role of electric flexibility in the future french grid ...1366231/fulltext01.pdf · master of...

Master of Science Thesis

KTH School of Industrial Engineering and Management

Energy Technology EGI-2019-678

TRITA-ITM-EX 2019:678

SE-100 44 STOCKHOLM

Role of electric flexibility in the future French grid with high renewable

integration

LILA HUET

2 Role of electric flexibility in a future French grid with high renewable integration Master Thesis – Lila HUET

Lila HUET | KTH – ENSE3

Master of Science Thesis EGI 2019-678

Role of electric flexibility in the future French grid with high

renewable integration

Lila HUET

TRITA-ITM-EX 2019:678

Approved

2019-10-28

Examiner

Hatef Madani

Supervisor

Hatef Madani

Commissioner

Contact person

Lila Huet



ABSTRACT ENGLISH – ENGELSKA – ANGLAIS

The operation of electric grids depends on the balance between the electric

generation and the demand. In France, the Transmission System Operator, RTE,

is responsible for the stability and the security of the grid. Today, the electric

generation follows the variations of the demand. However, environmental

concerns prompt to develop new strategies and policies for Energy Transition.

The development of Smart Grids, the uncertain future of nuclear generation, the

massive integration of renewable sources are the focus of those. Furthermore,

renewable energies generation is intermittent and can not be controlled. The

current strategy for the balance between generation and demand is challenged.

The electric grid has to be readjusted by adding more electric flexibility to ensure

its stability.

The electric flexibility is usually associated to storage technologies as

batteries or pumping stations. A state of art review is used to define this notion

and to evaluate the technological and economic maturity of different electric

flexibility vectors.

The following report is based on a selection of prospective scenarios,

development plans already launched in France, proposing a significant share of

renewable energies in a future energy mix and current French energy data. Two

studies were carried out : one at a regional level, for Bretagne and one at national

level for France.

An evaluation at 2050 is carried out to determine the load factors of

intermittent energies, consumption and residual demand in Bretagne. On the basis

of these prospective estimates, a need for electric flexibility can be determined

for the Bretagne region. This first study highlights an issue related to future needs

for electric flexibility. However, since the balance between production and

consumption is achieved at a national level, a second study on France is necessary.

The French need for electric flexibility is then estimated through a linear

optimization that evaluates the energy production required to achieve a

generation/consumption balance taking into account energy sources merit order.



KEYWORDS : electric flexibility, storage technologies, state of art review,

renewable energies, balance generation/demand, France, Bretagne, linear

optimization, merit order, nuclear, prospective scenarios



SAMMANFATTNING SWEDISH – SVENSKA – SUEDOIS

Det franska elnätets funktion beror på balansen mellan elproduktionen och

efterfrågan. Transmissionssystemoperatören, RTE, ansvarar för nätets stabilitet

och säkerhet. Idag följer den elektriska generationen variationerna i efterfrågan.

Miljömässiga frågor är emellertid snabba för att utveckla nya strategier och

strategier för energiövergång. Utvecklingen av Smart Grids, den osäkra framtiden

för kärnkraftsproduktion, den massiva integrationen av förnybara källor är deras

fokus. Vidare är generering av förnybara energikällor intermittent och kan inte

kontrolleras. Den nuvarande strategin för balans mellan produktion och

efterfrågan utmanas. Elnätet måste justeras genom att lägga till mer elektrisk

flexibilitet för att säkerställa stabiliteten.

Den elektriska flexibiliteten är vanligtvis förknippad med lagringsteknik

som batterier eller pumpstationer. En allmänt erkända tekniska används för att

definiera denna uppfattning och att utvärdera den tekniska och ekonomiska

mognaden hos olika elektriska flexibilitetsvektorer.

Följande undersökningar grundar sig på ett urval av framtida scenarier,

utvecklingsplaner som redan lanserats i Frankrike, och föreslår en betydande

andel förnybara energikällor i en framtida energimix och nuvarande franska

energidata. Två studier utfördes på olika perimetrar: på Bretagne-regionen och i

Frankrike.

En utvärdering vid 2050 utförs för att bestämma belastningsfaktorerna för

intermittent energi, förbrukning och återstående efterfrågan i Bretagne. På

grundval av dessa framtida uppskattningar kan ett behov av elektrisk flexibilitet

bestämmas för Bretagne-regionen. Denna första studie lyfter fram ett problem

som rör framtida behov av elektrisk flexibilitet. Men eftersom balansen mellan

produktion och konsumtion uppnås på nationell nivå krävs en andra studie om

Frankrike.

Det franska behovet av elektrisk flexibilitet uppskattas sedan genom en linjär

optimering som utvärderar den energiproduktion som krävs för att uppnå en

generation / konsumtionsbalans med hänsyn tagen till energikällans

meriteringsordning.



NYCKELORD : elektrisk flexibilitet, lagringsteknik, allmänt erkända tekniska,

förnybar energi, balansgenerering/efterfrågan, Frankrike, Bretagne, linjär

optimering, meriteringsordning, kärnkraft, framtida scenarier

RESUME FRENCH – FRANSKA – FRANCAIS

Le fonctionnement du réseau électrique français repose sur l’équilibre

entre la production et la consommation d’électricité. Le gestionnaire du réseau de

transport, RTE, est responsable de la stabilité et de la sécurité du réseau.

Aujourd’hui, la production électrique s’adapte aux variations de la consommation.

Cependant, des préoccupations environnementales incitent à la mise en place de

nouvelles stratégies et politiques pour la transition énergétique. Le

développement d’un réseau intelligent, l’avenir incertain du nucléaire et

l’intégration massive d’énergies renouvelables sont au centre de celles-ci. De plus,

la production électrique des énergies renouvelables s’avère intermittente et

fatale. La stratégie actuelle du maintien de l’équilibre production/consommation

est remise en question. Le système électrique doit être repensé en y intégrant plus

de flexibilité électrique pour en garantir la stabilité.

La flexibilité électrique est usuellement associée aux technologies de

stockage comme les batteries électrochimiques et les STEP hydrauliques. Un état

de l’art permet de définir précisément cette notion et d’évaluer la maturité

technologique et économique en France de ces différents vecteurs de flexibilité

électrique.

L’objet des recherches suivantes est basé sur une sélection de scénarios

prospectifs, de plans de développement d’ores et déjà lancés en France, proposant

une part importante d’énergies renouvelables dans un futur mix énergétique et

des données énergétiques actuelles françaises. Deux études ont été menées sur

différents périmètres : sur la région Bretagne et sur la France entière.

Une évaluation à 2050 est effectuée pour déterminer facteurs de charge des

énergies intermittentes, consommation et demande résiduelle en Bretagne. A

partir de ces estimations prospectives, un besoin en flexibilité peut être déterminé

sur le périmètre de la Bretagne. Cette première étude permet de mettre en

exergue une problématique liée aux futurs besoins de flexibilité électrique.

Cependant, l’équilibre entre production et consommation étant réalisé à un niveau

national, une seconde étude sur le périmètre français est nécessaire.

Le besoin français en flexibilité est alors estimé par le biais d’une optimisation

linéaire qui évalue la production énergétique nécessaire pour obtenir un équilibre

production/consommation en tenant compte de la préséance économique.



MOTS CLES : flexibilité électrique, technologies de stockage, état de l’art, équilibre

production/consommation, demande résiduelle, énergies renouvelables, scénario

prospectif, optimisation linéaire, nucléaire, préséance économique, France,

Bretagne



ACKNOWLEDGMENTS

I would like to thank all the people who have participated to this enriching

and interesting experience at ENGIE NextFlex over the past six months.

First of all, I would like to thank my two internship supervisors from Ense3 and

KTH, Mr. Hatef MADANI and Mr. Vincent DEBUSSCHERE, for their valuable

advices and the help they have been able to provide me during various follow-ups.

I would also like to thank Mr. Charles CAUCHE, my supervisor at ENGIE NextFlex,

for allowing me to carry out this experience, for giving me his confidence during

many tasks, for the time he has given me to answer all my questions, especially

during intense moments at work and for teaching me a part of his skills in the

creation of detailed and colourful Excel sheets.

I would like to express my gratitude to the following people for their warm

welcome, their time and their advice, which allowed me to draw my professional

path more precisely:

Mr. Youssef CHRAIBI, Business Developer at ENGIE NextFlex, for his precise and

precious advices and for the Swedish touches that have brought me back to the

land of kanelbullar,

Mr Christophe HUGUET, Managing Director of ENGIE NextFlex, for the trust he

has placed in me and the knowledge he has been able to teach me on various

subjects related to NextFlex,

Mrs Latifa IDOUCHE, Market & TSO Product Manager of ENGIE NextFlex, for her

daily good mood and her professional and human advice,

Mr. Yoann MAUDET and Mr. Julien MICHEL, Business Developers at ENGIE

NextFlex, who shared their previous and current professional experiences over a

cup of coffee and who were on my side in a new environment as electric flexibility

market,

Mr. Cyrille PELLIZZARO and Mr. Guillaume LEHEC of the SmartGrid team for

these discussions full of History, geopolitics, news, philosophy and environmental

debates that have fed my daily life

Mr Davide CONTI, my Italian colleague and his Flexipedia who brought sunshine

to the grey Parisian sky and reminded me of the importance of a Dolce Vita for my

future

Mrs Chantal LY, Mr Arnaud PASCAL, Mr Alexandre MOYRAND and Mr Alexandre

COSQUER, as well as all the staff of Energy Transition Services of ENGIE for their

daily presence, their help and their friendly welcome.



Finally, I would like to thank Mrs Camille PAJOT, PhD Engineer at G2ELab, a

research Laboratory, for her advices, reflections and immeasurable support in the

development of my Master Thesis.



Table of contents ABSTRACT ___________________________________________________________________________________________ 3

SAMMANFATTNING ___________________________________________________________________________________ 5

RESUME_____________________________________________________________________________________________ 6

ACKNOWLEDGMENTS _________________________________________________________________________________ 8

LIST OF FIGURES _____________________________________________________________________________________ 11

LIST OF TABLES ______________________________________________________________________________________ 12

GLOSSARY __________________________________________________________________________________________ 13

I – INTRODUCTION ___________________________________________________________________________________ 15

I.1 - Worldwide energy context _______________________________________________________________________ 15

I.2 - Aims and Questions ____________________________________________________________________________ 16

I.3 - Limitations ___________________________________________________________________________________ 16

I.4 - Structure _____________________________________________________________________________________ 18

II –STATE OF ART REVIEW ______________________________________________________________________________ 19

II.1 – Source of value for electric flexibility in France ______________________________________________________ 19

II.2 - Electric flexibility technologies ___________________________________________________________________ 27

III.3 - Technical and Economic comparison ______________________________________________________________ 29

III – METHODOLOGY __________________________________________________________________________________ 30

III.I - Choice of hypothesis from an Energy scenario ______________________________________________________ 30

III.2 - Merit Order Principle __________________________________________________________________________ 36

III.3 - Linear optimization to model energy systems ______________________________________________________ 37

IV – EVOLUTION OF ELECTRIC FLEXIBILITY DEMAND IN A REGION OF FRANCE, BRETAGNE __________________________ 40

IV.I – Description of Bretagne’s energy situation _________________________________________________________ 40

IV.2 – Study of the impact of an high RES on the electric flexibility demand ___________________________________ 44

IV.3 - Results of the study ___________________________________________________________________________ 47

IV.4 – Storage, Flexibility need and Development potential in Bretagne ______________________________________ 48

V – ANALYSIS OF THE FRENCH ELECTRIC FLEXIBILITY, ACCORDING TO NEGAWATT SCENARIO _______________________ 49

V.1 – First study on French scope : Linear optimization of the energy generation in 2017 ________________________ 49

V.3 – Second study on French scope : Linear optimization of the energy generation in 2050 without flexibility _______ 52

V.4 – Third Study on French scope : Integration of electric flexibility in the optimization _________________________ 54

VI – DISCUSSIONS ____________________________________________________________________________________ 56

VII – CONCLUSION ___________________________________________________________________________________ 57

REFERENCES ________________________________________________________________________________________ 58

APPENDICES ________________________________________________________________________________________ 61



LIST OF FIGURES FIGURE 1 - ELECTRICITY EXCHANGES BETWEEN FRENCH REGIONS IN 2017 [20] ..................................................................... 19 FIGURE 2 - INTERNATIONAL ELECTRICITY EXCHANGES IN 2017 FOR FRANCE [20] .................................................................. 19 FIGURE 3 - ELECTRICITY PRICE IN €/MWH ON THE EPEX SPOT ON 11/06/2018 [42] ............................................................. 22 FIGURE 4 - MECHANISMS OPERATED BY RTE TO ENHANCE ELECTRIC FLEXIBILITY CAPACITIES .............................................. 22 FIGURE 5 - RESERVES FOR THE FRENCH ELECTRICITY SYSTEM IN 2017 ACCORDING TO THEIR TIME HORIZON [29] ............... 23 FIGURE 6 - OPERATION OF THE VARIOUS RESERVES ON THE BALANCE OF THE GRID [29] ...................................................... 23 FIGURE 7 - FUNCTIONING OF THE CAPACITY MECHANISM [15] .............................................................................................. 25 FIGURE 8 - REDUCE THE ELECTRIC CONSUMPTION OF A SITE [43] .......................................................................................... 26 FIGURE 9 - ELECTRIC DEVICES WHICH COULD BE USED FOR DEMAND SIDE MANAGEMENT [43] ........................................... 26 FIGURE 10 – DEMAND RESPONSE FROM A SITE [1] ................................................................................................................. 27 FIGURE 11- NUCLEAR INSTALLED CAPACITY ............................................................................................................................. 32 FIGURE 12- MONOTONE OF FAILURE DELAY IN 2019 AND 2035 - WATT SCENARIO ............................................................... 32 FIGURE 13- INSTALLED STORAGE CAPACITY (GW) ACCORDING TO RES PROPORTION IN THE ENERGY MIX ........................... 34 FIGURE 14 - NEGAWATT APPROACH [5] ................................................................................................................................... 35 FIGURE 15- EVOLUTION OF MAIN RENEWABLE ENERGY SOURCES GENERATION BETWEEN 2015 AND 2050 IN KEEPING WITH

NÉGAWATT SCENARIO .................................................................................................................................................... 36 FIGURE 16 - ESTIMATION OF SRMC [8] .................................................................................................................................... 37 FIGURE 17 - EVOLUTION OF ANNUAL CONSUMPTION AND PEAK CONSUMPTION IN FRANCE AND BRETAGNE BETWEEN 2006

AND 2017, [33] ............................................................................................................................................................... 40 FIGURE 18 - BRETAGNE'S IMPORTED ELECTRICITY BALANCE FOR 2016................................................................................... 41 FIGURE 19 - COMPOSITION OF THE BRETAGNE ENERGY MIX OF ELECTRICITY GENERATION FACILITIES AS AT 31 DECEMBER

2016 (MW) ...................................................................................................................................................................... 41 FIGURE 20 - EVOLUTION OF THE RENEWABLE ENERGY INSTALLED CAPACITIES OF BRETAGNE .............................................. 42 FIGURE 21 - HIGH VOLTAGE LINES IN BRETAGNE [REF] ........................................................................................................... 42 FIGURE 22 - AIMS AND DEVELOPMENT OF RENEWABLE PROJECTS IN BRETAGNE .................................................................. 43 FIGURE 23 - VARIABILITY OF RENEWABLE LOAD FACTORS AND CONSUMPTION ON JANUARY 02, 2017 ............................... 44 FIGURE 24 - NUMBER OF RAMPING HOURS FROM 2013-2014-2015-2016-2017 .................................................................... 45 FIGURE 25 - OCCURRENCES OF RAMPING HOURS IN THE CASE STUDY 79 FOR THE YEAR 2030 ............................................. 47 FIGURE 26 - DEMONSTRATION OF A LINK BETWEEN THE NEED FOR FLEXIBILITY AND THE SHARE OF RENEWABLE ENERGIES

IN THE ENERGY MIX ........................................................................................................................................................ 48 FIGURE 27 - FRENCH ENERGY MIX IN DECEMBER 2017 ........................................................................................................... 49 FIGURE 28 - ANNUAL FRENCH ELECTRICITY GENERATION IN 2017 .......................................................................................... 49 FIGURE 29 - COMPARISON OF THE OBJECTIVES OF THE PPE AND THE FRENCH ENERGY MIX IN 2017 .................................... 50 FIGURE 30 - DISTRIBUTION OF THE NEED FOR FLEXIBILITY OVER THE YEAR 2050 ................................................................... 55 FIGURE 31- STORAGE TECHNOLOGIES RANKED BY ENERGY CAPACITY [16] ............................................................................ 62 FIGURE 32 – GIVEN SERVICES ET ADAPTED TECHNOLOGIES [17] ............................................................................................. 63 FIGURE 33- SIZE AND MATURITY OF STORAGE TECHNOLOGIES [17] ....................................................................................... 64 FIGURE 34 – RANKING OF STORAGE TECHNOLOGIES BY CAPEX IN ENERGY AND IN POWER [18] ........................................... 64 FIGURE 35- COMPARISON OF LCOS (€/MWH DELIVERED) FOR MAIN ELECTRICITY STORAGE TECHNOLOGIES IN 2013 AND IN

2050 [23]......................................................................................................................................................................... 65 FIGURE 36 - EVOLUTION OF ANNUAL GENERATION AND RENEWABLE ENERGY INSTALLED CAPACITIES IN KEEPING WITH

BRETON ELECTRIC PLAN [19] .......................................................................................................................................... 75 FIGURE 37 – EVALUATION OF LOAD FACTORS OF WIND POWER UNDER SOME HYPOTHESIS ................................................ 77 FIGURE 38- ACTIVATIONS ON THE ADJUSTMENT MECHANISM BY RTE IN 2017 [15] .............................................................. 79 FIGURE 39- INSTALLED CAPACITIES IN FRANCE ON 31/12/2017 [20]....................................................................................... 79 FIGURE 40- HYPOTHESIS OF EVOLUTION OF THE FRENCH ENERGY MIX FROM 2017 TO 2050 [4] .......................................... 81 FIGURE 41- REAL FRENCH GENERATION ON JANUARY 01, 2017 .............................................................................................. 82 FIGURE 42- MODELLED GENERATION IN FRANCE ON JANUARY 01, 2017 ............................................................................... 83 FIGURE 43- GENERATION DISTRIBUTION AND ELECTRIC FLEXIBILITY FLOW DURING A WINTER WEEK IN 2050 ..................... 84 FIGURE 44 – GENERATION DISTRIBUTION AND ELECTRIC FLEXIBILITY FLOW DURING A SUMMER WEEK IN 2050 ................. 85



LIST OF TABLES TABLE 1 - COEFFICIENT OF VARIATION OF UNAVOIDABLE ENERGIES................................................................... 46



GLOSSARY

ADEME – Agency for Environment and Energy Management

ANAH – National Habitat Agency

CR - Complementary Reserve. This reserve is a part of the tertiary reserve.

DSM – Demand Side Management. Consumption is reduced or increased on the

order of a third.

DSO – Distribution System Operator. In France, there is a unique DSO : Enedis.

G2ELab – Electrical Engineering Laboratory of Grenoble.

GHG – Greenhouse Gas

INSEE - National Institute of Statistics and Economic Studies

Load Factor – Ratio between generated electric energy on a defined period and

generated energy for an operation at its installed capacity on the same period by

on type of energy sources.

LP – Linear Programming

LRMC – Long Run Marginal Cost

MILP – Mixed-Integer Linear Programming

NextFlex – NextFlex is a former start-up from ENGIE incubator, which

integrated the business unit Global Energy Management of ENGIE and which

operates as aggregator with demand site management of industrial and tertiary

sites.

O&M – Operation and Maintenance

OMEGALPS – Optimization ModEls Generation As Linear Problems for Energy

Systems is a Python Library for the creation of optimization models for energy

management, developed by the G2ELab.

PPE - Plurennial Energy Programming

Ramp Power – Variation of the residual load from one half-hourly step to the

next one

RES – Renewable Energy Sources

Residual Demand – Electric demand minus unavoidable generation (wind

power, solar energy, marine renewable energy).

RR – Rapide Reserve. This reserve is a part of the tertiary reserve.

SRMC – Short Run Marginal Cost



TSO – Transmission System Operator. In France, there is one TSO : RTE.

UC – Unit Commitment



I – INTRODUCTION

I.1 - Worldwide energy context

Global warming is one of the main concerns of the energy engineering

world. A correlation has been found between this phenomenon and the high level

of GreenHouse Gas emissions1 (GHG emissions). For example, the United States

take second place on the podium of the world's major polluters. Responsible for

18% of global GHG emissions, they have an energy mix composed of 80% fossil

fuels2. According to the IEA, the International Energy Agency, fossil fuels

represent 85.5% of world energy generation, with 28% coming from coal, 24%

from natural gas and 33.5% from oil.

Fossil energy generation is based on resources which are not renewable. If we

refer to the current consumption of fossil fuels, their global stock will only extend

over the next 200 years. One solution to delay the depletion of fossil fuels stocks

and to reduce GHG emissions, would be to reduce the share of fossil fuels in the

energy mix. Thus, many policies and subsidies have been set up to replace fossil

fuels with renewable energies. Hydroelectricity, bioenergy, solar energy and wind

energy are energies that will not run out. The capacities of this kind of energy

sources are gradually increasing in many countries, such as Germany.

However, some of these energy sources, such as solar and wind energies, are

weather dependent. Thus, their generation may be difficult to anticipate. This

intermittent feature could create many instabilities of balance between electricity

generation and consumption. Today, this variable generation is compensated by

nuclear and fossil generation. However, in a context of massive integration of

renewable energies, a number of solutions will have to be implemented in order

to maintain the stability of the electricity grid.

Many scientists are sceptical about the potential of massive renewable energy

generation. Indeed, the objective of an integration of renewable energies set by

the European Commission is considered too optimistic3, because it would imply a

massive use of storage capacities. Considering current storage technologies, such

as batteries and hydraulic storage, this objective can only be achieved with a

significant economic investment. These two types of storage technology represent

a small part of the electric flexibility technologies, which can contribute to the

grid balance. For example, some countries are developing Demand Side

Management and Power Injection. Several European markets allow to enhance the

value of the electric flexibility as support for grid stability. In France, there is the

adjustment mechanism and in Sweden, the Nordic Regulation Power Market.

These markets are used throughout Europe and many projects are being developed

in the field of electric flexibility. One of them is the European project,

1 [26] 2 [27] 3 Renewable energy share should be more than 27% of the energy mix of each country of European Union in 2030- [43]



Flexiciency4, which carries out major demonstration projects on energy

efficiency, Demand Side Management and flexibility in France, Italy, Spain and

Sweden.

ENGIE's NextFlex team works on a daily basis as an aggregator of electric

flexibility, through the use of demand side management and power injections. By

carrying out this Master Thesis, I gave an overview of the sustainability of their

activity in a very prospective context.

The aim is to determine the role of electric flexibility in the French electricity grid

in 2050, according to hypotheses based on a recognised prospective scenario

proposing a massive integration of renewable energies.

I.2 - Aims and Questions

The objective of this project is therefore to determine whether electric flexibility

technologies can be considered as a technically viable solution for the French grid,

which would be mainly supplied by renewable energies.

To this end, one of the objectives is to establish a state of art review on electric

flexibility, to evaluate the potential of this flexibility in France but, above all to

truly define electric flexibility and its technologies. The second objective is to

assess the issues associated to a massive integration of renewable energies into

the power grid and to determine whether flexibility technologies can restore some

stability.

During this project, the following question will therefore be studied: What will be

the role of electric flexibility in the French electricity system of tomorrow with

high renewable energies integration ?

Many prospective energy scenarios attempt to assess the potential for fully

renewable energy generation in 2050. Most of them use bioenergy to counter the

intermittency of some renewable energies, without pushing their research

towards the development of electric flexibility solutions. NégaWatt and Ademe

companies carried out this kind of studies, with the hypothesis that nuclear

generation will end from 2050 onwards.

The interest of this project is to carry out the study of this often forgotten solution,

which can ensure the balance of the grid in the future, based on the generation

capacity assumptions of these same prospective scenarios. The main challenge of

this project is to analyse a scenario carried out by professional energy

consultants, to evaluate a solution that has been side-lined or forgotten and to

dimension its potential in France.

I.3 - Limitations

I.3.I - Restricted scope for studies

4 Information on the project on [44]



The study scope is focused on some areas which are considered isolated from the

rest of the grid. The aim is to determine if there is a causal relation between a

massive integration of renewable energies and an electric flexibility need. The

nowadays electric grid is a picture of globalization : electric lines proliferate

between and inside countries. The flexibility that China can offer to its neighbours

could be useful to an European country, if we extrapolate. We consider that the

Energy Transition in Europe will lead to a massive integration of renewable

energies in all Europe. This integration will lead to massive imports or exports of

electricity at some moments of the day. Thus those will have an important impact

on electric grids of the related bordering countries.

I.3.2 - Modelling energy power plants and energy system

Energies, which are considered in the future energy mix and in the actual one, are

associated to the most common type of technology, its characteristics and its

operational costs, in order to limit the model complexity.

In order to determine the units order used to meet electricity demand, a

constant short run marginal cost will be computed for energy sources. This project

considers that investment costs will be supported by the French government

between today and 2050. They are not taken into account in the generation cost,

by application of the merit order theory. The cost-in-use of storage technologies

is considered as null, in order to favour their use before international

export/import or demand response which implies a modification of consumers

habits.

Basic technical requirements to deliver demand response, as voltage

control and operating reserve services, are not taken into account in this study.

Indeed, most of nowadays demonstration projects focus already on those subjects

[1]. Whereas resulting technical potential for flexibility is not usually one of the

key topics of those projects.

Moreover, the balance between demand and generation is realised at a national

scale on each half an hour. The grid is supposed perfect : possible contingency of

electric lines and defaults on the grid are not taken into account.

I.3.3 - Calculations and estimations on the future

Estimations of demand and load factors are based on mathematical

approximations from actual data. This study compares those estimations to the

actual data, without taking into account hypothesis on climate change. French

demand is considered as stabilized from nowadays, unlike the hypothesis from

the used prospective scenario. This scenario considers an important efficiency of

the electric devices and an important reduction of demand, by almost 50% in

France.

Moreover, load factors are smoothed on an daily basis in order to limit the

complexity of program execution.



Generation costs and CO2 emissions costs are supposed unchanged from today.

The aim is to evaluate a way ahead between today and 2050 with actual technical

and economic maturity of different technologies.

I.4 - Structure

This thesis is structured in Chapters and Sections. After this first

introducing chapter, the structure will be organized as following.

Chapter II provides a State of art review on flexibility in France. It describes the

different actors and French mechanisms for electric flexibility value. It gives a

glance on the different electric flexibility vectors and provides a technical and

economic comparison between some vectors.

Chapter III introduces the method used in this thesis. It explains the choice of the

prospective scenario used for the study, the use of merit order and delivers a state

of art review on modelled energy systems by linear optimisation.

Chapter IV includes the development of the flexibility issue on a small perimeter :

the Bretagne region. It presents the energy situation in Bretagne, the hypothesis

used for this part of the study, the study itself and its results.

Chapter V expands the problem to the national level. As Chapter IV, it presents

the energy situation in France, the hypothesis used, the study and its results.

Chapter VI gathers the results of the two precedent analytic Chapters and

discusses on challenges and plans regarding the flexibility issue.

The conclusion of this thesis is synthetized in the final chapter.



II –STATE OF ART REVIEW

II.1 – Source of value for electric flexibility in France

II.I.I - Generation and Consumption of electricity balance, essential to the

safety of the electric grid

Electricity can only be stored in limited quantities. At any time, the quantity of

electricity injected into the grid must be equal to the quantity of electricity

withdrawn from it.

Interconnections of the transmission system ensure this balance at a national

level, in particular through regional solidarity and international interconnection

capacities. Indeed, some regions, such as Bretagne, are major importers of the

electricity they are not able to produce in sufficient proportion.

In case of imbalance, several types of instability5 can lead to a degradation of the

electricity grid equipment or to a national blackout. On September 28th, 2003,

Italy faced a major frequency deviation. Isolated from the European grid, the

country has been led to a general blackout6.

An imbalance between electricity generation and demand generates, in particular,

a grid frequency deviation from its nominal value7. This deviation must remain

infinitely low, in order to allow devices connected to the grid to operate.

5 Cascade of overloads, loss of synchronism, voltage collapse and frequency deviation are phenomena of instability that can lead to a national blackout. 6 [28] 7 This nominal value is fixed at 50 Hz in Europe and 60 Hz in North America.

Figure 2 - International electricity exchanges in 2017 for France [23]

Figure 1 - Electricity exchanges between French regions in 2017 [23]



A physical phenomenon allows an instantaneous first adjustment of the power

grid balance: the inertia of power generation devices. If power generation or

demand changes abruptly, the kinetic energy stored in the rotating machines of

the power plants would be released or captured. This will allow the frequency to

evolve more or less slowly. However, inertia mainly saves time to maintain the

stability of the grid frequency; a system operator is in charge of managing

potential imbalances in real time. The system operator must take into account the

technical constraints of the grid, regional and international interconnection

capacities and the forecast of peak consumption. He has an operational window

of a few hours to activate the multiple capacity reserves.

II.I.2 - Actors in the electric flexibility domain

II.I.2.A - THE TRANSMISSION SYSTEM OPERATOR (TSO)

Since 2000, RTE is the French transmission system operator. Responsible for the

infrastructure and operation of high and very high voltage power lines, RTE is

also responsible for grid maintenance, electricity flow management and technical

and economic conditions related to the access to the grid. The transmission system

operator is thus responsible for nearly 100,000 km of lines in 2018. By publishing

annual reports and providing free access to a database through an API, RTE

attaches great importance to a communication on its various projects, such as grid

planning. As a real operator of the grid, RTE is at the heart of the expectation of

energy companies, which must comply with its choices.

In addition, RTE must ensure the security of the country's electricity grid, in

particular by ensuring a balance, at national level, between the electricity injected

into the grid and the electricity extracted from it. A pooling of consumption and

generation forecast data, based on historical data, meteorological data and the

evaluation of the availability of generation capacities, allows to achieve an

approximate long-term balance between consumption and electricity generation.

This balance is then regularly updated, while maintaining safety margins. This

balance and those adjustments are achieved through different energy markets and

thanks to the different reserves developed by RTE. The TSO asks producers and

aggregators to provide to him the necessary control reserves, and sends signals

that activate tertiary reserves.

II.I.2.B - THE AGGREGATORS

This new category of actors has emerged with the liberalisation of the electricity

market and the significant development of competition in this sector.

Aggregators, as specialists in value potential of electricity generation or

consumption capacities, develop a portfolio of different sized sites. Often, these

sites do not have sufficient characteristics to access markets individually. The

aggregator therefore allows them to leverage their capabilities in different

markets by combining their potential with other sites to meet the necessary

criteria.

The role of this aggregator is multiple:



- As an expert in electric flexibility, he advises his clients on technical

specifications and valuation openings.

- In conjunction with the TSO, he is responsible for the administrative part,

concerning calls for tenders and auctions related to the various contracts.

- He is the operational manager for the management of activations between

RTE and its customers.

The field of flexibility is very competitive, particularly with the increasing need

for flexibility and storage with the integration of renewable energies. Many

energy companies are developing this activity such as Smart Grid Energy,

acquired by Vinci Energy or NextFlex developed in ENGIE's start-up incubator. In

2018, 22 electric flexibility aggregators share this market8.

II.I.2.C - CUSTOMERS AS SOURCES OF ELECTRIC FLEXIBILITY

Sites using electric flexibility aggregators can be divided into three categories:

- The important electro-intensive sites, which are able to deal directly with

the TSO.

- Electro-intensive sites or groupings of sites such as supermarket chains

or hospitals. Energy buyers are responsible for these types of sites. They

issue various calls for tenders to aggregators. These contracts are therefore

under strong competitive pressure.

- Independent sites of varying size. Most of these sites do not know their

potential in electric flexibility or the methods of possible valuation. The

sites are therefore solicited by the various aggregators.

These sites are the sources of value for electric flexibility aggregators; they are

therefore technically the suppliers of these aggregators. The real customer is then

RTE, the grid operator. The remuneration is paid by RTE to the aggregator and

then to the various sites in the portfolio.

II.I.3 - Valuation of electric flexibility capacities

Several sources of value can be interesting for electric flexibility capabilities:

- The electricity market,

- The distribution system operator,

- The transmission system operator.

The electricity market is an energy trade. Capacity over a certain period may be

sold or purchased 3 years before or during the half-hourly period prior to the

order being placed. By adapting its consumption to electricity market prices, it is

possible to earn cost differences related to this change in consumption.

8 A list of those aggregators is available in Appendix.



Today, variations in electricity prices on the French market are relatively small.

As the energy mix is mainly

based on nuclear generation,

price peaks are very rare. Not

very profitable, this source of

value is not interesting for

electric flexibility aggregators.

The French distribution system

operator, ENEDIS9, is also

interested in potential flexibility.

ENEDIS uses electric flexibility

to limit consumption in regions

where the grid is in difficulty and thus, to reduce investments in the distribution

grid.

The most reliable sources of value is the ones proposed by RTE. Filling sites can

enhance their electric flexibility in terms of energy and capacity:

- The site uses the energy that should have been consumed for a demand

response activation or that should have been injected on the grid for a power

injection activation. The remuneration is variable in €/MWh.

- The site undertakes to make its power capacity available for a predefined

period in return for a fixed remuneration in €/MW. The remuneration comes from

the availability of the capacity included in this commitment, and from the energy

sold during the solicitations.

The transmission system operator thus proposes several remuneration programs

depending on the response time and the power of the capacity used.

Figure 4 - Mechanisms operated by RTE to enhance electric flexibility capacities 10

9 ENEDIS is the former DSO, ERDF. 10 [18]

Figure 3 - Electricity Price in €/MWh on the EPEX SPOT on 11/06/2018

[45]



II.I.3.A - SYSTEM SERVICES

The transmission system operator has several reserves to balance the grid in real

time.

The Primary and Secondary

Reserves act in automated and

immediate adjustment

according to frequency

variations. If these two

reserves are not sufficient to

restore the grid balance, a third

reserve is activated manually:

the Tertiary Reserve. The

Tertiary Reserve is divided into

two programs: a Rapid Reserve

which can act after 13 minutes

and a Complementary Reserve

which can act after 30 minutes.

The following diagram

provides a graphical view of

the evolution of the various

reserves activations. When

the system is subjected to a

power variation, the

frequency will vary and the

primary reserve will be

activated. This reserve

stabilizes the frequency in

less than 30 seconds around

a value different from the

nominal value. The

secondary reserve is then activated and restores the frequency to its nominal

value in less than 15 minutes.

If these two reserves are not sufficient, the tertiary reserve is activated to adjust

the balance between generation and consumption more durably.

AUTOMATED RESERVES: THE PRIMARY AND SECONDARY RESERVE

Generation units and withdrawal sites, which can quickly modulate their

active power, participate in this first kind of control. Generating units with a

capacity greater than 40 MW and some electro-intensive sites must also

participate to the primary reserve.

Generating units with a capacity greater than 120 MW must participate to the

secondary reserve. They are called obligated. To participate in these programs,

Figure 5 - Reserves for the French electricity system in 2017 according to their time horizon [32]

Figure 6 - Operation of the various reserves on the balance of the grid [32]



sites and aggregators must meet with an obligated and must offer him to fulfil his

obligation.

All producers interconnected to European synchronous transmission grids

may participate in the primary reserve. It has been sized for the entire Europe by

ENTSOE. It can thus respond to the simultaneous loss of the two largest

generation groups in the European grid11. The French primary reserve is a 600

MW reserve.

When frequency instability occurs, all the groups participating in this reserve

restore the balance between 15 and 30 seconds. If there is a generation deficit,

the generating units automatically increase their generation proportionally as a

percentage of the power contracted with RTE and the withdrawal sites reduce

their consumption. The frequency stabilizes at a value different from the value of

the grid and international exchanges adapt to it.

If a site fails during a solicitation, it can be excluded from the program, but it is

not subject to any penalty.

The groups participating in the secondary reserve will receive a

generation/consumption setpoint defined by RTE following the primary setting.

As a result, the frequency returns to its nominal value and international trades

also recover. This setting takes less than 15 minutes to complete. The French

secondary reserve is estimated between 500 MW and 1180 MW depending on the

period of the year.12

THE TERTIARY RESERVE

Unlike the two previous reserves, the tertiary reserve is manually activated

by RTE dispatchers. It is used mainly if the secondary reserve is exhausted. This

reserve is divided into two programmes that differ in their activation delay.

Sites that can be mobilized in 13 minutes can participate to the rapid reserve. A

test period called technical approval allows sites to qualify for this program. Four

of the five tests must be passed. If the site fails during a solicitation, it is subject

to significant penalties from RTE. The complementary reserve is intended for sites

that can be mobilized in 30 minutes.

To select offers for these two programmes, RTE checks the technical adequacy of

those with its needs and then activates the corresponding offers by economic

priority.

A call for tenders is issued annually to participate in these two programs.13

II.I.3.B - THE ADJUSTMENT MECHANISM

11 The European Primary Reserve is a 3 000 MW reserve. 12 [32] 13 December 2017 tender evaluates the Rapide Reserve price at 24,3 k€/MW/year (in 2016, it was 28,6 k€/MW/year) and the Complementary Reserve price at 16,4k€/MW/year (in 2016 it was 18,2k€/MW/year).



This program is intended for sites that can perform demand side management. It

is also the least remunerative. An annual "Demand Response" call for tenders and

a weekly auction are carried out with RTE. Offers are selected by RTE on the basis

of economic priority. However, this program is also more accessible, with

activation delays up to two hours. Sites can therefore be activated automatically

or manually. A fixed remuneration is paid to the sites for the provision of their

capacity and a variable remuneration is paid in the event of its activation.

If a site fails during a solicitation, severe penalties are applied by RTE.

II.I.3.C - CAPACITY MARKET

The peak consumption has evolved in a singular way in recent years and in

particular faster than the average electricity consumption. In 2010, the NOME law

provides for the establishment of a capacity market to ensure the security of the

grid in the medium term during these peak periods. This mechanism has been

fully operational since January 2017.

Any electricity supplier must prove

to the public authorities that it has

the capacity to erase or inject power

to cover their customers'

consumption during a cold spell.

Generation and demand response

sites can certify their capacity if they

undertake to make this flexibility

available on some days of the year, called

PP2 days.

This certification14 is valid for a given year and for a number of capacity

guarantees15. Suppliers purchase these certificates from producers and demand

response sites to fulfil their obligations.

The PP2 days or Peak Period 2 days are defined by RTE at 7pm the evening for the

next day. These days may or may not be workdays from January to March and

from November to December. There are 10 to 25 PP2 days a year. The sites

participating in the capacity mechanism, provide their flexibility from 7am to 3pm

and from 6pm to 8pm on these days.

To check that suppliers have sufficient capacity guarantees, RTE defines their

obligation based on the actual consumption of suppliers' sites during PP1 days.

14 Minimum threshold for certification is 1 MW. 15 A capacity guarantee represents 0,1 MW available on PP2 days over a year. A Capacity guarantee is valued at 1000€.

Figure 7 - Functioning of the capacity mechanism [18]



These Peak Period 1 days are included in PP2 workdays. There are between 10 and

15 PP1 days a year, which are the days with highest consumption.

II.I.3.D - NEBEF

RTE is setting up a new market: the Notification of Exchanges of Blocks of Electric

Flexibility -NEBEF[4] mechanism. It is a wholesale market for demand response

by withdrawal sites. Demand response operators declare to RTE a quantity of

energy, which corresponds to a demand response block. The threshold of an

exchangeable demand response block on this market is 100 kW over half an hour.

II.I.4 - The principle of demand response

The reduction of electricity consumption at

RTE's request is called demand response. When

there is a generation deficit, demand response

may be requested, particularly during major

cold or heat waves. When a site is activated,

the load curve on the following figure, can be

observed. The site reduces its consumption or

partially or totally self-produces its

consumption using a generator.

In the latter case, the site can stop

withdrawing electricity from the grid, but has

to support costs related to the fuel used during

the demand response period.

However, in the future, the most convincing

source of recovery will no longer come from

generators but from sites able of modifying

their industrial processes or switching off

some devices. The ones, which can be switched

off for demand response, are shown in the

figure below.

The flexibility power of a site can be measured at half an hourly step by

determining the difference between a reference power and the average power

consumed at that half-hour step. These powers are determined thanks to load

curves, given by different grid operators.

Figure 8 - Reduce the electric consumption of a site [2]

Figure 9 - Electric devices which could be used for Demand Side Management [2]



Figure 10 – Demand response from a site [2]

The reference power is defined by the average power consumed over the full half-

hourly step before the activation for demand response, minus a mobilization delay

(usually 15 minutes or one hour).

This mobilization time is defined by the characteristics of RTE’s program for

which demand response is performed, or by the contract between the electric

flexibility aggregator and the customer responsible for the site.

II.2 - Electric flexibility technologies

There are different solutions to achieve a balance between consumption and

generation. The most commonly used are the use of controllable means of

generation and the exchange of electricity through international

interconnections. In 2017, France imported 36.2 TWh and exported 74.2 TWh16 via

neighbouring countries. Storage, power injection and demand response solutions

can also be considered to achieve the balance that will provide security for the

French grid.17

II.2.I - CAES

Energy storage by compressed air is developing strongly. Air is stored in

underground cavities through an electric compressor used during periods of low

electricity costs. A turbine system generates electricity. However, their efficiency

is about 40% with high heat losses and the number of available sites is low for

this type of technology. The development of adiabatic CAES on the surface would

reduce heat loss and eliminate the need for specific sites.

II.2.2 - Hydraulic storage – Pumping station

This storage solution is currently the most widely used in France. With an

installed capacity of 4.5 GW of pumping station and an efficiency of 80%, it is

used for seasonal storage and grid support. A turbine system generates electricity

16 [23] 17 [20] & [21]



and a pumping system stores it. However, this technology is widely used in France

and requires specific sites to be set up. A 1.5 GW pool of this type of pumping

stations is still available in France but will not potentially be exploited. The

installation of marine pumping stations could also prove to be a renewal for the

development of this technology.

II.2.3 - The flywheel and the super capacitor

The flywheel consists of a mass driven by an electric motor. It uses mechanical

rotation to store energy. Once the engine is stopped, the mass will continue to

run. The energy is generated by using a motor as an electricity generator, which

can reduce the rotational speed of the mass.

The super capacitor uses the accumulation of electric charges on an interface

between an ionic solution and an electronic conductor. A super capacitor can store

a large amount of energy. Their reactivity and longevity are important.

However, these technologies have a high self-discharge rate and therefore a low

autonomy time.

II.2.4 - Integration of electric vehicles

Electric vehicles are developing all over the world. These vehicles must be loaded

at various times of the day. They are often used in scenarios for a smart balance

of the grid. Charging your electric vehicle at off-peak consumption hours would

smooth the consumption. It can be assumed that in the future, these vehicles will

also be able to inject electric energy into the grid.

II.2.5 - Electrochemical and redox batteries

There are several kinds of electrochemical and redox batteries in development:

lead-acid, Lithium-Ion, Sodium-Sulphide, Sodium Chloride, Vanadium, Zinc-

Bromine. Their service life depends on the chemical components used. Their

reactivity and flexibility of sizing are their main advantages.

In addition, their efficiency rate is high (80%) and they can discharge for several

hours.

II.2.6 - Hydrogen Batteries

This technology uses electricity to break down water into hydrogen and oxygen.

The hydrogen is then stored in another state. It can be injected into the grid by

synthesizing natural gas, thanks to methanization, by using it in a hydrogen gas

power plant or in a fuel cell. Allowing a discharge in a few minutes, these batteries

have a potential for long-term storage but are currently very expensive. This

technology can be used for seasonal storage.

II.2.7 - Energy generation management

There are three types of energy generation management :

Increasing management is used to manage power plants. The grid operator asks

power plants to produce more energy than planned. This strategy is currently used



by the grid operator to achieve a balance between generation and consumption.

A second strategy is based on the adjustment mechanism and the rapid reserve. It

consists of power injection. Industrial or tertiary sites with generators can inject

power into the electricity grid at the request of a third party.

Finally, a new generation management is emerging with the development of

unavoidable renewable energies. In the event of a potential surplus of unavoidable

generation, the generation of renewable energies is curtailed.

II.2.8 - Demand Side Management

Demand Side Management is now used in France on various markets, such as the

adjustment mechanism. It helps to maintain the grid's balance during peak

consumption periods. The theory of this type of flexibility is presented in the first

part of this report. RTE has announced an available withdrawal capacity of 3 GW

in 201618. The French PPE has set a development of this capacity at 6 GW by 2023.

An ADEME report 19 estimates a technical-economic potential for 2035 in industry

of 4.3 GW and in the tertiary sector of 2 GW for activations of 30 minutes at 8 am.

However, there are still many technical and regulatory barriers to this type of

flexibility.

In addition, demand response from generators will soon no longer be valued on

the adjustment mechanism regarding the energy transition. Nevertheless,

generators allow a large part of the sites to participate to the adjustment

mechanism.

III.3 - Technical and Economic comparison

Today, few technologies are at a stage of maturity that can be commercialized.

Nevertheless, it can be assumed that CAES, Pumping stations, Hydrogen, RedOx

and Electrochemical Batteries technologies will be developed in the future. On one

hand, these technologies give significant energy storage and are sufficiently

mature. On the other hand, they make possible the support to the grid regarding

the different needs.20

Finally, these storage technologies are the most cost-effective but are still more

expensive to use than an increase of generation.

The potential of these technologies is therefore significant, and for many of them,

research is still necessary to reach a reasonable level of maturity for their use. In

addition, many economic and regulatory levers must be activated in order to

enable storage to contribute to the balance of the grid. The need for flexibility is

already present and will continue to increase with the integration of renewable

energies.

18 [41] 19 [3] 20 Data on storage technologies in Appendix



III – METHODOLOGY

III.I - Choice of hypothesis from an Energy scenario

French energy scenarios are numerous regarding the future energy mix. Three

major studies explore already the feasibility of a grid balance with 100%

renewable energy mix : RTE’s report, ADEME’s report and NégaWatt’s report.

Those studies are usually quoted in the French research domain, as references of

100% energy mix scenarios. However, they differ through their hypothesis and

their methodology.

Hypothesis of an existing scenario, carried out by professional consultants, are

used as a base for the study of this report. To evaluate the solution proposed to

maintain the grid balance and stability and to explain the choice made, it is

important to go through the methodology, the hypothesis and the results of those

three studies.

III.I.I – RTE’s report, « Bilan prévisionnel de l’équilibre offre-demande

d’électricité en France – Edition 2017 » [3]

It’s the ninth update21 of prospective scenario developed by RTE, the French TSO. The company used to update this report every year. This report studies 5 scenarios for the evolution of the French energy mix between today and 2035. It aims to verify viability conditions of those scenarios and to identify actions which have to be set if the scenario happens.

III.I.I.A - RTE’S METHODOLOGY

In RTE’s study, the balance between generation and demand is carried out on each

hour of each year between 2018 and 2035 by Monte Carlo simulations, taking into

account the security criteria of reliability of the electric grid, hydropower storage

and optimization of thermal technologies. Studies are realized with a physical

closure : more than 1,000 combinations of hazards test the operation of the grid

on these different scenarios. An economic closure ensures rentability on markets

and investments of actors on the generating and flexibility technologies.

This study takes into account European interconnections, 200 historical

temperature records, 60 thermal power plants and nuclear power plants

availability historical records, 200 wind power generation historical records, 200

solar energy historical records, 60 monthly hydroelectricity generation historical

records.

RTE identifies 15,000 energetic variants, based on multiple trajectories :

- Four trajectories on demand

- Three trajectories on RES integration in France

- Six principles on nuclear decommissioning

- Three trajectories on fuel and CO2 emissions costs

21 A Tenth Update of this study is available on RTE website.



- Thee trajectories for European grid development

- Three trajectories on RES integration in European countries

- Three trajectories on thermal energy in European countries

- Two trajectories on nuclear energy in European countries

Then, RTE studies 100 of them and reports the results from 50 more structuring

variants. Each variant includes 50,000 simulations for each hour of the year in

order to take into account different climate situations and energies availability.

Five scenarios have been built on the main variants :

- Ohm scenario on the solutions that should be set for Energy transition for

2025.

- Ampere scenario studies the reduction of proportion of nuclear energy in

the electricity generation without used new thermal technologies.

- Hertz scenario studies the evolution of new thermal technologies and a

slow integration of RES to replace nuclear energy.

- Volt scenario studies an acceleration of the RES integration in France and

the evolution of nuclear regarding economic opportunities

- Watt scenario agrees on decommissioning of nuclear power plants after 40

years of operation and on replacement of their capacity by RES.

III.I.I.B - WATT SCENARIO AND ITS HYPOTHESIS

The WATT scenario presents the highest RES integration and will be compared to

others scenarios with high RES development afterwards.

It assumes for 2035:

- A demand of 410 TWh

- An installed capacity of RES of 150 GW (Onshore Wind power 52 GW,

Offshore Wind Power 15 GW, Solar Photovoltaic 48 GW, Hydroelectricity 28

GW) with a proportion of 71 % in the energy mix

- An installed capacity of nuclear of 8 GW with a proportion of 11% in the

energy mix

- Thermal energies represents 18% in the energy mix

- CO2 emissions price is evaluated to 108 €/tons and the grid emissions is 32

Mtons of CO2.

- The capacity of import is 22 GW and the capacity of export is 28 GW



- RES development is high in all

Europe (increase by 327 GW)

- 6 GW for Demand response are

identified in France, 2 GW

additional of Pumping stations

to the actual pumping stations,

5.5 million of electric vehicles

and batteries become in 2035

profitable

- A hourly ramp of 6,200 MW/h

of the residual demand

III.I.I.C - RTE’S RESULTS

This scenario is analysed by RTE as a rupture scenario as the RES generation (314

TWh in 2035) cannot replace the nuclear generation of 54 oldest nuclear power

plants (350 TWh). It is not viable without a massive integration of thermal

technologies. Security of the grid is

one of the main challenges of this

scenario : an important opportunity

appears for demand response,

batteries, pumping stations or

electric vehicles, not as an

alternative but as a complement of

thermal technologies. But RTE

identifies an increase of short grid

failures in the WATT scenario. In

2035, among 100 simulations, 42% present at least one hour of failure and 5%

more than eleven hours of failure.

III.I.2 – ADEME’s report, “Un mix électrique 100% renouvelable ?” [3]

Ademe takes part to the execution of public policies in environment, energy and

sustainability domains. In order to support the RES policies, Ademe estimates in

this study necessary conditions as economic ones, technical ones and others, but

also different barriers to a massive development of RES in France.

III.I.2.A - ADEME’S METHODOLOGY

Figure 11- Nuclear installed capacity

Figure 12- Monotone of failure delay in 2019 and 2035 - WATT Scenario [9]



This study is based on an optimisation of the installed capacity and interregional

interconnections and on an optimisation through a year of the balance between

consumption and generation by hour step. France is considered as 21 regions

(administrative regions from 2015) to evaluate load factors energy potentials and

installed capacities. International exchanges capacity and installed capacity of

France neighbours are fixed before optimisation, based on the European scenario

Roadmap 2050, 80% RES [4].

The optimized energy mix is tested on several meteorological scenarios, based on

7 years of historical records. One of them corresponds to the two weeks cold wave

of February 2012. The chosen energy sources are based on hypothesis on annual

costs of installation and maintenance projected to 2050.

Fourteen variants have been studied with different integration of RES (40%,

80%, 95% or 100%). Variants deal with the demand, acceptability of grid

reinforcements, acceptability of wind power and solar energy installations,

technological advances.

A macro-economic study has been carried out to evaluate the impact of this kind

of energy mix on jobs, economic activity, commercial balance or CO2 emissions

[5].

III.I.2.B - HYPOTHESIS FOR THE ADEME’S 100 % RES SCENARIO

The LCOE is the energy cost in €/MWh, which takes into account annualized

investment costs with a 5.25% annualized rate, annual maintenance costs, fuel

costs, connection cost and annual energy generated.

Some energy sources have an installed capacity fixed due to their actual

development as for example hydroelectricity power plants. A few renewable

energy sources as renewable thermal sources as methanization are considered

controllable, others as unavoidable.

Ademe study assumes :

- Demand is 422 TWh with 60 TWh of controllable electric devices,

- 10.7 million of electric vehicles

- Installed capacities of 106.5 GW of Wind power, 63.4 GW of Solar energy,

0.2 of Marine renewable energy, 20.8 GW of Hydroelectricity and 4.4 GW

of Biomass,

- Erased consumption for demand response is postponed, with a theorical

maximum of up-flexibility (increase the demand) of 22 GW and of demand

response of 8 GW. Activation of flexibility is assumed null,

- Three types of storage are identified : 12 GW for short storage with a 6

hours discharge and a 0.81 efficiency, 7 GW of pumping station with a 32

hours discharge and 0.81 efficiency and 17 GW interseason storage with a

0.33 efficiency,



- Nuclear and fuel thermal energies are not a part of the energy mix,

- Grid is optimised by simulation to be adapted to the interconnection needs.

III.I.2.C - ADEME’S RESULTS

The study highlights the

opportunity to develop

storage capacity in a case of

massive RES integration.

Figure 13- Installed storage capacity (GW) according to RES proportion in the energy mix

The RES development implied a maximum international export capacity evaluated

to 23 GW and a maximum import one to 16 GW. Moreover, interregional exchanges

capacities increase by 36% from 2015.

Finally, the total investment cost for this type of energy mix is evaluated at least

at 600 billion €.

III.I.3 - Association NégaWatt’s report, “Scénario NégaWatt 2017-2050 –

Hypothèses et résultats” [6] [7]

The association NégaWatt, created in 2001, regroups energy professionals. This

entity has for goal the development of an energy policy based on energy efficiency,

energy sobriety and the development of renewable energies. According to those

three vectors of development, the association created a first energy scenario in

2003 and then updated it four times with finally the last version of the energy

scenario corresponding to 2017-2050 period.

III.I.3.A - NÉGAWATT’S METHODOLOGY

The association, for this study, evaluated the evolution between 2010 and 2050 of

:

- The energy consumption for buildings and electric devices,

- The transport modalities and energetic characteristics from the transport

domain,

- The industrial and agriculture fields,

- Energy generations.



The scenario is then based on an energy optimisation. The model ensures an

energy balance by year : energy uses are identified for each domain (residential,

tertiary, transport, industrial, agriculture), a finale energy demand and then a

primary energy demand. The demand is then crossed with availability of the

different energy sources. Thus, it represents more precisely evolutions of energy

sobriety on energy uses, efficiency on the entire energy

transformation and development of some energy

sources over others. The simulation is completed by a

electricity power balance for each hour.

The model gives a global overview of GHG emissions

and other environmental impacts. The physical model is

completed by an economic simulation based on the

transformation costs between energy vectors and on the

impact on jobs.

Two scenarios have been studied :

- Trend-based scenario, is a scenario based on the actual trends which can be

observed,

- NégaWatt scenario, is a scenario where the energy mix is deeply transformed.

Energy sobriety, energy efficiency and high development of RES are the main

hypothesis of this scenario.

With more than 1887 inputs, 700 000 data have been generated by 15 NégaWatt

experts to model each hour of a 34 years period. [8]

III.I.3.B - NÉGAWATT’S HYPOTHESIS

This scenario assumes :

- Nuclear energy is not a part of the energy mix after 2035. All nuclear power

plants are decommissioned after 40 years of operation.

- Fuel thermal energies are not a part of the 2050 energy mix.

- An 100% RES 2050 energy mix in France.

- Balance between demand and generation is obtained thanks to actual

capacities of pumping stations and to power-to-gas and cogeneration

development.

- Electric heaters are replaced by heat pumps.

- Fuel thermal power plants are converted into renewable thermal power

plants.

- Major efficiency developments are operated on energy devices, buildings

and means of transport.

Figure 14 - NégaWatt approach [7]



III.I.3.C - NÉGAWATT’S RESULTS

This study highlights different results on the possibility to change our actual

energy mix :

- The demand is divided by two compared to

2015 thanks to energy sobriety and energy

efficiency (272 TWh in 2050),

- 2050 energy mix is 100 % renewable, with

solid biomass as the major energy source in

2050,

- CO2 emissions are evaluated to 21 MTons in

2050,

- The import/export balance in 2050 is 18.9 TWh (60.6 TWh in 2015),

- The implementation of the NégaWatt

scenario should cost between 2015 and 2050

around 1,160 billion of euros, including 860

billion of euros for RES development.

However, this scenario generates cost

savings compared to the trend-based scenario.

III.I.4 - Finale choice of a prospective scenario

Those three studies are already compared in the RTE study. [9]

RTE carried out five different scenarios with a maximum RES proportion of 70%

in 2035, whereas ADEME took the decision to focus on a 100% renewable energy

mix in 2050. Both sized a theorical potential of flexibility based on different

technologies (batteries, pumping station, demand response,…).

The NégaWatt scenario seems realistic thanks to the important developments in

2050 for energy sobriety and energy efficiency. Indeed, meeting the demand in a

100% RES energy mix is possible because of a low demand and of two flexibility

vectors : power to gas and actual pumping stations capacities. This assumption is

not shared by the two others studies. Ademe and RTE assumed that energy

efficiency would only limit the electricity demand increase in the next years and

that demand would remain stable in the future.

Thus, it is interesting to test the NégaWatt scenario, with this assumption that

demand remains stable between 2017 and 2050.

III.2 - Merit Order Principle

Merit order is an optimisation method used to determine the operation schedule

of power plants at each hour step. In order to get an optimal dispatch, it consists

to start power plants, one by one, according to their variable generation cost. This

cost corresponds to the cost of producing one additional MWh. If we assume that

Figure 15- Evolution of main renewable energy sources generation between 2015 and 2050 in keeping with NégaWatt scenario



market is perfectly competitive and has perfect foresight, the market price is

given by the marginal generation cost of the last unit activated.

Power plants, which generate unavoidable energy, are the first ones to be called.

If the energy producer does not use them, the energy will be “lost”. Nuclear is also

known to have a really low marginal cost.

However, this approach is not a simulation of the French energy market and does

not give a foresight of the total cost generated in order to

meet the demand.

There are two types of Marginal Cost for

a generating unit : a Long-Run Marginal

Cost and a Short Run Marginal Cost.

They are defined as following [10]:

- The Long Run Marginal Cost “is

the cost of supplying an additional

unit (the marginal cost) assuming

that all factors of production can

be varied”.

- The Short Run Marginal Cost is

“the cost of supplying an

additional unit assuming that at

least one factor of production

(hereafter in this report assumed

to be capital investment) is fixed”.

As Capacities of the 2050 energy mix will be fixed according to NégaWatt

scenario, SRMC will be used to define the merit order.

This last one can be estimated with the following formula [11]. This equation can

be simplified in the case of this study as following :

𝑆𝑅𝑀𝐶 [€

𝑀𝑊ℎ] = 𝑂&𝑀 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝐶𝑜𝑠𝑡 [

€

𝑀𝑊ℎ] +

𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡[€

𝑀𝑊ℎ𝑡ℎ]

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦+

𝐶𝑂2 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 [𝑡𝑜𝑛𝑠

𝑀𝑊ℎ]∗𝐶𝑂2 𝑃𝑟𝑖𝑐𝑒[

€

𝑡𝑜𝑛𝑠]

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦

We consider perfect the grid and the generating units. Possibility of leaks in

thermal power plants is null. Thus, the fugitive emission factor is not taken into

account.

Moreover, benefits from American certificates are not taken into account as the

scope of study is France.

III.3 - Linear optimization to model energy systems

A study presents a review of several modelling tools used to analyse power

systems [12]. The Energy System Model REMix (Renewable Energy Mix), used in

several studies22, minimizes total energy system costs, as fuel costs, emission

22 [15], [49]

Figure 16 - Estimation of SRMC [11]



certificates and Operation and Maintenance costs. In one of those studies, capacity

dispatch and expansion are optimized, by decision variables. This tool is

developed in the language GAMS with the CPLEX solver, used in KTH courses on

optimization of power system. Another tool using GAMS language is known for

modelling energy system : EUCAD [13]. The European Unit Commitment And

Dispatch model is to used to minimize the European power system total cost of

operation, according to the power system balance and other technical constraints.

The dispatch of power plants is the one of the main economic problems linked to

the power system balance. The economic dispatch determines the optimal

generating units power output, according to operational constraints. To describe

more precisely this problem, a non-linear objective function with a lot of

constraints can be used. A study used this approach of optimization to solve

economic load dispatch on different power systems as 40 generating units in

Taiwan and a 18 thermal power plants of Crete Island system [14]. Thus, it

compares quadratic programming, a non-linear approach which is really complex

to solve due to its important dimension, and linear programming.

As described in the precedent sections, scenarios from RTE, Ademe and

NégaWatt are based on optimization models. Those used mixed integer linear

programming approach, one of the main approach of power plants modelling

which can be found in reports on this subject [15]. This method optimizes

economic dispatch and unit-commitments of several power plants, which includes

binary decision variables and operating constraints, for technological details.

Another approach is simplified Linear Programming (LP) which focuses only on

dispatch of power plants by merit order. Both methods have an optimizing

function. The optimizing function could be used, for example, to minimize the

operating costs of the power plants dispatch. A study23 models power plants, by

MILP approach, in two real power systems (New York, Texas) based on scenario

data from 2010. It analyses impacts of operating constraints in modelling. Other

approaches have also been explored, as Dynamix programming, Lagrange

relaxation, genetic programming, etc [16]. Another study reveals that Linear

programming overestimates power plants flexibility, compared to MILP method,

due to neglected technical constraints [15].

In this study based on flexibility, we will used MILP approach to integrate multiple

technical constraints. However, a number too important of constraints will

increase complexity of the model as its run time. Thus, a number of them will be

neglected, for example grid system constraints as grid is supposed perfect.

A study has been carried out with a MILP approach in order to choose

strategic and operational decisions for Greek Power System [17]. Strategic

23 Estimating Emissions from Electricity Generation Using Electricity Dispatch Models: The Importance of

System Operating Constraints [48]



decisions concern the construction of new plants and the capacity expansion

whereas operational decisions concern flows of electricity and energy resources.

This study takes also into account capacity for system reserves and grid

constraints.

In this study, the NégaWatt scenario will be questioned on the operational

implementation of flexibility, according to its assumptions on the grid and energy

capacities evolution.



IV – EVOLUTION OF ELECTRIC FLEXIBILITY

DEMAND IN A REGION OF FRANCE,

BRETAGNE

With an area of 27,208 km² and a population of 3.3 million inhabitants in

2017, Bretagne is a region in the middle of French energy debates. Many

renewable projects are implemented in this region, which requires financial

investments. This region is already subject to significant load shedding. It can be

noted that some Sarzeau’s inhabitants during the winter of 2017-2018 suffered a

three-day long power outage. The integration of renewable energy in some

scenarios could lead to increasing grid instability.

IV.I – Description of Bretagne’s energy situation

IV.I.I - Bretagne, a region depending on imported electricity

This region is very energy intensive, because of the importance of its agri-food

industry24, which accounts for 64% of the energy consumed there. Regional

annual consumption has been growing faster in recent years than national annual

consumption. Annual national consumption has remained more or less constant

over the past ten years, as shown in the following figure.

24 1429 sites from agri-food industry have been recensed in 2009 according to INSEE

Figure 17 - Evolution of annual consumption and peak consumption in France and Bretagne between 2006 and 2017, [36]

80%

90%

100%

110%

120%

2004 2005 2006 2007 2008 2009 2010 2011 20122013 2014 2015 2016 2017

Var

iati

on

co

mp

are

d t

o d

ata

fro

m 2

00

6

Annual Peak ConsumptionBretagne

Annual Peak ConsumptionFrance

Annual ConsumptionBretagne

Annual ConsumptionFrance



It can also be observed that the peak consumption is becoming more important

over the last decade. This appears in winter, as it is strongly linked to the use of

electric heating.

Electricity consumption increases on average by 150 MW per degree lost in

Bretagne and by 2,400 MW per degree lost in France.

However, Bretagne is a region

that consumes and does not

produce. Indeed, it has to import 86% of what it consumes. It is therefore very

dependent on the annexed regions. In 2011, the Loire-Atlantique produced 82% of

Bretagne's electricity consumption25.

Bretagne energy mix is mainly based on thermal and wind energy generation; 913

MW and 859 MW of wind and fossil thermal capacities

respectively are identified in the region.

Bretagne

therefore relies

on a basic

supply provided

by 4 nuclear

power plants26

located outside

the region, in

Chinon and Flamanville.

In Loire-Atlantique, the Cordemais power

plant and its two coal-fuelled units, and a Combined Cycle Gaz power plant in

Montoir de Bretagne are used as a semi-base for Bretagne consumption. Bretagne

generation also takes place in semi-phase through the generation capacity of

France's only tidal power plant, La Rance 27 and the Guerlédan dam.

25 [37] 26 Each unit of those Nuclear Power plant have an installed capacity of 1300 MW. 27 This tidal power plant has an installed capacity of 240 MW and a pumping capacity of 56 MW.

Figure 18 - Bretagne's imported electricity balance for 2016 [35]

Figure 19 - Composition of the Bretagne energy mix of electricity generation facilities as at 31 December 2016 (MW) [35]



Located in Finistère in the 1980s, the two combustion turbines at Dirinon and

Brennilis serve to fill the extreme peak, being able to supply 300 MW in less than

30 minutes28. Two fuel units from the Cordemais power plant and 20 MW from

diesel generators are used with these plants at the end of their life for peak

periods.

Renewable installations represent more than 62% of the Bretagne energy mix,

with 1,435 MW of generating capacity in 2016. Bretagne ranks second on the

podium among French regions with the highest share of renewable energy in their

energy mix.29 Renewable generation capacity has been constantly evolving in

recent years. The projects are thus developed around the potential of Bretagne.

IV.I.2 Future evolution of the RES proportion and of the demand in

Bretagne

Wishing to assert itself on a policy of energy transition, the Regional Council of

Bretagne signed on 14 December 2010 the Breton Electric Pact with the French

State, RTE, ADEME and ANAH. The aim is to ensure the Bretagne’s energy future

by controlling energy demand, developing renewable energies and securing

electricity supplies.

To this end, the Region of

Bretagne wishes, by 2020, to

reduce by a 3-factor the

growth in electricity demand

and multiply by 4 the

renewable electricity

generation, i.e. 3,600 MW.30

Regarding security of supply,

a 225-kilovolt underground

link linking the north and

28 The total installed capacity of those five units of combustion turbines is 480 MW. They operate on an average of 100 hours a year. [38] 29 [35] 30 [22]

Figure 20 - Evolution of the renewable energy installed capacities of Bretagne [35]

Figure 21 - High Voltage Lines in Bretagne [35]



south of the region was commissioned in 2017. This line creation is called the

"Safety Net" Project.

Indeed, northern Bretagne was extremely insecure, as it was supplied by only two

225 kV lines and one 400 kV line. These high voltage lines are not numerous in

the region; it is mainly supplied by an electricity distribution grid31. Faced with

the insecurity of its grid and a generation deficit in terms of electricity

consumption, Bretagne is multiplying interconnections and renewable generation

integration projects.

The "Celtic Interconnector" interconnection, which would connect Bretagne and

Ireland, would allow an even more stable supply of electricity.32 This project is

still under study but could be completed as early as 2030.

Finally, the main objective of the Breton Electric Pact is to achieve Bretagne

generation corresponding to 34% of its consumption in 2020. Generation in 2016

corresponded to 14% of Breton consumption. To achieve this objective, the

following projects are under development:

- In Landivisiau, a 422 MW gas-fired combined cycle power plant with a

capacity of 422MW will be commissioned in 2021 for an average operating

time of 2000-6000 hours per year. It would compensate for the closure of

the Cordemais power plant, Bretagne's main power supply.

- In Saint-Brieuc, an offshore wind farm with a total capacity of 500 MW

will be built by 2022.

- In Groix and Belle-Ile, 24 MW of floating wind projects will be

implemented in 2020.

Thus, Bretagne wishes to rely on the development of wind and photovoltaic

generation and biomass generation.33

In addition, the Regional Council of Bretagne has approved the objectives for 2020

of the Regional Climate, Air and Energy Plans. Based on a study of energy

31 Voltage lines in Bretagne are usually medium or low voltage lines (63kV/90kV). 32 [39] 33 Objectives of renewable generation from the Breton Electric Plan is in Appendix.

Figure 22 - Aims and Development of renewable projects in Bretagne



potentials, these plans provide for the development of 1,800 MW of wind farms

and 400 MW of solar farms.

The objectives of the Breton Electric Plan and the SRCAEs will be used to assess

the evolution of the need for Bretagne electric flexibility in relation to the

integration of renewable energies.

IV.2 – Study of the impact of an high RES on the electric flexibility

demand

IV.2.1 - Variability of unavoidable energy generations and consumption

The development of renewable energies is thus the spearhead of the region for its

energy future. However, some of them have one disadvantage: their generation

depends mainly on weather conditions or tides. This is the case for solar energy,

wind energy and marine renewable energy. Generation will not evolve in response

to demand variations.

The figure below shows a peak in Bretagne consumption on 02 January 2017

around 12:00 and one around 19:00.

These peaks are characteristics of electricity consumption in France. While the

peak in midday consumption may correspond to the maximum generation of solar

energy, the peak in early evening is difficult to balance. In the same figure, we

can observe the load factors of solar energy, wind energy and the Rance dam,

which uses tidal energy. This load factor refers to the generation on each half-

hourly step of each type of energy according to a percentage of the corresponding

installed capacity.

It can be difficult to anticipate the generation needed to supply the region, taking

into account a unavoidable energy generation that is sometimes difficult to

predict. This generation, which have to be anticipated, can be sized thanks to the

residual load. This load is a power in MW over a defined half-hourly step such

that :

𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝐿𝑜𝑎𝑑𝑡 = 𝐷𝑒𝑚𝑎𝑛𝑑𝑡 − 𝑊𝑖𝑛𝑑 𝑃𝑜𝑤𝑒𝑟𝑡 − 𝑃𝑉 𝑃𝑜𝑤𝑒𝑟𝑡 − 𝑅𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒 𝑚𝑎𝑟𝑖𝑛𝑒 𝑃𝑜𝑤𝑒𝑟 𝑡

-500

500

1 500

2 500

3 500

4 500

0%10%20%30%40%50%60%70%80%90%

100%

00

:00

01

:00

02

:00

03

:00

04

:00

05

:00

06

:00

07

:00

08

:00

09

:00

10

:00

11

:00

12

:00

13

:00

14

:00

15

:00

16

:00

17

:00

18

:00

19

:00

20

:00

21

:00

22

:00

23

:00

Dem

and

[M

W]

Load

Fac

tor

[%]

Evolution of Demand and Load factors of intermittents energies on 02/01/2017

FC Wind Power FC Marine Energy FC Solar Energy Bretagne Demand

Figure 23 - Variability of renewable load factors and consumption on January 02, 2017



This power thus makes it possible to see the need for non-renewable generation

and in particular the need for generation flexibility. In addition, the Ramp Power

is defined as the variation of the residual load from one half-hourly step to the

next one.

By using the Bretagne energy mix data

from 2013 to 2017 34 and by calculating the

corresponding ramp powers, the need for

generation flexibility could be defined on

an annual basis.

It can thus be observed that the need for

supply variation from one half-hourly step

to another rarely exceeds more or less 300

MW in recent years. For the time being,

this need can be largely offset by the

import of generation from the annexed

regions.

The data used for these different years can

be found in Appendix.

IV.2.2 - Calculation of

load factors and consumption in 2050

With a high share of renewable energy in the future mix, the intermittency of

unavoidable energies could have a greater influence on the need for supply

flexibility than until now.

First of all, it is necessary to evaluate the load factor of the different unavoidable

energies in 2030. To this end, a study was carried out on the load factors for the

years 2013 to 2017. Taking wind energy as an example, we can see that the load

factor varies from year to year. It is therefore not interesting to use an average of

the load factors over the last few years. The calculation of the coefficients of

variation of the load factors of wind energy generation in Bretagne over each half-

hourly period between 2013, 2014, 2015, 2016 and 2017 makes it possible to

determine an average coefficient of variation of the wind energy load factor of

81.1%.

To determine the load factors for the year 2030, a random variable between 1.811

and 0.189 is associated with each average load factor at the half-hourly step from

2013 to 2017 in order to incorporate the uncertainty of this prospective

approximation.

34 [36]

Figure 24 - Number of ramping hours from 2013-2014-2015-2016-2017

0%5%

10%15%20%25%30%35%40%

Number of Ramping hours in the year ranked by MW

2013 2014 2015 2016 2017



It can be seen that by modifying the random variable on each half-hourly step, the

load profile of wind energy undergoes too much variability. The random variables

will only differ from one day to the next.

These calculations were also made for the load factors of solar energy and the

Rance dam and for Breton consumption. The following average coefficients of

variation are obtained:

Table 1 - Coefficient of Variation of unavoidable energies

Wind Power

Load Factor

La Rance

Load Factor

Solar Energy

Load Factor

Bretagne

Demand

Average of the Coefficient of variation 81.1% 120.3% 23.8% 11.16%

We therefore obtain the load factors of these different types of energy for

2030 and the variation of Bretagne consumption.

However, taking into account this coefficient of variation of demand is

insufficient as single assumption. Indeed, according to some scenarios,

consumption in France and therefore in Bretagne is expected to increase by a

certain percentage. Many believe that an energy efficiency effort will lead to a

50% reduction in electricity consumption in 2050 compared to 2017. Others

consider that consumption will continue to increase in the coming years. To take

these different scenarios into account, the case studies will be multiplied

according to assumptions of a reduction or increase in consumption compared to

2017: consumption may be reduced in 2030 by 50%, 25%, 10% or 5%, may be

maintained or increase by 2%, 5% or 10%. Consumption will then be multiplied

by a random variable that differs from day to day between 0.888 and 1.112.

IV.2.3 - Assumptions on the energy mix

Different hypotheses are also set regarding Bretagne's energy mix. To determine

the residual load, only the development of unavoidable energy capacities can be

subject to new assumptions. We then consider ten different Bretagne renewable

energy mixes, created for this study :

- Three mixes will be subject to a major integration of two technologies

among wind, marine and solar energy.

- A mix considered a proportional integration of each type of technology

(wind, marine and solar energy).

- A mix will consider proportions similar to those of the current mix.

- Three mixes will allow the majority development of one of the three

technologies.

- A mix will use the objectives of the Breton Electric Pact.

- A final will consider that all current projects will be completed by 2030.



The share of renewable capacities in the overall energy mix will differ from case

to case.

IV.3 - Results of the study

Nearly 400 case studies have been carried out to obtain a wide range of results.

The load factor data determined above and the 2017 consumption data, assigned

with a random variable, are used under the different assumptions. The residual

load is calculated on each half-hourly step of the year 2030 for all study cases.

The ramp power on each half-hourly step of each case study is then obtained. It

can be observed that in a case where the integration of renewable energies is

important, the need for flexibility increases and appears more often than in recent

years.

In the case study 79, 72% of the annual generation in 2030 comes from

unavoidable renewable energies. The consumption of this case increased by 5%

compared to 2017. Nearly 1.4 GW of wind power capacity and 1.4 GW of solar

parks are installed and a development of marine renewable energy is considered

with a capacity of 10.8 GW. The demand for flexibility upwards or downwards

between two half-hourly steps is more important and recurrent. In this case, the

maximum ramp power is 1,048 MW, which is equivalent to almost one nuclear

unit.

By performing these same calculations for all case studies, the results in the

following Figure are obtained. We can see that the need for flexibility can reach

4,000 MW per half-hourly step, which corresponds to the average power

consumed in Bretagne. It is very unattractive to size power plants to cover such a

proportion of power, given that these moments with high flexibility requirements

are rare. In addition, on these different case studies, part of the unavoidable

generation is curtailed. We can see that some case studies reach nearly 120% of

the annual generation from unavoidable energy.

5% 4% 8% 10% 12%

36%

11% 7% 4% 5%0%

20%

40%

[-INF;-400[ [-400;-300[ [-300;-200[ [-200;-100[ [-100;0[ [0;100[ [100;200[ [200;300[ [300;400[ [400;+INF[

Occ

ure

nce

in 2

03

0 [

%]

Ramp Power [MW]

Number of Ramping Hours for the 2030 case with a high marine renewable energy integration

Figure 25 - Occurrences of ramping hours in the case study 79 for the year 2030



This curtailed generation could be used to meet these needs for flexibility by using

various storage technologies.

Figure 26 - Demonstration of a link between the need for flexibility and the share of renewable energies in the energy mix

IV.4 – Storage, Flexibility need and Development potential in

Bretagne

A great need for flexibility is therefore needed in Bretagne if the region wants to

obtain its energy independence and to stop the generation of power plants already

at the end of their useful life, such as the Dirinon and Brennilis Combustion

Turbines. The Landivisiau power plant would make allow to be closer to meet this

need, but not in a sufficient way. Storage could enable to take advantage of the

generated excess from unavoidable renewable energy.

ENEA Consulting has studied the potential of storage in Bretagne through its study

"Energy storage: Perspectives and Opportunities for Bretagne".35 The professional

consultant highlights the possibility of developing coastal marine pumping

stations. Located on the Bretagne coast, they could make it possible to store nearly

200 MW by marine pumping stations. In addition, Bretagne could take advantage

of these former slate quarries to transform them into CAES, which would store

nearly 200 MW each.

In addition, by 2020, nearly 300,000 demand response boxes must be distributed

in Bretagne inhabitants homes. A demand response potential of 600 MW would

then be exploitable.

In February 2012, Energy Pool36 released nearly 15 MW in Bretagne.

Thus, the potential in Bretagne to achieve its objectives is significant. However,

the supply of electricity is already supported by the rest of France and this may

still be the case in 12 years. In addition, the optimistic renewable energy targets

are common to the entire France.

35 [20] 36 Energy Pool is a French aggregator of electric flexibility

-

2 000

4 000

6 000

0% 20% 40% 60% 80% 100% 120% 140% 160%Max

imal

Ram

p P

ow

er

of

the

ye

ar [

MW

]

Proportion of unavoidable renewable energies in the annual production of Bretagne [%]

Impact of a massive renewable energies integration on the flexibility need in Bretagne in 2030



V – ANALYSIS OF THE FRENCH ELECTRIC

FLEXIBILITY, ACCORDING TO NEGAWATT

SCENARIO

V.1 – First study on French scope : Linear optimization of the energy

generation in 2017

The purpose of this study is to model the generation-consumption balance on each

half-hourly step of 2017 and to validate this optimization model by comparing it

with the actual data provided by RTE. Many writings identify models for

optimizing energy systems which take into account the technical constraints of

power plants, such as the report by Ana Viana and Joao Pedroso. This report

describes the activation constraints of a power plant37.

V.1.1 – Energy Situation in France in 2017 and Hypothesis

Today, the French energy mix has a 130 GW of installed capacity. French

generation is based on hydraulic and nuclear generation.38

With the 58 units of its 19 nuclear power plants, France is able to supply nearly

75% of national consumption. This has been stable in recent years, as shown in

Figure 11. In 2017, despite a very cold winter, demand reached 482 TWh. This

value is slightly lower than the previous year. France is indeed the most heat-

sensitive European country: for one degree Celsius difference, the variation in

consumption is on average 2,400 MW.

In addition, France is mainly an exporter; its import-export balance is 36 TWh for

201739.

37 [42] 38 Details of French installed capacities are in Appendix 39 [40]

Figure 27 - French Energy mix in december 2017 Figure 28 - Annual French Electricity Generation in 2017

Nuclear; 72%

Fossil Fuel; …

Renewabl…

French Generation in 2017



Nevertheless, France has also set

itself significant energy transition

objectives and is trying to achieve

them through numerous

renewable projects. In 2017, it

reaches 95% of the capacity

installed as a target for 2018, and

the balance between generation

and consumption is mainly

maintained by RTE, thanks to

exports and the various

adjustment markets.

V.1.2 – Methodology for the linear optimization

To carry out this model, I use the OMEGALPS40 python library dedicated to the

generation of optimization models for energy management, developed by the

G2ELab.

In this optimization model, we consider 5 types of unavoidable generation

(onshore wind, offshore wind, solar, marine renewable and run-of-river) and 9

types of piloting generation (nuclear, coal, gas, fuel, hydroelectric dam, biomass,

biogas, waste and geothermal). To simplify the model, each type of generation

will be associated with a specific technology. In addition, international trade will

not be taken into account until the third part of this study on the French

perimeter, even if it is considered as an important vector of flexibility.

First, an objective function is defined to model the merit order over each half-

hourly step. This economic precedence or merit order enable to classify

generation offers by increasing price.

The marginal cost for each type of generation is used to classify them. It should

be noted that renewable energies have a very low marginal cost, unlike high-tech

power plants dependent on expensive fuels. This short run marginal cost41 is

defined by the equation opposite 42:

40 Optimization ModEls Generation As Linear Programs for Energy Systems 41 SRMC is defined as the change in short run total cost for an extremely small change in output [47] 42 [11]

Figure 29 - Comparison of the objectives of the PPE and the French energy mix in 2017

-

20

40

60

80

Fleet 2017 Objectives2018

DownObjectives

2023

UpObjectives

2023

Inst

alle

d C

apac

ity

Tho

usa

nd

s

PPE Objectives

Wind Power Solar Power

Hydroelectrictiy Bioenergies



𝑆𝑅𝑀𝐶 [€

𝑀𝑊ℎ] = 𝑂&𝑀 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝐶𝑜𝑠𝑡 [

€

𝑀𝑊ℎ] +

𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡[€

𝑀𝑊ℎ𝑡ℎ]

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦+

𝐶𝑂2 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 [𝑡𝑜𝑛𝑠

𝑀𝑊ℎ]∗𝐶𝑂2 𝑃𝑟𝑖𝑐𝑒[

€

𝑡𝑜𝑛𝑠]

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦

All the marginal generation costs used for this study and the different variables

on which they are based are given in Appendix.

The start-up cost of nuclear and fossil fuel thermal power plants is also taken into

account in this objective function. It is defined as follows:

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑧 = ∑ ∑ 𝑀𝐶𝑖 × 𝑄𝑡,𝑖 × 𝑢𝑡,𝑖 × 0,5 + 𝑆𝐶𝑖

𝑁

𝑖=1

17520

𝑡=1

× 𝑢𝑠𝑡𝑎𝑟𝑡𝑡,𝑖 × 𝑄𝑡,𝑖

Where :

- i is the variable designating the type of generation from 1 to N

- t is the half-hourly step over the year 2017

- 𝑀𝐶𝑖 is the marginal cost of generation

- 𝑄𝑡,𝑖 is the generation of the type of generation i on the half-hourly step t

- 𝑆𝐶𝑖 is the start-up cost of generation type i

- 𝑢𝑡,𝑖 is a binary variable describing the state of power plant i

- 𝑢𝑠𝑡𝑎𝑟𝑡𝑡+1,𝑖 is a binary variable characterizing the start-up of a type of power

plant i

- 𝑢𝑠𝑡𝑜𝑝𝑡+1,𝑖 is a binary variable characterizing the stop of a type of power

plant i

We enter different parameters:

- The consumption of the year 2017 at the half-hourly rate[MW]

- Fatal energy load factors for 2017

- The installed capacities of each type of generation [MW]

- The number of plants for the types of generation that have a start-up cost

- Start-up costs [€/MW]

- Short Run Marginal generation costs[€/MWh]

On each half-hourly step, the optimization is also defined by the following

constraints:

- The consumption power 𝑃𝑐𝑜𝑛𝑠𝑡 must equal to the sum of the powers produced

by the different power plants:

𝑃𝑐𝑜𝑛𝑠𝑡= ∑ 𝑄𝑡,𝑖

𝑖

𝑓𝑜𝑟 𝑡 = 1 … 17520

- Some power plants operate only with a minimum power level 𝑄:

𝑄𝑡,𝑖 ≥ 𝑄 𝑓𝑜𝑟 𝑡 = 1 … 17520 𝑎𝑛𝑑 𝑖 = 1 … 𝑁

- The power of each type of generation may not exceed the installed capacity

𝑄:

𝑄𝑡,𝑖 ≤ 𝑄 𝑓𝑜𝑟 𝑡 = 1 … 17520 𝑎𝑛𝑑 𝑖 = 1 … 𝑁



- The binary variable 𝑢𝑠𝑡𝑎𝑟𝑡𝑡,𝑖 characterizing the start of the generating plant

depends on a binary variable of the plant status 𝑢𝑡,𝑖 :

𝑢𝑡+1,𝑖 − 𝑢𝑡,𝑖 ≤ 𝑢𝑠𝑡𝑎𝑟𝑡𝑡+1,𝑖 and 𝑢𝑠𝑡𝑎𝑟𝑡𝑡+1,𝑖 ≤𝑢𝑡+1,𝑖−𝑢𝑡,𝑖+1

2 𝑓𝑜𝑟 𝑡 = 1 … 17520 𝑎𝑛𝑑 𝑖 = 1 … 𝑁

- The binary variable characterizing the shutdown of a generating station is

defined by: 𝑢𝑠𝑡𝑜𝑝𝑡+1,𝑖 = 𝑢𝑠𝑡𝑎𝑟𝑡𝑡+1,𝑖 − 𝑢𝑡+1,𝑖 + 𝑢𝑡,𝑖 𝑓𝑜𝑟 𝑡 = 1 … 17520 𝑎𝑛𝑑 𝑖 = 1 … 𝑁

- A power plant that starts up must remain in operation for a minimum

period of time 𝑇𝑚𝑖𝑛𝑖:

𝑢𝑡,𝑖 ≥ ∑ 𝑢𝑠𝑡𝑎𝑟𝑡𝑡−𝑗+1,𝑖 𝑇𝑚𝑖𝑛𝑖𝑗=1 𝑓𝑜𝑟 𝑡 = 1 … 17520 𝑎𝑛𝑑 𝑖 = 1 … 𝑁

- A power plant that stops must remain stopped for a minimum time 𝑇𝑚𝑖𝑛𝑖:

1 − 𝑢𝑡,𝑖 ≥ ∑ 𝑢𝑠𝑡𝑜𝑝𝑡−𝑗+1,𝑖 𝑇𝑚𝑖𝑛𝑖𝑗=1 𝑓𝑜𝑟 𝑡 = 1 … 17520 𝑎𝑛𝑑 𝑖 = 1 … 𝑁

The optimization model would allow to obtain on each half-hourly step of the year

2017 the generation of each power plant i.

V.1.3 – Results of the optimization for the 2017 energy mix

However, the size of the optimization problem is too large, with nearly 391,000

non-zero variables for one day. The study was reduced to a one-day perimeter.

Thus, we can obtain the generation distribution43 of January 01, 2017 from the

model compared to the one44 from RTE's Open Data45.

The results of the optimization model and RTE data differ due to the absence of

pumping and international exchanges. On the other hand, the data used by the

different generating stations to post bids on the different electricity markets may

differ from the marginal cost proposed here.

However, the model allows a general approximation of the generation-

consumption balance of an autarky France. Using this basic model, the aim is to

determine whether, by 2050, the balance can be achieved with only the flexibility

to manage power plants.

V.3 – Second study on French scope : Linear optimization of the

energy generation in 2050 without flexibility

V.3.1 - Energy mix in 2050 according to NégaWatt Scenario

Many reports study various prospective scenarios for the year 2050. One of them

is the NégaWatt report46 which studies the absence of nuclear and thermal energy

43 See Appendix 44 See Appendix 45 [36] 46 [7]



in the energy mix in 2050. It uses a model that ensures an energy balance at the

annual step and a power balance at the hourly step.

Based on sobriety and energy efficiency, this scenario incorporates hypothesis of

massive development of renewable energies. By 2050, the entire French energy

mix would thus be renewable. The scenario estimates that consumption will

decrease by 50%, in particular by replacing electric heaters with heat pumps.

V.3.2 – Update of the methodology for the linear optimization

The energy mix proposed in the update of the NégaWatt scenario is used to

determine at half-hourly intervals the generation of each type of renewable

energy according to the previous model.

The following parameters are updated in the basic optimization model:

- Consumption in 2050 at half-hourly intervals [MW]

- The load factors of the unavoidable energies of 2050

- The installed capacities of each type of generation according to the

proportion of energy mix from NégaWatt scenario [MW]

The consumption and load factors of unavoidable energies are obtained according

to the same methodology as those determined for Bretagne case study. A daily

random variable within an interval dependent on the average coefficient of

variation of the data between 2012 and 2017 is associated with the average of the

data between 2012 and 2017.

V.1.3 – Results of the optimization for the 2050 energy mix

After an optimization test, it is noted that the model cannot be solved. The

constraint of balance between generation and consumption cannot be respected.

Indeed, the intermittency of fatal energies such as wind energy strongly impacts

this balance. Indeed, their share in the energy mix is significant. Curtailment is

not induced in the model.

During half-hourly intervals when the load factors of unavoidable energies are

important, unavoidable generation is higher than consumption: equilibrium is

impossible.

This energy produced and curtailed could be reused to make up for the

unavoidable generation deficit at certain times of the year, thanks to storage. This

could be an advantage for producers, unlike curtailment.



V.4 – Third Study on French scope : Integration of electric flexibility

in the optimization

V.4.1 – Update of the methodology : integration of flexibility and

curtailment

The possibility of curtailment, demand response, importing energy and storing

energy is integrated into the 2050 model by updating the equilibrium constraint

between generation and consumption in this way:

𝑃𝐶𝑜𝑛𝑠𝑜 𝑡 − 𝑃𝑀𝐷 𝑡 + 𝑄𝑒𝑓𝑓 𝑡 = ∑ 𝑄𝑡,𝑖

𝑖

+ 𝑄𝑠𝑡𝑒𝑝𝑡 − 𝑆𝑠𝑡𝑒𝑝𝑡 + 𝑄𝑖𝑚𝑝𝑜𝑟𝑡𝑡 + 𝐹𝑡 𝑠𝑢𝑟 𝑡 = 1 … 17 520

Where :

- 𝑃𝑀𝐷 𝑡 is the modulable power of Demand Response

- 𝑄𝑒𝑓𝑓 𝑡 is the curtailed power

- 𝑄𝑖𝑚𝑝𝑜𝑟𝑡𝑡 is the imported power

- 𝐹𝑡 the remaining need for flexibility

- 𝑄𝑠𝑡𝑒𝑝𝑡 is the power produced by pumping stations

- 𝑆𝑠𝑡𝑒𝑝𝑡 is the power stored by pumping stations

V.4.2 – Hypothesis on flexibility potential

These different flexibility systems are constrained by a maximum capacity set by

the different potentials exposed in the state of art review.

First of all, in 2017, France imported a maximum of 17.3 GW over a half-hourly

period to these border countries. The possibility of interconnections is an

important source of flexibility. The maximum import and export capacity is set at

this value.

Pumping stations are already used in France to balance the grid, with nearly 4.5

GW of installed capacity.

The development of a total for demand response of 6 GW in 2050 would also

reduce the generation-consumption imbalance, as estimated in the Ademe’s report

on flexibility47. It is assumed that by 2050, the government will no longer allow

the use of alternative technologies for demand side management and that energy

not consumed during the activation will be postponed.

V.4.3 – Results of the optimization

The optimisation model gives the electric flexibility needs in 205048. In 2050, the

controllable generation technologies are hydraulic dams, biomass, biogas,

geothermal energy and waste incineration. We can see that the needs are

47 [46] 48 Generation Distribution and electric flexibility are modelized for a winter day and a summer day in 2050. They are available in appendix.



significant and frequent, unlike those of the 2017 model, which are zero and

compensated by pilot-scale generation.

Pumping Storage and Demand Response are preferred to curtailment and

import/export in optimization as it reduces the target value by reducing the

generation of paid control units.

In this study, we size the flexibility needs at 52.8 GW : 17.3 GW of usual import,

the potential of 6 GW for Demand Response, the actual capacity for pumping

stations of 4.5 GW and 25 GW of the remaining flexibility requirement 𝐹𝑡. Actual

import and Pumping stations capacities will not raise at a high level in the future.

Moreover, the Demand Response potential of 6 GW came from Ademe forecast for

2035 and could hardly increase too. Other flexibility means should therefore be

developed in order to meet the remaining needs of 25 GW. The development of A-

CAES storage capacities and different types of batteries would provide part of this

additional flexibility needs, but requires a significant investment in the various

technologies.

In NégaWatt scenario, demand/generation balance is mainly obtain through

actual capacities of pumping stations and power-to-gas with its 18 GW of installed

capacity for biogas power plants. Moreover, import and export are strongly

reduced in this scenario. Moreover, import and export are strongly reduced in this

scenario. It is based on optimist assumptions on an important energy sobriety,

which reduces the need of electric flexibility. With more realistic assumptions on

demand and on interconnections, our study shows that the flexibility needs are

closer to 31 GW, (6 GW for Demand Response and 25 GW of the remaining

flexibility requirement), than to 18 GW.

0,1

%

0,3

%

1,2

%

3,6

%

5,9

% 8,8

%

28

,5%

45

,0%

5,3

%

1,3

%

0,1

%

]-IN

F;-

60

[

[-6

0;-

50

[

[-5

0;-

40

[

[-4

0;-

30

[

[-3

0;-

20

[

[-2

0;-

10

[

[-1

0;0

[

[0;1

0[

[10

;20

[

[20

;30

[

[30

;+IN

F

FLEXIBILITY NEEDS [GW]

FLEXIBILITY NEEDS IN 2050

Figure 30 - Distribution of the need for flexibility over the year 2050



VI – DISCUSSIONS In this work, power plants were aggregated by type of energy sources,

according to the most common technology in France. For more accuracy in the

modelling, it would be interesting to use the data for each type of technologies.

Nevertheless, those data are difficult to find and their use could complexify the

model.

The grid in this study is assumed perfect and able to support high hourly

ramps. An additional study on the grid could add some constraints of flexibility,

as the need of decentralized storage capacities and system reserves capacities

which should be secured.

The RTE scenario takes into account a more accurate capacity of import and

export from French neighbours, according to assumptions on Europeans country

energy mixes. Forecasted energy import/export could reduce the need of

flexibility as we assume that Europe will be more interconnected in the future.

However, European countries have important renewable energies objectives for

the future, so that it will not be possible to import energy in France according to

the needs by controlling another country production.

Moreover, load factors of renewable energy sources of this study are based on

mathematical approximations, as we assume that there is no climate change. It

will be interesting to use a forecast program for evaluating those load factors.

Electric flexibility vectors could be modelled precisely, using their using

cost49 and their technology constraints. The model would become really accurate

and should also be based on complex behaviour analysis.

The possibility to add some needed energy capacity could be integrated in

this model, through a loop where conception and management are coupled.

However, this type of model is mainly used for prospective study and not for a

test of a prospective scenario.

The security of supply is the base of European power systems, it can be

described by a really low Loss of Load Expectation. The LOLE is the number of

hours where available generating capacity is insufficient to serve the demand. In

France, it is evaluated at three hours. We can imagine a scenario more axed on

possible load shedding as in USA or in islands.

49 LCOS : Levelized Cost Of Storage is an actualized average of all storage costs on its life time



VII – CONCLUSION Electric flexibility is an essential vector for the future balance of French

generation and consumption. The management of power plants will not be the

only solution to maintain this balance. At first sight, an important development of

flexibility solutions may allow the transfer of unavoidable energy that could have

been curtailed, and may maintain the balance of the grid.

However, Pumping stations already in operation in France cannot be more

developed. The massive development of adiabatic surface CAES and

electrochemical or redox batteries will not be possible due to the small potentials

and important economic barrier in France.

Nevertheless, a mix of different storage, generation or consumption

management technologies could create a flexible grid. Thus, many economic and

regulatory levers must be activated in order to develop this new grid.

International interconnections may allow greater flexibility in the future

with the development of the European grid and its common markets. The

European expansion of unavoidable energies could reduce some effects of

intermittences. Though, if all European countries are moving strongly towards a

massive integration of renewables, the possibility to export electricity under such

conditions will be difficult on some time steps.

A 100% renewable mix will be achievable if the country abandons its

overgeneration strategy and moves towards optimising generation and efficient

storage. An energy-saving solution as described in the NégaWatt scenario would

be the most appropriate to maintain the balance of the grid, considering such

flexibility needs. Reducing energy consumption through energy efficiency and

grid flexibility, which are ENGIE's central strategic directions today, are therefore

the most appropriate solutions for developing carbon-free generation.

But, according to RTE assumptions, the future developments on energy

efficiency will only compensate the new electrical uses, such as electric vehicles,

and thus stabilize the electrical needs in the future. In this case, we showed that

the power system depicted in the NégaWatt scenario would need more flexibility

systems.



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APPENDICES APPENDIX 1 – LIST OF DEMAND RESPONSE AGGREGATORS IN 2018

Demand Response Aggregators EIC Code

ACTILITY 17X100A100R018RD

ALPIQ ENERGIE FRANCE 17X100A100F029CY

BHC ENERGY 17X100A100F0086K

BLUE ELEC 17X100A100R0711P

DANSKE COMMODITIES A/S 11XDANSKECOM---P

DIRECT ENERGIE 17X100A100R0172T

ECOMETERING 17X100A100R0709C

EDF 17X100A100R00182

ENERDIGIT 17X100A100F0079H

ENERGY POOL DEVELOPPEMENT 17X100A100R0416R

ENGIE SA 17X100A100R0227U

EQINOV DEMAND SIDE MANAGEMENT 17X100A100R06923

HYDRONEXT 17X100A100R026Y1

ILEK 17X100A100R0703O

METRON 17X100A100R06931

RES REACTIVE FLEXIBILITE SERVICES 17X100A100F0085M

SMART GRID ENERGY 17X100A100R0535J

SOLVAY ENERGY SERVICES 17X100A100R01235

SOVEN 17X100A100R0511X

VALORIS ENERGIE 17X100A100R0634H

VOLTALIS 17X100A100D0385M

XELAN 17X100A100R0716F

List developed by RTE on its customers webpage. [18]



APPENDIX 2 – DESCRIPTION OF MAIN PROGRAMS FOR ELECTRIC FLEXIBILITY VALORISATION PROPOSED BY RTE

APPENDIX– USUAL CAPACITY AND TIME CONSTANT OF DIFFERENT STORAGE TECHNOLOGIES

Figure 31- Storage technologies ranked by energy capacity [19]

Program Demand Response Rapide Reserve Primary Reserve

Format

Availability : 6h/20h working days . Possibility to reduce this time window, which will reduce the remuneration.

Availability : 24h/7 days Possibility to draw a distinction between working days and not working days.

Availability : 24h/7 days However, capacities can be activated only at 10% of installed capacity over a period longer than 30 seconds.

Remuneration Fixed and Variable Remunerations

Fixed and Variable Remunerations

Fixed Remunerations

Minimum Power 10MW 10MW 1 MW

Development

Capacity increase each year (2100 MW in 2016, 2500 MW in 2017), and minimum power has been fixed at 1 MW from 2017.

1000MW are picked each year by RTE. Proposed remunerations of this program decreased last years, with a recent stabilization.

Devices which can apply to those criteria are rare on sites. Their consumption is usually negligible compared to other devices of sites.



APPENDIX – ADAPTABILITY OF STORAGE TECHNOLOGIES TO GRID SERVICES

Figure 32 – Given services et adapted technologies [20]



APPENDIX – CAPACITY AND MATURITY OF STORAGE TECHNOLOGIES ET MATURITES DES TECHNOLOGIES DE STOCKAGE

Figure 33- Size and Maturity of storage technologies [20]

APPENDIX- CAPEX OF STORAGE TECHNOLOGIES

Figure 34 – Ranking of storage technologies by CAPEX in energy and in power [21]



APPENDIX– COMPARISON OF LCOS OF STORAGE TECHNOLOGIES IN 2013 AND IN 2050

Figure 35- Comparison of LCOS (€/MWh delivered) for main electricity storage technologies in 2013 and in 2050 [23]

With Levelized Cost Of Storage defined as:

Where r is the discount rate and n the number of operations



APPENDIX- VARIANTS USED FOR MAIN SCENARIO OF RTE STUDIES [9]



APPENDIX- ENERGY MIX IN 2025, 2030 AND 2035 FOR WATT SCENARIO

APPENDIX- IMPORT/EXPORT BALANCE FOR FRANCE IN 2035



APPENDIX- ANNUAL INVESTMENTS FOR WATT SCENARIOS



APPENDIX- INSTALLED CAPACITY FOR WATT SCENARIOS



APPENDIX- ANALYSED ENERGY POTENTIALS BY ADEME

APPENDIX- AVERAGED LCOE COMPARISON OF MAIN ENERGY SOURCES



APPENDIX- INSTALLED CAPACITIES, ANNUAL GENERATION AND EQUIVALENT HOUR AT NOMINAL CAPACITY FOR 100%

RES SCENARIO OF ADEME

APPENDIX- UP AND DOWN FLEXIBILITY POTENTIALS FOR 100 % RES SCENARIO OF ADEME



APPENDIX- NÉGAWATT STUDY METHODOLOGY

APPENDIX- ENERGY GENERATION ACCORDING TO TREND-BASED SCENARIO AND NÉGAWATT SCENARIO



APPENDIX- EVOLUTION OF GENERATION AND INSTALLED CAPACITIES OF ENERGY SOURCES ACCORDING TO NÉGAWATT

SCENARIO

(1) Proportion of renewable gas in the grid

(2) Including pumping stations



APPENDIX- EVOLUTION OF PRIMARY ENERGY VECTORS

APPENDIX- ANNUAL COSTS (INVESTMENT COSTS, GENERATION COSTS, ENERGY IMPORTS) FOR BOTH SCENARIOS

APPENDIX- STRUCTURE OF THE REMIX OPTIMIZATION MODEL USED IN [15]



APPENDIX – RENEWABLE ENERGY GENERATION OBJECTIVES FOR BRETON ELECTRIC PLAN BRETON

Figure 36 - Evolution of annual generation and renewable energy installed capacities in keeping with Breton Electric Plan [22]



APPENDIX - DATA OF ANNUAL AVERAGE POWER AND ENERGY IN BRETAGNE FROM 2013 TO 2017

2013 2014 2015 2016 2017

Peak Consumption

[MW] 4615 4226 4604 4388 4903

Annual demand

[GWh] 21750 20391 20872 21317 22262

Proportion of

unavoidable

generation over a

year [%]

10% 11% 12% 11% 10%

Unavoidable

energy over a year

[GWh]

2073 2159 2410 2261 2330

Total of Installed

capacities[MW] 1929 2061 2156 2294 2418

Wind power

installed capacity

[MW]

782 826 854 913 973

Solar energy

installed capacity

[MW]

150 167 178 190 205

Marine renewable

energy installed

capacity [MW]

240 240 240 240 240

Proportion of

installed capacity

of unavoidable

energies [%]

61% 60% 59% 59% 59%



APPENDIX – INTRODUCTION OF A RANDOM VARIABLE IN THE LOAD FACTOR OF WIND POWER IN

BRETAGNE

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Evaluation of Load Factors for Wind Power under some hypothesis

Load factor Wind Power 2015

Load factor Wind Power 2017

Average Load factor Wind Power from 2013 to 2017

Average Load factor Wind Power - Random Variable on each half an hour step

Average Load factor Wind Power - Random Variable for each day

Figure 37 – Evaluation of load factors of wind power under some hypothesis



APPENDIX– PROPORTION IN ENERGY MIX FROM DIFFERENT CASE STUDY OF MASSIVE RENEWABLE ENERGIES

INTEGRATION

Wind Power

Marine

Renewable

Energy

Solar Energy

1 20% 40% 40% Major integration of two

technologies 2 40% 20% 40%

3 40% 40% 20%

4 33% 33% 33%

Proportional integration of

each technology

5

67% 17% 17%

Proportions of integration

similar to those of the

current mix

6 10% 10% 80%

Major integration of one

technology 7 10% 80% 10%

8 80% 10% 10%

Wind Power

installed

capacity [MW]

Marine

Renewable

energy

installed

capacity

[MW]

Solar energy

installed

capacity [MW]

Planned

projects in

Bretagne 4 973 240 205

Breton Electric

Plan 2 810 240 400



APPENDIX – ACTIVATIONS ON THE ADJUSTMENT MECHANISM BY RTE IN 2017

APPENDIX – INSTALLED CAPACITIES IN 2017 IN FRANCE

Figure 39- Installed capacities in France on 31/12/2017 [23]

Figure 38- Activations on the adjustment mechanism by RTE in 2017 [18]



APPENDIX– TECHNICAL AND ECONOMIC DATA OF POWER PLANTS USED IN THE OPTIMIZATION MODEL

Inst

alle

d c

ap

aci

ty 2

01

7

[MW

]

Inst

alle

d c

ap

aci

ty 2

05

0

[MW

]

Ma

rgin

al C

ost

20

17

/20

50

[€

/MW

h]

Va

ria

ble

co

st o

f

pro

du

ctio

n O

&M

20

17

/205

0

[$2

004

/MW

h]

Ener

gy

effi

cien

cy

20

17

/205

0

Fuel

co

st [

€/M

Wh

th]

CO

2 e

mis

sio

ns

[to

nn

es/M

Wh

]

Sta

rt-u

p c

ost

[€

/MW

]

Min

imu

m p

rod

uct

ion

[%]

Min

imu

m t

ime

of

con

sta

nt

typ

e o

f

op

era

tio

n [

h ]

Nuclear 63 130 - 21,42/20,74 0,48 0,33/0,34 7,00 - 140,00 40 10,00

Coal 2 997 - 89,31/28,35 5,53/8,09 0,28/0,52 9,90/10,50 2041/189 60,00 40 8,00

Oil 4 098 - 127,56/154,83 4,43/8,51 0,39/0,40 46,70/57 282 - 38 2,00

Fossil gas 11 852 - 105,33/102,41 6,28 0,31/0,34 24,80/26,20 1 084 20,00 20 0,25

Solid Biomass 751 1 365 7,93/10,67 4,77/9,15 0,46/0,49 - 300 - nc nc

Biogas 1 103 18 475 6,61 9,52 0,46/049 - - - nc nc

Hydroelectricity

run of the river

and pondage

10 717 10 717 2,22 3,20 0,90 - - - nc nc

Hydroelectic

dams 10 168 10 168 2,22 3,20 0,90 - - - nc nc

Wind Onshore 12 127 77 380 3,21/2,89 4,62/4,16 1,00 - - - nc nc

Wind Offshore - 47 427 10,05/7,27 14,47/10,47 1,00 - - - nc nc

PV 7 300

107

310 - - 1,00 - - - nc nc

Marine

renewable

energy

243 3 402 39,51 56,90 1,00 - - - nc nc

Geothermal 15 50 - - 0,10 - - - nc nc

Waste 20 8 8,9/8,75 9,52 0,46/0,49 - 149 - nc nc

Price of $2004 is equivalent to 1.44€.

The ton CO2 price is fixed at 7€.

Data are from the NégaWatt scenario, the report DP1540 [24] and from the database NREL [25] used

in the ADEME report 100% renewable [3].



APPENDIX – EVOLUTION OF FRENCH ENERGY MIX IN 2050 IN KEEPING WITH NÉGAWATT SCENARIO

63

13

0

29

97

40

98

11

85

2

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71

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47

42

7

10

73

10

34

02

50

8

EVOLUTION OF FRENCH ENERGY MIX IN KEEPING WITH NEGAWAT T SCENARIO

2017 2050

Figure 40- Hypothesis of evolution of the French energy mix from 2017 to 2050 [7]



APPENDIX- REAL GENERATION DISTRIBUTION ON JANUARY, 01 2017

Figure 41- Real French Generation on January 01, 2017

-10

-

10

20

30

40

50

60

70

80

90

Gen

erat

ion

[M

W]

Th

ou

san

ds

Real generation on 01.01.2017

Oil Coal Gas Nuclear Wind Power Solar Energy Hydroelectricity (Lakes) Hydroelectricity (pondage and run of river) Waste Biomass Biogas Flexibility (Pumping and international flows) Demand



APPENDIX – MODELLED GENERATION IN FRANCE ON JANUARY 01, 2017

Figure 42- Modelled generation in France on January 01, 2017

0

10

20

30

40

50

60

70

80

90

Gen

erat

ion

[M

W]

Tho

usa

nd

s

Modelled generation in France on 01.01.2017

Nuclear Biomass Biogas

Geothermal Waste Hydroelectricity run of river

PV Wind Marine renewable energy

Coal Gas Oil

Hydroelectric Dams



APPENDIX – GENERATION DISTRIBUTION AND ELECTRIC FLEXIBILITY FLOW DURING A WINTER WEEK IN 2050

Figure 43- Generation distribution and electric flexibility flow during a winter week in 2050

-40

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100

120

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erat

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[M

W]

Tho

usa

nd

s

Generation distribution and electric flexibility flow during a winter week in 2050

Marine renewable energy Hydroelectricity Run of river and pondage

Solar energy Hydroelectric dams

Biomass Wind Onshore

Widn Offshore Biogas

Geothermal Waste

Imports Electric flexibility needs

Demand responser Pumping

Curtailment Demand



APPENDIX – MODELLED GENERATION DISTRIBUTION DURING A SUMMER WEEK IN 2050

Figure 44 – Generation distribution and electric flexibility flow during a summer week in 2050

-60

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usa

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Generation distribution and electric flexibility flow during a summer week in 2050

Hydroelectriciy run of river and pondage Marine renewable energy

Solar energy Wind Offshore

Wind Onshore Hydroelectric dams

Biomass Biogas

Geothermal Waste

Pumping Curtailment

Imports Electric flexibility needs

Demand response Demand

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