Assessment of the impact of the French capacity …/media/Files/us-files/...4.4 Comparison of an EOM...

30 JUNE 2016

Assessment of the impact

of the French capacity

mechanism on electricity

markets

ASSESSMENT OF THE IMPACT OF THE FRENCH CAPACITY MECHANISM ON ELECTRICITY MARKETS

Copyright © FTI Consulting, Inc., 2016 i

Contents

Contents ................................................................................................................................................................................... i

List of figures ......................................................................................................................................................................... v

List of tables ....................................................................................................................................................................... viii

Abbreviations and technical units ................................................................................................................................. ix

Preface .................................................................................................................................................................................... xi

Executive summary...................................................................................................................................................... 1 1.

1.1 Context and objectives of the study ............................................................................................................ 1

1.1.1 Drivers of the introduction of a capacity mechanism in France ................................................. 1

1.1.2 Interface between CMs and energy markets ..................................................................................... 1

1.1.3 Specific features of the French CM ........................................................................................................ 2

1.1.4 Objectives of this study ............................................................................................................................. 3

1.2 Modelling approach .......................................................................................................................................... 5

1.3 Assessment of the impact of the CM in France ....................................................................................... 6

1.4 Comparison of the impact of the CM with other policies implemented in Europe ................. 11

1.5 Conclusions ........................................................................................................................................................ 17

1.6 Structure of the report ................................................................................................................................... 18

Theory: Impact of policy interventions on electricity markets .................................................................. 19 2.

2.1 Price formation on power markets ............................................................................................................ 19

2.2 Policy and regulatory interventions ........................................................................................................... 22

2.3 Review of the different types of CM and their interface with electricity market ...................... 24

2.3.1 Issues with energy-only markets and rationale for a CM ............................................................ 24

2.3.2 The different types of CMs ..................................................................................................................... 26

2.3.3 Analysis of potential impacts of CMs on the energy market ..................................................... 27

Description of the French CM and its potential impacts on European electricity markets ............. 30 3.

3.1 Rationale for the introduction of a CM in France ................................................................................. 30

3.2 The legal and regulatory framework ......................................................................................................... 32

3.3 Description of the French CM principles and its specificities ........................................................... 32


ii Copyright © FTI Consulting, Inc., 2015

3.4 Identification of the potential impacts of the French CM on the energy market ..................... 35

Quantifying the impacts of the French CM ...................................................................................................... 37 4.

4.1 Introduction ........................................................................................................................................................ 37

4.2 Description of the modelling approach ................................................................................................... 37

4.2.1 Model description ...................................................................................................................................... 37

4.2.2 French power sector scenario and main background assumptions ........................................ 39

4.2.3 Modelling approach to assess the available capacity in two market design scenarios.... 42

4.2.4 Impact of the market design on risk aversion and cost of capital ........................................... 44

4.3 Assessment criteria .......................................................................................................................................... 46

4.4 Comparison of an EOM and the French CM design scenarios ........................................................ 46

4.4.1 Impact on security of supply ................................................................................................................. 46

4.4.2 Impacts on available capacity ................................................................................................................ 49

4.4.3 Economic efficiency ................................................................................................................................... 52

4.4.4 Impact on energy markets ...................................................................................................................... 60

4.4.5 CO2 emissions.............................................................................................................................................. 64

Comparison of the impact of the French CM with other public policies implemented in Europe65 5.

5.1 Introduction ........................................................................................................................................................ 65

5.2 Presentation of the scenarios for the four policy schemes ............................................................... 66

5.2.1 The German strategic and climate reserve ....................................................................................... 66

5.2.2 Policy support to renewable generation in Germany ................................................................... 70

5.2.3 Nuclear phase-out in Germany ............................................................................................................. 73

5.2.4 Carbon price floor in the UK .................................................................................................................. 75

5.3 Assessment of the impacts of the four interventions ......................................................................... 78

5.3.1 Security of supply ...................................................................................................................................... 78

5.3.2 Impacts on available capacity ................................................................................................................ 79

5.3.3 Impact on energy markets ...................................................................................................................... 84

5.3.4 Economic efficiency ................................................................................................................................... 93

5.3.5 CO2 emissions.............................................................................................................................................. 95

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


Copyright © FTI Consulting, Inc., 2016 iii

6.1 Impacts of the French CM compared to an EOM ................................................................................. 99

6.2 Comparison of the impacts of the French CM compared to other policy interventions .....100

Appendix A - Description of dispatch model ................................................................................................102 7.

European power plants database ......................................................................................................................102

European power market assumptions .............................................................................................................102

Geographic scope of the model .........................................................................................................................103

Price calculation .......................................................................................................................................................104

Back-casting calibration ........................................................................................................................................104

Power dispatch model credentials ....................................................................................................................106

Renewable power generation modelling ........................................................................................................107

Wind – Power production ...................................................................................................................................107

Solar – Power production ....................................................................................................................................107

Wind and solar bids in wholesale market ......................................................................................................108

Nordic and Alps hydro modelling ....................................................................................................................108

Pumped storage ......................................................................................................................................................108

On-site storage ........................................................................................................................................................108

GARCH Methodology .............................................................................................................................................108

Appendix B - Description of capacity market model ..................................................................................111 8.

Perfect competition capacity model .................................................................................................................111

Inter-temporal capacity model ...........................................................................................................................111

Interaction with power market model ..............................................................................................................111

Capacity market bids ..............................................................................................................................................112

Existing units ............................................................................................................................................................112

New units ...................................................................................................................................................................113

Remaining debt may represent a significant part of the avoidable cost of the existing plants.114

Appendix C - Description of key scenario modelling assumptions .......................................................115 9.

Macroeconomic context ........................................................................................................................................115

Economic growth ....................................................................................................................................................115

Demographic change ............................................................................................................................................116


iv Copyright © FTI Consulting, Inc., 2015

Energy efficiency .....................................................................................................................................................116

Demand .......................................................................................................................................................................116

Supply ..........................................................................................................................................................................117

FTI-CL Energy assumptions .................................................................................................................................117

Renewables ...............................................................................................................................................................118

Nuclear .......................................................................................................................................................................120

Combined Cycle Gas Turbines (CCGT) ............................................................................................................121

Coal ........................................................................................................................................................................122

Bibliography ......................................................................................................................................................................124


Copyright © FTI Consulting, Inc., 2016 v

List of figures

Figure 1-1: Comparison of LOLE between a CM design and an EOM design ................................................. 7

Figure 1-2: Impact of the CM on customer costs (difference between the CM and EOM

counterfactual scenario) ..................................................................................................................................................... 8

Figure 1-3: Impact of the CM on social welfare (difference between the CM and EOM counterfactual

scenario) ................................................................................................................................................................................... 9

Figure 1-4: Change in the available capacity (difference between the CM and EOM counterfactual

scenario) ................................................................................................................................................................................. 10

Figure 1-5: Average power prices in France and its neighbouring countries in the EOM and CM

scenarios ................................................................................................................................................................................ 11

Figure 1-6: Impact on available capacity of the CM and other policy intervention compared to the

counterfactual scenario .................................................................................................................................................... 13

Figure 1-7: Power price impact of the CM and other policy interventions compared to the

counterfactual scenario .................................................................................................................................................... 14

Figure 1-8: Impact on domestic cross-border flows of the different policy interventions compared to

the counterfactual scenario ............................................................................................................................................. 15

Figure 1-9: Impact on CO2 emissions of the different policy interventions compared to the

counterfactual in 2020 ...................................................................................................................................................... 16

Figure 1-10: Breakdown of the impact of the German strategic reserve on customer costs .................. 17

Figure 2-1: Wholesale electricity market clearing price in regular and scarcity conditions (with

inelastic demand) ................................................................................................................................................................ 21

Figure 2-2: Wholesale electricity market clearing price with and without mark-up on SRMC ............... 22

Figure 2-3: Taxonomy of CMs ........................................................................................................................................ 27

Figure 3-1: Maximum peak demand in different weather condition scenarios (winter 2016-2017) .... 31

Figure 3-2: French CM design ........................................................................................................................................ 34

Figure 3-3: General organisation of the CM (timeline) ......................................................................................... 35

Figure 4-1: Geographic scope of FTI-CL Energy’s European power market model ................................. 38

Figure 4-2: Peak demand duration curve and sample selection........................................................................ 39

Figure 4-3: Nuclear decommissioning paths ............................................................................................................ 41

Figure 4-4: Energy-only market design optimisation process ............................................................................ 43

Figure 4-5: Capacity market design optimisation process ................................................................................... 44

Figure 4-6: Energy revenue distribution across the modelled samples .......................................................... 45

Figure 4-7: LOLE in the CM scenario and in the EOM scenario.......................................................................... 47

Figure 4-8: Evolution of CCGT capacity in the CM scenario ................................................................................ 49

Figure 4-9: Evolution of peaking capacity in the CM scenario ........................................................................... 50


vi Copyright © FTI Consulting, Inc., 2016

Figure 4-10: Change in the available capacity (difference between the CM and EOM counterfactual

scenario) ................................................................................................................................................................................. 51

Figure 4-11: Evolution of capacity prices ................................................................................................................... 52

Figure 4-12: Impact of the CM on customer costs (difference between the CM and EOM

counterfactual scenario) ................................................................................................................................................... 53

Figure 4-13: Energy revenue distribution across the modelled samples in an EOM with a price cap at

26,000€/MWh ....................................................................................................................................................................... 56

Figure 4-14: Impact of the CM on social welfare (difference between the CM and EOM

counterfactual scenario) ................................................................................................................................................... 58

Figure 4-15: Average power prices in France and its neighbouring countries in the EOM and CM

scenarios ................................................................................................................................................................................ 61

Figure 4-16: Comparison of load duration curve between CM and EOM scenarios in 2030 .................. 62

Figure 4-17: Impact of CM on the French net export balance ........................................................................... 63

Figure 4-18: Impact of the CM on congestion rents .............................................................................................. 64

Figure 4-19: Impact of CM on CO2 emissions in France and in Europe in 2030 .......................................... 64

Figure 5-1: Germany’s planned capacity and climate reserve ......................................................................... 67

Figure 5-2: Installed capacity over 2005-2015 ......................................................................................................... 72

Figure 5-3: Renewable capacity development with and without renewable support ................................ 73

Figure 5-4: Evolution of nuclear capacity in Germany under Nuclear Energy Acts of 2010 and 2011 75

Figure 5-5: CPF implementation in the UK ................................................................................................................ 77

Figure 5-6: EU ETS and CPF assessment ..................................................................................................................... 78

Figure 5-7: LOLE in Germany in the EOM scenario and with the German strategic reserve set at 5

percent of the peak demand .......................................................................................................................................... 79

Figure 5-8: Impact of the CM and other policy interventions on the available capacity .......................... 81

Figure 5-9: Impact on the available capacity active in the market of the German climate reserve ...... 82

Figure 5-10: Impact on the available capacity of the German RES support .................................................. 83

Figure 5-11: Impact on the available capacity of the German nuclear phase-out plan ............................ 84

Figure 5-12: Impacts on domestic power prices of different policy interventions ..................................... 85

Figure 5-13: Impacts on the French power price of different policy interventions .................................... 86

Figure 5-14: Percentage of time when the price difference exceeds 5€/MWh as a result of different

policy interventions ............................................................................................................................................................ 90

Figure 5-15: Percentage of time when the price difference exceeds 1€/MWh as a result of different

policy interventions ............................................................................................................................................................ 91

Figure 5-16: Impact on cross-border flows of different policy interventions ............................................... 92

Figure 5-17: Impact on congestion rents of different policy interventions ................................................... 93

Figure 5-18: Breakdown of the impact of the German strategic reserve on customer costs .................. 95

Figure 5-19: Impact of the German climate reserve on CO2 emissions in 2020 ........................................... 96


Copyright © FTI Consulting, Inc., 2016 vii

Figure 5-20: Impact of the German RES support on CO2 emissions in 2020 ................................................ 97

Figure 5-21: Impact of the German nuclear phase-out on CO2 emissions in 2020 .................................... 97

Figure 5-22: Impact of the UK CPF on CO2 emissions in 2020 ........................................................................... 98

Figure 7-1: FTI-CL Energy’s European Power Market model ........................................................................ 103

Figure 7-2: Back-casting calibration – French hourly prices, November 2012 .......................................... 105

Figure 7-3: Back-casting calibration – GB hourly prices, October 2012 ....................................................... 105

Figure 7-4: Back-casting calibration – German hourly prices, October 2012 ............................................. 106

Figure 7-5: Back-casting calibration – Belgian hourly prices, October 2012 .............................................. 106

Figure 8-1: Interaction between the capacity and energy markets ............................................................... 112

Figure 8-2: Existing units ............................................................................................................................................... 113

Figure 8-3: New units ..................................................................................................................................................... 113

Figure 9-1: Electricity consumption against temperature in European countries .................................... 117

Figure 9-2: Installed capacity of hydropower by 2015 ....................................................................................... 119

Figure 9-3: Installed capacity of wind power by 2015 ........................................................................................ 119

Figure 9-4: Installed capacity of solar power by 2015 ........................................................................................ 120

Figure 9-5: Installed capacity of nuclear power by 2015 ................................................................................... 121

Figure 9-6: Installed capacity of CCGT by 2015 .................................................................................................... 122

Figure 9-7: Installed capacity of coal by 2015 ....................................................................................................... 123


viii Copyright © FTI Consulting, Inc., 2016

List of tables

Table 2-1: Potential impacts of CMs ............................................................................................................................ 28

Table 4-1: Renewable capacity in 2030 ....................................................................................................................... 42

Table 5-1: Historical nuclear shut down schedule in Germany .......................................................................... 74


Copyright © FTI Consulting, Inc., 2016 ix

Abbreviations and technical units

ARENH Accès Régulé à l’Electricité Nucléaire Historique

BMWi German Ministry of Economics and Energy

BNetzA Bundesnetzagentur, the German Energy Regulator

Brottes Act Law 2013-415

BWR Boiling water reactor

CCGT Combined-cycle gas turbine

CCS Carbon Capture and Storage

CHP Combined heat plants

CM Capacity Mechanism

CPF Carbon Price Floor

CPS Carbon Price Support

CRE French Energy Regulation Commission

DSR Demand Side Response

EC European Commission

EDF Electricité de France

EEG German Renewable Energy Act

EIA US Energy Information Administration

ENTSO-E European Network of Transmission System Operators for Electricity

EOM Energy-only market

ETS Emissions Trading Scheme

(G)ARCH (Generalized) Autoregressive Conditional Heteroskedastic

GB Great Britain

IEA International Energy Agency

INSEE Institut National de la Statistique et des Etudes Economiques

LOLE Loss of load expectation

MW Megawatts

MWh Megawatt hours

NOME Act Law 2010-1488

NPV Net present value

NTC Net Transfer Capacity

OCGT Open-cycle gas turbine

PWR Pressurised water reactor

RES Renewable Energy Sources

RTE French TSO


x Copyright © FTI Consulting, Inc., 2016

SEDC Smart Energy Demand Coalition

SRMC Short-run marginal cost

TSO Transmission System Operator

UK United Kingdom

VOLL Value of lost load

WEO14 IEA World Energy Outlook 2014

WEO15 IEA World Energy Outlook 2015


Copyright © FTI Consulting, Inc., 2016 xi

Preface

This report presents the key results of a study for RTE Assessment of the impact of the French

capacity mechanism on electricity markets which started in July 2015 and concluded with a public

launch event on 10 May 2016. FTI-CL Energy has been asked by RTE to:

Assess the French capacity mechanism and its impact on the electricity market, based on a

dynamic modelling of European electricity markets from 2017 to 2040; and

Compare the French capacity mechanism impact with the impact of other public policy

interventions.

FTI-CL Energy would like to thank RTE for their support as well as stimulation during the numerous

discussions throughout the study, as well as the wider group of policy and industry stakeholders

who contributed to the study through exchanges with the FTI-CL Energy experts.

Editorial of this report closed on 30 June 2016.

The views and analysis presented in this report are those of the FTI-CL Energy authors and not the

views of RTE or FTI Consulting, Inc. or its management, its subsidiaries, its affiliates, or its other

professionals. The right of Fabien Roques, Yves Le Thieis and Charles Verhaeghe to be identified as

the authors of this work has been asserted in accordance with the Copyright, Designs and Patents

Act 1988.

CONTACTS

Fabien Roques

Compass Lexecon

Senior Vice President

froques@

compasslexecon.com

Charles Verhaeghe

Compass Lexecon

Vice President

cverhaeghe@

compasslexecon.com

Yves Le Thieis

Compass Lexecon

Economist

ylethieis@

compasslexecon.com

22 Place de la Madeleine

4th Floor

Paris, 75008

France

Phone: +33 1 53 05 36 15

fax +33 1 53 05 36 16


xii Copyright © FTI Consulting, Inc., 2016

Published by FTI Consulting LLP

Copyright © FTI Consulting, Inc., 2016

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or

transmitted in any form or by any means, electronic, mechanical, photocopying, recording or

otherwise, without the prior permission of the publishers. Requests for permission should be

directed to [email protected] or mailed to: Compass Lexecon, 22 Place de la

Madeleine, 4th Floor, Paris, 75008

Notice:

The authors and the publisher of this work have used sources believed to be reliable in their efforts

to provide information that is complete and generally in accord with the standards accepted at the

time of publication. Neither the authors nor the publisher nor any party involved in the preparation

or publication of this work warrants that the information contained herein is in every respect

accurate or complete and do not assume responsibility for any errors or omissions or for the results

obtained from use of such information. The authors and the publisher expressly disclaim any

express or implied warranty, including any implied warranty of merchantability or fitness for a

specific purpose, or that the use of the information contained in this work is free from intellectual

property infringement. This work and all information are supplied "AS IS." Readers are encouraged

to confirm the information contained herein with other sources.

The information provided herein is not intended to replace professional advice. The authors and the

publisher make no representations or warranties with respect to any action or failure to act by any

person following the information offered or provided within or through this work. The authors and

the publisher will not be liable for any direct, indirect, consequential, special, exemplary, or other

damages arising therefrom.

Statements or opinions expressed in the work are those of the respective author. The views

expressed in this work do not necessarily represent the views of the publisher, its management or

employees, and the publisher is not responsible for, and disclaims any and all liability for the

content of statements written by authors of this work.

mailto:[email protected]


Copyright © FTI Consulting, Inc., 2016 1

Executive summary 1.

1.1 CONTEXT AND OBJECTIVES OF THE STUDY

1.1.1 Drivers of the introduction of a capacity mechanism in France

Concerns over security of supply have emerged in the past ten years in France as well as in a

number of European member states. The issue materialises in a specific way in France as peak

demand has increased more rapidly than energy consumption over the past decade. This results in a

high peak demand, which is very volatile from one year to another and the sensitivity to

temperature due to electrical heating is unique in Europe: during winter, when temperature

decreases by 1°C, consumption increases by 2,400 MW at peak.

Controlling the pace of peak demand growth is therefore critical for maintaining security of supply

in France. In order to provide better incentives to keep the power system in balance and to

complement energy efficiency policies, the government decided in 2010 – through a law on the new

organisation of the electricity market – to establish a capacity mechanism (CM) with a design aimed

at addressing the French power system specificities. The CM regulatory framework has been set up

gradually by a decree in 2012 and detailed rules in 2015 that establish a capacity obligation for

suppliers. The first delivery year will be 2017.

Moreover, in 2015 France passed a new law which provides some key directions for the energy

transition towards a low carbon economy.1 This law sets ambitious objectives for the development

of energy efficiency, as well as renewable and low carbon energy sources, while remaining cost-

competitive and maintaining an adequate security of supply.

1.1.2 Interface between CMs and energy markets

A number of European countries have implemented or are considering CMs of different types, such

as capacity payments, strategic reserves or capacity markets. The way in which these mechanisms

interact with the energy market and impact market participants differ considerably.

The European Commission (EC) launched in 2015 a sector inquiry into CMs in 11 member states2

and has also opened an in-depth investigation into the French market-wide CM.3

1 LOI n° 2015-992 du 17 août 2015 relative à la transition énergétique pour la croissance verte.

2 Press release of the EC, 20 April 2015: http://europa.eu/rapid/press-release_MEMO-15-4892_en.htm.

http://europa.eu/rapid/press-release_MEMO-15-4892_en.htm


2 Copyright © FTI Consulting, Inc., 2016

The EC is particularly sensitive to potential impacts on energy markets of such mechanisms. Energy

markets complemented by a CM have different incentives compared to a theoretical energy-only

market (EOM).4

The introduction of CMs or other national energy policies in theory has an impact on electricity

markets, both:

In the short term: The CM may influence the participation strategy (e.g. dispatch or bidding

behaviour) of existing operators in energy markets; and

In the medium to long term: The CM may influence investment, mothball and retirement

decisions of existing and new operators.

Whilst the intrinsic goal of CMs is to influence market participants’ behaviour in order to maintain

security of supply in line with the politically-driven reliability standard5, a sound CM design can limit

the impact to what is necessary to achieve the policy objectives of the mechanism and avoid

distortions6 of the electricity market.

1.1.3 Specific features of the French CM

In the case of the French CM, several key features have been designed in order to avoid any effect

on the bidding strategies and dispatch decisions of the participants in the energy market. For

instance, the capacity certification is based on the availability of capacities, which avoids any impacts

on the short-term merit order for dispatch, as capacity providers (generation or Demand Side

Response (DSR) operators) are not obliged to generate or to be activated. The main impact of the

French CM should therefore be concentrated on the medium- to long-term evolution of the

available generation and DSR capacity, through modification of market players’ investment,

mothball and retirement decisions.

3 Press release of the EC, 13 November 2015: http://europa.eu/rapid/press-release_IP-15-6077_en.htm.

4 A theoretical EOM is understood as a market without any forms of capacity remuneration or public

intervention to ensure security of supply.

5 A Loss of load expectation (LOLE) of three hours per year.

6 In the report, we distinguish impact and distortions. We define distortions as negative impacts of the

mechanism on the wholesale power market functioning, which would lead to an inefficient use of the

generation mix, not following the economic merit order.

http://europa.eu/rapid/press-release_IP-15-6077_en.htm



In addition, interconnection capacities have been taken into account implicitly in the design of the

French CM, which acknowledges the mutual dependence of EU countries in terms of security of

supply and therefore lowers domestic capacity requirements.

Furthermore, the French CM is not an isolated initiative: it is embedded in an in-depth market

design reform, with measures such as the completion of the electricity regional initiatives, the

revision of renewable energy sources’ (RES) support schemes to integrate RES in the markets, or

the direct participation of demand-response in all market structures.

1.1.4 Objectives of this study

FTI-CL Energy has been asked by RTE:

To assess the French CM and its impact on the electricity market, based on dynamic modelling

from 2017 to 2040; and

To estimate the impacts of different types of public policy interventions in order to provide

perspective on the results found for the French CM.

Our modelling aims to assess the impact of the CM in a realistic setting and to capture the current

electricity market functioning in practice, including its imperfections. Our model is calibrated in

order to reproduce the electricity market dynamics and this is validated by historical back-casting.

Our approach is therefore different from a theoretical exercise which would evaluate the impact of

the CM compared to a theoretical perfect energy market. We have for instance kept the current

price cap on day-ahead energy markets, i.e. of 3,000€/MWh. However, this current price cap is

considerably lower than the theoretical optimal price cap (estimates of the value of lost load are

close to 26,000€/MWh) due to various reasons such as asymmetric information, internalisation of

the probability of political intervention, and the absence of counterparties to accept such a high

price. We also run a sensitivity analysis without a price cap in order to test the robustness of our

modelling results under this assumption.

In order to evaluate the impact of the French CM, a counterfactual ‘energy-only market’ (EOM)

theoretical scenario is considered. This should not be understood as implying that the EOM scenario

is a “reference” market design, it is merely a counterfactual scenario illustrating what the market

design would be without a CM.

In the EOM counterfactual scenario, available capacity is driven by the investment, mothball, and

retirement decisions of rational profit-maximising players based on expected costs and revenues

taking into account the current price cap in the day-ahead market (3,000€/MWh) and by taking



into account revenues in the energy market and other market segments (balancing and reserve

markets). The EOM does not guarantee the reliability standard set by the government is met.

In the CM scenario, market participants have to meet the capacity obligation so the available

capacity is optimised in the same way as in the EOM scenario while respecting the reliability

standard set by the government and therefore taking into account the expected revenues in the

CM.

This report identifies the interactions between the CM and the electricity market, and quantifies the

effects of these interactions in the short, medium and long term, using a range of criteria and

indicators:

Security of supply criteria: we compute the loss of load expectation (LOLE). The reliability standard

defined by the French public authorities is three hours of LOLE.

Economic efficiency criteria: we evaluate the total consumer cost and the social surplus.

Available capacity criteria: we evaluate the impact on the installed capacity (both on the supply

side and on DSR).

Energy market impact criteria: we evaluate the impact on the power price, as well as the impact

on the total imports/exports between France and its neighbouring countries.

CO2 emissions criteria: we evaluate the impact on CO2 emissions both in France and in its

neighbouring countries.

In order to provide some context, we have also modelled other policy interventions in European

electricity markets to compare their impact with the impact of the CM. The objective is not to

evaluate these policies, but to provide some elements of comparison with the effect of the French

CM as regards the magnitude of their impacts. The other regulatory mechanisms that we have

assessed are the following:

The Strategic Reserve in Germany, including the forced inclusion of lignite plants in the reserve

(the so-called Climate Reserve);

The renewable energy sources (RES) support policy in Germany, comparing a scenario with higher

RES development, following the 2012 Renewable Energy Act (EEG) reform, to a development as

anticipated before 2011;

The Nuclear Phase-Out in Germany decided in 2011; and,

The Carbon Price Floor (CPF) introduced in 2013 in the United Kingdom (UK).



These public interventions are aimed at different objectives and, as such, are not directly

comparable. However, they provide a benchmark to compare across a range of metrics: (a) whether

the impact of the CM is significant; and (b) whether this impact is proportionate in light of its

benefits and of the impact of other public interventions that characterise current European power

markets.

Assessing whether the impact of the French CM is proportionate also requires taking into account

its benefits. This study therefore estimates social welfare gains associated with the French CM.

However, such a benefit assessment is not performed for the other policy interventions. The reader

is therefore advised not to draw hasty conclusions on the public interventions considered in this

study, which would not make sense without a dedicated cost-benefit analysis.

1.2 MODELLING APPROACH

To assess the impact of the French CM, we have modelled the European power system using our

FTI-CL Energy proprietary dispatch model based on the optimisation platform Plexos® coupled with

a capacity market model, which we have developed internally. The model is designed to simulate

the dynamics of European power markets in a realistic way and is calibrated to reproduce historical

prices.

The model provides an hourly optimal dispatch of each unit connected to the grid, taking into

account operational constraints, and for each year simulates the capacity market clearing price, and

computes optimal investment, mothball and retirement decisions. The model accounts for demand

fluctuations, renewables output and variability, and the specificities of hydro power and

interconnectors.

The model covers 15 European countries, including the United Kingdom, Ireland, France, Belgium,

the Netherlands, Germany, Austria, Italy, Switzerland, Denmark, Norway, Sweden, Finland, Spain and

Portugal. Countries beyond this geographic scope are modelled at an aggregated level. It takes into

account existing and planned interconnections between France and neighbouring countries.

For each year, the model optimises market participants’ operational, investment and retirement

decisions based on the net present value of their expected costs and revenues. It assumes perfect

competition and no asymmetry of information for market participants, but it includes some degree

of risk aversion, which leads to a lower cost of capital in the CM scenario compared to the EOM

scenario. A sensitivity analysis of the degree of risk aversion is presented in order to test the

robustness of our results.



A range of other reports which analysed the impacts of CMs have been published.7 However, our

study is to our knowledge the first one to use a realistic and dynamic characterisation of European

electricity markets in order to capture the effect of the CM in the transition until 2040 rather than

once equilibrium is reached. This is particularly of interest given the overcapacity that exists today in

some European countries due to the economic recession and the slow recovery of power demand

on the one hand and to the policy-driven development of RES on the other.

The set of forward looking assumptions used in the model are based on RTE’s and ENTSO-E’s

power system outlook scenarios. In particular, the French ‘diversification’ scenario is used as an

initial starting point to calibrate the model, although the electricity mix is optimised endogenously

based on market participants’ optimal decisions.

1.3 ASSESSMENT OF THE IMPACT OF THE CM IN FRANCE

As early as 2017 and for all subsequent years, the reliability standard set by the French public

authorities is not met in the EOM due to the closures of unprofitable plants. In contrast, the

CM allows meeting the reliability standard in all years.

The modelling of the EOM counterfactual scenario shows that market participants do not internalise

the full value of security of supply. Indeed, in the medium term (2017-2021), more generation

capacity is mothballed or shut down and DSR not developed due to the absence of sufficient

remuneration compared to what is expected in RTE’s adequacy assessment8 and compared to

what is necessary in order to maintain security of supply at the desired level. The LOLE gradually

increases in the EOM scenario and in 2017 already exceeds the reliability standard set by the French

public authorities.

In the long term (2022-2030), mothballed plants come back in the market, but there is a lack of a

strong enough economic signal to trigger timely new investments. As a result, the development of

DSR and new investments in combined-cycle gas turbines (CCGTs) are delayed, while no open-cycle

gas turbine (OCGT) is built on an economic basis.

7 See for instance: Union Française de l'Électricité (UFE) and German Association of Energy and Water

Industries (BDEW), 2014. Energy transition and capacity mechanisms: A contribution to the European

debate with a view to 2030. Frontier Economics, 2014. Impact Assessment of Capacity Mechanism.

8 RTE Bilan prévisionnel, 2015.



Consequently, in the absence of CM, security of supply deteriorates substantially, most notably in

but not limited to France. From 2017 onwards, the LOLE exceeds the three-hour reliability standard

and reaches up to ten hours per year in subsequent years. Conversely, the CM effectively guarantees

that security of supply meets the policy objective of three hours of LOLE (Figure 1-1).

Figure 1-1: Comparison of LOLE between a CM design and an EOM design

Source: FTI-CL Energy, 2016

On average from 2017 to 2030, the French CM reduces costs for French consumers by

400M€/year as the reductions in investment risk and energy and curtailment cost outweigh the

additional capacity costs (Figure 1-2).

In the medium term (2017-2021), the CM contributes to limiting costs for consumers as it slightly

reduces the energy cost component of the bills, as well as the cost of curtailment. On the other

hand, consumers have to pay for capacity availability, but the resulting capacity price remains low,

given that no major investment is required.

Between 2022 and 2024, the CM slightly increases costs for consumers as some investments, critical

to maintaining security of supply, are anticipated compared to the EOM scenario.

In the long term (after 2025), the CM significantly reduces costs for consumers through a decrease

in energy prices and in the amount of curtailed energy9, which largely compensates for the capacity

cost. One of the reasons for the reduction of total costs for consumers, in addition to lower

9 Curtailed energy is evaluated at the value of loss of load at 26,000€/MWh. The value is based on RTE

benchmark.

0

2

4

6

8

10

12

14

16

18

20

LO

LE

(h

rs)

CM - LOLE EOM - LOLE



curtailments, is that financing costs for new investments are lower with a CM, since the CM helps

secure revenues, which leads to a lower cost of capital and thereby reduces total costs. This is

especially true for peaking plants that only run for a limited period of time during infrequent cold

periods.

Figure 1-2: Impact of the CM on customer costs (difference between the CM and EOM counterfactual scenario)


The CM increases social welfare in France by more than 500M€/year on average with the

introduction of the CM over the outlook (Figure 1-3). The CM therefore benefits not only

consumers but also generators and DSR operators, who have more stable revenues with the

CM and benefit by more than 100M€/year on average over the outlook from capacity revenues

and reduced market risks and cost of capital.

Indeed, when additional DSR capacity and mothballed CCGTs are necessary to maintain security of

supply, capacity providers benefit from higher capacity prices, but this is partly compensated by

lower energy revenues with fewer price spikes.

-5

-4

-3

-2

-1

0

1

2

3

Cu

sto

me

r co

st

(€ b

illio

n)

Energy cost Ancillary service Capacity Cost

Cost of Unserved Energy Total (€m)



Figure 1-3: Impact of the CM on social welfare (difference between the CM and EOM counterfactual scenario)


As the CM aims at guaranteeing the adequacy between supply and demand, it has an impact

on the available capacity (Figure 1-4). The CM helps to select the most competitive

technologies for all timeframes to meet the reliability standard.

In the medium term, all oil plants are closed and three CCGTs are mothballed between 2017 and

2021 with or without the CM, but the CM induces the development of the most cost-effective

technologies to ensure adequacy, which is DSR in the medium term based on our cost assumptions.

The CM fosters the emergence of aggregators and the development of more DSR capacity than in

the EOM. However, these capacity needs could be fulfilled by other technologies, such as

mothballed CCGTs or closed peaking units, depending on cost assumptions.

In the long term, the CM triggers new investment in cost-effective capacities:

DSR covers at least 50 percent of the incremental capacity need beyond 2023 up to 2040);

From 2024, timely investments in new CCGTs are triggered; and,

From 2026, new OCGTs are allowed to be built, which are not economical without a CM.

It is also worth noting that the CM will likely be favourable to the development of competition and

new entry on the supply side, as it results in more DSR and gas-fired capacities than in the EOM

scenario, which are owned or can be invested in by several companies, including new entrants.

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

So

cia

l su

rplu

s (€

billio

n)

Capacity provider surplus Consumer surplus TSO surplus Total



Figure 1-4: Change in the available capacity (difference between the CM and EOM counterfactual scenario)


The CM does not modify the behaviour and strategies of market players in the energy market

or the short-term merit order. In the long term, it corrects the market failure where electricity

prices are not reflecting the true cost of security of supply and helps to obtain more optimal

available capacity given policy objectives (Figure 1-5).

The French CM is effectively designed not to alter market participants’ bidding strategy and

dispatch decisions in the short term. French capacity providers bid in the energy market on the basis

of their short-run marginal cost (SRMC) in the same way as they do in the EOM.10

The existence of a CM incentivises generators and demand-side response (DSR) operators to

maximise their availability during winter, but it does not force them either to generate or to modify

their bids in the energy market.

In a longer-term horizon, the average wholesale electricity price reduces slightly (by about 5 percent

in 2030) because of the additional capacity necessary to ensure resource adequacy which leads to a

lower occurrence of price spikes (Figure 1-5). Moreover, this price reduction effect is very limited in

neighbouring countries (about 1.6 percent in Germany and 2.2 percent in Great Britain in 2030).

10 We also take into account the possibility for – mostly peak – capacity providers to apply a mark-up on

their SRMC when system margins are tight, in order to recover part of their fixed costs.

0

1

2

3

4

5

6

7

8

9

10

Ava

ila

ble

ca

pa

cit

y (G

W)

CCGT OCGT OIL DSR



Figure 1-5: Average power prices in France and its neighbouring countries in the EOM and CM scenarios


Note: the number above each bar indicate the difference in average power price in €/MWh between CM and EOM.

1.4 COMPARISON OF THE IMPACT OF THE CM WITH OTHER POLICIES IMPLEMENTED IN EUROPE

As part of their national energy policy objectives and responsibilities, member states put in place a

range of policies which can lead to changes in the volume of available capacity. These policy

interventions aim to achieve national policy objectives either for security of supply or support to

specific technologies in order, for instance, to reduce CO2 emissions. CMs are one of these policy

interventions which can be implemented by member states in the energy sector.

The objective of this study is to contribute to the European policy debate by providing quantitative

estimates of the impacts of different policy interventions in electricity markets. The study compares

the impact of the French CM on the electricity market with the impact of policy interventions aimed

at guaranteeing security of supply and/or supporting specific technologies. The objective is not to

evaluate these policies, but to provide some context in order to assess the effect of the French CM

in comparison to other types of policy interventions. The impact of four policy interventions is

assessed:

-0.8 0

-0.4

-3.4 -1.1 -1.5

0

10

20

30

40

50

60

70

80

FR DE GB FR DE GB

2020 2030

Ave

rage

po

we

r p

rice

(€

/M

Wh

)

CM EOM



The German Strategic Reserve11: it is composed of a capacity reserve which targets security of

supply and a climate reserve which aims to reduce CO2 emissions (up to 2.7 GW of lignite power

to be retired from the energy market). We assume a perfect capacity reserve, which does not

affect the energy market. Our focus is therefore on the impact of the climate reserve. In the

counterfactual case, we assume that lignite plants are not retired before reaching the average age

of 45 years.

Policy support to renewable generation in Germany: the Renewable Energy Act (EEG) came into

force in 2010 to support a stronger growth of renewable energy sources (RES). In the

counterfactual case, a lower RES development scenario is considered, based on the expected

development of RES prior to the EEG.

The nuclear phase-out in Germany: all 17 German nuclear power plants are to be shut down by

2022. In the counterfactual case, an energy mix without nuclear phase-out is modelled.

The carbon price floor (CPF) in the UK: a carbon tax (the Carbon Price Support, CPS) topping up

the European Emission Trading Scheme (ETS) carbon price was introduced in 2014 and it

increased up to 18£/tonne between 2016 and 2020. In the counterfactual case, only the EU-ETS

price is charged to power plants operators.

Whilst the impact of the different policy interventions is difficult to compare as they aim at

different objectives, the modifications of the available capacity induced by some of the other

policy interventions modelled are much more significant than the changes driven by the French

CM.

The amount of additional capacity induced by the French CM is comparable in the long term to the

German strategic reserve. In contrast, the German high RES scenario brings in an additional capacity

of 43 GW in 2020 and 70 GW in 2030, while the nuclear phase-out policy results in a decrease of 6

GW in 2020 (Figure 1-6).

11 The planned capacity and its evolution are sourced from Platts, 2015 and from the White Paper by

the Federal Ministry for Economic Affairs and Energy on “an electricity market for Germany’s

energy transition”, which was the best information available when the study was performed.



Figure 1-6: Impact on available capacity of the CM and other policy intervention compared to the counterfactual scenario


Notes: The change in installed capacity of the strategic reserve takes into account the impact of the climate reserve.

Policy interventions may drive electricity prices upwards or downwards, but the impact of the

French CM in absolute terms on power prices is not greater than the other policies modelled.

The effect on average prices of the German strategic (climate) reserve is similar to the French CM’s,

but the nuclear phase-out in Germany or the CPF in the UK have a much more significant impact on

average prices than the French CM, and they more frequently affect the price formation. More

precisely, the nuclear phase-out increases German prices by about 4€/MWh or 10 percent in 2020

and by about 8€/MWh or 12 percent in 2030; the CPF increases GB prices by about 9 and 5€/MWh

in 2020 and 2030, equivalently 16 and 7 percent of the counterfactual prices. With regard to the RES

support, the impact on prices is very substantial in the medium term, but as the generation mix re-

balances itself in the longer run, the impact reduces.

-20

0

20

40

60

80

CM

Stra

tegic

(clim

ate

) rese

rve

Hig

h R

ES

Nu

cle

ar p

ha

se

-ou

t

CM

Stra

tegic

(clim

ate

) rese

rve

Hig

h R

ES

Nu

cle

ar p

ha

se

-ou

t

2020 2030

Insta

lle

d c

ap

acit

y d

iffe

ren

ce

(G

W)

CCGT OCGT WIND SOLAR NUCLEAR COAL CHP SR DSR Total



Figure 1-7: Power price impact of the CM and other policy interventions compared to the counterfactual scenario


The French CM has an impact on cross-border flows because the available capacity is modified

to maintain security of supply, but it is more limited than the impact of some of the other

policy interventions.

The French CM modifies cross-border flows by about 1 TWh in 2020 and by 11 TWh in 2030, whilst

some other policy interventions have a greater impact on cross-border flows: the German RES

support increases exports by 24 and 50 TWh in 2020 and 2030 per year respectively, and the UK CPF

increases import by 9 and 14 TWh per year in 2020 and 2030 respectively.

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

FR DE GB

Ave

rage

po

we

r p

rice

dif

fere

nce

(€

/M

Wh

) 2020

CMStrategic (climate) reserveHigh RESNuclear phase-outCPF

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

FR DE GB

Ave

rage

po

we

r p

rice

dif

fere

nce

(€

/M

Wh

) 2030



Figure 1-8: Impact on domestic cross-border flows of the different policy interventions compared to the counterfactual

scenario


The French CM has a marginal impact on CO2. Policy interventions aimed at decarbonisation

reduce CO2 emissions but other interventions may increase emissions.

Although the French CM does not aim to reduce CO2 emissions, it marginally reduces overall CO2

emissions in France and its neighbouring countries as more efficient CCGTs plants in France reduce

net electricity imports from thermal plants in neighbouring countries.

In contrast, some other policy interventions have strong and negative consequences in terms of CO2

emissions, especially the nuclear phase-out, which has significantly increased CO2 emissions.

-50

-40

-30

-20

-10

0

10

20

30

40

50

CM

Str

ate

gic

(clim

ate

) re

se

rve

Hig

h R

ES

Nu

cle

ar

ph

ase

-ou

t

CP

F

CM

Str

ate

gic

(clim

ate

) re

se

rve

Hig

h R

ES

Nu

cle

ar

ph

ase

-ou

t

CP

F

2020 2030

Ne

t e

xpo

rt d

iffe

ren

ce

(TW

h)



Figure 1-9: Impact on CO2 emissions of the different policy interventions compared to the counterfactual in 2020


The French CM reduces on average consumer costs, mainly because it reduces load shedding

(cost of curtailment) and the financing cost of new investments. The strategic reserve leads to

a high level of security, but it induces around 800M€ per year of additional costs for

consumers. These addition costs are mostly due to the climate reserve.

In the EOM scenario, the LOLE in Germany rises up to ten hours per year in the medium to long

term. The implementation of the strategic reserve largely covers this risk as the LOLE falls down to

0-1 hour per year.

The impact in terms of consumer costs is difficult to compare across the different mechanisms that

we modelled as they aim at different policy objectives, such that our findings should be interpreted

with caution. The strategic reserve and the French CM both aim at securing supply, even though the

strategic reserve also includes a specific measure to close lignite plants, which contributes to

decarbonisation. The costs arising from these two goals are distinguished in the following analysis.

The French CM on average reduces consumer costs, mainly because it reduces load shedding (cost

of curtailment) and the financing cost of new investments. In comparison, the modelling shows that

the strategic reserve generates a net cost for consumers estimated at around 800M€/year, as

presented in Figure 1-10. This is because: i) Consumers do not benefit from lower energy prices

insofar as capacity in the reserve is not valued in the market; ii) The contracting costs of a theoretical

strategic reserve are estimated to be 130M€; iii) The reduction of the security of supply risk is limited

in 2020 and is valued at around 260M€ in 2030; and iv) The ‘climate aspect’ of the reserve

-10.0%

0.0%

10.0%

20.0%

30.0%

-10.0

0.0

10.0

20.0

30.0

CM Strategic

(climate)

reserve

High RES Nuclear

phase-out

CPF

Em

issio

n d

iffe

ren

ce

(%

)

Em

issio

n d

iffe

ren

ce

(m

illio

n t

on

ne

s)

Emission difference (million tonnes) Emissions difference (%)



increases energy costs and contracting costs for consumers by around 650-870M€ per year. Indeed,

lignite plants are replaced by gas plants in the merit order, which increases wholesale prices on

average. In addition, their contracting costs are higher than those of the plants which would have

been contracted in a technology neutral strategic reserve (without forcing lignite plants in).

Figure 1-10: Breakdown of the impact of the German strategic reserve on customer costs


Therefore, most of the net cost for consumers is due to the ‘climate aspect’ of the strategic

reserve. Without this distortion in the constitution of the reserve, a theoretical ‘perfect’ strategic

reserve still induces a net cost in the first years of implementation. But over the period 2018 to 2030,

it is a zero-sum game for end consumers.

1.5 CONCLUSIONS

The internal energy market does not allow meeting all energy policy objectives by itself. Different

mechanisms are implemented to drive electricity market outcomes towards various EU policy

objectives including security of supply, renewable development and/or decarbonisation.

The French CM is designed to maintain security of supply at the reliability standard and the study

shows that it effectively achieves this goal in a cost efficient manner. The CM maintains the LOLE to

3 hours while it increases social welfare and reduces net costs for end consumers by 400 M€ per

-400

-200

0

200

400

600

800

1000

1200

Capacity reserve Climate reserve Strategic reserve Capacity reserve Climate reserve Strategic reserve

2020 2030Co

sts

of

the

str

ate

gic

an

d c

lim

ate

re

se

rve

s (

m€

/ye

ar)

Contracting cost Cost of unserved energy impactActivation cost Energy costAdditional Ramping cost Total costs



year on average. In comparison, a strategic reserve approach does not appear to be a more

economical alternative to secure supply.

The French CM does not modify the behaviour of market players in the energy market, but it

necessarily has an impact on the available capacity in the medium to long term, in order to meet its

objective of securing the power system in France. The modelling thus shows that it has an impact on

prices and cross-border flows in the long term, although this impact is comparable to or lower than

most of the other regulatory or policy interventions considered. Moreover, the French CM does not

increase CO2 emissions at the European level.

1.6 STRUCTURE OF THE REPORT

This report is structured as follows:

Section 3 presents the theoretical functioning of an energy market and discusses how policy

interventions may interact with the energy market;

Section 4 describes the French CM and its potential impacts on the electricity market;

Section 5 quantifies the impacts of the French CM;

Section 6 compares the impacts of the French CM with the impacts of other policy interventions;

Section 7 concludes on the key findings of our comparative analysis.

Appendix A presents FTI-CL Energy dispatch model;

Appendix B describes FTI-CL Energy capacity market model; and

Appendix C presents the key modelling assumptions in France and in neighbouring countries.



Theory: Impact of policy interventions on 2.

electricity markets

This section reviews the general mechanism of price formation in power markets and then

qualitatively analyses how policy interventions may affect power markets in the short and long term.

Lastly, it investigates the potential impacts of different forms of capacity mechanisms (CMs) on

power markets.

2.1 PRICE FORMATION ON POWER MARKETS

In electrical systems, generation and consumption need to be equal at all times,12 and the

possibilities to store electricity are limited and generally costly. Consequently, sales of electricity are

usually cleared in short-term markets on an hourly basis, or even sometimes on 15-minute basis.

Different prices may thus emerge at least every hour, taking into account the variation of demand

and the underlying generation sources activated to satisfy this demand.

Furthermore, the main market timeframe has historically been the day-ahead market: an auction is

typically organised every day for the 24 hours of the next day, taking into account interconnection

capacity between bidding zones through the “market coupling”.13 Pay-as-cleared is used in day-

ahead markets in Europe, i.e. the marginal price is applied to all accepted offers within an hour.

In competitive markets, generators are expected to bid their variable costs (“short-run marginal

cost” or SRMC) or their opportunity costs. If not, they risk being outbid by a competitor. The

clearing price of the wholesale electricity market equals the bid of the most expensive generator

that needs to be activated to meet load.

In such case, generators with lower SRMC earn “infra-marginal rent”, which is the difference

between their variable cost and the market price and contributes to the remuneration of the initial

investment and fixed costs. However, if all market players bid at their SRMC, the last-called (peak)

12 Taking into account losses when transmitting electricity from generation units to load centres

13 Market coupling is already implemented across most borders in the EU. As far as France is concerned,

only the Swiss border is not yet coupled.



generator or demand-side response operator does not earn anything additional to contribute to

financing investment and fixed costs.

However, a number of specificities of the electricity market would ensure additional revenues to

generators – including peaking units. These additional revenues stem in particular from: (i) the

scarcity rent; and (ii) a possible mark-up on SRMC.14

Scarcity rent

Because of the specific features of electricity (e.g. lack of commercial storage possibilities with the

exception of hydropower, low elasticity of demand in the short term, etc.), occasional capacity

shortages and price spikes are normal in well-functioning wholesale power markets.

In the long run, an optimal generation mix will likely fail to guarantee security of supply in all

circumstances. During the rare scarcity events – when demand for electricity and operating reserve

requirements exceeds available generation capacity – some consumers will have to be curtailed and

the clearing price will hit the price cap of the market and give infra-marginal rent, or “scarcity

rent”, to all generators (and DSR operators). In theory, the level of the price cap should be set at

the level of the value of lost load (VOLL)15 in order to provide adequate remuneration of

generators’ fixed costs. But in practice price caps in most electricity markets are set significantly

below VOLL.16

As an illustrative example, Figure 2-1 below depicts the wholesale electricity market clearing price in

both regular and scarcity conditions. On the left, demand is lower than available generation

capacity: the price is set at the marginal cost of the most expensive activated generator. On the

right, a scarcity event is depicted. With a price set at the price cap of the market, generators earn a

scarcity rent equal to the grey area.

14 For a thorough theoretical review of electricity markets, see for instance Joskow, P.L. and R.

Schmalensee. (1983). Markets for Power: An Analysis of Electric Utility Deregulation, Cambridge. MIT

Press.

15 The VOLL varies depending on the type of consumers and is typically around 20,000-30,000 €/MWh.

16 For a description of the various market imperfections that undermine power markets’ ability to

remunerate adequately generators fixed costs, see for instance Fabien Roques (2008), “Market

design for generation adequacy: Healing causes rather than symptoms”, Utilities Policy, Elsevier, vol.

16(3), pages 171-183.



Figure 2-1: Wholesale electricity market clearing price in regular and scarcity conditions (with inelastic demand)


Mark-up

The electricity market generally remains an oligopolistic market, and entry is limited in the short

term by the magnitude of the necessary investment and its lead time. As such, the electricity market

differs from a purely theoretical and perfectly competitive market model with free entry.

As a consequence, when the equilibrium between supply and demand tightens, the competitive

pressure diminishes and price-making firms or plants can influence the market price by raising the

price at which they are willing to sell their marginal output. By taking such actions, these plants may

risk selling less, but this risk is limited by the fact that few or no other plants would be able to

deliver the corresponding output. On the other hand, such bids would raise the price they would get

for all output that they do sell. This leads to a premium over the SRMC which is usually referred as a

“mark-up”.

As an illustrative example, Figure 2-2 depicts the wholesale electricity market clearing price in

energy markets with and without a mark-up. On the left, bids are strictly based on the SRMC. The

clearing price is equal to the (short-run) marginal cost of the most expensive activated generator.

On the right, the marginal generator applies a mark-up, which allows him – but also other

generators – to recover some (more) fixed cost, as highlighted by the area labelled “Rent”.

The extent to which generators may exercise market power depends on the structure of the market

and the resulting competitive pressure. As an example, market power exercise is more likely to occur

if there are fewer generators or when generation margins are tighter.



Moreover, a mark-up may not be considered as an abuse of market power if it remains limited and

proportionate, as it is necessary for peaking units to receiver their investment costs. The mark-up

over SRMC is usually closely monitored by regulators, especially as far as an incumbent or dominant

player is concerned.17 One of the key issues is for regulators to distinguish between “legitimate”

use of market power to recover investment costs, and price manipulation.

Figure 2-2: Wholesale electricity market clearing price with and without mark-up on SRMC


2.2 POLICY AND REGULATORY INTERVENTIONS

Policy and regulatory interventions may affect electricity markets in different ways, depending on

their aim and how they are designed:

In the short term, policy interventions may affect dispatch and prices; and

In the longer term, policy interventions may impact the available capacity, and therefore prices.

Short-term impact

In the short run, policy interventions could change plant dispatch and power prices either by

modifying bidding behaviour or by directly influencing the merit order. This could be illustrated by

the following examples:

17 E.g. See Rapports de la CRE sur le fonctionnement des marchés de gros de l’électricité, du CO2 et du

gaz naturel. http://www.cre.fr/marches/marche-de-gros/rapports-de-surveillance

http://www.cre.fr/marches/marche-de-gros/rapports-de-surveillance



Introduction of a new tax. A carbon tax, for instance, would increase the cost of generation

based on fossil fuels. On the one hand, it tends to increase prices, since the variable cost of

electricity produced by these resources increases, internalising the cost of CO2. On the other hand,

it modifies the merit order as the units that emit more CO2 would become less competitive than

units that emit less CO2.

Out-of-market technology support. Renewable energy sources’ (RES) support schemes may

cause distortions in price formation. For instance, some EU power markets have experienced

negative prices in recent years. This was in part due to the fact that renewable units were paid at a

fixed price, regardless of the market conditions, so in practice they bid at any price. Consequently,

some generation units – with start-up costs and up-and-down constraints, for instance – had to

pay to remain online. This leads to negative prices, while it would be economically more efficient

to reduce output of supported RES units.

Activation of strategic reserves. Rules for activating strategic reserves may directly have an

impact on the short-term markets. An example is if the strategic reserve is sold in the market at a

high price, but which is lower than the price cap: it acts as a lower price cap and reduces

incentives for market participants to balance their portfolio.

Long-term impact

Policy interventions may also have an impact in the long run. Indeed,

Consequences of short-term impacts on power prices. The short-term impacts – illustrated

previously – are passed on to the longer term by increasing the profitability of some investments

to the detriment of others, and they therefore modify investment strategies. For example, the

carbon tax mentioned above reduces resorting to CO2-emitting technologies in the short term

and consequently, their profitability, such that new investments favour cleaner technologies.

Decision on closure of plants. Other policy interventions directly modify the available capacity in

the short term by forcing or preventing closure of power plants, or putting plants in an out-of-

market reserve. This could be the case, for instance, when a government decides to force the

shutdown of nuclear plants or old fuel plants that emit high levels of pollutants. Such

interventions tend to increase prices as the market has to resort to more expensive plants to

satisfy demand, with these plants no longer being in the merit order.

Support for specific generation technologies. Such intervention may directly modify the

available capacity by pushing in and out some specific technologies. As an example, renewable

support schemes guarantee the profitability of RES investments in order to stimulate their

development. On the other hand, the development of RES may tend to crowd out other

investment.



2.3 REVIEW OF THE DIFFERENT TYPES OF CM AND THEIR INTERFACE WITH ELECTRICITY MARKET

2.3.1 Issues with energy-only markets and rationale for a CM

In theoretical energy-only markets (EOM), there is no specific mechanism to put a value on

generating capacity when the system becomes tight. This is based on the assumption that electricity

prices will rise if market players anticipate a shortage of capacity, inducing new investments.

The economic rationale of such an approach is theoretically grounded in the Peak Load Pricing

Theory,18 suggesting that marginal pricing can ensure the fixed cost recovery of investment based

on the scarcity rents that all power producers earn when the system is tight. The assumptions

underlying the current market design based on energy-only markets are: (i) that power prices could

climb to the VOLL at times of scarcity (i.e. the price that makes consumers indifferent between

consuming and not consuming); and (ii) that this would naturally lead market players to benefit

from periods of high prices to remunerate their fixed costs. Thus, prices would give adequate signals

for investment, leading to the level of security of supply which would be desired by consumers.

However, there are growing concerns on the applicability of this theory in practice. Many power

markets in Europe and elsewhere (e.g. in the U.S.) have implemented complementary measures to

address these concerns. Evidence suggests that electricity markets are usually far from perfect and

that there are high regulatory risks. In particular, power prices are not allowed in practice to reach

the VOLL, leading to a chronic shortage of revenue for plant operators – the so-called “missing

money” issue as referred to in the academic literature.19 Various reasons have been invocated, such

as:

It is difficult to capture the actual VOLL. In the absence of active demand-side participation –

especially for load that is not metered in real time – market participants have limited ways or

incentives to express their value for power at different times;

Price spikes are generally politically unacceptable. This is particularly true insofar as price spikes

are not only influenced by fundamentals, but could also be due to IT/process issues, lack of

18 See for instance Boiteux M., “La Tarification des demandes en pointe : application de la théorie de la

vente au coût marginal.”, revue Générale de l’Electricité 58 (August): 321-40, 1949.

19 See for instance Finon D. et V. Pignon, 2008, “Electricity and Long−Term Capacity Adequacy, The

Quest for Regulatory Mechanism Compatible with Electricity Market”, Utilities Policy.



transparency, or exercise of market power,20 which could lead to thorny situations if the price cap

is set too high.21

Therefore, revenues from scarcity rent and mark-up may not be sufficient to recover fixed costs,

leading to inadequate investment and retirement incentives. Ultimately, this may put security of

supply at risk, which leads some States to intervene and implement so-called CMs. Five main

arguments are commonly made:

Price caps and other barriers to scarcity pricing. A range of issues including price caps set

below the VOLL (e.g. due to political constraints) but also sometimes other operational practices

in the procurement and activation of reserves often undermine the ability of power prices to fully

reflect scarcity value and rise up to the VOLL at times of system stress. As a result, there is

“missing money” for all existing resources, which implies too low a level of investment in

capacity.22

Boom-bust investment cycles. Investments, especially in peak capacity, are risky, since even

small changes in the number of scarcity events can have a dramatic impact on the capacity

owners’ revenues. Further, these issues, combined with the long lead times for the construction

of generation capacity, may result in a boom-bust investment pattern, as investors might wait

before investing until the frequency of scarcity events provides unambiguous evidence that the

additional capacity will be profitable.

Investment coordination risk. In a theoretically pure market design, decisions to build new

capacity are made independently. This induces strategic uncertainty that may lead to under-

investment; because one’s investment in new capacity tends to be more profitable if others

invest less, there are incentives not to inform – or to misinform – competitors about one’s own

intentions.

Risk aversion. Even if prices are able to reach the VOLL and the revenues during these periods

allow power plants and demand-side response (DSR) capacities to cover their fixed costs, such

occurrences would be so rare and unpredictable that either the risk premium required to invest in

20 See, for example, price spikes in Belgium on 28 March 2011, due to a time change bug, or in France

on 19 October 2009, due to inefficient procedures and transparency issues.

21 http://www.creg.info/pdf/Etudes/F1099EN.pdf;

http://www.cre.fr/documents/deliberations/communication/pic-de-prix-de-l-electricite-du-19-

octobre-2009

22 See for instance Roques, F. (2008), “Market Design for Generation Adequacy: Healing Causes rather

than Symptoms”, Utilities Policy, 2008, vol. 16, issue 3, pages 171-183

http://www.creg.info/pdf/Etudes/F1099EN.pdf

http://www.cre.fr/documents/deliberations/communication/pic-de-prix-de-l-electricite-du-19-octobre-2009




the “last MWs” would be significant. This is frequently argued to hamper the financing of

peaking units.

Supply-side externalities. There are externalities on the supply-side. During a blackout or in case

of controlled load shedding, some consumers are forced not to consume and therefore buy less

energy, so generators earn lower revenues. However, the incentive to provide the reserves needed

to avoid blackouts may be too low compared to the incentive in an efficient market system,

because all suppliers profit equally from the positive market price resulting from an avoided

blackout.23

2.3.2 The different types of CMs

The goal of this section is not to analyse the pros and cons of different CMs for achieving the

objective they pursue, but to understand how they may have an impact on energy markets and

which of their features are key in this respect. Figure 2-3 presents different forms of CMs.

To simplify and limit potential combinations, we will focus our analysis on three different forms:

Capacity payment. Capacity payments are administratively set payments per MW capacity, paid

to all generators or to a selection of plants with some specific characteristics (e.g. flexible plants),

generally regardless of whether they are dispatched to run. Capacity payments are intended to

provide generators with additional revenues equivalent to their missing money.

Capacity markets (encompassing decentralised capacity obligations, capacity auctions and

reliability options market). A capacity market approach is based on a volume requirement: a

central body sets the amount of capacity to be auctioned or defines the parameters of the

obligation that each supplier should comply with. Capacity providers – generators or DSR

operators – compete to provide capacity to the auction mechanism or to obligated suppliers, and

they receive capacity revenues, which add up to their revenues from selling power in the

wholesale energy market. As a counterparty, capacity providers commit to be available during

certain periods, to place bids on energy markets, or to produce energy in case of scarcity.

Strategic reserve. In this approach, part of the installed generation or DSR capacity is contracted

and withdrawn from the market to be used only in scarcity situation, i.e. as reserve of last resort.

23 Cramton P. and Ockenfels A. (2011), “Economics and design of capacity markets for the power

sector”, available at: ftp://www.cramton.umd.edu/papers2010-2014/cramton-ockenfels-economics-

and-design-of-capacity-markets.pdf based on Joskow, P.L. and J. Tirole (2007), “Reliability and

Competitive Electricity Markets.”, Rand Journal of Economics, 38(1), 68-84.

ftp://www.cramton.umd.edu/papers2010-2014/cramton-ockenfels-economics-and-design-of-capacity-markets.pdf




Contracted plants or capacity receive a fixed payment but cannot sell energy in the market. On

the other hand, other plants in the market do not get any capacity revenue. If the plants in the

reserve would have been shut down otherwise (“perfect” reserve), plants in the market do not

benefit from higher revenues, but their energy revenues may increase if some of the plants put in

the reserve would not have been shut down otherwise.

Figure 2-3: Taxonomy of CMs


2.3.3 Analysis of potential impacts of CMs on the energy market

Table 2-1 presents the short and long-term impacts of CMs, as well as the key features of the design

that are likely to induce impacts. This brief assessment shows that CMs can be designed such that

they do not impact the short-term price formation. However, “the devil is in the details”, and

several features may induce short-term impacts:

In particular, when designing strategic reserves, activation rules must be looked at very carefully,

as they will likely have an impact on intraday and balancing markets, and a too low activation

price may cause distortions even on the day-ahead market – both prices and cross-border flows.

Similarly, a too low strike price for reliability options may also act as an implicit price cap in the

day-ahead market.

With regard to capacity markets, if participants are obliged to deliver electricity during certain

periods, this may force them to underbid to sell their output. It may therefore impact prices,

although this might be limited to rare periods, with limited impact, if the periods are well-defined.

Hence, availability-based capacity products may be preferable as they avoid any short-term

impact on price formation.



Capacity payments and capacity markets are likely to have a long-run effect on the available

capacity as they intrinsically aim at incentivising new investment to guarantee security of supply

through the market.

Capacity markets generally present an important advantage with regard to investment incentives:

as the approach is volume-based, they drive investment only when necessary and, as a market-

based mechanism, prices can adjust to the balance between demand and supply of capacity,

which limits risks of over- or under-investment, compared to capacity payments.

Conversely, strategic reserves provide an out-of-market response to security of supply issues,

recognising that the level of security of supply achieved through an EOM is not sufficient.

Therefore, if strategic reserves are well-designed, they have limited impact on the available

capacity active in the market. In practice, though, the procurement rules and the volume setting

for the reserve are not easy to define, which may deviate from a theoretical EOM. Furthermore,

rules to deal with mothballing decisions while avoiding windfall effects and freeriding behaviours

necessarily create frictions in the functioning of the market and on the entry and exit of plants.

A number of key design parameters are likely to have significant consequences if they favour

some technologies (e.g. by preventing DSR participation) or if they are badly calibrated, as they

may generate overinvestment and dampen prices in the long term (or, less probably,

underinvestment).

Table 2-1: Potential impacts of CMs

Key features Short-term impacts Long-term impacts

Capacity

payment

Level of capacity payment: may

lead to over- or under-investment

if too high or too low

Eligibility: a restriction to new

entrants may crowd existing

plants out

Conditions: payment applicable

to capacity installed, availability or

actual output

Unlikely to modify

bidding behaviour of

capacity providers as long

as based on availability

Risk of impacts on bids,

prices and cross-border

exchanges if payments

proportional to generation

(in €/MWh)

Likely to stimulate

additional investments

(compared to EOM)

Consequently likely to

impact prices and cross-

border exchanges without

inducing distortions a priori

Capacity

market

Level of capacity requirement:

risk of being too conservative,

leading to overinvestment

Eligibility: a restriction to some

technologies may create

distortions

Conditions: the design of the

obligation (availability vs. delivery)

Unlikely to modify


market participants if based

on availability

Risk of impacts on prices

and cross-border

exchanges when it forces

energy delivery or in case

Likely to stimulate

additional investments

(compared to EOM)

Allow coordination of

investments by setting the

forward capacity target or

the forward capacity

obligation and providing



Key features Short-term impacts Long-term impacts

may have different consequences,

especially delivery may induce

distortions by modifying bidding

strategies

Design specificities: auction

design, long-term contracts,

periods of obligation, strike price

setting (for reliability options) may

favour some parties compared to

others and impact prices

of reliability options, if the

strike price is too low, but

limited to rare occurrences

transparency on capacity

adequacy

Consequently likely to

impact prices and cross-

border exchanges without

inducing distortions of the

merit order a priori

Strategic

reserve

Activation rules and trigger

price: may affect prices especially

if the trigger price is too low

Volume requirement: if the

volume is too high, it may drive

capacity out of the market, with

an upward impact on prices and

higher investment incentives

Eligibility (technology):

restrictions may prevent or force

some technologies in the reserve,

and consequently impact the

available capacity

Participation conditions:

participation in the reserve may

prevent plants from going back to

the market once in the reserve,

which can modify mothball

decisions compared to EOM

Unlikely to modify


market participants, as

capacity providers are

withdrawn from the market

Risk of impacts on prices

and cross-border

exchanges when it is

activated or if too high

reserve requirement

incentivises profitable

plants to enter the reserve,

creating artificial scarcity in

the market

Technical constraints

usually imply day-ahead

activation, which affects

intraday and balancing

markets

A ‘perfectly’ designed

reserve should not have

impact on the available

capacity active in the

market: it affects only

unprofitable plants

necessary for security of

supply that would have

closed otherwise

May affect long-term

investment (a) through

short-term distortions, if

any, and (b) depending on

participation conditions

and reserve volume

determination




Description of the French CM and its potential 3.

impacts on European electricity markets

3.1 RATIONALE FOR THE INTRODUCTION OF A CM IN FRANCE

As other European countries, France is facing major challenges to successfully manage its energy

transition. The aim is to achieve a more sustainable energy mix, using more efficient and

environmentally friendly modes of production, transmission, distribution, and consumption;

integrating much more renewable energy sources; consuming less and better; and optimising power

systems.

As it stands, France has a rather cost-competitive and low-carbon power industry, mainly because of

energy choices made in the 1970s and 1980s. The use of its hydroelectric potential and the

development of nuclear power enhanced energy independence and affordability and reduced its

carbon footprint. It also promoted the use of electric heating to reduce dependence on fossil fuel

and take advantage of the large generation fleet.

But these choices have also given rise to a particularly intense peak demand phenomenon.

Moreover, until very recently, although growth in average electricity consumption has slowed down,

peak power demand has continued to trend sharply higher. A few figures illustrate this

phenomenon:

Maximum peak demand reached 102.1 GW during the winter 2011-2012, while the maximum

during the winter 2001-2002 was only 79.6 GW. In ten years, it has thus risen by 30 percent.

France comprises almost half of the temperature-sensitive power demand in Europe; if

temperature decreases by 1°C, consumption increases by 2,400 MW during winter peak hours.



Figure 3-1: Maximum peak demand in different weather condition scenarios (winter 2016-2017)

Source: RTE (2015), “Bilan prévisionnel”, http://www.rte-france.com/sites/default/files/bp2015.pdf

Such sharp and high peak demands, depending on very volatile weather conditions, cause concerns

over the security of electricity supplies in France, while investments needed to address these peaks –

and to meet the French security of supply standard – would likely be hardly profitable and highly

risky.

RTE resource adequacy forecast reports indeed showed risks of scarcity situations.24 In 2009 and

2010, when the law introducing the capacity obligation was discussed and passed, it was anticipated

that the security of supply standard would not be met from 2013 – and, more significantly, from

2015. The economic crisis and the slow recovery of the French economy, as well as the anticipation

of capacity revenue from the CM, may have postponed the problem, but the specificity of the

French situation remains.

24 See RTE resource adequacy forecast reports from 2009 to 2015:

http://www.rte-france.com/fr/article/bilan-previsionnel.

http://www.rte-france.com/sites/default/files/bp2015.pdf

http://www.rte-france.com/fr/article/bilan-previsionnel



3.2 THE LEGAL AND REGULATORY FRAMEWORK

Concerns about the functioning of electricity markets have been raised in France. There were serious

doubts about the ability of the electricity market: (i) to generate the right economic signals; and

thus, (ii) to guarantee a level of security of supply in accordance with the reliability criteria fixed by

the French authorities.

In response to these concerns, the French law n° 2010-1488 of 7 December 2010, reforming the

organisation of the electricity market (“NOME Act”) provides for the creation of a capacity

obligation scheme.25 It specifies that each supplier should contribute “in accordance with the

demand characteristics of its customers, in terms of power and energy, to the security of electricity

supply in continental France.”26 The Law 2013-415 of 15 April 2013 (“Brottes Act”) extends the

capacity obligation to transmission and distribution system operators (to cover for network losses)

and to consumers, who would not purchase all the electricity they consume from a supplier.

However, for simplicity, we will generally refer to obliged parties as suppliers, with the

understanding that it includes all relevant entities.

The Decree 2012-1405 of 14 December 201227 defines the general organisational framework for the

new scheme, while the Ministerial Order of 22 January 2015 establishes the precise rules that would

govern the CM and ensure its operational implementation.28

3.3 DESCRIPTION OF THE FRENCH CM PRINCIPLES AND ITS SPECIFICITIES

The Decree 2012-1405 of 14 December 2012 proposed to define a CM, monitored by the French

Energy Regulation Commission (CRE), through which providers of capacity certificates can trade

25 It is worth noticing that the introduction of a capacity mechanism in France is in line with Law 2013-

415 of 15 April 2013 (“Brottes Act”) which ask for making the load curve more flexible.

26 Loi NOME n° 2010-1488 du 7 décembre 2010 :

http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000023174854&dateTexte=&catego

rieLien=id

27 Décret n° 2012-1405 du 14 décembre 2012 relatif à la contribution des fournisseurs à la sécurité

d'approvisionnement en électricité et portant création d'un mécanisme d'obligation de capacité dans

le secteur de l'électricité : http://www.legifrance.gouv.fr/eli/decret/2012/12/14/DEVR1206335D/jo

28 Arrêté du 22 janvier 2015 définissant les règles du mécanisme de capacité :

http://www.legifrance.gouv.fr/eli/arrete/2015/1/22/DEVR1418335A/jo

http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000023174854&dateTexte=&categorieLien=id

http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000023174854&dateTexte=&categorieLien=id

http://www.legifrance.gouv.fr/eli/decret/2012/12/14/DEVR1206335D/jo




them with obligated parties to enable the latter to meet a legal obligation.29 Fundamental choices

have driven the architecture of the CM, namely:

Market-based mechanism without any public funding;

Equal treatment of new and existing plants;

Technology neutral mechanism (generation, storage, demand response, etc.);

No interference on the functioning of the Internal Energy Market (e.g. no change in market

coupling, no export restriction); and

Forward-looking mechanism.

This mechanism (with these characteristics) is intended to reward operators for the contributions

their capacities make to the power system by being available during periods of tight supply.

The core elements of the CM include:

Obligations – Obligations will be assigned to suppliers based on the actual consumption of their

customers (including transmission and distribution system operators, for their losses) during peak

periods. To meet its obligation, a supplier will have to secure capacity certificates, either by

certifying the capacities it operates (generation or demand-side capacities) or by purchasing

certificates from other market participants.30

Certifications – Operators of production and demand-response capacities commit to a certain

level of availability (certified capacity level) and are issued the corresponding amount of capacity

certificates. At the end of the delivery year, RTE calculates, for each operator, the difference

between the sums of certified capacity levels, reflecting the self-assessment based commitments

of capacity operators, and of effective capacity levels, based on checks. The operator is financially

liable for the amount of the settlement imbalance thus calculated.31

Market operations that include the design of the continuous capacity trading, beginning four

years ahead of delivery, as well as two imbalance settlement processes.

29 Additionally, the capacity mechanism should apply to all capacity; and not interfering on the

functioning of the internal energy market (e.g. no change in market coupling, no export restriction).

30 In practice, capacity portfolio managers balance obligations and certificates of suppliers or certified

and effective capacity levels of capacity providers and are financially liable for them, similarly to

balancing responsible parties in the energy market.

31 Same as above.



Figure 3-2: French CM design

Source: RTE (2014), “French Capacity Market”,

http://www.rte-france.com/sites/default/files/2014_04_09_french_capacity_market.pdf

Functioning of the French CM

The French CM is a multi-year scheme that starts four years before the delivery year and ends two

years after the delivery year. The general timeline of the mechanism, depicted in Figure 3-3, goes as

follows:

Four years before the delivery year, the CM begins when the obligation and certification

parameters are published. Capacity operators start certifying their capacities, in exchange for

which they will receive capacity certificates. Capacities that can be developed very quickly, such as

demand response capacities, can be certified up until the start of the delivery period.

Within the four years before delivery year, once the first capacity certificates have been issued,

suppliers start covering their obligations, based on their own forecasts and risk hedging

strategies, by: (i) acquiring capacity certificates from operators; or (ii) securing capacity certificates

from their own capacities. Capacity certificates can be traded until the transfer deadline (two years

after the end of the delivery period). Note that each supplier is informed of its obligation before

this deadline.

During the delivery year, data relating to the operation of capacities is gathered and verified,

particularly information about actual availability during the peak period associated with



certification. Capacity providers commit to being available during this period, but they are not

required to be generating.

At the end of the delivery period, imbalance settlement takes place. On one hand, for each

operator, an aggregate effective capacity level is compared to the sum of certified capacity levels.

If an imbalance is found, the capacity portfolio manager must pay an imbalance settlement

corresponding to that difference. The difference between a supplier’s obligation and the amount

of certificates held is calculated, and the supplier is notified of any imbalance.

Figure 3-3: General organisation of the CM (timeline)

Source: RTE, “French Capacity Market”, 2014

To conclude, the CM has already started for the first delivery years with the opening of the

certification period on 1 April 2015. This period has closed on 1 December 2015 for the delivery

years 2017, 2018 and 2019 for existing generation capacities.

3.4 IDENTIFICATION OF THE POTENTIAL IMPACTS OF THE FRENCH CM ON THE ENERGY MARKET

Short-term impact

The French CM does not present specific rules likely to have an impact on the dispatch in the short

term:



It is based on availability so it does not force generators (or DSR operators) either to produce

below their SRMC or to modify their bids; and

It does not withdraw capacity from the market.

It is coupled with ongoing reforms to improve the functioning of the energy and ancillary service

markets. For examples, DSR participation and aggregation are being extended gradually to all

markets, including capacity, reserves and wholesale markets, while smart meters are being rolled out

in order to facilitate the development of DSR and consumer engagement. As a result, the Smart

Energy Demand Coalition (SEDC) considers France as one of the most advanced markets for DSR

aggregation.32 Retail markets and other services are heading for a market-based model, such that

regulated tariffs have been phased out since the end of 2015 for large commercial and industrial

consumers and that ancillary services and reserve markets have been improved to be more flexible.

Moreover, the management of interconnection is being continuously improved. Market coupling is

implemented on all borders, except Switzerland, and liquidity and integration in intraday markets

has increased significantly over the past few years.

Long-term impact

The French CM aims at meeting a security of supply standard set by the Ministry. Thus, it will likely

foster DSR development, avoid excessive mothballs or retirements, and secure timely investment if

necessary in order to meet this criterion. As such, it may impact the long-term available capacity.

However, it is calibrated based on the predefined security criteria (three hours of loss of load

expectation, or LOLE), and overinvestment is avoided insofar as the contribution of neighbouring

countries, through interconnections, is taken into account. It is worth noting that the level of cross-

border contribution taken into account is fairly high: 7 GW out of the 9 GW of available import

capacity. This could be compared to the CM in the UK, for which the contribution was set to zero

(out of 4 GW of available import capacity) for the first CM auction.

DSR can participate in the CM on an equal footing, both explicitly to supply capacity or implicitly by

reducing capacity obligations. As such, the CM is rather favourable to DSR and it is expected to

stimulate DSR development.

32 SEDC (2015), Mapping Demand Response in Europe Today, http://www.smartenergydemand.eu/wp-

content/uploads/2015/10/Mapping-Demand-Response-in-Europe-Today-2015.pdf



Quantifying the impacts of the French CM 4.

4.1 INTRODUCTION

The objective of this section is to quantify the impacts of the French CM on the energy markets and

more generally on the European power system.

To do so, we describe our modelling approach and assumptions, as well as our evaluation criteria.

Then, we present the results of our simulations and the impact of the CM in France and in

neighbouring states, which we assess according the predefined evaluation criteria. We use the same

methodology to assess the impacts of other European policy interventions in Section 5.

4.2 DESCRIPTION OF THE MODELLING APPROACH

4.2.1 Model description

To assess the impact of the French CM, we have modelled the European power system using our

FTI-CL Energy proprietary dispatch model coupled with a capacity market model, which we have

developed internally. The model accounts for each unit connected to the transmission grid,

decentralised generation, renewable output and its variability, specificities of hydro power and

interconnectors, as well as for demand fluctuations.

The geographic scope of the model is shown below in Figure 4-1. It covers most western European

countries, including the UK, Ireland, France, Belgium, the Netherlands, Luxembourg, Germany,

Austria, Italy, Switzerland, Denmark, Norway, Sweden, Finland, Spain and Portugal. The model also

takes into account interconnections with other countries, beyond this geographic scope, which are

modelled at an aggregated level. It is therefore able to capture the effect of the interconnections

between the French market and neighbouring countries.



Figure 4-1: Geographic scope of FTI-CL Energy’s European power market model


We use a realistic model of the French and neighbouring markets calibrated to reproduce historical

market prices. Our dispatch model is based on well-established optimisation platform Plexos® and

provides an hourly optimal dispatch taking into account operational constraints. Our dispatch model

is described in detail in Appendix A, which also demonstrates the robustness of the model through a

back-casting exercise.

The capacity market (CM) model minimises the system cost while ensuring the reliability criteria. The

CM model simulates supply and demand curves and produces capacity prices defined as the

minimum capacity price necessary to meet the capacity requirement set to reach the regulated

reliability standard. The capacity market model is presented in details in Appendix B.

For each year, we optimise market participants’ operational, investment and retirement decisions

based on their expected costs and revenues. Both models are interlinked through an iterative

optimisation process descripted in Section 4.2.3. We assume perfect competition and no asymmetry

of information for market participants, but we include some degree of risk aversion. The model

“endogenises” economic decisions of market participants by ensuring plants do breakeven (based

on their avoidable costs) either only with energy and ancillary services’ revenues as in the EOM or

with energy and ancillary services’ revenues plus capacity revenue as in the CM.



4.2.2 French power sector scenario and main background assumptions

The core assumptions regarding the 2030 market fundamentals in France are based on RTE’s

“Diversification” scenario presented in the Bilan Previsionnel 2014, updated by the Bilan

Previsionnel 2015 for the medium term. These assumptions include the following items:

Demand: Demand projections are based on RTE data and forecast.

o Annual demand: In 2030, the “Diversification” scenario assumes an annual demand of

501TWh in average weather conditions. This is equivalent to a 5 percent increase compared

to 2014 consumption.

o Peak demand: The peak demand at 10 percent risk (one in ten years peak) increases by 5

GW to reach 105 GW in 2030.

o Hourly demand: Hourly power curves are based on RTE stochastic analysis. RTE realises

1,000 samples to test the short-term French power system reliability. For the purpose of the

analysis, RTE provided eleven samples representing the distribution of peak demand as

shown in Figure 4-2. These samples were selected and weighted in order to capture the

whole range of potential situations while grasping occurrences of extreme peaks, where the

likelihood of price spikes and load shedding materialises the most.

Figure 4-2: Peak demand duration curve and sample selection

Source: RTE and FTI-CL Energy, 2016

Commodity: Commodity prices are derived from the World Energy Outlook 2014 (WEO14)

published by the IEA. Prices are interpolated from October 2015 forwards to WEO14 long-term

projections.

70

75

80

85

90

95

100

105

110

115

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

GW



Interconnection: In line with RTE “Diversification” scenario, the winter import interconnection

capacity increases to 20 GW by 2030. This has to be compared to the 9 GW of import capacity

that was available during winter of 2014/2015.

Capacity: Capacity assumptions for nuclear, renewables and coal plants are derived from RTE’s

“Diversification” scenario, whilst combined-cycle gas turbines (CCGTs), open-cycle gas turbines

(OCGTs) and DSR capacities are optimised based on economic decisions of rational players:

o Nuclear: French nuclear capacity decreases to 47.7 GW by 2030. This is equivalent to an

average 1.5 GW nuclear capacity decrease per year over a ten-year period. Nuclear capacity

is assumed to decrease linearly (constraint by unit size) without respecting the normal ten-

year license schedule. The exact timing of nuclear decommissioning has a major impact on

new build requirement per year and therefore on the results of the study. We therefore

provide a sensitivity analysis which is described in the textbox below and which results are

presented later on in Section 4.4.3.



Sensitivity analysis to the timing of nuclear decommissioning

The exact timing of nuclear decommissioning has a major impact on new build requirement per

year and therefore on the results of the study.

The annual shut down nuclear capacity will trigger the need of new investments including new

CCGTs, OCGTs, DSR and efficiency measures. The average 1.5 GW of annual nuclear decrease

will be compensated by a combination of the above resources.

While the starting point in 2020 and the ending point in 2030 are known and set by RTE

Diversification long term scenario, there is no agreed phase-down timing to achieve the 2030

target yet.

To account for this uncertainty, we model two sensitivities around the reference linear decrease

(constraint by the unit size).

A convex sensitivity: It assumes a quicker phase-down in the early years and a slower

phase-down in the later years. New capacity requirement is therefore brought forward.

A concave sensitivity: It assumes a slower phase-down in the early years and a quicker

phase-down in the later years. New capacity requirement is therefore further postponed.

Figure 4-3: Nuclear decommissioning paths


40

45

50

55

60

65

70

2020 2022 2024 2026 2028 2030

Nu

cle

ar

ca

pa

cit

y (G

W)

Linear phase-down Convexe phase-down Concave phase-down



o Renewable: Renewable capacities are described in Table 4-1:

Table 4-1: Renewable capacity in 2030

Renewable technology Installed capacity 2030

Onshore wind 24.4 GW

Offshore wind 5.5 GW

Solar 16.4 GW

Thermal renewable 1.6 GW

Marine 1.5 GW

Source: RTE, Bilan prévisionnel 2014

o Coal plants: Coal capacity decreases to 1.7 GW by 2030.

o Other resources: Combined-cycle gas turbines (CCGTs), open-cycle gas turbines (OCGTs)

and DSR capacities are optimised based on economic decisions of rational players, in the

different market design scenarios. The optimisation process is presented in the section

below.

Further explanations on the background assumptions used in the power market model are

presented in Appendix C.

4.2.3 Modelling approach to assess the available capacity in two market design scenarios

Both market designs are modelled and the impact on the available capacity optimised using the FTI-

CL Energy European power market model. In each design, we optimise the available capacity based

on economic decisions of rational agents with perfect information. Plant operators choose to stay in

the market, exit, or mothball plants based on the most favourable Net Present Value (NPV) when

taking into account the expected profits in the energy and capacity markets. In addition, investment

decision in new CCGTs, OCGTs and DSR are also based on the most favourable NPV based on

expected costs and revenues, given that the other types of capacity additions (renewables, nuclear)

are assumed to be in line with RTE’s “Diversification” scenario. To do so, we consider the eleven

demand samples provided by RTE and we take into account the current price cap set at

3,000€/MWh.33

33 We also run a sensitivity analysis to test the impact of the removal of the price cap.



To determine the capacity mix at the equilibrium, we perform iterations to converge toward a

capacity mix in which the generators and DSR operators have positive NPV, based on the avoidable

costs and the market revenues, and where no additional investment would have a positive NPV. In

other words, the capacity mix is equilibrated so that (i) no existing plant has a negative NPV – only

considering its fixed O&M costs – (ii) no new build plant has a negative NPV – considering also

investment costs – and (iii) if an additional capacity was built, its NPV would be negative.

The two market design scenarios produce different optimal capacity mixes because they provide

different revenue streams to market participants:

Energy-only market. Available capacity is driven by economic decisions of rational players, based

on their expected revenues from the energy market. It takes into account the current price cap in

the day-ahead market and the revenues in other market segments (balancing and reserve

markets). The EOM does not a priori guarantee that the reliability standard set by the government

is met. Figure 4-4 summarises the optimisation process, which is performed in our model.

Figure 4-4: Energy-only market design optimisation process


Capacity market. Other market conditions being equal compared to EOM, in the CM, market

participants have to meet the capacity obligation defined to respect the reliability standard set by

the government. Therefore the available capacity is optimised taking into account the potential

revenues in the CM in addition to other revenue streams that also exist in the EOM. The

corresponding optimisation process operated by our model is presented on Figure 4-5.



Figure 4-5: Capacity market design optimisation process


4.2.4 Impact of the market design on risk aversion and cost of capital

Our modelling shows that market participants’ revenues are much more volatile in the case of an

EOM than if a CM is implemented. More specifically, in the EOM, the recovery of fixed costs relies on

the occurrence of price spikes, which only happen in the most extreme weather condition scenarios.

In other words, fixed cost recovery is concentrated in years with cold waves, which are expected

every ten to 30 years. Relying on such uncertain revenues is perceived as risky by investors, leading

investors to require a higher return to invest in such assets.

In comparison, the CM provides a more stable stream of revenues thanks to the capacity revenues,

while reducing the proportion of revenues depending on rare energy price spikes.

To account for the impact on the cost of capital of the higher risk faced by market participants in

the energy-only market scenario, a risk premium has been computed following the approach

presented by Arrow (1971).34 An investment is valued according to the formula:

Investment value=Expected revenue- α*risk premium

Where α is the risk aversion coefficient and the risk premium is calculated as the variation of the left

tail (low revenue part) of the revenue distribution.

34 Arrow. Essays in the theory of Risk bearing, North-Holland Amsterdam, 1971.



As suggested by Holt and Laury (2002)35 in the American Economic review, an estimated risk

aversion coefficient α of 2.5 can be assumed to correspond to an average risk averse market

participant. This value is consistent with the one used in other studies focussing on European

electricity markets.36

Considering the distribution of energy revenues obtained from the different samples modelled in

our study (shown in Figure 4-6), we have estimated the risk premium and integrated it into the cost

of capital: investments would only be realised in the EOM if the expected rate of return is higher

than the cost of capital taking into account the risk premium.

Based on our simulations, CCGT investors face a cost of capital increased by about one percentage

point in the EOM compared to the CM, while peak unit investors face a cost of capital increased by

about three percentage points.

Figure 4-6: Energy revenue distribution across the modelled samples


In order to evaluate the impact of this assumption of risk aversion on the robustness of our

modelling results, a sensitivity analysis is presented later on in Section 4.4.3.

35 Holt and Laury. Risk aversion and incentives effects, American economic review, 2002.

36 UFE (2015).

0%

50%

100%

150%

200%

250%

300%

350%

400%

450%

500%

En

erg

y re

ve

nu

e (€

/k

W)

Energy revenue - CCGT Energy - revenue - OCGT



4.3 ASSESSMENT CRITERIA

To assess the impacts of the implementation of the French CM, the following set of criteria are

compared in both market design scenarios:

Security of supply criteria: to assess the impact of the CM on security of supply, LOLE has been

computed in the CM and EOM scenarios. The reliability standard defined by the French public

authorities is three hours of LOLE.

Available capacity criteria: to assess the impact of the CM on the available capacity, the installed

capacity resulting from the different market scenarios and regulations have been compared.

Economic efficiency criteria: to assess the impact of the CM in terms of economic efficiency, the

total consumer cost and the social surplus have been computed in the CM and the EOM

scenarios.

Energy market impact criteria: to assess the impact of the CM on the functioning of the energy

market, energy prices have been compared and the total imports/exports have been computed in

the CM and the EOM scenarios.

CO2 emissions criteria: to assess the impact of the CM on CO2 emissions, the total CO2 emissions

in France and in its neighbouring countries have been computed in the different scenarios.

For each criterion, we discuss the impact of the CM in the short, medium, and long term. The short-

term only concerns the immediate effects on dispatch and bidding decisions, while the long term

refers to the time horizon beyond five to seven years, typically from 2023 onwards.

4.4 COMPARISON OF AN EOM AND THE FRENCH CM DESIGN SCENARIOS

4.4.1 Impact on security of supply

CMs aim at protecting consumers against the risk of power supply shortages. A reliability standard

of three hours of LOLE per year has been set by the French government. An appropriate CM design

should provide incentives to capacity providers to be available in the market in order to meet the

three hours reliability standard.

Capacity providers may sell capacity certificates, and this generates capacity remuneration which can

provide additional incentives to remain online or to drive investments in new generation or DSR

facilities in order to meet peak demand and to compensate for the expected decrease in nuclear

capacity. In contrast, in the EOM scenario, there is no guarantee that generators or DSR operators

have sufficient revenues to maintain adequate available capacity to meet the reliability standard.



This intuition is confirmed by our modelling. We have assessed the LOLE taking into account the

probability of occurrence of the different climatic year samples that we modelled. As shown in

Figure 4-7, the evolutions of the LOLE in the CM scenario and in the EOM scenario diverge

significantly, especially in the long-run. In an EOM, the LOLE is well above the reliability criteria of

three hours per year.

Figure 4-7: LOLE in the CM scenario and in the EOM scenario


In the EOM scenario, in the medium term (2017-2021), more generation capacity is mothballed or

shut down than is assumed in RTE’s adequacy assessment. The LOLE gradually increases in the

medium term in the EOM scenario, as more plants are mothballed or shut down and DSR is not

developing due to the absence of sufficient remuneration.

In the longer term (2022-2040), mothballed plants come back in the market, but there is a lack of

strong enough economic signal to trigger timely new investments. As a result, the development of

DSR and new investments in CCGTs are postponed, while no OCGT is built.

Consequently, in the absence of CM, security of supply in the medium to long term deteriorates

substantially in France. Already from 2017, the LOLE exceeds the three-hour reliability standard. It

reaches around ten hours on average per year from 2024 onwards, as significant capacity is

0

2

4

6

8

10

12

14

16

18

20

LO

LE

(h

rs)

CM - LOLE EOM - LOLE



withdrawn from the market due to the nuclear phase-down.37 In addition, we observe that the risk

of LOLE expands to neighbouring countries in the absence of CM.

Conversely, in the CM scenario, the annual LOLE remains around three hours’ reliability standard

for security of supply, meaning that the CM can effectively incentivise market participants to be

available or to keep plants running and to make timely investments in capacity expansions.38

We can thus conclude that security of supply in France substantially deteriorates if a CM is not

introduced. As early as 2017 and for all subsequent years, the reliability standard set by the

French public authorities is not met in the EOM due to the closures of unprofitable plants. This

results in higher risk of curtailments for consumers. In contrast, the CM allows the reliability

standard to be met in all years.

Other studies (Petitet (2016), Hary (2016)) also showed that the implementation of a CM allows to

meet the reliability standard and that security of supply would reduce in an EOM with a price cap at

3,000€/MWh.39

In order to test the impact of a potential removal of the price cap, we have run a sensitivity analysis

which is presented in a text box in Section 4.4.3. It shows that the reliability standard is met, but the

social welfare decreases significantly. Moreover, as the risk borne by the “last MWs” is very high –

they would recover investment costs only in very extreme scenario – we may wonder whether any

investors would actually invest in these “last MWs”.

37 This corresponds approximately to the cost of incremental DSR capacity (30,000€/MW/year) given a

price cap of 3,000€/MWh. Once LOLE reaches ten hours,, DSR can expect at least ten hours of prices

at 3,000€/MWh on average and therefore make investment in new DSR capacity profitable.

38 Slight variations around the 3 hours can be observed. This is due to the lumpiness of investment,

which cannot adapt to small variations of demand.

39 Petitet M., June 2016. Effects of risk aversion on investment decisions in electricity generation: What

consequences for market design?, conference paper for the 13th International Conference on the

European Energy Market (EEM).

Hary N., Rious V., Saguan M., April 2016. The electricity generation adequacy problem: Assessing

dynamic effects of capacity remuneration mechanisms, Energy Policy 91.



4.4.2 Impacts on available capacity

The CM is designed to reinforce energy market signals so that sufficient capacity is available to meet

demand in the long term and to satisfy the reliability standard. As a result, it has an impact on the

available capacity.

Figure 4-8 and Figure 4-9 present the evolution in terms of installed capacity for CCGT and peaking

power plants and DSR in France between 2017 and 2040 under the CM scenario. Our model shows

that, even in the CM scenario, revenues are not sufficient to maintain all existing plants in operation,

especially as all these plants are not necessary to meet the reliability standard. As a result, the most

expensive plants – remaining steam oil plants40 and some dispatchable combined heat plants (CHP)

– are closed before 2017 and several CCGTs are mothballed.

Figure 4-8: Evolution of CCGT capacity in the CM scenario


40 These steam oil plants were expected to be maintained in RTE 2015 bilan prévisionnel, but our

economic analysis shows that these plants cover their fixed O&M costs neither in the EOM scenario,

nor in the CM scenario. Obviously, this analysis may vary depending on cost assumptions, and these

plants could replace some DSR capacity if they have lower fixed O&M costs than in our assumptions.

-5

0

5

10

15

20

25

Insa

tlle

d c

ap

acit

y (G

W)

Current Installed capacity Mothball Return from mothball

New investment Available capacity



Figure 4-9: Evolution of peaking capacity in the CM scenario


Note: the graph shows that steam oil plants in light blue are not maintained, even in the CM scenario and are therefore

compensated by the capacity missing the CM in orange.

The gradual reduction in nuclear generation capacity driven by political decisions makes mothballed

CCGTs return to the market in 2021, and makes investment in new build generation capacity

necessary from 2024. The installed CCGT capacity reaches 13.6 GW by 2030. No OCGT plant is built

in the short and medium term because of insufficient profitability: in the CM scenario, the first new

OCGT is built in 2026, when DSR potential is already well-exploited.

Figure 4-10 shows the amount of additional capacity made available by the CM as compared to

EOM.

In the medium term, based on our cost assumptions, the introduction of the CM is mostly

favourable for the development of DSR, as it fosters the emergence of aggregators and the

development of more DSR capacity than in the EOM. Indeed, in the EOM scenario, DSR capacity

continues to decrease,41 as the energy market does not offer sufficient remuneration to maintain the

41 This is due to the gradual phasing-out of critical peak pricing regulated tariffs, which are no longer

offered for categories of consumers, while no equivalent offers are proposed by alternative suppliers

due to the lack of incentives through the energy market.

-6

-4

-2

0

2

4

6

8

10

Insa

tlle

d c

ap

acit

y (G

W)

OIL TURBINE STEAM OIL - BP 2015 OCGT

DSR Missing the CM Available capacity



existing DSR capacity and to attract DSR investment. The CM avoids a decline in DSR capacity in the

medium term and even accelerates its development. DSR thus covers most of the need for capacity

to meet the reliability standard in the CM, compared to EOM: DSR covers 100 percent of the

incremental capacity need until 2023. Most importantly, the CM develops the most cost effective

technologies. With our assumption, DSR appears more cost-effective but capacity needs could be

filled by other technologies, such as mothballed CCGTs or closed peaking units, depending on cost

assumptions.

In the long term, the CM triggers new investment in cost-effective capacities. As most of the

additional need for capacity can be covered by DSR in the medium term, it is only in 2024 that new

investment in generation will be triggered by the CM. The first new CCGTs are built in 2024 to

compensate for the reduction in nuclear capacity. From 2026 onwards, some OCGT plants are

needed in order to meet peak demand as the DSR potential gradually reaches its economic limits.

In conclusion, as the French CM aims at guaranteeing the adequacy between supply and

demand, it has an impact on the available capacity. The CM helps to select the most

competitive technologies for all timeframes.

Figure 4-10: Change in the available capacity (difference between the CM and EOM counterfactual scenario)


Figure 4-11 illustrates the capacity prices needed over time in order to obtain this additional

capacity in the CM scenario and meet the reliability standard. At the beginning of the study period,

only a low capacity price is expected. An excess in capacity pushes power plants out and only

limited capacity remuneration is necessary to maintain DSR capacity or develop the cheapest DSR

resource.

0

1

2

3

4

5

6

7

8

9

10

Ava

ila

ble

ca

pa

cit

y (G

W)

CCGT OCGT OIL DSR



The situation starts to change from 2022 when new and more expensive DSR is necessary and

higher prices are necessary to trigger its development. From 2024, new CCGT and OCGT capacity is

required in order to compensate for the gradual shutdown of nuclear power stations. These plants

would likely bid at around 25-30€/kW for investment decisions to be made. Thus the capacity price

increases from about 5€/kW to over 20€/kW, and then continues to rise to around 30€/kW.

Figure 4-11: Evolution of capacity prices


4.4.3 Economic efficiency

The CM aims to ensure that the reliability standard is met at least-cost for end consumers. We have

estimated the impact of the CM compared to the EOM both: (i) in terms of costs for consumers; and

(ii) in terms of social welfare.

Consumer cost

Figure 4-12 presents the impact of the CM on consumer costs compared to the EOM. On the one

hand, the CM benefits consumers through a lower cost of energy and a lower risk of loss of load. On

the other hand, the cost of capacity remuneration for generators and DSR operators is passed on to

consumers.

0

10

20

30

40

50

60

70

80

Cle

ari

ng P

rice

(€

/k

W)

Clearing price Range



Figure 4-12: Impact of the CM on customer costs (difference between the CM and EOM counterfactual scenario)


Based on the analysis of Figure 4-12 we can identify three distinct periods:

2017-2021: The CM contributes to limiting costs for consumers. The CM, by improving security of

supply, avoids price spikes when the supply is scarce and limits the risk of load shedding. On the

other hand, consumers have to bear the cost for capacity, but the capacity price resulting from an

implementation of a CM remains low given that no major investments have to be made. This

leads to a net benefit for consumers with respect to the EOM scenario, evaluated to 260M€/year

on average over this period.

2022-2024: The CM slightly increases costs for consumers as some investments critical to

maintain security of supply are anticipated compared to the EOM scenario. As the full potential of

affordable DSR capacity is already deployed, the incremental capacity needed to maintain security

of supply at the reliability standard is more expensive. The additional cost is not fully

compensated by the gain in terms of reduced price spikes and load shedding risk. This is because

in the EOM, given the ex-ante situation of excess capacity, the LOLE is contained between four

and six hours. As a result, the CM induces a net cost for consumers during this short period. It is

worth mentioning that, as capacity and energy prices are higher, the ARENH mechanism may be

competitive again – assuming that the 42€/MWh price for ARENH remains – which would allow

some transfers between baseload producers (EDF) and consumers, thus limiting the net costs for

consumers during this period.

After 2025: In the EOM scenario, the LOLE reaches ten hours, inducing more frequent price spikes

and occurrences of load shedding. The CM reduces significantly consumer costs as the decrease

in energy prices and in curtailed energy largely compensates for the capacity costs. In addition,

-5

-4

-3

-2

-1

0

1

2

3

Cu

sto

me

r co

st

(€ b

illio

n)

Energy cost Ancillary service Capacity Cost Cost of Unserved Energy Total (€m)



financing costs for new investments are lower with a CM, since it helps secure revenues, which

leads to a lower cost of capital. This is especially true for peaking plants that run for only a limited

period of time during infrequent cold waves.

The value of the reduction in curtailed energy represents 450M€/year on average for the period of

2017 to 2040. UFE thus estimates the reduction in loss of load costs thanks to the French CM of

510M€ in 2030, while our simulations show a reduction of 530M€ in loss of load costs in 2030.42

On average over 2017-2030, the French CM reduces costs for French consumers by 400M€/year

as the reductions in investment risk and energy and curtailment costs outweigh the additional

capacity costs.

It is intuitive that the CM is temporarily more expensive than the EOM in the transitory medium-

term period, because it brings forward the need of new investments to maintain the security of

supply at three hours: costs at anticipated, but the benefits are spread over a longer period.

However in the long term, the CM brings benefit to consumers significantly by reducing the costs of

both financing new investments and unserved energy.

In the box below, we summarise the results of a sensitivity analysis that we have carried out in order

to assess the impact of the price cap. The suppression of the price cap is equivalent to a price cap at

the VOLL, which we assume to be of 26,000€/MWh, i.e. the value at which consumers, which are not

already participating in the market via DSR capacity, would not buy but would reduce the

consumption instead. It should lead to an adequate level of security of supply, assuming consumers

are able to reflect their VOLL in the market, but it leads to a significant increase in cost (300M€ of

social welfare loss), as the investments to meet peak demand in rare situations are very risky and

would require an important risk premium. In practice, we may even wonder whether these

42 These findings are consistent with the analysis performed by UFE (2015). According to UFE (2015), in

the 2030 time horizon, implementing a CM in France while maintaining the current price cap at

3,000€/MWh will effectively reduce the loss of volume from 46 GWh to 11 GWh. There is a difference

in the assumption on the value of loss of load between our study and the UFE (2015). In the latter,

The loss of load is modeled by adding a virtual plant running at a very high variable cost of

15k€/MWh, which allows for higher fluctuations in RES and thermal-sensitive demand, resulting in

more occurrences of load shedding. For example, distributed load shedding with an additional energy

reserve has a capacity of 7,000 MW in France and 2,500 MW in Germany, while emergency load

shedding as the last resort to avoid a loss of load has a capacity of 4,000 MW in France and 5,000

MW in Germany.



investments would take place anyhow due to the high associated risk. Petitet (2016) reaches

comparable results. In her simulations with comparable risk aversion (with α between 2 and 3), the

reliability standard is met with a price cap at 20,000€/MWh – although it is slightly higher than with

a CM – but at a higher cost than with a CM.



Sensitivity analysis to the level of the price cap

Removing the price cap of 3,000€/MWh in the energy-only market would enable power plant

operators to earn higher revenues during tight periods. This increase in revenues should attract

more investment and thus decrease the frequency of scarcity situations.

Modelling an energy-only market design with a price cap set at the VOLL value (26,000€/MWh)

brings the LOLE back below the security standard set by the French government.

However, given the increased volatility of revenues for power plant operators as shown in the

chart below, the risk premiums induced by such a design increase substantially; CCGT investors

face a cost of capital increased by three points, while peak unit investors face a cost of capital

increased by five points.

Figure 4-13: Energy revenue distribution across the modelled samples in an EOM with a price cap at 26,000€/MWh


In 2030, such a design would decrease the social surplus by 300M€, driven by an increase of

energy cost (3,500M€), an increase of energy revenues for plant operators (3,700M€), an increase

of capital cost (450M€) and a decrease of unserved energy (50M€).

In practice, it is also worth noting that it is likely that, due to the risk aversion and the very low

probability that the “last MWs” would cover their fixed costs (the Sample 0 is associated with a

probability of 3/100, i.e. that such conditions are likely to occur one winter every 30 years),

investors would not invest in such “last MWs”.



Our modelling assumes, based on RTE “diversification” scenario, that 25 percent of the existing

nuclear capacity closes by 2030. The pace of nuclear plants closure is a key driver of investment

needs and thus affects the results of the analysis. In the text box below, we present the results of a

sensitivity analysis that we have run in order to assess the impact of the timing of nuclear partial

decommissioning. It shows that the faster the decommissioning pace is, the higher the benefits of

the CM are.

Social welfare

Figure 4-14 shows the effect of the CM on social welfare with respect to the EOM, which we then

split between the consumer surplus, the TSO surplus (congestion rent) and the capacity provider

surplus.

Sensitivity analysis to the timing of nuclear decommissioning

Assuming a quicker phase-down in the early years and a slower phase-down in the later years

(illustrated by the convex phase-down curve), the CM will bring forward the need for new energy

solutions to ensure security of supply. The CM design would then provide additional value to the

customer as it would allow customers to avoid facing higher loss of load while phasing down

nuclear. CM customer cost savings could increase up to 15 percent compared to the results

presented previously in Section 5 – Consumer cost.

Assuming a slower phase-down in the early years and a quicker phase-down in the later years

(illustrated by the concave phase-down curve) will postpone the need of new energy solutions to

ensure the security of supply. According to the CM design, additional costs will be slightly

increased in the early years, while benefits will be maintained but postponed. Overall the benefit

of the CM introduction will be slightly lower. CM customer cost savings could decrease by up to

15 percent compared to the results presented previously in Section 4 – Consumer cost.

While the timing of the results presented both in terms of installed capacity and energy costs are

subject to the nuclear progressive phase-down, the key messages remain.



Figure 4-14: Impact of the CM on social welfare (difference between the CM and EOM counterfactual scenario)


The impact of the CM in terms of social welfare is strictly positive throughout the whole study

period, because it lowers costs for consumers and enhances profit margins for generators. More

precisely, consumer surplus takes a larger share of the total surplus gains, but generators and

demand response providers also benefit by more than 100M€/year on average over the outlook

from increased capacity and energy revenues and decreased market risks.

On average, the CM improves social welfare, compared to the EOM scenario, by a net gain of

more than 500M€ per year. As a comparison, UFE (2015) evaluates the reduction total costs thanks

to the CM to 370M€.43 Petitet (2016) also shows that the implementation of a CM leads to a higher

social welfare compared to an EOM, especially (i) if there is a price cap as today at 3,000€/MWh and

(ii) in the presence of risk aversion (α between 2 and 3).

43 Contrary to UFE (2015), we believe that investors should not be considered when calculating the

increment in social welfare. Specifically, investors would require a premium reflecting the risk of

default of the company or, to a certain extent, the uncertainty surrounding the project. Assuming an

efficient market, the premium is set at a level that exactly covers hedging costs, and thus, makes

investors indifferent between lending and not lending. Therefore, whether in the EOM or in the CM

scenarios, investors’ utility should not be impacted – as long as we assume a liquid and competitive

financial market. This remark has an important implication for our calculation of social welfare.

Indeed, it implies that we have to take into account the additional costs induced by the higher costs

of financing without the CM when computing the increment in producer surplus.

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

So

cia

l su

rplu

s (€

billio

n)

Capacity provider surplus Consumer surplus TSO surplus Total



Part of the reduction in total costs is linked to the financing costs. Financing costs for new

investments are lower with a CM since the CM helps generators to secure a part of the revenues as

it reduces the volatility of their revenues and their reliance on extreme meteorological events and

scenarios. Hence, it results in a lower cost of capital. This is especially true for peaking plants, which

only run for a limited period of time during infrequent cold waves.

This reduction in financing costs allows generators to make new investments with a lower hurdle

rate, while improving security of supply for consumers. This is ultimately reflected in the consumer

bills: consumers benefit from a higher security of supply, while their bills – i.e. the consumer costs

minus the loss of load cost – display comparable numbers.

In the text box below, we show the results of a sensitivity analysis to assess the impact of different

degrees of risk aversion of investors in our results. The CM benefits increase in the case where risk

aversion is higher and conversely reduce in the case where risk aversion is lower. However, the

degree of risk aversion does not fundamentally change the general conclusions and key messages

of our analysis.



4.4.4 Impact on energy markets

Effect on average power prices

The French CM is effectively designed not to alter market participants’ bidding strategy and

dispatch decisions in the short term. French capacity providers bid in the energy market on the basis

of their short-run marginal cost (SRMC) in the same way as they do in the EOM. Indeed, one of the

main features of the French CM design is that capacity certification is based on the availability of the

capacity. The existence of a CM incentivises generators and demand-side response operators to

maximise their availability during winter, but it forces them neither to generate nor to modify their

bids in the energy market. Moreover, the French CM runs in parallel to the wholesale electricity

market and it does not withdraw any available capacity from this market. As a consequence, the

Sensitivity analysis to the degree of risk aversion of investors

The greater volatility of revenues of market participants in an EOM compared to the CM has an

impact on the cost of capital and investment hurdle rates, which in turns affects both the

security of supply and on customer costs and social welfare.

Assuming a higher risk aversion would lead to a higher risk premium, which would further

increase the customer cost savings delivered by the CM. In 2030, an increase of one point in

cost of capital would be equivalent to an increase of c80M€ to social welfare. It combines an

increase of generator revenues (+10M€), an increase of the energy cost (+12M€), an increase of

the capital cost (+55M€) and an increase of unserved energy (+3M€).

Conversely, assuming a lower risk aversion premium would decrease the customer cost savings

delivered by the CM as lower revenues would be required from the energy market to make an

investment breakeven. In 2030, a decrease of one point in cost of capital would be equivalent to

a decrease by c160M€. It combines a decrease in generator revenues (-40M€), a decrease of

energy cost (-66M€), a decrease of capital cost (-56M€), and a decrease of unserved energy (-

3M€).

While the absolute value of the results presented both in terms of installed capacity and energy

costs are subject to the risk aversion premium assessment, the key messages remain.

This is confirmed by Petitet (2016), which shows that social welfare decreases substantially as

risk aversion increases in the EOM scenarios she studied, while risk aversion has limited impact

on social welfare in the CM scenarios.



French CM does not present short-term market impacts, in the sense that it does not modify the

behaviour of players in the energy market and the short-term merit order outcome.

On the other hand, in the CM, more capacity is necessarily available to meet the reliability standard.

As a result, Figure 4-15 shows that the wholesale electricity price in 2020 in France in the CM

scenario is slightly lower than in an EOM. This is mainly due to the fact that the additional capacity

required to ensure adequacy at the reliability standard reduces the occurrences of scarcity periods,

and therefore of price spikes. Meanwhile, in 2020, the implementation of the French CM has little

influence on power prices in the neighbouring countries.

In the 2030 horizon, as new capacities are built to secure electricity supply in line with the CM policy

objective, the variation in the available capacity reduces power prices by 5.2 percent. Moreover, this

price reduction effect is extended to the neighbouring countries, and its extent is 1.6 percent in

Germany and 2.2 percent in Great Britain.

Figure 4-15: Average power prices in France and its neighbouring countries in the EOM and CM scenarios


Note: the number above each bar indicate the difference in average power price in €/MWh between CM and EOM.

The CM does not modify the behaviour and strategies of market players in the energy market

and the short-term merit order. On the other hand, its impact on the available capacity to meet

the reliability standard limits the occurrences of price spikes and reduces energy prices on

average.

-0.8

0

-0.4

-3.4 -1.1 -1.5

0

10

20

30

40

50

60

70

80

FR DE GB FR DE GB

2020 2030

Average power price (€/MWh)

CM EOM



Effect on price duration curve

The impact of the CM on electricity spot prices can be illustrated by comparing price duration curves

under the CM and EOM scenarios. In Figure 4-16, it is barely noticeable that the wholesale electricity

price in the 2030 scenario in France in the CM scenario is lower than that in an EOM. The price

difference between the two scenarios is more visible during peak hours, as additional available

capacity limits the occurrences of price spikes, but this effect is concentrated on few hours: overall

the CM only impacts hourly power price by more than 5€/MWh for less than 10 percent of the time.

Figure 4-16: Comparison of load duration curve between CM and EOM scenarios in 2030


Effect on imports and exports

As the implementation of a CM causes a change in the available capacity and in electricity prices, it

has an impact on cross-border exchanges. Figure 4-17 shows that, with the introduction of the CM,

France remains a net power exporter.

As more efficient generation and DSR assets are built, the CM slightly increases exports at mid-load

periods and reduces imports at peak times. The impact on cross-border exchanges is however

limited in the medium term. It becomes more substantial when additional CCGT capacity is built

thanks to the CM.



Figure 4-17: Impact of CM on the French net export balance


Effect on congestion rents

Figure 4-18 shows that the congestion rent is larger with the EOM market, despite the slight

increase in export resulted from the CM. This is mainly due to the fact that, in the EOM case,

capacity shortage and resulting price spikes occur more frequently in France and during these

periods, the price differential between France and neighbouring countries becomes very high and

brings additional revenues to interconnection owners.

-20%

-10%

0%

10%

20%

30%

-8

-4

0

4

8

12

2017 2020 2025 2030 2035 2040

Dif

fere

nce

of

ne

t e

xpo

rts C

M -

EO

M (

%)

Dif

fere

nt

of

ne

t e

xpo

rts C

M -

EO

M (

TW

h)

Net export difference (left) Net export difference % (right)



Figure 4-18: Impact of the CM on congestion rents


4.4.5 CO2 emissions

According to Figure 4-19, with a CM thermal generation and associated CO2 emissions slightly

increase in France in 2030. In parallel, equivalent quantities of generation – and consequently of CO2

emissions – are avoided in the neighbouring countries, such that the total amount of CO2 emissions

in the CM scenario in both France and its neighbouring countries is slightly lower compared to that

in the EOM scenario.

Figure 4-19: Impact of CM on CO2 emissions in France and in Europe in 2030


-12%

-10%

-8%

-6%

-4%

-2%

0%

-140

-120

-100

-80

-60

-40

-20

0

2017 2020 2030

Dif

fere

nce

of

co

nge

sti

on

re

nts

CM

- E

OM

(%

)

Dif

fere

nce

of

co

nge

sti

on

re

nts

CM

- E

OM

(millio

n €

)

Congestion rent difference (left) Congestion rent difference % (right)

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

-2

-1

0

1

2

3

4

FR BE DE ES GB IT CH

Dif

ffe

ren

ce

of

CO

2 e

mis

sio

ns

CM

- E

OM

(%

)

Dif

ffe

ren

ce

of

CO

2 e

mis

sio

ns

CM

- E

OM

(m

illio

n t

on

ne

s)

Absolute difference CM - EOM (left) Relative difference CM - EOM (right)



Comparison of the impact of the French CM 5.

with other public policies implemented in

Europe

5.1 INTRODUCTION

The previous section showed that the French CM achieves its objective of maintaining security of

supply in France in line with the reliability criteria, while reducing the costs for consumers and

enhancing social welfare. Such benefits require the capacity mix to adapt, which results in higher

exports and fewer price spikes (and less load shedding). In this section, we compare these impacts

with those of other policy interventions in neighbouring countries, in order to put them into

perspective and assess their relative magnitude. The objective of this section is to contribute to the

European policy debate by providing quantitative estimates of the impacts of different policy

interventions in electricity markets, but not to evaluate these policies with regard to their objectives.

As part of their national energy policy objectives and responsibilities, Member States put in place

policies which can lead to changes in the volume of available capacity. These policy interventions

usually aim to achieve national policy objectives such as guaranteeing security of supply or

supporting specific technologies. CMs are one of these many public policy interventions which can

be implemented by the Member States in the energy sector.

The EC wants to avoid policy interventions introducing distortions which could affect the internal

electricity market. The qualification of these policies’ impact as a “distortion” is questionable, as

these policy interventions are meant to have consequences. For instance, policies aiming to ensure

security of supply at a specific reliability criterion should lead to greater available capacity. Similarly,

policies aiming to meet environmental objectives should provide a higher share of renewable

energy or low carbon generation capacity. In both cases, it is logical that the changes of the

available capacity resulting from the policy interventions have influences on the market.

However, it seems important that policy interventions’ impact on the internal electricity market are

proportionate, i.e. limited to the minimum necessary to achieve their objective. This sections aims

to provide a comparative quantitative analysis of the CM impact with four other policy interventions:

The German Strategic Reserve is composed of a capacity reserve which targets security of supply

and a climate reserve which aims to reduce CO2 emissions (up to 2.7 GW of lignite power to be



retired from the energy market) while contributing to security of supply. In the counterfactual

case, we assume that lignite plants are not retired before reaching the average age of 45 years.

Policy support to renewable generation in Germany: the Renewable Energy Act (EEG) came into

force in 2012 to support a stronger growth of renewable energy sources (RES). In the

counterfactual case, a lower RES development scenario is considered, based on the expected

development of RES prior to the EEG.

The nuclear phase-out in Germany: all 17 German nuclear power plants are to be shut down by

2022. In the counterfactual case, an energy mix without the nuclear phase-out is modelled.

The carbon price floor (CPF) in the UK: a carbon tax (the Carbon Price Support or CPS) topping

up the ETS carbon price was introduced in 2014 and it increased up to 18 £/tonne between 2016

and 2020. In the counterfactual case, only the EU-ETS price is charged to power plants operators.

It is worth noticing that the objectives pursued by these schemes vary and differ from the French

CM. However, security of supply or decarbonisation are key policy objectives in the European energy

strategy. Also the determination of the energy mix remains a national prerogative in Europe, as per

the article 194 of the Lisbon treaty. Whilst the comparison of the effects of these policies should be

interpreted with care, it provides useful background to evaluate the impact of the CM. Our

modelling shows that the CM has a comparable or smaller impact on the electricity market than

these policy interventions introduced in different countries.

5.2 PRESENTATION OF THE SCENARIOS FOR THE FOUR POLICY SCHEMES

5.2.1 The German strategic and climate reserve

Presentation of the mechanism

At the heart of the design of the future German power market proposed in the White Paper44

published in July 2015, the Federal Ministry for Economic Affairs and Energy has decided to

transform the power market into an “electricity market 2.0”, with a capacity mechanism in the

form of a strategic reserve.

The future power market will be backed up by capacity reserve, which is planned to amount to 5%

of peak demand, i.e. around 4.3 GW. The power plants that constitute this reserve do not participate

44 “An electricity market for the Energy transition”,

https://www.bmwi-energiewende.de/EWD/Redaktion/EN/Newsletter/2015/07/Meldung/white-

paper.html.

https://www.bmwi-energiewende.de/EWD/Redaktion/EN/Newsletter/2015/07/Meldung/white-paper.html




in the electricity market. Due to poor profitability in the energy market, it is expected that a part of

these plants would be closed otherwise. The strategic reserve will be made of two categories.

Climate reserve: announced as a means to achieve the government’s emission reduction

targets, up to 2.7 GW of the strategic reserve will come specifically from lignite power plants,

which would otherwise have been kept in the market and would have generated more and

emitted CO2.

Capacity reserve: the rest of the strategic reserve will be secured through an auction mechanism

to select the least cost offers.

The legislation to implement the climate reserve was due for spring 2016, and the capacity reserve is

scheduled to be implemented from 2018 onwards. A first lignite plant will be placed in the climate

reserve in 2016, and the lignite capacity in the reserve will reach 2.7 GW in 2019.

Figure 5-1: Germany’s planned capacity and climate reserve

Source: Platts and FTI-CL Energy, 2016

Meanwhile, climate policies concerning coal and lignite plants in Germany and their specific

consequences continue to be the subject of debates. Several studies conducted by Frontier

Economics (2015)45 recently compare three different options: the strategic reserve, a carbon levy,

and promoting CHP. They consider that the strategic reserve and promotion of CHP can be less

turbulent to the coal industry. In addition, the implementation of a carbon levy on old coal-fired

45 Frontier economics has elaborated three reports to assess the proposals of a carbon levy, a climate

reserve and promotion of CHP generation.

0

1

2

3

4

5

2016 2017 2018 2019 2020 2021 2022

GW

Climate reserve (lignite) Capacity reserve (technology neutral)



power stations is currently under discussion of the German Ministry of Economics and Energy

(BMWi). In order to achieve the national target of a CO2 reduction of 22 million tonnes by 2020,

these studies reveal that all three proposals lead to a higher cost of emission abatement compared

with simply buying and withholding emission certificates under the EU Emissions Trading Scheme,

and that the carbon levy results in a higher cost for consumers. Therefore, uncertainty in the German

climate policy had not been resolved at the moment when our study was finalised.

Modelling approach

The German Strategic Reserve is a CM that addresses security of supply issues, even though a

component of the reserve, the climate reserve, also aims at reducing CO2 emissions. The amount of

capacity that is moved out of the wholesale market is about 4.3 GW per year, representing 5 percent

of the level of peak demand in Germany.

In a “perfect strategic reserve”, the energy market should theoretically function equivalently to an

energy-only market, as it is expected that the plants that are integrated in the reserve would have

been closed otherwise and that the use of the reserve should not interfere with the energy market.

According to the high level description of the German capacity reserve46, these main criteria seem to

be respected and the reserve is supposed to be used only in situation when the day-ahead market

will not clear. However, in practice, at least three issues may alter the functioning of the market:

Impact on intraday markets: the activation of the reserve may not affect the day-ahead market

because activation decisions are taken after the closure of the day-ahead market. However, once

a part of the capacity in the strategic reserve is called into operation, this amount of generation

will surely interfere with intraday trading and balancing. As a result, distortions may be created at

the intraday and balancing stage. Acknowledging the limit of our model, we do not take into

account the interactions between intraday and day-ahead markets.

Volume to be procured: it is assumed that the volume to be procured is optimally chosen,

meaning that: (a) only plants that are necessary to ensure security of supply are contracted and

(b) an appropriate reserve capacity is procured, so that plants contracted in the reserve would not

have remained in the market otherwise. If this assumption does not hold, the impact on the day-

ahead market of the strategic reserve will not be negligible. In this case, we expect prices and

volumes to be different from what we have modelled, and different from the theoretical EOM.

46 “An electricity market for the Energy transition”,

https://www.bmwi-energiewende.de/EWD/Redaktion/EN/Newsletter/2015/07/Meldung/white-

paper.html





Climate reserve: the climate reserve forces lignite plants to be placed in the reserve, although

they might be profitable or at least more profitable than some other plants. From an economic

point of view, putting less profitable plants into the reserve instead of lignite would be less costly.

As a consequence, the climate reserve has a direct effect on the available capacity and merit

order, as well as on total costs, compared to an actual EOM, because these lignite plants would

have been kept in operation, but other, less economic, plants would probably have closed instead.

While modelling the impact on the energy market, we have taken this aspect into account.

Our modelling approach distinguishes between the two categories of the strategic reserve: the

capacity reserve and the climate reserve. With respect to the capacity reserve, we consider a

theoretical “perfect” market design based on the available information on the design of the

strategic reserve, which assumes:

The volumes contracted within the reserve should not exceed what is necessary and should not

incentivise plants which are already covering their avoidable costs in the energy market to enter

this reserve. Consequently, leaving aside the climate reserve’s impact, the available capacity in

the market is similar to that of an EOM in the case of a perfectly designed strategic reserve.

The activation should not impact the wholesale prices and dispatch; the reserve is activated only if

the day-ahead market does not clear and induces load curtailment without the activation of the

reserve.

Such theoretical perfect market design for the capacity reserve should produce no impact on the

dispatch and the available capacity as compared to the EOM.

However, in practice, the activation of the strategic reserve necessarily affects intraday and

balancing markets’ prices and flows, but these are not modelled in our simulations. As intraday

markets develop and become more central in the market, these impacts may become substantial.

With respect to the ‘climate aspect’ of the reserve, we consider a theoretical climate reserve

which assumes:

The lignite plants forced into the reserve would have been economical to run in the market up to

the end of their technical lifetime. The dispatch and prices are therefore impacted by the climate

reserve;

The activation of the lignite plants in the reserve has no impact on the wholesale prices and

dispatch.



Such theoretical reserve should therefore have an impact on the wholesale prices and dispatch

limited to the base-load nature of the plants forced into the reserve. Affordable base-load power

would be replaced by more expensive base-load power.

However, in practice, given the ramp-up constraints of the lignite plants, the activation of the

climate reserve would have additional impacts on the wholesale market during the activation period.

But these are not modelled in our simulations.

The impact of the German strategic reserve including the climate reserve on European power

markets has been modelled by comparing two scenarios:

In the reference case, the climate reserve is assumed to be implemented as announced in the

latest energy bill draft. 2.7 GW of lignite plants are progressively taken out of the power market

and maintained as operational from 2016 to 2023, then closed. It is then projected that similar

efforts will be made in the future to reduce the lignite generation throughout the entire

modelling horizon.

In the counterfactual scenario, existing lignite plants are assumed to retire upon achieving their

average lifetime of 45 years in an energy market without the climate reserve.

As the capacity necessary to secure supply is taken out of the market and placed in the reserve,

more price spikes occur compared to a situation – as in the French CM scenario – where this

capacity would be in the market. But if we compare the frequency of price spikes to an EOM, then it

is comparable. This allows other generators in the market to cover their fixed costs. As these plants

can be activated in case the day-ahead market does not clear, they serve as a secured resort for

security of supply. However, withdrawing lignite plants, which have lower variable costs than CCGTs

or OCGTs based on our scenarios of fuel and CO2 prices, will result in a higher power price on

average.

5.2.2 Policy support to renewable generation in Germany


In 2000, the first Renewable Energy Act (EEG) came in force in Germany. The purpose of the law was

“to facilitate the sustainable development of energy supply, particularly for the sake of protecting

the climate and the environment, to reduce the costs of energy supply to the national economy

(also by incorporating external long-term effects), to conserve fossil fuels and to further promote



the development of technologies for the generation of electricity from renewable energy

sources”.47

The act introduces financial support to renewable plants for a period of 20 years following the year

that the plant comes into operation.

The act has since then been revised several times. In 2011, the German government decided to give

a new spur in RES development, through the “Energiewende” (energy turnaround) concept. This

led to the 2012 revision of the EEG, which also intended to further encourage direct marketing. The

2012 EEG provided that producers of renewable power also had the option to market the electricity

themselves, without receiving the fixed feed-in tariffs paid under the EEG. Instead they could claim a

market premium in addition to the revenue obtained by the sale of the electricity. Furthermore, in

the 2014 Revision, the EEG sets targets of up to 45 percent of total generation to come from

renewables by 2025 and to 60 percent by 2035. Total new renewable build is subject to annual

capacity limits by technology.

As a result, government support for renewables prompted a strong growth of wind, solar and

biomass. In 2015, Germany had an installed capacity of 40 GW of wind, 41 GW of solar PV and 8 GW

of biomass. Between 2011 and 2015, Germany built around 12 GW of onshore wind and 16 GW of

solar PV, and the expected trend continues to add around 2 GW of wind and 1.5 GW of PV per year.

47 Federal and Ministry for Economic Affairs and Energy (BMWi):

http://www.bmwi.de/English/Redaktion/Pdf/renewable-energy-sources-act-eeg-

2014,property=pdf,bereich=bmwi2012,sprache=en,rwb=true.pdf.

http://www.bmwi.de/English/Redaktion/Pdf/renewable-energy-sources-act-eeg-2014,property=pdf,bereich=bmwi2012,sprache=en,rwb=true.pdf




Figure 5-2: Installed capacity over 2005-2015

Source: ENTSO-E and FTI-CL Energy, 2016

Modelling approach

The policy support to renewable generation in Germany impact on European power market has

been modelled by comparing two scenarios:

In the reference case, the policy support led to a high level of renewable development between

2011 and 2015. The development of new renewable capacity is then projected to fall between

ENTSO-E and BNetzA (the German energy regulator) projections.

In the counterfactual scenario we assume a lower policy support to renewable generation and

that such a lower support would have led to a growth of renewable capacity as in the conservative

scenario of ENTSO-E System Outlook and Adequacy Forecast 2011-2025. This is assumed to

represent the projection of the renewable capacity expansion path before the support to

renewable increased in 2011.

Figure 5-3 below shows the renewable development in both scenarios.

0

5

10

15

20

25

30

35

40

45

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Insta

lle

d C

ap

acit

y (G

W)

DE Wind Onshore DE Wind Offshore DE Solar



Figure 5-3: Renewable capacity development with and without renewable support


The policy support to renewable impacts on power markets is then derived from the assessment of

the precedent set of criteria in both scenarios.

5.2.3 Nuclear phase-out in Germany


The German government formed in 1998 had the phasing-out of nuclear energy as one of the main

features of its policy. In 2009, the newly elected government cancelled the phase-out, but in 2011,

after the Fukushima accident, it was reintroduced with eight reactors shut down immediately.

Until March 2011, Germany was producing around a quarter of its electricity from nuclear energy

using 17 reactors. By August 2015, nine reactors with a total net capacity of more than 9600MWe,

i.e. 47 percent of the total nuclear capacity in Germany, had been shut down.

Table 5-1 below compares the nuclear “phase-out schedules” existing at different times with

actually realised phase-out.

0

10

20

30

40

50

60

70

Insta

lle

d C

ap

acit

y (G

W)

Onshore Wind - Low RES scenario Solar - Low RES scenario

Onshore Wind - Ref RES scenario - FTI Solar - Ref RES scenario - FTI



Table 5-1: Historical nuclear shut down schedule in Germany

Plant Type

Net

capacity

(MWe)

Start date

Planned

shut down

as of 2001

Planned

shut down

as of 2010

Actual

shut down

1 Biblis A PWR 1167 Feb-75 2008 2016 2011

2 Neckarwestheim

1

PWR 785 Dec-76 2009 2017 2011

3 Brunsbüttel BWR 771 Feb-77 2009 2018 2011

4 Biblis B PWR 1240 Jan-77 2011 2018 2011

5 Isar 1 BWR 878 Mar-79 2011 2019 2011

6 Unterweser PWR 1345 Sep-79 2012 2020 2011

7 Phillipsburg 1 BWR 890 Mar-80 2012 2026 2011

8 Krümmel BWR 1260 Mar-84 2016 2030 2011

9 Grafenrheinfeld PWR 1275 Jun-82 2014 2028 06/2015

Source: http://www.world-nuclear.org/info/Country-Profiles/Countries-G-N/Germany/

According to the 13th amendment to the Nuclear Energy Act ("Nuclear Energy Act") enacted by the

German government in June 2011, all 17 German nuclear power plants should be shut down by

2022.48

Figure 5-4 compares the evolution of German nuclear capacity according to the two Nuclear Energy

Acts, enacted in September 2010 and June 2011.

48 The text of the Nuclear Energy Act (in German) is available at

http://dip21.bundestag.de/dip21/btd/17/060/1706070.pdf (accessed on 16/12/2015).

http://www.world-nuclear.org/info/Country-Profiles/Countries-G-N/Germany/



http://dip21.bundestag.de/dip21/btd/17/060/1706070.pdf



Figure 5-4: Evolution of nuclear capacity in Germany under Nuclear Energy Acts of 2010 and 2011

Source: Nuclear Energy Act 2011 (in German) is available at http://dip21.bundestag.de/dip21/btd/17/060/1706070.pdf and

http://www.world-nuclear.org/info/Country-Profiles/Countries-G-N/Germany/ (accessed on 16/12/2015).

Modelling approach

The nuclear phase-out impact on European power market has been modelled by comparing two

scenarios:

In the reference case, the nuclear phase-out is acted upon in 2011, with nuclear decommissioning

dates set according to the official plan. Nuclear capacity quickly decreases to reach zero in 2023.

In the counterfactual scenario an energy market without nuclear phase-out, it is assumed that

nuclear decommissioning dates are set according to the national plan in place before the

announcement. German nuclear capacity progressively decreases to reach zero in 2036. This

would have an impact on the available thermal capacity.

The nuclear phase-out impacts on power markets are then derived from the assessment of the

precedent set of criteria in both scenarios.

5.2.4 Carbon price floor in the UK


The CPF mechanism was introduced in the UK on 1 April 2013. It is designed to provide an incentive

to use and invest in low carbon power generation by providing certainty to the carbon price in the

UK electricity generation sector. The CPF affects the price of carbon in the UK electricity generation

market by taxing the fossil fuels that are used to generate electricity. In particular, the mechanism





implies that businesses using fossil fuels to generate electricity are required to pay Carbon Price

Support (CPS) rates on those fuels.

The CPS tops up the EU Emissions Trading System (EU ETS) carbon price to a target level for the

electricity generation sector. Precisely, the CPF level is calculated as a sum of CPS and EU ETS carbon

price. The CPF is, as indicated, a floor; the carbon price in the UK would be set at the CPF, unless the

EU ETS price would rise higher than the CPF. The CPF trajectory as of 2011 was announced to begin

at around 15.70£/tCO2 in 2013 and follow a straight line to 30£/tCO2 in 2020, rising to 70£/tCO2 in

2030 (in real 2009 prices). 49

However, EU ETS carbon prices in 2013-2014 were substantially lower than was expected when the

CPF was introduced. If kept in place, the planned CPF trajectory would cause a large and increasing

gap between the carbon price faced by UK energy users and those faced abroad. This would result

in UK firms facing significantly higher energy prices than those of competitors abroad, which raises

energy bills for households and dampens competitiveness of industries.50

This led to a reform of the Carbon Price Support in March 2014 which increased the carbon tax

almost twice from 9.54 to 18.08£/tonne from 1 April 201551 but capped it at 18£/tonne from 2016

until March 2020.52 The carbon tax would fall below £18 only if EU ETS price rises substantially. As

the chart below illustrates, this has effectively reduced the carbon price trajectory.

49 “Planning our electric future: a White Paper for secure, affordable and low‑carbon electricity” (July

2011) by the UK Department of Energy and Climate Change.

50 https://www.gov.uk/government/publications/carbon-price-floor-reform

51 “Carbon price instruments for the Power Sector” by Sandbag, March 2015.

52 https://www.gov.uk/government/publications/carbon-price-floor-reform

https://www.gov.uk/government/publications/carbon-price-floor-reform




Figure 5-5: CPF implementation in the UK

Source: DECC, All prices are in real Euro 2015

Modelling approach

Figure 5-6 below shows the two CO2 UK assessments used in the scenario. The CPF impact on

European power market has been modelled by comparing two scenarios:

In the reference case, the CPF is assumed based on the CPS reform of 2014 setting the CPS at

18£/tCO2 throughout the modelling horizon, i.e. the UK CO2 price will be higher than the EU ETS

price by 18£/tCO2 (in orange in the graph).

In the counterfactual modelling scenario of an energy market without CPF, it is assumed that the

CPF is not implemented. UK power plant operators’ emissions are charged at the EU-ETS price as

other European market participants (in blue in the graph).



Figure 5-6: EU ETS and CPF assessment


The CPF impacts on power markets are then derived from the assessment of the precedent set of

criteria in both scenarios.

5.3 ASSESSMENT OF THE IMPACTS OF THE FOUR INTERVENTIONS

5.3.1 Security of supply

Many of the considered mechanisms do not directly target security of supply (e.g. RES support,

nuclear phase-out, CPF). However, they might be coupled with other mechanisms that ensure

security of supply, such as the strategic reserve in Germany or the CM in Great Britain.

Furthermore, in the long term, the electricity generation mix tends to rebalance, so that the security

of supply is not affected: it converges either to a level achieved by an EOM or to the targeted level

by a market with a CM in place. In the medium term, only the nuclear phase-out might have had an

impact on margins, but the relative oversupply capacity and the resort to interconnections has kept

lights on despite sharply reduced margins. The main issue was actually linked to network

constraints, making it difficult to transmit energy from North Germany to South Germany at times of

high demand in the South.

However, the German strategic reserve targets security of supply and, as such, has a direct impact

on its level. Therefore, we have estimated the level of security of supply in Germany, represented by

the LOLE, before and after the activation of the strategic reserve. The size of German strategic

reserve will likely be based on the anticipated average annual peak load; 5 percent of it, i.e. around

0

20

40

60

80

100

120

2015 2020 2025 2030 2035 2040 2045 2050

€/t

CO

2

EU-ETS price

FTI reference scenario - Current CPF price with a constant gap above the EU-ETS price

Initially planned CPF price by the government



4.3 GW, will be retained as a capacity reserve.53 Figure 5-7 compares the LOLE in Germany with and

without the strategic reserve for the period of 2016-2040.

Figure 5-7: LOLE in Germany in the EOM scenario and with the German strategic reserve set at 5 percent of the peak demand


Our modelling demonstrates that, in the EOM scenario, without any form of CM, the LOLE in

Germany rises up to ten hours per year. Nonetheless, the implementation of the strategic reserve

largely covers for this risk as the LOLE falls down to zero to one hour per year. The strategic reserve

therefore actually induces a security of supply level higher than the reliability standards observed in

some other European countries.

5.3.2 Impacts on available capacity

Figure 5-8 compares the impact on the available capacity between the CM in France and other

modelled policy interventions in 2020 and in 2030.

With the French CM, France has mainly more DSR and builds some additional OCGTs and CCGTs

(based on our costs assumptions). In total, the additional capacity brought by the CM is below 5

GW, amongst which 50 percent to 100 percent is DSR.

53 German Federal Ministry for Economic Affairs and Energy, White Paper on “an electricity market for

Germany’s energy transition”, July 2015: http://www.bmwi.de/English/Redaktion/Pdf/weissbuch-

englisch,property=pdf,bereich=bmwi2012,sprache=en,rwb=true.pdf

0

2

4

6

8

10

12

14

16

18

20

LO

LE

(h

rs)

German - LOLE German LOLE - with Strategic (climate) reserve

http://www.bmwi.de/English/Redaktion/Pdf/weissbuch-englisch,property=pdf,bereich=bmwi2012,sprache=en,rwb=true.pdf




In contrast, other policies induce profound changes in the generation available capacity. The

German RES support brings in an additional capacity of 43 GW in 2020, and 70 GW in 2030, while

the nuclear phase-out policy results in a decrease of 6 GW and an increase of 1.7 GW in total

available capacity in the medium and long term. The German nuclear phase-out results in extending

the life of coal and lignite plants and more CCGTs being built, totalling more than 10 GW of

additional thermal capacity.

In Great Britain, the introduction of the CPF increases the generation costs for thermal plants. In the

medium term, our model shows that it affects the merit order and dispatch in the energy market.

However in the long term, the impact on the available capacity is limited given the following

reasons:

The gap between CPF and EU ETS is not high enough to stimulate substantial CO2-free

investment, and investment in renewable, CCS, or new-build nuclear technologies will still be

mainly driven by support schemes.

The GB CM remains as the main driver for the long-term development of domestic generation

capacity, together with technology-specific support policies. The GBCM restraints new investment

in generation to be located outside Great Britain, as it is designed to guarantee sufficient

domestic supply considering interconnectors’ contribution. Hence, foreign generation capacity is

not allowed to participate directly in GB CM. Consequently, investment on domestic generation

cannot be substituted by foreign investment.

By widening the gap between Great Britain and its neighbours’ power price, the CPF encourages

new interconnectors to be built. Several projects might not have been realised otherwise.

The 1-5 GW impact on the available capacity of the French CM as compared to the EOM in the long

run is much smaller than the impacts of most of other considered policy interventions. It is much

smaller than the impact on the available capacity of the German nuclear phase out or renewable

support and it is comparable to the strategic reserve in Germany that adds 4.3 GW to the EOM

available capacity.



Figure 5-8: Impact of the CM and other policy interventions on the available capacity


In conclusion, the impact of the different policy interventions is difficult to compare as they

aim at different objectives. However, the modifications of the available capacity induced by

some of the other policy interventions modelled such as the German nuclear phase-out or

renewable support are much more significant than the changes driven by the French CM.

Below we analyse each of the interventions in more detail.

German climate reserve

Figure 5-9 shows the variations in installed capacity of the CCGT and coal/lignite plants in 2020 and

in 2030, respectively. In the medium term, lignite generation is withdrawn from the market because

of the climate reserve. In the longer term, the in-market coal/lignite capacity will be lower resulting

from the climate reserve, allowing for more CCGTs to be developed. By putting lignite plants in the

strategic ‘climate’ reserve, Germany will avoid some other thermal plants being mothballed or

shut down. In the 2030 time horizon, climate reserve maintains some thermal capacity and reduces

that of coal/lignite generation relative to the counterfactual scenario in which climate reserve does

not exist.

-20

0

20

40

60

80

CM

Stra

tegic

(clim

ate

) rese

rve

Hig

h R

ES

Nu

cle

ar p

ha

se

-ou

t

CM

Stra

tegic

(clim

ate

) rese

rve

Hig

h R

ES

Nu

cle

ar p

ha

se

-ou

t

2020 2030

Insta

lle

d c

ap

acit

y d

iffe

ren

ce

(G

W)

CCGT OCGT WIND SOLAR NUCLEAR COAL CHP SR DSR Total



Figure 5-9: Impact on the available capacity active in the market of the German climate reserve


Note: Capacities outside of the market, in the strategic reserve, are excluded.

German renewable support

Due to fast development of RES in Germany, electricity prices have been largely dampened in recent

years. This has led to a crowd-out of conventional technologies and deterrence to new investment.

Thus, a considerable number of mothballs and retirements have been observed. According to Figure

5-10, with a larger share of wind and solar infeed, a smaller share of CCGT generation is needed in

the long term.

CCGT COAL CCGT COAL

2020 2030

-4

-3

-2

-1

0

1

2

3

4

0

5

10

15

20

25

30

35

40

45

Dif

fere

nce

clim

ate

re

se

rve

- n

o c

lim

ate

rese

rve

(GW

)

Insta

lle

d c

ap

acit

y (G

W)

Climate reserve (left) No climate reserve (left) Difference (right)



Figure 5-10: Impact on the available capacity of the German RES support


German nuclear phase-out

In Figure 5-11, the German nuclear phase-out influences the available capacity substantially. We see

that with the gradual phase-out of nuclear power, CCGTs capacity will be increased by 10 GW by

2030 compared to the counterfactual scenario. The importance of the German nuclear phase-out

plan has been reiterated by many studies. For instance, Grave et al. (2012)54 emphasise the need for

massive construction of gas-fired power plants in case of a nuclear phase-out, in which natural gas

capacity will need to increase by more than 30 GW by 2030, representing 20 percent in generation

mix. Another study published by Prognos (2011)55 indicated that gas capacity will evolve from 21.3

GW to 49 GW between 2015 and 2030. Compared with these numbers, we offer a more conservative

result as flexible measures such as the CM and strategic reserve are accounted for in the reference

scenario.

54 Grave, K., Paulus, M., & Lindenberger, D. (2012). A method for estimating security of electricity supply

from intermittent sources: scenarios for Germany until 2030. Energy Policy, 46, 193-202.

55 Prognos, 2011. Energieszenarien 2011.

http://www.prognos.com/fileadmin/pdf/publikationsdatenbank/11_08_12_Energieszenarien_2011.pdf

CCGT WIND SOLAR CCGT WIND SOLAR CCGT WIND SOLAR

2015 2020 2030

-50

-25

0

25

50

0

10

20

30

40

50

60

70

80

Dif

fere

nce

Hig

h R

ES

- L

ow

RE

S (

GW

)

Insta

lle

d c

ap

acit

y (G

W)

High RES (left) Low RES (left) Difference (right)



Figure 5-11: Impact on the available capacity of the German nuclear phase-out plan


5.3.3 Impact on energy markets

Effect on average power prices

As the French CM reduces the occurrence of scarcity periods, there are fewer price spikes. This

mechanism reduces the average wholesale price (by 5 percent in the long term). However, this

impact is concentrated on a limited number of hours.

The different schemes that we have analysed have different impact on power prices and dispatch.

The short-term impact of the strategic reserve in Germany remains unknown insofar as the

activation rules are not yet decided. We have assumed that the activation of the strategic reserve

will be triggered only in case of curtailment in the day-ahead spot market. In such a case, this

activation will not modify the day-ahead prices compared to a theoretical EOM situation (leaving

aside the question of the climate aspect of the reserve), even if the activation of strategic reserve

avoids loss of load. It would though probably have an impact on intraday prices and exchanges,

which we have not modelled in this study. Also, should the activation rules be different, the impact

on power prices might be different and distortions in wholesale power prices may appear due to a

forced change of the merit order.

For instance, the CPF directly changes the offered price of power plants by adding a tax component

to the variable costs, whereas other mechanisms, including the French CM, do not affect offered

prices, but have an impact on prices in the long term through their impact on the available capacity.

We present simulations of price impacts in the following paragraphs.

CCGT NUCLEAR CCGT NUCLEAR CCGT NUCLEAR

2015 2020 2030

-10

-5

0

5

10

15

0

5

10

15

20

25

Dif

fere

nce

Ph

ase

-ou

t - N

o p

ha

se

-ou

t (G

W)

Insta

lle

d c

ap

acit

y (G

W)

Nuclear phase-out (left) No nuclear phase-out (left) Difference (right)



Similarly to our analysis for the French CM, we have computed the dispatch and prices with and

without the different policy interventions. The comparison of the impacts on power prices of all

policy interventions is presented in Figure 5-12. This analysis shows that the CPF has the largest

impact in the medium term, whereas in the long term it is the nuclear phase-out.

Figure 5-12: Impacts on domestic power prices of different policy interventions


Figure 5-13 shows the impacts of the foreign policy interventions on the French power prices.

Except for the climate reserve, the other three interventions, namely the RES support, nuclear phase-

out, and the CPF, have a significant impact on the French power prices. In particular, the nuclear

phase-out has a larger impact in the long run, bringing a price increase of 2.9€/MWh, whereas the

RES support and the CPF affect the French power price in the medium run, driving the price variance

by 1.3€/MWh.

-6

-4

-2

0

2

4

6

8

10

Ave

rage

po

we

r p

rice

dif

fere

nce

in

th

e

do

me

sti

c m

ark

et

(€/M

Wh

)

2020

CM Strategic (climate) reserve

High RES Nuclear phase-out

CPF

-6

-4

-2

0

2

4

6

8

10

Ave

rage

po

we

r p

rice

dif

fere

nce

in

th

e

do

me

sti

c m

ark

et

(€/M

Wh

)

2030

CM Strategic (climate) reserveHigh RES Nuclear phase-outCPF



Figure 5-13: Impacts on the French power price of different policy interventions


More precisely, the different mechanisms analysed have the following impact on prices in the

medium and longer term:

Strategic reserve in Germany. In the case of the German climate reserve, it has a comparatively

lower impact on prices as security of supply is complemented by out-of-market actions; by

construction, prices in a theoretical EOM or in a market with “perfect” strategic reserve are

similar. The “climate aspect” of the German strategic reserve increases German prices by

1.7€/MWh on average. Moreover, if the plants in the strategic reserve were activated in the merit

order of the energy market, the power price would then be 4.3€/MWh lower than in the EOM, i.e.

the impact of the reserve would be similar than of the CM, as shown in the text box below.

-2

-1

0

1

2

3

Ave

rage

po

we

r p

rice

dif

fere

nce

in

th

e

Fre

nch

ma

rke

t (€

/M

Wh

)

2020

Strategic (climate) reserve High RESNuclear phase-out CPF

-2

-1

0

1

2

3

Ave

rage

po

we

r p

rice

dif

fere

nce

in

th

e

Fre

nch

ma

rke

t (€

/M

Wh

)

2030

Strategic (climate) reserve High RES

Nuclear phase-out CPF



High RES scenario in Germany. Massive deployment of RES in Germany in the past few years has

dampened electricity prices considerably in Germany. For example, this price depression is as

significant as 4.3€/MWh in 2020 and 1.0€/MWh in 2030. Moreover, it also has noticeable

influence on the power prices in the German neighbours. In 2020, the rapid growth of RES in

Germany results in a price reduction of 1.4€/MWh in France. In the long term, this number

diminishes to 0.6€/MWh in 2030. This limited impact of the high RES scenario on power prices in

the long run can be explained by several reasons. First, the low/high RES scenarios have a

common starting point in 2012, when a large share of RES was already deployed. Second, the new

capacity mix with the low RES requires a more frequent use of CCGT without changing other

marginal technologies in the merit order curve. Additionally, the RES development can drive the

power price both upwards and downwards. For example in our simulation for 2030 for 83 percent

of time, the high RES scenario reduces the German power price by 8.7€/MWh on average in line

with the merit order effect. However, for the rest 17 percent of time (1,489 hours), the high RES

scenario leads to a higher the German price, which is 35.9€/MWh higher than that in produced by

Impact of not valuing plants in reserve in the market

Our modelling approach compares the market outcomes with the intervention to the market

outcomes without the intervention. When it comes to security of supply, one major difference

is that, without the strategic reserve, security of supply standard would not be achieved. It

therefore assumes that the reference scenario would not guarantee security of supply.

Alternatively, we could consider that the reference scenario would be a market delivering

sufficient capacity to meet the security of supply standard. Thus, to simulate that, and as

Germany does not have explicit security of supply standard to our knowledge, we have

assumed that plants contracted in the reserve would have participated in the market. Hence,

the market satisfies the security of supply objectives.

Not dispatching the plants of the strategic reserve within the merit order of the market leads to

a loss in social welfare and in consumer costs, as more expensive plants than those in the

reserve are sometimes activated. Alternatively, if we assume that the capacity mix dispatched

through the market should deliver the level of security of supply required, then the plants of

the strategic reserve would be dispatched more frequently and would reduce prices, to the

benefit of the consumers. In that case, the power price would then be 4.3€/MWh lower than

in the EOM or with a strategic reserve only activated outside the market. This impact is

similar to the CM effect.



the low RES scenario. Indeed, the penetration of RES crowds out mid-merit plants, resulting in

more frequent recourse to peaking plants and in more frequent price spikes. The two forces,

driving prices downwards and upwards, even out the average effect of the high RES.

Nuclear phase-out in Germany. Our dispatch model confirms that the nuclear phase-out drives

power prices up significantly. More precisely without the phase-out plan of nuclear power,

wholesale prices are 3.8€/MWh lower in 2020 in Germany. Consequently, prices in France are from

2 to 2.9€/MWh higher than the counterfactual level between 2020 and 2030.

CPF. The CPF has a direct effect on electricity prices, as it adds a tax component to variable costs;

hence, GB prices rise by 8.9€/MWh in 2020 and by 4.9€/MWh in 2030. Moreover, this domestic

price increase is spread over to prices in neighbouring countries through cross-border power

trades. For instance, under the influence of the GB CPF, the French power price increases by

0.9€/MWh in 2020, but in the long term, the effect of CPF outside Great Britain is rather limited, at

0.4€/MWh.

If the impact of CM is higher than that of the climate reserve using the EOM as a reference, the CPF

in the UK or the nuclear phase-out in Germany has a much more significant impact on average, and

they impact the price formation much more frequently. Thus, the CPF increases GB prices by 7 to 16

percent; the nuclear phase-out increases German prices by 10 to 12 percent. With regard to the RES

support, the impact on prices is very substantial in the short to medium term, but as the generation

mix rebalances itself in the longer run, the impact reduces around 2030. Comparatively, the impact

of the French CM appears limited or comparable:

Evaluation of the impact of renewables based on an econometric approach

The effect on the German wholesale power price of the RES is separately studied by our team

using an econometric approach. In this model, we found that a 1 GW increase in PV

generation would decrease, on average, the German power price by 1.12€/MWh and a 1 GW

increase in wind generation by 0.63€/MWh.

Analogously to the CPF, the price reduction in Germany has also an influence on the price

levels in other European countries. For instance in France, we found that one additional GW of

PV generation in Germany would reduce the French power price by 0.67€/MWh and a same

unit of wind generation reduces the French power price by 0.46€/MWh.



Medium term. In 2020, mechanisms implemented in the UK or in Germany, especially RES

support, nuclear phase-out and CPF, have a larger impact on French prices that the CM. A fortiori,

the impact of the CM on French power prices is significantly less important than the impact of

these mechanisms in their country of origin: the impact in absolute value of the CPF on GB prices

is 8.7€/MWh higher than the impact of the CM on the French prices; the absolute impact of RES

support or nuclear phase-out on German prices is 3.5€/MWh higher than the impact of the CM on

French prices.

Longer term. In 2030, the CM has an impact on the French available capacity and avoids

numerous periods of load curtailment and consequent price spikes. Its impact on power prices

then becomes more significant, while remaining below the impact of other schemes; the impact

of the CPF on GB prices is, in absolute terms, still 4.5/MWh higher than the impact of the CM on

French prices, and the impact of nuclear phase-out on German prices is 5.4€/MWh higher than

the impact of the CM on French prices.

Effect on price duration

The extent of price difference between the scenarios with and without the CM, showed in Figure

5-14, is relatively small. The CM results in a price difference larger than 5€/MWh for 3 percent of

time in 2020 and 10 percent of time in 2030, while the proportion of hours with significant

price differences is much higher in the case of other policy interventions:

Strategic reserve. Comparably, the German climate reserve seems to have a slight impact in the

medium and long run, since the electricity price in Germany rises more than 5€/MWh for only 4

percent and 3 percent of time in 2020 and 2030 respectively. This effect hardly spreads out to

France and to Great Britain (0 to 2 percent).

RES support. It affects the German power price the most, leading to a noticeable price difference

in 17 percent, 26 percent and 54 percent of hours in 2015, 2020 and 2030, respectively.

Nuclear phase-out. Additionally, a notable price difference in the case of the nuclear phase-out

suggests a constantly significant impact in Germany from the medium term to the long term and

that its long-term impacts are more diffused in the French market than in the GB market.

CPF. In 2020, the UK CPF policy results in a price difference beyond 5€/MWh in 73 percent of the

year 2020 and 42 percent of the year 2030, meaning that the CPF policy influences the GB power

price significantly in the medium and long run. The resulting price difference in France decreases

from 9 to 7 percent, while that in Germany increases from 4 to 6 percent.



After all, the impact of the French CM on the wholesale electricity market is much smaller compared

to other policies that directly intervene in energy markets.

Figure 5-14: Percentage of time when the price difference exceeds 5€/MWh as a result of different policy interventions


Figure 5-15 presents the percentage of time per year when the wholesale price difference between a

reference case and a counterfactual case exceeds 1€/MWh. It describes a similar picture. The impact

of the French CM on power prices is relatively small, rising from 19 percent to 37 percent over time,

whereas other policy interventions have a larger influence and some of them can reach as high as 90

percent.

Hence compared to the other policy interventions, the impact of the French CM on power prices is

minor. The CM does have a long-run effect on the power price. However, this is the consequence of

a modification of available capacity, and this effect is much smaller relative to those of the other

policy interventions.

0%

10%

20%

30%

40%

50%

60%

70%

80%

FR DE GB

% o

f ti

me

- p

rice

dif

fere

nce

exc

ee

ds

5€

/M

Wh

2020



CPF

0%

10%

20%

30%

40%

50%

60%

70%

80%

FR DE GB%

of

tim

e - p

rice

dif

fere

nce

exc

ee

ds

5€

/M

Wh

2030



CPF



Figure 5-15: Percentage of time when the price difference exceeds 1€/MWh as a result of different policy interventions


In conclusion, policy interventions may drive electricity prices upwards or downwards, but the

impact of the CM in absolute terms on power prices is not greater than the other policies

modelled.

Effect on imports and exports

In addition to their impact on prices, public interventions result in different cross-border flows,

compared to a scenario without their introduction. Figure 5-16 shows their aggregated impact over

the year.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FR DE GB

% o

f ti

me

- p

rice

dif

fere

nce

exc

ee

ds

1€

/M

Wh

2020



CPF

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FR DE GB

% o

f ti

me

- p

rice

dif

fere

nce

exc

ee

ds

1€

/M

Wh

2030

CM Strategic (climate) reserveHigh RES Nuclear phase-outCPF



Figure 5-16: Impact on cross-border flows of different policy interventions


Note: The left axis indicates the scale in volume; the right axis indicates the scale in percentage (orange dots).

The CM modifies cross-border flows by 1 TWh in 2020 and by 11 TWh in 2030). This order of

magnitude is similar to the impact of the ‘climate aspect’ of the strategic reserve in Germany,

which changes flows by 5 to 7 TWh per year. In Germany, the RES support, diminishing the German

electricity price, has a major impact on cross-border flows; that is, net exports from Germany will

increase to 24 TWh in 2020 and continue to increase to almost 50 TWh in 2030. However, this

tendency is countered by the need for imports caused by its RES supports and nuclear phase-out

plan. For instance, in comparison with the counterfactual scenario, the phase-out leads to an

increase of 17 TWh in net imports in 2020, and this amount is doubled in 2030. Similarly, the

implementation of the CPF in the UK induces a rise in cross-border imports by 9 TWh in 2020 and by

14 TWh in 2030, since it scales up the power price.

As a result, we can conclude that the other European policy interventions have mostly a larger effect

on cross-border flows, and in comparison with them, the effect of the French CM on cross-border

trades can be regarded as the least significant.

In conclusion, because the available capacity is modified to maintain security of supply, the CM

has an impact on cross-border flows, but it is more limited than the impact of some of the

other policy interventions



Effect on congestion rents

Compared to other policy interventions, as shown in Figure 5-17, the impact of the CM on

congestion rents is relatively small. The German RES support increases, in the long run, congestion

rent by 565M€ in 2030, in contrast to a moderate impact in the medium run. Due to its considerable

impact on export flows, the German nuclear phase-out plan affects its domestic congestion rent

significantly, especially in the long term, totalling at 926M€ in 2030. The change brought by the CPF

policy, corresponding to the amount of export flows, amounts to 167M€. In contrast, the CM only

slightly affects the congestion rent in France, by bringing a small increase of 3M€ in the medium

term, and a fall of 43M€ and 126M€ in the long term.

Figure 5-17: Impact on congestion rents of different policy interventions


Note: The left axis indicates the scale in volume; the right axis indicates the scale in percentage (orange dots).

5.3.4 Economic efficiency

As the four schemes we have analysed target different objectives, the impact in terms of consumer

costs is difficult to compare and it does not make sense to compare their impact on costs and

economic efficiency.

However, regarding the strategic reserve, as it addresses security of supply like the French CM, costs

could be interesting to compare. However, it also addressed decarbonisation through the climate

CM

Str

ate

gic

(clim

ate

) re

se

rve

RE

S s

up

po

rt

Nu

cle

ar

ph

ase

-ou

t

CP

F

CM

Str

ate

gic

(clim

ate

) re

se

rve

RE

S s

up

po

rt

Nu

cle

ar

ph

ase

-ou

t

CP

F

2020 2030

-100%

-50%

0%

50%

100%

-1000

-500

0

500

1000

Co

nge

sti

on

re

nt

dif

fere

nce

(%

)

Co

nge

sti

on

re

nt

dif

fere

nce

(m

illio

n €

)



reserve, so we have tried to disentangle the two cost aspects by assessing the incremental cost of

the climate reserve, compared to a technology neutral strategic reserve.

When the strategic reserve and climate reserve are fully operational, the introduction of the

strategic reserve has an impact on the customer cost through three levies:

Energy cost increase: A theoretical strategic reserve should not change the energy cost in the

market, as it is designed not to have any impact compared to the EOM, but we have to add

activation costs of the reserve. In addition, because of the climate reserve, as base load plants

(lignite plants) are removed from the wholesale power market, the energy cost necessary to meet

demand increases, increasing the power price for customers;

Strategic reserve contracting cost increase: To maintain capacity in the strategic reserve,

consumers have to cover the fixed O&M costs of the plants in the reserve. In addition, lignite

plants, which would have been maintained in the market, have an opportunity cost of not

generating in the market, so the introduction of the climate reserve increases the strategic reserve

cost as lignite plants are contracted in the reserve at a higher cost than the plants which would

have been contracted in a theoretical strategic reserve where lignite plants are not forced into the

reserve.

Reduced cost of loss of load: the strategic reserve aims to secure supplies and, as such, may

reduce the energy that needs to be curtailed in scarcity situations.

Figure 5-18 shows the increased customer cost spread out on the strategic reserve cost and the

energy cost. The strategic cost delta increases over time as the lignite opportunity cost increases

overtime. In our modelling and based on public information, the German strategic reserve could

induce around 800M€ per year of additional costs for consumers. Consumers do not benefit

from lower energy prices insofar as capacity in the reserve is not valued in the market.

Consumers do not benefit from lower energy prices insofar as capacity in the reserve is not valued

in the market.

The contracting costs of a theoretical strategic reserve are estimated at 130M€.

The reduction of the security of supply risk is limited in 2020 and is valued at around 260M€ in

2030.

The ‘climate aspect’ of the reserve increases energy cost and contracting costs for consumers

by around 650-870M€ per year. Indeed, lignite plants are replaced by gas plants in the merit

order, which increases wholesale prices on average. In addition, their contracting costs are higher



than those of the plants which would have been contracted in a technology neutral strategic

reserve (without forcing lignite plants in).

Figure 5-18: Breakdown of the impact of the German strategic reserve on customer costs


Therefore, most of the net cost for consumers is due to the ‘climate aspect’ of the strategic

reserve. Without this distortion in the constitution of the reserve, the strategic reserve would still

induce a net cost in the first years of implementation but, over the period 2018-2030, it would be a

zero-sum gain for end consumers. This is to be compared with the net gain generated by the French

CM, estimated at around 400M€/year.

5.3.5 CO2 emissions

As previously stated in the report, the French CM slightly increases domestic CO2 emissions, but

decreases CO2 emissions in other European countries, such that the total amount at the European

level diminishes. Keeping this result in mind, it is important to identify the aggregate effect on CO2

emissions of the other policy interventions.

It is also worth realising that the different schemes we are considering for the comparison aim at

different objectives. In particular, three of them directly target the reduction of CO2 emissions –

-400

-200

0

200

400

600

800

1000

1200

Capacity reserve Climate reserve Strategic reserve Capacity reserve Climate reserve Strategic reserve

2020 2030

Co

sts

of

the

str

ate

gic

an

d c

lim

ate

re

se

rve

s (

m€

/ye

ar)

Contracting cost Cost of unserved energy impactActivation cost Energy costAdditional Ramping cost Total costs



namely the ‘climate part’ of the strategic reserve, the RES support, and the CPF – while the French

CM addresses security of supply issues. We therefore expect that the impact in terms of reduction of

CO2 emissions will be more positive for these mechanisms.

As shown in Figure 5-19 in Germany, the domestic emission level drops by 5 Mt in 2020 and by 26

Mt thanks to the climate reserve policy that puts lignite plants in the strategic reserve. Furthermore,

this policy has no impact in Great Britain, in contrast to a 1.1 Mt increase in France. Besides that, its

impact on Belgium, Spain and Italy remains neutral.

Figure 5-19: Impact of the German climate reserve on CO2 emissions in 2020


Supporting RES can clearly allow CO2 emissions to drop in Germany. More precisely, Figure 5-20

shows that up to 7 Mt CO2 emissions are avoided in Germany in 2020. In 2030, this number rises to

14 Mt if the government continues to support the development of wind and solar generation. In

other European countries, the effect on emissions of the German RES support is not significant.

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

-6

-4

-2

0

2

4

6

FR BE DE ES GB IT

Dif

fere

nce

Clim

ate

re

se

rve

- N

o c

lim

ate

re

se

rve

(%

)

Dif

fere

nce

Clim

ate

re

se

rve

- N

o c

lim

ate

re

se

rve

(millio

n t

on

ne

s)

Difference Climate reserve - No climate reserve (left)

Difference % Climate reserve - No climate reserve (right)



Figure 5-20: Impact of the German RES support on CO2 emissions in 2020


On the other hand, Figure 5-21 reveals a significant 23 Mt increase of CO2 emissions in Germany

after the implementation of the phase-out, and a slight increase in France, Belgium, Spain and Italy.

Figure 5-21: Impact of the German nuclear phase-out on CO2 emissions in 2020


-4%

-3%

-2%

-1%

0%

1%

-8

-6

-4

-2

0

2

FR BE DE ES GB IT

Dif

fere

nce

Hig

h R

ES

- L

ow

RE

S (

%)

Dif

fere

nce

Hig

h R

ES

- L

ow

RE

S

(millio

n t

on

ne

s)

Difference High RES - Low RES (left) Difference % High RES - Low RES (right)

-3%

0%

3%

6%

9%

12%

15%

-5

0

5

10

15

20

25

FR BE DE ES GB IT Dif

fere

nce

Nu

cle

ar

ph

ase

-ou

t -

No

nu

cle

ar

ph

ase

-ou

t

(%)

Dif

fere

nce

Nu

cle

ar

ph

ase

-ou

t -

No

nu

cle

ar

ph

ase

-ou

t

(millio

n t

on

ne

s)

Difference Nuclear Phase-out - No nuclear phase-out (left)

Difference % Nuclear Phase-out - No nuclear phase-out (right)



Comparatively, the nuclear phase-out in Germany has a strongly negative impact on CO2 emissions

while other schemes, whose objective is to reduce emissions, have positive impacts on CO2

emissions.

According to Figure 5-22, the CPF reduces CO2 emissions in Great Britain substantially; up to 12 Mt

of emissions are avoided in 2020 in Great Britain, but this decrease is partially recouped by a small

increase in CO2 emissions in other European countries. For example, this number in France will be

1.3 Mt in 2020.

Figure 5-22: Impact of the UK CPF on CO2 emissions in 2020


It is also worth noticing that the French CM does not have a negative impact on neighbouring

countries’ emissions and cannot be blamed of exporting emissions. Conversely, the CPF leads to an

increase of CO2 emissions in neighbouring countries of around 4400 tonnes. The nuclear phase-out

plan will increase CO2 emissions in other countries by 3800 tonnes.

To conclude, the CM has a negligible impact on CO2 emissions, whereas it does not aim to

address CO2 emissions but security of supply. As such, it does not contravene the EU objective of

reducing CO2 emissions, and it addresses another EU objective – that of security of supply.

-20%

-10%

0%

10%

20%

-15

-10

-5

0

5

10

15

FR BE DE ES GB IT

Dif

fere

nce

CP

F - N

o C

PF

(%

)

Dif

fere

nce

CP

F -

No

CP

F (

millio

n t

on

ne

s)

Difference CPF - No CPF (left) Difference % CPF - No CPF (right)



Conclusions 6.

6.1 IMPACTS OF THE FRENCH CM COMPARED TO AN EOM

To assess in details the impact of the French CM, we have modelled the European power system

using our dispatch model coupled with or without a CM. This has allowed the simulation of energy

prices, dispatch and flows in France and in Europe, on an hourly basis, as well as investment,

mothball and closure decisions, with and without the introduction of the CM in France.

We have analysed the impact of the CM, by contrast to an EOM with a price cap at 3,000€/MWh,

with regard to several criteria:

Security of supply. The introduction of the CM guarantees that the security of supply standard is

met. Conversely, in the EOM, the LOLE exceeds the three hour policy standard and reaches nine

hours on average per year. It reaches ten hours on average per year from 2024 onwards.

Economic efficiency. The CM is economically efficient at addressing security of supply issues, as it

increases social welfare by more than 500M€/year over the period 2017-2040. These gains are

partly enabled by a reduction of financing costs for capacity providers, especially peak capacity

providers, who may secure part of their revenues in the CM. Moreover, contrary to usual

thoughts, end consumers are the main beneficiaries of the economic surplus induced by the CM,

as the CM reduces consumer costs by 400M€ per year between 2017 and 2030, thanks to the

reduction of loss of load expectation and reduction of investment financing costs.

Impact on the available capacity. In order to maintain security of supply at the desired reliability

standard, the CM helps bringing in additional capacity compared to the EOM. In the medium

term, based on our cost assumptions, it fosters the development of demand-side response. It is

only from 2024 that the CM allows more generation capacity (CCGTs and then OCGTs) in the

market. In the longer term, around additional 5 GW are built, and at least half of it is DSR.

Impact on the energy market. The French CM does not impact bidding and dispatch strategies,

as it is based on availability, such that there is no short-term impact of the CM on energy market

and the only potential impact results from additional capacity in the future. In the medium term,

the impact on prices is limited as additional DSR is called upon only in extreme situations. In the

long term, additional capacity reduces the long-term price by around 5 percent, and this is

concentrated on peak hours, where the system is tight. As a consequence, the net export balance

increases by up to 10 TWh.



Impact on CO2 emissions. The CM slightly increases CO2 emissions in France, because of higher

domestic generation. However, this increase is more than entirely compensated by a decrease in

neighbouring countries.

6.2 COMPARISON OF THE IMPACTS OF THE FRENCH CM COMPARED TO OTHER POLICY

INTERVENTIONS

Following the same methodological approach, we have assessed the impact of other policy

interventions in Europe, namely:

The new Strategic Reserve planned in Germany, including the forced inclusion of lignite plants in

the reserve (so-called Climate Reserve);

The RES support policy in Germany;

The Nuclear Phase-Out in Germany; and

The Carbon Price Floor in the UK.

Particularly in the modelling of the strategic reserve, we have distinguished the capacity reserve and

the climate reserve while assuming a “perfect” or theoretically optimal market design, in which

activation of the reserve will not modify dispatch or available capacity benchmarked with an EOM

scenario: only plants about to be mothballed or closed participate.

The comparison with other mechanisms aims to put things into perspective, in order to assess

whether the impact of the CM is significant or not compared to other public interventions. These

other mechanisms often have different objectives: decarbonisation, development of RES, or national

decision on the generation mix. Any comparison of the effects should therefore be interpreted with

care, but they give an interesting comparison to grasp what could be deemed proportionate or not.

We have analysed the impact of these interventions and compared them to the CM’s impact, with

regard to the same criteria:

Security of supply. Only the German strategic reserve aims at security of supply. Without the

strategic reserve, security of supply deteriorates from 2023, to reach around ten hours of LOLE.

The strategic reserve brings this value down to below one hour due to a political choice regarding

the volume of reserve, which is a very high security of supply standards compared to what we

usually observe in Europe. Regarding other mechanisms, the market tends to rebalance, such that

the actual impact in terms of security of supply is limited.

Economic efficiency. For the same purpose, the impact of the strategic reserve in terms of

consumer costs is completely different. Indeed, the strategic reserve increases consumer costs by



around 800M€, while the CM is beneficial for consumers in terms of costs. This increase in costs is

mainly due to the climate aspect of the reserve: without this distortion in the constitution of the

reserve, the strategic reserve still induces a net cost in the first years of implementation but, over

the period 2018-2030, it is a zero-sum gain for end consumers. The impact is though less positive

than the CM.

Impact on the available capacity. Policy interventions generally have a much stronger impact on

the available capacity than the CM. RES support in Germany can lead to change in the installed

capacity of more than 70 GW, while the CM impact is lower than 4 GW in the medium term and

limited to 5 GW on average in the long term. The impact of the strategic reserve, taking into

capacity put in reserve, is comparable, to 4.3 GW.

Impact on the energy market. Public interventions may drive electricity prices upwards (e.g. the

UK CPF or the nuclear phase-out) or downwards (RES support), but the impact of the CM in

absolute terms on power prices is not greater than the other policies modelled. For instance,

while the CM does not modify French power by more than 4€/MWh on average, the CPF can

increase GB prices by more than 10€/MWh and the nuclear phase-out by up to 8€/MWh.

Furthermore, the impact of the CM on prices is limited to limited periods (mostly peak hours). As

a consequence, the impact on cross-border trade is reduced compared to other interventions,

especially RES support.

Impact on CO2 emissions. The CM has a neutral impact on CO2 emissions. Conversely, policies

that target decarbonisation and RES development have a downward impact on CO2 emissions.56

In contrast, some other policy interventions have strong and negative consequences in terms of

CO2 emissions, especially the nuclear phase-out, which has significantly increased CO2 emissions,

and which effect will last for a while.

56 Although we have not assessed the downward impact of RES development on CO2 prices, which may

limit the reduction of CO2 emissions.



Appendix A - Description of dispatch model 7.

The European Power Market Model developed by FTI-CL Energy, is implemented in the commercial

modelling platform Plexos® Integrated Energy Model. This modelling platform is most commonly

used in the European electricity industry by utilities, regulators and transmission system operators.

Plexos® allows finding solutions quickly using advanced optimisation procedures taking into

account of a large number of variables and complex constraints of transmission network and power

plants. It also provides a flexible and user-friendly interface allowing testing multiple scenarios, to

perform stochastic sampling and optimisation, and to present the results in a graphical form.

EUROPEAN POWER PLANTS DATABASE

FTI-CL Energy has developed a database of European power plants. It contains technical specificities

of all thermal European plants and is used as the basis of the dispatch model.

The database is regularly updated to include the latest announcements from plant operators,

utilities and regulators.

It is completed by a range of scenarios on decommissioning dates for existing plants,

commissioning dates for current and future projects, and projection on renewable developments.

EUROPEAN POWER MARKET ASSUMPTIONS

FTI-CL Energy European Power Market model runs on a set of key inputs developed in-house. For

the forward price assessment, a consistent set of assumptions based on public data as well as on

FTI-CL Energy’s European expertise has been used. The power dispatch model uses:

Demand projections. FTI-CL Energy’s long-term power demand projections in European

countries are derived from a combination of GDP growth, policy effectiveness, and the expected

technological change. In the reference case, uptake of energy efficiency measures drives

electricity demand lower, but electrification of the transport and heat sectors (together with GDP

growth) offset this reduction.

Supply projections. These are based on climate and energy policies and technology development

cost. In particular, future capacity mix scenarios in European countries are based on the existing

thermal plants retirement or mothballing based on released publication, energy policies or

economic modelling (including LCPD and IED decisions), existing Low Carbon technologies

retirement or life extension based on current and future energy policies, new thermal plant



capacity scenarios based on economic modelling and new Low Carbon technologies scenarios

based on future energy policies.

Transmission projections. Based on the ENTSO-E data and FTI-CL Energy’s expertise in the

European power market, a transmission database referencing historic NTCs and future

interconnection projects has been created.

Commodity price projections. Commodity prices are one of the main determinant of the short-

run marginal cost (SRMC) of most power generators, and thus a primary driver of wholesale

power prices. We have developed internal scenarios based on publicly and privately released data

from IEA’s World Energy Outlook and EIA’s Annual Energy outlook projections. Commodity

price projections are regularly reviewed to account for latest changes in energy regulation.

Each scenario is internally consistent and represents a plausible combination of assumptions on the

considered variables.

GEOGRAPHIC SCOPE OF THE MODEL

In conjunction with these proprietary datasets, FTI-CL Energy has developed a European power

dispatch model. This model covers the markets of the UK, Ireland, France, Belgium, the Netherlands,

Germany, Switzerland, Austria, Denmark, Norway, Sweden, Finland, Italy, Spain and Portugal.

Countries beyond this geographic scope are modelled at an aggregated level. The geographic scope

of the model is shown in Figure 7-1 below.

Figure 7-1: FTI-CL Energy’s European Power Market model




PRICE CALCULATION

This model uses a detailed bottom-up methodology: the supply from flexible thermal power plants

is modelled individually to meet the demand net of the supply of must-run renewable generators.

The dispatch is determined to minimise the costs of generation in the northwest Europe while

satisfying the unit commitment constraints of generators as well as the flow constraints over the

European transmission network. The model uses the zonal transmission network representation that

matches with the price zones currently implemented in Europe and the commercial transmission

boundaries.

The model calculates the price in each price zone as the marginal value of energy delivered in that

zone based on the simulated bids of flexible generators. In reality, these bids closely follow the

estimated short-run variable cost of power generation. Therefore, the estimated clearing prices

correspond to the marginal cost of electricity. Such estimation of electricity prices based on the

marginal cost is reasonable when the capacity margin above the demand is high and there is high

competition between generators to serve the demand.

However, when demand in an area reaches the levels close to the generating and import capacity,

generators have less competitive pressure to bid at the SRMC-level. In such cases, the clearing price

is set above the marginal cost of the most expensive running unit. Such prices allow the marginal

units to cover the variable cost of production and further contribute to covering their fixed costs.

A dedicated model to reflect this bidding behaviour, referred as to the "Fixed-cost recovery

mechanism" or FCRM has been developed. This model calculates the mark-ups of the generators’

bids over the marginal cost depending on the capacity margin.

BACK-CASTING CALIBRATION

The model has been calibrated with respect to the historical price profiles observed in a number of

European countries. For example, Figure 7-2 below shows the results of the back-casting57

calibration of the prices calculated by the model against the realised prices in 2012 in France; Figure

7-3 below shows the results of the back-casting calibration of the prices calculated by the model

57 Back-casting is a process by which we use our model to forecast prices over a historic period and

then compare to the actual prices observed over the same historic period. The closer the modelled

results to the actual results the greater comfort we can draw that our model will produce reliable

forecasts over the future.



against the realised prices in 2012 in Great Britain; Figure 7-4 below shows the results of the back-

casting calibration of the prices calculated by the model against the realised prices in 2012 in

Germany; and, Figure 7-5 below shows the results of the back-casting calibration of the prices

calculated by the model against the realised prices in 2012 in Belgium.

The high precision of the hourly price profiles is achieved through a realistic representation of the

dynamic constraints of thermal plants and an accurate calculation of the demand net of the must-

run production from renewable and distributed generation.

Figure 7-2: Back-casting calibration – French hourly prices, November 2012

Source: FTI-CL Energy’s European hourly dispatch model calibration, 2016

Figure 7-3: Back-casting calibration – GB hourly prices, October 2012


0

20

40

60

80

100

120

1

17

33

49

65

81

97

113

129

145

161

177

193

209

225

241

257

273

289

305

321

337

353

369

385

401

417

433

449

465

481

497

513

529

545

561

577

593

609

625

641

657

673

689

705

€/MWh

Hours

Modelled Actual

0

20

40

60

80

100

120

140

160

180

200

1

19

37

55

73

91

10

9

12

7

14

5

16

3

18

1

19

9

21

7

23

5

25

3

27

1

28

9

30

7

32

5

34

3

36

1

37

9

39

7

41

5

43

3

45

1

46

9

48

7

50

5

52

3

54

1

55

9

57

7

59

5

61

3

63

1

64

9

66

7

68

5

70

3

72

1

73

9

€/M

Wh

Hours

Modelled Actual



Figure 7-4: Back-casting calibration – German hourly prices, October 2012


Figure 7-5: Back-casting calibration – Belgian hourly prices, October 2012


POWER DISPATCH MODEL CREDENTIALS

The FTI-CL Energy dispatch power model has been initiated internally by our experts to provide a

robust and reliable source of market intelligence. Recognising that the best source of market

insights stems from stakeholders, it has been developed collaboratively using our experts’ insights

and stakeholders’ contributions.

Recently, the model has been fine-turned on two principal components:

FTI-CL Energy team closely worked with utilities in the Nordic countries to further improve their

hydro modelling in order to understand the impact of increased flexibility sources on the power

systems.

0

20

40

60

80

100

1201

18

35

52

69

86

103

120

137

154

171

188

205

222

239

256

273

290

307

324

341

358

375

392

409

426

443

460

477

494

511

528

545

562

579

596

613

630

647

664

681

698

715

732

€/MWh

Hours

Modelled Actual

0

20

40

60

80

100

120

1

18

35

52

69

86

103

120

137

154

171

188

205

222

239

256

273

290

307

324

341

358

375

392

409

426

443

460

477

494

511

528

545

562

579

596

613

630

647

664

681

698

715

732

€/MWh

Hours

Modelled Actual



FTI-CL Energy team closely worked with Transmission System Operators (TSOs) and

interconnector’s developers to further improve their wind modelling in order to model the

impact of increased variability on the power systems and cross-border flows.

Having been used extensively with clients, the European dispatch model is now widely recognised as

a robust and reliable source of power market intelligence.

RENEWABLE POWER GENERATION MODELLING

Given the impact of renewable variability on future power systems, we have developed specific

methodologies to represent and forecast wind and solar production and model hydro flexible

generation. The model also includes pumped storage modelling and has the flexibility to model on

site storage. The renewable power forecast methodologies are completed by an in-depth

understanding of the economic impact of renewable production on power prices.

Wind – Power production

Following extensive analysis on the impact of wind variability on future power systems, for multiple

clients such as TSOs, interconnection operators, and European utilities, a specific wind model – the

“FTI-CL Energy Hybrid wind model” has been developed. It combines:

Wind manufacturers’ theoretical power curve applied on historic re-calibrated wind speed data

collected from weather stations across Europe; and

Historic wind power production.

This combined methodology strengthens the wind modelling capability as it goes beyond wind

turbine manufacturers’ data and uses historic technical performances at the heart of the “wind

speed to power converter” algorithm.

Solar – Power production

As solar technical performances are continually improved, solar production has been modelled in

detail to include future technical improvements and technologies. Besides using historic solar

production, a dedicated methodology to model the impact of future technical improvements, such

as capturing diffuse solar irradiation, has been developed.

As per wind modelling, we collect irradiation data from weather stations scattered around Europe

and convert the irradiation values into power values by using a statistical analysis on the relationship

between average solar irradiation and national solar production. This relationship captures the

inverter efficiency and diffusion coefficient.



Wind and solar bids in wholesale market

To capture the fatal characteristic of renewable generation, the wind and solar production as a

must-run generation have been modelled. Existing and under construction sites are allowed to bid

negatively up to their renewable incentives level, creating occasionally negative prices.

Following work on the negative price impact and regulation, we have adapted our model to include

a number of wind and solar site vintages in order to accurately model the level down to which

renewable plants will bid before being curtailed. This acknowledges that overtime renewable

generation will be merchant-only and won’t have external incentives to create negative prices.

Nordic and Alps hydro modelling

The model specifically focuses on an explicit modelling of the production flexibility provided by the

Nordic and the Alps hydro reservoirs. Hydro production is one of the main determinants of the

electricity prices in the Nordic region and one of the main sources of flexibility in the Alps. The

hydro model is designed to dynamically replicate the seasonal optimisation performed by those

producers. The modelling is based on two elements:

Hydro constraints, such as reservoir maximum levels and weekly natural inflows have been

calibrated following extensive research on historic and future hydro data; and

Given the calibrated constraints, our dispatch model includes a state-of-the-art algorithm

designed to calculate the “water value”, i.e. the value of water held in storage. It then uses the

water value of the hydro plants in the short-run optimisation.

This detailed approach further improves the dispatch model robustness, providing additional

flexibility to the European power system.

Pumped storage

Pumped storage facilities are the actual main source of storage on the European power systems.

Our model includes a specific add-on to correctly account for this source of flexibility. It optimises

its pumping and dispatch schedule on a weekly basis.

On-site storage

Our model provides flexibility to model on site storage impact on power system. These additional

features could be analysed in further sensitivities.

GARCH METHODOLOGY

In addition to the standard wind and solar forecast methodology used in the power dispatch model,

the historic impact of renewable production on Belgian wholesale power prices over the period



2008-2014 has been assessed by using an econometric approach developed by FTI-CL Energy

experts based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH)

methodology.

The GARCH model is an econometric technique that is well suited to capture the fluctuation and

clustering of volatility. This is particularly useful when analysing price volatility, for example as

Roques and Phan did in studying the impact of growth in renewables on power price volatility,58 a

study very relevant to Belgium’s case. The GARCH model solves a number of econometric issues

when modelling price, such as high price volatility due to the non-storable nature of electricity, and

daily and seasonal fluctuations. A conditional heteroscedasticity model such as GARCH is an

appropriate solution to handle this sort of volatility. The GARCH model is also particularly relevant

for the study of the effect of renewables because it estimates both the mean and variance of the

dependent variable.

The theoretical framework of the GARCH model was introduced by Bollerslev in 198659 as an

extension of the Autoregressive Conditional Heteroskedastic (ARCH) model with a more flexible lag

structure. Since then, it is commonly used to analyse variations of the commodity market. Knittel

and Roberts (2005) were among the first to use GARCH model in the field of energy economics by

modelling electricity prices.60 Others who have also used GARCH to model electricity prices include

Petrella and Sapio (2009).

The GARCH model estimates the dependent variable, in this case price, using two simultaneous

equations, one that estimates today’s price using all the previous time periods’ price, and another

that estimates the conditional variance using all the previous time periods’ conditional variance

plus all the previous time periods’ error terms squared.

The GARCH model is used to estimate the impact of intermittent renewables in Germany on French

and German prices. The econometric analysis shows that both wind and solar generation in

Germany have a negative effect on French prices. Increasing solar and wind generation by 1 GW

58 Phan, Sebastien and Roques, Fabien (2015) “Is the Depressive Effect of Renewables on Power Prices

Contagious? A Cross Border Econometric Analysis”

59 Bollerslev, Tim (1986). “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of

Econometrics 31 p. 307-327.

60 Knittel, C. R., & Roberts, M. R. (2005). An empirical examination of restructured electricity prices.

Energy Economics, 27(5), 791-817.



decreases the average French price by 0.67€ and 0.45€, respectively. Wind and solar generation in

Germany also decreases prices in Germany and interconnections contribute to the convergence of

French and German prices.

In terms of volatility, on average, renewables increased price volatility in both countries. Cross-

border exchanges, through more interconnections, decrease price volatility in both countries,

although the reduction of volatility is more pronounced in France than Germany.



Appendix B - Description of capacity market 8.

model

The capacity market model is integrated with the energy market model over the entire

planning horizon.

PERFECT COMPETITION CAPACITY MODEL

The model assumes perfect competition and perfect information between market participants. It

minimises the system cost while ensuring the reliability criteria. Capacity certificate trades are

modelled through an annual trade which computes the annual capacity price as the intersection of

the supply curve and the demand curve.

INTER-TEMPORAL CAPACITY MODEL

The model simulates the recurrent annual capacity certificate trades over several years:

Decision to retire, invest or mothball units depends on the expectation of the future income over

the next years.

These decisions are made based on a multi-period strategy taking into account the inter-temporal

constraints

The model has been set up to run over a number of future periods 2017 to 2040.

INTERACTION WITH POWER MARKET MODEL

Capacity market offers are based on the “missing money” and depend on the revenues expected

in the energy and ancillary services’ markets. Plant energy market revenues are fed to the capacity

market model from the Energy market model developed in Plexos®.



Figure 8-1: Interaction between the capacity and energy markets


CAPACITY MARKET BIDS

A competitive bid in a capacity market should reflect the plant’s “missing money” – the part of

the avoidable fixed cost not covered by the expected energy and A/S revenues.

A capacity bid of existing and new units is based on the avoidable cost of being capacity resources

that are not covered by the expected net revenues in the energy and ancillary services markets.

Existing units

The avoidable cost of being a capacity resource is represented by:

Fixed annual operating and maintenance expenses; and

Debt depreciation (equity cost is assumed to be sunk cost).



Figure 8-2: Existing units


New units

The avoidable cost of being a capacity resource is represented by:

Fixed annual operating and maintenance expenses;

Investment costs (annualised); and

Financing costs.

Figure 8-3: New units




Remaining debt may represent a significant part of the avoidable cost of the existing plants.

Investments in power plants are financed through equity and debt. For the existing plant, the equity

part of the investment often represents a sunk cost. Remaining debt is often recoverable in case a

plant is sold and thus represents avoidable cost of being a capacity resource.

Therefore, remaining debt can represent a part of the capacity bid of existing plants. Its share in the

bid depends on a number of factors:

Debt-to-equity ratio; and

Debt depreciation schedule and the amount of the remaining debt in a given year. The weight of

the debt is higher in newer plants.



Appendix C - Description of key scenario 9.

modelling assumptions

MACROECONOMIC CONTEXT

To construct the forecast scenarios of evolution of electricity demand, the main determinants

analysed are GDP, energy efficiency, demography and the relative price of electricity over other

energies. The projection data are sourced from BP 2015.

Economic growth

The economic growth remains an important determinant of electricity demand, especially of

industrial and services demand. Thus, changes in the level of electricity consumption largely reflect

cyclical business developments.

To a lesser extent, the macroeconomic context can be influent on purchases of household

appliances or insulation work, even induce behavioural effects on consumption. The situation being

the bearer of uncertainties, it is necessary to retain a range of possible evolution of GDP sufficiently

broad and based on recent analysis, to better fit the effects of the current economic context.

The European economic activity is expected to benefit from the end of few barriers in the short-

term, with a fall in the oil market, another monetary easing, low interest rates and an important

depreciation of the euro since the summer 2014. The consumption recovery in Germany, due to

rising incomes, should also be a strong trend for a return of GDP growth in Europe.

France has benefited relatively less from this context than countries like Germany, Spain or the UK.

However, the recovery in temporary employment in the last quarter 2014 is a strong indicator of

better growth levels than in previous years (2012, 2013 and 2014). Growth assumptions retained for

French GDP in 2015 and 2016 are based on a recent forecast panel from recognised external

sources. In a prudential approach, given the variability of the previous forecasts, the low trajectory

was slightly undervalued. Beyond two years, the assumptions are based on a recent report from

INSEE, which traces three French growth scenarios for the period 2015-2025, with average trend

levels ranging from 1.2 percent to 1.9 percent and a median value of 1.5 percent.



Demographic change

Demographic change is a decisive factor of residential and tertiary consumptions and economic

growth. To define the possible evolutions of population, the assumptions are based on the latest

INSEE projections, with a resetting of the starting point in 2014. These assumptions involve in all

cases a growth of the population between 2014 and 2020 in France.

The number of households has a direct effect on the number of main residences, so on the

residential consumption. The number of persons per household tends to decrease in 2020, making

growth in the number of households more dynamic than the one of population. The power

consumption of tertiary sector depends on the evolution of the workforce. The assumptions are

based on the latest projections available from INSEE.

Energy efficiency

Improving the energy efficiency of equipment and buildings should be continued as a result of

energy policies and dissemination of innovations. In order to achieve the EU targets to reduce by 20

percent in 2020 energy consumptions and emissions of greenhouse gases, it is provided that the

European directives on eco-design and energy labelling are generalised to all energy-related

products sold in the European market.

According to RTE estimations, the energy efficiency on all the sectors and practices could help to

save between 5 percent and 7 percent of 2014 consumption in 2020. The main driver of this energy

efficiency is that of standard norms and regulations imposed to new buildings and equipment.

DEMAND

In previous years, power demand in Europe was affected by the difficult economic context: it

decreased by almost 1 percent per year on average since 2007. This decrease is a continuation of a

slowdown of the growth in demand for many years, induced by an economic growth less intense

and structurally less energy-intense than in previous decades, as well as an improvement of energy

efficiency.

Overall, the projections of total electricity demand show growth rates very moderate until 2020, in

the continuity of the historical inflection observed (average annual growth rate of about 1 percent in

the 2000s).

This power consumption is temperature sensitive. In winter, because of the electrical heating,

consumption is greater when temperatures are rigorous. In summer, the consumption may increase

with warm temperatures, mainly through the use of air conditioning.



The power consumption levels are very different in different countries, but the temperature

sensitivity phenomenon is still visible: for cold temperatures, consumption increases as the

temperature decreases. France is by far the country where this phenomenon is most observed. In

first approximation, sensitivity to temperature in France is 2.5 times higher than that of Great Britain,

4.5 times higher than that of Germany, and 5 times higher than that of Italy and Spain.

In the southern countries (Italy and Spain), the use of air conditioning during periods of high

temperatures is also visible.

Figure 9-1: Electricity consumption against temperature in European countries

Source: BP, 2015

SUPPLY

FTI-CL Energy assumptions

FTI-CL Energy’s assumptions are based on ENTSO-E previsions. For the medium term, ENTSO-E

scenario B (“Best Estimate”) is used. For the long term (2030), the reference is an average between

ENTSO-E v1 and ENTSO-E v3.



Renewables

Hydropower generation, which is the oldest renewable source of electricity, retains an essential part

in the energy mix of several West European countries. France (25 GW), Italy (22 GW) and Spain (19

GW) have the most important plants.

Hydroelectric capacities are not growing today, except in Switzerland and Austria. These two

countries are those in which the consumption part from hydropower is the most important (more

than 50 percent percent).

Since the 2000s, wind energy and solar have developed strongly due to environmental policies in

most European countries. The climate and energy package (2008) and the European directive

associated set renewable growth targets. At the European level, these policies have led to a massive

development of the different sectors. The observed dynamics vary depending on the countries.

In 2015, Germany has the largest capacity installed in Western Europe for wind (39 GW) and solar

(38 GW). The UK has experienced the largest European increase of solar, with 2.2 GW in 2014 and

also has the largest offshore plant. These two countries still have significant ambitions in the

medium term.

Southern Europe also benefited from the strong potential development of solar and wind sectors. In

Spain, the energy produced by wind represents 22 percent of electricity mix. In Italy, the energy

produced by the photovoltaic sector represents 10 percent of the mix. However, after several years

of strong development, these sectors are now growing more slowly.

Belgium retains a moderate dynamic of wind and solar and there are several offshore wind projects.

The forward assumptions include the achievement of the majority of these projects by 2020.



Figure 9-2: Installed capacity of hydropower by 2015


Figure 9-3: Installed capacity of wind power by 2015


0

10

20

30

40

50

FR GB DE ES IT

Insta

lle

d c

ap

acit

y b

y 2

01

5 (

GW

)

HYDRO

0

10

20

30

40

50

FR GB DE ES IT

Insta

lle

d c

ap

acit

y b

y 2

01

5 (

GW

)

WIND ONSHORE



Figure 9-4: Installed capacity of solar power by 2015


Nuclear

There is a great diversity of nuclear situations in Europe. While some countries such as France have

opted for an electricity generation mainly based on nuclear, other countries such as Italy, Portugal,

or Austria have no reactor in service.

With 63.1 GW of installed capacity, France has more than 50 percent of the nuclear power in all

ENTSO-E countries. The other two main countries are Germany (12.1 GW) and the UK (9.4 GW). The

nuclear plant in Belgium is more modest (6 GW) but electricity accounted for over 40 percent of the

mix in 2014.

Some countries such as Germany or Belgium have decided to implement the decommissioning of

nuclear power, in 2022 for Germany and in 2025 in Belgium. Spain and the Netherlands have not

announced a phase-out of nuclear power but no new plant project have been drawn. Switzerland

has decided not to renew nuclear plants beyond 50 years of operation; two plants should be closed

by 2020.

Conversely, the UK included nuclear in its future energy mix. Downgrades of two plants are planned

in 2015 and then four in 2019, while plans for new plants have been announced. The most advanced

project is that of two EPR provided for 2025.

0

10

20

30

40

50

FR GB DE ES IT

Insta

lle

d c

ap

acit

y b

y 2

01

5 (

GW

)

SOLAR



Figure 9-5: Installed capacity of nuclear power by 2015


Combined Cycle Gas Turbines (CCGT)

In early 2000s, low gas price assessment allowed imagining the strong economic competiveness of

CCGT. The first plants were installed in Europe in a context of opening the market to competition

and growth of electricity consumption. Drowned by the positive assessment, the growth of the

sector was very fast in many countries. Spain (25 GW installed in 2014), UK (28 GW) and Italy (37

GW) saw the connection of a large number of units in few years. The development has been more

constrained in France (5.7 GW). It is in Italy that the share of energy by CCGT was the largest in 2014

(35 percent).

After these years of growth, CCGT meets economic difficulties today because of competitiveness of

coal, development of renewables, and lifeless demand. The utilisation rates of CCGT are now low,

well below those of the early 2000s.

In Spain and Italy, the consumption’s decline and the significant development of renewables

combined with the massive construction of CCGT led to situations of high overcapacity.

In Germany, despite having less weight in the energy mix, the sector is facing the same difficulties.

The closing of recent plants due to economic reasons (1.5 GW), despite being considered part of the

units with the best energy efficiency in Europe, was announced. Nevertheless, some plants are

actually built so the capacity reduction is limited.

0

10

20

30

40

50

60

70

FR GB DE ES IT

Insta

lle

d c

ap

acit

y b

y 2

01

5 (

GW

)

NUCLEAR



In Belgium, CCGT plants are threatened with closure or mothball. The establishment of a strategic

reserve by the Belgian government in 2014 could maintain some of these plants. But after 2017, any

certainty about maintaining strategic reserve exists.

Among the countries studied, it is in the UK that the prospects seem the most optimistic. Some

CCGTs are now under construction. The probable closure of coal plants in the coming years leads to

using assumptions of CCGT growth in the medium-term horizon.

Figure 9-6: Installed capacity of CCGT by 2015


Coal

In Western Europe, coal plants are mainly installed in Germany (46 GW) and the UK (18 GW). It is

also in these two countries that electricity produced by coal represents the most important share of

the energy-mix, respectively 46 percent and 28 percent. Coal capacities are less important in Spain

(10 GW) and Italy (7 GW).

In the medium term, a large number of coal units should be decommissioned due to the age of the

plants and environmental constraints decided by the European Directive.

The UK has a lot of old units and should see the installed capacity declining by 30 percent in the

next five years. In Italy, social and environmental constraints impact the most polluting plants. Some

units have been adapted, while others are threatened by legal procedures. Furthermore, producers

have announced plans to reduce the number of units, because of overcapacity. In Belgium, only one

0

10

20

30

40

50

FR GB DE ES IT

Insta

lle

d c

ap

acit

y b

y 2

01

5 (

GW

)

CCGT



coal unit is currently open. Her next closure has been announced and no new project has been

ordered as of today.

In Germany and the Netherlands, the evolution of the sector differs from that of other countries.

Facilities will be decommissioned but new groups will be built to partially offset the closures

announced.

Figure 9-7: Installed capacity of coal by 2015


0

10

20

30

40

50

FR GB DE ES IT

Insta

lle

d c

ap

acit

y b

y 2

01

5 (

GW

)

COAL



Bibliography

Arrêté du 22 janvier 2015 définissant les règles du mécanisme de capacité. Available from


Arrow, K. J. (1971). Essays in the theory of risk-bearing. North-Holland, Amsterdam-London.

Boiteux, M (1949). La Tarification des demandes en pointe : application de la théorie de la vente au

coût marginal, revue Générale de l’Electricité 58 (August): 321-40.

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity, Journal of

Econometrics 31 p. 307-327.

Cramton, P. and Ockenfels, A. (2011). Economics and design of capacity markets for the power

sector. Available from ftp://www.cramton.umd.edu/papers2010-2014/cramton-ockenfels-

economics-and-design-of-capacity-markets.pdf

Commission de Régulation de l’Énergie (2014). Le fonctionnement des marchés de gros de

l’électricité, du CO2 et du gaz naturel. Available from http://www.cre.fr/marches/marche-de-

gros/rapports-de-surveillance

Commission de Régulation de l’Énergie (2009). Délibération de la CRE sur le pic de prix de

l’électricité du 19 octobre 2009. Available from

http://www.cre.fr/documents/deliberations/communication/pic-de-prix-de-l-electricite-du-19-

octobre-2009

Commission de Régulation de l’Electricité et du Gaz (2011). The price spike on Belpex DAM for 28

march 2011. Available from http://www.creg.info/pdf/Etudes/F1099EN.pdf

Décret n° 2012-1405 du 14 décembre 2012 relatif à la contribution des fournisseurs à la sécurité

d'approvisionnement en électricité et portant création d'un mécanisme d'obligation de capacité

dans le secteur de l'électricité. Available from

http://www.legifrance.gouv.fr/eli/decret/2012/12/14/DEVR1206335D/jo/texte

European Commission press release, 20 April 2015. Available from http://europa.eu/rapid/press-

release_MEMO-15-4892_en.html








http://www.creg.info/pdf/Etudes/F1099EN.pdf

http://www.legifrance.gouv.fr/eli/decret/2012/12/14/DEVR1206335D/jo/texte

http://europa.eu/rapid/press-release_MEMO-15-4892_en.html

http://europa.eu/rapid/press-release_MEMO-15-4892_en.html



European Commission press release, 13 November 2015. Available from

http://europa.eu/rapid/press-release_IP-15-6077_en.html

Finon, D. et Pignon, V. (2008). Electricity and Long−Term Capacity Adequacy; The Quest for

Regulatory Mechanism Compatible with Electricity Market, Utilities Policy.

Frontier Economics (2015). Impact Assessment of CMs – report for the Federal Ministry for Economic

Affairs and Energy. Executive summary available at http://www.frontier-

economics.com/documents/2014/09/security-of-supply-in-the-electricity-sector-impact-

assessment-of-potential-capacity-reliability-mechanisms-for-germany.pdf

German Federal Ministry for Economic Affairs and Energy (BMWi) (2014). Act on the Development of

Renewable Energy Sources (RES Act 2014), Unofficial translation. Available from

http://www.bmwi.de/English/Redaktion/Pdf/renewable-energy-sources-act-eeg-

2014,property=pdf,bereich=bmwi2012,sprache=en,rwb=true.pdf

German Federal Ministry for Economic Affairs and Energy (2015). An electricity market for the

Energy transition, Summary. Available from https://www.bmwi-

energiewende.de/EWD/Redaktion/EN/Newsletter/2015/07/Meldung/white-paper.html

http://www.bmwi.de/English/Redaktion/Pdf/weissbuch-

englisch,property=pdf,bereich=bmwi2012,sprache=en,rwb=true.pdf

Grave, K., Paulus, M., & Lindenberger, D. (2012). A method for estimating security of electricity

supply from intermittent sources: scenarios for Germany until 2030. Energy Policy, 46, 193-202.

Guidelines on State Aid for environmental protection and energy 2014-2020, 28 June 2014.

Available from http://eur-lex.europa.eu/legal-

content/EN/TXT/PDF/?uri=CELEX:52014XC0628(01)&from=EN

Hary N., Rious V., Saguan M., April 2016. The electricity generation adequacy problem: Assessing

dynamic effects of capacity remuneration mechanisms. Energy Policy 91.

HM Revenue and Customs (2014). Carbon price floor: reform. Available from


http://europa.eu/rapid/press-release_IP-15-6077_en.html

http://www.frontier-economics.com/documents/2014/09/security-of-supply-in-the-electricity-sector-impact-assessment-of-potential-capacity-reliability-mechanisms-for-germany.pdf







http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52014XC0628(01)&from=EN

http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52014XC0628(01)&from=EN




Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American economic review,

92(5), 1644-1655.

Joskow, P.L. and R. Schmalensee (1983). Markets for Power: An Analysis of Electric Utility

Deregulation, Cambridge. MIT Press.

Joskow, P.L. and Tirole, J. (2007). Reliability and Competitive Electricity Markets, Rand Journal of

Economics, 38(1), 68-84.

Knittel, C. R., Roberts, M. R. (2005). An empirical examination of restructured electricity prices.

Energy Economics, 27(5), 791-817

Loi n° 2010-1488 du 7 décembre 2010 portant nouvelle organisation du marché de l'électricité.

Available from

http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000023174854&categorieLien=id

Loi n° 2015-992 du 17 août 2015 relative à la transition énergétique pour la croissance verte.

Available from

http://www.legifrance.gouv.fr/affichLoiPubliee.do?idDocument=JORFDOLE000029310724&type=ge

neral&legislature=14

The Nuclear Energy Act (in German). Available from

http://dip21.bundestag.de/dip21/btd/17/060/1706070.pdf (accessed on 16/12/2015)

Petitet M. (2016). Effects of risk aversion on investment decisions in electricity generation: What

consequences for market design?, conference paper for the 13th International Conference on the

European Energy Market (EEM).

Phan, S. and Roques, F. (2015). Is the depressive effect of renewables on power prices contagious? A

cross border econometric analysis. CEEM, Fondation Paris-Dauphine.

Platts (2015). http://www.platts.com/

Prognos (2011). Energieszenarien 2011. Available from

http://www.prognos.com/fileadmin/pdf/publikationsdatenbank/11_08_12_Energieszenarien_2011.pd

f

http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000023174854&categorieLien=id

http://www.legifrance.gouv.fr/affichLoiPubliee.do?idDocument=JORFDOLE000029310724&type=general&legislature=14

http://www.legifrance.gouv.fr/affichLoiPubliee.do?idDocument=JORFDOLE000029310724&type=general&legislature=14


http://www.platts.com/





Roques, F. (2008), Market design for generation adequacy: Healing causes rather than symptoms,

Utilities Policy, Elsevier, vol. 16(3), pages 171-183.

RTE (2014), French Capacity Market, Available from http://www.rte-

france.com/sites/default/files/2014_04_09_french_capacity_market.pdf

RTE resource adequacy forecast reports from 2009 to 2015, available from http://www.rte-

france.com/fr/article/bilan-previsionnel

Sandbag (2015).Carbon price instruments for the Power Sector.

SEDC (2015), Mapping Demand Response in Europe Today, Available from

http://www.smartenergydemand.eu/wp-content/uploads/2015/10/Mapping-Demand-Response-in-

Europe-Today-2015.pdf

Sinden, G. (2005). Characteristics of the UK wind resource: Long-term patterns and relationship to

electricity demand, Energy Policy

The UK Department of Energy and Climate Change (2011). Planning our electric future: a White

Paper for secure, affordable and low‑carbon electricity.

Union Française de l'Électricité (UFE) and German Association of Energy and Water Industries

(BDEW), (2014). Energy transition and capacity mechanisms: A contribution to the European debate

with a view to 2030.

UFE (2015). France-Germany Study – Energy transition and CMs, Report to UFE and BDEW. Available

from http://ufe-electricite.fr/IMG/pdf/france-germany_study_report-2.pdf





http://www.smartenergydemand.eu/wp-content/uploads/2015/10/Mapping-Demand-Response-in-Europe-Today-2015.pdf

http://www.smartenergydemand.eu/wp-content/uploads/2015/10/Mapping-Demand-Response-in-Europe-Today-2015.pdf

http://ufe-electricite.fr/IMG/pdf/france-germany_study_report-2.pdf


Published by FTI Consulting LLP

Copyright © FTI Consulting, Inc., 2016

CONTACT

Fabien Roques

Compass Lexecon

Senior Vice President

froques@

compasslexecon.com

Charles Verhaeghe

Compass Lexecon

Vice President

cverhaeghe@

compasslexecon.com

Yves Le Thieis

Compass Lexecon

Economist

ylethieis@

compasslexecon.com

Assessment of the impact of the French capacity …/media/Files/us-files/...4.4 Comparison of an EOM...

Documents

Transcript of Assessment of the impact of the French capacity …/media/Files/us-files/...4.4 Comparison of an EOM...