiency of conventional power plants

The efficiency of the different technologies and their development until 2050 can be seen in Figure 3-2. The data for the years from 2012 on is according to (Gatzen 2008, p. 125) and (Schröder et al. 2013, p. 79) while the past data is according to (Ellersdorfer 2009, p. 100). The efficiency is affecting the fuel costs by direct consumption and tax as well as CO2 cost. Furthermore an increase in efficiency will decrease the relative variable cost. For the model it is necessary to know the year each power plant was built to allocate the according efficiency affected data of this year. The Figure 3-2 shows that the two water powered technologies are assumed to have reached their very high efficiency potential and this is not expected to rise any further. The other conventional power plants can be grouped into two trends. Both trends show a steady and high incline until the year 2012. From that year on COAL and LIGNITE continue the increasing trend with a comparably high incline. The remaining technologies show a slightly less rising trend.


- 25 -

electrical power generation efficiency of energy conten of fuel [%]

100% 90% 80%

WATER

70%

STORAGE_WATER CCGT

60%

COAL

50%

LIGNITE

40%

OIL

30%

F_OTHER

20%

OCGT

10%

URAN 1900 1907 1914 1921 1928 1935 1942 1949 1956 1963 1970 1977 1984 1991 1998 2005 2012 2019 2026 2033 2040 2047

0%

Figure 3-2 Development of electrical efficiency of conventional power plants

3.2.2.5 Availability of conventional power plants The availability factor is the time a power plant is able to produce electricity divided by the time period. An empirical study of the VGB PowerTech e.V., the European technical association for power and heat generation, gathered information of its members over the years 2000 until 2009. This study provides data concerning the Equivalent Availability Factor (EAF) that is why it is used for the model. The EAF not only considers the time a power plant is not able to produce electricity but also the time it is forced in part load. Reasons for that are minor defects, repair and inspections. (see Table 3-4) Remarkable is that whilst the conventional power plant types all have similar availabilities, with uranium the lowest, the water powered technologies show widely differing availabilities. The storage water power plant has the highest availability with 97 %, almost twice as high as the water power plant. Technology

availability [%] CCGT 88.60 COAL 84.20 F_OTHER 85.70 LIGNITE 84.50 OCGT 85.70 OIL 85.70 STORAGE_WATER 97.00 URANIUM 82.60 WATER 40.00

Source (Prost 2010, p. 27) (Prost 2010, p. 27) (Prost 2010, p. 27) (Prost 2010, p. 27) (Prost 2010, p. 27) (Prost 2010, p. 27) (Deutsche Energie-Agentur GmbH (dena) 2008, p. 6) (Prost 2010, p. 27) (Deutsche Energie-Agentur GmbH (dena) 2008, p. 6)

Table 3-4 Availability of conventional power plants


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3.2.2.1 Fuel price of conventional power plants As shown by a comparison study of the Forschungsradar Erneuerbare Energien (Kunz 2012), the projected fuel cost till 2050 differ widely among the studies. Therefore the fuel price development will function as a parameter for scenarios. For the reference scenario the fuel prices of (Konstantin 2008, p. 290) are projected with inflation to the reference year 2012. They include transportation cost to the power plant and a profit of the trader. The growth rate is set to be an exponential increase of 3 %. The fuel costs of storage water and water power plants are assumed to be zero. 120 100 CCGT

fuel price [€/MWh]

80

COAL F_OTHER

60

LIGNITE OCGT

40

OIL 20

URAN

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050

0

Figure 3-3 Development of the fuel prices of conventional power plants

3.2.2.2 Fuel tax of conventional power plants The tax for oil and gas is defined in the Energiesteuergesetz (EnergieStG) §2 (3). The result of converting the units to €/MWh can be seen in Table 3-5. A special case are nuclear power plants their tax is regulated in the Kernbrennstoffsteuergesetz (KernbrStG) §3. This regulates that every nuclear power plant operator is required to pay 145€ for one gram of uranium. For simplicity reasons it is assumed that only uranium 235 is used. The F_OTHER fuels contain the fuel defined as 2710 19 61 to 2710 19 69 of the European “Kombinierten Nomenklatur”, a directory for different types of goods.

3 - Methodological approach Technology CCGT COAL F_OTHER LIGNITE OCGT OIL STORAGE_WATER URANIUM

fuel tax [€/MWh] 5.50 0.00 25.00 0.00 5.50 5.00 0.00 4.84

- 27 Source EnergieStG EnergieStG EnergieStG EnergieStG KernbrStG

Table 3-5 Fuel tax of conventional power plants

3.2.2.3 CO2 emissions of conventional power plants Relevant for the model are two parameters, the technology specific CO2 factor and the price for the CO2 emissions. CO2 is the only emission considered because the power plant operator has to buy emission allowances to be able to emit the gas. The CO2 factor describes how many tons of CO2 gas is emitted when producing an MWh of electricity. Water power and nuclear power are assumed to not emit CO2. Technology CCGT COAL F_OTHER LIGNITE OCGT OIL STORAGE_WATER URANIUM WATER

CO2_factor [t/MWh] 0.33 0.69 0.33 0.80 0.55 0.33 0.00 0.00 0.00

Source (Strauß 2013, p. 324) (Strauß 2013, p. 324) (Strauß 2013, p. 324) (Strauß 2013, p. 324) (Strauß 2013, p. 324) (Strauß 2013, p. 324)

Table 3-6 CO2 emission factors of conventional power plants

With the CO2 factor and the CO2 price it is possible to calculate the emission cost for every power plant. Similar to the fuel price development it is not possible to forecast the CO2 price reliably, especially due to the high dependence on political decisions. Therefore it will be used to calculate scenarios. This enables the model to forecast the effect of a politically decided shortage of CO2 emission allowances on the CO2 emission and the electricity price. For the base scenario the CO2 price will be set to 5 €/t of CO2 with a linear increase of 20 % each year. 3.2.2.4 Start-up time and cost of conventional power plants An important part of the model is the implementation of intertemporal constraints of the power plants. They are a result of the specific technical parameters describing the flexibility of the dispatch as well as


- 28 -

the unit commitment. Although these parameters are highly power plant specific the noted sources draw general conclusions. The start-up time is the time a power plant needs to leave the idle state, synchronizing the generator with the grid and being ready to produce. The main restrictions for a fast start-up are differences of temperature in parts of the system. This leads to pressure differences which are not allowed to exceed specific values. This is especially the case for power plants designed to deliver base load power. The two most evident examples are nuclear and pumped storage power plants. While a nuclear power plant needs 24 hours (Schröder et al. 2013, p. 65) to start-up a pumped storage power plant is able to produce full load in 60 seconds (Jiaqi, Harley 2010, p. 1).


Start-up time [h] 1.00 (Hundt et al. 2009, p. 24) 2.00 (Hundt et al. 2009, p. 24) 1.00 (Traber, Kemfert 2011, p. 252) 2.00 (Hundt et al. 2009, p. 24) 0.25 (Hundt et al. 2009, p. 24) 1.00 (Traber, Kemfert 2011, p. 252) 0.02 (Jiaqi, Harley 2010, p. 1) 24.00 (Traber, Kemfert 2011, p. 252) must Assumption run

Table 3-7 Start-up time of conventional power plants

The start-up costs highly depend on the time the power plant has been shut down. Furthermore it is important to include start-up patterns often occurring in reality for example shutting-down a coal plant at night and starting-up in the morning. For this reason it is distinguished between a hot, warm and cold start. The time frames (Schröder et al. 2013, pp. 57–58) referring to idle time are:    The start-up costs are mainly composed of additional fuel related costs for hot, warm and cold start. The significantly higher wear of a cold start makes it necessary to include depreciation costs. They occur due to a shortening of the lifetime, higher maintenance cost and a higher forced outage rate. (Lefton, Kumar n.d.) When there is no data available for hot and warm start, literature with no distinction between these is used. As expected the start-up of base load power plants inflict the most cost compared to more flexible power plants. The only exception is Lignite power with a constant cost distribution over all the types of start-ups. (see Table 3-8)


- 29 -

Start-up cost hot [€/delta MW]

Start-up cost warm [€/delta MW]

Start-up cost cold [€/delta MW]

Start-up cost cold depreciation [€/delta MW]

Source

CCGT

23.00

33.00

45.00

10.00

COAL

28.50

42.00

74.00

5.00

F_OTHER

18.92

18.92

18.92

5.00

LIGNITE

27.90

27.90

27.90

3.00

OCGT

16.50

21.00

28.50

10.00

OIL

18.92

18.92

18.92

5.00

STORAGE_WATER

0.00

0.00

0.00

0.00

(Schröder et al. 2013, p. 60) (Schröder et al. 2013, p. 60) (Schröder et al. 2013, p. 60) (Schröder et al. 2013, p. 60) (Schröder et al. 2013, p. 60) (Schröder et al. 2013, p. 60) Assumption

URANIUM

35.07

35.07

35.07

1.70

WATER

0.00

0.00

0.00

0.00

(Schröder et al. 2013, p. 60) Assumption

Table 3-8 Start-up cost of conventional power plants

3.2.2.5 Minimal load, up- and downtime of conventional power plants The maximal load is determined by the size of the power plant, analogical a minimal load is needed to model a realistic unit commitment. This minimal load is determined by the lowest generation state a power plant can effectively operate at. Below that a stable operation cannot be guaranteed. (Schröder et al. 2013, p. 64) A minimal up- and downtime is needed to model the incentive of the operators to run the power plant as steady as possible. In reality this is the case because operators try to limit the stress on the parts of a power plant thru excessive start-up and shut-down procedures. Table 3-9 shows that Uranium and Lignite power plants have a long period of minimal up- and down time compared to flexible power plant types. Min Pel Min block time [% of Pel] [h] CCGT 0.4 1 COAL 0.3 3 F_OTHER 0.4 2 LIGNITE 0.5 5 OCGT 0.5 0.25 OIL 0.4 2 STORAGE_WATER 0.1 0 URANIUM 0.6 24 WATER 0.1 0

up Min time [h] 1 3 2 8 0.25 2 0 24 0

Table 3-9 Minimal load, up- and down time of conventional power plants

down Source

(Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4)


- 30 -

3.2.2.6 Ramping gradients and cost of conventional power plants The ramping load gradient limits define the upper and lower bound of the ability of a power plant to increase and decrease the power output over a period of time. As can be seen in Table 3-10 gas powered plants have a much better ability to adapt to changing demand conditions. The results reflect the distinction between base load and peak load power plants impressively. Storage water, the most flexible power plant type, has 20 times the ability to ramp in both directions than the nuclear power plants. Remarkable is that nevertheless lignite power plants are less flexible with a ramp-up rate no even half the rate of nuclear power. Also coal is less flexible in ramping than nuclear power. Further notable is that whilst OCGT has, as expected, very high possible ramping rates the other gas fired technology CCGT is more in the range of nuclear and coal fired plants.


Ramp-up rate

Ramp-down rate

[% of Pel/h]

[% of Pel/h]

360

360

180

300

240

300

120

300

1200

1200

240

300

6000

6000

300

300

0

0

Source (Hundt et al. 2009, p. 24) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Steck, Mausch 2008, p. 4) (Hundt et al. 2009, p. 24) (Steck, Mausch 2008, p. 4) Assumption (Steck, Mausch 2008, p. 4) Assumption

Table 3-10 Ramping gradients of conventional power plants

Ramping cost are only available in literature for coal and gas fired plants. To be able to model these costs it is assumed that for nuclear, lignite and oil powered plants ramping costs of coal apply. The reason is that they are comparably flexible indicating similar ramping costs. Besides the ramping gradients the costs also intensify the suited operation mode of the power plant technologies. In addition to being technically the most flexible, gas and water powered facilities have the lowest costs for ramping.

3 - Methodological approach ramp_up_cost [€/MW] CCGT 0.25 COAL 1.3 F_OTHER 1.3 LIGNITE 1.3 OCGT 0.66 OIL 1.3 STORAGE_WATER 0 URANIUM 1.3 WATER 0

- 31 ramp_down_cost [€/MW] Source 0.25 (Schröder et al. 2013, p. 63) 1.3 (Schröder et al. 2013, S. 63) 1.3 (Schröder et al. 2013, S. 63) 1.3 (Schröder et al. 2013, S. 63) 0.66 (Schröder et al. 2013, S. 63) 1.3 (Schröder et al. 2013, S. 63) 0 Assumption 1.3 (Schröder et al. 2013, p. 63) 0 Assumption

Table 3-11 Ramping cost of conventional power plants

3.2.2.7 Part load efficiency of conventional power plants When operating a power plant below its full capacity the efficiency will decrease, leading to a higher consumption of fuel and an increase in emissions relative to the power output. Figure 3-4 shows the effect of a power plant needing a certain amount of energy to keep it running, meaning the further the commitment is deviating from the optimal the higher this share is per produced unit of energy. To be able to implement this, the part load efficiency needs to be linearized. For computational efficiency reasons the model will not use the piecewise linear approximation proposed a. o. by (Angel Ortega Vazquez 2006, p. 203). Instead it will use a linear approximation penalizing part-load operation. (see chapter 4.2.2.5) The data available in literature will be adjusted accordingly. The data used for partload-efficiency is illustrated in Figure 3-4. The data is mainly from (Deutschen Energie-Agentur GmbH (dena) 2005, p. 280). There is no data available for the part-load efficiency of oil and the two water powered technologies. For oil the assumption was made that it is following the coal part-load-efficiency. For water and storage water it is assumed that there is no loss through part-load operation. Figure 3-5 shows the part-load efficiency over the relative loading compared to the block size. It can be seen that uranium has the worst part-load characteristics, meaning the further it is operated from full load the more energy will be lost. The nuclear power plant is followed by lignite which also has a comparably bad part-load behavior. Power plant operators in general try to operate their facilities rather at higher loads for the reasons above. For the two types with comparably bad part-load efficiency behavior this is even truer. The fact that it can be more economical for a power plant operator to operate the plant at full load instead of ramping it down results in the formation of negative prices, this will be described in chapter 8. The flexible gas powered plants show comparable good part-load efficiency with an efficiency loss of only around 20 % at 35 % load. Remarkable is also that the efficiency of coal stays comparably high throughout the range of operation, only loosing around 5 % at the lowest operation level.

part-load efficiency factor of nominal efficiency


- 32 -

120% 100% CCGT COAL

80%

F_OTHER LIGNITE

60%

OCGT 40%

OIL STORAGE_WATER

20%

URANIUM WATER

0% 30%

40%

50%

60%

70%

80%

90%

100%

110%

Relativ loading of block size

Figure 3-4 Part-load-efficiency of conventional power plants

3.2.2.8 Pumped storage The storage available in Germany is more than 80 % pumped storage power plants, therefore these are modelled separately (Mahnke, Mühlenhoff 2012, p. 5). For pumped storage power plants additional data is needed to model the specific operations management. Besides the maximal and minimal generation capacity of the generator the efficiency of the pumps to refill the reservoir is needed. The input data needed for modelling the reservoir are the size of the storage water reservoir, limiting the maximal capacity of water stored, as well as the minimal storage level of the reservoir, necessary to assure the functioning of the eco system. For a realistic operations management the self-discharge rate of the reservoir through evaporation and infiltration also need to be included. According to (Mahnke, Mühlenhoff 2012, p. 10) the maximal capacity of the reservoir is six times the block size with a self-discharge rate of 0.5 % per day. The minimal storage level, also called depth of discharge (DoD), is according to (Fuchs et al. 2012, p. 19) 20 %. The efficiency of the pumps is set to 88 % according to (Gatzen 2008, p. 125). 3.2.2.9 Combined Heat and Power (CHP) To model the benefit of combined heat and power plants it is necessary to integrate the heat load. Therefore a heat credit is introduced which will be deduced from the variable costs of the power plant. The amount of heat produced compared to the produced amount of electricity varies considerably for


- 33 -

the technologies in the literature sources. (see (Dielman, Kuperjans 2008, p. 1)) Figure 3-5 shows the heat credit and its development according to (Scholz 2010, p. 56).

heat credit [€/MWhtherm]

60 50 40 30 20 10 0

Figure 3-5 Development of the heat credit of conventional power plants according to (Scholz 2010, p. 56)

Small CHP systems are mostly operated to cover a certain stable heat demand all year to be economic. As a result they can be assumed as so called must-run producers, not taking the electricity demand into account for operation scheduling. In contrast power plants are mostly electricity price and demand oriented. Therefore they are not modelled as must-run plants, they rather need to cover a certain heat demand. This is perceived to be the long-distance heat derived from a normalized heat demand and the total long-distance heat load according to (Ziesing 2013, p. 32). The development of the load is simplified through a linearization of the projected load in (Nitsch et al., p. 79). The results are based on the political goal to achieve 25 % share of CHP production of the total electricity production. (Nitsch et al., p. 78) To achieve that the expansion of the CHP power plants needs to be accelerated, reflected in the steep incline from 2012 to 2020. The projected load than continues to increase however with a much levelled out incline until 2025. From that year on it starts to decline until 2050. The study describes that this is due to an overall increase of the production of electricity in this time period from other sources. (Nitsch et al., pp. 78–79)


- 34 -

70000000

heat load [MWhthermal/a]

60000000 50000000 40000000 30000000 20000000 10000000 0 2012

2017

2022

2027

2032

2037

2042

2047

Figure 3-6 Development of the heat load

3.2.2.10 Reserve requirements Germany is part of the Union for the Coordination of Transmission of Electricity (UCTE). This means it needs to provide a share of the reserve requirements defined for this region. The requirements are divided into primary, secondary and tertiary reserve, differentiating by responding time and prequalification requirements for the power plants taking part. In Figure 3-7 the reserve requirements according to (Konstantin 2008, p. 411) and (consentec, p. 23) are shown. The primary reserve is calculated as a share of the whole UCTE requirements of 3000MW on the basis of the percentage of total production. The other reserves are calculated by the Transmission system operators (TSO) on the basis of a statistical method minimizing the blackout risk. Therefore it can be assumed that a rising share of renewable energy will increase these requirements. For the model an increase of 5 % is assumed.


- 35 -

reserve requirements [MWelectric]

6000 5000 primary_reserve_pos

4000

secondary_reserve_pos 3000

tertiary_reserve_pos primary_reserve_neg

2000

secondary_reserve_neg tertiary_reserve_neg

1000

2050

2047

2044

2041

2038

2035

2032

2029

2026

2023

2020

2017

2014

2011

2008

0

Figure 3-7 Annual reserve requirements in Germany

3.2.2.11 Transmission grid The transmission constraints are no key interest of the model because the formation of the stock exchange price where only the marginal cost are considered indicate that the grid constraints do not influence the price. Nevertheless this needs to be tested therefore the grid will be included based on today’s transmission capacities and planned transmission lines according to the European Network of Transmission System Operators for Electricity`s (entsoe) website. (see Figure 3-8) The transmission is implemented through a nodal model. The analyzed spatial resolution is the NUTS-1 level, corresponding to the Germany states. Every node is characterized through a residual load and numerous power plants able to produce at that node. Further the nodes are connected according to the capacities of the real grid. This results in 57 power lines, including the ones connecting the neighboring countries. The costs for transmitting electricity is assumed to be 20 €/MW. If it is needed the model is designed to be easily extended with a more advanced transmission grid.

4 - Model structure

- 36 -

transmission capacity [MWelectric]

25000

20000

15000

10000

5000

RP BY HE SN BE ST NI NI RP NI BY SH SH ST HE BB HB HE SL HE RP TH BB TH BB NI BY NI SN

0 BW

BY BE BB HBHH

HE MV NI NW RP SL SN from region (lower) to region (upper)

ST

SH

TH

Figure 3-8 Transmission capacity from state to state of Germany

3.2.2.12 Import-Export For the implementation of the electricity exchange of Germany with neighboring countries the maximum physical power flows of the available years 2011, 2013 and 2014 according to the entsoe website are assumed as the capacity available for import and export. The assumption is made that the price for import and the benefit for export is the average EEX electricity price of the year 2012 rising 5 % every year till 2050 where it reaches 120 €/MWh.

4 Model structure In general models try to describe the real world and its interdependences mathematically in qualitative and quantitative ways. It is not feasible to consider all the interdependencies in the model, thus the main problem is to identify and include the relevant aspects while abstracting other less important features. The selection of relevant aspects highly depends on the time horizon and the focus of the analysis. (Dieckhoff 2011, p. 26) The resulting structure outlined in Figure 4-1 is an input output model. The relevant aspects are reflected in the chosen input parameters described in chapter 3.2.2 and the characteristics of the model itself (chapter 4.2). In addition to the fixed input there are variable input parameters necessary to

4 - Model structure

- 37 -

examine different scenarios. These are the fuel and emission costs which strongly correlate with the economic situation of the different power plant types. The variation in fuel costs is interesting mainly due to unpredictable fuel prices which are affected by a variety of factors, technological development and geopolitical crises to just name a few. The scenarios based on emission costs allow examining the effect of political decisions which the cost of CO2 is mainly depending on. The development of the power plant park is affected by these two parameters already in the KW_invest model so no further adaptions in the GESOP model are needed. The development as a scenario parameter means the intertemporal technological development. This will reveal what effect adding flexibility to conventional power plants will have on the energy system.

Figure 4-1 GESOP model structure

4.1 Previous works The unit commitment problem is a classical optimization problem with a lot of research conducted on it in the past years due to its relevance. In addition the MILP formulation gained importance due to dramatic improvements in the ability of solvers. (Van den Bergh et al. 2013, p. 4) This model is therefore based on a large variety of papers from different research groups. The need for an interface to the existing models all wrote in R encouraged to process the input in this language and adapt a similar structure.

4 - Model structure

- 38 -

4.2 Model description 4.2.1 R-program As mentioned above the input processing is implemented in an R program. The task is to gather all data from other models and the literature research processing it to function as an input for the optimization model. Besides a complete coverage of the relevant input a flexible structure is needed to calculate different scenarios and enable the adjustment to different input needs. In addition the program is required to be transparent enabling the easy understanding and further development. To achieve these tasks the program is built modularly, the following chapter structure reflects these modules. Another benefit of the modular structure is that small adjustments in one section do not require an overall restart of the program. 4.2.1.1 Residual load A key parameter for the optimization model is the residual electrical load which needs to be met during operation. The guaranteed feed-in of renewable energy allows calculating the residual load as follows:

(2) Where nbHours is the hour and region the region of consideration. The total load for Germany which is openly available on the entsoe website for 2012 is distributed by the gross domestic product (GDP) of those regions, assuming the GDP output correlates with the electricity demand. The development of the load is assumed to follow the energy efficiency goals of the government to reduce it by 10 % until 2020 and 25 % until 2050 referencing on 2008 (Funk 2013, p. 7). The renewable electric energy is comprised of solar (photovoltaic), biomass, wind onshore and wind offshore energy. The RES_E_invest model is explorativly modelling the stock of these technologies until 2050. With that data and the normalized feed-in pattern of the reference year 2012 it is possible to calculate the feed-in assuming the weather is not significantly changing in this time period. This normalized feed-in is the output of a sophisticated weather model developed at the Fraunhofer ISE. The model delivers normalized feed-in patterns for solar and wind power with a 15 minute temporal and NUTS-3 spatial resolution. (Killinger 2013, p. i) The feed-in pattern of biomass is assumed to be constant over time, not considering direct marketing aspects for simplicity. The hourly feed-in of renewable electric energy is calculated as follows:

(3)

4 - Model structure

- 39 -

Besides that, individually modelling is needed for wind offshore energy due to a lack of data in the RES_E_invest model. The development of the stock is assumed to follow the goals of the government of a stock of 15 GW in 2020 and 25 GW in 2030. (BMWi 2014, p. 78) For the development of the offshore stock from 2030 on exist no data therefore the development until 2030 is extended until 2050 steadily. The regional distribution between the Baltic Sea and Northern Sea are assumed to remain on the reference year level. The result can be seen exemplarily in Figure 4-2. Here the residual load is shown for the reference year 2012 and relevant following years. The reference year is characterized by a relatively steady residual load and a well-established daily load pattern. This load pattern is still visible in the year 2020. In contrast to the residual load of 2012 though the volatility increased dramatically and there are already negative residual loads occurring occasionally. It has to be kept in mind that the depicted residual load here is the sum of all the residual loads of the different regions. This means on the regional level negative residual loads are already occurring before 2020. The trend of a more unstable and in total lower residual load is continued in the year 2030. Finally in the year 2050 the residual load is negative in a big part of the time. Further there cannot be identified a certain regular load pattern, not a daily and not a weekly one. Remarkable is that although the sum of the residual is falling considerably, there are times when the residual reaches the height of the reference year. This indicates that in these times the renewable energies contribute much less to the production. Additionally the daily pattern shows that considerable amounts of solar energy are produced resulting in a very volatile daily load pattern. Solutions for that are discussed in chapter 8.

4 - Model structure

- 40 -

Figure 4-2 residual load of the scenario years

4.2.1.2 Heat load The yearly heat load is calculated with the reference year heat load and the electricity load plan for every year. The assumption that the CHP power plants feed their heat into a local heat grid allows to not considering the regional distribution of the heat load. When multiplying this yearly total heat load with a normalized feed-in pattern the result is the needed heat load pattern for every hour and year.

(4) 4.2.1.3 Reserve requirements The reserve requirements are calculated similarly as the heat load. The difference is that it is fixed for every year, meaning it is only one calculation required per year, distinguishing between primary, secondary and tertiary reserve requirements. The influence of the special prequalification rules for power plant parameters to be able to participate in this market is assumed to be negligible. The yearly reserve requirement of every year is calculated by multiplying the reference year reserve requirement with the normalized reserve development of the year under consideration.

4 - Model structure

- 41 -

(5) The KW_invest model is also delivering details on the so called operation mode of every power plant. This means which power plants are in normal operation, taking part in the market, compared to power plants in cold reserve. Cold reserve means these power plants are only used in extreme situations where a very high demand is met by a very low supply. Because they do not participate in the market, generation units in this operation mode do not need to be considered in the model. 4.2.1.4 Power plant list The power plant list is a crucial part of the input determining the quality of the results. The task is here to combine the output of the KW_invest model, the literature research as well as many other parameters depending on the details in these two. For example the fuel price per MW produced electricity is determined by the type, the building year and the considered year. This makes it the most advanced module of the R-program, more complex than the modules above combined. The following formulas are closely oriented on the implementation itself, allowing the best understanding of the various interdependencies. To ensure an appropriate space only the most important parameters are described in detail, whilst sketching the calculation of the self-describing parameters. The basic information considering the size of the power plant, the regional location, the type as well as the existence of a CHP technology can directly be taken from the KW_invest output with only small adjustments made by the program. With the help of numerous parameter Table s consisting the data of the literature research, it is possible to build the complete detailed list needed for the optimization model. The intertemporal as well as the part load efficiency parameters of the power plants can directly be derived from the technology. These parameters are assumed to not change over the time horizon. They are only altered manually to generate scenarios. Another important parameter which can be simply determined this way is the minimal commitment of a block. The parameters requiring a more sophisticated calculation are dependent on one or more details of the basic information in combination with time dependent parameters of the literature research. Among them are the fuel and generation efficiency depending on the building year and the technology of power plant. The fuel efficiency is necessary to calculate the specific fuel price in every year as well as the specific fuel tax. The final fuel cost in the optimization model is affected by more parameters than the fuel price and tax alone, this will be described in detail in chapter 4.2.2.

4 - Model structure

- 42 -

(6)

(7) Similarly to the fuel efficiency the generation efficiency is also important as a calculation step for a lot of parameters later in the optimization model. The variable costs here consist of additional cost for inspection and repair of the power plant as well as auxiliary materials and other consumables than fuel. It is necessary to calculate the specific variable operation and management costs and the CO2 factor as follows.

(8)

(9)

4.2.2 Optimization model In this chapter the optimization program is described in detail. The structure of the chapter is closely oriented on the actual code of the program. (see Error! Reference source not found. and Error! Reference source not found.) 4.2.2.1 Total costs As mentioned above the optimization model is written in the GAMS language. It is implemented as a minimization problem of total operation costs. The total costs are defined as follows:

( 10 )

According to this formula the model is minimizing the summation of all these costs over the period under consideration, the period length can be chosen freely. The only costs subtracted are the benefits of CHP power plants for the delivered heat. All the costs are now described in detail with being the time steps of the period,

being the number of blocks taking part in the market and

being the commitment of one power plant. The commitment

4 - Model structure

- 43 -

is not considering losses of part load necessary for later calculations. The variable, fuel and tax costs are calculated similarly, multiplying

with the specific cost

∑

∑

.

( 11 )

∑

∑

( 12 )

∑

∑

( 13 )

As mentioned above the fixed costs are not further used for analysis. Nevertheless they are included as an option for further use.

∑

∑

( 14 )

Transmission costs are inflicted on every unit of electricity flowing through the grid. The summation is thus over all transmission lines making up the grid.

∑

∑

( 15 )

The import-export costs are modelled similarly but for them there needs to be a distinction between imports and exports. The costs can be negative when importing or positive when exporting energy. The import-export capacities are modeled with the help of the transmission lines connecting the surrounding countries with the regions bordering them.

∑

∑

( 16 )

The emission costs are only comprised of CO2 emission costs. The calculation of these costs requires the emission factor

and the price of CO2 emission certificates.

∑

∑

( 17 )

4 - Model structure

- 44 -

The start-up costs require the introduction of integer decision variables. The binary variable is 1 when the block is leaving the idle state at the beginning of the time step and 0 otherwise. With this variable and the distinction between the different start-up types it is possible to calculate the start-up costs very realistically. To determine the costs the model is assuming that when a block is starting-up the costs are proportional to the minimal commitment of the block. If the start-up type of

is a cold start the depreciation cost are also added.

∑

( 18 )

∑

In addition to intertemporal constraints described in 5.2.2.7, the costs inflicted by changing the load play an important role for the model to achieve a realistic operation mode. In the model it is distinguished between ramping up and down.

∑

∑

( 19 )

The storage water power plants need a special modelling due to the function as a storage and generation facility. This will later be in more detail in the chapter 4.2.2.6. The costs directly influencing the total costs are the variable costs for the pumps of the power plants refilling the reservoir.

∑

∑

( 20 )

The incentivisation of operating the power plants at full load is implemented through a part-load penalty which will increase according to how far the power plant is operating off the optimum. In literature there were no data available on the topic of the part-load penalty costs. Therefore as an assumption for these costs the fixed costs are used.

∑

∑

( 21 )

The only costs always non positive are the heat credit costs, or heat credit benefits, of CHP power plants. In contrast to the equations above where the commitment of a unit here the actual unit commitment considering part-load efficiency losses

is used, is needed

to calculate the benefits from delivering heat in addition to the produced electricity. The delivered heat amount is assumed to be the same as the delivered electricity.

4 - Model structure

- 45 -

∑

( 22 )

∑

4.2.2.2 Residual load generation equilibrium After the detailed description of all the components of the objective function follow the constraints. They limit the solution space of the optimization problem and thus determine the possible feasible solutions. The most important of these is the constraint ensuring that always enough electricity is produced to meet demand, the residual load. This not only needs to be the case in total but also for every region. The grid is needed so that this constraint does not mean that the required energy would have to be produced by the power plants located in the specific region. The transmission grid as well as the import-export of electricity is covered by the transmission difference variable which will be described in the chapter 4.2.2.3.

( 23 )

∑

4.2.2.3 Transmission and import-export The transmission grid is modeled with connections from one region to another. The realistic modelling requires constraints for the capacity of transmission line as well as every import-export line.

( 24 ) The

which is relevant for the residual load constrain, is the results of the

summation of every electricity flowing in the region minus every electricity flowing out. This equation connects the import-export modelling with the grid enabling a simplification of the model.

∑

∑

( 25 )

4.2.2.4 Power plant commitment The power plant commitment is a crucial part of the model. This part of the electricity market is modelled in the most detail to ensure a realistic unit commitment. The first two constraints set limits to the commitment of the power plants. This requires the introduction of a binary decision variable

which is 1 if the power plant is committed and 0 otherwise. The equations

4 - Model structure

- 46 -

are derived according to (Morales-Espana et al. 2013, p. 5). The following equation means that the power plant is either starting-up or shutting-down in the case there is a change in the state of commitment from

to

.

( 26 ) This requires another equation ensuring that a power plant is either committed or not commitment.

( 27 ) With these equations it is possible to add other constraints regarding the details of the power plant commitment. The following two describe the maximal and minimal commitment. Here the availability is introduced for the maximal commitment.

( 28 ) The need of a minimal commitment when synchronized with the grid results in a minimum load constraint.

( 29 ) 4.2.2.5 Part-load efficiency The part-load equations incentivize the operation of the power plants at rather full load than part-load. This is necessary to model the operation of the power plants realistically. Various implementations were assessed for their computational complexity. Among them an approach using different states of efficiency proposed by (Eikenberg 2014, p. 28). (see Figure 4-3)

4 - Model structure

- 47 -

part-load efficiency factor of nominal efficiency

1.4

1.2

1

0.8

0.6

0.4

0.2

0 0.7

0.95

1


Figure 4-3 Step approach for part-load efficiency

The problem with these implementations is that they all require the introduction of at least two additional binary decision variables. The testing of the computation time suggested that the added complexity through these variables do not justify the increased accuracy. This is why an approach was chosen penalizing the operation at part-load. This penalty is added to the total costs of the objective function, see formula 10. Needed parameters are the interval of low part-load efficiency and the efficiency factor line.

. With them it is possible to calculate the incline of the linearized part-load

4 - Model structure

- 48 -

1.2

Part-load efficiency factor

1 0.8 0.6 0.4 0.2 0 0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1


Figure 4-4 Part-load efficiency factor calculation

4.2.2.6 Technology specific modelling There are two power plant technologies which require a separate modelling due to their differences to the other technologies. These are storage water and CHP power plants. Today the CHP power plants are mostly heat driven according to (Wünsch et al. 2011, p. 54). The Leitstudie (Nitsch et al., p. 125) is predicting an increasing share of electricity driven power plants in the future. This does not allow the assumption of these plants as must-run power plants. On the other hand complete electricity market participation is not realistic either. The solution is a mix of an approach subtracting the heat credit from the total cost and the coverage of a certain heat demand. The heat credit equation was described already. In contrast to the electricity demand which has a spatial resolution the heat demand is only covered in total for Germany.

∑

( 30 )

The nature of functioning as a storage and generation facility requires a more sophisticated approach for storage water power pants. This has to circumference the operation of the reservoir, self-discharge and separate modelling of the pumps and generator. The generation part of the storage water power plants is integrated in the normal conventional power plant modelling. To manage the reservoir the introduction of the level of storage

and a factor for the self-discharge

are needed. This is constrained by two equations determining the maximal and

4 - Model structure

- 49 -

minimal level possible. The pumps are modelled similarly as the generator. The pumps have a minimal and maximal level at which they operate. With these constraints only one additional constraint is needed to ensure realistic operation.

(

)

( 31 )

4.2.2.7 Up- and down-time The minimal up- and down-time of the power plants is complex in its implementation requiring a lot of constraints and equations. Nevertheless it is a crucial part of the unit commitment and therefore needs to be modelled. The added complexity is mainly due to the extension of the considered time steps. For the definition of the state changing of a power plant it is only necessary to know the current and the state before. The minimal times however have a much longer period under consideration. The solution is a variable counting the time steps a power plant is on- and offline. This approach is proposed a.o. by (Martens et al. 2011, p. 3) for a so called off-time counter. For this thesis it is also applied to the ontime, resulting in an on-time counter. The variables needed are the off-time counter variable and on-time variable chosen to be the number of time steps in total

as well as a large number, which will be .

( 32 )

( 33 )

( 34 ) Similarly the up-time counter constraints.

( 35 )

( 36 )

4 - Model structure

- 50 -

( 37 ) With this information it is now possible to ensure that the power plants are only shut-down and startedup when the minimum time for these is fulfilled. Where cases. The start-up time

is the minimal-time for the two

required by the power plant to leave the idle state is not

modelled separately. Rather it is added in the following constraint ensuring that in this time period the power plant is not delivering electricity. It would be more realistic to model this in more detail because the power plant is delivering electricity during the start-up. The gain of accuracy is not assumed to justify the computational effort. The minimal on-time is similar but without the summation of a shutdown time.

( 38 ) ( 39 ) Another use for the counters is the distinction between the different start-up types. For this the proposed approach of (Martens et al. 2011, p. 3) is adopted. The first constraint is assuring that only one start-up type is used. While the second and third constraints set the upper and lower time limits

and

.

∑

( 40 )

∑

( 41 )

∑

( 42 )

4.2.2.8 Ramping and reserve The allowed ramping in positive and negative direction is constraint by a power plant specific technical limit. The interesting constraint here is the following, describing the ramping in just one equation through the connection with

. The ramping required for the start-up and the shut-

down are included in the constraint so that these are not part of the ramping itself.

4 - Model structure

- 51 -

( 43 )

With the knowledge of the ramping it is possible to implement a sophisticated reserve market. Because of the so far very limited possibilities of storing electricity it is in reality a crucial instrument to guarantee a reliable system operation. This market is able to differentiate between the three types of reserve, primary, secondary and tertiary reserve. These are distinct by the reaction time and the TSO which has to deliver the electricity. The primary reserve is provided by all the TSOs. It needs to be activated within 30 seconds and be available for up to 15 minutes. The secondary reserve in contrast needs to be delivered by the TSO affected with a responding time of five minutes with the same time horizon. Finally the tertiary (minutes) reserve is activated manually by the TSO. The provision time is from 15 minutes to 1 hour. Later the delivering TSOs are compensated through the balancing group affected. (Amprion n.d.). (see Figure 4-5)

Figure 4-5 Illustration of the reserve types (Amprion n.d.)

The modelling of this market requires the distinction between spinning and non-spinning power plants. In the literature spinning means that a power plant is started-up but it does not necessarily need to be delivering energy to the grid. In this thesis this distinction is not relevant with the result that spinning means that a power plant is turned on and producing and non-spinning means a power plant is turnedoff. The first step is to define the available ramping up

and ramping down

which is spinning and non-spinning. The following seven constraints describe this definition. This definition is illustrated for the

in Figure 4-6.

4 - Model structure

- 52 -

( 44 )

( 45 )

( 46 )

( 47 )

( (

) )

( 48 )

( 49 )

( 50 ) The Figure 4-6 illustrates the available spinning ramping down of a power plant when in operation. At the first time step the power plant has a unit commitment total size

of around 60 % of the

. The total size for the illustrated power plant is here 300. From that level of

commitment the power plant has an avaialble spinning negative ramping of the possible ramping down if this is not bigger than the actual commitment

minus the minimal commitment

. In this illustration the available ramping down spinning in the second time step is the technically possible ramping down is performed and

because no load change

is not bigger than the actual commitment minus the minimal

commitment. For the thrid time step this however changes because the power plant is ramped down in its opertaion. Now the amount the power plant is ramped down needs to be considered, this is done through the integration of

. In addition to the check performed before if the

possible ramping is bigger than the difference of actual commitment and minimal commitment the actual ramping needs to be considered. This is done by substrating the actually performed ramping from the available ramping.

5 - Model implementation and validation

- 53 -

The model describend above throught the formulars 44-50 and the Figure 4-6 is determining not only the available ramping down of the spinning power plants. The model is doing that similaryl for ramping up spinning and non-spinng as well as ramping down non-spinning power plants. With that information it is possible to model the reserve market.

Figure 4-6 Illustration of the available ramp down rate of one power plant

Now the only step left is to connect the results of the model with the requirements in reality. Due to the similar time frame of up-to 15 minutes it is assumed that the spinning reserve of the model is fitting to the summation of the primary and secondary reserve. This tertiary reserve is assumed to be covered by the non-spinning ramping available.

5 Model implementation and validation In this chapter the implementation of the model will be described followed by the validation of the results.

5.1 Implementation The implementation showed that there are computational difficulties requiring a new method to overcome them. The first step to lower the computational complexity was to set the time step size to one hour rather than the quarter hour time frame the stock exchange is operating at. This unfortunately did not solve the main problem which was a very high need for memory. The evaluation of the model showed that this is mainly based on the long optimization horizon of one year. The solver needs to


- 54 -

consider 8760 single time steps for one optimization. The solution is a rolling horizon approach. In this approach the optimization horizon is reduced to 24 hours. In contrast to a yearly optimization the rolling horizon approach lowers the required memory considerably. Another positive aspect is that this approach is closer to reality of the stock exchange spot market which is traded the day before. Nevertheless there are new challenges arising when implementing this approach. The model now has to be able to pass on the previous states of different variables to ensure a consistent optimization. The previous state is the state of the last time step of the previous rolling optimization horizon. There are two variables sufficient for describing the whole state of the system. These are the current total commitment of all the power plants plants

and the reservoir level of storage water power

. All the other variables of the system can be derived by the model when

optimization the first step of the following rolling horizon. The only exclusions are the two counting variables of the up-time

and down-time

. An interesting finding

when implement this approach is that the solution time is highly correlating with the rolling horizon time length. The computational time was halved when reducing this from 28 to 24 hours for example. This indicates a remarkable time saving compared to the optimization of a whole year. Furthermore the testing showed that the model has to have an overlapping optimization period. In this way it is avoided that the storage water power plants reservoir are always emptied at the end of the rolling horizon period. (see Figure 5-1)

Figure 5-1 Rolling horizon method

Another challenge is that the code of the GAMS program now has to be individually generated for all the different rolling optimization steps. The solution is that the code is generated in a separate R-script allowing the automated generation of the entire GAMS program. The R-script input parameters are the length of the rolling horizon and the overlap, making it a very flexible model.


- 55 -

5.2 Validation For the validation the results of the model are compared with the real data to be able to assert the relevance and accuracy of the model. The problem is that it is not practical to wait for future data being available. The solution is a backtesting of the model on past data. With this method it is possible to make very clear assertions using statistical procedures. (Möst 2009, p. 14) The validation also includes the adjustment of parameters. The parameters used for the validation are the year 2012 and a time step length of one hour. Furthermore there were four weeks selected, each of them representative for the seasonal circumstances. The rolling horizon was implemented as a 24 hour time frame with an overlap of two hours. The computational complexity is a key interest because of the reasons mentioned above. The calculation was performed with the solver CPLEX on a 64 Bit desktop PC with four Intel® CORE™ i5-3570 CPUs at 3.40 GHz a RAM of 8.00 GB. The optimality criterion of the integer solution was set to 0.05 %. The resulting computational detail can be seen in Table 5-1. year

week individual blocks

time steps [h]

overlap [h]

computation time [min]

2012 2012 2012 2012

1 13 26 39

24 24 24 24

2 2 2 2

32 min 31 min 20 min 28 min

702 702 702 702

1 year computation time [h] 69 74 43 50

memory use [GB] 6 6 6 6

Table 5-1 Computational complexity of validation

The notable variation in the computational time can be explained when looking at the different residual load curves for these weeks in Figure 5-2. Although the absolute residual load is in the same range the first week is the most volatile and the 26. week the least volatile.


- 56 -

60000

week 26

week 39

power [MW]

50000 40000 30000 20000 10000

1 22 43 64 85 106 127 148 2137 2158 2179 2200 2221 2242 2263 2284 4297 4318 4339 4360 4381 4402 4423 4444 6457 6478 6499 6520 6541 6562 6583 6604

0

hour of year [h] production_total

residual load

Figure 5-2 Residual load and total production of the reference weeks of the validation year 2012

The detailed analysis which can be seen in Figure 5-3 shows that the production is not precisely following the residual load. In times with a very sudden load change the model anticipates that and the production remains on a higher level to be able to ramp up when the load is increasing. This reflects the forecasted load which the TSO needs to meet at all time, therefore the anticipating ramping up of the production to meet demand hikes is realistic. 60000

Power [MW]

50000 40000 30000 20000 10000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163

0 hour of year[h] production_total

residual load

Figure 5-3 Residual load and total production of the first week of the validation year


- 57 -

The validation process showed that without the previously assumed irrelevant transmission constraint the resulting generation patterns are not reflecting the real data. Nevertheless when implementing the real grid on interregional level the pattern fits the real data. The possible reasons for this will be discussed in the chapter 8. The formation of the stock exchange price is through the merit order. This describes that the most expensive power plant delivering electricity in the market is setting the price for electricity in this time step. The costs considered for this price are only the marginal costs. From this price forming mechanism follows that the model needs to be able to calculate the total unit commitment pattern in every hour. Therefore this is firstly evaluated before validating the calculated stock exchange price. For the validation the unit commitment pattern of the reference year 2012, for which the data is available through the entsoe website, is compared with the model results for the same time period of the four reference weeks. The difference in the data is that the entsoe data is not distinguishing between the two gas types OCGT and CCGT, they are combined under the term GAS. Another difference is that the power plants defined as WATER in the model are called RoR. Further it is not clearly defined what F_OTHER includes for that data. This does not affect the model strongly because this power plant type is not used in the model at all for this year. 50000 power [MW]

40000 30000 20000 10000 1 23 45 67 89 111 133 155 2145 2167 2189 2211 2233 2255 2277 2299 4313 4335 4357 4379 4401 4423 4445 6459 6481 6503 6525 6547 6569 6591 6613

0

hour of year 2012 [h] production_eex_URANIUM

production_eex_RoR

production_eex_LIGNITE

production_eex_COAL

production_eex_GAS

production_eex_OIL

production_eex_F_OTHER

production_eex_STORAGE_WATER

Figure 5-4 Reference four week unit commitment pattern


- 58 -

power [MW]

50000 40000 30000 20000 10000 1 23 45 67 89 111 133 155 2145 2167 2189 2211 2233 2255 2277 2299 4313 4335 4357 4379 4401 4423 4445 6459 6481 6503 6525 6547 6569 6591 6613

0

hour of year 2012 [h] production_URANIUM

production_WATER

production_LIGNITE

production_COAL

production_CCGT

production_STORAGE_WATER

Figure 5-5 Model results of the four weeks unit commitment pattern

For a better visualization one week will be used. Nevertheless for the evaluation all four weeks will be taken into account. The more detailed analysis of one week will help to reveal the performance of the model according to the daily pattern of the different unit operations as well as the share of the different technologies.

power [MW]

40000 30000 20000 10000 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163

0 hour of year 2012 [h] production_eex_URANIUM

production_eex_RoR

production_eex_LIGNITE

production_eex_COAL

production_eex_GAS

production_eex_OIL

production_eex_F_OTHER

production_eex_STORAGE_WATER

Figure 5-6 Reference one week unit commitment pattern


- 59 -

power [MW]

40000 30000 20000 10000 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163

0 hour of year 2012 [h] production_URANIUM

production_WATER

production_LIGNITE

production_COAL

production_CCGT


Figure 5-7 Model results of a one week unit commitment pattern

The visualization shows that the general pattern of operation is very well replicated. The daily changes in load are met with the ramping of the right types of power plants. The only minor deviation here is that in reality the nuclear power plants are operated a bit more volatile which will not affect the formation of the price considerably. In addition to that the operation pattern for lignite shows a daily pattern in the model. They are ramped down in the night due to a lower load. In reality however this pattern cannot be recognized so clearly. For inflexibility reasons they keep operating through the night on almost the same level as during the day. The share of the different technologies is modelled within a range of 25 % for all power plant types except storage water. (see Table 5-2) production production production production production production URANIUM WATER LIGNITE COAL CCGT STORAGE [GW] [GW] [GW] [GW] [GW] WATER [GW] reference 6851 model 5888 reference/model 116 %

391 532 73 %

9719 9502 102 %

4169 4378 95 %

1339 1406 95 %

120 624 26 %

Table 5-2 Unit commitment pattern model accuracy

The only considerable deviation in the share of the use of the technologies occurs for storage water power. Here the model produces almost four times more power than in reality. This mistake can easily be dealt with by reducing the available storage water power capacity in the input of the model. The stock exchange price for the reference year of the model called cost_marginal_max compared to the real EEX data called EEX_price is shown in Figure 5-8. To be able to analyze the results the four reference year weeks are chosen for a detailed view.


- 60 -

250 200

100 50 0 -50

1 271 541 811 1081 1351 1621 1891 2161 2431 2701 2971 3241 3511 3781 4051 4321 4591 4861 5131 5401 5671 5941 6211 6481 6751 7021 7291 7561 7831 8101 8371

electricity price [€/MWh]

150

-100 -150 -200 -250

hour of the year 2012 [h] cost_marginal_max

eex_price

Figure 5-8 Stock exchange price validation for the reference year 2012

The final result is illustrated in Figure 5-8 with the actual EEX price of the reference year and the model results for the stock exchange price. This Figure shows that the general level of the stock exchange price is similar to the general level of the model result stock exchange price. Further the daily and weekly pattern of the volatile stock exchange price can also be reflected in the model results. However it can be seen that the more volatile stock exchange price in the winter season is not replicated as good as the summer season. Another analysis instrument is to design ordered annual price duration curve. Figure 5-9 shows that the overall level of the price is fitted but that the extreme price level occurring in just a few hours over the year cannot be modeled. Additionally the steps are visible which resemble the different technologies.


- 61 -

250 200

100 50 0 -50

1 271 541 811 1081 1351 1621 1891 2161 2431 2701 2971 3241 3511 3781 4051 4321 4591 4861 5131 5401 5671 5941 6211 6481 6751 7021 7291 7561 7831 8101 8371

electricity price [€/MWh]

150

-100 -150 -200 -250

hours of the year 2012 [h] cost_marginal_max

eex_price

Figure 5-9 Ordered annual price duration curve

In addition to that the general analysis of a whole year there are four reference weeks chosen for a more detailed analysis. Figure 5-10 shows that the model is following the real data stock exchange price in steps rather than the comparably smooth behavior. The reason for that is that when the load is rising or falling to a certain level there are suddenly other power plant technologies producing to be able to meet demand. This affects the MAE because the difference from this behavior and the smoother real data stock exchange line results in a higher error. Possible solution methods will be discussed in chapter 8.


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100 80

price of electricity [€/MW]

60 40 20

-20

1 21 41 61 81 101 121 141 161 2149 2169 2189 2209 2229 2249 2269 2289 4301 4321 4341 4361 4381 4401 4421 4441 4461 6473 6493 6513 6533 6553 6573 6593 6613

0

-40 -60 -80

-100

stock exchange price

eex_price

hour of year 2012 [h] Figure 5-10 Stock exchange price validation of the four reference weeks

The validation of the stock exchange price is as straight forward as the validation of the unit commitment pattern. The same representative weeks are chosen to statistically evaluate the relevance of the results. The implementation showed that a parameterization of the price is needed to get relevant results. The parameterization is done by an optimization of the mean absolute error (MAE) and the mean percentage error (MPE) These two are a standard measurement tool for the backtesting of energy system models. (Möst 2009, p. 14)

∑ |̌

̂

|

|̌

̂

|

∑ Where ̌

is the results of the model and ̂

̂

( 51 )

( 52 )

is the reference value. The result is a

parameter of -30.84 €/MW for the stock exchange price. The MAE and MPE are also used as an evaluation tool for the accuracy of the model. The statistical evaluation is concluded in Table 5-3.


week_1 week_13 week_26 week_39 Total of the four weeks

MAE [€/MW]

MPE [%]

18,65447 9,529343 11,18795 8,219597 11,89784

25,8 16,8 25,1 19,6 21,8

- 63 -

Table 5-3 Statistical evaluation of the reference weeks

Figure 5-10 and Table 5-3 both reflect the same result, which is that week 1 and 26 have a much higher MPE whilst week 13 and 39 have a much lower MPE. This indicates that the model is much better at calculating the summer period than the winter period. A possible reason is the increased influence of climate factors hard to reproduce by the model in the winter. Further there are factors influencing the stock exchange price not explainable by the available data. (see red area in Figure 5-10) Possible solutions will be discussed in chapter 8. Besides these extreme cases the parameterized model is replicating the daily and weekly pattern with a MPE of maximum 25 %. Nevertheless there are possibilities to enhance the model which will also be described in chapter 8. Also there will be a comparison of the performance with other models as a benchmark. Due to the storage constraints along with the variability of the load and supply, electricity is a timeheterogeneous good. This means the value of electricity depends on the time it is produced (Hirth 2013, p. 219). The market value factor allows drawing conclusions on the value of a type of electricity generation. For this the feed-in pattern is compared with a base load feed-in. The relation of the actual return on sales and the return on sales for base load is the market value factor (MVF). ∑ ∑

∑ ∑

∑

( 53 )

∑

The resulting market value factors for the different renewable energy sources can be seen in Table 5-4.

market MVF model MVF PE [%]

Solar 0,9380

Wind-onshore 0,9790

Wind-offshore 0,9718

water 1

biogas 1

0,9367

0,9786

0,9712

1

1

-0,14

-0,04

-0,06

0

0

Table 5-4 Average market value factors of the reference weeks

The result reflects the expected values stated in different studies a.o. (Hirth 2013, p. 225). The model results extraordinarily fit the real data with an average percentage error (PE) of under 0,15 % for all the renewable energy types. The fact that the model is much more accurate in calculating the MVF than the

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stock exchange price indicates that the pattern of the price as well as the price spreads are very well modelled. Reasons for that will be discussed in chapter 8.

6 Case study In this chapter the different scenarios and their results will be described. This is followed by the analysis of the results and a sensitivity analysis of the model.

6.1 Scenario description The computational complexity and the limited time frame of this thesis restrict the number of scenarios to be analyzed. Therefore the focus of the case study will lie on scenarios for the CO2 price. This will demonstrate the potency of the model and the relevance for the different stockholders. Further demonstration will be achieved also in the sensitivity analysis where the fuel prices and also the intertemporal constraints will be varied. The scenarios can be seen in Figure 6-1. The scenarios reflect a

120 100 80 60 40 20

CO2_50

CO2_100

42

40

38

36

34

32

30

28

26

24

22

20

18

16

14

12

8

10

6

0 base

CO2 emission certificate price [€/t]

political decision widely perceived as necessary to avoid the shift to lignite power generation.

v.oil

Figure 6-1 scenario C02 price variation

The variation of the parameters also needs to be made in the input of the KW_invest model see chapter 3.2.1.2 ensuring it to be consistent. The resulting development of the conventional power plant park can be seen in the Figures 6-2 and 6-3. The capacity of nuclear power is following in all scenarios the politically decided phase out until 2022. Similarly the capacity of oil and coal power plants seem to not be affected considerably by the increase of the CO2 price. The constant capacity of storage water is due to a pending implementation in the KW-

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Invest model. The figures show that the main influence of an increase of the CO2 price lies in the building of lignite and gas power plants. The base scenario is characterized by a significant expansion of lignite capacity, whilst most other power plant types decline with a constant relative share. The KW_invest model does not take intertemporal constraints into account therefore the investment predominantly in lignite power plants needs to be assessed critically through the GESOP model when the results of the GESOP model are described. The CO2 50 scenario is characterized by a similar expansion of lignite power as the base scenario although the hike in the year 2030 cannot be seen. Significant difference can be seen when looking at the CO2 100 scenario. Here the remarkable expansion of lignite is avoided, indicating that at least this emission price is needed to do so. The gap of the lowered capacity of lignite is almost entirely substituted with the relatively low CO2 intensive gas power. 90000

available electrical capacity [MW]

80000 70000

STORAGE_WATER F_OTHER

60000

OIL 50000

CCGT OCGT

40000

COAL 30000

LIGNITE

20000

WATER URANIUM

10000 0 2012

2020

Figure 6-2 Conventional power plant park of the base scenario

2030

2050

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CO2 100 scenario

90000

90000

80000

80000 available electrical capacity [MW]

available electrical capacity [MW]

CO2 50 scenario

70000 60000 50000 40000 30000 20000 10000

70000 60000 50000 40000 30000 20000 10000

0

0 2012

2020

2030

2050

2012

2020

2030

2050

Figure 6-3 Conventional power plant park of the CO2 50 and CO2 100 scenario

6.2 Scenario results The scenarios are modelled for the base year 2012 and the years 2020, 2030 and 2050. The time calculated are the four representative weeks already used before. The rolling horizon parameter is 24 hours with an overlap of two hours. For the best understanding of the scenarios the results will be presented in a detailed resolution of one week regarding the price pattern, in addition to a presentation of average values for the average price. Figure 6-4 shows the price pattern of the base scenario. The development of the price is characterized by a moderate incline until the year 2030, which is expected through the increase of fuel costs. Remarkable is that the predominantly base load type power plant park is not suited to deal with sudden steep inclines of the residual load. This effect can already be seen in the year 2030 with the full consequences visible in the year 2050. In this year with the base load parameters a stable operation seems not possible, which can be seen in the sudden price hikes. Remarkable is that the model decides rather to produce a lot of electricity not used instead of using more flexible power plants. (see Figure 6-5) Remarkable is also that the stock exchange price in 2020 is lower than in 2012 despite the assumed rise in fuel costs. This can be explained by the circumstance that the residual load is decreasing through the forgoing expansion of renewable energy while the conventional power plant park stays relative constant. The comparably large capacity remaining of nuclear energy also contributes to this effect. This result is in line with the latest decrease in the stock exchange price.

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600

stock exchange price [€/MWh]

500 400 300 200 100

4297 4302 4307 4312 4317 4322 4327 4332 4337 4342 4347 4352 4357 4362 4367 4372 4377 4382 4387 4392 4397 4402 4407 4412 4417 4422 4427 4432 4437 4442 4447 4452

0 -100 2012

hour of modelled year 2020 2030 2050

Figure 6-4 price pattern of the reference week for the base scenario

50000 40000 30000 20000

0 -10000

4297 4303 4309 4315 4321 4327 4333 4339 4345 4351 4357 4363 4369 4375 4381 4387 4393 4399 4405 4411 4417 4423 4429 4435 4441 4447 4453

load [MW]

10000

-20000 -30000 -40000 -50000 -60000

hour of modelled year [h] production_WATER

production_LIGNITE

production_COAL

production_CCGT


heat_load

residual

Figure 6-5 Operation pattern of a reference week in 2030 for the base scenario

When analyzing Figure 6-5 it can be noted that the heat load does not significantly affect the operation of the power plants for this week. The reason is that the analyzed week is in summer characterized by a

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much lower heat load than the winter weeks. Further the main share of power is delivered by lignite power plants. Due to the small ability of following a volatile residual load through ramping the power plants are still operated at relatively constant level even when the residual load is far negative. This residual load pattern is mainly a result of the comparably high share of solar energy which is producing considerably amount of electricity around noon. When there are days with a low production of solar energy, for example from hour 4405 until 4447 the lignite power plants are not able to meet the suddenly rising demand. To cope with this situation the model will start-up coal and especially gas fired power plants. Further storage water energy is used for the most extreme hikes in demand. The extreme price hikes in the highlighted areas in Figure 6-4 can now be explained when looking at the Figure 6-5. A very high feed-in of solar energy during the day and a sudden rise of the residual load from the negative when this feed-in stops results in a very volatile residual load pattern. The most extreme residual load change in the year 2030 is a total change from -49 GW to +27 GW in just 8 hours. This pattern is reflected in the model by the commitment of very flexible but expensive gas power plants resulting in the sudden price hikes. The base scenario power plant park proofed not to be able to cope with the very sudden hikes in the residual load for some individual weeks in the year 2050. This is especially the case in summer where the high feed-in of solar energy results in extreme volatility. The result of infeasibility is mainly due to the rolling horizon optimization. Through this the model has not perfect foresight over the whole year. Rather the model has perfect foresight over the period of the rolling horizon which is in this implementation set to 26 h. When there is a sudden residual load change it can be the case that there is no possible solution to meet this by the operation of the power plants. This happens only for the base scenario, indicating that the power plant park is not suitable for the residual load. Possible solutions will be discussed in chapter 9. The power plant park of the CO2 50 scenario is already able to better cope with the difficulties of a volatile residual load. Here this effect is avoided until the year 2050. This can be explained by the slightly higher share of flexible power plants compared to the base scenario.

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800 700 600 500 400 300 200 100 0 4297 4303 4309 4315 4321 4327 4333 4339 4345 4351 4357 4363 4369 4375 4381 4387 4393 4399 4405 4411 4417 4423 4429 4435 4441 4447 4453 4459

modelled stock exchange price [€/MWh]

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hour of modelled year [h] 2012

2020

2030

2050

Figure 6-6 price pattern of the reference week 26 of the year 2012 for the CO2 50 scenario

The CO2 100 scenario shows a similar yet overall reduced price range of the years until 2030. Notable is that the price hikes also first emerge in the year 2050. In contrast to the CO2 50 scenario the doubling of the price at the end of the reference week is also avoided. This indicates that the increased stock of flexible power plants, here especially gas, is necessary to ensure a stable operation. The price hikes in the year 2050 are less numerous and in general smaller in their manifestation. The downside of this

modelled stock exchange price [€/MWh]

slightly more constant operation is an overall higher price in the time of relative constant operation. 500 450 400 350 300 250 200 150 100 50 0 hour of modelled year [h] 2012

2020

2030

2050

Figure 6-7 price pattern of the reference week 26 of the year 2012 for the CO2 100 scenario

This relatively constant operation can be seen when analyzing a winter week with a high heat load. (see Figure 6-8) The residual load is most of the times negative, except for two hikes near the beginning and

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end of the analyzed week. The operation pattern of the power plants is thus mainly determined by the relatively constant heat load. This load is covered by coal, lignite and mainly gas power plants. This is the reason why the price level is higher in the CO2 100 scenario when there is relative constant residual load compared to the base scenario. Nevertheless the price hikes are much better maintained in this scenario leading to an overall less price level. 40000 30000 20000 10000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163

load [MW]

0 -10000 -20000 -30000 -40000 -50000 -60000

hour of the modelled year [h] production_WATER

production_LIGNITE

production_COAL

production_CCGT

production_OIL

production_F_OTHER


heat_load

residual

Figure 6-8 Operation pattern of a reference winter week in 2030 for the CO2 100 scenario

Figure 6-9 shows the results for the corresponding summer week. Here the unit commitment is characterized by a daily pattern of the residual load. The heat load does not play a considerable role.

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50000 40000 30000 20000

0 -10000

4297 4303 4309 4315 4321 4327 4333 4339 4345 4351 4357 4363 4369 4375 4381 4387 4393 4399 4405 4411 4417 4423 4429 4435 4441 4447 4453

load [MW

10000

-20000 -30000 -40000 -50000 -60000


production_COAL

production_OCGT

production_CCGT

production_OIL

production_F_OTHER


production_LIGNITE

heat_load

residual

Figure 6-9 Operation pattern of a reference week in 2030 for the CO2 100 scenario

Figure 6-10 shows the results for a summer week of the year 2050. The small heat demand is covered by a very constant operation of lignite and coal power. Noticeable is that the sudden hikes in the residual load are almost solely met by CCGT and OCGT power plants. It is remarkable that the model decides to operate OCGT power plants because they inflict much higher variable costs resulting in the price hikes in Figure 6-7. This indicates that the ramping of the CCGT plants in not enough to be able to cope with the rising demand. This is also the explanation why the stock exchange price of the CO2 100 scenario is characterized by sudden price hikes although the power plant park seems to be comprised of enough flexible power plants. This indicates that the technological development of the intertemporal characteristics of the different generation technologies plays an ever increasing role for the results of the model and that it might be advisable to integrate this development to the model parameters. This is further discussed in chapter 8.

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60000

40000

0 4297 4303 4309 4315 4321 4327 4333 4339 4345 4351 4357 4363 4369 4375 4381 4387 4393 4399 4405 4411 4417 4423 4429 4435 4441 4447 4453 4459

load [MW]

20000

-20000

-40000

-60000

-80000


production_LIGNITE

production_COAL

production_OCGT

production_CCGT


heat_load

residual

Figure 6-10 Operation pattern of a reference week in 2050 for the CO2 100 scenario

Besides the analysis of the scenarios using a detailed evaluation of one reference week a comparison of the average stock exchange price of all the four reference weeks helps to see the overall effects of the scenario variations. By analyzing all four reference weeks the seasonal effects will be moderated. Figure 6-11 shows that the average stock exchange price of the base scenario is falling considerably to almost only half the level of the reference year in 2020. This is the price lowing effect of the extensive expansion of lignite capacity. The advantage of comparably low running cost is compensated by the disadvantage of the inflexibility, making it the second most expensive scenario already in 2030, and the most expensive in 2050. The CO2 50 scenario results in the highest average stock exchange price in 2020 and 2030. The CO2 scenario shows a constant but more moderate increase. Remarkable is that while the CO2 100 scenario has only almost half the price in 2030 this gap closes until 2050. Nevertheless it is the cheapest scenario regarding the average stock exchange price.

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average modelled stock exchange price [€/MWh]

250

200

150

100

50

0 2012

2020

2030

base

CO2 50

Co2 100

Poly. (base)

Expon. (CO2 50)

Expon. (Co2 100)

2050

Figure 6-11 average stock exchange price of the four reference weeks of the scenarios

The market value factors are besides the stock exchange price another key interest of the model. Therefore they are in the following evaluated for wind on- and offshore as well as for solar energy. Figure 6-12 shows the results of the MVF of the four reference weeks from the reference year until 2050 for the three scenarios. The wind on- and offshore MVF stay relatively constant. The wind onshore MVF are increasing slightly in all scenarios. For the wind offshore MVF the development is more differentiated. Notable is that they are decreasing for the base and CO2 50 scenario while rising in the CO2 100 scenario. To most consistent development can be seen for the solar power MVF, they are decreasing in all scenarios. A decreasing MVF means that the produced electricity has a lower value than the generation of base load power. The reason for that is that solar energy is producing electricity when there is a comparably low residual load level. Therefore the solar energy generation meets a low stock exchange price resulting in a lower MVF than one. The results show that this effect is intensified for all scenarios. The consequences and conclusions which can be drawn from that fact will be discusses in chapter 7.

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base scenario 1.2

Market value factor

1 0.8 solar 0.6

wind-onshore

0.4

wind-offshore

0.2 0 2012

2020

2030

2050

CO2 100 scenario

1.2

1.2

1

1 Market vlaue factor

MArket value factor

CO2 50 scenario

0.8 0.6 0.4 0.2

0.8 0.6 0.4 0.2

0

0 2012

2020

2030

2050

2012

2020

2030

2050

Figure 6-12 Development of the market value factors for the scenarios based on the four reference weeks

The reason for raising the CO2 emission certificate price, on which the scenarios are based, is a political will to lower the emissions. The CO2 emission certificates are traded on a market, therefore this is done through the tightening of the supply of emission certificates. To assess the success of this political instrument the overall CO2 emissions are analyzed. This is done by comparing the total CO2 emissions for one reference week over the future years. The consideration of one reference week is less valid than the consideration of the whole year. This however is not possible because of the limited time horizon of the master thesis and the considerable amount of computational time needed for calculating a whole year. Nevertheless the characteristics of the residual load of the one analyzed week stay the same. Therefore the results of a comparison of one week are still valid. The results are shown in Figure 6-13. Firstly it can be noted that for all scenarios there is a considerable decrease of CO2 emissions. Even in the base scenario the emissions can be decreased until 2020 to 64 %

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and 36 % in 2050 of the reference year. The reasons are that even in the base scenario the CO2 emission certificate price is rising to 43 €/t CO2 in the year 2050 and the increasing share of renewable energy production replacing conventionally generated electricity. The instant increase of the CO2 emission certificate price to 50 €/t CO2 has a notable effect on the total CO2 emissions. They are decreased further to 48 % in the year 2020 and to 33 % in 2050. The effect is relativized until 2050 through the convergence of the CO2 emission certificate price. The difference in 2050 between the base and the CO2 50 scenario is only 3 % relative to the reference year. Nevertheless the goal is not solely to lower the CO2 emissions to a certain level in 2050. For the climate change problem every emitted ton of CO2, on the way of achieving a CO2 emission efficient economy, needs to be considered. Therefore the fast decrease of the emissions is desired. As expected this is best achieved by raising the CO2 emission certificate price to 100 €/t CO2. This measurement will cut the emissions already to only 39 % in 2020 and 25 % in 2050. Surprising might be that the effect of an increase to 100 €/t CO2, despite having a considerable effect in 2020, loses some of the effectiveness relative to the other scenarios. 5000000 4500000

CO2 emissions [t/week]

4000000 3500000 3000000 2500000 2000000 1500000 1000000 500000 0 2012

2020 base

CO2 50

2030

2050

CO2 100

Figure 6-13 CO2 emissions of the reference week 26

Another interesting aspect of the analysis is the transmission grid and its workload. For the analysis it is relevant to consider seasonal changes because of the expected effect of stronger winds in the winter season requiring more transmission from northern to southern regions. There were no relevant variations observed for the different scenarios therefore the analysis is conducted using four reference weeks covering all seasons of the base scenario. To get a clear Figure average values are calculated represented in Figure 6-14.

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The Figure shows the expected result that the big cities are all importing considerable amounts of electricity. Notable is here that this import is reduced for Berlin and Hamburg whilst it stays on the same level for Bremen after a rise in the year 2020. Further there is the trend of first rising imports in the southern regions of Baden-Württemberg and Bayern with a peak in 2030 and a decrease until 2050. The trend in the opposite direction can be seen for the northern regions. Especially the expansion of windoffshore energy in Niedersachsen for example results in considerable amounts exported energy. This reflects the anticipated development which requires the extension of the transmission lines between the northern and southern region of Germany. 80000

import-export difference [MW/h]

60000 40000 20000 0 BW

BY

BE

BB

HB

HH

HE

MV

NI

NW

RP

SL

SN

ST

SH

TH

-20000 -40000 -60000 -80000 -100000 2012

2020

2030

2050

Figure 6-14 Hourly regional import-export difference of the four reference weeks of the base scenario

This effect would be even more significant if the solar expansion would not moderate the difference of produced electricity between the regions. It can be seen that the RES_E model decides primarily to build solar power plants in the southern and western regions. This also explains the big amount of export from Niedersachsen. This state not only has wind offshore but also a considerable amount of solar power. The projected regional distribution of solar power in the year 2030 can be seen in Figure 6-15 with the installed capacity in Wpeak/m2.

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Figure 6-15 Area specific installed capacity of solar power in the year 2030 (Senkpiel et al. 2010, p. 25)

Another interesting aspect of the model is the development of the flexibility costs. As shown in chapter 4.2.1.1 the future power plant park has to cope with increasing load changing gradients. These costs therefore include the costs of ramping and starting-up. The starting-up costs are composed of the costs for the different start-up types and the cold start-up depreciation. The flexibility costs are determined by the volatility of the residual load meaning the more ramping necessary the more costs for ramping and starting up of the power plants is needed. Further the total level of the residual load is determining the magnitude of the hikes in load meaning with a lower residual load there are fewer power plants necessary to change their load resulting in fewer costs. Figure 6-16 shows the overall trend of considerably rising flexibility costs, almost doubling every century. Therefore the conclusion can be drawn that the influence of a decreasing residual load is much lesser than the rising volatility of the residual load. When analyzing the different scenarios the main conclusion is that the more the power plant park is comprised of facilities designed to cope with ramping the lower the flexibility costs are. Notable is however that the rise of the CO2 emission certificate price to 50 €/t CO2 results in higher flexibility costs than the base scenario. The biggest difference can be seen in the 2050 while there is a relatively constant rise from 2012 until 2030. In chapter 8 it is discussed if the extremely rising flexibility costs for the base scenario in the year 2050 can be prevented through other mechanisms rather than adding flexibility to the power plant park.

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800000 700000 flexibility costs [€/h]

600000 500000 400000 300000 200000 100000 0 2012

2020 base

CO2 50

2030

2050

CO2 100

Figure 6-16 Development of the flexibility costs for the scenarios

6.3 Sensitivity analysis The aim of the model is to predict the stock exchange price as a result of the unit commitment of every power plant, this is achieved by optimizing the total operation costs. The sensitivity analysis is an important method to assess the uncertainties of the model. It is evaluating the effects of variations of the input parameters on the results. The assessed periods are the four reference weeks of the year 2012 of the base scenario. Two parameters are varied in this analysis. These are chosen due to their likelihood of differing from the assumed development. The first one are the fuel prices. They are openly traded and affected by a variety of other factors, making a prediction very difficult. The second one is assumed to get more attention with an increasingly fluctuation of the residual load. The intertemporal parameters are assumed in the model to be constant over the assessed period, therefore a variation in the sensitivity analysis might also be interesting as an result apart from the evaluation of sensitivity alone. The variation is implemented in 25 % steps from -50 % to +50 %, the result of which can be seen in Figure 6-17.

modelled average stock exchange price variation [%]

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160% 140% 120% 100% -50%

-25%

0%

25%

50%

80% 60% 40% 20%

parameter variation share of assumed input parameter [%] fuel price variation

intertermporal constraints variation

Figure 6-17 sensitivity analysis of the fuel prices and the intertemporal constraints for the base scenario and the four reference weeks

The results show that when the intertemporal constraints are tightened to 25 % the price is not affected at all. A relevant effect can first be seen when they are further tightened to 50 % which results in an increase of the stock exchange price of 10 %. The loosening of the constraints has a similar effect in the opposite direction. The only difference is that the price is already showing a decrease starting from smaller variations. This result is remarkable because the variation of the power plant park as a result of the different scenarios showed a considerable effect on the price. The conclusion can be drawn that for short term predictions of the stock exchange price in the nearer future the variation of the intertemporal constraints do not considerably affect the result. Nevertheless the scenario analysis showed that in the medium and long-run with a much more volatile residual load the intertemporal constraints will have an increasing effect on the results of the model. The variations of the fuel price have a significant effect on the stock exchange. Even small variations have an overproportional effect on the stock exchange price. The reason for that becomes evident when looking at the overall operation cost distribution. The fuel costs contribute the biggest share to the total costs. (see Figure 6-18)

7 - Result interpretation

- 80 -

120%

share of total operation costs [%]

100%

80%

2.74% 3.92% 6.42%

heat-credit

9.60%

start-up costs

14.33%

part-load penalty costs

negative ramping costs positive ramping costs

60%

10.05%

emission costs transmission costs

40%

41.52%

fixed costs tax costs fuel costs

20%

variable costs 16.70%

0%

Figure 6-18 constitution of total operation costs of the four reference weeks of the base scenario of the year 2012

The sensitivity analysis has shown that the sensitivity of the GESOP regarding the intertemporal constraints is depending on the prediction horizon. Expectably the variation of costs parameters, with the fuel costs the most important, have a very high impact on the stock exchange price. The scenarios showed that the share of fuel costs is expected to rise further; therefore it is advisable to assess the assumptions of the level of the fuel price as well as the development in great detail.

7 Result interpretation Although a considerable variety of aspects of the energy market are integrated, all model results should be interpreted keeping simplifications and methodological shortcomings in mind. Among them are the simplified grid implementation, the missing extension of hydro storage facilities, the missing back coupling of the GESOP and the KW_invest model and the missing development of flexibilities of the conventional power plants. The scenario analysis proved the model to be suitable for a wide variety of analytical studies. In the scenario analysis a mainly political decision to raise the CO2 emission certificate price was assessed. The main finding is, that the more flexible power plant park, incentivized through the higher CO2 emission certificate price is able to better cope with the increasingly volatile residual load in the future. Another expected result is that the increase of the CO2 emission certificate price will have a lowing effect on the climate impacting CO2 emissions. In addition to that expected results it is a remarkable result that the average stock exchange price of electricity is lower in the medium and long-run for a higher CO2

8 - Discussion

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emission certificate price. The reasons for that were explained above. The main outcome of these results is that raising the CO2 emission certificate price is an effective and under the used assumption economical advisable instrument. The development of the MVF showed that the increasing penetration of the electricity market with renewable energy results in a decreasing market value for solar energy. This is consistent with the development of the recent years and findings of other models which found a strong negative correlation of the MVF and the market share of solar energy. (Hirth 2013, p. 232) The MVF affects the investment decision of these technologies in a time after the guaranteed feed-in tariff. The development of the wind power MVF however did not show this correlation in the model. The MVF of wind-offshore and onshore stay on the same level or even slightly increase. Other models find a small negative correlation between the share of wind energy and the MVF. The reason for the difference to the GESOP model might be the as steady assumed interconnection capacity between the regions. (Obersteiner 2012, p. 229) found that the wind market value factor is reduced if interconnector capacity is increased. This indicates that the development of the transmission grid should be integrated in the GESOP model. The stock exchange price proofed to be much more complex to be forecasted in great detail than the MVF. Especially the extreme values of the stock exchange price could not always be replicated by the model. The validation showed a MPE of around 20 % for the validation year. Reasons for that are assumed to be the complex behavior of import-export flows which requires a consideration of the neighboring countries to fully capture these effects. Further the stock exchange price is influenced by other factors for which no data is available for example daily changing fuel prices, therefore it is not possible to capture these effects completely. Besides these errors the model replicates the price pattern and the price level satisfyingly for the further use in the E2S model. The power plant operation pattern indicates that with an increasing share of base load power plants it is necessary to implement instruments to cope with situations of a very volatile residual load. The installation of a high share of gas power plants proofed to be a solution. Nevertheless other solutions might have more favorable stock exchange prices discussed in chapter 9.

8 Discussion The limited time of this thesis required to simplify aspects of the model and to reduce the analyzed time period. Further the computational complexity of the model limited the aspects which could be analyzed. The model is very potent for analyzing different aspects on different levels ranging from a much aggregated level for the stock exchange price to the commitment and operation pattern on the level of single power pants. The used MIP formulation results in a huge variety of analytical possibilities,

8 - Discussion

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unfortunately this approach is much more computational complex than a linear optimization problem. The computational effort was reduced already by the implementation of the rolling horizon method. Nevertheless further reductions should be pursued. The flexibility of the model qualifies it for a big variety of tasks. The thesis focused on the modeling of the stock exchange price and the market value factors as a result of the operation pattern. Nevertheless the model can easily be adjusted for other studies for example regarding the grid or the import-export development. Another focus of this thesis was the validation of the results of the KW_invest model because it is not taking into account the intertemporal constraints of the power plant park. Therefore the GESOP model results are interesting for the adjustment of the KW_invest model. Here the difficulty is how to implement the exchange of data. The analysis of the scenarios showed that the results of the KW_invest model for the base scenario proofed to not have enough flexibility of the power plant park available for the expected very volatile residual load in the following years of 2030. Possible functional enhancements of the model which are derived from the results are the implementation of an extension of the storage facilities, which is already worked on. (Santa 2014) The limited potential of storage water indicates that other technologies will play a role in the future energy system therefore the implementation should also include these. Besides this the results of the scenario analysis indicated further enhancements necessary to the implemented grid. This should be implemented endogenous rather than exogenous. An optimal implementation here would take into account the results of the model regarding the power flow for the transmission lines and the losses of the transmission. Similarly the import-export aspect of the energy system should be investigated regarding the relevance for the model results. Another aspect which is statically implemented is the heat load. The results showed that during the winter season with a high share of renewable energy the heat load is a relevant aspect for the operation of the power plants. Therefore an endogenous modelling would also be a possible enhancement. Another interesting aspect of the stock exchange price is the occurrence of negative prices. Already in the reference year 2012 there are instances of this phenomenon. The expected development of increasing times of negative residual loads makes the formation of the negative prices more likely in the future. The model is designed to shut-down the power plants in times of negative residual load and only keep the CHP plants running to cover the heat load. For the modelling of negative prices another approach would be needed.

9 - Conclusion and prospect

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9 Conclusion and prospect The first and foremost result of this study is that the developed GESOP is a potent and versatile instrument for various study cases, combining the explorative aspects of the E2S models and an unit commitment optimization approach. It can be used for short term and long-term investigations of different aspects of the energy system. In addition it is a tool to assess the results of the E2S model. Nevertheless there is room for improvements regarding the computational time and the implementation of more advanced aspects of the energy system. The model is transparently implemented and it is therefore possible to pursue further development. Problems demonstrated in the long-run to cope with the volatile residual load results in many more possible instruments which could be analyzed by the model. Among them are demand side management systems to shift load in the time of hikes to smoothen the residual load curve or the extension of the grid. Besides these developments the interaction with the ES2 model is a future challenge which should be automated rather than the manually implementation used in this thesis. A possible implementation would be the yearly interactive calculation of the E2S and the GESOP model combining the explorative and optimization aspects of both models.


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