Impact of Bidding Zone Configuration on the French ...751985/FULLTEXT01.pdf · Declaration of Authorship I, Alexandre Canon, declare that this thesis titled, ’Impact of Bidding

Degree project in

Impact of Bidding Zone Configurationon the French Electricity Network

Alexandre Canon

Stockholm, Sweden 2014

XR-EE-EPS 2014:005

Electric Power SystemsSecond Level,

KTH Royal Institute of Technology

Master Thesis

Impact of Bidding Zone Configurationon 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

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

KTH ROYAL INSTITUTE OF TECHNOLOGY

AbstractElectricity 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 pur-

pose 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 capac-

ity 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 problem-

atic situation, the modification of the bidding areas configuration is often considered as

a solution. Different studies developed methods based on nodes aggregation or mini-

mization 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.

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 Reseau de Transport d’Electricite.

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.

iii

Contents

Declaration of Authorship i

Abstract ii

Acknowledgements iii

Contents iv

List of Figures vii

List of Tables ix

1 Introduction 1

2 Background and Literature Review 3

2.1 Importance of the Exchanges between different Bidding Zones . . . . . . . 3

2.1.1 Social Welfare and Day-Ahead Social Welfare . . . . . . . . . . . . 3

2.1.2 No Commercial Exchanges . . . . . . . . . . . . . . . . . . . . . . 4

2.1.3 Interconnection between two areas: Importance of the Transmis-sion Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.4 Congestion Management . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Capacity Calculation and Allocation . . . . . . . . . . . . . . . . . . . . . 7

2.2.1 Capacity Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.2 Capacity Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Bidding Zones Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.1 Bidding Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.2 A suitable context for Bidding Zone Configuration studies in Europe 12

2.3.3 Market Splitting already implemented in some European countries 13

2.3.4 Price signals considerations . . . . . . . . . . . . . . . . . . . . . . 14

2.3.5 Loop Flows problematic . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3.6 THEMA Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.7 Bidding Zones studies: an open topic . . . . . . . . . . . . . . . . . 16

3 Model Presentation 18

3.1 Electricity Market Modelling . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1.1 Economic Dispatch . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

iv

Contents v

3.1.2 Monte-Carlo Simulation . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1.3 Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.1 Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.2 Renewable Energy Sources . . . . . . . . . . . . . . . . . . . . . . 22

3.2.3 Hydraulic Production . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2.4 Thermal Production . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2.5 Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.3 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.3.1 Market Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3.2 Minimum Stable Power and Minimum up/down time Constraints . 28

3.3.3 Hurdle Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.4 Load Flow Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.4.1 DC Load Flow on Antares . . . . . . . . . . . . . . . . . . . . . . . 32

3.4.2 AC Load Flow with network model . . . . . . . . . . . . . . . . . . 34

4 Methodology 35

4.1 New Bidding Zone Configuration . . . . . . . . . . . . . . . . . . . . . . . 35

4.1.1 Congestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.1.2 Redispatching Costs . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.2 Choice of the Net Transfer Capacity . . . . . . . . . . . . . . . . . . . . . 38

4.3 Economic Impact Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.3.1 Commercial Exchanges Evolution . . . . . . . . . . . . . . . . . . . 39

4.3.2 Price Convergence Indicator . . . . . . . . . . . . . . . . . . . . . . 39

4.3.3 Price Divergence Indicator . . . . . . . . . . . . . . . . . . . . . . . 40

4.3.4 Social Welfare study . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.4 Loop Flow study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.4.1 Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.4.2 Unscheduled Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.4.3 European Loop Flows . . . . . . . . . . . . . . . . . . . . . . . . . 46

5 Results 48

5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.2 Localisation of Congestions in France . . . . . . . . . . . . . . . . . . . . . 50

5.2.1 New Bidding Zone configuration: France North and France South 50

5.2.2 Commercial Capacity Determination . . . . . . . . . . . . . . . . . 52

5.3 Modification of the European Commercial Exchanges . . . . . . . . . . . . 54

5.3.1 Influence of the new Configuration . . . . . . . . . . . . . . . . . . 55

5.3.2 Seasonality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.3.3 Exchanges Evolution when there is a congestion in France . . . . . 59

5.4 Economic Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.4.1 Price Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.4.2 Price Divergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.4.3 Social Welfare evolution . . . . . . . . . . . . . . . . . . . . . . . . 64

5.5 Loop Flows Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.6 Limits of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5.6.1 Impact on Congestion Management . . . . . . . . . . . . . . . . . 71

Contents vi

5.6.2 Data limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5.6.3 Capacity Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.6.4 Indicators used in the Loop Flows Study . . . . . . . . . . . . . . . 72

6 Conclusion 74

A Grid Transmission Capabilities Computation (Source: RTE) 76

B Impedance Computation Principle (Source: RTE) 77

C List of the performed simulations during the study 79

D Seasonality of the Commercial Exchanges in France 81

E Price Divergence Results for other European borders 83

F European Net Values for the four simulations used in Loop Flowsstudy 85

Bibliography 90

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 trans-mission capacity (Source: RTE) . . . . . . . . . . . . . . . . . . . . . . . . 5

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

2.4 Illustration of the difference between Scheduled Exchanges and PhysicalFlows (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 constraintsor not . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

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

3.7 Marginal Price Evolution if the constraints are neglected (compared tothe 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 PhysicalFlows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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

vii

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 theinterconnection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

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

5.7 Commercial Exchanges from France South to France North (mean valueof 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 BiddingZone configuration) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.9 Switzerland used as New Path for Electricity . . . . . . . . . . . . . . . . 61

5.10 Evolution of Price Convergence due to new Bidding Zone Configurationthroughout 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 valueover 50 Monte-Carlo years) in the different Bidding Zones . . . . . . . . . 66

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

5.15 Loop Flows measure for one Bidding Zone configuration; for two BiddingZones configuration - June 10am . . . . . . . . . . . . . . . . . . . . . . . 68

5.16 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicatorfor one Bidding Zone configuration - January 9am . . . . . . . . . . . . . 69

5.17 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicatorfor two Bidding Zones configuration - January 9am . . . . . . . . . . . . . 70

5.18 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicatorfor one Bidding Zone configuration - June 10am . . . . . . . . . . . . . . . 70

5.19 Commercial Exchanges and Physical Flows; Unscheduled Flows Indicatorfor two Bidding Zones configuration - June 10am . . . . . . . . . . . . . . 71

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 Flowsimulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 twoFrench 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 whenFR2-FR1 is constrained . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.6 Evolution of the Congestion time rates in European interconnections whenFR1-FR2 is constrained . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

ix

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 alloca-

tion 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 config-

uration 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.

1

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.

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

3

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 (figure 2.1).

Figure 2.1: Supply curve (blue) and demand curve (red) for both isolated areas Aand 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


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) (figure 2.2).

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 (figure 2.3). 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× Price Difference (2.1)

Figure 2.2: Supply curve (blue) and demand curve (red) for both areas A and B withlarge 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 elec-

trical system. Different methods are available depending on the considered timeframe.

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


Figure 2.3: Supply curve (blue) and demand curve (red) for both areas A and B incase 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 invest-

ments 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


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 mech-

anisms 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


Figure 2.4: Illustration of the difference between Scheduled Exchanges and PhysicalFlows (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.

PTDFk,ij =∆Ptrans−k

∆Pij=

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 inter-

connection) are simultaneously feasible.


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 figure 2.5

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,


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 auc-

tions. 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


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 develop-

ment 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 mar-

ket 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.


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, localisa-

tion 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...


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 en-

sure 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.


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 con-

gestion 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


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].


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 MeasuresModification of the topology of the grid (substation, PST)

Load/Generation Patterns Modification

Medium Term Measures

Capacities values reductionNew Bidding Zone Configuration

Flow-Based Market CouplingMarket 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 (Table 2.1).

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 un-

planned 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


to start a Bidding Zone Review process (figure 2.9). 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]

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 Antares – 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.

Antares 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

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.

Min(Ω) =∑t

∑G

∑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.


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 xi of the considered variable [16].

mX =1

n

n∑i=1

xi (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[mX ] =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 Antares 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. Figure 3.1 shows

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


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 config-

uration by aggregating smaller areas. The available information is presented in the

following sections. In order to make decisions, a sensitivity analysis of the input/scenar-

ios should be made, for example with the four scenarios of ENSTO-E Scenario Outlook

and Adequacy Forecast [17].


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 (figure 3.2) 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.


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


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.

lweighed(z) =∑i

A(i, z)lnet(z) for 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 com-

puted 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)=

(lweighed(i)

lweighed(j)

)βfor all (i, j) (days) (3.5)

With:nb days∑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 Pmax (3.7)

nb days∑i=1

h∗∗(i) =

nb days∑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 (figure 3.3).

Intra Daily Modulation Parameter = max

(Daily peak in the day

Mean 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


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


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.

FOR =F

A+ F(3.10)

POR =P

A+ P(3.11)

OOR =F + P

A+ F + P(3.12)

OOR =FOR+ POR− 2.FOR.POR

1− FOR.POR(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,


Other and Storage, only one year of historical data is available for each area (no opti-

mization, no marginal cost). The storage management is disregarded; that represents a

large approximation since pumped-storage hydro power plants have a significant influ-

ence on the market results.

3.2.5 Links

On Antares 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.


Table 3.1: List of the Power Plants with their characteristics

Power Capacity Bid Pmin Min up downPlant (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 infor-

mation 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 Table 3.1. A load profile of the area is fictitiously determined (figure 3.4).


Figure 3.4: Load Profile during the day (for the example purpose, completely ficti-tious)

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 Antares: 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 Antares 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


all meaning. The evolution of the marginal cost in the different cases is presented below

(figure 3.5). 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 constraintsor 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 3.7 give 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 con-

figuration 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.


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 tothe case with constraints)

3.3.3 Hurdle Costs

The hurdle costs are available parameters for the inter-area links modelling. They rep-

resent 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 Antares enables to determine via a DC Load Flow


Figure 3.7: Marginal Price Evolution if the constraints are neglected (compared tothe 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 Antares.

3.4.1 DC Load Flow on Antares

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.


The assumptions of the DC Load Flow [18] and the use of impedances in the model

represent a solution to compute the physical flows which occur throughout the links

between areas since the physics of the electricity network are modeled.

The power transmission through a power line can be defined by the relation below (3.14).

P12 =U1U2

X12sin(δ1 − δ2) (3.14)

Where:

P12 is the transmitted power through the line (node1 – node2)

X12 is the line reactance

Ui is the voltage at node i

δi is the rotor angle at node i

Load flow computation based on definition (3.14) leads to a non-linear problem which

can be solved by iterative methods. In order to simplify the problem, the following

assumptions are often accepted:

The voltages are considered constant: U1 = U2 = 1pu

The phase angle difference from one side to another of a transmission line can be

disregarded: δ1 − δ2 ≈ 0 and the sinus function can be linearized: sinx ≈ x

Relation (3.14) is therefore simplified and the transmitted power through the line be-

tween node 1 and node 2 can now be computed by (3.15):

P12 =δ1 − δ2X12

(3.15)

It can be shown [18] that based on formula (3.15), there is a relation between the

transmitted power through all lines in the system Ptrans and the net production matrix

Pnode for all areas (3.16). The matrix M could be seen as a PTDF matrix since it

corresponds to the way the flows are modified through line when the area balances

change.

Ptrans = MPnode (3.16)

If equivalent impedances are needed to study physical flows between areas, the model

allows defining Kirchhoff’s laws. For each mesh into the European electricity network,

the constraints below (3.17) are implemented with Fi the amount of power transmitted


through the link and Xi the equivalent impedance. The methodology used by RTE to

compute those impedances is given in Appendix B.

∑i∈mesh

FiXi = 0 (3.17)

By using this method, even if the model is initially based on economic considerations, a

simplified load flow computation is performed for each hour. The approximated “physi-

cal flows” are determined since impedances and injections/consumptions are known and

this computation can be done for each hour in combination with the economic dispatch.

3.4.2 AC Load Flow with network model

Physical flows on the European electricity network are of interest to evaluate the flow

resulting from the commercial exchanges with 2 bidding zones or one bidding zone for

France (cf. chapter 4). In order to obtain these flows, the RTE load flow model is used.

This model is based on an AC load flow algorithm. It takes into account the actual

nodes, lines, transfers of the grid. The model has been used for relevant hours; for

some typical hour, the production plan and the consumptions are defined. Based on

the inputs (load, generation dispatch, exchanges...), the model determines the physical

flows through the grid.

The load flow computation used is based on a Newton-Raphson algorithm. This algo-

rithm enables to determine the physical quantities of interest such voltage, active and

reactive powers for each node.

Chapter 4

Methodology

In this chapter, the methodology used for the study is presented based on the theoretical

frame and the rationale for the model choices. Methods to determine the new bidding

zone configuration are developed. Then, indicators are given to assess the impact of

this market evolution. The section ends with methodology regarding a loop flows study.

This part is quite different from the impact study based on economic considerations

since it combines both physical flows and commercial exchanges at the same time.

Based on Antares economic dispatch results, both commercial exchanges and physical

flows are of interest. Commercial exchanges are determined since they are only based on

the electricity market nature. Electricity market modelling is performed following the

process and assumptions given in chapter 3. For each hour, the production is determined

and the exchanges between bidding zones are found in order to minimize the overall

cost. If the Kirchhoff’s laws are taken into account in the model, the impedances are

defined and the physical flows are obtained. In case, there are not any constraints on

the flows through the links between areas, the exchanges are only constrained by the

transmission capacities. This leads to an important observation: the capacities defined as

parameters for each line do not represent the same thing is the impedances are considered

in the system or if they are disregarded. If Kirchhoff’s laws are activated (physical

flows obtained as outputs), these capacities are physical constraints; in the other case

(commercial exchanges as outputs) they represent Available Transfer Capacities (ATC).

4.1 New Bidding Zone Configuration

The new bidding zone configuration has to be defined according to an unbiased and

rational methodology. In this study, the definition of the new bidding zones in France

is based on internal congestions observations.

35

Chapter 4. Methodology 36

The system is simulated in order to get the physical flows in France. The impedances are

therefore taken into account in the model. The transmission capacities which correspond

to physical limits are known for the internal links between the 25 French areas. However,

it has been decided that the system would be simulated without any transit limitations

in France. By using unconstrained results, all possible congestions into the system are

visible.

Two methods can be applied: a configuration only based on congestions and a way of

reasoning based on redispatching costs.

4.1.1 Congestions

The first method in order to define the new bidding zone configuration is based on

congestion observations in the system. The principle is to find the congested links and

then define the new bidding zones in such way that both ends of the congested lines are

in different zones. Thus, the exchanges which would occur throughout the congested line

once the new configuration is implemented are submitted to the market mechanisms.

The congestion is now visible through the results and could lead to different incentives

in the electricity market.

4.1.2 Redispatching Costs

Redispatching is a measure that the Transmission System Operator (TSO) can take

to deal with internal congestions. A simple case is presented below to explain this

congestion management.

A fictitious system is composed by two areas A and B (with respectively λA and λB as

electricity prices) and a transmission line with a capacity of 100MW. If the optimum

situation in the market leads to a physical flow of 50MW through the line, there is not

any congestion in the system and the prices are equal in both areas (figure 4.1).

Figure 4.1: No Congestion situation, Physical Flow lower than the line capacity

However if the same fictitious system would lead to a transfer of 150MW from area A

to area B to maximize the overall social welfare, congestion occurs in the system. Only

100MW are transferred (physical flows limited by the capacity of the link between areas


A and B) and the price in area A remains lower than in area B. If the constraint is not

taken into account during the capacity allocation, the TSOs could use redispatching to

relieve the transmission line by trading the extra 50MW. That represents a cost for the

TSO since it acts “against the market” to relieve the congestion (figure 4.2).

Figure 4.2: Congestion through the line, Redispatching example

For a given border between areas A and B, for each hour t the redispatching cost can

be defined as (4.1) in case of congestion and null if there is not any congestion:

Redispatching cost(A,B, t) = (Ptrans − Plim). |λA − λB| (4.1)

With:

Ptrans: Transmitted power through the link to maximize the social welfare (in case there

is not any physical constraint), at hour t

Plim: Physical capacity of the link

In this method, the new bidding zone configuration in France could be defined by ag-

gregating small French areas in order either to minimize the overall redispatching cost

or to regroup areas with close prices. Thus, a division of the country in small bidding

zones would be necessary and then the configuration would be based on larger zones

(results of the aggregation process).

This method has not been used in this master thesis since the actual French redis-

patching process is different. Indeed, redispatching is applied on a national level and

therefore, it is not necessarily power plants in areas A and B that would be used to

deal with this bottleneck. In case of internal redispatching, redispatching costs for the

TSO in France are obtained by considering both the electricity spot price in the country

and the adjustment price of the needed group. Formula (4.1) would not represent the

actual cost for the French TSO. More information regarding the power plants involved

in redispatching measures in France are needed to obtain a more suitable model.

Thus, the bidding zone configuration defined in this study is based on the occurrence of

congestions through internal French transmission lines.


4.2 Choice of the Net Transfer Capacity

Once the new configuration chosen, the question of the commercial capacity of the link

between the new bidding zones is of interest. Indeed, only one link between the new

bidding zones remains and for all other equivalent transmission lines, the capacity is set

to null. The value of the capacity on the remaining link has a significant impact on the

obtained results (cf chapter 5).

A capacity calculation should have been performed in order to get the value of the com-

mercial capacity between the new bidding zones. The general method used to perform

such computation is given in section 2.2. But in the study case presented in chapter 5,

the critical branches and the PTDF matrix are not available. It is therefore not possible

to perform any capacity calculation.

Thus, the capacity is set by an arbitrary choice. It has been decided to consider both

physical flows and commercial exchanges to define these values. The system is simulated

taking into account the impedances of the links in France without any Grid Transmission

Capability (GTC) in order to observe the unconstrained physical flows between the new

bidding zones. Then, the system is run without any Kirchhoff’s laws; the unconstrained

commercial exchanges (commercial capacity set to infinite) through the link of interest

are measured.

The internal commercial capacity in France is then defined based on the unconstrained

physical flows and commercial exchanges. Two different values have been chosen in

order to perform a sensitivity analysis and emphasize the importance of the commercial

capacity value on the results: a case with strong constraints on the link and another one

slightly less constraining.

4.3 Economic Impact Study

In this section the general methodology to study the impact of the new bidding zone

configuration in France from an economic point of view is presented. These indicators

enable to assess whether or not such measures are beneficial for the European electricity

market.

This method has been used for the study case presented in the results part. It can

obviously be generalized for every bidding zone configuration of interest. The main goal

of this part is to present a general methodology which can be applied to study the impact

of market splitting and compare different possible solutions.


4.3.1 Commercial Exchanges Evolution

The commercial exchanges are known in Europe when France has only one bidding

zone. If the market design and the bidding zone structure are modified, the European

transactions will also change.

The first step in an impact study is to observe the evolution of the commercial exchanges

at the level of the different borders. The main tendencies can be computed by using

results from the Monte-Carlo synthesis (for example the mean value of hourly commercial

exchanges, computed for 50 Monte-Carlo years). More statistics can be used to be more

specific and obtain more information regarding the way the interconnection is used and

the exchange values in each direction.

A deeper analysis is possible by using duration curve. These curves enable to show

the amount of time the exchange is larger or equal to a given value. In this study, the

exchanges values are given as a percent of the available GTC or ATC in the link. The

10th and 90th percentile are also specified on the graphs. This presentation is perfect

to have a complete overview of an interconnection behaviour since it gives all necessary

data: minima, maxima, shares in each direction. . .

4.3.2 Price Convergence Indicator

Price convergence into the electricity market is often considered as a criterion of interest

to define the overall efficiency of the interconnections management. Indeed, a large

price convergence reflects a small occurrence of congestion. Based on this principle, an

indicator has been created to compute the price convergence for each interconnection in

the system.

In section 3.3, hurdle costs are presented. These fictitious costs avoid irrelevant flows in

the system but they also have an impact on the prices. A price convergence is considered

if the price difference on both sides of an interconnection is null. Because of these hurdle

costs, even if there is convergence, the price difference is not necessary null.

Convergence criteria are to be defined in order to create a relevant indicator. Thus, the

system is simulated with the copper plate model assumptions. The copper plate model

corresponds to a system where the whole Europe is only one bidding zone. All capacities

are set to infinite and therefore there are not any constraints. Thus, the electricity prices

in all simulated areas are supposed to be exactly the same for each hour; there is full

convergence. Based on these results, it is possible to determine the price difference due

to the hurdle costs for each interconnection.


The indicator will therefore consider both the price difference in the studied simulation

and the one obtained with the copper plate simulation. For each hour and each border,

there is not convergence if the price spread in simulation Vi is higher than the copper

plate price difference (4.2). The final value corresponds to the time rate where there is

convergence over the 50 Monte-Carlo years (4.3).

Price Convergence(Vi, b, t) =

1 if (∆λVib,t −∆λCPb,t ) = 0

0 if (∆λVib,t −∆λCPb,t ) > 0

(4.2)

With:

∆λVib,t: price difference throughout border b, at hour t, for simulation Vi

∆λCPb,t : price difference throughout border b, at hour t, for copper plate model

Price Convergence Indicator(Vi, b) =

∑t Price Convergence(Vi, b, t)

number of hours in the year(4.3)

Due to the assumptions considered in the simulations and the time horizon 2030, the

absolute value is not of interest. In order to study the impact of the new bidding

zone configuration, the evolution of this indicator will be presented for the different

interconnections (difference between the initial value where France is one bidding zone

and the simulation with the new situation).

4.3.3 Price Divergence Indicator

This section also deals with electricity spot prices. The indicator presented above shows

the impact on the price convergence for each interconnection: it is possible to know if

the convergence has increased or on the contrary if the new bidding zone configuration

provokes divergence on both sides of a link. The limit of this indicator is that all price

divergences are considered the same way; the price divergence is only considered as an

absence of convergence but the price difference is not given.

A price divergence indicator has therefore been created to study the impact on the price

difference. Once again, the hurdle cost problematic has to be considered and the copper

plate simulation results are part of the computation.

Formula (4.4) gives the relation used to compute the price divergence indicator. It is

mainly based on a root-mean square deviation which enables to aggregate the magnitudes

of difference between the considered simulation V and the copper plate model CP into

a single measure. This “equivalent distance” is interesting to the extent that the whole


divergence on both ends of an interconnection is represented by a simple indicator which

emphasizes the large difference and decreases the impact of small ones. Quantitative

information is available regarding the electricity prices analysis thanks to this indicator.

Price Divergence Indicator(V, a1, a2) =

√∑t∈T∑2

i=1(mpVai,t−mpCPai,t)2

2. |T |(4.4)

The “global” divergence from the ideal copper plate model is determined. It can therefore

been assessed if the electricity market evolution is beneficial by bringing the whole system

closer to the fictitious target model where there is only one bidding zone in Europe.

4.3.4 Social Welfare study

The social welfare in an electricity market is an abstract measure which gives a general

overview of the quality of the market for all players: consumers, producers and TSOs.

Different definitions are used in the scientific papers which deal with this indicator [19].

A clear definition of the social welfare used in this study is given by formula (4.8).

For each bidding zone i in the system, the social welfare can be computed as the sum

of:

The consumer surplus (4.5): difference between the amount the consumers are

willing to pay in area i for hour t and the price they really have to pay

Consumer Surplusi,t =[CDDi,t − λi,tDi,t

](4.5)

With:

CD: Fictitious price which corresponds to the maximum price the consumer is

ready to pay

Di,t: Load in area i at hour t

λi,t: Electricity Spot Price in area i at hour t

The producer surplus (4.6): difference between the income obtained by selling

the electricity in area i and the operating cost

Producer Surplusi,t = [Gi,tλi,t −OCi,t] (4.6)

With:

Gi,t = Di,t +∑

j 6=i Pij : Amount of energy produced in area i during hour t (load

taking into account the transmitted powers with the neighbouring bidding zones)


λi,t: Electricity spot price in area i at hour t

OCi,t: Total operating cost in area i at hour t

The trading surplus (4.7): corresponds to the congestion rent. It is assumed

that this rent is equally shared between the two TSOs on both sides of an inter-

connection.

Trading Surplusi,t =∑j 6=i

Pij,t2

(λj,t − λi,t) (4.7)

With:

Pi,j,t: Transferred power through the link between areas i and j, at hour t.

(Pi,j,t > 0 if export; Pi,j,t < 0 if import)

λi,t: Electricity spot price in area i at hour t

Thus, the social welfare is obtained with the relation below (4.8). It can be shown that

the overall social welfare computed for all simulated bidding zones in the market is given

by (4.9).

δWi,t =[CDDi,t − λi,tDi,t

]+ [Gi,tλi,t −OCi,t] +

∑j 6=i

Pij,t2

(λj,t − λi,t) (4.8)

δWtotal,t = CDDtotal,t −OCtotal,t (4.9)

In all simulation, the same load is kept for the 50 years of Monte-Carlo. The only

unknown variable in the relation above is the price the consumers are willing to pay.

Since the impact of the new bidding zone configuration is of interest, an evolution of

the social welfare is presented as results of the study. The load being exactly the same

each hour for all areas, the only assumption to do is to consider that the consumers are

willing to pay the same price in all market configurations. The bidding zone creation or

the modification of commercial capacities does not modify the value CD. The first term

of the consumer surplus is simplified in the social welfare comparison.

Another important aspect of the social welfare evolution is the transfer of surplus from

one actor to another. Indeed, it can be very interesting to know for whom the market

structure modification is beneficial.

4.4 Loop Flow study

This section presents another part of the study regarding the loop flows problematic.

The area pricing is nowadays seen as a possible solution in order to deal with this


Table 4.1: Clusters used to implement the economic results in the AC Load Flowsimulations

Clusters for the different kinds of power plants

Hydro generation (Run-of-river, Storage)

Nuclear

Coal

Fuel

Gas (turbine, combined cycle)

Wind generation

Fictive power plants

phenomenon. Indeed, by creating new bidding zones, some exchanges would be subjected

to capacity calculation and allocation mechanisms and that could potentially have an

impact on the unscheduled flows in the system.

The methodology is based on both economic dispatch and load flow simulations. Indeed,

the commercial exchanges as well as the physical flows between the bidding zones are

needed to determine the amount of loop flows and transit flows (cf. definitions given in

section 2.3).

4.4.1 Data preparation

The simulations run on Antares software to obtain commercial exchanges, use a certain

set of data based on 2030 forecasts. Different files for the physical network are available

with the same time horizon both for peak demand and low consumption.

First, it is necessary to do some modifications of the data in order to obtain consistent

data in order to determine both commercial exchanges and physical flows.

In France, each power plant and each load have been placed in the 25 areas. In Antares

it is aggregated by clusters but for the load flow simulation it has to be divided between

each individual production units. The clusters used for the production are given in the

Table 4.1:

The economic dispatch is computed for the 50 Monte-Carlo years for different configura-

tions in the system: initial case and new bidding zone configuration. Two characteristic

hours have been chosen:

Peak load in winter: January

Peak load in summer: June


The loop flow study cannot be automated; this solution has therefore been decided in

order to obtain two different configurations in the system regarding consumption and

production plan. For each hour, the results are given by the mean values over the 50

simulated years to take into account the possible random events and get a representative

sample.

If needed with the chosen hours, demand-side management, storage, unsupplied energy

or spilled energy are quantities to take into account in order to obtain perfect supply-

demand equilibrium in AC Load Flow simulations. Demand-side management (DSM)

is by definition a decrease of the consumption in case of load peak. It appears therefore

very clear that the best way to consider DSM in the physical model is to decrease the load

in the areas where the fictitious thermal power plants which represent this phenomenon

on Antares are producing. Regarding storage, it is also easier to use the same method

as DSM.

The network model for 2030 is not accurate enough for some kinds of production. The

production plan found for solar power plants and combined heat and power (CHP)

cannot be properly fulfilled. The only solution is to decrease the load in the concerned

areas. This does not represent an important error since photovoltaic or thermal solar

production are mainly decentralized and the amount of CHP is relatively small compared

to the total production.

Wind generation is partially modeled in the physical network. If the available maxi-

mum power in the area is large enough to cover the planned production (cf. economic

dispatch), the production is completely taken into account in the simulation. If the

installed fleet is too small, the wind generators produce their maximum capacity and

the rest is deduced from the load.

In France, production and load are exactly the obtained values in economic situations

since the physical model is accurate enough. Abroad, the network is not so detailed.

Rough production fleets are available; the net values are therefore the priority. Since the

production in the base case is consistent, only the loads are modified in order to obtain

the good balance in each country. Portugal, Northern Ireland, Ireland and Great Britain

do not have any physical model. Their net values are therefore taken into account in the

load of neighboring countries in order to ensure perfect supply-demand equilibrium for

the whole Europe. In France, the HVDC links are represented and set to the commercial

values.

All modification applied to the physical base case are given in figure 4.3.

Consistent economic dispatch and load flow simulations are finally available for two

hours. This enables to determine both the results from the electricity market (production


Figure 4.3: Modifications performed from the Base Case to obtain consistent PhysicalFlows

plan, commercial exchanges, electricity prices. . . ) and the network behavior (physical

flows, all constraints. . . ).

4.4.2 Unscheduled Flows

The amount of unscheduled flows can be easily computed once the commercial exchanges

as well as the physical flows are known for the exact same situation. Indeed, commercial

exchanges represent all the scheduled transactions due to market mechanisms and the

physical flows correspond to the power transfers which actually happen in the network.

An intuitive indicator would be to compare these two quantities in order to compute all

“unscheduled flows” on each interconnection (4.10). This value would be in the same way

as the physical flows if more power is transferred compared to the market expectations

and in the opposite way of the physical flows if the commercial exchanges are higher.

Unscheduled F lows = Physical F lows− Commercial Exchanges (4.10)


By analyzing this indicator when France is only composed of one bidding zone and with

the new configuration, the impact of the market structure modification is visible.

This study gives a relevant overview of the differences between the economic situation

and the physics in the system. However, it is completely impossible to determine which

part of these unscheduled flows are loop flows. Transit flows which are flows through

a bidding zone due to a commercial exchange between two other bidding zones are

also taken into account in the indicator. The inherent meshed nature of the European

network necessarily causes these transit flows. The results obtained should therefore be

taken with a grain of salt.

4.4.3 European Loop Flows

A Loop Flow is a physical flow through a bidding zone caused by commercial exchanges

having origin and destination in one other bidding zone (cf. definition chapter 2).

The main idea in this section of the loop flows analysis is to obtain a measure which gives

the amount of the loop flows only. As explained earlier, transit flows due to commercial

exchanges between the different bidding zones are inevitable. The principle is to deviate

from the real situation by deleting the induced flows by all transactions between bidding

zones in Europe and only consider internal exchanges. All net values have therefore to

be null in Europe.

When there is not any exchange between bidding zones, all the remaining flows are loop

flows. In figure 4.4, only internal exchanges are considered in each bidding zone (solid

arrows) and the dashed arrows represent the loop flows provoked by these exchanges. For

instance the physical flow observed between areas A and B gives the amount of loop flows

created by internal exchanges in A, B and C at the same time on this interconnection

A-B.

This study obviously leads to a fictitious market situation. However, if the methodol-

ogy to balance the net values in each bidding zone is conserved from one simulation

to another, the results could be compared and the impact of the new bidding zone

configuration on the overall amount of loop flows can be determined.

In order to set the net values of all bidding zones to zero, the load is modified in such

way that there is perfect supply-demand equilibrium in each area.


Figure 4.4: Loop Flows due to Internal Exchanges in three Bidding Zones

Chapter 5

Results

In this chapter, the impact of a new bidding zone configuration in France is studied

and the results obtained by performing the methodology presented in chapter 3 are

given. First, the general situation of the study case is presented. Then, the bidding

zone configuration in France is determined and its influence on the European network

both based on economic and physical considerations is of interest.

Many simulations have been run in the study both to find a possible way to divide

France in two bidding zones and to estimate the influence of this new market structure.

This represents an application of the presented methodology and a first analysis of the

obtained results. All simulations with the assumptions taken into account in each case

are listed in Appendix C.

5.1 Background

The aim of the study is to determine the impact of market splitting in France on the

European Electricity system. The list of countries considered in the simulations of the

European system is given in Table 5.1. The model used for France as a starting point

is more accurate. Indeed, the available set of data used in this study corresponds to the

25 areas represented in figure 5.1.

This organization of the French territory has been defined by RTE based on technical

considerations. Indeed the area definition has been realized in order to gather together

coherent transmission lines which present physical flows in the same direction. Thus, the

obtained transmitted power flows between these 25 areas could represent relevant phys-

ical flows since they could be considered as physical flows on an equivalent transmission

line (figure 5.1).

48

Chapter 5. Results 49

Table 5.1: List of the Simulated Countries and Abbreviations

Country Abbreviation

Austria AT

Belgium BE

France FR

Germany DE

Great Britain GB

Ireland IE

Italy IT

Luxembourg LUX

Netherlands NL

Northern Ireland IE N

Portugal PT

Spain ES

Switzerland CH

In base case configuration, each European country corresponds to one bidding zone.

France production and consumption is modeled by 25 small areas but all these areas are

part of the same bidding zone. Thus, for each hour, there is a unique electricity price in

France. The aim of the new bidding zone determination is to aggregate some of these

small areas in order to define bidding zones in France based on congestion observation.

For each bidding zone and each of the 25 French areas, the production and consump-

tion data described in part 2 are available. The chosen time horizon is 2030 and for

each simulation, 50 Monte-Carlo years are run. The set of data corresponds to the as-

sumptions of the median scenario (Moderate consumption growth, Slight decrease of the

French nuclear installations, Development of Renewable Energies). Different scenarios

are available: median, new generation mix, large consumption, low consumption. The

methodology is applied on the median one. Robustness of the defined bidding zone

configuration and the methodology as a whole could be studied by using other set of

data.

The main objective of the study is to determine a new bidding zone configuration ob-

jectively defined thanks to congestion observation and then evaluate the impact of this

market splitting based both on economic and technical considerations. It could give an

overview of the whole European system behaviour in case France would be divided in

several bidding zones.


Figure 5.1: Areas used as starting point in the Study

5.2 Localisation of Congestions in France

5.2.1 New Bidding Zone configuration: France North and France South

In order to determine a suitable bidding zone configuration in France, the congestions

are of interest in the French electricity network. Thus, the European system is simulated

taking into account the impedances between the different areas and infinite GTCs. It

is therefore possible to observe all constraints in France. The obtained physical flows

are then compared to the physical limits of the equivalent links and the congestions are

visible.

Figure 5.2 gives a rough overview of the main tendencies of the physical flows in France.

The given quantities represent the mean values of the hourly physical flows (synthesis


computed based on simple sampling method throughout 50 Monte-Carlo years). Thus,

arrows which represent the flows are colour-coded in order to observe the probability to

have constraints on the considered links.

If the arrow is red, the physical flows are higher than the GTC and therefore congestion

appears on the link at least 10 percent of the time; the arrow is orange when the flows

are higher than 90 percent of the physical limit at least 10 percent of the time; it is

yellow with a threshold of 75 percent of the GTC. Green is used for links which do not

reach the considered thresholds.

Figure 5.2: Mean values over 50 years of the hourly Physical Flows in France (color-coded congestion)

Based on the results above, the new bidding zone configuration has been decided in

order to obtain both ends of congested lines in different zones. The blue line represents

the new border which divides France in two bidding zones. The congestions on links

Area19-Area6, Area17-Area5, Area16-Area2, Area16-Area3 are considered. Then, to

finish the division, the border between Area14 and Area10 has been chosen. Even if

there is no congestion on this link, a large amount of power is transferred. It would also


Table 5.2: Structure of the New Bidding Zones Configuration in France

Bidding Zone Abbreviation French areas

France North FR1 Areas 1 to 10

France South FR2 Areas 11 to 25

be impossible to separate Paris from the northern areas to the extent that the network

is designed in such way that production from Area9 is supposed to transit in Area10 to

cover the load in areas 1 to 4.

Thus, two bidding zones are created in France: France North and France South. Table

5.2 gives the structure of the two French bidding zones. Other constraints in France

especially in the South have been disregarded. It could be possible to create three

different bidding zones based on the dividing methodology. In the following study case,

the two bidding zones solution is developed since it corresponds to the most probable

evolution of the market structure from European point of view.

At first sight, the new bidding zone configuration does not seem as optimal. Indeed,

based on congestion study on the French territory, physical flows through this new border

are not in the same direction. The commercial exchanges between FR1 and FR2 will

be submitted to market mechanisms but this limitation does not necessarily imply an

efficient congestion management since some physical flows in opposite directions could

relieve the total transmission even if there are congestions.

These congestion management considerations enable to define the new bidding zones.

However, the impact of the new configuration on the French physical flows is not possible

with the simulation model (limits of the model in section 5.7).

5.2.2 Commercial Capacity Determination

The new bidding zone configuration in France is now defined. Available Transfer Capa-

bilities (ATCs) for all interconnections in Europe are needed for the Electricity market

modelling. The commercial capacity determination between the two French bidding

zones is decisive since it has a significant impact on the market behaviour.

If the ATC is set to infinite, France is considered as one unique bidding zone and the

new configuration has no impact on the market. In reality, a capacity calculation taking

into account all critical branches in the network and relevant N-1 situations would be

performed to find a suitable value of ATC between FR1 and FR2. In this study, this

computation is not possible since the PTDF matrix and accurate network description

are not available.


An observation of both unconstrained commercial exchanges and physical flows has been

performed in order to define the internal French ATC. Two simulations have been run:

Simulation 1:

The impedances are taken into account and based on DC Load Flow theory, “physical

flows” are found. Unconstrained physical flows are obtained in France by setting all

GTCs to infinite. Figure 5.3 gives the distribution of the unconstrained hourly physical

flows from France South to France North (FR2-FR1) over 50 Monte-Carlo years (i.e.

synthesis computed by simple sampling method over the 50 simulated years at a time

resolution of one hour). It can be noticed that the mean value of these flows through

the border is around 900MW. Thanks to this curve, the percentage of time where there

is physical congestion on the border for a given capacity can be found.

Simulation 2:

In this purely economic study, the Kirchhoff’s laws are not defined. Unconstrained

French commercial exchanges are observed since the commercial capacity between FR1

and FR2 is set to infinite. ATCs abroad are constraining. This market simulation is

exactly the same as if France was only bidding zone. The only difference is the measure

of these exchanges through the new French border.

Figure 5.3: Statistics of Physical Flows France South-France North over 50 Monte-Carlo years


Table 5.3: Congestion Time rates depending on the capacity link between the twoFrench Bidding Zones

Symmetrical capacity Physical Flows Commercial ExchangesFrance North-France South (Time rate>capacity) (Time rate>capacity)

500 87.38% 88.31%

1000 75.08% 79.06%

1500 63.43% 69.86%

2000 52.71% 60.51%

2500 42.98% 51.50%

3000 34.39% 43.14%

3500 27.02% 35.74%

4000 20.84% 29.27%

In Table 5.3 below, the percentage of time where there is congestion is given for differ-

ent values of capacities between FR1 and FR2. The symmetrical capacities represent

different quantities depending on the nature of the simulation. For the physical flows,

the values given in the first column are GTCs whereas for commercial exchanges, they

are considered as ATCs.

A sensitivity analysis has been performed by choosing two different values of commercial

capacities. The chosen value should not be too high since the new configuration would

have a very small impact on the market and not too small since we still need power

transfer between the two French bidding zones.

Thus, the two different ATCs for the new bidding zone configuration are:

2500 MW: constraining case with around 50% of time commercial congestion in

the system

4000MW: less constraining than the previous value (only 30% of time in conges-

tion)

5.3 Modification of the European Commercial Exchanges

By modifying the bidding zone configuration in France, the commercial exchanges in

Europe are modified. The first step in an impact study consists of an analysis of these

transactions.


Table 5.4: Colour Scale for net values in the different maps

Amount Colour Scale for Imports Colour Scale for Exports

Minimum

Minimum - 20%

20% - 40%

40% - 60%

60% - 80%

80% - Maximum

Maximum

5.3.1 Influence of the new Configuration

The system is simulated for 50 Monte-Carlo years by taking into account the ATCs. The

impedances are disregarded in order to obtain commercial exchanges in the European

network. The impact of the new bidding zone configuration can be analyzed in a first

approach thanks to a comparison of the main tendencies of these exchanges through the

different interconnections.

Figure 5.4 gives the results in the initial case where France is considered as only one

bidding zone. On the left, the mean values of the flows over the 50 simulated years are

given. The colours represent the net value of the bidding zone; the scales are given in

Table 5.4. Since information is lost with a mean hourly value over 50 years, the figure

to the right gives more information especially the time rates in both ways of use of the

interconnection with the mean value in each case and the percentage of time where the

link is not used.

The legend for the time-split arrows given in the maps with flows statistics is given

below, with the example of the interconnection between France and Spain on figure 5.4:

The interconnection is used 43% of the amount of hours over 50 simulated years

in the direction France-Spain. The mean flow in this direction is 3291MWh/h.

The interconnection is used 52% of the amount of hours over 50 simulated years

in the direction Spain-France. The mean flow in this direction is 3408MWh/h.

The “0”-Flows rate is 4%. During this time, there is not any exchange through

the interconnection.


The situation is therefore obtained for 2030 if the market structure remains unchanged.

This simulation represents the base case and the impact will be computed in comparison

with these results.

Figure 5.4: Commercial Exchanges (MWh/h) in initial Bidding Zone Configuration:mean values over 50 Monte-Carlo years (left); Statistics in both ways of the intercon-

nection (right)

The ATCs are conserved abroad and between the two French areas the capacity is set

to 2500MW or 4000MW (cf. Section 5.2). In the initial case where France is only one

bidding zone, the following links had an infinite commercial capacity: Area14-Area10,

Area16-Area2, Area16-Area3, Area17-Area5, Area19-Area6. To model the two bidding

zones, all the commercial capacities of the interconnections given above have been set

to zero except for Area16-Area2 which presents the defined ATC (FR1-FR2) as charac-

teristic.

Figure 5.5 and figure 5.6 give the commercial exchanges results once the new bidding

zone configuration is introduced for the two different values of ATC between France

North and France South.

First of all, the results given above show a relatively low impact of the new configuration

on the European commercial exchanges. Indeed, the main tendencies of commercial

exchanges in Europe are unchanged and some values are slightly modified. It can be

noticed that the use of interconnections with Switzerland has increased with the new

French bidding zones.

The sensitivity analysis performed on the commercial capacity FR1-FR2 gives results

that could have been intuitively foreseen. The larger the capacity is chosen, the lower

the impact is on the system. In case the ATC between FR1 and FR2 is 2500MW, the


Figure 5.5: Commercial Exchanges (MWh/h) in new Bidding Zone Configuration(2500MW capacity): mean values over 50 Monte-Carlo years (left); Statistics in both

ways of the interconnection (right)

Figure 5.6: Commercial Exchanges (MWh/h) in new Bidding Zone Configuration(4000MW capacity): mean values over 50 Monte-Carlo years (left); Statistics in both

ways of the interconnection (right)


commercial exchanges evolution is larger than with a choice of 4000MW. A constraining

capacity in France decreases the overall European power transfer between north and

south and therefore modifies the initial market operations.

5.3.2 Seasonality

Seasonality of the commercial exchanges through the link between France North and

France South is of interest since it gives information on the way the interconnection

is used during the year. During the year, the mean values of commercial exchanges

from France South to France North over 50 Monte-Carlo years have been computed at

noon. Figure 5.7 gives the evolution of these quantities over the year for both one (blue

curve) and two (red curve) bidding zones configurations in France. The seasonality of

the exchanges in France for peak load situation is deduced from this study.

Figure 5.7: Commercial Exchanges from France South to France North (mean valueof each hour 12am)

The new bidding zone configuration limits the flows through the link since its capacity is

set to 2500MW but the overall seasonality remains unchanged. Thus, the market struc-

ture modification does imply a limitation of the flows but there is not any modification

of the use of the French territory during the year. Similar studies for the hour 4am and

4pm are given in Appendix D and show the same conclusions.


Table 5.5: Evolution of the Congestion time rates in European interconnections whenFR2-FR1 is constrained

Constraints on France South – France North

Border Congestion time rate Congestion time rateevolution at direct capacity evolution at indirect capacity

FR2-CH 24.58% -1.01%

CH-FR1 11.01% -7.01%

CH-DE 0.95% -1.64%

FR2-ES 1.25% -12.59%

FR2-IT 1.53% -0.42%

BE-FR1 0.00% -14.83%

DE-FR1 1.53% -9.49%

Another comment on these graphs is the fact that the French electrical network is mainly

used from South to North during summer and in the other way during winter. This can

be explained by the strong solar production in summer and the large correlation between

French consumption and temperatures.

5.3.3 Exchanges Evolution when there is a congestion in France

The commercial exchanges are slightly modified with the new bidding zone configuration

in France but the overall behaviour remains unchanged. The main impact of the creation

of two zones in France is a decrease of the north-south European power flow.

Since the exchanges are limited in France, it would be interesting to determine whether

some borders are used as alternative paths in Europe. The following study compares the

percentage of time where there is congestion through all European borders of interest

while the link France South – France North (Table 5.5) or France North – France South

(Table 5.6 ) is congested. The values given in the tables below correspond to the con-

gestion time rates difference between the two bidding zones configuration (commercial

capacity 2500MW) and the initial market structure. This indicator gives a rough idea

of which paths the electricity is willing to take when the French transmission system is

limited.

The results above demonstrate that European borders are effectively affected by the

French commercial limit. Depending on the way the French link is congested, the impact

can be either positive or negative.

If France South – France North is constrained the two borders between French bidding

zones and Switzerland are more constrained. Since the flows between South and North

are decreased in Europe the following links are relieved: from Spain to France, from

France to Belgium and from France to Germany.


Table 5.6: Evolution of the Congestion time rates in European interconnections whenFR1-FR2 is constrained

Constraints on France North – France South

Border Congestion time rate Congestion time rateevolution at direct capacity evolution at indirect capacity

FR2-CH -11.94% 11.89%

CH-FR1 -0.85% 24.99%

CH-DE 0.00% 10.80%

FR2-ES -10.94% 5.38%

FR2-IT -15.15% 3.42%

BE-FR1 -0.52% 2.96%

DE-FR1 -14.11% 1.39%

Similarly, if France North – France South is constrained Swiss borders are more loaded.

Since the flows between North and South are decreased in Europe the exports from

France towards Spain and Italy are smaller and the import from Germany decreases.

The borders involving Switzerland seems to be the alternative paths for the commercial

exchanges which are not able to flow through France. A deeper analysis of these links

has been performed based on duration curves (cf. figure 5.8). These curves determine

the percentage of time an hourly commercial exchanges is lower or equal to a given value

over 50 Monte-Carlo years. For instance the solid blue line reflects that around 50% of

the time, the exchange from FR2 to Switzerland is lower or equal to the ATC FR2-CH

value. If the border is congested in one way or the other, the plots reach a plateau

and the amount of time under constraints can be determined. The results below give in

dashed line the situation for three Swiss links in one bidding zone configuration and in

full line for the new market splitting when France South – France North is constrained;

the duration curves are computed considering only the flows during the hour of French

congestion.

When France is split into two zones, all duration curves are translated to the left. That

means that the considered borders are more used in the direct way. It can be noticed

that if the France South – France North connection is congested, the exchanges from

France South to Switzerland, from Switzerland to France North and from Switzerland

to Germany are higher than the unconstrained case. The new path in this situation can

therefore be defined as in figure 5.9: the red arrow corresponds to the unconstrained

case where the needed commercial exchanges flows from FR2 to FR1 and the blue ones

reflects the limited transfer in France and the path though Switzerland. The exact

opposite situation is obtained when France North – France South is constrained.


Figure 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 con-

figuration)

Figure 5.9: Switzerland used as New Path for Electricity


5.4 Economic Aspects

In this section, the economic impact of the new bidding zone configuration is assessed

by using the different indicators presented in the methodology part. For the different

computed quantities, the evolution from the initial bidding zone configuration to the

new one is given. Indeed, the absolute results are not of interest to the extent that

the study is performed with a time horizon 2030 with strong assumptions. Since the

parameters in all simulations are conserved, a comparison of the prices and the social

welfare is completely relevant.

This impact study corresponds to the insertion of two bidding zones in the European

electricity market with the definition of a commercial capacity. It is assumed that this

new configuration does not affect the other European capacities: all ATCs abroad remain

constant.

5.4.1 Price Convergence

As explained in the methodology, due to hurdle costs, a convergence criterion is defined

for each hour over the 50 simulated years. For each hour and for each simulation,

we measure the number of hours where there is convergence throughout the different

European borders. Then the convergence rates are compared to obtain the impact of

the new bidding zone configuration (cf. figure 5.10):

Two bidding zone configuration with 2500MW of commercial capacity between

FR1 and FR2 minus the initial market structure (in blue)

Two bidding zone configuration with 4000MW of commercial capacity between

FR1 and FR2 minus the initial market structure (in red)

The impact on the price convergence is relatively low: the maximum variation is around

8%. The comparison realized between France and Switzerland is not exploitable since

the quantities are different due to the dividing. Indeed, it is not possible to compare

a price convergence between Switzerland and France as one bidding zone and then the

convergence with the two French areas.

As a general remark, the borders between France South and Spain or Italy present a

higher convergence. The situation is more critical in between France North – Belgium

and Switzerland – Germany. Price convergence is often seen as a relevant measure of the

effectiveness of the interconnections. The impact of the new bidding zones in Europe


Figure 5.10: Evolution of Price Convergence due to new Bidding Zone Configurationthroughout European borders

varies from one border to another and based on this indicator, only local conclusions

can be drawn.

5.4.2 Price Divergence

The price convergence measures the time rate where electricity prices on both ends of

an interconnection are equal. The main issue with this kind of measure is that price

divergences are always taken into account the same way. The indicator used in this

section enables to determine the degree of divergence throughout a given border. Thus,

a price difference of a few cents would not be considered as a large spread reaching large

amount (more than 100e/MWh).

During the simulations it could happen that a part of the load is not covered. This

energy not served has a cost called Value of Lost Load (VoLL). This VoLL value has

a significant impact on the obtained value with the divergence indicator computed by

(4.4). To conserve a certain consistency in the results, the price of offers at all cost in

France has been chosen: 3000e/MWh. Once again the focus of this analysis should be

on the evolution and not on the absolute results.

Figure 5.11 gives the monthly evolution of the price divergence criterion computed on

both ends of the interconnection between France North and France South. Each hour, the


indicator is computed for the initial market configuration and the two new situations (two

bidding zones with 2500MW or 4000MW of inter-zonal link capacity). Then monthly

averages are calculated taking into account the 50 Monte-Carlo years to obtain plausible

reference year. Similar results for other European borders are available in Appendix E.

Figure 5.11: Price Divergence Indicator Evolution for the border FR1-FR2

The price divergence indicator gives a measure of the overall divergence compared to the

ideal copper plate model. Large values reflects pronounced divergence compared to the

fictitious case where Europe would be one large bidding zone (all ATCs set to infinite)

and larger spreads on both ends of the interconnection.

As expected, the bidding zone creation in France provokes a larger divergence between

FR1 and FR2. The impact is larger during winter where the commercial exchanges

reach their maximum values. The 4000MW configuration is a transitional state between

the constraining situation (2500MW) and the one bidding zone configuration. Same

observations are visible for other European countries.

5.4.3 Social Welfare evolution

In many studies dealing with electricity market, the social welfare corresponds to the

most suitable indicator to assess the impact of a measure on the system. The same

method as the one used in price analysis is applied for social welfare comparison. For


each hour over 50 Monte-Carlo years, the social welfare is computed in the different

bidding zones and monthly mean values are finally found.

Figure 5.12: Hourly European Social Welfare Evolution throughout Monte-Carlosynthesis year

The results given in figure 5.12 represent the evolution of the hourly average social

welfare values (mean values for each month) in Europe when France is divided by the

new configuration. Indeed, the value of social welfare in itself does not represent a

concrete quantity but its evolution quantifies the influence of the measure taken in the

system.

The impact is negative in Europe since a net decrease of the total social welfare is ob-

served. Once again the influence is larger during winter since the capacity introduced

in France provokes more constraints when the exchanges are high. The other European

capacities have not been modified. Thus, the new bidding zone configuration only in-

troduces constraints in the system: this social welfare evolution is therefore consistent

with the expectations.

Figure 5.13 gives the evolution of the producer, consumer and trading surpluses for each

bidding zone. These values are obtained by comparison of the mean hourly surpluses

over 50 Monte-Carlo years for the initial market situation and the constraining bidding

zones configuration (2500MW of commercial capacity).

Since the load and the amount the consumers are willing to pay are assumed to be con-

stant in the two different bidding zone configurations, the consumer surplus represents


Figure 5.13: Hourly Consumer, Producer and Trading surpluses Evolution (meanvalue over 50 Monte-Carlo years) in the different Bidding Zones

in this case a measure of the electricity price evolution. The new bidding zone configu-

ration slightly decreases the Italian spot prices and increases the other European prices.

Thus, consumer surplus is negative in all European bidding zones except in Italy.

The new bidding zone configuration provokes a transfer of surplus from the consumers

to the producers. France, Germany and Spain would be the most impacted countries.

Trading surplus significantly appears in France and Switzerland total social welfare due

to new congestions for the borders involving these countries (including the FR1-FR2

congestions). In Italy the market structure evolution would be beneficial for consumers.

The results given above do not exactly reflect the reality of the system since a new

bidding zone configuration in France would change the European commercial exchanges

and some commercial capacities would be modified. The two French bidding zones have

necessarily an impact on the capacity calculation and therefore on the ATCs values.

The capacity calculation is not conceivable in this study since the PTDF and critical

branches are not available. In order to illustrate the influence of this capacity calculation,

some ATCs should be increased in Europe.


5.5 Loop Flows Study

This study is completely different from the previous sections developed earlier in the

impact study since it combines both economic results and load flow computations.

Two hours have been defined for this analysis in order to obtain different and represen-

tative situations. These two hours have been chosen to be consistent with the evolution

of the load during the year in Europe and in France and then the mean results over the

50 hours obtained in each Monte-Carlo year have been determined:

January 9am

June 10am

Appendix F gives the European net values for the 4 simulations used in this study.

The loop flow analysis is therefore to consider carefully since only two hours are studied.

For the economic impact study, all hours during 50 Monte-Carlo years are taken into

account. In each case, the system is simulated both when France is only composed by

one bidding zone and with the new bidding zone configuration (2500 MW of commercial

capacity). Thus, it is possible to analyze the evolution of the indicators due to market

splitting in two completely different situations regarding production and consumption

purposes.

Figure 5.14 and figure 5.15 give the loop flows measurement realized by setting all net

values in Europe to zero. All transactions through interconnections being null, there are

not any transit flows in the system. This method enables to determine only loop flows

by computing the physical flows through the links between bidding zones.

All the net values have been set to zero. This modifies the generation unit pattern and

the loop flows created by the exchanges inside a bidding zone are changed. Moreover,

France is the only country in the simulation with the actual generation units and con-

sumption dispatch to a node level. For the other countries an equivalent network is used

and only the net positions of the bidding zones are accurate.

A rough overview of loop flows through French interconnections is available though.

The phenomenon appears to be more distinct in summer. It seems also that the new

bidding zone configuration increases the amount of loop flows in the system for France

North-Belgium and France South-Italy and decreases them for France-Switzerland and

France-Germany.


Figure 5.14: Loop Flows measure for one Bidding Zone configuration (left); for twoBidding Zones configuration (right) - January 9am

Figure 5.15: Loop Flows measure for one Bidding Zone configuration (left); for twoBidding Zones configuration (right) - June 10am


From figure 5.16 to figure 5.19, the commercial exchanges and the physical flows are

given on the left and the unscheduled flow indicator is presented on map to the right.

The unscheduled flows are the difference of the physical flows and the commercial cross

border exchanges. This indicator enables to measure the amount of flows which were

not foreseen in the market mechanisms through European interconnections.

Figure 5.16: Commercial Exchanges and Physical Flows (left); Unscheduled FlowsIndicator (right) for one Bidding Zone configuration - January 9am

In winter, the amount of unscheduled flows for borders France North – Belgium and

France South – Italy increases due to the new French bidding zones and decreases for

France – Switzerland and France North – Germany. In summer, the situation is com-

pletely the opposite with an increase of unscheduled flows with Italy and Belgium and

larger values with Germany and Switzerland.

The new bidding zone configuration defined in this study does not represent a viable

solution since its impact on the unscheduled flows depends on the period of the year.

The robustness of the market configuration is determinant and, based on these results,

market splitting in France would not improve the unscheduled flows problematic in Eu-

rope. Furthermore the unscheduled flow indicator does not represent a suitable quantity

to evaluate the loop flows and transit flows. By considering unscheduled flows, it is

impossible to accurately determine the source to sink of the problems.


Figure 5.17: Commercial Exchanges and Physical Flows (left); Unscheduled FlowsIndicator (right) for two Bidding Zones configuration - January 9am

Figure 5.18: Commercial Exchanges and Physical Flows (left); Unscheduled FlowsIndicator (right) for one Bidding Zone configuration - June 10am


Figure 5.19: Commercial Exchanges and Physical Flows (left); Unscheduled FlowsIndicator (right) for two Bidding Zones configuration - June 10am

5.6 Limits of the study

This section presents the different limits of the model chosen in this master thesis.

The consequences of these particularities are analyzed and solutions to address these

shortcomings are proposed.

5.6.1 Impact on Congestion Management

First of all, the model chosen cannot define impedances once the new bidding zone con-

figuration is applied. Indeed, if several bidding zones appears in France, the Kirchhoff’s

laws definition is not possible anymore. Only one link between the two bidding zones

remain functional and all other lines capacities are set to null. A new computation of

the equivalent impedances would be necessary. Since this process is mainly based on

historical network snapshots, a new set of Kirchhoff’s laws cannot be obtained. The

study of the impact on congestion management in France is not conceivable.

5.6.2 Data limitations

In the electricity market modelling, the model used for European countries is not as

accurate as the French one. Indeed, France is modeled by 25 small areas whereas all


other bidding zones are only represented by one node. For Load Flow computation, the

available network is also more detailed in France.

There is a significant lack of consistency between the data used to determine the com-

mercial exchanges and the ones for physical flows. Many assumptions have to be taken

into account on the pattern of the generation units for example.

The study performed in this paper would lead to more reliable results with a complete

set of data for whole Europe. Other market splitting such as a combination between

German and French bidding zones configuration could be performed and compared.

A robustness analysis is needed. Many scenarios are possible for 2030. For example

ENTSO-E is studying 4 scenarios for this time horizon [17]. The projects that will be

deeply analyzed are the ones needed in the four scenarios. For a bidding zone reconfig-

uration, the same robustness has to be performed before making any decision.

5.6.3 Capacity Calculation

The implementation of the European bidding zone configuration needs to be combined

with completely coordinated capacity calculation. Indeed, the commercial capacity has

been chosen based on the constraining level involved and the importance of the European

capacities modification has been demonstrated. An accurate computation based on the

network critical branches and the different N-1 situations should be performed to obtain

more relevant results.

The model could be slightly modified to take into account new European market mecha-

nisms such as Flow Based. Indeed, this new methodology would completely modify the

obtained results and it would make sense to perform a study for the time horizon 2030

taking it into account.

5.6.4 Indicators used in the Loop Flows Study

The loop flow measure where all net values in Europe are set to zero deviates from the

reality of the system since it is necessary to delete all exchanges between bidding zones.

Thus, the obtained results give a rough overview of the amount of loop flows without

giving the ex act value.

The unscheduled flow indicator presents also some limitations [4]:

The scheduled flow is only supposed to flow through the border which is impossible

in a meshed network.


The actor can choose several paths to schedule their exchanges. In the study case,

when there is not enough capacity between FR1 and FR2, the actors are scheduling

their exchanges through Switzerland.

The indicator cannot take into account the impact of all commercial cross border

exchanges on all borders.

Another indicator could be used in order to assess the impact of the new bidding zone

configuration on the amount of transit and loop flows into the European system. The

PTDF Flow indicator enables to evaluate the difference between the measured physical

flows through interconnections and the computed flows between bidding zones based

on the net values and the PTDF matrix. This approach closer to the Flow-Based

methodology compares a measured physical flow with a value which is closer to a result

of capacity allocation where the influence of all exchanges on all interconnections is

considered. Since the PTDF matrix is not available for this study case and the time

horizon 2030, this computation was not possible.

Chapter 6

Conclusion

The impact of the modification of the bidding zone configuration in France has been

performed in this master thesis report. In the base case configuration, France corre-

sponds to only one bidding zone. Based on congestion management considerations, a

new bidding zone configuration has been defined and France has been divided into two

different bidding zones (France North and France South). A general methodology both

to define this new configuration and to study its impact on the European electricity

system has been developed.

The implementation of a new bidding zone configuration in France appears to be quite

negative as a general point of view.

A coherent division of the country is quite impossible considering the physical flows

distribution in France. The new border defined by the two bidding zones configuration

chosen in this paper is crossed by large flows in opposite directions. Thus, the efficiency

of this new market configuration on internal French congestions is therefore certainly

challenged.

Regarding economic considerations, market splitting introduces constraints into Euro-

pean system and decreases the overall performances. The impact on the commercial

exchanges is quite low and new paths are found in order to bypass the market limita-

tions. The importance of the European ATCs evolution has been proved and a capacity

calculation should be performed to obtain more accurate results and determine the real

impact on the electricity prices and social welfare. In this study, the impact on the

European social welfare is negative because of the constraints introduced by the new

bidding zone configuration. The influence on commercial capacities for other borders

could not be assessed.

74

Chapter 6. Conclusion 75

The loop flows problem might be the main reason which leads to market splitting in

Europe. Based on the results obtained in this study, a new bidding zone configuration

in France is not robust enough to deal with this phenomenon and only local conclusions

can be drawn.

This paper presented a general methodology in order to define a relevant bidding zone

configuration and study its impact on the electricity market based on both physical and

economic considerations. Based on this study, an optimal zonal configuration regarding

the number of zones in France and the topology of the system could be found. The set

of data used in this study presents some limitations and the pertinence of the results

obtained would be higher with more consistent inputs for both market modelling and

load flow simulations.

Future studies could be performed to develop further this very important research field.

The implementation of capacity calculation and Flow Based methodology into the sim-

ulation models could increase the pertinence of the results.

Other possible solutions can be investigated to compete with market splitting such as

grid investments, HVDC link, more coordination through interconnections management,

cost sharing... A comparison of these methodologies and their impact on the European

system could enable obtain the optimal solution in the current European market devel-

opment.

Appendix A

Grid Transmission Capabilities

Computation (Source: RTE)

The “Grid Transmission Capability” (GTC) is a parameter defined for each link between

the 25 French areas. In order to see the congestions which occur into the French electrical

network, it is necessary to define equivalent physical limits between these zones. This

appendix gives the general method used by RTE in order to compute the GTCs used in

the study.

If there is only one line between two areas, the GTC is defined as 70% of the capacity

of the line. In case there are several lines between two areas, the process is slightly

different.

A base case is simulated, all physical flows between areas (through the equivalent links)

are known. Then the method used in order to define the GTCs is a simplified capacity

calculation (general methodology given in chapter 2).

The GTCs are obtained by increasing the physical flows between two areas for each N-1

situation (a N-1 situation occurs when there is a failure on one line in the system) until

a constraint is hit on one of the critical branch in the system. This process uses the

notion of PTDF in order to know how is influenced the flows through a line when the

transmission between two areas is modified. The minimum value computed is chosen as

the GTC in order to withstand any N-1 situation.

76

Appendix B

Impedance Computation

Principle (Source: RTE)

25 areas have been defined in France. In order to find the new bidding zone configuration,

a statistical study of the physical flows inside the country is of interest. In order to apply

the DC Load Flow theory presented in chapter 4, impedances of the equivalent links are

needed between the areas.

This appendix shortly present the way of reasoning of RTE in order to compute such

impedances.

First of all, for the considered system, network snapshots s are available for different 2030

scenarios (large or small consumption, large amount of renewable energies. . . ). Then

an optimization process (given below) is performed in order to define the impedances in

such way that the difference between the already known physical flows of the snapshots

and the computed physical flows (based on these impedances) is minimized. Thus, an

admittance matrix is defined in order to obtain theoretical physical flows which are as

close as possible to the real ones. This computation is realized for all snapshots; equiv-

alent impedances are obtained and they are valid for every possible network evolutions.

The validity of these impedances is analyzed with an indicator which compares the dif-

ference between the theoretical physical flows and the real ones for each situation.

77

Appendix B. Impedance Computation Principle (Source: RTE) 78

Variables: Y , T0 (and therefore Ti,j,s)

Parameters: Fi,j,s, θs, Is

minY,T0∑s,i,j

(Fi,j,s − Ti,j,s)2

Subject to:

Y θs = Is ∀s

Ti,j,s = Yi,j(θis − θjs) + T0ij ∀s, ∀i,∀j

Ti,j,s: obtained physical flows with admittance matrix Y between areas i and j for

simulation s

θs: phase matrix, simulation s

Is: injection matrix, simulation s

T0ij : flows when there is not any exchange in the system, between areas i and j

Appendix C

List of the performed simulations

during the study

Simulation 1

Impedances taken into account

Hurdle Costs

GTC infinite in France (no constraints in France), constraints in Europe (between

bidding zones)

No Minimum Up/Down Time

Simulation 2 : Copper Plate Model

No Impedances

Hurdle Costs

GTC infinite in Europe (only one BZ)


Simulation 3 : 1 Bidding Zone configuration in France

No Impedances

Hurdle Costs

79

Appendix C. List of the performed simulations during the study 80

GTC infinite in France (only one bidding zone), constraints abroad


Simulation 4 : 2 Bidding Zones configuration in France

No Impedances

Hurdle Costs

GTC infinite in France North and France South (2 bidding zones in France), con-

straints abroad

Commercial capacity infinite between the two French bidding zones


Simulation 5 : 2 French bidding zones - high constraints through FR1-FR2

No Impedances

Hurdle Costs


straints abroad

Commercial capacity between the two French bidding zones: 2500 MW


Simulation 6 : 2 French bidding zones - low constraints through FR1-FR2

No Impedances

Hurdle Costs


straints abroad

Commercial capacity between the two French bidding zones: 4000 MW


Appendix D

Seasonality of the Commercial

Exchanges in France

Commercial Exchanges (mean values for 4am during a year) from France South toFrance North

81

Appendix D. List of the performed simulations during the study 82

Commercial Exchanges (mean values for 4pm during a year) from France South toFrance North

Appendix E

Price Divergence Results for

other European borders

Price Divergence Evolution [two bidding zones configuration (2500MW capacity) minusone bidding zone configuration] in Europe

83

Appendix E. Price Divergence Results for other European borders 84

Price Divergence Evolution [two bidding zones configuration (2500MW capacity) minusone bidding zone configuration] in Europe

Appendix F

European Net Values for the four

simulations used in Loop Flows

study

85

Appendix F. European Net Values for the four simulations used in Loop Flows study86

Net values in MWh/h (mean values over 50 Monte-Carlo years) - 1 Bidding Zoneconfiguration January, 9am


Net values in MWh/h (mean values over 50 Monte-Carlo years) - 2 Bidding Zonesconfiguration January, 9am


Net values in MWh/h (mean values over 50 Monte-Carlo years) - 1 Bidding Zoneconfiguration June, 10am


Net values in MWh/h (mean values over 50 Monte-Carlo years) - 2 Bidding Zonesconfiguration June, 10am

Bibliography

[1] Amprion; Apx Endex; Belpex; Creos; Elia; EnBW; Epexspot; RTE; Tennet. “CWE

Enhanced Flow-Based MC feasibility report”. Technical report, October 19th, 2011.

[2] ACER. “Framework Guidelines on Capacity Allocation and Congestion Manage-

ment for Electricity”. Technical report, FG-2011-E-002, Ljubljana, Slovenia, July

29th, 2011.

[3] CASC. “Rules for Capacity Allocation by Explicit auctions within Central West Eu-

rope Region (CWE), Central South Europe Region (CSE) and Switzerland”. Tech-

nical report, [Available at http://www.casc.eu/en/Resource-center/CWE–CSE-

and-Switzerland/Auction-Rules].

[4] ENTSO-E. “Bidding zones review process - Technical Report”. Technical report,

ENTSO-E, Brussels, Belgium, January, 2001.

[5] Barbara BURSTEDDE. “From nodal to zonal pricing: a bottom-up approach

to the second-best”. Technical report, IEEE 9th International Conference on the

European Energy Market (EEM), Florence, Italy, 2012.

[6] Copenhagen Economics. “The economic consequences of capacity limitations on

the Oresund connection”. Technical report, December 11th, 2006.

[7] NordPool. [Available at: http://www.nordpoolspot.com/market-

data1/maps/power-system-overview/power-system-map/]. Technical report.

[8] ACER. “The influence of existing bidding zones on electricity markets - Consulta-

tion document”. Technical report, Ljubljana, Slovenia, July 31st, 2013.

[9] ENTSO-E. “ToR for the early implementation of the NC CACM concerning a

Bidding Zone Review in CWE (Belgium, France, Germany, Luxembourg, Nether-

lands), Denmark-West, CEE (Austria, Czech Republic, Germany, Hungary, Poland,

Slovenia, Slovakia) Switzerland and Italy”. Technical report, Market Integration

Working Group, October, 2012.

90

Bibliography 91

[10] EA Energy Analyses; Hagman Energy and COWI. “Congestion management in the

Nordic market - evaluation of different market models - Final report”. Technical

report, May, 2008.

[11] CEPS; MAVIR; PSE; SEPS. “Unplanned flows in the CEE region - In relation to

the common market area Germany-Austria”. Technical report, January, 2013.

[12] RWTH Aachen University. “Impact of a German/Austrian market splitting on the

transmisison grid of Austrian Power Grid (APG) and critical lines in Germany,

Poland and Czech Republic”. Technical report, IAEW (Institut fur Elektrische

Anlagen und Energiewirtschaft) - slideshow in english, September 20th, 2011.

[13] Frontier Economics and Consentec. “Relevance of established national bidding

areas for European power market integration - an approach to welfare oriented

evaluation”. Technical report, October, 2011.

[14] THEMA. “Loop Flows - Final Advice”. Technical report, THEMA Consulting

Group TE-2013-36, September 30th, 2013.

[15] ENTSO-E. “Draft - Network Code on Capacity Allocation and Congestion Man-

agement (CACM)”. Technical report, [Available at https://www.entsoe.eu/major-

projects/network-code-development/capacity-allocation-and-congestion-

management/], September 27th, 2012.

[16] Lennart SODER and Mikael AMELIN. “Efficient operation and planning of power

systems”. Technical report, Department of Electric Power Systems, School of Elec-

trical Engineering, KTH Royal Institute of Technology, Stockholm, 2011.

[17] ENTSO-E. “Scenario Outlook and Adequacy Forecast 2013-2030”. Tech-

nical report, [Available at: https://www.entsoe.eu/about-entso-e/system-

development/system-adequacy-and-market-modeling/soaf-2013-2030/], 2013.

[18] Lennart SODER. “EG2060 Electricity Market Analysis”. Technical report, De-

partment of Electric Power Systems, School of Electrical Engineering, KTH Royal

Institute of Technology, Stockholm, 2013.

[19] Matti SUPPONEN. “Influence of national and company interests on European

electricity transmission investments”. Technical report, Aalto University, School of

electrical engineering, Helsinki, 2011.

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Impact of Bidding Zone Configuration on the French ...751985/FULLTEXT01.pdf · Declaration of Authorship I, Alexandre Canon, declare that this thesis titled, ’Impact of Bidding