Impact of Bidding Zone Configuration on the French Electricity Network

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Degree project in

Impact of Bidding Zone Configuration on the French Electricity Network

Alexandre Canon

Stockholm, Sweden 2014

XR-EE-EPS 2014:005 Electric Power Systems

Second Level,

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KTH Royal Institute of Technology

Master Thesis

Impact of Bidding Zone Configuration on the French Electricity Network

Author:

Alexandre Canon

Supervisor:

M. Mahir Sarfati

Examiner:

Dr. Mohammad Reza Hesamzadeh

RTE Supervisor:

M. Vincent Protard

A thesis submitted in fulfilment of the requirements for the degree of Master Thesis

in the

Electric Power Systems Electricity Market Research Group

April 2014

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Declaration of Authorship

I, Alexandre Canon, declare that this thesis titled, ’Impact of Bidding Zone Configu- ration on the French Electricity Network’ and the work presented in it are my own. I confirm that:

This work was done wholly or mainly while in candidature for a research degree at this University.

Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated.

Where I have consulted the published work of others, this is always clearly at- tributed.

Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.

I have acknowledged all main sources of help.

Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.

Signed:

Date:

i 07/03/2014

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KTH ROYAL INSTITUTE OF TECHNOLOGY

Abstract

Electricity Market Research Group

Master Thesis

Impact of Bidding Zone Configuration on the French Electricity Network by Alexandre Canon

At the beginning, the first operation concern of electricity transmission networks is to ensure the security of power system. These interconnections are also used for the purpose of exchanging electricity from one country to another. They have a central role to achieve the integrated European electricity market by allowing electricity supplier to sell energy to a customer in another EU country. This enables market players to trade electricity depending on opportunities and prices in various bidding areas in Eu- rope. The interconnections contribute therefore to the effectiveness of the European electricity market. The volume of trade is however limited by the physical limitations of the transmission lines, which are determined by the TSOs through cross border capacity calculations and assigned to the actors based on different market mechanisms (e.g.

capacity allocation).

The fast development of renewable energy sources has increased the imbalances between supply and demand. This further increases constraints on transmission lines, including the interconnections between neighboring countries. In order to manage this problematic situation, the modification of the bidding areas configuration is often considered as a solution. Different studies developed methods based on nodes aggregation or minimization of re-dispatching costs to define the price areas. However, the impact of this kind of measure on the overall system is not well studied.

This master thesis work presents a general methodology to study the impact of a new bidding zone configuration in the French electrical network from a market point of view.

In order to define a relevant bidding zone configuration in the system, physical flows on the lines are determined and the two ends of binding links are located in two different bidding zones. Then, electricity price, social welfare evolution and modification of the flows due to the new generation pattern are presented in order to evaluate and analyze the impact of the new bidding zone configuration on the market.

The modelling limits are analyzed in order to evaluate the proposed approach.

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Acknowledgements

I would like to express my gratitude to all the professionals who have been available in order to answer my questions and help me during these six months within the French Transmission system Operator RTE R´eseau de Transport d’Electricit´e.

Thus, I am particularly grateful for the assistance given by Mr Vincent Protard, Mr Lucian Balea and all members of the Cross Border Market Design department. Their collaboration and the valuable advice they gave me enabled me to fulfil this research study and to experience a rewarding internship.

Finally I would like to thank my supervisor Mr Mahir Sarfati, PhD candidate in Electric Power systems, and my examiner Dr Mohammad Reza Hesamzadeh for their availability and their involvement. Their support throughout this project had been very helpful in the carrying out of this master thesis.

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List of Figures

2.1 Supply curve and demand curve for both isolated areas A and B (Source:

RTE) . . . 4

2.2 Supply curve and demand curve for both areas A and B with large transmission capacity (Source: RTE) . . . 5

2.3 Supply curve and demand curve for both areas A and B in case of congestion (Source: RTE) . . . 6

2.4 Illustration of the difference between Scheduled Exchanges and Physical Flows (Source: RTE) . . . 8

2.5 Comparison of different Admissible Domains for NTC/ATC or Flow- Based Method . . . 9

2.6 Capacity Allocation Mechanisms (Source: RTE) . . . 10

2.7 Nordic System with Market Splitting in Sweden and Norway . . . 13

2.8 Loop Flow and Transit Flow definitions . . . 14

2.9 Activities in the Bidding Zone Review Process . . . 17

3.1 Antares Interface with the considered European areas in the Simulations . 21 3.2 French hourly Consumption (mean value over 50 Time-Series years) . . . 22

3.3 Intra Daily Modulation Parameter enables to smooth the Hydraulic Pro- duction (Source: RTE) . . . 25

3.4 Load Profile during the day (for the example purpose, completely fictitious) 29 3.5 Marginal Prices throughout the day taking into account the constraints or not . . . 30

3.6 Operating Cost Evolution if the constraints are neglected (compared to the case with constraints) . . . 31

3.7 Marginal Price Evolution if the constraints are neglected (compared to the case with constraints) . . . 32

4.1 No Congestion situation, Physical Flow lower than the line capacity . . . 36

4.2 Congestion through the line, Redispatching example . . . 37

4.3 Modifications performed from the Base Case to obtain consistent Physical Flows . . . 45

4.4 Loop Flows due to Internal Exchanges in three Bidding Zones . . . 47

5.1 Areas used as starting point in the Study . . . 50

5.2 Mean values over 50 years of the hourly Physical Flows in France . . . 51

5.3 Statistics of Physical Flows France South-France North over 50 Monte- Carlo years . . . 53

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List of Figures viii

5.4 Commercial Exchanges (MWh/h) in initial Bidding Zone Configuration:

mean values over 50 Monte-Carlo years; Statistics in both ways of the interconnection . . . 56 5.5 Commercial Exchanges (MWh/h) in new Bidding Zone Configuration

(2500MW capacity): mean values over 50 Monte-Carlo years; Statistics in both ways of the interconnection . . . 57 5.6 Commercial Exchanges (MWh/h) in new Bidding Zone Configuration

(4000MW capacity): mean values over 50 Monte-Carlo years; Statistics in both ways of the interconnection . . . 57 5.7 Commercial Exchanges from France South to France North (mean value

of each hour 12am) . . . 58 5.8 Duration curves for borders with Switzerland when FR2-FR1 is con-

strained (solid line: 2 Bidding Zones configuration; dashed line: 1 Bidding Zone configuration) . . . 61 5.9 Switzerland used as New Path for Electricity . . . 61 5.10 Evolution of Price Convergence due to new Bidding Zone Configuration

throughout European borders . . . 63 5.11 Price Divergence Indicator Evolution for the border FR1-FR2 . . . 64 5.12 Hourly European Social Welfare Evolution throughout Monte-Carlo syn-

thesis year . . . 65 5.13 Hourly Consumer, Producer and Trading surpluses Evolution (mean value

over 50 Monte-Carlo years) in the different Bidding Zones . . . 66 5.14 Loop Flows measure for one Bidding Zone configuration; for two Bidding

Zones configuration - January 9am . . . 68 5.15 Loop Flows measure for one Bidding Zone configuration; for two Bidding

Zones configuration - June 10am . . . 68 5.16 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator

for one Bidding Zone configuration - January 9am . . . 69 5.17 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator

for two Bidding Zones configuration - January 9am . . . 70 5.18 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator

for one Bidding Zone configuration - June 10am. . . 70 5.19 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator

for two Bidding Zones configuration - June 10am . . . 71

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List of Tables

2.1 Measures proposed by THEMA to deal with Loop Flows problematic. . . 16 3.1 List of the Power Plants with their characteristics. . . 28 4.1 Clusters used to implement the economic results in the AC Load Flow

simulations . . . 43 5.1 List of the Simulated Countries and Abbreviations . . . 49 5.2 Structure of the New Bidding Zones Configuration in France . . . 52 5.3 Congestion Time rates depending on the capacity link between the two

French Bidding Zones . . . 54 5.4 Colour Scale for net values in the different maps . . . 55 5.5 Evolution of the Congestion time rates in European interconnections when

FR2-FR1 is constrained . . . 59 5.6 Evolution of the Congestion time rates in European interconnections when

FR1-FR2 is constrained . . . 60

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Chapter 1

Introduction

Interconnections have a significant role in the European electricity market since they increase the trading possibilities within the system and improve the overall security of supply. The capacity through the different interconnections is however a scarce resource due to physical limitations. Market mechanisms such as capacity calculation and allocation enable to determine the amount of electricity which can flow on the interconnections and how to assign it to the market players. Many challenges appeared with the increase of renewable energy sources in the system. The evolutions of the energy policies in each European country and the foreseen entry into force of the network code have also a significant impact on the electricity market.

This master thesis report deals with a topical research field: the bidding zone configuration in a meshed electrical network. The current context in the European system is suitable for this kind of project to the extent that this evolution of the market is often seen as a possible solution to deal with different issues: congestion management, localisation price signals, balance between internal and cross-border flows...

The main objective of this paper is to define a general methodology in order to define a relevant bidding zone configuration in France and study its impact on the European electricity market based both on physical and economic considerations.

The present report is organized as follows:

The second chapter gives an overview of the research field and a literature review of the studies related to this subject. This section enables to approach the main challenges at stake with bidding zones studies and the possible consequences of market splitting in Europe.

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Chapter 1. Introduction 2

Then the models used for the simulations as well as the set of data are presented. The assumptions taken into account and the limitations of the study are also clarified.

Chapter four corresponds to the general methodology to define a new bidding zone on the French territory and then, based on different indicators, the impact of such evolution is assessed. In this perspective, exchanges evolution, electricity prices and social welfare are of interest for economic purposes and indicators for loop flows study are defined.

The results chapter gives the conclusions obtained when the methodology is applied for a case study with a median scenario at time horizon 2030. Congestion management considerations enable to define new bidding zones in France and the impact on the European system is presented. To finish the limits of the study and future possible studies are given.

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Chapter 2

Background and Literature Review

2.1 Importance of the Exchanges between different Bid- ding Zones

The European electricity network represents nowadays 35 interconnected countries with the same objectives: network safety, security of electricity supply and economic efficiency.

This large meshed network improves the stability of the whole system (larger inertia of the system) and enables to increase the total social welfare within the electricity market.

2.1.1 Social Welfare and Day-Ahead Social Welfare

The notion of social welfare in an electricity market can be defined in different ways according to the considered study. On the whole, this quantity enables to measure the overall market value for the involved actors based on different considerations. Thus market performances are taken into account but other aspects can also be involved such as ecological concerns (impact on the carbon dioxide emissions), impact on the society, social modification...

In order to have an unbiased definition, the day-ahead social welfare is often used because it is only based on economic parameters of the overall system. The definition used for this report is the sum of the different actors’ surpluses within the market:

Consumer surplus: difference between the amount a consumer is willing to pay minus the market price

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Chapter 2. Background and Literature Review 4

Producer surplus: difference between the price a producer is actually selling his energy minus his bidding price on the market

Congestion rent if there are interconnections in the system (definition given below)

In this paper, social welfare implicitly refers to the day-ahead social welfare since other considerations depend on subjective interpretation and indicators.

2.1.2 No Commercial Exchanges

Considering two areas, it is very easy to understand why such interconnections are essential. Assuming perfect competition, if the two areas are isolated, the price cross in each area corresponds to the intersection between the demand curve (aggregated loads with decreasing order prices) and the supply curve (aggregated offer with ascending order prices). Thus, only demand bids with a larger price than the spot price and offer bids with a lower price than the market price are accepted. This price maximizes the total surplus (for both producers and consumers) and only the cheapest power plants are started. Each area presents its own electricity price (figure2.1).

Figure 2.1: Supply curve (blue) and demand curve (red) for both isolated areas A and B (Source: RTE)

2.1.3 Interconnection between two areas: Importance of the Trans- mission Capacity

Now if the two areas are interconnected by a link which presents a certain transmission capacity, the total operation cost (overall price the producers have to pay to produce the needed generation) to satisfy the whole demand can be decreased.

Assuming that area A presents a lower price than area B, it would be beneficial (from the Day-Ahead social Welfare point of view) to export energy from A to B. Price in A

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would increase (more expensive power plants are started to cover the export power) and price in B would decrease (expensive groups are stopped since import helps cover the load).

If the transmission capacity is large enough, the exchanges will occur until the prices in areas A and B are the same (this is the economic optimum) (figure2.2).

In the case where the transmission capacity is not enough to reach this optimum point, congestion will occur. Price in A will increase, price in B will decrease but a price difference between the two areas will remain (figure2.3). This price difference generates a so-called “congestion rent” (2.1). The congestion rent is collected by the TSOs in order to reinforce the interconnections and increase the cross-border capacities.

Congestion Rent = Amount transmitted through the line × P rice Dif f erence (2.1)

Figure 2.2: Supply curve (blue) and demand curve (red) for both areas A and B with large transmission capacity (Source: RTE)

The principle explained above can be generalized for n areas. An efficient congestion management is one of the key to improve both technical and economical performances of the European network.

2.1.4 Congestion Management

Inside a bidding zone, measures are taken in order to deal with congestion into the electrical system. Different methods are available depending on the considered timeframe.

Thus, in short term, congestions can be solved with costly and non-costly measures.

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Figure 2.3: Supply curve (blue) and demand curve (red) for both areas A and B in case of congestion (Source: RTE)

TSOs will first use non-costly measures like modification of the topology of the grid and Power Shifter Transformer (PSTs) taps changes. If these measures are not able to solve the constraint, TSOs can apply costly measures such as redispatching or counter-trading.

Counter-trading corresponds to a transaction initiated by the TSO between two areas to relieve a congestion between these two areas. The location of the energy modification is based on the merit order or another independent method. Redispatching represents an increase and decrease of the level of production of specified unit in order to reduce a given constraint. The choice of the affected units is based on their sensitivity on the constraint. Redispatching can be internal (inside a country), external (measures in a country to relieve a constraint in another area) or cross-border (increase of production in one country and decrease in another one). For long-term solutions, TSOs reinforce the grid by building new lines and PSTs. Grid enforcements represent the ideal solution to the extent that the amount of congestions is decreased in the system. These investments are expensive and have to be balanced with the overall gain due to congestion reductions.

In either solution, the grid can be exploited without any constraint. Inside the bidding zone, congestions are somehow ignored in order to freely trade electricity with a unique price. In Europe, such approach is impossible since the network is not strong enough, and will never be to solve all congestions. Alleviate all the network constraints does not represent the optimum solution in economic point of view. The management of congestions will therefore remain. Two mechanisms in order to manage capacity are used: first the capacity calculation which computes the volume of electricity to be traded

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between areas and secondly the capacity allocation which allocates the available capacity to the market players.

2.2 Capacity Calculation and Allocation

2.2.1 Capacity Calculation

The transmission capacities between different bidding zones are very interesting from a market point of view. It is therefore of interest to use efficient and coordinated mechanisms to compute these limits given by the TSOs. The capacities cannot be defined arbitrarily by TSOs since it impacts other bidding zones, these capacities have to be agreed on the regional level. An objective and systematic method of computation is necessary.

Two notions have to be distinguished: physical flows which are the actual flows on the grid and commercial exchanges that result from transactions between market players.

The European electrical system cannot be considered as a unique copper plate since the network is meshed and the power flows are ruled by the Kirchhoff’s laws. Thus, if the commercial exchange between France and Germany is increased by 100MW, it is impossible to get a physical flow of 100MW between those two countries. The power flow will follow different paths according to the network topology and system situation (figure 2.4). The TSO computes the interconnections capacities based on the possible situation during the exchanges. The main goal of the capacity calculation method is to convert the calculated available physical margins on power lines into commercial capacities for different timeframes (annual, monthly, weekly, D-2, D-1).

NTC Method

The first method used by RTE with the neighbouring bidding zones is based on Net Transfer Capacities (NTC). Based on a base case (topology, exchanges assumptions, consumption and production forecasts), for different fault hypothesis in the system (N, N-1. . . ) the physical margin (physical capacity minus the flow on the line for the given situation) is computed for the most impacted lines. The physical margin is then divided up between the different possible paths (cf. Kirchhoff’s laws) in an equitable way (same margin for all the considered paths). In order to compute the commercial capacity for each interconnection, the notion of Power Transmission Distribution Factor (PTDF) is introduced. This factor shows how the flows on a line are modified when the exchange between two bidding zones is modified. For instance, if the commercial exchange between

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Figure 2.4: Illustration of the difference between Scheduled Exchanges and Physical Flows (Source: RTE)

two bidding zones A and B is increased by 100MW and the flow through a given line increases by 30MW; the PTDF for this border and branch is 0.3. This example shows that the new power flow has to be known in order to compute the PTDF matrix. It is necessary to determine which node will be modified when a change of import/export occurs through an interconnection. Generation Shift Keys (GSKs) are used to translate an incremental cross-border exchange into incremental nodal injections. By this method, it is possible to determine the power flow modifications in the electrical system due to changes in the commercial exchanges between bidding zones. Then, the PTDF matrix can be computed.

P T DF_k,ij = ∆P_trans−k

∆P_ij = Changed Intensity on Line k

Changed Exchange between Bidding Zones i and j (2.2)

Once the margins are divided up and the PTDF matrix is computed, the commercial capacities are easily obtained by dividing the physical margins by the PTDF. For each interconnection, the commercial capacity is computed for relevant N-1 situations. Since the system has to remain safe in all N-1 situations, the final commercial capacity is defined by the lowest obtained value. Thus, all commercial capacities (for each interconnection) are simultaneously feasible.

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Flow-Based Method

Another method developed in order to compute the interconnection capacities is the so-called Flow-Based (FB) method, which is on the eve of a CWE go live [1]. This methodology takes into account the constraints on the most impacted lines in the system for all the considered outage scenarios. Based on this information and the PTDF matrix, a supply domain is defined in order to ensure the security of the network. The figure2.5 shows the security domain for both market coupling models (ATC and FB) for a simple case composed of 3 bidding zones. The x-axis corresponds to the commercial exchanges from country A to country B and the y-axis the exchanges from country A to country C. The yellow area defines the admissible domain. It can be shown that the market possibilities are higher in the FB method since the whole security domain is available for the market. In the ATC/NTC method, the domain is limited due to TSOs choices (fixed commercial capacities).

Figure 2.5: Comparison of different Admissible Domains for NTC/ATC or Flow- Based Method [1]

Compared to the ATC/NTC method, which could be a choice made by the TSO, the FB method gives the security domain itself. In both ATC and FB Market Coupling,

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the optimization problem presents the same objective function (maximize the total day ahead social welfare) but with different formulation of grid constraints. The trading opportunities are considerably increased in a FB model, since the net positions are market driven, the odds to get a price convergence between the different bidding zones is higher than with a simple ATC model. The model does not compute fixed limits for the commercial exchanges but gives a domain where the net positions (and therefore exchanges) can be optimized.

2.2.2 Capacity Allocation

There are two main capacity allocation mechanisms used in the European Electricity Market: explicit auctions and implicit allocation. In the first one, the energy exchanges and the transmission capacity allocation are decoupled. Actors need to acquire the energy and the capacity separately. With implicit allocation, the energy and the cor- responding transmission right are simultaneously traded; actors only need to take into account the energy trading since everything is coupled. The implicit allocation is the target model for the Day Ahead Integrated European Market [2].

In the different borders between France and its neighboring countries, the capacity allocation mechanisms are mostly as follows (figure 2.6):

Figure 2.6: Capacity allocation mechanisms (Source: RTE)

The long-term transactions are done through explicit auctioning: annual, monthly auctions. A secondary market begins where the different actors can trade the commercial capacity they acquired. At the beginning of D-1, the transmission rights have to be nominated in order to be effective. Then, the principle of “Use It Or Sell It” (UIOSI) is

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applied: if an auction is not used, it has to be sold again, the actor will be financially compensated according to the Harmonized Auctions Rules [3]. Market players can nom- inate transmission rights in both ways of an interconnection. Netting is applied. For example if an actor nominates 100 MW from a bidding zone A to a bidding zone B and another 50 MW from B to A, the used capacity is only 50 MW from A to B. This method enables to take into account the actual exchanges program for each link between bidding zones in both ways in order to propose all the available capacity and therefore optimize the use of the interconnection. Indeed, the nominations performed on both ends can either use a share of the computed capacity or relieve a part of it if they are in the opposite direction.

During the day-ahead market, either the explicit auction is used, or an implicit auction is applied. In the second case, the transmission capacities are traded with the energy.

In the first case, the process is similar to the long-term market. The only difference is the principle of “Use It Or Lose It” (UIOLI): if an auction is not used, it is lost and the actor is not financially compensated for not using the capacity. The remaining netted capacity will then be offered to the intraday market with implicit or explicit auctions.

In this context where the interconnections capacities are essential for an efficient development of integrated European electricity market, using the adequate capacity calculation method associated with an adequate allocation mechanism. The target model for the day ahead in a meshed network given in the network code on Capacity Allocation and Congestion Management (CACM) is Flow Based Market capacity calculation and market coupling for the allocation mechanism [2].

2.3 Bidding Zones Studies

2.3.1 Bidding Zones

“A bidding zone is the largest geographical area within which Market Participants are able to exchange energy without Capacity Allocation” [4]. It is also assumed that there is not any constraint inside a bidding zone and in case of internal congestion the Trans- mission System Operator (TSO) is responsible to relieve the involved transmission line.

Each bidding zone is therefore considered as a copper plate from the market point of view and the only possible congestions would occur between these bidding zones. Capac- ity calculation and allocation are used to manage exchanges through interconnections between bidding zones.

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Chapter 2. Background and Literature Review 12 2.3.2 A suitable context for Bidding Zone Configuration studies in

Europe

Market splitting as a whole and its impact on the electricity market has become a large subject of interest. Thus, the studies related to that domain are relatively limited and mainly confidential since most of them are internal investigation and research.

Measures have to be taken in order to achieve an efficient congestion management. The study presented in [5] is the perfect example of such context. The author analyses the impact of zonal pricing on total system cost for different potential delimitations. The clustering method used is based on an algorithm (Ward’s minimum variance method) which enable to aggregate areas with price which cause the minimum decrease in ho- mogeneity quality of the cluster. Different cases are studied and compared in Europe according to the number of defined clusters. Although the study is only based on a total system prices comparison, it is possible to conclude that national borders are not necessary suitable clusters and it is very complicated to find an optimal solution.

The development of this kind of study could enable to investigate different problematic in the current European market configuration:

The balance between the amount of congestion management costs to reflect either in the market (different electricity spot prices in the bidding zones) or through the consumer price (redispatching costs have an impact on the consumer costs within a bidding zone)

To send relevant long-term price signals to face the new market organization where the function of transmission is separated from the electricity generation and the large development of renewable energy sources

Study the impact of both internal and cross-border transactions; this axis embrac- ing loop and transit flows problematic.

The reconfiguration seems to be the perfect solution for all the problems faced by the TSOs, but other solution can be found for each of the three issues: congestion, localisation and impact of internal congestion on other bidding zones. For example, development of new lines, introduction of new market design... all of these new solutions will help to deal with the increase of electricity trades, the important development of renewable sources and their integration into the market...

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Chapter 2. Background and Literature Review 13 2.3.3 Market Splitting already implemented in some European coun-

tries

Market splitting is implemented in some countries in Europe. In this section, a brief overview of the reasons which led to modify the electricity market mechanisms and choose to create several bidding zones is presented.

From one country to another, the causes for market splitting implementation are very different and strongly related to the market situation in the considered bidding zone.

Norway, for instance, resorts to market splitting in case of structural congestions, to ensure the security of supply, to prevent the impact of important maintenance activities. . . The bidding zone configuration is very flexible and they can change the borders inside the country quite easily.

The production in Sweden is mainly located in the North of the country whereas the consumption is in the South. When congestions occurred in the Swedish system, the export towards ¨Oresund connection was limited and both losses for Danish customers and gains for Swedish producers used to happen. Four bidding zones have therefore been created (figure 2.7) since it was an interesting option to deal with internal bottlenecks without modifying the interconnection capacity with Denmark and without unreasonable countertrade commitments [6].

Figure 2.7: Nordic System with Market Splitting in Sweden and Norway [7]

In Poland, the electrical system is facing several problems and congestions due to a weak transmission grid. A nodal energy market is currently of interest and could be implemented in the Polish system in order to improve the situation.

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New bidding zone configuration is a considered solution in order to improve congestion management, prevent from impacts due to planned works in the system. . . Due to an important meshed network in Europe, other problematic such as price signals or loop flows can increase the interest in this new market design.

2.3.4 Price signals considerations

Price signals are an essential point in order to properly develop the European network.

These signals are considered good and useful if they accurately reflect the network’s physical constraints. Modifying the bidding zone configuration in such way that congestion would be visible through congestion rent (price difference between 2 bidding zones due to congestion) is studied as an eventual congestion management method [8].

The investments would be considerably improved in case of relevant and accurate price signals in the network.

The possible inconsistency between generation investments timeframes and bidding zones lifetimes represents a significant shortcoming. Secondly if a large amount of new generation units is built in the same area, the price will change and the signal as well.

Other mechanisms, like relevant subsidies, are another solution.

2.3.5 Loop Flows problematic

A commercial exchange realized between two bidding zones does not only affect the flow between these bidding zones. It has a significant influence on other zones: loop flows or transit flows. The examples below (figure 2.8) give the definitions of these different kinds of flows for a simple situation with three bidding zones involved: Areas A, B and C.

Figure 2.8: Loop Flow and Transit Flow definitions

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Loop Flow can be defined as the flow over bidding zones (areas B and C) caused by a scheduled exchange within another bidding zone (origin and destination in area A).

Transit Flow can be defined as the flow over bidding zones (area B) caused by origin (in area A) and destination (in area C) in two different bidding zones.

Loop flows and transit flows can decrease the available capacity of neighbouring countries even if they are not involved in the scheduled exchanges. Furthermore, loop flows are not controlled through capacity calculation and allocation, counter-trading or redispatching are therefore the only way to get rid of them in case they accentuate a constraint in the system. Creating new bidding zone could enable to transform loop flows into transit flows and take them into account in a flow based market coupling mechanism [9]. Transit and loop flows have to be handled with regional coordinated capacity calculation and allocation.

In the European meshed network, the presence of unscheduled flows is bound to happen and sometimes completely unexpected. Loop flows or transit flows are not necessarily synonyms of constraints since these flows can both have a restraining and relieving effect on the grid [10]. The more exchanges are submitted to allocation mechanisms on a small area (i.e. small bidding zone), the more the system is controlled and predictable on the other hand a smaller area will introduce larger uncertainties for the consumption and renewable energy sources. Market splitting is therefore of interest in order to decrease the impact of unscheduled flows on the European electricity market.

The bidding zones configuration issue is an important matter since unconstrained flows within a given area could cause unplanned flows and impact the neighbouring systems.

As they are not constrained by any capacity calculation and allocation mechanisms;

the flows have a priority access in the electricity network. Different studies have been performed to determine the impact of such exchanges in Europe based on statistical point of view using available database or model to compute the flows and compare them with the physical ones. The Flow-Based method combined with a new bidding zone configuration could be a solution. It is however very complicated to know who is responsible for a given unplanned flow (all flows which were not foreseen during the market processes) and every country should keep in mind that their own exchanges have also an impact on these flows. It is therefore more adequate to sum all the created flows instead of studying only one isolated bidding zone. A study has also been performed to show that loop flows are completely inevitable in the European meshed network and instead of modifying the bidding zone configuration they should be taken into account in the network codes. The problematic related to loop flows and bidding zone configurations has been approached in [11] [12] [13].

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Table 2.1: Measures proposed by THEMA to deal with Loop Flows problematic Measures considered to deal with loop-flow issues (THEMA report) Short Term Measures Modification of the topology of the grid (substation, PST)

Load/Generation Patterns Modification

Medium Term Measures

Capacities values reduction New Bidding Zone Configuration

Flow-Based Market Coupling Market Signals for Renewable Sources

Long Term Measures Grid Investments

2.3.6 THEMA Study

THEMA study in [14] emphasizes the need to find a solution about this phenomenon which provokes a decrease in the market efficiency, endanger the system and have a negative effect on the incentives effectiveness. Different measures for different timeframes are proposed (Table2.1).

THEMA used historical data for both market and physical flows for a quantitative analysis. Based on these results, an idea of the amount of unscheduled flows in Europe is presented. Then using a model on software GAMS, different solutions in order to decrease the amount of loop flows are studied. A physical grid model is needed to investigate the loop flows origin. THEMA did not have any physical model; it is therefore not possible to conclude on loop flows origin since the impedances are not taken into account.

The consulting group proposed different solutions in order to decrease the impact of unplanned flows on the system. The conclusion reflects that Flow-Based Market Coupling combined with an adequate bidding zone configuration is a possible solution [14]. A coordinated grid development is also highly recommended.

2.3.7 Bidding Zones studies: an open topic

The bidding zones configuration is therefore a highly topical issue since this network evolution could be of interest in different cases: bottlenecks management, price signals, loop flows... However, this research field is quite new and many considerations have to be taken into account. The current knowledge on market splitting is not enough to accurately understand the impact on the market liquidity, the competition – for instance smaller bidding zones could increase the risk of market power...

In this context, through the Network Code on Capacity Allocation and Congestion Man- agement [15], Agency for Cooperation of Energy Regulators (ACER) invited ENTSO-E

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to start a Bidding Zone Review process (figure2.9). In order to study the influence of existing bidding zones on electricity market and their possible modifications, ACER and ENTSO-E cooperate and the Terms of Reference [9] were presented during the Regula- tory electricity Forum in Florence in November 2012. The involved TSOs elaborated a technical report based on the current network situation [4]. This report mainly focuses on congestion issues and management based on different aspects: physical congestions in the network, security of supply, considered measures, indicators used to determine unscheduled flows. . . Once the review launched, different bidding zones configurations will be analyzed and compared taking into account both technical and economic aspects.

Figure 2.9: Activities in the Bidding Zone Review Process [9]

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Chapter 3

Model Presentation

In this part, the different models used both for electricity market modelling and load flow simulations are presented. The content mainly focuses on the economic part which is the most used in the bidding zone configuration impact study. The inputs used in the simulations are also detailed.

3.1 Electricity Market Modelling

The internally developed software used for the simulations – called Antar`es – enables to combine both electricity market modelling (economic dispatch) via supply-demand equilibrium under constraints and Monte-Carlo simulations. The model is designed in order to simulate the market mechanisms inside a bidding zone but also to take into account the exchanges through interconnections. Thus, it is possible to simulate the whole European system.

3.1.1 Economic Dispatch

Economic dispatch principle is based on the supply and demand mechanism [15]. In a given area, the electricity spot price is assumed to be determined by the intersection of the supply and demand curves. This trade enables to maximize the total surplus from a general point of view.

Antar`es uses the cheapest power plants to cover the load in the most efficient way considering the constraints. Thus, economic dispatch is based on a minimization of the overall cost in the system (3.1) subject to different constraints such as power plants availability, links properties, defined relations between different flows... The time span

18

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Chapter 3. Model Presentation 19

used is of one year and the time resolution is one hour in order to be consistent with the resolution used in the French wholesale electricity market.

M in(Ω) =X

t

X

G

X

z

B(t, g, z)Q(t, g, z) (3.1)

Where:

B(t,g,z) is the bid of the power plant G in zone z at hour t

Q(t,g,z) is the generated power in power plant G in zone z at hour t

In the model used in this study, the approach is composed of three different steps:

A first optimization for hydro and thermal generation is run disregarding the unit commitment constraints. This enables to use only linear programming since the approximation gets rid of the integer variables.

Then the thermal constraints (minimum stable power, minimum up/down time, must-run conditions...) are forced.

To finish, knowing the committed thermal fleet for the given hour, a second linear optimization is run.

3.1.2 Monte-Carlo Simulation

The main advantage of the software used in the study is the possibility to combine electricity market modelling with Monte-Carlo simulations. In the electricity market, many parameters are uncertain. There is a probability that a power plant is not available during a given hour due to maintenance work or failure in the system, renewable energy sources (wind speed, solar energy) depend on the considered year...

By using different coherent set of data, typical years can be simulated. The software can also determine whether or not a power plant is available based on the outage rate and duration given as input... Moreover, the study is based on 2030 forecasts data, then, using Monte-Carlo simulations enables to obtain more realistic and accurate results.

Monte-Carlo method is based on estimation of output variables by random observations [16]. Since it is completely impossible to know the 2030 characteristics regarding weather conditions, group availability and amount of consumption, Monte-Carlo represents an interesting alternative.

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An estimate of the expected value of a random variable X can be obtained by computing the mean value (3.2) of n independent observations x_i of the considered variable [16].

m_X = 1 n

n

X

i=1

x_i (3.2)

This Monte-Carlo method, called simple sampling, is the one applied in this study.

Different observations of uncertain input parameters are realized and then, the electricity market is simulated with these different values. The mean values of the output variables give a better estimate of the reality of the system. It can be shown that the variance of the estimate obtained with the simple sampling Monte-Carlo method is given by (3.3).

V ar[m_X] = V ar[X]

n (3.3)

The number of observations has a significant impact on the results accuracy. It has been decided in the study to simulate 50 years of Monte-Carlo. This sample is historically large enough to obtain relevant results and it is possible to deal with the results via Excel.

3.1.3 Interface

With Antar`es software, an area is defined as a node in the interface. In this node, consumption and production data are given (please refer to section 3.2) and for each Monte-Carlo year simulated, the program randomly pick one the available time-series.

In each area optimization is performed. Links between areas are also modelled and some constraints can be implemented to take more parameters into account. Figure3.1shows the graphical interface of the software used.

The following countries are simulated: Austria, Belgium, France, Germany, Great Britain, Ireland, Italy, Luxembourg, Netherlands, Northern Ireland, Portugal, Spain and Switzer- land.

3.2 Inputs

The input data in electricity market studies are very important since they determine the time horizon and the purpose of the study. In this study, programmes of production and consumption realized by RTE for the time horizon 2030 are used. This choice enables

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Figure 3.1: Antares Interface with the considered European areas in the Simulations (color orange for the 25 French areas)

to be more flexible on the considered assumptions and gives one possible evolution of the future network expansion. The following assumptions are taken into account:

Moderate consumption growth

Slight decrease of the French nuclear installations

Development of Renewable Energies

The initial set of data available corresponds to different already-made time-series both for consumption and the different kind of productions for European countries. In France, the same data are available but for 25 areas. This enables to be more accurate on the production distribution inside the country and determine a new bidding zone configuration by aggregating smaller areas. The available information is presented in the following sections. In order to make decisions, a sensitivity analysis of the input/scenarios should be made, for example with the four scenarios of ENSTO-E Scenario Outlook and Adequacy Forecast [17].

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Chapter 3. Model Presentation 22 3.2.1 Load

The load is obtained for each country based on historical data and forecasts at the time horizon 2030 with the assumption of a moderate growth. 100 time-series years (TS years) are available which means that for 100 years, the consumption for each of the 13 countries is known at a time resolution of one hour. The graph below (figure3.2) gives the mean French consumption over 50 TS years.

Figure 3.2: French hourly Consumption (mean value over 50 Time-Series years)

Since more accurate data are needed in France, the consumption in each of the 25 areas in France must be known. Based on actual network situations, it is possible to compute the load in each area and compare it with the total consumption in France. Thus, a coefficient is defined for each area and every hour, the load is known in the 25 French areas.

3.2.2 Renewable Energy Sources

The renewable energy sources data is also based on historical ones. 100 TS years are available for wind generation and 3 TS years are available for solar production. Thus, for each Monte-Carlo year simulated, the model will randomly choose one of the TS years for each type of production.

It is necessary to make sure that for one type of production, the software always pick the same TS years for all the considered areas. Indeed, that way enables to obtain coherent and relevant production since a windy or sunny year will have an impact on many areas.

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Wind and solar productions are obtained by using a time-series analyzer. This tool enables to find the most suitable parameters of the distribution based on historical data. The models defined as output of this function are therefore very close to the reality. For instance, if wind speed has to be simulated, a Weibull distribution would be the most suitable choice. Then, by analyzing the historical time-series, the parameters of the distribution, the daily profile, the spatial correlation are found to correspond to the real values. A simple turbine model could enable to transform these wind speed values into wind production. The data used in this study have been obtained through this process in order to directly find wind production. In this case, a beta distribution is an interesting alternative.

3.2.3 Hydraulic Production

Hydraulic production is often the most problematic one in most of the electricity market models since it is very difficult to foresee the hydraulic generation when the electricity price are not known. Indeed, the production depends on the market situation since hydraulic power plants try to maximize their profit. Thus, the energy is saved in the reservoirs if the prices are low and the available power is used when it is the most profitable.

The initial set of data for hydraulic production is composed of historical observations.

60 TS years are available with the overall amount of hydraulic energy to produce per month in each area. The share of Run-Of-river (ROR) production for each month in the different area is also known.

The ROR energy to produce throughout a given month is known. Thus, the hourly ROR production in each considered area is obtained by dividing this value by the number of hours in the month.

For the Storage Power (SP), the process is more complex. The historical data give the amount of energy to produce due to the storage for each area and for each month (60 TS years). The hourly production for a zone z is determined by the method presented below.

First of all, a “weighed” load is computed for each area using equation (3.4). This value takes into account the net load of the considered area which represents the load minus the non-dispatchable generation. This net load is to be covered by hydraulic production or dispatchable generation. The “weighed” load considers also the fact that the hydraulic production in a given area does not necessarily depend only on the load in this considered area by using the allocation matrix A. For example, in the studied

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system, there are 25 areas in France; the hydraulic production in a given area depends on the French consumption and not only on its own load. In the study, it is assumed that the hydraulic production in one country only depends on its own consumption.

l_weighed(z) =X

i

A(i, z)l_net(z) f or every area i (3.4)

A process called inter-daily generation breakdown enables to find the amount of energy to produce each day knowing the available energy throughout the month. First, target values which correspond to optimum energies to produce throughout the day are computed based on (3.5). This equation shows that there is not any linear relation between the load and the hydraulic production. Historical data show that the power factor is around 2. In the available data, β = 1.5 is chosen. The sum of all the target values has to be the already-known monthly production.

h(i)

h(j) = l_weighed(i) l_weighed(j)

β

f or all (i, j) (days) (3.5)

With:

nb days

X

i=1

h(i) = monthly production(i) (3.6)

The amount of hydraulic energy to produce within the day in each area is computed by solving an optimization problem. This process minimizes a fictitious cost proportional to the difference between the daily energy and the target value presented above (computed by (3.5)), subject to constraints given in formulas (3.7)(3.8).

0 ≤ h^∗∗(i) ≤ 24 P_max (3.7)

nb days

X

i=1

h^∗∗(i) =

nb days

X

i=1

h(i) (3.8)

Then it is necessary to define a last parameter (given in (3.9)) which enables to limit the production peak during the day and therefore smooth the hydraulic generation throughout the day (figure3.3).

Intra Daily M odulation P arameter = max

Daily peak in the day M ean power throughout the day

(3.9) To conclude on the hydraulic production, the model combines both historical data and optimization in order to be as realistic as possible. The ROR production is easily

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Figure 3.3: Intra Daily Modulation Parameter enables to smooth the Hydraulic Pro- duction (Source: RTE)

obtained with the same production every hours of the same month. The SP data process gives the amount of energy to produce for each area and each day of the year. Then, this daily value is optimized at an hourly time span throughout the day taking into account the intra daily modulation parameter.

3.2.4 Thermal Production

The thermal production modelling is of interest to the extent that this type of production will give the electricity spot price every hour in the system. Indeed, the marginal cost of renewable energies is assumed to be null. Thus the marginal price is fixed by the most expensive thermal power plant used to cover the load.

Different clusters are defined for each area: Nuclear, Lignite, coal, Gas, Oil, Mixed Fuel, Miscellaneous dispatchable generation. Parameters are to be defined:

Capacity of the power plants

Number of units

Minimum stable power

Min up/down time: some power plant cannot be started for one hour only, they have to produce for a certain amount of time

Marginal cost of production

Start-up cost, fixed costs

Market bid

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Through Monte-Carlo simulations, random events are taken into account. It is therefore necessary to give outage rates and durations both for planned outages (maintenance work. . . ) and forced outages (failure). Formulas (3.10) and (3.11) shows the Forced Outage Rate (FOR) and the Planned Outage Rate (POR) definitions with F the number of hours in forced outage, P the number of hours in planned outage and A the number of hours where the power plant is available.

The Overall Outage Rate (OOR) given in (3.12) which is of interest when simulating the Monte-Carlo years does not corresponds to the sum of the two previous rates. Based on the formulas below, equation (3.13) used in the model can be defined.

F OR = F

A + F (3.10)

P OR = P

A + P (3.11)

OOR = F + P

A + F + P (3.12)

OOR = F OR + P OR − 2.F OR.P OR

1 − F OR.P OR (3.13)

The model allows the definition of power plant capacity modulation. Thus, the efficiency of the power plants can be modified throughout the period of the year. This function is especially used for thermal power plants.

Demand Side Management (DSM) is also modelled as a fictitious thermal power plant.

In case of loss of balance between supply and demand of electricity, DSM consists of a temporary decrease of the consumption on a given location or for some actors in the system (compared to their regular load). This method can be seen as an alternative to the installation of new power plants by relieve the network in difficult situations such as a fault, a large consumption increase or to compensate the renewable energies intermittence. In the model used, DSM is not available from April to October.

For each of the 50 Monte-Carlo years simulated in the study, the first step is a time-series generation where the availability of the power plants is randomly determined following the given parameters for each cluster. Then knowing the availability and the capacity of each power plant, the optimization can be launched. Regarding the minimum stable power constraints and the minimum up/down times, further information are given in section 3.3.

Some types of production are not taken into account in the thermal clusters presented above. Thus, for Combined Heat and Power, Bio Mass, Bio Gas, Waste, Geothermal,

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Other and Storage, only one year of historical data is available for each area (no optimization, no marginal cost). The storage management is disregarded; that represents a large approximation since pumped-storage hydro power plants have a significant influence on the market results.

3.2.5 Links

On Antar`es software, each area is modelled as a node and the electrical interconnections between two zones are defined as a simple link where different parameters can be given.

The model used for these links and the assumptions - taken into account or not - have a significant impact on the results. Indeed, the output of the models can be completely different if the impedances are given or not, according to the transmission capacities definition...

To simplify the model, only the 400kV network is considered in the simulations. Every other voltage levels are aggregated in the 400kV stations. For each link between areas, the following parameters can be defined:

Transmission capacity which can be defined in both direct and indirect direction (in case the capacity is not symmetrical). If physical flows are of interest, Grid Transmission Capabilities “GTC” are used; in case of economic studies, Avail- able Transfer Capacities “ATC” are defined. In the European model used in the study, ATC used abroad are based on historical data. In France, GTC have been computed by RTE; the methodology is briefly presented in Appendix A.

Hurdle costs. These costs represent a fictitious fee for using a transmission line (see section 3.3).

Constraints. This module enables to define conditions on the flows through links, relations between flows (Kirchhoff laws)...

3.3 Assumptions

Based on the model used and its limits, some assumptions have to be done. The main decisions regarding the electricity market modelling are presented below.

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Table 3.1: List of the Power Plants with their characteristics

Power Capacity Bid Pmin Min up down

Plant (MW) (euros/MWh) (MW) Time

Nuclear 1 60 50 0 No constraint

Nuclear 2 40 60 0 No constraint

Thermal 50 200 30 3h

3.3.1 Market Assumptions

The market is supposed to be ideal. Thus the assumptions of perfect competition and perfect information are considered [16].

There is perfect competition in the market when some conditions are fulfilled:

The players are assumed to be rational

The players are free to trade with each other

There is no market power

The perfect information criterion is valid if all players have access to all relevant information to take their decisions.

Regarding the price elasticity of the load, this assumption is not taken into account since the demand is assumed to be known every hour for each simulated year (section 3.2).

3.3.2 Minimum Stable Power and Minimum up/down time Constraints

As presented in part 3.2, different constraints regarding the thermal power plants can be taken into account in the optimization process. In this report, the minimum stable power and minimum up/down constraints have been disregarded. This choice is due to one of the limit of the used model. Indeed, in order to consider these constraints, the model will create four periods of six hours during the day. For each period, a test is performed to assess whether or not the constraint is fulfilled and the latter is forced if necessary. This can considerably modify the results to the extent that the marginal cost is computed by adding 1MWh/h of consumption in each area and the least expensive available power plant defines the price cross.

This problematic can easily be seen on a simple example. Three power plants are considered in a given bidding zone. The characteristics of the generating fleet are given in Table3.1. A load profile of the area is fictitiously determined (figure 3.4).

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Figure 3.4: Load Profile during the day (for the example purpose, completely fictitious)

Based on this very simple example, it is possible to compare the real case with the software behavior. Most of the time, the load is covered by the two nuclear power plants. If the consumption exceeds 100MWh/h, the thermal power plant is necessary, thus it is called hours 5, 7, 17 and 19. Depending on the considered assumptions, the following happens:

Real case: since the group is called hour 5, 17 and has to remain on for three hours, the thermal power plant produces 30 MW from 5am to 7am and from 5pm to 7pm.

With Antar`es: since the thermal power plant is called hour 5, the model will check if the constraint is fulfilled from hour 1 to hour 6; the same process will occur for the four periods during the day. Thus, the thermal power plant produces 30 MW from 4am to 9am and from 4pm to 9pm.

By taking into account the minimum stable power and min up/down time constraints on the model, the thermal power plant is producing twice longer than in reality. Due to the minimum stable power, during some hours a part of the load is covered by thermal generation instead of nuclear. The marginal prices obtained with Antar`es are therefore very low. Indeed, the marginal price given in the model will be based on the nuclear operating cost if the nuclear power plants are not fully used. The economic dispatch loses

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all meaning. The evolution of the marginal cost in the different cases is presented below (figure3.5). The black dashed curve which represents real marginal cost is very different from the one obtained on the model with all constraints (red dashed curve). The blue one corresponds to the results obtained on the model by disregarding the constraints.

Figure 3.5: Marginal Prices throughout the day taking into account the constraints or not

If the minimum stable power and minimum up/down time constraints are neglected, the system is less accurate since this means that any thermal group can be fired for only one hour and produce the amount of power that is necessary. The obtained results will of course present differences compared to reality. Figure 3.6 and figure 3.7give the mean value of the difference for operating costs and electricity spot prices when the constraints are neglected compared to the initial case taking into account all assumptions. It can be shown that the impact is more important in Italy. Regarding France, the operating cost and the marginal prices are slightly modified. Disregarding the minimum stable power and minimum up/down time constraints provokes an increase of the French operating cost (+5%) and the marginal price (around +10%). As a general remark, the impact is different according to the considered country but remains acceptable.

However the purpose of this study is to study the impact of a new bidding zone configuration in France. Thus most of the results will be presented as comparison; if the assumptions are coherent in the different simulations, the results are relevant.

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An economic impact study is possible if the economic dispatch results have a real sense.

The decision to delete these assumptions enables to obtain marginal prices as relevant optimization process outputs.

Figure 3.6: Operating Cost Evolution if the constraints are neglected (compared to the case with constraints)

3.3.3 Hurdle Costs

The hurdle costs are available parameters for the inter-area links modelling. They represent a small cost for using the interconnections capacity.

They enable to avoid irrelevant flows into the system. Indeed, since the objective function of the optimization problem is to minimize the overall cost, only useful flows will appear in the results.

The impact of these hurdle costs on the results is studied in the chapter 4 and solution to deal with them especially regarding marginal prices is presented.

3.4 Load Flow Simulations

Two methods are used in this study to determine the physical flows into the European network. The first one based on Antar`es enables to determine via a DC Load Flow

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Figure 3.7: Marginal Price Evolution if the constraints are neglected (compared to the case with constraints)

the equivalent physical flows between all simulated areas. This is possible by defining impedances for each links.

For the loop flows study, a more advanced model based on AC Load Flow computation is run to obtain the physical flow in each line in Europe and therefore be able to compare them with the commercial exchanges obtained with Antar`es.

3.4.1 DC Load Flow on Antar`es

The DC Load Flow method is used in the study in order to obtain the physical flows in France. Based on electricity market mechanisms, the model internally developed by RTE enables to compute the production plan in the different areas for each Monte-Carlo year with an hourly time span.

In a “meshed” network, there are some loops into the system and therefore different possible parallel paths between two locations. A method to determine in which way the flows are distributed among those paths is necessary in case physical flows are of interest.

Impact of Bidding Zone Configuration on the French Electricity Network

Impact of Bidding Zone Configuration on the French Electricity Network

Alexandre Canon

KTH Royal Institute of Technology

Master Thesis

Impact of Bidding Zone Configuration on the French Electricity Network

Declaration of Authorship

Abstract

Acknowledgements

Contents

List of Figures

List of Tables

Chapter 1

Introduction

Chapter 2

Background and Literature Review

2.1 Importance of the Exchanges between different Bid- ding Zones

2.2 Capacity Calculation and Allocation

2.3 Bidding Zones Studies

Chapter 3

Model Presentation

3.1 Electricity Market Modelling

3.2 Inputs

3.3 Assumptions

3.4 Load Flow Simulations