Energy Management Strategies Applied to Photovoltaic-Based Residential Microgrids for ﬂexibility Services Purposes

THÈSE DE DOCTORAT DE

L’UNIVERSITÉ DE NANTES COMUE UNIVERSITÉ BRETAGNE LOIRE

ÉCOLE DOCTORALE N° 601 Mathématiques et Sciences et Technologies de l’Information et de la Communication Spécialité : Génie Électrique

Par Rémy VINCENT

Energy management strategies applied to photovoltaic-based residential microgrids for ﬂexibility services purposes

Thèse présentée et soutenue à Saint-Nazaire, le 15 Juillet 2020 Unités de recherche : Institut de Recherche en Énergie Électrique de Nantes Atlantique, France Thèse N° :

Rapporteuses : Corinne ALONSO Professeur des Universités, Université Paul Sabatier, Toulouse Manuela SECHILARIU Professeur des Universités, UTC

Composition du Jury : Attention, en cas d’absence d’un des membres du Jury le jour de la soutenance, la composition du Jury ne comprend que les membres présents

Examinatrice : Corinne ALONSO Professeur des Universités, Université Paul Sabatier, Toulouse Examinatrice : Manuela SECHILARIU Professeur des Universités, UTC Président du Jury : Josep M. GUERRERO Professor, Aalborg University, Danemark Dir. de thèse : Mourad AIT-AHMED Maître de Conférences Hors-Classe HDR, Polytech Nantes Codir. de thèse : Mohamed Fouad BENKHORIS Professeur des Universités, Polytech Nantes Encadrant de thèse : Azeddine HOUARI Maître de Conférences, IUT de Saint-Nazaire

Invitée : Lamya BELHAJ Electric Machine and Power Electronics expert, PSA Groupe

ACKNOWLEDGEMENTS

Firstly, I would like to express my sincere gratitude to my supervisor Dr. Mourad Ait-Ahmed for his continuous support during my Ph.D study and related research, for his patience, kind- ness and motivation. Besides, his excellent guidance helped me throughout the whole process of research and writing of this thesis. I could not have imagined having a better supervisor for this study. Besides my supervisor, I would like to thank the rest of my thesis committee: Prof. Mohamed Fouad Benkhoris and Dr. Azeddine Houari, for their insightful comments and encouragement, but also for couple of questions which incented me to widen my research from various perspectives (especially regarding the management of uncertainties). I also would like to thank jury members who accepted to assess this thesis and for their availability the day of the ﬁnal defense. Many thanks to Prof. Corinne Alonso and Prof. Manuela Sechilaru, reviewers of this work, for their constructive key-points. Besides, also many thanks to Prof. Josep M. Guerrero, examiner of this thesis for its insightful remarks. My sincere thanks also goes to Dr. Lamya Belhaj, Institut catholique d’arts et métiers (Icam) and Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA) who provided me an opportunity to apply for a Ph.D student position. Without their precious support it would not have been possible to conduct this research. I would like to thank my family: my parents and to my sisters for supporting me throughout writing this thesis and my life in general. To that extent, I would like to thank Luce who has heartened me during so many years. Besides, I would like to extend heartfelt thanks to all my friends (particularly Diane and Allison for being my friends for ages). Last but not the least, I thank my fellow labmates - Océane, Quentin, Corentin, Ryad, Nidhal, Jean-Marie, Antoine and Rémy - for the stimulating discussions (and particularly Sarra for her famous "little tea" break), for the long days at the lab we were working on our respective papers to submit them before deadlines, and for all the fun we have had in the last three years.

3 Throughout space there is energy. Is this energy static or kinetic? If static our hopes are in vain; if kinetic - and this we know it is, for certain then it is a mere question of time when men will succeed in attaching their machinery to the very wheel work of nature.

NIKOLA TESLA, "Experiments With Alternate Currents Of High Potential And High Fre- quency" an address to the Institution of Electrical Engineers, London (February 1892).

4 TABLEOF CONTENTS

List of ﬁgures 10

List of tables 11

Acronyms 12

Introduction 18

1 Smart Grids and Microgrids: towards more smartness 24 1.1 Introduction...... 25 1.1.1 Renewable energy in Europe...... 25 1.1.2 Incentives and deployment programs for renewable energy sources in Europe...... 28 1.1.3 Residential energy consumption and Renewable Energy Sources (RES) integration...... 34 1.1.4 Microgrid deﬁnition and speciﬁcations...... 35 1.1.5 General summary...... 38 1.2 Microgrid challenges and perspectives...... 39 1.2.1 Microgrid challenges and perspectives...... 39 1.3 Microgrid sizing and management strategies...... 44 1.3.1 Optimization methods...... 44 1.3.2 Microgrid sizing...... 51 1.3.3 Microgrid management...... 52 1.4 Research work, methodology, and thesis outline...... 58 1.4.1 Objectives and outline...... 58

2 Sizing optimization of pv-bess based community microgrid 60 2.1 Distributed energy resources modeling...... 61 2.1.1 Photovoltaic generation...... 63 2.1.2 Battery energy storage system...... 67 2.1.3 Loads...... 70 2.1.4 Aggregator...... 72

5 TABLE OF CONTENTS

2.1.5 Optimization...... 73 2.2 Problem formulation...... 80 2.2.1 Context...... 80 2.2.2 Key Performance Indicators...... 81 2.2.3 Proposed cost functions...... 82 2.3 Results and discussion...... 84 2.3.1 Results (single-family houses)...... 84 2.3.2 Results (community microgrid)...... 91 2.3.3 Results summary...... 93 2.3.4 Discussion...... 96 2.4 Conclusion...... 98

3 Relevance of time horizon-based battery energy management strategies for community microgrids 99 3.1 Introduction to horizon-based energy management strategies...... 99 3.2 System model...... 101 3.2.1 Solar irradiation uncertainties...... 103 3.2.2 Energy storage system degradation...... 106 3.3 Management framework...... 108 3.3.1 Key zone designation...... 109 3.3.2 State selection...... 111 3.3.3 Proposed cost functions...... 113 3.4 Results and discussion...... 115 3.4.1 Uncertainty management...... 115 3.4.2 24 and 48-hour horizon comparison...... 117 3.4.3 Sensitivity analysis...... 122 3.4.4 Discussion...... 127 3.5 Conclusion...... 128

4 Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties 129 4.1 Context for community microgrid operation under uncertainties...... 129 4.2 System model...... 131 4.2.1 Smart persistence model...... 132 4.2.2 Recursive Least Squares predictor-corrector algorithm...... 133 4.2.3 Algorithm performance comparison...... 134 4.2.4 Aggregator model...... 139

6 TABLE OF CONTENTS

4.3 Management framework...... 140 4.3.1 Microgrid bidding process mechanism...... 141 4.3.2 Case studies...... 141 4.4 Results and discussion...... 144 4.4.1 Results...... 144 4.4.2 Sensitivity Analysis...... 153 4.4.3 Discussion...... 155 4.5 Conclusion...... 157

Conclusion and perspectives 159

Appendices 163

A Extra information related to renewable energy and microgrids 164 A.1 Current data for energy consumption and production...... 164 A.2 Feed-in Tariffs in France...... 167 A.3 Experimentel smart grid projects in Europe...... 169 A.4 Flexibility services...... 170 A.5 Microgrid challenges...... 173 A.5.1 Technical challenges...... 173 A.5.2 Regulatory/Social challenges...... 174 A.5.3 Financial challenges...... 175 A.5.4 Technical perspectives...... 176

Bibliography 187

Résumé en français 206

7 LIST OF FIGURES

1 Diagram presenting relations between different Chapters of this thesis...... 22

1.1 Electricity consumption by sector (2017)...... 34 1.2 Household energy consumption (2018)...... 35 1.3 Urban microgrid classic scheme...... 37 1.4 Illustration of graphical construction method for a photovoltaic-wind system satisfying a given load satisfaction...... 46

2.1 Studied smart home scheme...... 61 2.2 Community microgrid general scheme...... 62 2.3 Price per installed kWp in function of system size...... 65 2.4 Battery capacity degradation over time...... 68 2.5 Price per installed kWh in function of battery size...... 69 2.6 Household consumption ("classic", 2009-01-01)...... 71 2.7 Household consumption ("RT2012", 2009-01-01)...... 71 2.8 Household consumption (community, 2009-01-01)...... 72 2.9 Illustration of velocity update for a given particle i ...... 74 2.10 Particle swarm optimizer convergence illustration...... 74 2.11 Objectives involved in optimal sizing...... 80 2.12 Convergence curve of particle swarm optimizer for scenario 1 (Discount rate = 0%) 85 2.13 Convergence curve of particle swarm optimizer for scenario 1 (Discount rate = 0.5%)...... 85 2.14 Pareto front for scenario 4 ("RT2012" house)...... 88 2.15 Pareto front for scenario 4 ("Classic" house)...... 88 2.16 Household power ﬂow for scenario 4 ("Classic", 1st Jan of year 1)...... 89 2.17 State of charge for scenario 4 ("Classic", 1st Jan of year 1)...... 89 2.18 State of charge for scenario 4 ("Classic", worst year)...... 90 2.19 Net present value evolution among scenarios (single-family households)..... 94 2.20 Renewable energy penetration rate evolution among scenarios (single-family households)...... 94 2.21 Net present value evolution among scenarios (community microgrid)...... 95

8 LIST OF FIGURES

2.22 Renewable energy penetration rate evolution among scenarios (community microgrid)...... 95

3.1 Example of Markov Transition Matrix for clear-sky index in January...... 104

3.2 Probability distribution for current state E10 extracted from January Markov Tran- sition Matrix...... 105

3.3 Cumulative probabilities function F for current state E10 ...... 105 3.4 Example of stochastic global tilted irradiation in January...... 106

3.5 State of health degradation (%) in function of Crate for a given cycle...... 107 3.6 Illustration of 24 and 48h horizons abilities to manage storage...... 109 3.7 Solar power production and residential load for a given day...... 110 3.8 Spot and feed-in price for a given day...... 110 3.9 Zone designation for a given day...... 110 3.10 Histogram of global profitability variations over 50 random years...... 116 3.11 Convergence curve of particle swarm optimizer...... 119 3.12 Evolution of profitability and renewable energy penetration rate in function of allowed battery capacity for solar surplus storage...... 123 3.13 Evolution of profitability in function of battery capacity...... 124 3.14 Evolution of profitability in function of aggregator margin...... 124 3.15 Solar and residential power flow for a given day...... 125 3.16 Spot and feed-in price for a given day...... 125 3.17 Battery power flow in case 1: 0% aggregator margin...... 126 3.18 Battery power flow in case 2: 20% aggregator margin...... 126

4.1 General overview of microgrid behavior...... 131 4.2 Accuracy example of reference forecast and proposed method...... 136 4.3 Probabilistic solar energy distribution in studied location...... 136 4.4 Accuracy of reference forecast and proposed method with historic data..... 138 4.5 Global residential microgrid operation scheme...... 140 4.6 Flowchart of proposed microgrid power management framework for smart persistence and recursive least squares-based methods...... 142 4.7 Residential microgrid power flow for a given day (case I)...... 144 4.8 Residential microgrid power injection flow for a given day (case I)...... 145 4.9 State of charge for a given day (case I)...... 146 4.10 Battery power flow evolution over 10 years (case I)...... 147 4.11 State of health evolution over 10 years (case I)...... 147 4.12 Satisfaction ratio density (case I)...... 148

9 LIST OF FIGURES

4.13 Mean satisfaction ratio density for 100 random years (case I)...... 148 4.14 Residential microgrid power ﬂow for a given day (case II)...... 149 4.15 Residential microgrid power injection ﬂow for a given day (case II)...... 150 4.16 State of charge for a given day (case II)...... 151 4.17 Example of satisfaction ratio density (case II)...... 151 4.18 Mean satisfaction ratio density for 100 random years (case II)...... 152

4.19 Evolution of practical balance (actual profit) in function of penalty coefficient (kagg) 153 4.20 Evolution of mean satisfaction ratio and practical balance in function of battery size (both cases)...... 154 4.21 Relative improvements of profitability and satisfaction provided by proposed method in function of battery size...... 155

A.1 Gross inland energy consumption by source (2017)...... 164 A.2 Energy production by source (2017)...... 165 A.3 Electricity generation by source (2017)...... 165 A.4 Share of energy from renewable sources in the European Union (2017)..... 166 A.5 Incentives evolution for photovoltaic-based projects in France (2002-2017).... 167 A.6 Evolution of available photovoltaic power in France (2001-2017)...... 168 A.7 Spot prices (EPEX SPOT market, french area) for year 2016...... 175

10 LIST OF TABLES

1.1 Policies and incentives towards renewable energy among the some European Union countries...... 30 1.2 Pros and cons of optimization methods...... 50

2.1 Solar system related costs...... 65 2.2 Battery specifications...... 67 2.3 Battery system related costs...... 69 2.4 Photovoltaic feed-in tariffs and self-consumption incentives...... 73 2.5 Optimal system size and profitability (scenario 1)...... 84 2.6 Optimal system size and profitability (scenario 2)...... 86 2.7 Optimal system size and profitability (scenario 3)...... 86 2.8 Optimal system size and cost (scenario 4)...... 87 2.9 Optimal system size and cost (scenario 4*)...... 90 2.10 Optimal system size and profitability (scenario 1)...... 91 2.11 Optimal system size and profitability (scenario 2)...... 91 2.12 Optimal system size and profitability (scenario 3)...... 92 2.13 Optimal system size and cost (scenario 4)...... 92 2.14 Optimal system size and cost (scenario 4*)...... 93

3.1 Clear-sky index quantification...... 103 3.2 Battery states and description...... 112 3.3 Input parameters...... 116 3.4 Financial balance...... 116 3.5 24-hour horizon financial balance (profitability after 10 years for systems 1, 2 and 3)...... 118 3.6 48-hour horizon financial balance (profitability after 10 years for systems 1, 2 and 3)...... 120 3.7 Energy management strategy evolution in function of years (48-hour horizon, system 1)...... 122 3.8 Energy management strategy in function of battery cost...... 123

4.1 Performance comparison of orders applied in equation (4.5)...... 134

11 LIST OF TABLES

4.2 Performance comparison of two weather forecast techniques under uncertainties 137 4.3 Performance comparison for year 2019...... 137 4.4 Proﬁtability and aggregator satisfaction over 10 years...... 152

12 ACRONYMS

AC Alternating Current ADPED Approximate Dynamic Programming based Economic Dispatch AI Artiﬁcial Intelligence ANN Artiﬁcial Neural Network ANSI American National Standards Institute API Application Programming Interface AR(I)MA Autoregressive (Integrated) Moving Average AVR Automatic Voltage Regulator B2G Battery to Grid BEMS Battery Energy Management System BESS Battery Energy Storage System BMS Building Management System BRP Balance Responsible Party CAISO Califorina Independant System Operator CAMS Copernicus Atmosphere Monitoring Service CEC California Energy Commission CHP Combined Heat and Power COE Cost of Energy CPF Cumulative Probability Function CRE Commission de Régulation de l’Energie DC Direct Current DE Differential Evolution DEMS Domestic Energy Management Systems DER Distributed Energy Resources DG Distributed Generation

13 Acronyms

DOD Depth Of Discharge DR Demand Response DSM Demand Side Management DSO Distribution System Operator DSR Demand Side Response DTU Documentation Technique Unique EA Evolutionary Algorithms ECES European Committee for Electrotechnical Standardization EDF Electrcité De France EENS Expected Energy Not Supplied EHV Extra High Voltage EMM Energy Market Management EMS Energy Management System ENTSO-E European Network of Transmission System Operators ESG Environmental, Social, and Governance EPEX European Power EXchange ESS Energy Storage Systems EU European Union EV Electric Vehicle FERC Federal Energy Regulatory Commission FIP Feed-in Premium FIR Finite Impulse Response FIT Feed-in Tariff FL Fuzzy Logic FUSE-IT Future Uniﬁed System for Energy and Information Technology GA Genetic Algorithm GHG Greenhouse Gases GSE Gestore Servizi Energetici GWO Grey Wolf Optimizer

14 Acronyms

HDKR Hay, Davies, Klucher and Reindl HIL Hardware-In-the-Loop HOMER Hybrid Optimization Model for Electric Renewable HV High Voltage HVAC Heating, Ventilation and Air-Conditioning Icam Institut Catholique d’Arts et Métiers ICT Information and Communication Technologies IEC International Electrotechnical Commission IEEE Institute of Electrical and Electronics Engineers IoT Internet of Things IREENA Institut de Recherche en Energie Électrique Nantes Atlantique ITEA Information Technology for European Advancement ISO Independent System Operator KPI Key Performance Indicator LCOE Levelized Cost of Energy LMP Locational Marginal Pricing LOLP Loss Of Load Probability LPSP Loss of Power Supply Probability LV Low Voltage MAE Mean Absolute Error MAPE Mean Absolute Percentage Error MAS Multi-Agent System MASCEM Multi-Agent System for Competitive Electricity Markets MC Monte Carlo MERRA Modern-Era Retrospective analysis for Research and Applications MFO Moth-Flame Optimizer MG Microgrid MGCC Microgrid Central Controller MIBEL Mercado Ibérico de la Electricad

15 Acronyms

MILP Mixed Integer Linear Programming MOPSO Multi-Objective Particle Swarm Optimization MPPT Maximum Power Point Tracking MTM Markov Transition Matrix Mtoe Megatonnes of oil equivalent MV Medium Voltage NEMA National Electrical Manufacturer’s Association NOCT Normal Operating Cell Temperature NPV Net Present Value NREL National Renewable Energy Laboratory NSGA-II Non-dominated Sorting Genetic Algorithm-II NYISO New York Independent System Operator OFGEM Ofﬁce of Gas and Electricity Markets OMIE Operador del Mercado Ibérico de Energía - Polo Español OMIP Operador del Mercado Ibérico de Energía - Polo Portugués OpenADR Open Automated Demand Response P2P Peer to Peer PCC Point of Common Coupling PHEV Plug-in Hybrid Electric Vehicle PSH Pumped-Storage Hydroelectricity PSO Particle Swarm Optimization PSOGSA Hybrid Particle Swarm Gravitational Search Algorithm PV Photovoltaic PWM Pulse-Width Modulation RAPS Requested Average Price Spread RAR Resource Adequacy Requirement RES Renewable Energy Sources RLS Recursive Least Squares S-PER Smart Persistence

16 Acronyms

RMS Root Mean Square RMSE Root-Mean-Square Error RnE Renewable Energy ROI Return Over Investment RTE Réseau de Transport d’Electricité RTO Regional Transmission Operator RTP Real-Time Pricing SC Smart City SEAS Smart Energy Aware Systems SG Smart Grid SMB Small and Medium-sized Businesses SOC State Of Charge SOH State Of Health SSUEP Set of Sequential Uninterruptible Energy Phases SVC Static Voltage Controller TGC Tradable Green Certiﬁcate THD Total Harmonic Distortion TOU Time Of Use TSO Transmission System Operator TURPE Tarif d’Utilisation des Réseaux Public d’Electricité UPS Uninterruptible Power Source USA United States of America V2G Vehicle to Grid VAT Valued Added Tax VPP Virtual Power Plant VUF Voltage Unbalance Factor WCA Water Cycle Algorithm WOA Whale Optimization Algorithm

17 INTRODUCTION

Context

Smart Cites and Smart Grids

Smart City (SC) belongs to a concept that is subject to a numerous amount of definitions. In 2014, a report published by the International Telecommunications Union cited more than 100 definitions forSC[159]. Smart Grid (SG) andSCs are intimately related.SGs are involved inSCs in order to efficiently manage their own energy consumption as well as energy consumed by citizens. Such management is performed thanks to a local Renewable Energy Sources (RES) optimization and load demand at various time steps. The key objective is to propose a smarter way to manage grids and mitigate imbalances between energy demand and supply in order to facilitate the integration of RES and new use cases such as : Electric Vehicle (EV) management, Internet of Things (IoT) and so on. Integration of Renewable Energy (RnE), balance management for supply and demand taking into account market mechanisms, reduction of Greenhouse Gases (GHG), energy efficiency improvements, security of energy supply for the consumer are key points of policies for energy transition that the European Union (EU) enacted in 2008 [62] and revised in 2018 [63]. Nowadays, cities are a trending topic in discussions related to society evolution and urbanization growth. All over the world, the ratio of people living in cities is estimated to be around 55% in 2018 and expected to reach 68% in 2050. In Europe, this ratio is estimated to be around 74% in 2018 and expected to reach 83.7% in 2050 [34]. Moreover, according to the population growth, residential areas have become a significant stakeholder in future grid transactions due to rapid urbanization and development of smart meters. For instance, in 2015, the residential sector represented 27% of worldwide electricity consumption [126, 89]. Moreover, in theEU-28, households consumed 807 TWh of electricity (27.8% of available energy for final consumption in this zone) [67]. The vision of futureSCs that relies onSGs for energy supply, RES integration and respect of European policies will demand an expensive and long-term reengineering of infrastructures as well as the communication systems. This view must integrate techno-economic, social and environmental parameters. The major question is to demonstrate that citizens and the whole society are able to bring in all the benefits of this new paradigm such as: cleaner planet, low-

18 Introduction carbon economy as well as cost-effective energy systems. Thus, extending the Microgrid (MG) concept in the residential sector will help grids to mitigate problems due to RnE integration and help the grid through various ﬂexibility services. Moreover, current networks are radial and power is unidirectional, energy is delivered through a feeder. Power ﬂows in future grids will be multi-directional, thusSGs based on mesh topology are considered to integrate various local power sources and to minimize losses. In order to control this kind of topology, smart management methods must be developed. Therefore, economic solutions are currently deployed to achieve European objectives introduced above in the residential sector. In order to promote integration of RES in the residential sector, various European countries enacted subsides to help people install solar panels on their roofs. Besides, Feed-in Tariff (FIT)[127] were introduced to limit uncertainties regarding Return Over Investment (ROI)[15]. Theses tariffs are revised periodically to avoid speculation. In France, energy markets were liberalized in 2007 for residential consumers [57]. Residential consumers are able to choose their energy supplier. For energy injection, they can sign in for regulated FIT to any stakeholder.

Problems

Due to their intrinsic intermittent characteristics, RES are renowned to be classified as non dispatchable and difficult to forecast accurately. This is particularly true if they are compared to conventional power plants. That is why, despite the fact that a policy towards RnE deployment in residential sector is a good idea to increase RES share in the energy mix, a major share of non dispatchable power sources in the global production and a limited market access of the residential section will generates several issues such as: — Non optimal energy allocation. Energy (spot) markets currently not available for residential users because of their small size. It would be technically possible for clustered residential users (under the from of a residentialMG) to access such markets through aggregators [102] and thus, be able to help the main grid through various flexibility services [169]. In this context, various potential business models for aggregators managing residential MG were presented in [27]. Considered business models are: energy supply to mid-scale customers with time variable tariffs including peak-load optimisation, simple trading of aggregated renewable electricity on spot markets, valorisation of local generation apartment buildings. Nevertheless, current strategies are quite simple. For instance, energy trading is always considered with a fixed 24h time horizon. — Problems to supply the base load caused by RES’ low charge factor (such as Photovoltaic (PV) and wind) and forecasting errors.

19 Introduction

— Storage not considered as a RES in subsides frameworks expect for large entities that ensure voltage/frequency regulation thanks to (non)-spinning reserve. Storage is often discarded in the residential sector because of its current cost and lack of incentives. How- ever, it would provide an extra degree of freedom for cost-competitive sizing and energy management techniques.

Regarding forecast errors, grid managers will have to deal with a growing amount of residential solar panel arrays that are non dispatchable and difﬁcult to forecast due to local climatic variations. Thus, European policies may be harmful for the main grid. Thus, as written before, massive integration of RES in grids implies for all stakeholders to shift from one paradigm to another regarding electrical grids sizing and management. Ensuring safety of energy supply to consumers at all time clearly becomes more complicated considering a large share of RES in the global energy mix. To cope with this major issue, various solutions such as storage systems, enhanced energy management methods as well as forecast methods can be deployed.

Contribution

In view of the global trends and policies regarding energy transition and migration towards low-emission electricity sources, problems presented above need to be dealt with. This thesis proposes to address:

— Energy market integration and lack of cost-competitiveness of RnE sources (par- ticularlyPV systems) by providing a communityMG sizing method suitable for residential consumers which takes into account local applications (power injection, self- consumption...) and regulations. Moreover, a novel energy arbitrage strategy based on time-horizons is proposed to further improve proﬁtability.

— Short-term forecasting errors coming from weather variations with a statistical auto regressive predictor-corrector solar irradiation forecast model.

Methods presented above aims to study the relevance of residential sector participation in energy markets. These contributions applied to realistic single-family homes and community MG are detailed below:

— Sizing optimization: a sizing optimization must be carried out in order to avoid pro- hibitive operation costs and to satisfy adequately the load considering either a single- family house or a communityMG subject to subsides and with various objectives such as: self-consumption, partial injection, total injection or off-grid operation. In this thesis, both

20 Introduction

mono and multi-objective optimizations based on Particle Swarm Optimization (PSO) are proposed to deal with both subsides and various objectives presented above.

— Energy management optimization: an energy management optimization must be carried out because of uncertainties generated by RES. In this thesis, energy management strategies that are suitable in the context of communityMG involved in energy/ﬂexibility markets are presented. Two energy management strategies are introduced below:

— First strategy is based on time horizons for energy arbitrage proﬁtability improvement. Time horizon for energy arbitrage applications is currently set at 24h in the literature. Proposed contribution assesses potential beneﬁts of extending this horizon to 48h. This extended horizon allows extra battery usage patterns.

— Second strategy is based on forecast accuracy performance in the context of bid- based energy markets. The objective of this strategy is to provide accurate hour- ahead solar irradiation forecasts in order to provide relevant energy injection forecasts to an aggregator. Mismatches between forecasts and actual injected energy are penalized according to contract terms. Proposed forecast method based on least squares is compared to "smart persistence" reference method commonly used in the literature for short-term forecasts.

Thesis Outline

This work involves 4 chapters which deal with key points presented above. The outline of this thesis is presented below:

— Chapter 1 deals with a review of various challenges thatSG andMG are facing and will have to face in the future. Among others, economical operation challenge is a key point. Moreover, various sizing and management strategies proposed in the literature are discussed. Some of them focus on residential applications because of its relative importance in terms of consumed energy all over the world. Finally, a point of view, objectives of this thesis as well as followed scientiﬁc process are presented.

— Chapter 2 introduces models used in this thesis.PV, Battery Energy Storage System (BESS), residential loads as well as various aggregator interaction models are presented. This chapter also proposes mono and multi-objective optimal sizing method based on PSO for on-site energy production in residential areas based on solar power taking into account: (i) future energy cost in order to ensure ﬁnancial safety regarding energy prices, (ii) load satisfaction rate / User comfort satisfaction rate and (iii) RnE penetration rate.

21 Introduction

Studied site is a single-family house. In the scope of this work, this site is extended to a communityMG. — Chapter 3 proposes to assess the relevance of an energy management strategy based on 48-hour horizon compared to a 24-hour horizon one in order to perform energy arbitrage. This study considers a residentialMG based onPV generation and storage connected to the main grid. Proposed 48-hour energy management strategy provides additional management possibilities such as the ability to delay trades (charge today, discharge tomorrow) and a larger range of hours to use the storage. PSO is used to solve the optimization part. Besides, a sensitivity analysis is investigated to assess the economic impact of forced storage of solar surplus power in order to increase self-consumption rate and storage size. — Chapter 4 proposes an enhanced energy management framework which aims to efﬁ- ciently address uncertainty issues due to local climatic variations in a peninsula context. The proposed framework uses a ten-state Markov chain to generate stochastic solar irradiation as well as a forecast correction method based on recursive least-squares updated every hours in order to efﬁciently take part in hour-ahead power bidding process. Besides, a sensitivity analysis is investigated regarding impact of storage size and aggregator penalty on operation cost and commitment indices.

Figure1 displays relations between Chapters presented above. Methods presented in Chap- ter 1 are adapted to match with the residential context and sizing results from Chapter 2 are used in Chapters 3 and 4.

Sizing results

Review of MG Time-horizon Forecast Optimal sizing for sizing and based energy uncertainties Sizing methods single-family Sizing results Addition of forecast energy arbitrage applied management for homes and uncerttainties management to community community MG in community MG methods MG peninsular zones Energy management methods

Chapter 1 Chapter 2 Chapter 3 Chapter 4 Figure 1 – Diagram presenting relations between different Chapters of this thesis

Finally, in the last part of this manuscript, conclusions that can be drawn for each Chapter and the main contributions of this thesis are summarized. General ideas for future extensions of this research work are also discussed. This thesis is related to an European project named Future Uniﬁed System for Energy and Information Technology (FUSE-IT). FUSE-IT project involved 20 partners (industries and uni- versities) among Europe and was leaded by Cassadian Cybersecurity. The goal was to provide

22 an unified interface to manage both power flows and smart sensors for enhanced security in critical sites (hospitals and so on). Besides, this work was financed by Institut Catholique d’Arts et Métiers (Icam) and super- vised by laboratory Institut de Recherche en Energie Électrique Nantes Atlantique (IREENA) which is associated to University of Nantes.

Publications

Contributions presented above are highlighted in the following papers: — Publications in international journals — R. Vincent, A. Houari, M. Ait-Ahmed and M. F. Benkhoris. Influence of different time horizon-based battery energy management strategies on residential microgrid profitability. Journal of Energy Storage, Volume 29, 2020. — R. Vincent, M. Ait-Ahmed, A. Houari and M. F. Benkhoris. Residential microgrid energy management considering flexibility services opportunities and forecast uncertainties. International Journal of Electrical Power & Energy Systems, Volume 120, 2020. — Communications in international conferences — R. Vincent, M. Ait-Ahmed, A. Houari and M. F. Benkhoris, "Microgrid Modeling and Power Quality Enhancements Using Low-Level Control Methods Based on Robust RST Controller," IECON 2018 - 44th Annual Conference of the IEEE Industrial Elec- tronics Society, Washington, DC, 2018, pp. 213-218. — R. Vincent, M. Ait-Ahmed, A. Houari and M. F. Benkhoris, "Residential microgrid photovoltaic panel array sizing optimization to ensure energy supply and financial safety," 6th International Conference on Control, Decision and Information Technolo- gies (CoDIT), Paris, France, 2019, pp. 226-231. — L. Qiao, R. Vincent, M. Ait-Ahmed, and T. Tianhao, “Microgrid modeling approaches for information and energy fluxes management based on PSO,” ICINCO 2019 - Proc. 16th Int. Conf. Informatics Control. Autom. Robot., Prague, Czech Republic, vol. 1, pp. 220–227, 2019. — Communication in national conference/workshop — L. Qiao, R. Vincent, M. Ait-Ahmed, T. Tianhao. Microgrid Modeling Approaches for In- formation and Energy Fluxes Management (IREENA/SMU). Fifth Sino-French Work- shop on Information and Communication Technologies (SIFWICT), Nantes, France, 2019.

23 CHAPTER 1

SMART GRIDSAND MICROGRIDS: TOWARDS MORE SMARTNESS

The concept ofSG was clearly defined by the United States of America (USA) for the first time in the Energy Independence and Security Act of 2007 [129] and by European Commission in Standardization Mandate to European Standardisation Organisations to support European Smart Grid deployment of 2011 [37]. Smart grids are electrical network that are able to cost efficiently integrate various stakeholders (producers, consumers as well as entities able to be both) in order to optimize its economical operation while providing a sustainable power system with low losses and high quality levels as well as security of supply.

Distributed Generation (DG) involved inSGs and intermittent RES such asPV arrays and wind turbines (particularly low voltage sources) can cause as many problems as they may solve. In [103], authors proposed an approach to view localDG and loads as a unique subsystem which is able to help the main grid thanks to ﬂexibility services or to switch to off-grid mode if needed. This subsystem is named microgrid.

This Chapter is dedicated to investigating various challenges thatSGs andMGs are facing and will have to face in the future. Among others, economical operation challenge is a key point. In order to achieve such goal, various sizing and management strategies have been proposed in the literature. Some of them focus on residential applications because of its relative importance in terms of consumed energy all over the world. The ﬁrst section introduces RnE in Europe, incentives and deployment programs as well as technical solutions for RES integration in households. Moreover, theMG concept will be thoroughly deﬁned and challenges that should be overcome will be investigated.In the second section,MGs’ challenges and perspectives will be discussed. In the third section, sizing and management strategies in order to improveMG economical operation will be presented and their respective limitations will be highlighted. In the fourth section, contributions from this work will be presented.

24 1.1. Introduction

1.1 Introduction

RES are defined as resources that are able to regenerate over a short time period. Solar (thermal andPV), wind, hydro power, biomass as well as geothermal and ocean-based sources are considered as RES. Nowadays, RES represent a fair alternative solution in order to replace conventional power plants such as coal-fired and oil-based plants for both grid-connected and off-grid applications. Nevertheless, RES have some drawbacks and cause technical challenges that have to be dealt with in order to ensure cost efficient and reliable energy supply. This section introduces history of RnE in Europe, its deployment as well as incentives and programs in order to achieve fixed objectives by the European Commission. Because of its important share of consumed energy, residential sector is a key point in the context ofSGs because of its numerous degrees of freedom. A residentialMG is able to provide a various set of flexibility services, such possibilities will be introduced below. However, massive introduction of intermittent power sources in current grids is far from easy. Thus, several challenges such as regulations, economical/social barriers or technical limitations that come with the residentialMG concept are also presented.

1.1.1 Renewable energy in Europe

European objectives

Climate change concerns started in the last century and lead to the establishment of the Kyoto protocol in December 1997 [28].EU members signed this protocol. Moreover, a sig- niﬁcant increase of the RnE share in European electricity grids was voted by the European commission. The "2020 climate and energy package" is a set of regulations to ensure the EU meets its objectives by the end of the year 2020 [62]. This set of binding legislation’s objectives were targeted byEU leaders in 2007 and enacted in December 2008. 2020 climate and energy package’s main targets are the following ones (2030 objectives [63] are displayed in italic):

— 20% (40%) cut in GHG emissions (from 1990 levels). Target value depends on the country’s wealth (a maximum of 20% increase is allowed for less wealthy ones).

— 20% (32%) of EU energy from renewable sources (this target relates to gross ﬁnal energy consumption).

— 20% (32.5%) improvement in energy efﬁciency 1.

1. In this case, energy efﬁciency means consuming less by improving efﬁciency of variety of products such as heaters, household appliances, lighting, reducing the consumption of new buildings, implementing smart meters in order to improve energy management [64].

25 Chapter 1 – Smart Grids and Microgrids: towards more smartness

In 2017, average penetration of RES in gross energy consumption was equal to 17.5% [67] in theEU-28. This value is not far from 2020 objectives. This ratio shows that European policies are correctly enforced despite of ﬂaws generated by a massive RnE integration. In order to facilitate the comparison between current situation in European countries and ﬁxed objectives regarding RnE integration, more details and explanations are available in Appendix A.1.

Advantages of RnE

The increase of the RnE share in the energy mix has advantages for the grid:

— Provide clean energy (admitting that setup and maintenance emissions are not taken into account or neglected).

— Allow to supply consumer’s sites far away from the main grid if the consumer does not require huge amounts of power.

— Allow to be partially or totally self sufﬁcient in a case of a grid major breakdown.

— Allow ﬁnal users to become so-called "prosumers" as they are able to both produce and consume electricity if they have local RnE sources (mainlyPV panels for households).

Drawbacks of RnE

As brieﬂy mentioned before, currently, RnE sources suffer from several ﬂaws that limit their development all over the world:

— Intermittent behavior and lack of accurate forecast that generates:

— Annoyance for grid Transmission System Operator (TSO)s and Distribution System Operator (DSO)s as well as other stakeholders because it can perturb the following actions:

— Participating to frequency and voltage regulation.

— Supporting the grid in case of a black start.

— Ensuring safety of power supply in islanded mode.

— Need for storage capacity in order to mitigate intermittent behavior. This currently adds signiﬁcant extra costs to the global system but as technology evolves, storage price decreases.

— Need of dispatchable power sources must be considered if no storage is taken into account for applications where safety of power supply must be ensured or to respect

26 1.1. Introduction

commitments regarding injected energy. However, such dispatchable sources are often based on fossil fuels (gas turbines and diesel generators) which do not fit with the main objectives of reduction of GHG emissions and RnE integration. — Higher upfront costs than conventional power plants (this point tends to become arguable because prices are going down thanks to improvements in performance and manufacturing processes) — To combat this drawback, incentives are available in many countries. This incentives are generally composed of: tax credits, rebates or even FIT/Feed-in Premium (FIP) or even Tradable Green Certificate (TGC). Such incentives are discussed in Section 1.1.2. Creation of incentives leads to an another drawback. As the source of such incentives to attract private investments comes from public funds, extra taxes that impacts electricity price are enforced resulting in a continuous and significant aug- mentation of retail electricity price over time for final consumers as demonstrated in Section 1.2.1. — Geographic limitations Indeed, some RnE sources (particularlyPV and wind sources) can be really complicated to forecast accurately (depending on their location) and therefore, controlling the production due to their inherent intermittency (for solar and wind) is not a simple task. Besides, for multi- source generation sites or clusters of generation apart from each others (for instance offshore wind turbines andPV panel fields in the country side), optimal dispatching can be impossible because energy is produced at places where it is not needed. Hydroelectric, geothermal and biomass power plants are great technologies to provide reliable clean energy as they can deliver power upon request and do not suffer strong intermittency as wind and solar technologies can. But they suffer from lack of places (adapted sites for hydroelectric and geothermal utilities) or require a huge areas for natural resources storage (for instance: a forest for wood-based biomass plants) which is a strong limitations regarding their respective deployment in theEU. As written above, major challenges have to be dealt with in order to deploy RES massively. As large-scale RES deployment attracts grid operators as a way to reduce their GHG emissions (caused by their fossil fuel consumption), such deployment will generate serious challenges regarding normal grid operation. Namely, it will lead to frequency and voltage stability issues particularly in insular locations or in areas where there is no major generation site (for instance, in France, in place where there is no nuclear power plant nearby). Finally, in the literature, challenges regarding RES integration briefly introduced above can be sorted in 4 categories: technical, supply and demand (markets), environmental and social/political barriers [145, 60]. These challenges will be further discussed in Section 1.2.1.

27 Chapter 1 – Smart Grids and Microgrids: towards more smartness

1.1.2 Incentives and deployment programs for renewable energy sources in Eu- rope

Incentives and programs in theEU

Financial incentives were set up after the EU Directive 2001/ 77/EC and the EU Directive 2009/28/EC were enacted respectively in 2001 and 2009 and lead to a target of 20% gross final consumption of energy coming from RES[62]. Policies are different amongEU countries. For instance, France, Germany, Spain, Italy and United Kingdom have set various kind of incentives. Incentives in these countries can be sorted into 2 categories: tariffs (FIT and FIP) and certificates (TGC)[127]. FIT often involves a long-term purchase agreement (15-20 years), a tariff based on costs related to RnE generation and a guaranteed access to the main grid. In this context, FIT is fixed above spot price and generally does not evolve during all the agreed time period. FIT being fixed above spot prices acts as a premium to attract RES generation projects. According to involved RES and project size (households or bigger entities), FIT value may vary. In a call of tenders, FIT are proposed by bidders. FIP offers a premium related to spot price. As FIP will not be constant over contract period, global income for RnE producers will be less predictable. Finally, TGC is an instrument set up by governments in order to make sure RES producers fulfill promised quantity of RnE. Associated mechanism is quite simple, involved stakeholders have to supply a certain quantity of RnE. Proof of generation is represented by so-called TGC. In order to fulfill agreements there are several ways. Certificates can be obtained by: producing RnE, buying RnE and associated certificates or buying certificates (without buying associated RnE). For all of theses policies, there are pros and cons: — Pros: — FIT: guarantee of a stable and secure market for investors, hedge against price volatility, enhanced market access for stakeholders. — FIP: stimulates price competition and can cost less than FIT policy to public finances. — TGC: guarantees a regulation of capacity deployment and is generally cheaper than a FIT policy. — Cons: — FIT: distorted electricity price, price competition is not encouraged, does not directly address high upfront investment costs related to RES projects (this argument tends to become irrelevant due to cost reductions). — FIP: does not provide FIT advantages

28 1.1. Introduction

— TGC: no specific certificate for a given technology thus it promotes mature technologies instead of newer ones (because of ROI difference). Certificates rely on supply and demand (market mechanism) thus their value could be theoretically null in case of over investments. Table 1.1 shows different policies adopted by 5EU countries introduced above. It can be noted that every listed countries adopted (at least) a FIT policy. Policies introduced above involve several types of RES such as:PV, wind, hydro, geothermal, biomass and so on. One of the most represented type isPV (especially for household due to the fact that it is easy to set up). Some policies displayed in Table 1.1 have been abrogated in order to make future projects to focus on self-consumption [1]. For instance, Germany switched from premium FIT to a net metering scheme in 2012 [1] (unique bill where produced energy is deducted from electricity bill, generally injected electricity is subject to a low FIT in order to motivate self-consumption). Spain switched from FIT to FIP where future projects have to bid prices in a call for tenders [61]. Denmark started schemes to promote RnE generation (especially wind due to geographic characteristics) since 1981. In 1984 introduction of FIT boosted the amount of grid-connected wind turbines and in 2000, TGC superseded FIT because of plan "Energy 21" [119, 48]. Nowa- days in Denmark, several incentives are enforced such as: guaranteed loans, net-metering (it translates into an exemption for certain plant operators from paying the public service fees), FIP/TGC and call for tenders [173]. France enforced FIT since 2002 and FIP since 2015 (FIP does not apply to solar generation) because European guidlines require that renewable energy must be progressively exposed to market competition. Moreover, other mechanisms such as tax regulations (income tax credit and Valued Added Tax (VAT) discount) and call for tenders are also enforced [168].

3. Original name: Regimen Especial 4. Original name: Tariffa Onnicomprensiva/Certiﬁcati Verdi 5. Original name: Bekendtgørelse af lov om fremme af vedvarende energi 6. Original name: Obligation d’Achat 7. Original name: Mécanisme de compensation

29 Chapter 1 – Smart Grids and Microgrids: towards more smartness

Country Policies Policy name(s) Period Sources GER FIT Law on renewable energies 2 2000-2012 [176] SPA FIT Special Regime 3 2000-2012 [61] ITA FIT/TGC All-inclusive tariffs/Green Certiﬁcates 4 2000-2013 [79, 80] UK FIT/TGC FIT/Renewable Obligation 2000- [130, 131] DNK FIP/TGC Promotion of Renewable Energy 5 2009- [173] Mandatory buying program 6 FRA FIT/FIP 2002-/2015- [40, 168] Compensation mechanism 7

Table 1.1 – Policies and incentives towards renewable energy among the some European Union countries

This part brieﬂy presented incentives and programs in theEU which are currently active or canceled. French incentive and their impact overPV sources deployment is further detailed in Appendix A.2. All over theEU, incentives allowed the emergence of variousSG/MG projects. Some of them are presented thereafter.

Ongoing development in Europe

EU-28 investments towardsSG include 950 projects in the end of 2017, totalling for 5 B e [154]. Amount of ongoing projects peaked in 2013 (394 ongoing projects) and amount of investments peaked in 2012 ( 936 Me). R&D projects represent 53% of funded projects whereas demonstration projects represent 47%. The high amount of R&D projects implies that some research is still required even if someSG technology is about to enter in commercialisation phase. Regarding financed projects, there is currently a lack of private investments towardsSG. Only 15% of all projects are fully financed by private investors, the rest rely partially or totally on national/European investment schemes. On the R&D side, only 10% of projects are fully financed by private entities. Currently, European equipment, services andSG Information and Communication Technologies (ICT) developers invest in projects with the aim to test and deploy their solution in real environment. However, their inclination to invest be affected by several causes explained in Appendix A.5.

Ongoing development in France

Réseau de Transport d’Electricité (RTE) sees opportunity and great potential in (offshore) wind power, marine currents and waves [147]. The involvement of such RnE sources in the grid

30 1.1. Introduction will contribute to the energy transition success. French TSO"RTE" invested 1549 M e in 2016 and will continue to invest into the development of its infrastructure [148]:

— Integrate more RnE sources in order to respected ﬁxed objectives. Their objective is to reach 23% of the consumed energy by the end of 2020.

— Reduce the nuclear share of consumed energy to 50% by the end of 2025.

— Support the development of new RnE sites according their given characteristics (for instance: offshore wind power in the north of the country andPV arrays in the south).

— Compensate RnE intermittency problems by bringing more smartness into the current grid and promote market mechanisms to ensure the power quality and balance between supply and demand.

By half of 2019, RTE stated that 21% of consumed energy originated from renewable sources (considering measurements over a rolling year) [149].

ExperimentalSG projects

TheEU promotes RES integration thanks to FIT and TGC systems. TheEU also ﬁnances several EuropeanSG projects in order to deal with various aspects of RES integration. Some projects focus on security of energy supply, self-consumption, ﬂexibility services (such as Demand Response (DR)), new use cases and associated business models related to RnE. Several ex- emples are listed below:

— Kergrid [142] (2009-2017) project is a concept of smart microgrid integrated inside a building (ofﬁces) as known as "smart building" located in Brittany, France. The main goal of this project was to demonstrate the relevancy of storing surplus energy in order to use/sell it later. However, this project highlighted the lack of economical and regulatory frameworks for storage capacity usage in order to interact with the main grid. Neverthe- less, this project validated the relevance of peak-shaving as a service in order to help the main grid during peak hours. Brittany is considered nearly as a remote area regarding the presence of major power plants (no active nuclear power plants in Brittany), thus transmission and distribution losses are not marginal. Thus, a service such as local peak shaving could help the grid to ensure safety of supply and reduce the amount of power outages in this area.

— GreenLys [77] (2012-2016) is a smart grid experiment located in Lyon/Grenoble, France. This project aims to adapt load demand (thanks toDR mechanisms) with future grids’

31 Chapter 1 – Smart Grids and Microgrids: towards more smartness

constraints generated by the massive integration of renewable sources. This project involved 1000 households et 40 professional users (tertiary firms). Majors findings showed the presence of a power rebound after the load shedding period and a lack of household motivation to followDR guidelines. The power rebound was caused by a suboptimal coordination of different stakeholders (every stakeholder was shedding at the same time and restarted shedded loads at the same time). This generated a new peak, few hours after the original one. Regarding household motivation, it was mainly due to the fact that generated savings where too low compared to the generated annoyance ofDR. — Smart Energy Aware Systems (SEAS) [90] (2014-2016) is an Information Technology for European Advancement (ITEA) 2 European project that involved 7 European countries. This project aims to overcome the problem of inefficient energy consumption due to a lack of control means. In order to do so, SEAS proposed an onthology 8 dedicated to propose a global standardized framework to coordinate energy, automation and communication systems at consumption sites and in order to optimize global systems’ efficiency. The major outcome was the realization of Energy, Software and Services Application Pro- gramming Interface (API)s which are already used by several European energy-related companies in order to improve urban energy management through standard control of transport, water, heating, lighting, weather information and traffic regulation. Like other projects, proposed framework will help to integrate massively RnE sources. — EcoGridEU [141] (2011-2015) is an islanded microgrid demonstrator located in Born- holm, Danmark and where wind represents 50% of electricity consumption. 2000 residential consumers participated with flexibleDR to Real-Time Pricing (RTP) signals. This project is similar to the GreenLys project regarding the concept ofDR. The goal of this project was to assess the relevance of market mechanisms and how final users will take part in such market throughDR and local generation (rooftopPV). Contrary to Greenlys, households were more motivated not only by lowering their electricity bill (major point) but by doing something good for their community. Besides demonstrating that RTP and DR are a reliable solution for future grids, this project showed that the relevant ofDR also depends on people’s long-term involvement in such programs. These projects share a common objective: to find technical solution in order to seamlessly integrate RnE sources in the global energy mix. They proposed various solutions to do so. Demonstrators went from a single building to a part of an island. Involved services were various but there is a trend for "market" related services (DR and arbitrage). Finally, projects that involved residential users emphasized on the importance of keeping people committed to their

8. Onthology stands for a representation, formal naming and deﬁnition of the categories, properties and relations between the concepts, data and entities involved in several domains.

32 1.1. Introduction

DR scheme. In order to do so, such scheme should provide them signiﬁcant electricity bill reduction as it is perceived as the main source of success and award by end users. Moreover, some of these projects as well as several others are further described in Ap- pendix A.3.

33 Chapter 1 – Smart Grids and Microgrids: towards more smartness

1.1.3 Residential energy consumption and Renewable Energy Sources (RES) integration

Recently, according to the development of smart grids and smart cities, residential areas have become a significant stakeholder in future grid transactions due to rapid urbanization and development of smart meters. For instance, with 5480 TWh consumed in 2015, the residential sector represents 27% of worldwide electricity consumption [126, 89]. In theEU-28, with 2895 TWh of electricity available for final consumption in 2017, households consumed 807 TWh of electricity (27.8% of available energy for final consumption). Figure 1.1 shows electricity consumption per sector in theEU for 2017 [67].

Other: 5.9%

Households: 27.8% Industry: 37.5%

Commercial and public services: 28.8% Figure 1.1 – Electricity consumption by sector (2017)

Figure 1.1 also displays an "other" category, this category involves transports, agriculture, forestry and fisheries as well as electricity consumed by the energy sector itself. Figure 1.2 shows a classic electrical household consumption by sector according to main french electricity producer Electrcité De France (EDF)[49]. The main part of the consumption is represented by heating appliances (62%), followed by other electric devices such television, small electric devices and so on (20%), hot water (11%) and finally by cooking devices (7%). Energy is mainly consumed in order to heat homes, this is explained by the debatable quality of some heating devices (large amount of so-called "toasters" are still used in a lot of households) and by the lack of good thermal insulation (a lot of households were built before or around the first oil crisis). It can be noted that hot water and space heating needs represent more than two third of household energy needs. Hot water and (to a lesser extent, depending on user’s confort) space heating are dispatchable loads. In the literature they are often used as a degree of freedom in order to optimizeMG operation. Because of their ability to be (partially) dispatchable, they are good candidates forDR and other load shifting applications even with a self-consumption objective.

34 1.1. Introduction

Cooking: 7% Hot water: 11%

Other electric devices: 20% Space Heating: 62%

Figure 1.2 – Household energy consumption (2018)

In the beginning of this millennium, German government offered lucrative funding opportunities based on fixed FIT towards residential small-scalePV-arrays. In 2017, residentialPV systems reached the cumulative peak power of 5.9 GWp in Germany [175]. This amount of PV peak power could generate several issues such as, grid imbalances or political issues regarding finances allowed toPV deployement. Thus, Germany decided to revise its FIT. With the stepwise decrease of guaranteed FIT (which is also noticeable in France and displayed in Figure A.5), domesticPV owners are encouraged to give more and more priority to alternative strategies such as self-consumption or to gather as large entities and deal with aggregators in order to valorise their energy production [72]. Therefore, along with the emergence of smart grid technologies and decreasing FIT introduced above, concepts of residentialMG and Domestic Energy Management Systems (DEMS) appeared in the literature [76] (the term domestic can refer to a wholeMG and not necessarily to a single home). This context created new opportunities for residential consumers to: become prosumers, facilitate RES integration and offer flexibility services to the utility grid [158, 122, 75, 12].

1.1.4 Microgrid deﬁnition and speciﬁcations

A smartMG is a self-sustaining cluster of Distributed Energy Resources (DER) and loads that operates as a whole control entity in both grid-connected and off-grid modes. This concept appeared in the literature in 2001 [103].MG concept and its future are extensively discussed in the literature [134, 86], various experiments are performed all over the world [82, 108, 54]. Its loads can be fixed or flexible. It is able to provide both power and heat to the area where it is implemented (if there is a Combined Heat and Power (CHP) unit) and its electrical boundaries are clearly defined. Flexibility services and load management are also key applications for a smartMG, thus it has an Energy Management System (EMS) which performs data aggregation and delivers orders to connected electrical devices. These components are: smart meters and

35 Chapter 1 – Smart Grids and Microgrids: towards more smartness switches, protective, control and communication devices as well as automation systems. The MG has also an Energy Storage Systems (ESS) such as a set of batteries or supercapacitors that acts in coordination withDG units to ensure stability and to supply loads seamlessly in case of islanding. ESS can also be used for economical applications like energy arbitrage [134, 121, 69, 103, 107]. In the literature, a typical smartMG has the following characteristics: — Electrical boundaries are clearly fixed and it is often connected to the Low Voltage (LV) or Medium Voltage (MV) distribution grid. — It has fixed and/or flexible loads. — It has small DER which can be dispatchable or not (usually less than 100 kW and generally represented by wind turbines,PV panels, batteries...). — It is not planned/dispatched by a central unit (like the utility grid). — It can operate both in grid-connected and off-grid modes. — It has an ESS (not mandatory, but helpful for islanded operation). — It has a generation capacity greater than the peak critical load to satisfy the off-grid operation. — Due to its objective to supply electrical and heats loads, Institute of Electrical and Elec- tronics Engineers (IEEE) recommends 10 MVA of maximum capacity perMG. Figure 1.3 displays a typical topology of an urbanMG commonly represented in the current literature. TheMG represented in Figure 1.3 shows several elements that are commonly involved in such structure: local energy production (herePV panels), loads (houses), storage capacity (static batteries and/orEVs) as well as an EMS located on the Direct Current (DC) bus. The main role of this management system is to ensure reliable and sustainableMG operation as well as providing a range of grid services if some of them are technically implemented.

36 1.1. Introduction

Utility Grid

Microgrid DC Bus Solar Power BESS

AC Residential Load

Figure 1.3 – Urban microgrid classic scheme

Flexibility services

MGs are expected to enhance RES usage and mitigate their intrinsic issues thanks to ESS by enabling grid services and flexibility products [169, 83, 100]. Grid and flexibility services are sorted in several application families such as ancillary services (frequency regulation, black- start, droop control), behind-the-meter (PV-ESS, peak shaving, self-consumption maximization, Uninterruptible Power Source (UPS)), energy trade (arbitrage), grid support (voltage support, EV integration, balance management) and combined applications (combination of multiple services presented above in both islanded and grid connected mode) [83, 88]. Some of flexibility services introduced above are extensively detailed in Appendix A.4. The potential ofDG to provide flexibility services started to be discussed since 2000 [94]. The literature presents plenty of case studies dealing with grid services profitability. In this case, profitability is not only about money but it can be about emissions reductions, power quality or about any other objective that can benefit to theMG entity [17, 95, 161, 30]. Incentives should be applied to allow small actors such asMGs to provide such services (currently, they cannot compete with traditional entities in a liberalized flexibility services market because of their size).

37 Chapter 1 – Smart Grids and Microgrids: towards more smartness

1.1.5 General summary

Section 1.1 shows the current status of renewable energy integration in theEU and associated objectives fixed by the European Commission. In order to achieve such goals, several European countries enacted various kinds of incentives such as tariff-based programs and financed several research programs which will facilitate the emergence ofSGs in Europe to help theEU to meet its objectives. Moreover, this work focused on the residential sector as a potential way to take part in the massive integration of RnE sources (particularlyPV panels on roofs). The residential sector is a good candidate because residential areas have become a significant stakeholder in future grid transactions due to rapid urbanization and development of smart meters. This sector represents nearly a third of total electricity consumption. Therefore, along with the emergence of smart grid technologies and decreasing FIT over time, concepts of residentialMGs emerged in the literature. One of the main key characteristic ofMGs is their ability to perform various flexibility services. This ability is regarded as a potential solution to (at least partially) address both lack of competitiveness of RnE sources and drawbacks generated by said RnE sources presented above. Moreover, competitiveness improvements will have a positive impact on citizen and by extension, on the whole society. In order to efficiently provide flexibility services in a residentialMG context, several specific challenges need to be dealt with. Current literature proposes several perspectives to do so. Most of them rely on an efficient design of theMG. Efficient design can be achieved by both intelligent sizing and use of intelligent energy management strategies. Next Section focuses on points presented in this paragraph.

38 1.2. Microgrid challenges and perspectives

1.2 Microgrid challenges and perspectives

In previous Sections, context for the integration of RnE sources in the European global mix was presented. Presentation involved European policies (objectives), incentives and projects related to the integration in the residential sector as well as the emergence of theMG concept to help stakeholders to achieve such objectives. Howe,MGs still have to deal with various challenges in order to ensure an excellent integration of RnE sources. This Section focuses on technical challenges and perspectives observed in ongoing research.

1.2.1 Microgrid challenges and perspectives

SG/MG concept suffer from several barriers that have to be overcome in order to allow SGs/MGs to realize their full potential [162, 110, 32, 55, 84]. Technical challenges are presented below. Moreover, extra technical as well as regulatory/social challenges and economic challenges are discussed thereafter and more points are presented in Appendix A.5. These details can help to understand the ins and outs of whatMG are currently facing.

Technical challenges

ESS integration is a key challenge in order to optimize energy management because of the amount of flexibility service ESS can provide. For instance, for end users, ESS can perform peak-shaving, Time Of Use (TOU) energy cost management, specific requirement regarding power quality, maximizing self-consumption ratio, ensure continuity of energy supply and help to integrateEV. In current residentialMG storage solutions are limited by upfront costs and low density of power and energy. Besides, storage solutions presented in the literature such as lithium or lead-based batteries suffer from ageing problems which involve capacity and power decrease that lead to non-optimalMG operation. Weather forecast is a fundamental input for prediction of someDG and load power profiles [111,4]. Accurate forecast is really useful for power production planning and can become critical for insularMG which may not have important dispatchable power sources [6] or to efficiently perform grid-connected flexibility services. Weather effects on power system operation and on MG resilience were reviewed in [133]. Extreme weather events such as high temperatures or heat waves could limit transfer abilities of transmission lines and lead to extra losses and voltage sag. Some other events such as high winds, cold waves, lightning strikes, heavy rains and floods could dramatically impactMG normal operation. Moreover, in the context of liberalized energy markets, energy producers are expected to respect their commitments regarding bidden amount of delivered energy. Economical penalties could be high and endangerMG operation.

39 Chapter 1 – Smart Grids and Microgrids: towards more smartness

Regulatory/social challenges

There is currently a lack of regulatory framework to ensure compliance with legislative and regulatory requirements forSG/MG deployment as highlighted by Kergrid project. Current regulations were designed a long time ago for centralized and unidirectional electricity generation networks. With the risingSG concept, these regulation appear to be obsolete. The fact that traditional regulatory systems are not harmonized tend to discourage research in innovativeSG technologies and add ambiguity regardingSG possibilities. RegardingMG operations,MG connection/disconnection schemes as well as interconnection rules which are complex and needed for several cases have to be clearly defined (liability, technical procedure and so on) by regulators [110]. As European consumers keep their right to choose their energy provider, managing various energy providers in aMG will add a new layer of complexity. Moreover, existing tariffs are not adapted for consumers located inside entities where there is a high self-consumption ratio. Because of their limited amount of energy requirement from the grid, grid fees no longer represent actual network cost in this case. Regarding social challenges, customers requirements forSG related innovations are lacking due to several reasons: (1) inadequate or nonexistent incentives to make end users to adopt EMS, (2) lack of educational projects to rise general public’s awareness aboutSG, (3) reluc- tance of end users to adoptSG installations and to participate inDR programs. Consumers ad- vocate that some ofSG functionalities such as remote disconnection and TOU may undermine end users. For instance, in [172], authors found that among 8,702 residents, TOU programs allowed to decrease on-peak use compared to a control group, but effects were small. They stated that perceived savings were the strongest predictor of people’s intent to remain in TOU. Moreover, some residents which could have an inaccurate perception of monetary savings may joinDR programs and thus undermine the goals of these programs[172]. These social issues are confirmed by findings presented in GreenLys demonstrator whereas EcoGrid demonstrated that when perception of saving is good enough, commitment is respected over the long term.

Financial challenges

European electricity have been fully liberalized since 2007. This liberalization aimed to allow new actors to come in this market and therefore increase competition. Finally it would allow end users to get access to cheaper energy. Price drop for end users after market liberalization is theoretical. Competitive markets to lead to the development of cost effective process thus reducing energy price. Nevertheless, market opening to new competitors does not always imply efﬁciency and competition towards prices.

40 1.2. Microgrid challenges and perspectives

In [136], authors explain that introduction of new competitors in the European market led to a downtrend of wholesale energy prices that started in 2012 whereas an uptrend regarding retail electricity price for households have been observed between 2008 and (at least) 2016. Indeed, wholesale prices (European average) decreased from 8 ce/kWh in 2008 to 3 ce/kWh in 2016. Contrary to wholesale prices, residential electricity prices have increased by 26% between 2008 (16.24 ce/kWh, including taxes) and 2016 (20.53 ce/kWh). Besides, average electricity price for households in theEU-28 for 2019 was 21.59 c e/kWh [36]. This contradiction shows that introduction of a wholesale European market does not necessarily reduce electricity prices for end users. Another important point is price volatility. Because electricity market is now liberalized, energy price is a statement of natural outcome related to supply and demand. For now, in France, end users are not directly exposed to spot prices but it may be possible in the future. Such exposition could lead to access to cheaper energy because of the observed downtrend in wholesale prices. Whereas new ﬂaws regarding security of energy supply could occur because of strong volatility during events where big conventional power sources are not available. Finally, observed decorrelation between wholesale and retail household prices suggests that household are not yet fully exposed to wholesale prices general trend or that external factors play an important role in retail prices (renewable and climate change policies costs, market inefﬁciency or other factors).

Technical perspectives

ESS can address several technical issues while enhancing the overall performance of energy networks. In [11], ESS is presented as an entity able to compensate the stochastic nature of RnE sources and help their integration in current grids. ESS will allow a better control of supply and demand thanks to a better usage of local energy sources, this aspect is useful for for load leveling and peak shaving applications (either for internal purposes or as a grid service). Moreover, ESS are considered as frequency regulation service providers in off-grid locations where there is a need to ensure power balances in various and difﬁcult operating conditions. Residential network energy management under uncertainties performance improvements can be obtained thanks to several methods. It is possible to address uncertainty issues by considering stochastic methods to compute the day-ahead value of an operating reserve [178]. This operating reserve is computed taking into account the value of a risk index. In order to achieve various objectives such as minimization or operation cost or minimization of CO2 emissions, this work also involves an optimal planning among DER to ensure the supply of the operating reserve. In order to decrease the risk of errors, there are several forecast methods presented in the literature. An extensive list of forecast techniques is presented thereafter in Section 1.3.3.

41 Chapter 1 – Smart Grids and Microgrids: towards more smartness

Regulatory perspectives

According toMG regulatory challenges, a new framework will be required to allow them to develop correctly. A specific European ESS framework is envisaged by European Network of Transmission System Operators (ENTSO-E)[59]. The framework should clarify connection and disconnection procedures as well as possible flexibility services to the main grid in order to secureMG interactions with the utility grid. This framework will address issues regarding regulations towards usage of stored energy for flexibility services thate were highlighted in Kergrid project, Section 1.1.2. A revision of current electric tariffs would be considered in order to reflect the real cost of services provided by the utility to theMG and take into account value added to the network by theMG (decrease of network reinforcement costs, black start capacity and so on). Tax management for energy consumed within theMG should be carefully considered.MG operators should operate under a different regulatory regime because of their intrinsic differences with conventional energy producers and their status should be adapted to prevent exces- sive administrative burden. In this regulatory regime, the right to freely choose energy suppliers should be ensured.

Financial perspectives

There are several potential value streams forMG, these streams are listed below: — Protection of retail users (households) against uptrend of electricity prices. — Services to system operators: frequency and voltage regulation, reserve, black start and DR services (using static ESS orEV). — Local subsides and FIT for RnE generation or self-consumption (this stream is strongly dependent on local policies/frameworks and may not be reliable in the long term). — Energy arbitrage and optimized energy injection. Energy is traded with the main grid to proﬁt from wholesale price volatility (using static ESS orEV). — Quality of supply: energy security of supply and high level of RnE remunerated by end users. RegardingDR perspectives, this topic generally addresses both technical and ﬁnancial issues. For instance, in [10], presented smart home system incorporated the conventional energy management principles represented in Demand Side Management (DSM)/DER management strategies and merged them in an integrated framework that simultaneously addressed 3 objectives such as: permanent energy savings, decrease of energy demand, and peak load mitigation. In this paper, authors have taken into account major aspects that will make end users

42 1.2. Microgrid challenges and perspectives committed inDR programs (mainly the bill reduction as highlighted by GreenLys and EcoGrid projects in Section 1.1.2): maximization of savings through minimization of operation costs, maximization of user convenience thanks to preferred time range to start electric appliances and maximization of thermal comfort. However, this work could be improved by taking into consideration that the EMS could propose more bill-saving time ranges (sometimes outside the preferred time range) to end users and let them choose a trade off between temporary incon- venience and extra savings. Moreover, this work assumed that the comfort temperature was between 23 and 27°C. Extra savings could be realized if some users could set a personal comfort temperature. Regarding electricity aggregators, several business models have been reviewed in order to highlight the emergence and the relevance of a new market role [27]. Real use cases were reviewed such as: supplying mid-scale customers with variable tariffs and optimize their peak load (Next Kraftwerke, Germany), trading aggregated RnE on spot markets (Next Kraftwerke, Belgium) and valorisingDG of customers in apartment buildings (oekostrom, Austria) [71].

Summary

Among technical challenges presented above, there are 2 common key-points that are shared: (i) the need to provide an accurate sizing of a considered residential microgrid as well as (ii) intelligent energy management strategies. Research in these two key-points will allow the residentialMG: (i) to ensure its stability and reliability, thus solving some of technical issues presented above; (ii) to comply with local regulations (even if this domain is lacking suitable content) and (iii) to provide various ﬂexibility services that will help this system to become more competitive and thus to reduce the cost of energy for households. Thus, in the rest of this Chapter, literature review is more focused on these two main aspects.

43 Chapter 1 – Smart Grids and Microgrids: towards more smartness

1.3 Microgrid sizing and management strategies

In the scope of this work, emphasis was placed on the analysis of sizing and energy management strategies. These strategies are key-points in order to improveMG economical sustainability and overall performance regarding ﬂexibility services. Optimal sizing and energy management problems are generally non-convex/non-linear optimization problems [114]. Thus, because of the general complexity of such problems, a suitable method must be selected in order to perform a correct sizing or an energy management optimization. That is why, this Section will start by presenting optimization methods used in the literature for both sizing and energy management problems. Then a literature review of ongoing research focused on sizing optimization will follow. This review will highlight papers’ respective objectives, their common points as well as their common limitations. Finally, the same review method focused on energy management will close this Section.

1.3.1 Optimization methods

In the literature, optimization is performed by in-house algorithms or by using pre-existing softwares. There are several softwares that can be used to solve optimal sizing problems. Among programs such as HYBRID2, iHOGA (based on Genetic Algorithm (GA) optimizatin) and RETSCREEN (backed by the governement of Canada) [74], there is a well-known program called Hybrid Optimization Model for Electric Renewable (HOMER)® developed by National Renewable Energy Laboratory (NREL)[155]. HOMER® aims to simplify the task of evaluating designs for both off-grid and grid-connected power systems and it allows the user to model a system with various ready-made components and to perform optimization as well as sensitivity analysis and cost computations. Optimization is taken care of by HOMER Optimizer® proprietary algorithm. Its major advantage is that the designer is quickly able to assess the performance of several configurations and find the best configuration for a given objective especially if the designer wants a classic system. Regarding drawbacks, this software does not capture voltage and frequency variations. Hence, no transient analysis (which is a major requirement for stability analysis) can be performed. Besides, extra analysis which captures line losses or reactive power production cannot be carried out [132]. Moreover, this software is not a free software (not free as: one must pay for it and there is no available source code), thus it is not possible to perform studies to assess the performance of HOMER Optimizer® against other optimization algorithms without a software licence.

44 1.3. Microgrid sizing and management strategies

In [183, 74, 164, 123], several optimization methods have been introduced. These methods can be listed in several groups and subgroups:

— Deterministic methods

— Graphical construction method.

— Analytical methods.

— Iterative methods.

— Non-deterministic methods

— Probabilistic methods.

— Artiﬁcial intelligence and evolutionary algorithm-based methods.

— Co-optimization methods.

Deterministic methods are characterised by the fact that for a given input, they will always provide the same output and therefore their associated algorithm will always go through the same sequence of states. Contrary to deterministic methods, non-deterministic methods do not provide the same output for a given input. Such behavior can be useful for complex problems with an area of function solutions where there are numerous local minimums.

Graphical construction

Graphical construction is based on a Cartesian plane composed of, for instance, variables

NPV and Nwind which are respectively quantities ofPV panels and wind turbines. For a given average load demand d (Wh), one can ﬁnd a combination ofPV panels and wind turbines such as:

d ≤ NwindEwind + NPV EPV (1.1) where Ewind and EPV are respectively average wind and solar energy yields (Wh) for a unit. In order to satisfy this inequation, it is possible to ﬁnd a lot of possible combinations in this plane (see Figure 1.4 for illustration). In Figure 1.4, several constraints (dashed lines) for different d values (for instance, winter, spring and summer load demands) are represented, then one can draw the Pareto front for this system (curve in red). In order to minimize the cost of generation to satisfy said seasonal load demand, one can use the following cost function (if one can assume that the cost is a linear function of bothPV panels and wind turbines quantities) [117]:

C = CPV NPV + CwindNwind (1.2)

45 Chapter 1 – Smart Grids and Microgrids: towards more smartness

Nwind

Area of possible systems

not possible

NPV Figure 1.4 – Illustration of graphical construction method for a photovoltaic-wind system satisfying a given load satisfaction

where C is the required capital cost (C), CPV and Cwind are respectively costs (C/unit) of PV panels and wind turbines. Therefore, optimal system (PV panels and wind turbines) can be computed using the following equation:

∂N C PV = − PV (1.3) ∂Nwind Cwind As it can be noted by the presentation above, this method is easy to implement but it has a main drawback. Because of the considered 2D area of possible solutions, only two decision variables can be considered in the process of optimization. Besides, this methods works only for average values (demand and generation), thus this method is generally used to solve simple problems [117].

Analytical methods

Analytical methods are based on resolving equations/matrices from models that describe system size or operation taking into account its feasibility, one of the well known analytical method is Mixed Integer Linear Programming (MILP) optimization [164]. For instance, one can perform an analytical method based on Eigenvalue analysis in order to optimize system stability under various load conditions. Because of being based on system equations, this method allows the simulation of several conﬁgurations, and, in order to assess optimal conﬁgurations it is possible to use one or several performance indexes. Ref. [74], reported that HOMER was a widely-used simulation tool for performance assessment of energy systems using analytical method because this software resolves system

46 1.3. Microgrid sizing and management strategies equations in order to assess its performance or compute its required size. Nevertheless, this method requires long time series [164] (often 1 year or more) of generation and consumption data in order to carry out simulations.

Iterative methods

Iterative methods use an initial guess to generate a sequence of improving approximate solutions for a given problem. The n-th approximation is derived from the previous ones. This recursive process stops when the optimal solution is reached according to stopping criteria or for a fixed number of iterations. Iterative methods are can be easy to implement (implementation of Artificial Intelligence (AI)-based algorithms can be difficult) but computation time can be a major drawback notably for complex systems and if inputs are not correctly constrained. Some iterative methods such as Gauss-Sidel require a fixed initial guess, thus they are deterministic. Moreover, Gauss-Sidel method can be used to solve non-linear problems but it must be adapted for it [170]. In contrary, Evolutionary Algorithms (EA)s such asGA and PSO that also have an iterative behavior are non-deterministic because of their mutation/random search function. Thus,EAs will be treated in the next group. This part closes the introduction of deterministic methods. This group of methods has the common characteristic of being relatively simple to implement whereas they may not be suitable for complex, non-linear systems (either the method will not work/will be stuck on a local minimum) or computation time will be very long depending on model precision. Next part will present the group of non-deterministic methods.

Probabilistic methods

Probabilistic methods employ a degree of randomness as part of their logic. More precisely, during the process, such methods rely on random draws. For instance, during a given step, one algorithm can draw a 0 or 1 value according to uniform law and perform a given action according to the output. For instance, probabilistic approaches of sizing or assessment of optimal dispatching consider the effect of uncertainties in order to optimise system design. From generation, consumption or price probability density functions, such methods will generate a large set of scenarios (in order to do so, Monte Carlo procedure can be used [70] or using a uniform distribution law to draw scenarios from a Markov Transition Matrix (MTM)). Then, a global objective function that usually corresponds to the weighted average of the operating costs of drawn scenarios will be solved. In order to reduce computation time, scenarios are evaluated using a scenario-

47 Chapter 1 – Smart Grids and Microgrids: towards more smartness reduction technique, like k-means method. Finally, the global function can be optimized using a deterministic method 9 such as MILP[70]. Usually, these probabilistic techniques consider one or two performance indicators in order to perform the global sizing or energy management optimization [74]. A major advantage of probabilistic methods is that they do not require inputs time-series based data as they rely on probability density functions, for instance, to computePV/wind generation. The main drawback of this kind of technique is that it is not possible to characterize the dynamic changing performance of the studied system [74].

Artiﬁcial intelligence and evolutionary algorithms-based methods

These methods are inspired by processes that can be observed in our environment. The general idea is to make a set of solutions suitable for a given problem to evolve in order to find the best possible results. These methods belong to both probabilistic and iterative domains because they use iteratively random processes. AI/EA-based methods have the following features: can learn from examples (for Artificial Neural Network (ANN)-based optimizers); are "fault tolerant" in the way that they are able to handle noisy and/or partial data and once trained can quickly perform prediction and general- ization (for ANN-based optimizers).AI andEA are often used to for sizing and energy management applications in the literature because they can handle mixed-integer, complex as well as combinatorial problems that cannot be solved by step-by-step methods [163]. Multi-objective optimization methods are included in this field. According to [74, 164], this field is probably the best to solve multi-objective problems. Two common methods are used to solve such problems: (i) by merging objective functions into a single one, (ii) by the determina- tion of a Pareto optimal solution set. In the literature, a lot of papers useEAs [41, 45, 101, 113, 20] to solve problems in theMG context. The major advantages are the fact that such method is able to handle complex systems that could not be optimised by more conventional methods and that this kind of optimization is able to solve multi-objective problems which often involves conflicting objectives. Moreover, for certainEAs such as PSO, the computation burden is limited due to the simplistic nature of the algorithm. Nevertheless such methods involve complex system design and require from the designer some knowledge in order to tune correctly ANNs orEA algorithms.

9. In this case, it is possible to categorize such method as an hybrid method.

48 1.3. Microgrid sizing and management strategies

Co-optimization methods

Co-optimization methods involve two or more different methods presented above in order to perform sizing and energy management optimization recursively. For instance, an optimal sizing is computed for a given energy management strategy. Then the energy management optimization is performed to ﬁnd the best possible dispatching for such sizing and the process repeats itself until the co-optimization method converged and that the algorithm provided an optimal sizing that works best with its associated energy management. The major advantage of co-optimization is that combining two different methods for both sizing and energy management allows the algorithm to ﬁnd an optimal design associated to an optimal energy management strategy and not only the best sizing for a classic energy management strategy. For this reason, co-optimization algorithms are often used in the literature for microgrid applications [106, 179] but such methods are encountered less frequently thanEA- based methods. Nevertheless, the main drawback is that complex system design is required and knowledge of associations of algorithms are required in order to use such algorithms correctly.

Beneﬁts and drawbacks in methodologies of RES

In order to efﬁciently display characteristics of various optimization methods presented above, their respective parameters are gathered in Table 1.2 that summarizes the beneﬁts and drawbacks (“Pros and Cons”) of each optimization approach.

Method Beneﬁts Drawbacks

Only 2 parameters can be Graphic construction Easy to implement included in this optimization No need of time-series data Cannot represent dynamic Probabilistic (frequency distributions) performance of a system Lack of design ﬂexibility (system Analytical Simple and fast models impacts performances) Computation time Easy to implement Iterative (for complex systems (depends on algorithm) if input is not constrained) Relative low computation burden AI&EA to obtain the global optimum Complex system design of a objective function

49 Chapter 1 – Smart Grids and Microgrids: towards more smartness

Method Beneﬁts Drawbacks

Can optimize at least two conﬂicting objectives; Co-optimization Possible combination of Complex system design sizing and energy management optimization Table 1.2 – Pros and cons of optimization methods

From the literature review presented above, it can be noted that there is a clear trend in the development of multi-objective optimization methods (often based onEA methods). This is due to the fact that multi-objective methods are able to solve complex problems and thus provide solutions to optimize systems with conflicting objectives whereas with other methods, it would be difficult to find such solutions or even not possible at all. AmongEAs, PSO is one of the most commonly used algorithm for both mono and multi- objective optimization in the literature thanks to its relative low computational requirements and maturity through several improvements (stopping criteria, constriction factor and so on) [116]. Finally, there is a link between sizing and energy management strategies. Indeed, there is an optimal sizing for a given energy management strategy and inversely. The main problem is to know which one should be fixed because once sizing or energy management is fixed, the other strategy can be optimized accordingly. Despite the fact that it is possible to perform co-optimization in order to overcome this problem, it will not be necessary for residential applications. Indeed, due to regulatory constraints, it is not possible to imagine every possible energy management scenarios. Currently in France, households can perform only a limited amount of scenarios such as: self-consumption (with or without storage), surplus power injection and full injection. For instance, it is not possible to switch from full to surplus power injection scenario or to store surplus energy in order to inject during evening peak-hours. Firstly it would not be economically interesting due to FIT policy, secondly it would cause imbalance problems on the utility side (if a large amount of households would do such action) because the signed contract was valid for solar surplus injection and not for evening/night energy injection. In this context, energy management scenarios will be fixed, thus there is no particular need for co-optimization. Actually, the general idea is to propose an optimal sizing that respects current constraints introduced above in order to be able to deploy it today. However, energy management strategies that are not yet available could be tested on such sizing in order to assess that current sizing is still relevant for such applications even if the sizing was done without considered of such energy management strategies.

50 1.3. Microgrid sizing and management strategies

1.3.2 Microgrid sizing

Among various factors that impact the behavior of a system, optimal sizing is of particular interest inMG optimization. In the literature presented below, authors used various methods to perform sizing optimisation taking into account different objectives and constraints. However, several common points can be highlighted. These points are listed below.

— Load supply: this point is one the most common point encountered in the literature. Supply of loads can be complete or partial. When partial load supply is considered, some Key Performance Indicator (KPI)s such as Loss of Power Supply Probability (LPSP) are considered to minimize duration of unsatisﬁed loads. — Operation cost minimization: probably the most common objective encountered in the sizing literature. This objective is quite obvious and regarded as the main objective for a lot studies. Indeed, the stakeholders will not shift so easily to RnE generation if there is no available cost-competitive solutions for decision-makers and investors. — Environmental constraints: CO2 is considered as the main KPI regarding the minimization of emissions duringMG operation. However, according to the authors’ knowledge, optimal sizing taking into account global carbon footprint (manufacturing, transport, maintenance, etc...) of each device involved in a MG (instead of only considering CO2 emissions of dispatchable units) was not already considered in the current literature. The general behavior consists in proposing RnE sources and storage capacity as an alternative to conventional dispatchable sources.

Articles presented below are grouped according to their common points and they are analysed in order to assess the interest of methods they used according to their respective objectives. It is also possible to group articles according to the complexity of the studied problem. In this group, considered systems as well as problems are quite simple. The main objective is to ensure partially or totally load demand while minimizing the global operation cost. For instance, [74] compiled several research articles in their review that presented usage of graphic construction forPV-wind system that must ensure a given load demand and desired LPSP. According to [74],PV-wind orPV-BESS combinations are generally considered when researchers use this method. As demonstrated above, graphic construction techniques are limited to two optimization variables thus it is normal to ﬁnd common DER couples such as PV-wind orPV-BESS systems for such technique. Sometimes, the problem vary slightly and authors adds a degree of freedom regarding the sizing. Indeed, in [96] the goal was, for a given load demand, to select suitable sites forMG deployment, what kind of DER to use and their respective size. Due to their limitations, methods presented in this group are only suitable for simple systems with classic objectives.

51 Chapter 1 – Smart Grids and Microgrids: towards more smartness

This group involves the optimization of more complex systems and the consideration of multi-objective problems. There are more recent articles that deal with complex systems and multi-objective optimization than recent articles that belongs in the first group. Thus there is a clear trend in current academic research for multi-objective optimization. For instance, in [44, 45] focused their research on ageing models for 3 storage technologies: lead-acid and lithium- ion batteries as well as supercapacitors. This work aims to propose an optimal sizing able to minimize cost of stored energy, to maximizePV energy self-consumption rate and minimize the quantity of energy not served to the load. Such tri-objective optimization cannot be solved with methods presented in the group above because of the problem complexity, thus, in order to solve this tri-objective optimization problem, authors used aGA (particularly Non-dominated Sorting Genetic Algorithm-II (NSGA-II)). A similar approach based onGA is presented in [182] but in this case, energy management operation varies (splitted in 3 different modes) according to battery State Of Charge (SOC). In [21], a multi-objective optimization ofPV-BESS using NSGA-II taking into account the operation cost and security of supply (represented by LPSP) was presented. Objectives are similar to the ones encountered in the previous group but in this case, the system was taking into account uncertainties and thus, it was not possible to compute a sizing optimization with averagePV generation values that are required for graphical construction. To conclude about findings presented above, multi-objective optimization based onEAs seems to be the best choice for optimization problems with conflicting objectives such as minimization of cost of operation, maximization RnE penetration and Expected Energy Not Sup- plied (EENS) in a residential context where systems are highly constrained due to various technical and regulatory limitations.

1.3.3 Microgrid management

Introduction to urbanMG energy management

As written before,MGs are a potential solution for RnE sources integration such asPV panels and wind turbines. With a proper EMS, massive intermittent DER such asPV and wind power can take part in the generation process. Generally, DER such asDG, storage and dispatchable loads are controlled by a Microgrid Central Controller (MGCC) which is in charge to provide both power (and heat) to the local area. Communication infrastructure is used by the MGCC to control energy generation and distribution all over theMG in order to optimize its operation. Generally, techno-economicMG optimization is focused on ﬁnding an optimal solution (or a front of solutions in the case of multi-objective optimization) for several problems related toMG operation such as:

52 1.3. Microgrid sizing and management strategies

— Improvement of RnE penetration ratio. — Reduction of grid dependency or islanded operation, power quality management in islanded operation. — Optimal BESS operation for load management, arbitrage, peak-shaving.... — Optimal economic operation for one or several ﬂexibility services (multi-objective optimization). — Reduction of CO2 emissions.

Such techno-economic optimization regarding energy management should be carried-out taking into account conﬂicting objectives such as: (1) balance between energy generation and consumption, (2) price of energy generation and consumption (from grid), (3) potential penalties charged by external parties caused by failure to perform a given ﬂexibility service, (4) BESS technical limitations.

BESS management

Storage characteristics should be always optimized for various services such as arbitrage, ancillary services (frequency regulation) or evenDR. Optimized planning is required in order to provide such services in an efficient way. Optimal planning can be performed within a short period of time (usually several minutes to 1 hour, lower time frames are reserve for low level control methods which are not in the scope of this thesis) or over a longer period of time (i.e. 24 to 48 hours). Shorter periods will be used in order to correct flaws of longer periods and long horizons will be used, for instance, to propose a complete optimized pattern for battery management (charge/discharge). Research articles presented below share common points such as ones presented below. — Main objectives: maximization ofMG profitability, improvement of RnE sources penetration — Flexibility service: as economical objective or to solve a specific problem. — Time horizon: usage of a 24h time horizon for optimization. — Study period: it is set to 1 year or less. — Optimization techniques: usage of similar optimization techniques for energy management (often based onEAs). — Offline optimization: planning is performed by offline optimization algorithms that adjusts objective function solution for the online control of DER. — Fixed SOC: strategies presented above reset their SOC at the end of each

53 Chapter 1 – Smart Grids and Microgrids: towards more smartness

In the following, the literature’s status quo is briefly captured with respects to: battery energy management strategies, optimization methods and frameworks as well as the relevancy of used periods and horizons. In [180], authors presents an energy management optimization that aims to minimize residential households electricity bills by performing an optimal dispatching of theirPV generation and storage capacity. TheGA optimizer is motivated to do so thanks to the usage of TOU and step-tariffs. In this paper, time horizon for optimization is set to 24h. This choice of time horizon prevent the optimizer to use extra energy management patterns such as the ability to store energy specifically for day-ahead operation. Moreover, in this paper, authors have not considered battery degradation over time. Such assumption can lead to significant differences regarding the value of global energy savings. Besides, this paper does not take into account forecasts errors and thus do not provide any online correction method in order to correct its planning and reset battery SOC at a fixed value at the end of each day to ensure continuity of the pattern. Such characteristics in the optimization algorithm will generate a lack of revenue due to non-optimal behavior of both forecast algorithm and BESS. Similar conclusion can be made of [160] because this papers share the same cost reduction objective and only adds an EV besides static storage. Some papers takes battery degradation into account. For instance, in [85], authors proposed a BESS management strategy using cycle-based depreciation cost which aims to reduce the amount of partial cycles that can occur in a communityMG context. Results show smoother battery cycles but this study focuses on specific scenarios and a given strategy. Nevertheless, the study is conducted only on 4 specific cases with a time frame of 48 hours each, so the robustness of the global system may be debatable. Moreover authors have not justified their choice for such time horizon and have not assessed the relevance of such horizon compared to a 24-hour horizon. As cycle-based depreciation can be quite inaccurate regarding the way storage was used, authors from [153] and [181] authors focused their respective work on alternative storage depreciation methods. The first paper proposes a depreciation estimation based on C- rate evaluation in the context of energy arbitrage while the second paper is based on Depth Of Discharge (DOD) and advises on a specific average DOD in function of the battery controllable depreciation cost. Both methods address partially the issue generated by cycle-based depreciation estimation but extra research should be carried out in order to merge both C-rate based and DOD based methods in order to provide a more accurate estimation of storage degradation over time. Differences between articles presented above can be noted among problems authors solved as well as the precision of selected models and the realism of hypothesis taken into account in their respective studies. Some drawbacks still exist in the research work presented above.

54 1.3. Microgrid sizing and management strategies

Firstly, 24-hour horizons are commonly used in order to optimizeMG daily or day-ahead operation. This limits BESS management possibilities to a specific time frame. For instance, using a 24-hour horizon prevents the possibility to delay BESS usage in order to maximize the profitability. Secondly, variations of certain parameters cannot be correctly represented considering only a 24-hour study period or even one year study period. For instance, State Of Health (SOH) degradation of BESS andPV efficiency degradation require more than one year to have a significant impact on global MG operation and thus affection initial energy management strategy (assuming BESS is not used in a way that could generate premature SOH degradation). Finally, a reset of SOC at the end of each time horizon was observed in presented articles, this action may lead to useless battery usage and then impact on overall battery life andMG operation cost. To conclude, despite the fact that adapting the length of a time horizon for optimize can allow extended planning possibilities for a smarter management, there are still some limitations regarding such "long" time horizons. Research articles presented above share common points such as the usage of a 24-hour time horizon for optimization and the fact that BESS planning is performed by offline optimization algorithms that adjusts objective function solution for the online control of their generation units and storage capacity. Thus, there is no online correction actived in a shorter time frame (1h) that allow the system to modify its planning along a given day if forecasted inputs appear to be wrong (such as solar irradiation or wind speed forecasts). Thus, the next section presents research related to forecasts methods applied to short term forecasting.

Energy management under uncertainties - short term forecast methods

Due to the growing share of intermittent RnE generation in grids all over the world. Weather forecast becomes a fundamental input for prediction of renewableDG and load power profiles [111,4]. Accurate forecast is useful for power production planning and can become critical for insularMGs which may not have important dispatchable power sources [6] or to efficiently perform flexibility services in case of insularMGs or entity committed to a certain level of forecast accuracy towards a stakeholder. Moreover, there is a real need of short term energy management methods based short term forecast in order to correct flaws generated by offline daily or day-ahead (24-hour time horizon) optimization. This is due to the fact that not every RnE sources can be reliability forecasted on a 24-hour time horizon or more. Besides, weather forecasts, there is also price volatility that can be hardly anticipated over a full day because such value depends on many variables (numerous stakeholders in energy markets). Thus, prediction and correction over a short time horizon such as 1 hour can help aMG to finely tune its original planning.

55 Chapter 1 – Smart Grids and Microgrids: towards more smartness

Research articles presented above share common points such as:

— scenario-based inputs for considered forecast method;

— absence of consideration of grid/aggregator penalties for energy injection;

— forecasts algorithms generally do no rely on a corrector part to modify their daily or day- ahead forecasts.

Thus, literature presented below focuses on various methods available in order to perform accurate forecast on location subject to significant climatic variations. Indeed, forecast corrections are not necessary in locations where the weather is predictable over a long time period (i.e deserts). Authors of [14] proposed an energy management framework which involves proactive and reactive approaches to address scenario-based uncertainties associated with power generation and demand. This paper proposes a forecast technique that is recomputed each hour in order to provide a more accurate forecast thanks to newly-acquired data. But, because of uncertainties are scenario-based, authors considered that probable scenarios cannot deviate from more than 10% of the initial daily forecast. This implies that scenarios that involves significant climatic variations are not taken into account and thus preventing the proposed model to work correctly in islands or peninsular locations subject to frequent climatic variations. Contrary to [14], Ref. [87] investigatesMG operation taking into account worst-case scenario thanks to the usage of Taguchi’s orthogonal array to find the worst case scenario among a set of probable scenario generated by the Monte Carlo (MC) procedure. This allows the probability of a strong climatic variation and therefore it is more suitable for locations subject to significant climatic variations. Nevertheless, in this probabilistic method, there is no online correction that would regenerate new scenarios during the day in order to update the daily planning and thus, improving theMG operation cost. Authors from [150], introduces a similar forecast mechanism based on scenarios like articles above. In this case, the goal is to useEVs as ESS with a stochastic availability in order to inject/absorb both active and reactive power besides considering probable weather scenarios and market prices. This work considered 5 probable scenarios to assess its robustness which makes it subject to the same remarks as [14]. Author in [178] provided a forecast solution based on ANNs for both day-aheadPV and load that output results with low normalized Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE). Nevertheless, this good performance is obtained at this expense of a lot of training and the need of various weather data such as historical solar irradiation, temperature, wind speed and humidity. Moreover, in order to perform the solar irradiation forecast, the system relies on an accurate forecast of the day-ahead temperature. Besides, the residential microgrid

56 1.3. Microgrid sizing and management strategies system involved a dispatchable power source (gas turbine) besides BESS in order to ensure its operational reserve. Gas turbines are not a common power source when residential microgrids are considered, extra investigation with a more conventionalPV-BESS that represents more accurately households could be carried out. Differences between articles presented above can be noted among problems authors solved as well as the diversity of selected forecast methods and the realism of hypothesis taken into account in their respective studies. Regarding limitations, research articles presented above involves ofﬂine planning for daily and/or day-ahead forecasts which may not be reliable enough in location where weather is subject to strong variations. Therefore, online correction algorithms can be useful to enhance global accuracy regarding solar irradiation forecasts. For instance, [14] used newly acquired data at each step time to provide online correction of their respective temperature forecasts. Moreover, said research work generally deals with uncertainties by generating a set of scenarios using scenario reduction techniques or by assuming a limited possible deviation from the forecast (i.e 5-10% errors) except for [178]. In [178], an ANN is set to forecast day-ahead prevision thanks to an extensive historical list of weather parameters such as: ambient temperature, humidity, air pressure, solar irradiation... Day-ahead irradiation forecasts are provided by the ANN thanks to external ambient temperature forecast entity and measured daily local solar irradiation. There is also a lack of forecast prediction correction system except for [14] where a rolling horizon is used. Besides, the grid is mainly viewed as a safety where surplus power can be injected and missing power can be drown to ensure power supply to the loads. In general, no penalty is considered for mismatching values between power injection prediction and real injected power. In the context ofMG where climatic variations are strong and sudden, an accurate forecast method is required to decrease as much as possible forecast errors that could generate power unbalance on the main grid and thus increaseMG operation cost.

57 Chapter 1 – Smart Grids and Microgrids: towards more smartness

1.4 Research work, methodology, and thesis outline

1.4.1 Objectives and outline

Objectives

There are numerous barriers that prevent development of theMG concept. Each of presented barriers deserves specific research. In this work, research was focused market participation of residentialMG through an aggregator in order to deal with stakeholder size limitation. Market participation involves several barriers related to energy management such as: ESS management and weather forecast in the context of flexibility services such as: arbitrage and bid-based energy injection. These flexibility services will provide an additional source of income to deal with the challenge of electricity price inflation for retail users. Literature review showed that in the context of energy arbitrage, 24-hour horizons are commonly used in order to optimizeMG daily or day-ahead operation. This limits BESS management possibilities to a specific time frame. Moreover, Research work mainly deals with uncertainties by generating a set of scenarios using scenario reduction techniques or by assuming a limited possible deviation from the forecast (i.e 5-10% errors) or by advanced techniques that require a lot of computation and large amount of historical data as well as external forecasts. There is also a lack of forecast prediction correction systems. Besides, the grid is mainly viewed as a safety where surplus power can be injected and missing power can be drown to ensure power supply to the loads. In general, no penalty is considered for mismatching values between power injection prediction and real injected power. The main goal of this thesis is to propose a sizing method suitable for residential areas taking into account local regulations and possible technical interactions with the grid through various scenarios in order to provide a realistic and regulation-compliantMG sizing for the application of current and future energy management strategies. Moreover, this work proposes to enhance profitability of future potential residentialMGs thanks to an energy management strategy for arbitrage based on extended time horizons. Finally, this work also propose to assess the performance of a statistical autoregressive predictor-corrector forecast algorithm in the context of bid-based energy markets with a similar objective of profitability enhancement and stakeholder satisfaction.

58 1.4. Research work, methodology, and thesis outline

Thesis outline

The content of this thesis is divided in 4 Chapters:

— Chapter 2 is dedicated to the introduction of models used in this thesis.PV, BESS, residential loads as well as various aggregator interaction models are presented. This chapter also proposes mono and multi-objective optimal sizing method based on PSO for on-site energy production in residential areas based on solar power taking into account: (i) future energy cost in order to ensure financial safety regarding energy prices, (ii) load satisfaction rate / user comfort satisfaction rate and (iii) RnE penetration rate. Studied site is a single-family house. In the scope of this Chapter, this site is extended to a community MG. — Chapter 3 is devoted to assess the relevance of an energy management strategy based on 48-hour horizon compared to a 24-hour horizon one in order to perform energy arbitrage. This study considers a residentialMG based onPV generation and storage connected to the main grid. Proposed 48-hour energy management strategy provides additional management possibilities such as the ability to delay trades (charge today, discharge tomorrow) and a larger range of hours to use the storage. PSO is used to solve the optimization part. Besides, a sensitivity analysis is investigated to assess the economic impact of forced storage of solar surplus power in order to increase self-consumption rate and storage size. — Chapter 4 aims to propose an enhanced energy management framework which aims to efficiently address uncertainty issues due to local climatic variations in a peninsula context. The proposed framework uses a ten-state Markov chain to generate stochastic solar irradiation as well as a forecast correction method based on recursive least-squares updated every hours in order to efficiently take part in hour-ahead power bidding process. Besides, a sensitivity analysis is investigated regarding impact of storage size and aggregator penalty on operation cost and commitment indices.

Finally, conclusions of this work are presented and ideas for potential further research work are proposed.

59 CHAPTER 2 SIZING OPTIMIZATION OF PV-BESS BASEDCOMMUNITYMICROGRID

Nowadays, as explained in the first chapter, several challenges are growing in the field of conventional electrical transmission and distribution grids. One of them is to deal with the increase of RnE penetration. Such massive adoption of RnE sources will generate some constraints that can perturb TSO and DSO normal operation as well as other stakeholders. To prevent such problems, houses withPV systems also often comes with a storage system. With such abilities, homes or neighbourhood with local energy production and storage can be considered as a smartMG[103]. Households are retail consumers and pay high premium for electricity compared to professionals/energy market players that get bulk tariffs. Such tariffs andPV panel/storage prices decrease along years may allow on-site energy generation and storage to be cost-effective compared to classic grid-connected household depending on: possible local/national incentives, future electricity tariffs, household load profile, investment costs (system costs, future cash flows, discount rate and inflation rate) [125]. The goal of this chapter is to propose an optimal sizing method for on-site energy production in residential areas based on solar power taking into account: 1. Future energy cost in order to ensure financial safety regarding energy prices. 2. Load satisfaction rate / User comfort satisfaction rate. 3. RnE penetration rate. Studied site is a smart-household located in Saint-Nazaire, France. In the scope of this work, this site will be later extended to a community. PSO is used for both single-objective and multi- objective optimization in various scenarios related to self-consumption and power injection. These scenarios are presented to demonstrate the cost-effectiveness of on-site energy generation compared to a classic grid-connected household and to asses both user and load satisfaction rates. Besides, models and methods presented in this chapter will be used in chapter 3 and 4 for energy management operations.

60 2.1. Distributed energy resources modeling

This chapter is organized as follows: section 2.1 presents models used to compute energy production and consumption in a household as well as grid interaction hypothesis; section 2.2 presents ﬁnancial equations, scenarios and associated cost functions as well as optimization method; section 2.3 presents comprehensive results comparison where proposed scenarios are compared to a classical grid-connected household and between each other; ﬁnally, section 2.4 underlines the contributions of this work.

2.1 Distributed energy resources modeling

StudiedMG is a ﬁctitious house located in Saint-Nazaire, France. Studied EMS is composed of: a solar panel array installed on house’s roof, a smart meter (named "Linky" and is owned by the major DSO), a solar inverter, a BESS and various home appliances used by a family of 4 people. Figure 2.1 presents the considered system.

SMART METER MPPT SOLAR PANEL DC UTILITY GRID ARRAY HOME AC ENERGY STORAGE APPLIANCES INVERTER Linky TM

Figure 2.1 – Studied smart home scheme

AMG should be designed to satisfy one or several objectives. Thus, its components are represented with a different level of detail according to needed detail precision. Models presented below are used in order to simulate aMG. In this thesis, both sizing and energy management optimizations are considered as "high-level" optimization. Thus, there is no need to use high precision models. Therefore, steady-state models are accurate enough to perform such optimization. In this work, focus will be made on the following models:PV panels, lithium batteries, residential loads, energy prices and aggregator relations. Moreover, this ﬁctitious house takes part as an element of a communityMG. This community MG is be considered as the main load in the next two chapters. Figure 2.2 represents the extendedMG as known as communityMG. Letter A represents

61 Chapter 2 – Sizing optimization of pv-bess based community microgrid rooftop solar panel arrays for "Classic" (old) houses and for "RT2012" (new) houses. Letter B represents BESS integrated in the communityMG. Letter C represents the DEMS where the Point of Common Coupling (PCC) is located. It gathers data from solar energy generation, load consumption, storage status and electricity prices (for peak and off-peak tariffs as well as yearly price increase). It communicates with an aggregator that allows the studiedMG to monetize power injections. Power and information buses deployed all over the residential neighbourhood are represented by plain and dashed black lines.

Power ﬂow A Solar Panels B BESS Data ﬂow C DEMS

Utility Grid A

Microgrid B C

Figure 2.2 – Community microgrid general scheme

This section is divided in subsections where models used in this thesis are introduced and detailed. To facilitate comprehension, this data is divided in several parts:PV system, BESS system, loads and aggregator. Besides, economic indicators such as Levelized Cost of En- ergy (LCOE) and Requested Average Price Spread (RAPS) are presented in their respective subsection.

62 2.1. Distributed energy resources modeling

2.1.1 Photovoltaic generation

Theoretical solar irradiation

In this thesis, theoretical solar irradiation is used in stochastic solar irradiation model based on historical data and Markov chains (chapter 3 and 4). In this chapter solar irradiation is based on historical data (2009-2018). Theoretical irradiation is represented by a global irradiation on a titled plane IGT . It can be computed using Hay, Davies, Klucher and Reindl (HDKR) model [43] (which is often discussed in literature [22, 124, 92]) and is presented in the following formula:

IGT = IBT + IDT + IRT (2.1)

−2 with IBT as beam irradiation on a titled plane (W m ), IDT as diffuse irradiation on a titled −2 −2 plane (W m ) and IRT as reﬂected irradiation on a titled plane (W m ). Beam irradiation on a titled plane IBT can be set with the formula below:

IBT = RBKsky(1 − f(Ksky))KatmI0h (2.2) where RB is ratio on beam irradiation on tilted plane to that on horizontal plane and is detailed in

[92]. Ksky is the clear-sky index (0.1 means really cloudy weather, 1 means clear sky). f(Ksky) is used in HDKR model and detailed in [22]. Ksky was computed from historical data (2009- IGH 2018 history from [118]) using the following ratio: Ksky = where IGH and IGH−clear are IGH−clear respectively global horizontal irradiation and global horizontal irradiation in clear-sky conditions. −2 I0h refers to extraterrestrial horizontal irradiation (W m ) and is detailed in [92]. Katm is the atmosphere correction factor and represent solar irradiation losses that occur when radiations are going through the atmosphere (in clear sky conditions). Its value depends on atmosphere composition and solar irradiation incidence angle. An average value of 0.7 is recommended

[152]. Then, diffuse irradiation on a tilted plane IDT is provided by:

!! 1 + cos β q β 3 I = A B + C 1 + 1 + f(K ) sin (2.3) DT 2 t 2 where A = I0h.Ksky.f(Ksky), B = Ksky.(1 − f(Ksky)).RB and C = 1 − Ksky.(1 − f(Ksky)) and β refers to the plane’s tilt angle (rad). A tilt angle value of 30° was selected according to technical report Documentation Technique Unique (DTU) 40.11 (norm NF P 32-201) for slate rooftops or DTU 40.21 (norm NF P 31-202) for tile rooftops in this area. Besides, the studied domestic energy model in [144] introduces 4906 residentialPV systems located in south of Germany with an average rooftop tilt angle of 27.36°. Finally, the reﬂected irradiation on a titled plane IRT

63 Chapter 2 – Sizing optimization of pv-bess based community microgrid formula is: 1 − cos β I = K I ρ (2.4) RT sky 0h ground 2 with ρground as the ground albedo usually equal to 0.2 [124].

Photovoltaic power output

This part presents a steady-statePV panel array model. Output solar power PPV available to be used for desired application is detailed in the equation below:

PPV = ηPV ηinvNpanelSpanelIGT (2.5) with ηPV the solar panel array efﬁciency, Npanel the amount of installed solar panel on site, 2 2 Sarray (m ) its surface (around 1.6 m ) and ηinv the built-in solar Maximum Power Point Tracking

(MPPT) inverter efﬁciency. ηPV is presented below:

ref ηPV = ηPV (1 − CT (Tcell − 298)) (2.6)

ref where ηPV is reference efﬁciency of a given panel (measured in standard conditions, here ηP V,ref equals 19.7%). CT is temperature coefﬁcient (0.258%/°K). Tcell is cell operating temperature (°K). Tcell is explicited below:

I T = T + (NOCT − 293) GT (2.7) cell ambient 800 with Tambient as ambient air temperature (°K). NOCT as Normal Operating Cell Temperature (NOCT) (317 °K). Ambient temperature is also extracted from [118], data from a 10-year history (2009-2018) was used. Solar panels efﬁciency fades over years thus their output power for a given year y can be obtained using the following equation:

y init y PPV = PPV (1 − Rsdr) (2.8)

init where PPV is initial output solar power without system performance degradation (W) Rsdr is system degradation rate over time (0.4%). In this work, selected solar panels with initial peak power of 330 Wp will still provide 90.5% of their initial power after 25 years.

PV system related costs

APV system involvesPV panels, one or several solar inverters (which are able to manage BESS if there is any) as well as structural and electrical components. Components’ costs to

64 2.1. Distributed energy resources modeling compute the cost of a complete system are displayed in Table 2.1[73].

Category Modeled Value Equipment Module price (ex factory) 0.38-0.5 C/Wp Inverter price (ex factory) 0.14-0.2 C/Wp Structural components (racking) 0.1 C/Wp Electrical components 0.25 C/Wp Supply chain costs (% of equipment costs) 30% Company Direct installation labor 30 C/h Installation duration 1.6-4 h/panel Permitting, inspection, and interconnection 500 C + 140 C Sales, marketing and Overhead 0.6 C/Wp Proﬁt 17% VAT 20% Table 2.1 – Solar system related costs

Permitting, inspection, and interconnection involves ﬁxed fees for residential solar systems: 500 C corresponds to DSO fee for interconnection with the main grid [56] and 140 C (165 C if there is a BESS) corresponds to an inspection check to ensure that installed system respects electrical norms [38]. Therefore, price per installed kWp was computed using data from Table 2.1 and is displayed in Figure 2.3.

6000 Without DSO inspection fees With DSO inspection fees 5000

4000

3000

2000 0 5 10 15 20 25 30 35 40 kWp Figure 2.3 – Price per installed kWp in function of system size

Operation and maintenance costs related to solar installations mainly involves inverter re-

65 Chapter 2 – Sizing optimization of pv-bess based community microgrid placement fees and can be annualized taking into account yearly price inﬂation of 1%.

Levelized Cost Of Energy

LCOE( C/kWh) is often used in the literature by industrial sector to make a comparison regarding several power sources (renewable based or not) cost effectiveness [143, 16]. This value gathers all cost involved in the power source’s life cycle such as: initial capital investment, ﬁxed and variable operating cost as well as applicable taxes. Result is the break-even price for a generated kWh that investors should get to cover their investments and all related operating costs. Put differently, LCOE is a virtual stable energy price requested to make the sum of all incomes and expenditures during entire operational period of the given source equal to zero.

Y Cproject,P V −y y −y −y X + CO&M (1 + Rd) − C (1 + Rd) (1 + Ri) LCOE = Y inc (2.9) Ey (1 + R )−y(1 + R )−y y=1 PV d i with:

— Cproject,P V : project cost (C, solar generation part: see Table 2.1). y — CO&M : cash outﬂow generated by operation, maintenance and replacement costs (C, solar inverter replacement over 10 years). y — Cinc incentives for self-consumption purposes (C, for pure self-consumption or surplus power injection only). y — EPV : expected system energy yield for year y (kWh).

— Ri: general repricing rate (1%).

— Rd: discount rate (0-2%).

LCOE will be used as a performance indicator and will provide information regarding cost per produced kWh for each system.

66 2.1. Distributed energy resources modeling

2.1.2 Battery energy storage system

In this thesis, studied BESS is based on lithium-ion RESU Gen 2 48V battery manufactured by LG Chem [24]. Battery speciﬁcations are depicted in Table 2.2

RESU Models RESU 3.3 RESU 6.5 RESU 10 RESU 13 Total energy (kWh) 3.3 6.5 9.8 13.1 Usable energy (kWh) 2.9 5.9 8.8 12.4 Maximum charge/discharge power (kW) 3 4.2 5 5 Table 2.2 – Battery speciﬁcations

It can be noted in 2.2 that the ratio of usable energy over total energy implies a theoretical DOD of 90%. Moreover, C-rate value depends on selected model. For the sake of simplicity and to prevent premature degradation, usable energy was reduced to 75% of total energy (with SOC limits detailed below) and maximal charging/discharging power was limited to 0.5C [174].

State Of Charge (SOC)

It’s SOC is given by the following equation [23] where SOCt equals:

 SOC , t = 0  init  t ηchPbatt∆t t t SOCt = min(SOCt−1 + Bt ,SOCmax), ∀t Pbatt ≤ Pmax, (charge) (2.10)  cap  P t ∆t  batt t t max(SOCt−1 + t ,SOCmin), ∀t |P | ≤ P , (discharge) ηdchBcap batt max where SOCinit,SOCmin,SOCmax are initial (100%) for the first hour of simulations, minimum t (15%) and maximum (90%) values for battery SOC[174]. Pbatt is the battery power flow (W) at time t and corresponds to the power value available to supply loads or for grid injection t purposes (Pbatt is positive if BESS is charged, negative if BESS is discharged and is limited t t by Pmax = 0.5C). SOC and Pbatt values are limited as recommended by the manufacturer to prevent efficiency drops and premature storage ageing due to chemical degradation during fast power transfers [8]. ηch and ηdch are charging and discharging efficiency (95% provided by t Building Management System (BMS) inside hybrid inverters). ∆t stands for step time (h). Bcap refers to BESS capacity at time t (Wh). In equation 2.10, the first line initiates state of charge, the second line is used during charging periods, finally the last line is used during discharging periods.

67 Chapter 2 – Sizing optimization of pv-bess based community microgrid

State Of Health (SOH)

SOH is also an important aspect of storage life cycle. Some battery manufacturers provide calendar ageing determined by a certain amount of cycles and others provide it through global energy throughput. In this case, it is assumed that batteries are stored inside houses and maintained at room temperature. Besides, manufacturer warranty ensures a residual capacity of 60% after 10 years if Pmax = 0.5C threshold was respected and that global energy throughput was below 24.3 MWh per 10 kWh module. Thus, remaining battery capacity was computed as follows:

y rated y Bcap = Bcap (1 − Rb) (2.11)

rated with Bcap as the battery rated capacity, Rb as the battery degradation factor (0.495%) and y a given year (between 1 and 10). Figure 2.4 illustrates calendar degradation considered for BESS in this study.

100

70 State Of Health (%)

60 0 2 4 6 8 10 Time (year) Figure 2.4 – Battery capacity degradation over time

Battery system related costs

A BESS also involves several components such as: batteries, structural and electrical components. Prices are presented in Table 2.3[185].

68 2.1. Distributed energy resources modeling

Category Modeled Value Equipment Battery price (ex factory) 170-300 C/kWh Structural components 25 C/kWh Electrical components 25 C/kWh Supply chain costs (% of equipment costs) 30% Company Direct installation labor 30 C/h Installation duration 3-4 h/system Permitting, inspection, and interconnection see Table 2.1 Sales, marketing and Overhead 150 C/kWh Proﬁt 17% VAT 20% Table 2.3 – Battery system related costs

Therefore, price per installed kWh was computed using data from Table 2.3 and is displayed in Figure 2.5.

1400

1200

1000

800

600

400 0 5 10 15 20 BESS size (kWh) Figure 2.5 – Price per installed kWh in function of battery size

Requested Average Price Spread

RAPS( e/kWh) is used to compute cost of energy being stored over a system lifespan. It is useful to know what is this cost for several reasons such as computing cost-effective price for

69 Chapter 2 – Sizing optimization of pv-bess based community microgrid various storage technologies [16]. This price spread is provided by:

Y Cproject,batt y y −y −y X + (CO&M (1 + Ri) + (1 − ηrt)Ce,c)(1 + Rd) (1 + Ri) RAP S = Y (2.12) Ey (1 + R )−y(1 + R )−y y=1 batt,d d i

with:

— Cproject,batt: project cost (C, BESS part: see Table 2.3). y — Ebatt,d: amount of energy discharged during year y (kWh).

— ηrt: round-trip efﬁciency (equals ηch × ηdch) y — Ce,c: cost of charged energy during year y (C, repricing included if any)

2.1.3 Loads

In this thesis, residential loads are represented by households living in single-family houses. Houses built according to different norms are introduced below. Moreover, in order to compare the economical relevance multiple households compared to the sum of single households taken separately, a community microgrid is introduced in this subsection

Single-family houses

One hour step time residential load curves were generated with a software named Load- ProfileGenerator [109]. Studied load represents a family of 4 people (2 adults, 2 children) living in a 120 m2 house. This fictitious house is located in downtown Saint-Nazaire, France (47.27° N, 2.21° W). In order to get more realistic data, 10-year load was generated using history temperature profiles at Saint-Nazaire, France from 2009 to 2018. Household energy consumption is around 15 MWh per year depending on climatic conditions. This energy consumption represents energy consumed by houses built 30-40 years ago. Energy consumption involves household appliances, a 300 L hot water tank with an electric water heater which is operated preferably during off-peak period (between 23h and 7h) and electric space heating to ensure around 7 MWh of heat needs per year which translates into around 150 kWhpe/m2/year of primary energy consumption for space heating needs. Electric consumption is displayed in Figure 2.6.

70 2.1. Distributed energy resources modeling

6000 Hot water Space heating 4000 Global consumption

Power (W) 2000

0 5 10 15 20 25 30 35 40 45 Time (h) Figure 2.6 – Household consumption ("classic", 2009-01-01)

Besides, in order to compare with "classic" houses load was generated taking into account that energy consumption for space heating, hot water and lighting was below norm RT2012 threshold: 50 kWhpe/m2/year. In contrast with the previous house model, RT2012 house space heating and hot water needs are ensured by heat pumps. Thus, for energy-saving households, energy consumption is around 4.8 MWh per year depending on climatic conditions. Electric consumption is displayed in Figure 2.7.

5000 Hot water 4000 Space heating Global consumption 3000

2000 Power (W) 1000

0 5 10 15 20 25 30 35 40 45 Time (h) Figure 2.7 – Household consumption ("RT2012", 2009-01-01)

71 Chapter 2 – Sizing optimization of pv-bess based community microgrid

Community area

Residential areas involves houses built at different periods. To reflect In order to compare economic efficiency of neighborhood with single-family homes regarding energy management and injection profits, an allotment composed of 50 houses was considered. This allotment involves 2 equally distributed single-family house profiles introduced above. Power comsumption is presented in Figure 2.8.

250 Hot water 200 Space heating Global consumption 150

100 Power (kW) 50

0 5 10 15 20 25 30 35 40 45 Time (h) Figure 2.8 – Household consumption (community, 2009-01-01)

2.1.4 Aggregator

An aggregator is a type of grid participant and acts as an energy service provider. This entity is able to moderate electricity consumption of a group of consumers according to energy demand on the main grid in order to ensure power balance. An aggregator is also able to operate on behalf of a group of prosumers producing their own electricity by selling the surplus power. An aggregator is often a Balance Responsible Party (BRP). It is responsible for power balance between producers and consumers within a specific area. In this study, the aggregator and BRP is EDF for both energy consumption and injection. Electricity tariffs are structured according to peak and off-peak times of day. TOU tarrifs are fixed as follows: 0.1337 C/kWh for off-peak period and 0.1781 C/kWh for peak period. In order to reflect energy price inflation for the past 10 years, grid prices will increase by 2.96% each year.PV feed-in tariffs depends on system size and they are divided in 2 categories: injection of whole production and surplus power injection. Tariffs for full injection are increased each year by 1% whereas tariffs for surplus injection are fixed. Moreover, incentives are proposed to households that plan to installPV systems for self-consumption purposes (self-consumption

72 2.1. Distributed energy resources modeling with or without surplus injection). These incentives are paid once a year during 5 years. Corre- sponding values are displayed in Table 2.4[39].

System (KWp) Full injection (C/kWh) Surplus power injection (C/kWh) Incentive (C/y) 0-3 0.1853 0.1 390 3-9 0.1575 0.1 290 9-36 0.1207 0.06 190 36-100 0.1051 0.06 90 100-500 call for tenders call for tenders 0 500-8000 " + spot " + spot 0 Table 2.4 – Photovoltaic feed-in tariffs and self-consumption incentives

For largePV systems, prices for injection have to be proposed in a call for tenders. In the scope of this work, call for tenders injection price are set with the following value: 0.05 C/kWh (repriced by 1% each year). For massive systems (last line of Table 2.4), injection prices are spot prices. Spot prices are represented by median values of historical prices from 2016 to 2018 [139]. Besides, injection prices for massive systems have a lower bound ﬁxed at 0.05 C/kWh (repriced by 1% each year).

2.1.5 Optimization

To solve optimization problems presented in section 2.2, particle swarm optimization was chosen. PSO is a meta-heuristic algorithm tied with swarm theory and is computationally in- expensive regarding memory and speed [47]. In the domain of microgrids, PSO is a proven and reliable method often employed for various optimization problems such as optimal sizing [99] or energy management [85]. In the scope of this thesis, both single and multi-objective optimization based on PSO were used. Both kind of optimization are introduced below.

Single-objective optimization

PSO concept is quite simple and can be easily implemented in a few lines of computer code. PSO is similar toGA in that the algorithm is initiated with a population of random solutions. Each particle involved in the swarm is able to store hyperspace coordinates associated with its personal best solution (fitness) it has reached so far. Both personal best coordinates and associated fitness are stored. In algorithm1, personal best coordinates for a given particle i are named pb,i. Global best gb is also stored in order to keep track of the overall best coordinates and associated fitness achieved by any particle so far.

73 Chapter 2 – Sizing optimization of pv-bess based community microgrid

Then, PSO concept consists in changing particle’s velocity at each time step to make it move towards pb,i and gb coordinates. Particle velocity is weighted by random values for both personal and global parts. This concept is illustrated in Figure 2.9 where the search space is a 2d hypercube. y

c2R2(gb − xi(t)) x (t + 1) g i b pb,i(t)

c1R1(pb,i(t) − xi(t))

xi(t) vi(t) x Figure 2.9 – Illustration of velocity update for a given particle i

Finally, particles will move towards global best solution in the course of iterations. This convergence is illustrated in Figure 2.10. y y

p2(t)

v3(t) p3(t) v2(t)

v3(t) v2(t) gb gb

v (t) p3(t) p1(t) 1 p2(t)

v1(t) p1(t) x x (a) Iteration 1 (b) Iteration n Figure 2.10 – Particle swarm optimizer convergence illustration

This concept translates into a pseudocode which is displayed in algorithm1. In this study, PSO convergence and stability was improved using constriction factor and computation time was improved using MaxDistQuick stopping criterion [184]. Constriction coefﬁcient χ, c1 and c2 as well as R1 and R2 are deﬁned according to [26]. MaxDistQuick compares the score difference of each particles with the best one and stops the algorithm if a certain percentage of them are below a given threshold. In this work, particles’ percentage was set to 50% and

74 2.1. Distributed energy resources modeling threshold was set to 0.1. This translates into the fact that PSO is stopped if more than 50% of the swarm score differ from less than 0.1 point from the best score.

At the end of algorithm1, gb is the best solution and f(gb) is the best score. Algorithm 1 introduced above is set to minimize a cost function. It is also possible to maximize a cost function by changing ≤ into ≥. Regarding PSO parameters, stopping criteria is triggered by reaching a ﬁxed amount of iterations (100 iterations) or by satisfying MaxDistQuick conditions [184]. Swarm size was set to 30 particles.

Algorithm 1 Single-objective particle swarm optimization algorithm Require: ~x Ensure: min (f(~x)) for Particlei ∀i ∈ 1 : N do Initialize particle’s position ~xi(0) according to constraints and boundaries Initialize it’s best position such as ~pb,i ← ~xi(0) Initialize particle’s velocity such as ~vi(0) ← 0 Compute particle score: f(~xi(0)) if f(~xn(0)) ≤ f(~xi(0)), ∀n 6= i then Initialize global best position: ~gb ← ~xn(0) end if end for while stopping criteria are not met do for Particlei ∀i ∈ 1 : N do Update particlei velocity: ~vi(t + 1) ← χ~vi(t) + c1(~pb,i − ~xi(t))R1 + c2(~gb − ~xi(t))R2 Update particlei position: ~xi(t + 1) ← ~xi(t) + ~vi(t + 1) Check constraints and boundaries and update particlei position if needed Compute particle score: f(~xi(t + 1)) if f(~xi(t + 1)) ≤ f(~pb,i) then ~pb,i ← ~xi(t + 1) end if if f(~xi(t + 1)) ≤ f(~gb) then ~gb ← ~xi(t + 1) end if end for Update stopping criteria status end while

75 Chapter 2 – Sizing optimization of pv-bess based community microgrid

Multi-objective optimization

As written before, PSO is suitable for single-objective optimization. According to [31], PSO is suitable for multi-objective optimization mainly because of its high velocity of convergence. However, in order to handle properly multi-objective optimization, PSO algorithm proposed above must be modiﬁed. The major difference between single-objective and multi-objective optimization is the concept of Pareto front [31].

Deﬁnition 2.1.5.1. (Multi-objective optimization problem) Given a function f :Ω ⊆ Rn −→R, ∗ ∗ ∗ ∗ Ω 6= 0 and ~x ∈ Ω, ﬁnd the vector ~x = [x1, x2, ..., xn] which will satisfy q inequality constraints

gi(~x) ≥ 0 ∀i ∈ {1, 2, ..., q} (2.13) p equality constraints

hi(~x) = 0 ∀i ∈ {1, 2, ..., p} (2.14) and will optimize the vector function

~ f(~x) = [f1(~x), f2(~x), ..., fk(~x)] (2.15)

T where Ω is the feasible search space and ~x = [x1, x2, ..., xn] is the vector of decision variables.

Deﬁnition 2.1.5.2. (Pareto optimality) A vector ~x∗ is Pareto optimal if ∀~x ∈ Ω and K = 1, 2, ..., k

∗ ∀i ∈ I, fi(~x) = fi(~x ) (2.16) or ∃i ∈ K such as ∗ fi(~x) > fi(~x ) (2.17)

It translates into the fact that if ~x∗ is Pareto optimal, there is no ~x that would be able to decrease a given fi with i ∈ K without simultaneously increase at least one other fj with j ∈ K and j 6= i.

Deﬁnition 2.1.5.3. (Pareto dominance) ~u = [u1, u2, ..., uk] dominates ~v = [v1, v2, ..., vk] if ∀i ∈

{1, 2, ..., k}, ui ≤ vi ∧ ∃i ∈ {1, 2, ..., k}ui < vi. Dominance is noted u v.

Deﬁnition 2.1.5.4. (Pareto optimal set) For a proposed multi-objective problem f~(~x), Pareto optimal set S∗ is deﬁned as:

∗ n 0 o S = x ∈ Ω | @x ∈ Ω: f~(x~0) f~(~x) (2.18)

Finally, it is possible to deﬁne mathematically the Pareto front based on previous deﬁnitions.

76 2.1. Distributed energy resources modeling

Deﬁnition 2.1.5.5. (Pareto front) For a proposed multi-objective problem f~(~x), Pareto front F ∗ is deﬁned as: ∗ n ∗ ~ o F = x ∈ S | f = [f1(~x), f2(~x), ..., fk(~x)] (2.19)

In the case of multi-objective optimization, it is essential to generate a reliable Pareto front. Due to PSO high convergence velocity, it is possible that the algorithm does not explore full range of each decision variables or that remote regions of the search space are not explored properly. It may end to a partial Pareto Front. To solve these problems, authors in [31, 157] added: (1) an external repository to store non-dominated particle solutions and positions as well as to select a leader "particle", and (2) two well-known mutation operators fromEA applied to the swarm in order to ensure extensive exploration of search space. To facilitate comprehension, features presented above are explained in the following list:

1. External repository: its main objective is to store historical record of nondominated vectors generated along the search. It is also called "secondary population" in the literature [31]. This repository involves two main parts: the controller and the adaptive grid.

(a) Controller: at the beginning of the search, the external repository is empty. Non- dominated vectors from initialized swarm are stored in the repository. At each iteration, new nondominated vectors are compared with stored vectors and if a new vector is dominated by a stored individual, then such vector is automatically rejected. Whereas, if none of stored vectors dominates the vector wishing to be stored in the repository, then the new vector is added to the repository. When the "secondary population has reached its capacity limit (200 solutions have been selected as a limit), adaptive grid procedure enters into action.

(b) Adaptive grid: the basic idea of the adaptive grid is to uniformly divide objective functions search space into "regions" that are mathematically called hypercubes (here 10 hypercubes 1 per dimension were selected, however, this amount is problem dependant). Each hypercube is a "region" that contains a certain amount of nondominated solutions. Adaptive comes from the fact that hypercubes dimensions are computed from solution coordinates. Its computational cost is low because hypercube dimensions have to be updated only when there is a nondominated solution that lies outside grid’s current bounds. In this case, the whole grid has to be recomputed and each solution has to be relocated. In order to stay within repository capacity limit, solutions that lies in high density hypercubes are discarded when a new nondominated solution is found within the current grid boundaries or when the new solution

1. If the ranges of objective functions are not scaled, the adaptive grid is formed by hyperparallelepids.

77 Chapter 2 – Sizing optimization of pv-bess based community microgrid

lies outside the previous grid boundaries. This ensures a uniform distribution of nondominated solutions along the Pareto front.

(c) Leader selection: the concept of global best gb presented in the single-objective part is replaced the concept of leader. Here quality (fitness) of each hypercube containing more than one solution is computed by dividing any number x > 1 (in this work, x = 10 was selected according to [31]) by the amount of solutions they contain. The objective is to decrease quality of highly populated hypercubes. A roulette-wheel selection is applied to select the hypercube where the leader particle will be selected. Once the selection is performed, the leader particle is randomly selected within the selected hypercube. 2. Mutation operators:[157] proposed to divide the swarm in three equal parts and to apply no mutation on the first third, an uniform mutation on the second third and finally an non-uniform mutation to the rest of the swarm ("primary population"). Uniform mutation is applied to a certain percentage of the second third of the swarm (here 50%), it corresponds to a particle’s position mutation within decision variable search space boundaries. Non uniform mutation is applied to 5% of the last third of the swarm and decreases along iterations. Non-uniform mutation impacts particle’s position in the same way as uniform mutation. Remark. When the current position of a particle is better than the position contained in its memory, particle’s best position should be updated. In order to decide which position should be stored in the memory, Pareto dominance criterion is used. If the current best position dominates the current position, then position stored is kept. Otherwise the new position replaces the one stored. Finally, if neither of the positions are dominated by each other, one of them is selected randomly to be stored in the memory. Algorithm2 displays pseudocode of Multi-Objective Particle Swarm Optimization (MOPSO) taking into account modification presented above.

78 2.1. Distributed energy resources modeling

Algorithm 2 Multi-objective particle swarm optimization algorithm Require: ~x Ensure: min f~(~x)

for Particlei ∀i ∈ 1 : N do Initialize particle’s position ~xi(0) according to constraints and boundaries Initialize particle’s velocity such as ~vi(0) ← 0 Compute particle score: f~(~xi(0)) Store particle’s position that represent nondominated vectors in Repository Generate adaptive grid and associated hypercubes Initialize particle’s best position memory such as ~pb,i ← ~xi(0) this memory is also stored in Repository end for while stopping criteria are not met do for Particlei ∀i ∈ 1 : N do Select "leader" particle and return its position ~pleader Update particlei velocity: ~vi(t + 1) ← χ~vi(t) + c1(~pb,i − ~xi(t))R1 + c2(~pleader − ~xi(t))R2 Update particlei position: ~xi(t + 1) ← ~xi(t) + ~vi(t + 1) Apply both uniform and non-uniform mutation operators Check constraints and boundaries and update particlei position if needed Compute particlei score: f~(~xi(t + 1)) Update Repository and update adaptive grid ~ if f(~xi(t + 1)) f(~pb,i) then ~pb,i ← ~xi(t + 1) ~ ~ ~ else if f(~xi(t + 1)) f(~pb,i) and f(~pb,i) f(~xi(t + 1)) then ~pb,i ← random selection between ~pb,i and ~xi(t + 1) end if end for Update stopping criteria status end while

79 Chapter 2 – Sizing optimization of pv-bess based community microgrid

2.2 Problem formulation

In this part, problem formulation is introduced. Main objectives and scenarios are presented in section 2.2.1, Net Present Value (NPV) is described in section 2.2.2 and proposed cost function associated to a speciﬁc scenario are listed in 2.2.3.

2.2.1 Context

The main goal of this Chapter is to propose an optimalPV array and BESS sizing method for a residential application. Optimal sizing relies on 3 objectives which are presented in Figure 2.11 and detailed below.

Cost efﬁciency

RnE penetration rate Load satisfaction rate Figure 2.11 – Objectives involved in optimal sizing

Cost efﬁciency stands for future energy cost in order to ensure ﬁnancial safety regarding energy prices. Load satisfaction rate stands for the ability to ensure load supply and can be translated into user comfort satisfaction rate because a lack of load supply will generate users complaints. RnE penetration rate is explicit and stands for the ratio of RnE consumed over total energy consumption. In order to illustrate the impact of each objective, optimal sizing is proposed for several scenarios:

1. On-site production system for self-consumption without any possibility of power injection and no BESS. This scenario will be named "pure self-consumption".

2. On-site production system for self-consumption with possibility of surplus power injection and no BESS. This scenario will be named "self-consumption with surplus power injection".

3. On-site production system for full power injection and no BESS. This scenario will be named "full power injection".

80 2.2. Problem formulation

4. On-site production and BESS designed to ensure safe off-grid operation. This scenario will be named "off-grid operation". This scenario will be treated as a bi-objective optimization in order to simultaneously maximize NPV to minimize and EENS.

For each scenario, PSO algorithm is used to optimize the size of installed solar system in order to maximize each system’s NPV over 20 years.

2.2.2 Key Performance Indicators

This part presents KPI used to asses the relevance of optimal sizing for each scenario. NPV is used to asses the economic value of optimal sizing, EENS refers to load/user satisfaction rate. RnE penetration rate will be used as the third indicator but will not be further detailed.

Net Present Value

NPV is the difference between the present value of cash inflows and the present value of cash outflows over a period of time. NPV is used various financial activities such as: capital budgeting and investment planning. NPV is useful to analyze the profitability of a projected investment or project. A positive NPV implies that an investment is profitable, NPV equal to 0 implies that proposed project adds to value and a negative NPV implies that investor should avoid doing such investment. NPV equation is introduced below:

Y y y y y y y y X Csave(1 + Rgrid) + Cinj(1 + Rbuy) + Cinc − CO&M (1 + Ri) NPV = −C + (2.20) project (1 + R )y(1 + R )y y=1 d i

with:

— Cproject: overall project cost (C). y — Csave: cash inflow generated by savings from grid bill for year y (C). y — Cinj: cash inflow generated by injections into the distribution grid for year y (C). y — CO&M : cash outflow generated by operation, maintenance and replacement costs (C, i.e. solar inverter and new batteries replacement every 10 years). y — Cinc incentives for self-consumption purposes (C, for pure self-consumption or surplus power injection only).

— Rgrid: grid repricing rate (2.96%).

— Rbuy: power injection repricing rate (1%, 0% for surplus power injection).

— Ri: general repricing rate (1%).

81 Chapter 2 – Sizing optimization of pv-bess based community microgrid

— Rd: discount rate (0-2%).

Discount rate Rd is used to discount future cash flows in order to provide a convenient way to compare different investments [115]. For instance, setting Rd = 0 signifies that proposed on- site energy consumption system is compared to no other investment; setting Rd = 2% signifies that proposed system is compared to a fixed-rate investment that generates 2% of interests each year (besides inflation). In this work, Rd value will be set to 0 (to compare with cash), 0.5% (to compare with a saving account) and 2% ( to compare with a life insurance savings plan).

Expected Energy Not Supplied (EENS)

EENS (Wh/period) refers to the total energy that is not delivered at the system load points over a given period (often a year). It translates into an energy shortage during a period of time when power required by loads is greater than the available power generation [3]. EENS is presented in the following equation:

T X n t t t o EENS = max Pload − PPV − Pbatt ∆t, 0 (2.21) t=1 with

t — Pload: residential load (W).

2.2.3 Proposed cost functions

In order to ﬁnd the optimal solar panel array and BESS size, cost functions based on NPV for scenario 1,2 and 3 are presented below. Cost functions for scenario 4 are based on NPV and EENS because the main objective is to satisfy off-grid operation.

Scenario 1: "pure self-consumption"

Scenario 1 consits in optimal sizing ofPV panel array for self-consumption purposes,PV system efﬁciency will be decreased in order to match load demand. Cost function for scenario 1 involves project cost, maintenance, operation and replacement costs, incentives for self-consumption as well as savings from grid bill. Cost function f1 is displayed below:

  Y y y y y y X Csave(1 + Rgrid) + Cinc − CO&M (1 + Ri) max(f1) = max −Cproject +  (2.22) (1 + R )y(1 + R )y y=1 d i

82 2.2. Problem formulation

Scenario 2: "self-consumption with surplus power injection"

In this scenario, power coming fromPV panels is consumed on-site and surplus power is injected to the main grid. Cost function for scenario 2 involves project cost, maintenance, operation and replacement costs, incentives for self-consumption, savings from grid bill and cash inﬂows from surplus power injection. Cost function f2 is displayed below:

 Y y y y y y y y  X Csave(1 + Rgrid) + Cinj(1 + Rbuy) + Cinc − CO&M (1 + Ri) max(f2) = max −Cproject +  (1 + R )y(1 + R )y y=1 d i (2.23)

Scenario 3: "full power injection"

Scenario 3 represent a system which injects all the power it produces. Cost function for scenario 3 involves project cost, maintenance, operation and replacement costs and cash inﬂows from power injection to the distribution grid. Cost function f3 is displayed below:

 Y y y y y  X Cinj(1 + Rbuy) − CO&M (1 + Ri) max(f3) = max −Cproject +  (2.24) (1 + R )y(1 + R )y y=1 d i

Scenario 4: "off grid operation"

In this scenario, the site is assumed to be off-grid and thus optimal sizing should ensure at least partial load demand. Thus, the optimal sizing should optimize two objectives: maximization of NPV and minimization of EENS. Besides, grid bill savings are assumed to be equal to 0 because it is not possible to compare with grid-connected households. In the context of off-grid operation, proﬁtability compared to other investments may not be taken into account. Thus, discount rate is assumed to be equal to 0 Cost functions f4,1 based on NPV and f4,2 based on EENS are displayed below:

  Y y y y X Cinc − CO&M (1 + Ri) max(f4,1) = max −Cproject +  (2.25) (1 + R )y y=1 i and T ! X n t t t o min(f4,2) = min max Pload − PPV − Pbatt ∆t, 0 (2.26) t=1

83 Chapter 2 – Sizing optimization of pv-bess based community microgrid

2.3 Results and discussion

In this section, simulation are carried out with a "RT2012" house, a "classic" house and a neighborhood load to demonstrate the effectiveness of the proposed optimization. For scenario 1, 2 and 3, simulations are performed using a residential grid-connected houses and a grid- connected communityMG where the main goal is to maximize system proﬁtability thanks to optimal sizing. For scenario 4, off-grid houses and off-grid communityMG are considered. Besides, a sensitivity analysis is carried out to assess the impact of the discount rate on global proﬁtability. In order to have a reference regarding electricity consumption expenses, 20-year cumulative expenses for grid-connected houses without any DER are listed below: — "RT2012" cumulative expenses: 20392 C — "Classic" cumulative expenses: 56537 C

2.3.1 Results (single-family houses)

Scenario 1: "pure self-consumption"

Scenario 1 involves a house that installed a rooftop solar panel array for self-consumption purposes only. This system is considered without grid injection, thus without DSO fee. Results for "RT2012" and "classic" houses are displayed in Table 2.5.

House "RT2012" "Classic" Discount Rate 0 0.5 2 0 0.5 2 Optimal peak power (kWp) 0 0 0 1 1 0 LCOE (cC/kWh) N/A N/A N/A 21.47 22.33 N/A RnE penetration rate (%) 0 0 0 6.83 6.83 0 EENS (kWh/20 years) 0 0 0 0 0 0 NPV( C) 0 0 0 399 234 0 Table 2.5 – Optimal system size and proﬁtability (scenario 1)

Table 2.5 shows that considering solar panel array installation for self-consumption purposes only is relevant only for "classic" houses with signiﬁcant energy consumption. Optimal sizing found the best proﬁtability for a 1 kWp system. This system covers 6.83% of the overall consumption.

Figures 2.12 and 2.13 show convergence of PSO used to optimize cost function f1. It can be noted that the iteration amount is different. This is due to the fact that PSO is stopped sooner than expected (100 iterations) thanks to MaxDistQuick stopping criterion.

84 2.3. Results and discussion

400

390

380

370

360

350 5 10 15 20 25 30 35 40 45 50 Iterations Figure 2.12 – Convergence curve of particle swarm optimizer for scenario 1 (Discount rate = 0%)

235

230

225

220

215

210 10 20 30 40 50 60 Iterations Figure 2.13 – Convergence curve of particle swarm optimizer for scenario 1 (Discount rate = 0.5%)

Scenario 2: "self-consumption with surplus power injection"

As presented before, scenario 2 involves a residential single-family house that installed solar panels for self-consumption with surplus power injection. Results for "RT2012" and "classic" houses are displayed in Table 2.6. In Table 2.6, results shows that scenario 2 is proﬁtable for both kind of houses if a discount rate equal to 0 is taken into account. Moreover, for "classic" house, this scenario is proﬁtable for every considered value of discount rate. Regarding installed system peak power, optimal sizing shows 1 kWp for the "RT2012" household and, depending on discount rate value, between 2 and 4 kWp. Overall RnE penetration rate remains below 20% for every cases.

85 Chapter 2 – Sizing optimization of pv-bess based community microgrid

House "RT2012" "Classic" Discount Rate 0 0.5 2 0 0.5 2 Optimal peak power (kWp) 1 0 0 4 3 2 LCOE (cC/kWh) 20.77 N/A N/A 16.17 17.15 20.66 RnE penetration rate (%) 16.34 0 0 18.44 15.70 12.02 EENS (kWh/20 years) 0 0 0 0 0 0 NPV( C) 131 0 0 1872 1256 201 Table 2.6 – Optimal system size and proﬁtability (scenario 2)

Scenario 3: "full power injection"

Scenario 3 involves single-family houses that installed solar panels for full injection purposes. Results for "RT2012" and "classic" houses are displayed in Table 2.7.

House "RT2012" and "classic" Discount Rate 0 0.5 2 Optimal peak power (kWp) 9 9 0 LCOE (cC/kWh) 16.07 16.69 N/A RnE penetration rate (%) 0 0 0 EENS (kWh/20 years) 0 0 0 NPV( C) 2600 1305 0 Table 2.7 – Optimal system size and proﬁtability (scenario 3)

Table 2.7 shows that optimal sizing for both houses is the same because in this scenario, the system injects 100% of produced power. The optimal solar system size in this scenario is 9 kWp. This value is due to the fact that injected energy price varies in function of system size. Besides, this price variation is not linear (see Table 2.4 for more information about injection tariffs). Thus there are threshold effects that appear during the optimization. Moreover, taking into account a value of 1.6% for discount rate, optimal NPV (218.21 C) is reached with a 3 kWp system. With this discount rate value, the NPV is comparable with the to "classic" house NPV in the scenario 2 with a discount rate value of 2.

Scenario 4: "off grid operation"

Scenario 4 proposes to ﬁnd the optimal sizing for aPV-BESS system considering off-grid operation. As presented above, in this scenario, batteries are charged by solar surplus power and discharged to supply the considered household load. This energy management is able to ensure off-grid operation at all time if sizing is performed carefully. Results for "RT2012" and

86 2.3. Results and discussion

"Classic" houses are displayed in Table 2.8.

House "RT2012" "Classic" Optimal peak power (kWp) 27.39 92.07 LCOE (cC/kWh) 88.40 94.47 Optimal battery size (kWh) 53.42 214.63 RAPS (cC/kWh) 147.33 105.53 RnE penetration rate (%) 100 100 EENS (kWh/20 years) 0 0 NPV (kC) -145.27 -437.83 Table 2.8 – Optimal system size and cost (scenario 4)

Table 2.8 shows that optimalPV peak power and battery capacity for both cases are quite signiﬁcant. In both cases, NPV is negative. Negative NPV means that such investment should be avoided. But in this case, the objective is to ensure a certain ratio of autonomy if not full autonomy. For "RT2012" households, the optimal sizing result is 27.39 kWp for solar array peak power and 53.42 kWh for BESS size. For "Classic" households, the optimal sizing result is 91.74 kWp for solar array peak power and 214.63 kWh for BESS size. Corresponding LCOE are: 88.40 cC/kWh and 94.47 cC/kWh. Regarding the RAPS, corresponding values are: 147.33 cC/kWh and 105.53 cC/kWh. RAPS values are bigger than usually encountered in the literature because the battery is not used as much as it should be in order to minimize said RAPS. In other words, the battery is bought a certain price and the total energy throughput is too low compared to battery abilities. Finally, because of its bigger load demand, "Classic" household require a bigger system.

87 Chapter 2 – Sizing optimization of pv-bess based community microgrid

Figures 2.14 and 2.15 depicts Pareto front for both houses. According to desired EENS ratio, it is possible to compute a speciﬁc sizing that will ensure a certain autonomy ratio.

100 Non-dominated solutions (Pareto front) 80

40 EENS (%) 20

0 0 50 100 150

Figure 2.14 – Pareto front for scenario 4 ("RT2012" house)

100 Non-dominated solutions (Pareto front) 80

40 EENS (%) 20

0 0 50 100 150 200 250 300 350 400 450

Figure 2.15 – Pareto front for scenario 4 ("Classic" house)

Figure 2.16 shows solar generation and BESS power flow for a given day. It can be noted that solar power is displayed twice. Theoretical solar power represents possible solar generation if MPPT efficiency was not decreased to meet load and BESS requirements. Battery power flow is positive during charge phases and negative during discharge phases. Figure 2.17 represents BESS SOC associated with battery power flow presented in Figure 2.16.

88 2.3. Results and discussion

104 3 Load 2.5 Theoretical solar power Throttled solar power Battery power flow 2

1.5

1 Power (W) 0.5

-0.5

-1 5 10 15 20 Time (h) Figure 2.16 – Household power ﬂow for scenario 4 ("Classic", 1st Jan of year 1)

SOC (%) 80

75 5 10 15 20 Time(h) Figure 2.17 – State of charge for scenario 4 ("Classic", 1st Jan of year 1)

89 Chapter 2 – Sizing optimization of pv-bess based community microgrid

Figure 2.18 represent battery SOC for the worst year of simulated period. SOC was bounded between 15 and 90%. It can be noted that this constraint is respected and the lowest SOC value was 15%.

40 SOC (%) 20 X 31 0 Y 151000 2000 3000 4000 5000 6000 7000 8000 Time(h) Figure 2.18 – State of charge for scenario 4 ("Classic", worst year)

Scenario 4*: "off grid operation considering recycled batteries"

This scenario is the same as scenario 4 but considers batteries salvaged from discarded EV. Therefore, initial battery SOH is assumed to be 80% instead of 100%. Besides, it is also assumed that degradation is linear, thus batteries will be replaced every 5 years (from a SOH of 80% to 60% because it was assumed that brand new batteries takes 10 years to reach a SOH of 60%). Results are displayed in Table 2.9

House "RT2012" "Classic" Optimal peak power (kWp) 19.8 91.74 LCOE (cC/kWh) 65.34 94.06 Optimal battery size (kWh) 104.05 190.46 RAPS (cC/kWh) 68.79 96.80 RnE penetration rate (%) 100 100 EENS (kWh/20 years) 0 0 NPV (kC) -91.14 -422.22 Table 2.9 – Optimal system size and cost (scenario 4*)

90 2.3. Results and discussion

2.3.2 Results (community microgrid)

Scenario 1: "pure self-consumption"

Scenario 1 involves a communityMG that installed solar panels for self-consumption purposes only (without grid injection, thus without DSO fee). Results for this neighborhood are displayed in Table 2.10.

Area "Community" Discount Rate 0 0.5 2 Optimal peak power (kWp) 61.05 57.09 36.3 LCOE (cC/kWh) 17.04 17.45 17.84 RnE penetration rate (%) 11.94 11.34 7.78 EENS (kWh/20 years) 0 0 0 NPV (kC) 47.35 38.30 19.51 Table 2.10 – Optimal system size and proﬁtability (scenario 1)

Results in Table 2.10 shows that optimal sizing is proﬁtable for every discount rate value considered in scenario 1. Considering on-site generation for a whole community is more profitable than considering each house separately. This is due to the fact that load consumption is bigger than load consumption of a given house and that system cost per Wp installed decreases when considering bigger systems.

Scenario 2: "self-consumption with surplus power injection"

Scenario 2 involves a communityMG that installed solar panels for self-consumption with surplus power injection. Results for this neighborhood are displayed in Table 2.11.

Area "Community" Discount Rate 0 0.5 2 Optimal peak power (kWp) 100 99 78 LCOE (cC/kWh) 13.50 14 15.80 RnE penetration rate (%) 16.82 16.72 14.28 EENS (kWh/20 years) 0 0 0 NPV (kC) 103.02 87.99 46.13 Table 2.11 – Optimal system size and proﬁtability (scenario 2)

Results in Table 2.11 shows that optimal sizing is proﬁtable for every discount rate value considered in scenario 2. In this case, the possibility to sell energy to the main grid increase

91 Chapter 2 – Sizing optimization of pv-bess based community microgrid overall proﬁtability. Nevertheless, surplus power injection prices are limited systems up to 100 kWp (for bigger systems, a call for tenders should be considered). Thus the optimal sizing is subject to threshold effects for 100 kWp systems.

Scenario 3: "full power injection"

Scenario 3 involves a residential neighborhood that installed solar panels for full injection purposes. Results for this neighborhood are displayed in Table 2.12. Results are similar to

Area "Community" Discount Rate 0 0.5 2 Optimal peak power (kWp) 9 9 0 LCOE (cC/kWh) 16.07 16.69 N/A RnE penetration rate (%) 0 0 0 EENS (kWh/20 years) 0 0 0 NPV( C) 2600 1305 0 Table 2.12 – Optimal system size and proﬁtability (scenario 3) single-family houses taken separately because in this scenario, value of residential load does not affect the cost function. Conclusions are similar as single-family houses in scenario 3.

Scenario 4: "off grid operation"

Scenario 4 proposes to ﬁnd the optimal sizing for aPV-BESS system. As presented above, in this scenario, batteries are charged by solar surplus power and discharged to supply the residential load. This energy management is able to ensure off-grid operation at all time if sizing is performed carefully. Results for the communityMG are displayed in Table 2.13.

Area "Community" Optimal peak power (MWp) 2.19 LCOE (cC/kWh) 59.76 Optimal battery size (MWh) 11.11 RAPS (cC/kWh) 57.19 RnE penetration rate (%) 100 EENS (kWh/20 years) 0 NPV (MC) -8.53 Table 2.13 – Optimal system size and cost (scenario 4)

92 2.3. Results and discussion

Scenario 4*: "off grid operation considering recycled batteries"

This scenario is the same as scenario 4 but considers batteries salvaged from discarded EVs. Therefore, initial battery SOH is assumed to be 80% instead of 100%. Besides, it is also assumed that degradation is linear, thus batteries will be replaced every 5 years (from a SOH of 80% to 60% because it was assumed that brand new batteries takes 10 years to reach a SOH of 60%). Regarding recycled battery price, a residual value of 30% of the initial price was considered. Results are displayed in Table 2.14

Area "Community" Optimal peak power (MWp) 1.63 LCOE (cC/kWh) 45.02 Optimal battery size (MWh) 35.44 RAPS (cC/kWh) 38.88 RnE penetration rate (%) 100 EENS (kWh/20 years) 0 NPV (MC) -6.24 Table 2.14 – Optimal system size and cost (scenario 4*)

2.3.3 Results summary

In this section, results summary is displayed to facilitate results comparison between cases. In the same fashion, Figure 2.19 2 depicts NPV evolution among scenarios for "RT2012" and "Classic" single-family homes. Figure 2.20 shows RnE penetration rate evolution for the same context. Figure 2.21 3 depicts NPV evolution among scenarios for a communityMG. Figure 2.22 4 shows RnE penetration rate evolution for the same context. EENS is not represented graphically because its value is either 0 or 100% depending on the scenario, thus interest for graphical representation is limited. Besides, in order to be able to easily compare every scenario, a discount rate of 0% was selected for graphical display purposes.

2. In Figure 2.19, from scenario 1 to scenario 3, NPV is displayed in e whereas in scenarios 4 and 4* NPV is displayed in ke. 3. In Figure 2.21, from scenario 1 to scenario 3, NPV is displayed in ke whereas in scenarios 4 and 4* NPV is displayed in Me. 4. In Figures 2.20 and 2.22, RnE penetration rate is ﬁxed to 0 because RnE is fully injected into the grid, therefore it is not possible to compute a percentage of penetration regarding the global grid. Besides, because of full injection technical and regulatory limitations (injection is performed through a distinct PCC), there is no impact (other than ﬁnancial impact) on studied site.

93 Chapter 2 – Sizing optimization of pv-bess based community microgrid

3000 RT2012 Classic 2000

1000 NPV

-1000 1 2 3 4 4* Scenario Figure 2.19 – Net present value evolution among scenarios (single-family households)

100 RT2012 80 Classic

RnE penetration rate (%) 0 1 2 3 4 4* Scenario Figure 2.20 – Renewable energy penetration rate evolution among scenarios (single-family households)

94 2.3. Results and discussion

100

NPV 40

1 2 3 4 4* Scenario Figure 2.21 – Net present value evolution among scenarios (community microgrid)

100

RnE penetration rate (%) 0 1 2 3 4 4* Scenario Figure 2.22 – Renewable energy penetration rate evolution among scenarios (community microgrid)

95 Chapter 2 – Sizing optimization of pv-bess based community microgrid

2.3.4 Discussion

In this section, results for each scenario presented above are discussed.

— Scenario 1:

— For "RT2012" single-family households taken separately, pure self-consumption is never profitable whatever the discount value is. For "Classic" single-family households taken separately, results showed that the optimalPV array size is equal to 1 kWp. This system size is not big but still profitable for this kind of household. — Regarding the communityMG, thanks to load and solar generation sharing, results showed that the optimalPV array size varies from 36 to 61 kWp depending on discount rate value. The profitability of the community MG is greater than the sum of single-family households (25 "RT2012" + 25 "Classic") profitability taken separately. This is due to the fact that generation and load are shared and also because of extra system savings based on its scale. Moreover, RnE penetration rate varies from 7.7 to 12%. The presence of solar panel arrays increased significantly the RnE share of green electricity consumed by the communityMG.

— Scenario 2:

— For "RT2012" houses taken separately, self-consumption with surplus power injection is barely profitable. Results shows an optimal sizing of 1 kWp for such households. For "Classic" single-family households taken separately, results showed that the optimalPV array size varies between 2 and 4 kWp depending on the discount rate value. In this case, the NPV is significant and there is a real interest for families living in this kind of houses to install rooftop solar panel arrays. Moreover, RnE penetration rate varies from 12 to 18%. The presence of solar panel arrays increased significantly the RnE share of green electricity consumed by this kind of household. — Regarding the communityMG, thanks to load and solar generation sharing, results showed that the optimalPV array size varies from 78 to 100 kWp depending on discount rate value. The profitability of the community MG is greater than the sum of single-family households (25 "RT2012" + 25 "Classic") profitability taken separately. This is due to the same facts as in scenario 1. Finally, RnE penetration rate varies from 14.3 to 16.8%. The presence of solar panel arrays increased significantly the RnE share of green electricity consumed by the communityMG. Compared to scenario 1, RnE penetration rate was increased up to 84% (with discount rate value of 2%).

— Scenario 3:

96 2.3. Results and discussion

— In this case, due to the price scheme linked to solar system size, there is no difference between "RT2012", "Classic" and "Community"MG loads. The optimalPV system size is 9 kWp. Besides, this system does not increase RnE penetration rate in the point of view of the consumer (injected power is considered negligible in front of the whole country power generation). — Scenario 4: — Wheter considering single-family houses or the communityMG. Optimal sizing shows that installed systems size is significant.PV panel size varies from 27 kWp to 92 kWp depending on the house and is equal to 2.19 MWp for the communityMG. BESS size varies from 53 to 215 kWh depending on the house and is equal to 11.11 MWh for the communityMG. — It can be noted that BESS is oversized for a both sites regarding a whole year. Indeed, the storage is emptied up to his lower SOC limit only once during the considered period. Figure 2.17 illustrates this fact. — It can also be noticed that LCOE is high for every cases (except scenarios 2 and 3). This is due to the fact thatPV generation is often throttled in order to ensure power balance. Figure 2.16 illustrates this throttling issue. This finding shows that additional load management strategies in order to use all availablePV generation and avoid throttling issues are a mandatory requirement. Without such load management strategies, self-consumption and off-grid scenarios will never be cost-competitive alternatives compared to partial and full injection scenarios. Thus, systems that aim to increase RnE penetration (through storage or not) can: (i) implement new income streams such as flexibility services in order to make such configuration a cost-competitive alternative compared to classic configurations such as full injection and/or (ii) implement smart load management strategies in order to maximize usage of local generation and decrease the need of storage capacity. Both strategies will lead to better profitability thanks to new income streams and/or optimization of operation (that will lead to lower cost per generated/storage energy unit). — Finally, recycled batteries sourced fromEVs are a cost-efficient way to provide storage capacity. Scenarios 4* prove that NPV value is improved when recycled batteries are considered. Please note that it is assumed that recycled batteries (in fact second- hand batteries) will not suffer from major operation failures due to their age/degraded capacity.

97 Chapter 2 – Sizing optimization of pv-bess based community microgrid

2.4 Conclusion

In this Chapter, an optimalPV-BESS sizing method for grid-connected/off-grid single-family households and communityMG based on retail prices and repricing rates was proposed. Steady-state models used to modelPV, BESS, loads as well as the aggregator were introduced in section 2.1. Optimal sizing method was applied to 4 scenarios introduced in Section 2.2 in order to represent current possible technical choices for households. Then, cost function were formulated to represent said scenarios. The MOPSO (presented in section 2.1) was used to optimize proposed cost function. Main highlights and remarks of this Chapter are listed below:

1.MG component models, single-family household loads ("RT2012" and "Classic" and economical models taking into account future value of money were introduced. Data and parameters presented in this Chapter reflect current technical and legal possibilities for the residential sector to participate in the European energy transition with a realist economical approach. 2. The optimal sizing algorithm focuses on 3 main objectives: cost effectiveness, load/user satisfaction rate and RnE penetration rate. Therefore, 4 scenarios were introduced to illustrate said technical and legal possibilities. Scenario were named: "pure self-consumption", "self-consumption with surplus power injection", "full power injection" and "off grid operation (considering recycled batteries)". 3. Results from optimal sizing showed that: (1) considering single-family houses, the installation of a rooftopPV system was profitable only for scenario 2 and 3; considering the communityMG every scenario was profitable thanks to load sharing and savings on system cost due to its larger scale. (2)PV array and BESS size in order to achieve complete autonomy are very substantial. Moreover, if the objective is to ensure complete autonomy, scenario 4* demonstrated that using recycled batteries fromEV is a good solution to significantly decrease overall system cost assuming that this second-hand storage will not suffer from major operation failures due to its age/degraded capacity.

Nevertheless, in this Chapter, BESS degradation was based only on calendar degradation method. Battery energy management strategies were limited and focused only on off-grid operation. Weather forecast uncertainties which are a major issue for reliable energy management were not considered. In Chapter 3, battery management strategies (energy arbitrage) for grid- connectedMG will be presented and, in Chapter 4 a novel statistical weather forecast model for short term prediction in regions with strong climate variations will be introduced and compared to a reference model available in the literature.

98 CHAPTER 3 RELEVANCE OF TIME HORIZON-BASED BATTERYENERGYMANAGEMENT STRATEGIESFORCOMMUNITY MICROGRIDS

3.1 Introduction to horizon-based energy management strategies

RES are well known for their inherent intermittency, thus renewable energy production have to be stored in order to correctly supply loads. Thus several energy management strategies related to the residential sector were presented in the literature with the aim of maximising MG profitability, increasing RES penetration as well as providing a specific flexibility service [93, 97, 19] (see chapter 1 for further explanation about energy management strategies). To that end, this chapter proposes to explore the influence of an energy management strategy based on 48-hour horizon compared to a 24-hour horizon one in order to perform energy arbitrage. Presented system considers a residentialMG based onPV generation and storage connected to the main grid. Proposed 48-hour energy management strategy provides additional management possibilities such as the ability to delay trades (charge today, discharge tomorrow) and a larger range of hours to use the storage. This part involves a communityMG (see Figure 2.2 in Chapter 2) connected to the main grid. Its operation is controlled by a DEMS with an integration of a BESS in the context of self-consumption and market participation through an aggregator. As discussed in the chapter 1, some improvements can be performed in this area of research. Firstly, 24-hour horizons are commonly used in order to optimize MG daily or day-ahead operation. This limits BESS management possibilities to a specific time frame. For instance, using a 24-hour horizon prevents the possibility to delay BESS usage in order to maximize the profitability. Secondly, variations of certain parameters such as battery remaining capacity and cost of storage over time cannot be correctly represented considering only a 24-hour study pe-

99 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids riod or even one year study period. For instance, SOH degradation of BESS andPV efﬁciency degradation require more than one year to have a signiﬁcant impact on global MG operation and thus affection initial energy management strategy (assuming BESS is not used in a way that could generate premature SOH degradation). This Chapter proposes to handle drawbacks presented above while main highlights are detailed below:

1. Enhanced energy management framework (compared to Chapter 2 Section 2.1) for residential grid-connected MG operation that incorporates bi-directional power exchanges with the grid. Studied MG has access to dynamic market prices thanks to an aggregator. This study involves 24-hour and 48-hour horizon profitability comparison. 2. BESS model which include SOH degradation taking into account charge and discharge pattern and magnitude for long-term study 10 years. Storage is used for arbitrage applications and to improve self-consumption rate throughPV surplus power storage. 3. Battery power flow optimization algorithm able to select the best scenario for a given day (single or multiple day trades, injection report...) taking into account day-ahead data in the 48-hour horizon case. The algorithm also includes profitability selection for a given scenario, buy/sell zones designation to decrease amount of decision variables (unknowns) and dynamic stopping criterion in order to reduce computation time.

Note: in this residential context, work was focused on grid interaction with the main grid. Thus, specific field of research such as DSM was not considered in this thesis. Besides, solar irradiation uncertainties and weather forecast prediction will be considered in the next Chapter. The rest of this Chapter is organized as follows. Section 3.2 presents the proposed system model extensions from Chapter 2 which involves solar generation based on Markov chains and BESS degradation based on SOH. Proposed energy management framework is detailed in Section 3.3. Numerical results regarding 24 and 48-hour horizon profitability comparison as well as sensitivity analysis results (based on storage price and size, RnE penetration rate and aggregator margin) are discussed in section 3.4. Section 3.5 draws the conclusions of this Chapter.

100 3.2. System model

3.2 System model

Models used for simulations are presented in this section. In this part as well as in this whole thesis, a 1-hour time step is used for models and simulations. Studied management horizons are respectively 24 and 48-horizons, besides studied period during simulations is 10 years. In order to provide various KPIs, several enhancements regarding system models were integrated in models presented in Chapter 2 Section 2.1. These modifications are listed below: — Solar irradiation is now based on Markov chains (discussed in Section 3.2.1). Previous solar irradiation data was only based on historical data. — BESS degradation model is now based on SOH to reflect the impact of each charge and discharge over battery lifespan (discussed in section 3.2.2). Previously, degradation was only represented by calendar ageing. — Aggregator capacities are now extended and involve aggregator’s margin and access to RTP (discussed below). Previously, only FIT was considered in the context of aggregator and BRP interaction. Regarding aggregator model, despite the fact that various possible aggregator business models exist [27], only trading of aggregated renewable electricity on spot markets and valuation of distributed generation of residential customers were addressed. In this study, the MG is able to buy and sell energy on the spot market through the aggregator. Price spread can be applied between buy and sell options, therefore a margin can be applied on feed-in prices. In the scope of this Chapter, margin rates, noted ragg, varying from 0 to 20% will be represented in the sensitivity analysis. A spot price time series was generated from mean values of a 3 years (2016-2018) electricity price history extracted from [65]. In order to reflect average European household electricity prices (21.13 e/kWh)[68], previously computed tariffs were multiplied by 5 to obtain household-based spot prices.

101 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

Finally, in order to provide a realistic communityMG sizing,PV panel array size and BESS size was extracted from Chapter 2. In this Chapter, the followingMG setup is studied: — System 1: 25 "RT2012" and 25 "Classic" houses, 671PV panels spread all over roofs and a 168 kWh BESS. This sizing was able to provide a 30% of required energy the households (EENS of 70%) and because of its main source of energy apart from the grid was renewable, its RnE penetration rate was also equal to 30%. — System 2: 25 "RT2012" and 25 "Classic" houses, 757PV panels spread all over roofs and a 1257 kWh BESS. This sizing was able to provide a 50% of required energy the households (EENS of 50%) and because of its main source of energy apart from the grid was renewable, its RnE penetration rate was also equal to 50%. — System 3: 50 "RT2012" houses, 426PV panels spread all over roofs and a 455 kWh BESS. This sizing was able to provide a 50% of required energy the households (EENS of 50%) and because of its main source of energy apart from the grid was renewable, its RnE penetration rate was also equal to 50%. The two ﬁrst systems represent the evolution of suburbs in Saint-Nazaire. Old houses had large plots. Such plots were later divided in order to allow the building of new houses. The third system represents new neighborhoods that could take part inMG deployment projects. Note: for system 3, computation was performed for both 30% and 50% autonomy ratios. Results for 30% ratio shows that the system only requires 220PV (no BESS) to achieve this goal. Thus, for this study, an autonomy ratio of 50% is considered for system 3.

102 3.2. System model

3.2.1 Solar irradiation uncertainties

In this Chapter (see Chapter 2 section for further details on theoretical solar irradiation), solar irradiation uncertainties are represented by stochastic clear-sky index variations. Such variation can be interpreted as stochastic cloud cover which will impact solar irradiance value received on a plane. First of all, solar irradiance history data from 2009 to 2018 was collected to generate a MTM. Solar irradiance data used in this work is provided by Copernicus Atmosphere Monitoring Service (CAMS)[118]. This data is subject to a minimal spatial resolution of around 4-5 km. According to [138], satellite-based reanalyses data set are suitable for hourlyPV simulations across Europe. Clear-sky index history Khis is obtained by dividing the global irradiation by the clear sky irradiation, thus Khis ∈ [0, 1]. MTM-based methods require input data with a ﬁnite amount of states, thus previously computed clear-sky index history vector must be discretized. Discrete 9-state clear-sky index was used in [50] which is well-known Oktas scale. In [22], 10- state clear-sky index was used in order to allow uniform distribution of states between 0 and 1 by 0.1 steps and also to stay close to the Oktas scale. Here, the 10-state version was selected and associated MTM will be used to form annual stochastic cloud cover. Table 3.1 represents conversion between clear-sky index value and associated cloud cover state.

Table 3.1 – Clear-sky index quantiﬁcation clear-sky index State

[0, 0.1] E1 (extremely cloudy) . . . . [0.9, 1] E10 (clear-sky)

Then, for a given set of n states, MTM is a (n, n) matrix and provides all transition probabilities to move from a state Ei to a future state Ej. MTM M is typically represented as follows:

Next State

E1 ··· En   M = E1 p1,1 ··· p1,n (3.1)   .  . .. .  .  . . .    En pn,1 ··· pn,n Current State Pn Moreover, one property of M is that j=1 p(i, j) = 1. p(i, j) represents transition probability to move from state Ei to state Ej. Its computation starts by counting transitions from state Ei to state Ej between two consecutive elements of Khis. Such transition is denoted r(i, j)t and it equals to 1 if the transition occurs between the t-th and (t + ∆t)-th element, else it is set to

103 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

PT 0. This sum of transition occurrences noted R(i, j) = t=1 r(i, j)t is divided by total possible transition amount from a given state Ei. In this case, for January, period T equals 744 hours. Equation 3.2 sums up the deﬁnition of p(i, j) given above.

R(i, j) p(i, j) = Pn (3.2) j=1 R(i, j)

Due to signiﬁcant climatic variations between winter and summer, MTMs were generated for each month. The application of MTM methods gives a 10 × 10 matrix illustrated in Figure 3.1. It can be noted that probabilities p(i, i) mainly have bigger values than p(i, j)|i6=j, this means that for a given hour, weather has a higher probability to remain in the same state during next hour.

Figure 3.1 – Example of Markov Transition Matrix for clear-sky index in January

Besides, row 1 and column 1 of matrix displayed in Figure 3.1 are equal to 0. This is due to the fact that night values were not taken into account and that no Khis below 0.1 was historically recorded during 2009-2018 period in Saint-Nazaire. Kt generation algorithm is based on cumulative frequency function F inversion. Function FEi gives cumulative transition probabilities from state Ei. This function is presented in equation 3.4. A random selection k on F is computed thanks to an uniform distribution U(0, 1) such as:

E = F −1(k) (3.3) j Ei

104 3.2. System model with: j X F(i, j) = M(i, p) (3.4) p=1

In equation 3.3, future state Ej is computed. Figure 3.3 is introduced thereafter in order to illustrate equation 3.3 and 3.4. Figure 3.2 represents probability distribution to switch from state E10 at time t to any future state at time t + 1 (these probabilities are represented in Figure

3.1). Figure 3.3 displays FE10 in January. For instance, if a value k = 0.3 (represented by the red line) is set as a parameter of F −1 this function will output future state E . Each generated E10 9

0.6

0.4

0.2 Probability distribution 0 E E E E E E E E E E 1 2 3 4 5 6 7 8 9 10 Future state E Figure 3.2 – Probability distribution for current state E10 extracted from January Markov Transi- tion Matrix

0.8

0.6

0.4

0.2

Cumulative probabilities 0 E E E E E E E E E E 1 2 3 4 5 6 7 8 9 10 Future state E Figure 3.3 – Cumulative probabilities function F for current state E10 state is added into stochastic clear-sky index Kt. Kt is generated for each year of the simulation period with a 1-hour step-time (thus Kt contains 8760 values). Figure 3.4 depicts an example of stochastic hourly global tilted irradiation (with a tilt angle of 30°) over a month with considered clear-sky index generated using the MTM described above.

105 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

800 I (Clear Sky) GT I (Stochastic) 600 GT

400

200 Solar irradiation (W/m²)

0 Jan 01 Jan 04 Jan 07 Jan 10 Jan 13 Jan 16 Jan 19 Jan 22 Jan 25 Jan 28 Jan 31 Time (h) 2021 Figure 3.4 – Example of stochastic global tilted irradiation in January

3.2.2 Energy storage system degradation

SOH is also an important aspect of storage life cycle. Some battery manufacturers provide calendar ageing determined by a certain amount of cycles and others provide it through global energy throughput and charge/discharge associated Crate. Thus Crate based SOH degradation is considered to estimate residual capacity over time. It is possible to compute SOH using DOD or Crate. According to [153], using Crate allows to model impact of charge/discharge aggres- siveness over battery life expectancy whereas DOD-based model only computes depth of said charge/discharge impact. Assuming that operating temperature range and a maximum C-rate of 3 were respected (technically maximum Crate will be limited to 1 because of 1 hour time step), SOH can be estimated as follows:

d (Ct ) SOH = SOH − soh rate (3.5) t t−1 100 where SOHt is bounded between SOHinit (100%) and SOHmin (60%). Maximum SOH degradation depends on the application. For instance, in car-based applications, batteries are discarded when SOH reaches 80% [13]. For MG-based applications, batteries can be used with t a residual SOH as low as 60% [83], [13]. Function dsoh(Crate) is graphically represented below by Figure 3.5:

In Figure 3.5, slope values k0, k0.5, k1 and k2 are displayed in the following vector k =

[0.0145, 0.016, 0.0182, 0.0192] [153]. These values are valid for cycles realized at the given Crate and for batteries stored at room temperature (25°C). In this work it is assumed that charging and discharging have the same effect on SOH, thus slopes values are divided by a factor 2 when dealing only with a charge or discharge event.

106 3.2. System model

·10−2

2.63

1.67

0.76

SOH degradation (%) 0.36 0 0 0.5 1 2 3

Crate

Figure 3.5 – State of health degradation (%) in function of Crate for a given cycle

In this case, BESS operation cost varies dynamically according to charge/discharge Crate. Day Thus in equation 3.6, CBatt (e/kWh) represents a full charge/discharge round-trip cost of a given day :

2 Day .Bprice.Brated.dsoh(Crate ) CDay = SOHinit−SOHmin (3.6) Batt Day 2 Brated 2.xloss.(Crate ) .1000 . (SOCmax − SOCmin).SOHDay − 1000 Brated with Bprice (e/kWh) as the cost of purchase for a battery, Brated as battery’s rated capacity

(kWh), SOC and SOH as State Of Charge and State Of Health (%) and xloss (kW) as a power loss coefﬁcient for quadratic power losses (equal to 1.88 for both charging and discharging operations [153]). In the worst possible case, the storage can be (dis)charged in one hour (because of the selected 1-hour time step for simulation). Thus in this scenario, the corresponding Day Crate is equal to 1 (full charge in one hour and full discharge in one hour). As SOH does not vary signiﬁcantly over a day, equation 3.6 gives a good estimation of round-trip costs. There are other possibilities to compute a daily charge/discharge cost, for instance DOD- based methods or a posteriori methods. In the case of a posteriori methods (such as RAPS), the user must know the global energy throughput value in order to have a cost estimation for a given charge/discharge event. It may be unreasonable to use this kind of method as the cost must be known for the next day to perform optimal planning.

107 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

3.3 Management framework

The main target of this Chapter is to minimize theMG operation cost by optimizing battery energy management. To achieve this goal, 24 and 48-hour horizons are compared. Figure 3.6 provides an example to illustrate enhanced abilities to maximize profitability thanks to extended horizons. Positive values stand for charging periods (or zones) whereas negative values stand for discharging periods (or zones). In this example illustrated by Figure 3.6, 48h algorithm delays discharge action from hour 19 to hours 37 and 38 because it is more profitable to save on round-trip related costs. Thus 48-hour horizon delays battery usage from the evening of day d to the evening of day d + 1. In this study, it is assumed that day-ahead prices values are known at day "d" thanks to day- ahead fixing market and that they will not vary at day "d+1" thanks to market liquidity. For each of these horizons, there are different BESS management possibilities. Batteries can be operated to do energy arbitrage and increasing self-consumption ratio using various scenarios for a given day. These scenarios (except "end-of day refill" only valid for 24-hour horizon) presented thereafter are valid for both horizons. Battery use cases are presented as follows: 1. stay idle, 2. charge, 3. discharge, 4. proceed to one trade (one charge/discharge cycle), 5. proceed to two trades, 6. proceed to refill batteries at the end of each day (24-hour horizon only) The last enumerated action performs a refill a the end of each day. The value of this refill is

fixed by setting "end-of-day" State Of Charge SOCeod at the end of each day if necessary. In the 48-hour horizon case, trades can be delayed. For instance, energy can be stored during a given day in order to be consumed or sold to the grid next day. Thanks to a wider horizon compared to 24-hour horizon, there is no need to proceed to an end of day battery refill. In order to reduce computation time dedicated to BESS power flow optimization, this work proposes to designate key zones for period (24 and 48-hour horizons) where it would be the most profitable to perform a given scenario. Then the profitability of each scenario is tested in order to select the best one. Finally the battery power flow is optimized using PSO with regards to associated cost function and constraints.

108 3.3. Management framework

1 24h horizon 48h horizon 0.5

-0.5 BESS usage zones

-1 5 10 15 20 25 30 35 40 45 Time (h) Figure 3.6 – Illustration of 24 and 48h horizons abilities to manage storage

3.3.1 Key zone designation

Key zones are represented by two main zones: "buy" and "sell" zones. "Buy" zones represents set of hours where the spot price is low whereas "sell" zones represent sets of hours where the spot price is high. These zones are limited to 6 hours respectively (which is a period large enough to integrate both morning and evening peaks). For instance, zones for storage arbitrage (4h and 10h for arbitrage and 24h for regulation) were considered in for the region of New York in the scope of New York Independent System Operator (NYISO) energy arbitrage business considerations [171]. Besides, in France, it is possible to access the spot market throught an aggregator. For instance, NEXT-KRAFTWERKE [102] provides such access toMGs and RnE producers to participate in the energy market and the possibility to be a part of their Virtual Power Plant (VPP). These groups can be divided into subzones (which are limited to 3 hours). Sell zones can be splitted in "early" and "late" sell zones. Early and Late often match with morning and rush hour peaks of energy demand. Buy zones can be splitted in buy "night" zone and buy "intraday" zone. "Night" corresponds to the lowest price zone during off-peak period (generally between 9pm and 8am). "Intraday" zones stands for possible buy zones between sell subzones. "Intraday optimal" zone is located between sell subzones if prices are low enough. "Intraday surplus" zone corresponds to hours outside sell zones where there is surplusPV power. Finally in the case of 24-hour horizon, there is a "end of day" buy zone dedicated to reﬁll storage up to a speciﬁc SOC if needed. The result of this designation is displayed in Figure 3.9 for a given day which is represented by Figures 3.7 and 3.8. In this example (Figure 3.9), the buy zone "night" (in blue) involves hours 4, 5 and 6. Sell

109 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

150 Solar Power Solar Surplus Power 100 Residential Load

50 Power (kW)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Figure 3.7 – Solar power production and residential load for a given day

20 Spot Price Feed-in Price 15

5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Figure 3.8 – Spot and feed-in price for a given day

0.8 Buy Zone (Night - Optimal) 0.6 Buy Zone (Intraday - Optimal) Buy Zone (Intraday - Surplus) 0.4 Buy Zone (End of Day - 24h model only) Sell Zone 0.2 Sell Zone (Early) Sell Zone (Late) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Figure 3.9 – Zone designation for a given day zone (in green) is composed of "early" (in light blue) and "late" (in burgundy) subzones which respectively involves hours 11-13 and hours 18-20. Hours 22-24 display "end of day" zone

110 3.3. Management framework

(in purple) in order to perform low cost battery refill in the 24-hour horizon case. In orange, "intraday" buy zone (hours 14-16) represents potential buy zone for a second round-trip if it is profitable to perform such action. "Intraday surplus" zone (in gold) involves solar surplus power and low price. Last zone is useful in the context of self-consumption improvement as it represent time period where prices are low compared to sell zone, thus charging storage during these hours will allow the MG to be more profitable compared to a battery charge during hours 11-13 where solar surplus power is also available.

3.3.2 State selection

Algorithm 3 State selection (24-hour horizon) Require: spot prices (buy and feed-in) for a given day (e); zones; household load. Ensure: Best state for a given day for Day ∈ 1 : 365 do Day Day Day Day Day Day if min (CNight) + CBatt > max (CSell ) and min (CIntraday) + CBatt > max (CLate) then StateDay ← 0 th πDay ← 0 end if Day Day Day Day Day if max (CMixEarly) − min (CNight) + max (CMixLate) − min (CIntraday) − CBatt > Day Day Day Day max (max (CMixSell) − min (CNight), max (CMixLate) − min (CIntraday)) and StateDay 6= 0 then StateDay ← 4 th Day Day Day Day Day πDay ← max (CMixEarly) − min (CNight) + max (CMixLate) − min (CIntraday) − CBatt end if if StateDay 6= 0 and StateDay 6= 4 then StateDay ← 3 th Day Day Day πDay ← max (CSell ) − min (CBuy ) − CBatt end if end for

Each scenario is represented by a state. States used to represent them are explicited thereafter. State 0: idle battery; state 1: charge only; state 2: discharge only; state 3: one charge/discharge cycle (which include end of day refill if needed); state 4: two charge/discharge cycles (which also include end of day refill if needed). Theses state are summed up in Table 3.2: In order to select the most profitable battery state for 24 or 48-hour horizon, a selection algorithm is computed before the optimization part. Algorithm3 is presented above. In algorithm3, profitability is checked in order to select the best possible scenario. Firstly one-trade profitability is checked. If a day is too expensive, then its state equals 0. Then, if the day is profitable, two-trade profitability is checked (it is compared to an one-trade case). If

111 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

Table 3.2 – Battery states and description Battery state Description Used by 0 Idle both horizons 1 Charge only 48-hour horizon only 2 Discharge only 48-hour horizon only 3 One round trip (cycle) both horizons 4 Two round trips (cycles) both horizons two-trade case is more protifitable than one-trade case, day’s state is set to 4. Finally if a day’s Day state is neither 0 or 4, it is set to 3. CNight (e/kWh) represents spot prices in the "night" zone for a given day. Similar notation is used for "sell", "intraday", "early and "late" zones taking into account efficiency losses. The prefix "mix" represents an extra weight to modify prices in case Day of discounted feed-in prices compared to spot prices. CBatt represents roundtrip battery cost th (e/kWh) as explicited in Section 3.2.2. πDay represents the theoretical profitability for a given day. This value is theoretical because it is computed using min and max prices as well as the worst possible battery round trip cost (aggressive charge/discharge during 1 hour). Profitability is expected to be at least the theoretical value. In the case of a 48-hour horizon, there are two extra states that can be checked (state 1 and 2). Thus, algorithm4 (an addition to algorithm3) is presented thereafter and a brief summary of its behavior is explicited below:

1. For each day, battery state and arbitrage proﬁtability is computed until the day before the last day of studied period (for day 365, day ahead is the ﬁrst day of the next year).

2. If a report (buy at a given day and sell the day after) is more proﬁtable, battery state is set to 1 as well as state report variable. Lowest buy price for a given day is saved to be used later.

3. If a report was found to be more profitable, the profitability of a bigger report is compared to the sum of present report profitability and day ahead profitability.

To put this explanation in a nutshell, assuming it is more profitable to charge the battery at day 1 and discharge it at day 2 than treating each day separately, an arbitrage report is performed. At day 2, prior to discharge the battery, the profitability of reporting again to day 3 is compared to the present report (charge at day 1 and discharge at day 2) and the profitability of day 3 computed aside. In algorithm4, index "DayAhead" stands for the day ahead current day and index "Report" stands for specific variables which are related to the 48-hour horizon specific scenario possibilities.

112 3.3. Management framework

Algorithm 4 State selection (48-hour horizon add-on) Require: spot prices (buy and feed-in) for a given day (e); zones; household load. Ensure: Trade report if necessary StateReport ← 0 for Day ∈ 1 : 365 do th Determine StateDay and πDay th Determine StateDayAhead and πDayAhead if StateReport = 0 then DayAhead Day Day th th if max (CSell ) − min (CBuy ) − CBatt > πDay + πDayAhead then StateDay ← 1 StateReport ← 1 Day CBuy,Report ← min (CBuy ) end if else if StateReport = 1 then th Day Day πDay ← max (CSell ) − CBuy,Report − CBatt DayAhead Day th th if max (CSell ) − CBuy,Report − CBatt > πDay + πDayAhead then StateDay ← 0 StateReport ← 1 else StateDay ← 2 StateReport ← 0 end if end if end for

3.3.3 Proposed cost functions

There are specific cost functions for battery charge and discharge operations. Charge cost function is firstly formulated as the sum of energy cost used to charge BESS, cost of energy losses during charge and battery degradation cost (D) and its corresponding equation is presented below: T X t t t 2 Cch = Cspot.(Pch + xloss.(Crate) ).1000 + D .∆t (3.7) t=1 with D = 1 .B .B .d (Ct ). In equation 3.7, Ct (e/Wh) corresponds SOHinit−SOHmin price rated soh rate spot t to spot price provided by the aggregator, Pch means charging power (W), value of T depends on studied zone’s time duration (hours) and xloss represents battery internal losses coefficient (fixed at 1.88) [153]. This charging cost function takes into account grid price, amount of power charged per hour as well as charging losses extra cost. In order to ensure that correct amount t of energy is stored in the BESS, equation 3.7 result Cch is penalized in function of Pch values

113 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids such as: T T 6 X t X t Cch = 10 − Pch.∆t, ∀ Pch.∆t < E (3.8) t=1 t=1 Day PT t 2 with E = (SOCmax − SOCbegin).Bcap + t=1(xloss.(Crate) .1000).∆t. In equation 3.8, SOCbegin Day stands for battery SOC before performing an operation (a charge), Bcap (Wh) is the remaining battery capacity for a given day. In the case of discharge operations, cost function is computed differently if the discharged power at time t is bigger than the load and if there is an aggregator margin (thus making feed-in price different from spot price). Equation 3.9 represent discharge cost function for discharge t t power Pdch values smaller than Pload (residential load value at time t). In this case, cost is computed with a negative sign in order to reflect profitability. Discharge profitability involves gains from energy sold to the grid/saved from the grid minus energy losses during discharge phase minus battery degradation cost (D):

T X t t t 2 Cdch = − Cspot.(Pdch − xloss.(Crate) ).1000) − D .∆t (3.9) t=1

t t For Pdch values bigger than Pload (this case corresponds to battery discharge in order to supply load and to inject energy into the main grid), equation 3.9 becomes equation 3.10:

T X 0 Cdch = − F − G − D .∆t (3.10) t=1

! t t 2 0 t t t Pdch−Pload with F = Cspot.(1 − ragg). Pdch − Pload − xloss. Day .1000 and with Bcap ! t 2 t t Pload G = Cspot. Pload − xloss. Day .1000 . Price spread between feed-in price and spot price Bcap will allow the optimizer to schedule BESS discharge to maximize proﬁtability. In this work, feed- in price can be discounted compared to spot price. A big discount will make the optimizer to decide that the best discharge pattern to apply is theMG load pattern. In order to ensure that correct amount of energy is stored in the BESS, equation 3.11 result Cdch is penalized in t function of Pdch values such as:

T T 6 X t X t Day Cdch = 10 . Pdch.∆t, ∀ Pdch.∆t > (SOCbegin − SOCmin).Bcap (3.11) t=1 t=1

114 3.4. Results and discussion

3.4 Results and discussion

In this Section, simulation is carried out to demonstrate the effectiveness of the proposed algorithm using a 48-hour horizon over an existing 24-hour horizon commonly used in the literature. Simulations are performed using a residential grid-connectedMG where the EMS’s main goal is to maximizeMG profitability thanks to energy arbitrage. Firstly, solar generation uncertainties are studied to make sure that they do not impact simulation results significantly. Sec- ondly, profitability of 24 and 48-hour horizon method is compared. Finally, a sensitivity analysis is carried out to assess the impact of fourMG parameters on its global profitability. StudiedMG parameters in the following sensitivity analysis are listed below:

— RnE penetration rate (%).

— Storage price (C/kWh),

— Storage size (kWh).

— Aggregator margin (%).

3.4.1 Uncertainty management

Impact of solar irradiation uncertainties is assessed by performing a study on 24-hour horizon algorithm applied toMG setup 1. 50 randoms years are generated and first year financial balances are computed 50 times in order to provide relevant results. Model parameters are provided in Table 3.3 (other relevant data such as panel efficiency, etc... is provided throughout Chapter 2).

Project cost (excluding battery related expenses which are computed using Cbatt) is equal to: Y X −y Cproject = Cproject,P V + CO&M (1 + Rd) (3.12) y=1 with Cproject,P V as the product of panel quantity, panel peak power and solar system price,

CO&M as average annual outlays cost (e, repriced each year at 1%), Rd as discount rate and Y as study period (in years). Global profitability is computed by subtracting liabilities from assets provided in financial balance Table 3.4. Using results from Table 3.4, Figure 3.10 provides global profitability variations caused by stochastic solar generation for a period of 50 years when 24-hour horizon algorithm is used. Results displayed in Figure 3.10 shows that for a random year the mean profitability equals -96,856 e with a standard deviation of 914.25 e (0.94%). This cost must be compared to the

115 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

Table 3.3 – Input parameters

Parameter Value Unit PV panels Panel quantity 671 Panel peak power 330 Wp Solar system price 2.17 C/Wp Average annual outlays (system maintenance) 8665 e Battery Energy capacity 168 kWh BESS system cost 291.3 e/kWh End of day SOC( SOCeod) 50 % Financials Discount rate 0 % Aggregator margin 0 %

Table 3.4 – Financial balance

Parameter Unit Assets Solar energy production (balance) kWh (e) Grid injection from storage (balance) kWh (e) Liabilities Load consumption (balance) kWh (e) Storage consumption (balance) kWh (e) Storage depreciation e MG setup and maintenance e Global Proﬁtability e

Figure 3.10 – Histogram of global proﬁtability variations over 50 random years

116 3.4. Results and discussion reference cost (withoutMG) to supply the community load during one year. The average electricity cost for one-year supply of such community load equals 109,460 e. The cost difference allows this neighborhood to save 11.51% of their annual energy-related expenses.

3.4.2 24 and 48-hour horizon comparison

In this section, 24 and 48-hour horizon models performing with the same 10-year solar pattern are compared. These models are applied on threeMG systems presented in the beginning of this Chapter.

24-hour horizon

Table 3.5 shows results for systems 1, 2 and 3 using a 24-hour horizon generally used in the literature. "End of day" battery SOC SOCeod was set to 50% in order to ensure SOC continuity between two days. Compared to their non-MG equivalent (taking load costs as global cost):

— System 1 offers 10% of reduction on the considered global bill. — System 2 offers 21.9% of reduction on the considered global bill. — System 3 offers 12.7% of reduction on the considered global bill.

Moreover, it can be noted that, for every system, global profitability allow the community to save money compared to the same community without any installed RES. Systems 1 and 2 are similar, the main difference between them is the BESS size. In the context of energy arbitrage, battery size has a significant impact on profitability. This impact is noticeable when global profitability and arbitrage profitability are compared between both systems. System 3 involves only "RT2012" houses, thus results are completely different from other systems. In the scope of this study, storage arbitrage profitability is maximized if traded energy volume is maximized. Thus, profitability is increased if SOCeod = SOCmin because "end of day" period is generally not the cheapest one regarding spot prices (it is often more profitable to charge during early morning before sunrise).

117 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids 8.76,87.3% % 72.23 30,149 kWh 33.40 64,38 24,622 113,329 -514,460 33.17 69,655 81.37 197,458 7,339 -854,101 % 27.91 185,889 (150,775) 796 29.56-0-0-68.16-2.27 2,172 13.24-0-0-80.68-6.08 (589,887) 49.72-0-0-49.37-0.90 -985,114 2,408 (491,768) 2,679 Unit 23,823 (250,580) 761 SOH (1,094,600) Remaining 4,822 profitability 3) Arbitrage (system (426,079) Value (43,611) 2,001 (averaged) 217 0-1-2-3-4 States (1,094,600) 4,822 ratio penetration (790,987) RnE BESS 2,569 to stored 2) surplus (system RnE Value (758,684) 3,565 Indicators Performance Key 1) (system Profitability (74,774) Value Global 206 ( (669,926) maintenance 3,140 and setup MG depreciation Storage consumption Storage consumption Load Liabilities storage from injection Grid production energy Solar Assets Parameter al . 4hu oio nnilblne(rfiaiiyatr1 er o ytm ,2ad3) and 2 1, systems for years 10 after (profitability balance financial horizon 24-hour – 3.5 Table C project 6,2 3,1 380,801 631,514 567,122 ) MWh MWh MWh MWh e e e e ( ( ( ( e e e e ) ) ) )

118 3.4. Results and discussion

Another simulation was performed assuming there was no "end of day" charge required. In this case, global profitability for systems 1, 2 and 3 is listed below. — System 1: -977,237 e (with a storage arbitrage profitability of 15,216 e) — System 2: -789,302 e (with a storage arbitrage profitability of 178,128 e). — System 3: -491,451 e (with a storage arbitrage profitability of 53,158 e). Figure 3.11 displayed below illustrates the fitness curve for a charging phase performed in a given day using the 24-hour algorithm.

19 )

e 18

17 Fitness (

16 10 20 30 40 50 60 70 80 90 100 Iterations Figure 3.11 – Convergence curve of particle swarm optimizer

This Figure represents PSO convergence for a given day in order to ﬁnd best battery charge global cost using electricity cost from a "buy" zone. With MaxDistQuick, required amount of iteration can be dynamically reduced to save extra computation time.

48-hour horizon

Results shows that, for every system, global profitability over 10 years is improved by using 48-hour horizon compared to a classic 24-hour horizon. Global profitability improvements are listed and detailed below. — System 1: profitability improvement equals to 0.85%; assuming that "end of day" charge is not used in 24-hour model, profitability improvement is still equal to 0.06%. — System 2: profitability improvement equals to 8.4%; assuming that "end of day" charge is not used in 24-hour model, profitability improvement is still equal to 0.93%. — System 3: profitability improvement equals to 4.7%; assuming that "end of day" charge is not used in 24-hour model, profitability improvement is still equal to 0.34%.

119 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids 8.36.27.4% % 74.34 kWh 54,842 34.23 44,719 65.52 489,768 185.162 64,356 34.70 82.63 271,157 -782,288 (117,549) 725 15,774 179,925 % 27.99 (589,887) 2,408 Unit 30.35-6.63-6.63-54.05-2.32 6,058 13.15-5.18-5.18-70.35-6.13 (236,747) 693 (405,692) 2,552 51.3-5.3-5.3-37.2-0.9 -976,683 3) (system (426,079) Value 2,001 (1,094,600) 4,822 21,249 SOH Remaining profitability Arbitrage (32,500) (averaged) 191 0-1-2-3-4 (770,760) States 2,447 (1,094,600) 4,822 2) ratio (system penetration Value RnE BESS to (758,684) stored 3,565 surplus RnE Indicators Performance Key 1) (system Profitability (69,251) Value Global 180 ( (669,926) maintenance 3,140 and setup MG depreciation Storage consumption Storage consumption Load Liabilities storage from injection Grid production energy Solar Assets Parameter al . 8hu oio nnilblne(rfiaiiyatr1 er o ytm ,2ad3) and 2 1, systems for years 10 after (profitability balance financial horizon 48-hour – 3.6 Table C project 6,2 3,1 380,801 631,514 567,122 ) MWh MWh MWh MWh e e e e ( ( ( ( e e e e ) ) ) )

120 3.4. Results and discussion

Theses results may appear insignificant because profitability improvement is low but focus should be pointed towards arbitrage profitability. Arbitrage profitability is not the major source of income in considered scenarios, thus profitability improvement can appear really low if only global profitability is taken into account. By increasing BESS size, storage arbitrage will obviously will represent a bigger share of global profitability. In order to assess the performance of proposed algorithm, storage arbitrage profitability improvement should be computed. Results shows that storage arbitrage profitability over 10 years is improved by using 48-hour horizon compared to a classic 24-hour horizon. Results of storage arbitrage are listed and detailed below. — System 1: profitability improvement equals to 114%; assuming that "end of day" charge is not used in 24-hour model, profitability improvement is still equal to 3.66%. — System 2: profitability improvement equals to 63.38%; assuming that "end of day" charge is not used in 24-hour model, profitability improvement is still equal to 3.94%. — System 3: profitability improvement equals to 81%; assuming that "end of day" charge is not used in 24-hour model, profitability improvement is still equal to 3.16%. Results for every systems show that there is a significant arbitrage profitability improvement when proposed 48-hour horizon algorithm is used instead of 24-hour horizon. Variations of profitability improvements ratios are caused by battery size (bigger BESS implies cheaper price per kWh). The major point of this comparison is that results between 24-hour horizon algorithm (without "end of day" battery refill) and proposed 48-hour algorithm are similar. Profitability improvement are respectively equal to 3.66%, 3.94% and 3.16% for systems 1, 2 and 3. Besides, it seems also important to assess the behavior evolution of the proposed algorithm. Table 3.7 shows state ratios evolution over years. It can be noted that battery is used differently along studied period in order to deal with its degradation and maximize profitability. State 0 tends to increase whereas other states tends to decrease or to stay stable. This is due to the fact that battery degradation implies cost increase of battery usage for a similar quantity of energy.

121 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

Table 3.7 – Energy management strategy evolution in function of years (48-hour horizon, system 1) Year 1 2 3 4 5 State 0 45.48% 45.75% 47.40% 47.67% 50.68% 1 6.03% 5.75% 5.75% 6.58% 5.21% 2 6.03% 5.75% 5.75% 6.58% 5.21% 3 41.10% 41.64% 40.27% 38.36% 38.08% 4 1.37% 1.1% 0.82% 0.82% 0.82% Year 6 7 8 9 10 State 0 52.05% 54.25% 54.79% 56.99% 58.08% 1 4.93% 4.93% 4.93% 4.38% 4.38% 2 4.93% 4.93% 4.93% 4.38% 4.38% 3 37.26% 35.07% 34.52% 33.42% 32.33% 4 0.82% 0.82% 0.82% 0.82% 0.82%

3.4.3 Sensitivity analysis

Sensitivity analysis is performed onMG setup 1 with the 48-hour horizon model. Four parameters are involved in this sensitivity analysis. These parameters are: RnE penetration rate, storage price (C/kWh) storage size (kWh) and aggregator margin (%). For RnE penetration rate parameter, proposed algorithm will prioritize battery charging with solar surplus if there is any available power for a given day.

RnE penetration rate

RnE penetration rate is deﬁned as on-site consumed energy provided byPV divided by total energy consumed by the communityMG. In order to maximize RnE penetration ratio, solar surplus power is stored in the BESS instead of being injected to the grid (this impacts proﬁtability). This power is used later in the day according to the scenario to supply the load.

Besides, SOCRnE is introduced as a ratio that force the algorithm to allow a share of BESS capacity to store surplusPV power instead of charging BESS during low price hours as usual. As written before, the goal here is to maximize on site usage of solar generation.

Figure 3.12 displays profitability and RnE penetration rate in function of SOCRnE. SOCRnE represents a share of battery capacity which is committed to solar surplus power charge. Re- sults shows that the larger this share is, the lower the profitability is. It can be noted that a RnE allocation of more than 80% provides a less significant RnE penetration rate improvement.

Moreover, RnE penetration rate value is a bit less than 30% for SOCRnE = 100%. This is due to the fact that when battery is discharged, a share of its energy is injected to the grid and

122 3.4. Results and discussion

30 -976 RnE penetration rate 29.5 Global profitability -978 29

-980 28.5

RnE penetration rate (%) 28 -982 0 20 40 60 80 100 SOC (%) RnE Figure 3.12 – Evolution of proﬁtability and renewable energy penetration rate in function of allowed battery capacity for solar surplus storage not consumed by the load. In this simulation, proﬁtability is still the key driver for BESS energy management strategy.

Storage price

Table 3.8, represents state distribution in function of battery cost. For a cost of 100 e/kWh,

Table 3.8 – Energy management strategy in function of battery cost State 0 1 2 3 4 Cost 100 e/kWh 1.56% 0.06% 0.06% 72.98% 25.34% 200 e/kWh 26.19% 4.35% 4.35% 62.35% 2.74% 300 e/kWh 51.3% 5.3% 5.3% 37.2 % 0.9% 400 e/kWh 73.75% 4.4% 4.4% 17.18% 0.27% 500 e/kWh 83.86% 3.75% 3.75% 8.63% 0.02% there is a majority of state 3 (one cycle in a given day) followed by state 4 (two cycles in a given day) and nearly no state 0, 1 and 2. For 200 e/kWh, there is a majority of state 3 followed by state 0 (do not perform any charge/discharge). State 1 represents 4.35% as well as state 2. Delays performed by 48-hour horizon algorithm in order to improve profitability happen averagely during 3.75% of a year. Finally, state 4 represents 2.74%. For higher prices (from 300 to 500 e/kWh), results shows that state 0 skyrockets up to more than 80%. State 3 and 4 decrease significantly while states 1 and 2 which represent arbitrage delays decrease slightly compared to state 3 and 4. Finally, despite the fact that proposed algorithms adapts according to BESS price, overallMG profitability and BESS price will be inversely proportionate.

123 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

Storage size

Figure 3.13 shows MG proﬁtability in function of installed BESS capacity (from 0 to 1250 kWh). Proﬁtability increases linearly with battery capacity. This is mainly due to the fact that

-800

-850

-900

-950

-1000 0 200 400 600 800 1000 1200 BESS size (kWh) Figure 3.13 – Evolution of proﬁtability in function of battery capacity proﬁtability scales according to traded energy volume.

Aggregator margin

Figure 3.14 displays MG profitability in function of aggregator’s share of profit (applied to spot prices). Higher the aggregator’s cut is, lower the profitability is. High aggregator margins

-976.5

-977

-977.5

-978

-978.5 0 5 10 15 20 Aggregator margin (%) Figure 3.14 – Evolution of proﬁtability in function of aggregator margin make the proposed algorithm to modify BESS discharge pattern to prioritize load satisfaction instead of grid-injection at best possible price. To illustrate this point, an example is provided below. Both 0% and 20% aggregator margin cases are presented in order to show it’s effect on

124 3.4. Results and discussion

BESS discharge pattern. Figure 3.15 displaysMG’s solar power production (and solar surplus power) as well as load consumption. Figure 3.16 displays spot and feed-in prices (assuming a 20% margin). In case 1, aggregator margin is set to 0% (spot price = feed-in price). Figure 3.17 shows battery power ﬂow (positive for charge, negative for discharge). Discharge is set at hour 9 because of expensive spot price. Discharge pattern involves a "to load" (energy consumed by the residential load) part and an "to grid" part (energy injected to the grid). Energy is mainly consumed on-site and the rest is injected into the grid.

150 Solar Power Solar Surplus Power 100 Residential Load

50 Power (kW)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Figure 3.15 – Solar and residential power ﬂow for a given day

35 Spot Price 30 Feed-in Price

10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Figure 3.16 – Spot and feed-in price for a given day

125 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

100 Charge 50 Discharge (to load) Discharge (to grid) 0

-50 Power (kW) -100

-150 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Figure 3.17 – Battery power ﬂow in case 1: 0% aggregator margin

In case 2, aggregator margin is set to 20%. Figure 3.18 displays similar information as Figure 3.17. In this case, battery discharge pattern differs from case 1. The optimizer set battery power flow to match with load consumption because it is financially more profitable not to draw power from the grid than injecting all available power during one hour (for instance hour 9) and supply the load thanks to the grid from hour 9 to 10 and at hour 19. Figure 3.18 accurately illustrates the influence of aggregator margin and importance of energy management according to spot prices and load consumption. Depending on spot prices, power flow patterns will vary. Thus having access to future spot prices values is a key advantage for EMS in order to improve MG cost of operation.

100 Charge Discharge (to load) 50 Discharge (to grid)

Power (kW) -50

-100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Figure 3.18 – Battery power ﬂow in case 2: 20% aggregator margin

126 3.4. Results and discussion

3.4.4 Discussion

This section discusses results presented above. Results showed that under same parameters (same MG, same 10-year solar pattern) 48-hour horizon algorithm allows the MG to be more profitable. Greater profitability is proved compared to either 24-hour model with a battery "end of day" charge or without such "end of day" battery charge. Besides, profitability improvement was noted on both studiedMG setups. Proposed 48-hour algorithm provides similar profitability improvement ratios for different storage sizes. Thus it is possible to affirm that profitability gains are mainly driven by extended energy management possibilities offered by 48-hour horizon rather than by extended battery size. This profitability improvement is achieved thanks to 48-hour horizon algorithm’s specific possibilities such as: 1. Ability to charge BESS on a wider range of hours (between "late" sell zone of a given day and "early" sell zone of the next day). 2. Ability to delay trades (main reason of extra profitability compared to 24-hour strategy without "end-of-day" battery charge). Regarding the evaluation of self-consumption rate impact on profitability, it can be noted that a trade off between profitability and self-consumption rate is possible by setting a specific share of battery capacity to store solar surplus power. It is also notable that setting more than 80% of battery SOC to store solar surplus power does not increase significantly self-consumption rate. This result means that used battery sizing method is suitable to store a large majority of solar surplus provided in this configuration. This solar surplus power corresponds to available surplus power in "intraday surplus" zones. Storage price has a significant impact on MG profitability. It can be noted that for BESS prices (100 e/kWh) lower than current prices, battery management strategy prioritize states 3 and 4 (two cycles in a given day) over arbitrage delay to maximize profitability. With such storage prices and retail prices, it becomes worthwhile to use storage for multiple intraday energy trades. Aggregator’s cut also has a significant impact on MG profitability. Besides price spread between spot price (buy) and feed-in (sell) prices makes the proposed algorithm to modify BESS discharge pattern to maximize profitability. As feed-in price variations compels the studiedMG to move towards self-consumption instead of grid injection during energy arbitrage events, such prices could be used as leverage by aggregators or regulators.

127 Chapter 3 – Relevance of time horizon-based battery energy management strategies for community microgrids

3.5 Conclusion

In this Chapter, a battery energy management framework based on time horizons was proposed. Two time horizons (24 and 48h) were introduced in this work. Both time horizon- based methods aim to maximize communityMGs profitability by using energy arbitrage method while taking into account energy storage degradation. Both horizon-based energy management strategies were applied on three systems: (i) a communityMG with 25 new / 25 old houses and a sizing to ensure 30% of energy needs, (ii) same communityMG with an autonomy ratio of 50%, (iii) a communityMG with 50 "RT2012" houses and an autonomy ratio of 30%. Proposed 48-hour horizon has specific possibilities such as a wider range of available hours to charge the BESS and the ability of delay trades compared to 24-hour algorithm. Major findings are listed below: — Proposed method has a profitability improvement equals to 114% for system 1, 63% for system 2 and 81% for system 3 compared to 24-hour horizon (with battery SOC reset at the end of each day) classically used in the literature. Profitability improvement is still equal to 3.66% for system 1, 3.94% for system 2 and 3.16% for system 3 just by switching from 24-hour to 48-hour horizon-based algorithm without end-of-day SOC reset. — Optimization related to profit and RnE penetration rate maximization showed that with proposedMG sizing and operation, setting more than 80% of battery SOC to store solar surplus power does not increase significantly RnE penetration rate. This result means that used battery sizing method is suitable to store a large majority of solar surplus provided in this residential configuration. — Sensitivity analysis also showed that relation between profitability and aggregator margin is not linear. This is due to the fact that the algorithm focuses on self-consumption for high margin values thus allowing theMG to be less dependant from spot prices. Depending on the considered sizing and energy management strategy, this study certifies that a practical residentialMG is worthwhile in the context of real-time electricity tariffs and energy arbitrage management algorithms using a 48-hour horizon and its extended management possibilities over a 24-hour horizon. In this Chapter, solar irradiation was modeled from MTM in order to assess model’s robustness over an important number of years and not to assess robustness of forecast methods. Moreover, spot price was assumed to be fixed during day-ahead fixing process, thus no price uncertainties were assumed. In the next Chapter, solar irradiation uncertainties will be taken into account and a novel statistical predictor-corrector short term forecast method will be introduced.

128 CHAPTER 4 RESIDENTIALMICROGRIDENERGY MANAGEMENT CONSIDERING FLEXIBILITY SERVICESOPPORTUNITIESAND FORECAST UNCERTAINTIES

4.1 Context for community microgrid operation under uncertainties

This Chapter belongs to communityMG operation subject to strong climatic variations and integration of BESS in the context of flexibility services such as ones introduced above and particularlyPV-ESS for residential applications. In Chapter 3, study was focused on energy arbitrage (using storage to buy and sell energy to the spot market stakeholders). For this application, forecast data was assumed to be 100% reliable, thus, uncertainties were not taken into account. This Chapter proposes to integrate solar irradiation uncertainties in the hour-ahead energy bidding context with an aggregator. Nonetheless, weather forecast is fundamental for prediction of RnE sources (especially solar and wind sources) and load power profiles [111,4]. Due to its inherent stochastic nature, solar irradiance is difficult to predict and even more in locations which are subject to strong climatic variations such as Saint-Nazaire. Because of its specific location in the peninsula of Guérande and at the mouth of the river Loire, forecast errors can be very common. Accurate forecast is useful for power production planning and can become critical for insularMG which may not have major dispatchable power sources [6] or to efficiently perform grid-connected flexibility services such as: behind-the-meter services (PV-ESS), ancillary services or UPS services. Thus, in order to be profitable when performing flexibility services, forecast errors should be minimized. Some drawbacks still exist in this area of research presented in Chapter 1, Section 1.3.3. Firstly, stochastic power generation was bounded within 10% variation from the daily forecast

129 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

[14] or scenarios were selected to keep a limited quantity (5-10 scenarios, most probable ones) [150]. Thus the probability of extreme variation between reality and forecast was excluded. Such extreme variations could be caused by local weather variations due to specific climatic conditions orMG area topography (such as islands or peninsulas) or averaged meteorological input data. Secondly, the grid is mainly viewed as a safety net where surplus energy can be injected and missing energy can be obtained to ensure power supply to the loads. Avoiding forecasting error impact on power injection potential fees is acceptable for small entities such as single-family houses equipped withPV panels because they are associated with a BRP that deals with such impact for them. For instance, french energy producer EDF has a fixed rate buy-back process named "EDF OA" for small solar produced below 100 kWp which includes BRP activities [52]. This FIT program was detailed in Chapter 1, Section 1.1.2. For bigger entities such as proposedMG (around 220 kWp), fees should be considered when injected power does not match bidden value by the MG operator to the aggregator or to the TSO/DSO. These fees are quite hard to estimate because there are integrated in contracts. Finally, variations of certain parameters such as battery and hardware degradation cannot be correctly represented considering only a 24-hour study period or even a 1-year study period. For instance, SOH degradation of BESS requires more than 1 year to have a significant impact on globalMG operation (assuming BESS is not used in a way that could generate premature SOH degradation). This work proposes to handle drawbacks presented above while major topics are detailed below:

1. Studied residentialMG has access to spot market prices thanks to an aggregator and provide flexibility service through hour-ahead power injection bids. Unsatisfied bids incur economical penalty charged by said aggregator (as TSOs applies penalties to BRPs [53]) because of power imbalance unsatisfied bids generate. 2. Enhanced forecast model based on stochastic solar irradiance generation using Markov chains combined with well-known Recursive Least Squares (RLS) predictor-corrector method which aims to improve hour-ahead solar irradiance forecast thanks to local irradiance measurements. Proposed model performance is compared to the reference model named "smart persistence". 3. Storage is used as a buffer in order to increase bid satisfaction rate. Sensitivity analysis is carried out to determine the impact of BESS size on bid satisfaction rate and economical balance. Besides, analysis is also accomplished to assess the economical impact of BESS residual capacity degradation.

130 4.2. System model

This Chapter is organised as follows. Section 4.2 presents the proposed system model which involves a new aggregator behavior. Proposed management framework is detailed as well as case studies in section 4.3. Numerical results for twoMGs with and without proposed forecast method are discussed in section 4.4. Section 4.5 draws the conclusions of the presented work.

4.2 System model

In this part, studied communityMG is similar as Chapter 3, Section 3.2. Figure 2.2 displayed in Chapter 2 represents the studiedMG. System 1 introduced in Chapter 3 is selected to be the studied sizing of this communityMG.

— System 1: 25 "RT2012" and 25 "Classic" houses, 671PV panels spread all over roofs and a 50 kWh BESS. In this study, the main objective is not to achieve a certain ratio of autonomy but to respect amount of injected power at each hour.

Power Data

MAIN GRID - AGGREGATOR

Forecast Penalties

MICROGRID CONTROLLER Power ﬂow FORECAST AND ENERGY MANAGEMENT CORRECTION ALGORITHM

Infrastructure status Energy management

INFRASTRUCTURE

PV, BESS, LOADS SOLAR SENSOR

Figure 4.1 – General overview of microgrid behavior

Figure 4.1 displays the general behavior of considered communityMG. There are three layers, these layers are listed and detailed below:

131 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

— Infrastructure layer: this layer containsMG models and outputs power production, consumption and storage status as well as local solar irradiation.

— Microgrid controller layer: this layer computes local power balance and priorize self- consumption.PV power generator is forecasted thanks to the forecast algorithm. Then future power available for injection is communicated to the aggregator while power of current period of time is injected. At each time step, the corrector part is used to adapt the forecast of the next hour.

— Aggregator layer: the aggregator accepts every bids from theMG and applies penalties (introduced thereafter) if injected power at time t does not match bidden power at time t − 1.

Besides, in this case, a RLS predictor-corrector forecast model is proposed to ensure a reliable hour-ahead power bidding process in the context of intraday electricity markets. This proposed predictor-corrector model will be compared to the reference model for short term solar irradiation forecasting. This model is named Smart Persistence (S-PER). Besides, aggregator behavior is different. Here the aggregator charges penalties if bidden quantity of energy is not fully delivered. S-PER predictor-corrector model and aggregator behavior are further detailed below.

4.2.1 Smart persistence model

S-PER model is a commonly used model [5,9,7]. In the literature, this model is considered adapted for time horizons up to 1 hour [42]. The so-called S-PER model is often used as a baseline to compare more accurate models. Thus, it will be considered as reference forecast and will be compared to the proposed method. Behavior of S-PER forecast model is quite simple. It computes the clear-sky index at time t (which describes the current cloud cover) and multiply it by the future theoretical clear-sky global titled irradiation that could occur at time t + 1 if there was absolutely no clouds. Broadly speaking, this methods applies the current sky condition to the next hour taking into account solar travel. S-PER model equation is presented below:

t+h t t+h IGT = KskyIGT −clear (4.1)

t+h where IGT is the global titled irradiation at time t + h (h is the considered time horizon, here 1 t t+h hour), Ksky is the clear-sky index at time t and IGT −clear is the clear-sky global titled irradiation t+h t at time t + h. Extensive information about IGT −clear and Ksky can be found respectively in Chapter 2, Section 3.2.1 and Chapter 3, Section 3.2.1 in order to compute these values.

132 4.2. System model

4.2.2 Recursive Least Squares predictor-corrector algorithm

The proposed method aims to minimize forecast errors using history data. Proposed method consists in applying a recursive least-squares correction to a daily average forecast (using average clear-sky index). In this method, true solar irradiance error e at time t is deﬁned as:

et = mt − mˆ t (4.2) where mt and mˆ t are irradiance measurement and prediction at time t. If there is a time correlation in the prediction error sequence then it is possible to generate an error estimator to correct the prediction based on past (known) errors. The estimated prediction error is deﬁned as follows:

eˆt+i|t = ft(e1, ..., et) (4.3) where eˆt+i|t stands for estimated prediction error for time t + i generated at time t based on known errors {e1, ..., et}, thus the corrected prediction can be set by:

mˆ t+i|t =m ¯ t+i − eˆt+i|t (4.4)

where m¯ t is the gross prediction used in this model. Gross prediction at time t was provided by a simple simulated weather forecast generator (that could be assimilated to an external inaccurate weather forecast service) which generates gross estimation of a daily irradiation.

Gross irradiation prediction is computed by averaging daily Ksky values and multiplying it by clear-sky irradiation of the considered day. To sum it up, average prediction is equal (for every t time t) to Ksky−mean × IGT −clear. In this Chapter, error estimator fi is deﬁned via Finite Impulse Response (FIR) ﬁlter presented in equation (4.5). To save computation time, the algorithm is computed only during daytime. tr is sunrise time and ts is sunset time:

        eˆts ets −ets−1 · · · −ets−n 1 a1          .   .   . .. .   .   .  =  .  −  . . . 1  .  (4.5)         eˆtr etr −etr−1 · · · −etr−n 1 an+1 | {z } | {z } | {z } | {z } εˆ Y C Θ where {a1, ..., an+1} are FIR filter parameters. Order n is set to 1 in this study. Estimated prediction values εˆ can be set to −C.Θ assuming that filter parameters stored in Θ generate no real errors (Y = 0). Then, the estimator and the gross prediction are used in equation (4.4) to compute the final prediction.

In order to avoid large matrices, recursive computation is used. For instance, vector ΘN+1

133 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

computed at time N is deﬁned by components from time N and matrix such as: ΘN+1 = f (ΘN ,Yk|k ∈ (1,N)). Firstly, initialization parameters are computed using the equation below:

 | Pinit = (Cinit.C ) init (4.6) | Θinit = Pinit.Cinit.Yinit

In order to prevent singularity event during inversion of matrix C that could happen at the beginning of each day, matrix Pinit can be set using Csunset from previous day. Then the general recursive system is presented below:

 K = P .c| . 1 + c .P .c|  N N−1 N µ N N−1 N  1 PN = . (PN−1 − KN .cN .PN−1) (4.7)  β  ΘN = ΘN−1 + KN . (yN + cN .ΘN−1)

where βfir is a constant set as 1 for order n = 1 and 0.2 for higher orders. Besides, µfir = 1 . βfir and µfir are used in order to provide weight to historical errors that happened few βfir time steps ago. βfir value was obtained by trying values between 0.1 and 1 and comparing them using RMSE obtained between proposed forecast method and true solar irradiation. The value of βfir associated with the lowest RMSE was selected. To justify selected order n results for different orders are displayed in Table 4.1 (simulations were performed over 1000 random years).

Order / best βfir 1 / 1 2 / 0.1 3 / 0.1 nRMSE (average) 0.278 0.281 0.279 MAP E (average) 9.917 10.128 10.024 Table 4.1 – Performance comparison of orders applied in equation (4.5)

It can be noted that increasing the order does not provide any improvements regarding accuracy, thus as written before, order 1 was selected. Finally, performance of the proposed output presented above (in Table 4.1) is obtained by averaging outputs of both methods.

4.2.3 Algorithm performance comparison

To measure the forecast correction method performance, KPI such as RMSE, Mean Abso- lute Percentage Error (MAPE) and standard deviation will be used. RMSE, MAPE and standard deviation are frequently used in the context of weather forecast error estimations [42, 156, 46].

134 4.2. System model

RMSE is used as a mean to measure differences between values predicted by a model or an estimator and observed values. In this study, RMSE is used to compute difference between solar irradiance prediction and real measurement, it is normalized using the mean value of measurement series and detailed below as nRMSE: q 1 PT 2 t=1(m ˆ t|t−1 − mt) nRMSE = T (4.8) 1 PT T t=1 mt where mˆ t|t−1 is the prediction at time t generated at time t − 1 and mt is the real measurement made at time t. Mean absolute percentage error is a way to express accuracy of forecast methods. It differs from RMSE because it gives the same weight to every errors. MAPE is computed using the following equation: T 100 X mˆ t|t−1 − mt MAP E = (4.9) T m t=1 t In the context of this study, MAPE is suitable for error interpretation. Forecast skill SKILL is a measure in percent of superiority of a forecast method over another. It is commonly used in the literature to compare solar irradiance forecast methods [5]. The associated equation is given below: nRMSE SKILL = (1 − RLS ) × 100 (4.10) nRMSES−PER For instance, if the result of equation (4.10) is 10%, it means that proposed method outperforms reference method by 10% regarding nRMSE. So, forecast skill can be used as a relative perfomance indicator. Forecast skill can also be used with MAPE. Figure 4.2 shows tracking performance of the proposed RLS-based predictor-corrector forecast algorithm against reference for a given ﬁctitious day in year 2020. It can be noted that both algorithm show a similar pattern and that proposed algorithm tends to be more accurate during hours 10, 13 and 15.

135 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

700 Reality Smart Persistence 600 Proposed Method

500

400

300

Solar Irradiance (W/m²) 200

100

0 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time (h) Apr 20, 2020 Figure 4.2 – Accuracy example of reference forecast and proposed method

To compare model performance, normalized RMSE and MAPE are computed over 1000 random years to assess that performance difference between the compared forecast methods are not due to a speciﬁc solar irradiation pattern. Figure 4.3 illustrates the probabilistic solar energy distribution over 1000 years in the studied location.

Figure 4.3 – Probabilistic solar energy distribution in studied location

136 4.2. System model

Performance indicators normalized RMSE and MAPE presented above are displayed in Table 4.2.

Forecast Method S-PER RLS nRMSE (average) 0.309 0.278 nRMSE (standard deviation) 0.010 0.009 SKILL (%) 10.10 SKILL (standard deviation) 0.60 MAP E (average) (%) 12.236 9.917 MAP E (standard deviation) 0.259 0.286 SKILL (%) 18.95 SKILL (standard deviation) 0.83 Table 4.2 – Performance comparison of two weather forecast techniques under uncertainties

Proposed method ensures a performance improvement compared to the reference forecast method used alone (whether in relation with RMSE or MAPE). Regarding RMSE, proposed method provides an average skill of 10.10% (with a standard deviation of 0.60) compared to the reference forecast method. For MAPE, proposed method provides an average skill of 18.95% (with a standard deviation of 0.83) compared to the reference forecast method. This method based on recursive least-squares provides better accuracy than smart persistence method. The proposed method does not require ANN nor satellite-derived data and numerical weather prediction method at the same time. According to [5,9], smart persistence method is hard to outperform in location where strong climatic variations occur such as islands or peninsulas and for short-term forecast windows (i.e from 5 min to 1 h). Besides, extra simulation was performed to ensure performance of proposed method over the year of 2019 in Saint-Nazaire using historical global horizontal irradiation from [118]. RMSE and MAPE for this study are displayed in Table 4.3.

Forecast Method S-PER RLS nRMSE 0.290 0.261 SKILL (%) 10.10 MAP E (%) 13.83 12.07 SKILL (%) 12.71 Table 4.3 – Performance comparison for year 2019

In this case, skill score is equal to 10.10% using normalized RMSE and to 12.71% using MAPE as input.

137 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

Using real global horizontal solar irradiation history data from [118], results shows that proposed method provides better forecast accuracy with real history data as input compared to smart persistence method. Figure 4.4 presents both models applied on May 30th, 2019 in order to illustrate hour-ahead forecast on a real data.

900 I (History) GH 800 I (S-PER) GH I (RLS) GH 700

600

500

400

300 Solar irradiance (W/m²)

200

100

0 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time (h) May 30, 2019 Figure 4.4 – Accuracy of reference forecast and proposed method with historic data

138 4.2. System model

4.2.4 Aggregator model

In the context of flexibility opportunities, as briefly described before, theMG is able to bid on hour-ahead power injection to ensure frequency support. In case where injected power does not t|t−1 t fit with bidden power, a penalty function pagg = f(kagg,Pbid ,Pinj) is applied by the aggregator. Because of lack of information regarding penalty functions or values applied by aggregators are other involved stakeholders, a quadratic penalty function is proposed in this work. Lack of data about it is mainly due the fact that penalties are integrated into private contracts between two or more parties. This penalty function is presented thereafter:

 2 P t|t−1 − P t p = k . bid inj agg agg  t|t−1  (4.11) Pbid where kagg stands for penalty coefﬁcient (for instance, kagg = 20 results in a global fee equal to t 100% of missing energy cost if the prediction relative error equals 22.5%), Pinj is the injected t|t−1 power to the grid at time t and Pbid is the power announced by the MG at time t − 1 to be delivered at time t in the bidding process context. It can be noted that if available power for injection is greater than bidden power, surplus energy is stored in the BESS if it is not fully loaded. In the case where surplus energy cannot be stored, overall system efﬁciency is decreased to cancel bidding error. The aggregator charges missing energy to theMG according to the following equation: t t t Cmiss = Pmiss.Cspot.pagg.∆t (4.12)

t t t|t−1 t where Cmiss (e) is charged cost at time t, Pmiss = Pbid − Pinj (W) is the missing amount of t power at time t and Cspot (e/Wh) is aggregator electricity spot price at time t.

139 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

4.3 Management framework

This section presents the proposed energy management framework. A communityMG with grid injection abilities takes part in a power bidding process proposed by an aggregator. The MG participates in a bidding process presented in Figure 4.5. In the scope of this Chapter, focus is made on bidding and injection events, thus economic considerations regarding photovoltaic LCOE and price of consumed power from the main grid (except if grid power is used to charge batteries) are not taken into account. Such costs are discarded because the bidding mechanism performance will not impact them. For instance, Having twice the size regarding solar array (thus having a lower LCOE because of scale savings) will not increase forecast accuracy. Moreover, only gross proﬁt is considered in studied cases. In order to facilitate comprehension of the residentialMG behavior, Figure 4.5 depicts the information ﬂow gathered by the DEMS in order to perform an accurate forecast and take part in the bidding process.

Step 1: Step 2: Step 3:

MG operation and Status Forecast Generation & Correction Power delivery at time t+1 based Aquisition at time t Bidding with the aggregator on comittements made at time t

Solar Panels Solar Load Bidding Power Utility Sensors DEMS Process Delivery Grid

BESS

Figure 4.5 – Global residential microgrid operation scheme

EMS integrated in current communityMG has the following behavior:

1. Solar irradiation and generation, DEMS parameters, residential loads are monitored and stored in the DEMS (Fig. 4.5, Step 1).

2. DEMS forecasts hour-ahead solar generation and takes part in the bidding process with the aggregator. At time t, DEMS communicates power value to be delivered at time t + 1 to the aggregator (Fig. 4.5, Step 2).

3. ResidentialMG delivers power at time t + 1 (power can differ from expectations due to forecast errors) (Fig. 4.5, Step 3).

140 4.3. Management framework

4. At all times, solar power generation is consumed on site to supply loads, only surplus power is traded. 5. If available surplus power is different from bidden power, storage will help to mitigate such imbalances. System efﬁciency is decreased in order to match with bidden value if storage is full and available surplus power is bigger as expected. If available surplus power is lower than expected, it is injected to the grid at discounted price. Then, bidding process mechanism introduced in (Fig. 4.5, Step 2) is further detailed in section 4.3.1 and case studies are depicted in section 4.3.2.

4.3.1 Microgrid bidding process mechanism

The residentialMG follows an economic process towards main grid for 10 years which is presented below and displayed in Figure 4.6. On the first day, initialMG parameters are set. At the beginning of each day (except the first one), a battery check is performed in order to know if there is a need to charge battery at 50% or not in order to help the DEMS to respect its future bids. For every hour, an hour-ahead forecast is performed using previous irradiation data and local measurements (Fig. 4.6, BID part). For hour = 1, because it is the beginning of a new day, there is no need to check if battery support should be used (MG economic operation is not computed because load cost is not in the scope of this study). In case of battery support, storage availability is computed using battery SOC and requested power. If the request can be fulfilled, (dis)charge process is performed and new SOC as well as SOH are updated (Fig. 4.6, BESS SUPPORT part). Then, bid satisfaction ratio and MG economic status are computed. Regarding the presented process, it is assumed in this study that the aggregator accepts every bids. TheMG aims to deliver the amount of power at time t which was committed at time t − 1.

4.3.2 Case studies

In order to show the effectiveness of the proposed model, this part presents and compares S-PER and RLS forecast methods applied to a communityMG. The studiedMG is the system 1 presented in Chapter 3, Section 3.2.

— System 1: 25 "RT2012" and 25 "Classic" houses, 671PV panels spread all over roofs and a 50 kWh BESS.

Regarding ﬁnancial equations, in the scope of this study, every proﬁt or cost is considered as a gross value: no VAT, country-based taxes, amortization plan or asset depreciation were taken into account.

141 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

BEGIN

System Initialization

SoC = SoCinit hour & day = 1

SoH = SoHinit year = 1

SoC = SoC No yesterday,hour=24 day = 1 & year = 1 ? SoH = SoHyesterday,hour=24

Yes Charge BESS SoC < 50% ? SoC = 50% Yes No

Compute Available No hour = 1 & day = 1 ? BESS Surplus Power SUPPORT Yes

Yes Hour-ahead forecast: Injectable Power = Bidden Power ? smart persistence case BID No Hour-ahead forecast: Charge BESS if Yes Excess of Power ? RLS case possible No

Discharge BESS if Load PV Model possible

Update BESS Status (SOC, SOH) BID Surplus Power Injection Evaluate Bid Satisfaction Ratio

No Evaluate MG hour = Financial Data hour = 24 ? economic operation hour + 1

Yes

hour =1

No day = 365 ? day = day + 1

Yes

day = 1

No year = 10 ? year = year + 1

Yes

END Figure 4.6 – Flowchart of proposed microgrid power management framework for smart persistence and recursive least squares-based methods

Case I:MG with S-PER forecast method

The ﬁrst case involves the communityMG introduced above. Smart persistent forecast method is applied to forecast hour-ahead solar irradiation. The operation cost regarding in-

142 4.3. Management framework jection events and bids with the aggregator is computed using the following equation:

T X t t t CcaseI =∆t (Cspot.Pinj − Cmiss) t=1 ! (4.13) T d (Ct ) − ∆t X Ct .P t + k .C .B . soh rate spot grid ESS ESS rated 100 t=1 where CESS (e/kWh) is the battery cost per installed kWh, fixed at a value of 291 e/kWh using equations of Chapter 2 Section 2.1.2 and Brated (kWh) is the BESS rated capacity fixed at 50 kWh. kESS represents the weight added to the storage cost because total investment does not involve 100% of the available SOH. For instance, if a BESS manager plans to use the storage up to a final SOH of 50%, the coefficient kESS equals 2. In this study, kESS = 2.5 because SOH degradation is limited to 60% according to [13]. In order to make sure that the storage can be used in both charge and discharge mode, at the beginning of each day SOCinit is set to

50% if the remaining SOC from previous day is inferior to this value. Pgrid (W) represents grid power needed to ensure that the SOC is set accordingly. Each new day, SOC is reset to 50% using the cheapest available power between 1am and 4am. Moreover, kagg = 20 was used as penalty coefﬁcient value for both cases.

Case II:MG with RLS forecast method

The second case involves the sameMG with RLS forecast correction method applied instead of S-PER method. The operation cost regarding injection events and bids with the aggregator is computed using the same equation as case I.

143 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

4.4 Results and discussion

4.4.1 Results

These simulations where performed on a personnal computer using MATLAB®. Computa- tion time for one case (10 years) is 10 seconds using MATLAB® 2018a on a computer with a CPU intel Core m3 6Y30 (2GHz) and 4 GB of RAM.

Case I

Case I represents a MG that uses smart persistence forecast method and involves a 50 kWh battery. Figure 4.7 shows the residentialMG power ﬂow for a given day in case I. In this Figure, contract power and injected power towards the grid are represented with negative values in order to ease readability.

150

100

Power (kW) 0

S-PER Forecast -50 Real Solar Power Contract Power Injected Power Residential Load -100 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time (h) Apr 30, 2025 Figure 4.7 – Residential microgrid power ﬂow for a given day (case I)

In Figure 4.7, it can be noted that S-PER forecast overestimates solar power generation from hour 8 to 10 and signiﬁcantly underestimates it during hours 11 and 13. Usage of BESS helped theMG to deliver to amount of power contracted with the aggregator. For instance,

144 4.4. Results and discussion at hour 9, despite S-PER overestimation, injected power matches aggregator’s expectations (contract power). At hour 10, there is a mismatch between contract power and injected power because of both forecast error and unavailability of stored energy (battery SOC reached its lower limit of 15% as it can be seen in Figure 4.9). Figure 4.8 presents power ﬂow towards the utility grid during injection events for the same day and illustrates performance comparison of S-PER systems with and without storage. Figure 4.8 shows 4 curves: contract (bidden) and injected power curves for a system without storage (No Batt) and same curves for the previously presented system (with a 50 kWh BESS). As there is no difference between systems with or without storage regarding contract power because of same forecast method, there are differences regarding injected power values.

0 Contract Power (No Batt) -10 Injected Power (No Batt) Contract Power (With Batt) Injected Power (With Batt) -20

-30

-40

-50 Power (kW)

-60

-70

-80

-90 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time (h) Apr 30, 2025 Figure 4.8 – Residential microgrid power injection ﬂow for a given day (case I)

At hour 8, 9, 12 and 15 the system without storage is not able to respect entirely its commitments compared to the system with storage. Moreover, at hour 10, the remaining energy available in storage helped to inject more energy than what would have been possible without storage (even if commitments are not entirely respected). Figure 4.9 shows battery SOC evolution over the same day. As explained in section 4.3.1, the battery is loaded (if needed) up to a SOC of 50% at the lowest possible cost during early

145 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties morning (from hour 1 to 4). In this case, storage was not charged because its SOC was over 50%. Then, at hour 7, extra surplus power (which can be noted in Figure 4.7) was used to charge the battery up to a nearly 80%. At hour 8 and 9 energy was drawn from the storage to ensure that injected power matches with aggregator’s power expectations. At hour 10, storage’s remaining energy is used to decrease imbalance between contract and injected surplus power.

The battery SOC is now equal to SOCmin value of 15%. Storage has a similar behavior is order to mitigate imbalances for the rest of this day. Moreover, it can be noted that SOC values always stays between its boundaries introduced in Chapter 2 Section 2.1.2.

100

20 State of Charge (%) 0 00:00 04:00 08:00 12:00 16:00 20:00 Time (h) Apr 30, 2025 Figure 4.9 – State of charge for a given day (case I)

This battery usage illustrates the difference of performance regarding respect of contract power between systems with and without storage. Figure 4.10 depicts battery power ﬂow evolution over 10 years. Represented period in Fig- ure 4.10 goes from the beginning of year 2020 to the end of year 2029. Due to the capacity degradation over time, it can be noted that the magnitude of exchanged power decreases as time goes on. Note from Figure 4.10 is conﬁrmed by Figure 4.11 that shows battery SOH over 10 years. Storage capacity goes from nominal capacity (SOH of 100%) to a remaining capacity of 70.27%. As the lower SOH limit is 60%, MG operator should consider to renew its storage capacity in the foreseeable future. Besides, it can be noted that there is a seasonality in the SOH degradation. The storage is obviously used more often during sunny periods (summer) when there is more surplus energy to inject than during cloudy/rainy periods (winter). Thus, in Figure 4.11, SOH degradation is nearly nonexistent during winter periods. In order to illustrate S-PER system performance in a respect of commitments point of view, a KPI such as satisfaction ratio is computed. This satisfaction ratio is equal to injected power

146 4.4. Results and discussion

-20 Power Flow (kW)

-40 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 Years Figure 4.10 – Battery power ﬂow evolution over 10 years (case I)

100

80 State of Health (%)

70 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 Years Figure 4.11 – State of health evolution over 10 years (case I) divided by contract power. Satisfaction ratio is represented by the equation below:

PT t t=1 Pinj SKPI = (4.14) PT t|t−1 t=1 Pbid with T = 87600 for 10 years. Figure 4.12 depicts the simulated satisfaction ratio density the first year of case I. The mean satisfaction ratio for this given year is 97.7%. To assess that performance presented in Figure 4.12 does not depend on a specific solar pattern, a statistical study was conducted over 100 random (first) years. Statistical study results are displayed in Figure 4.13. Considering 100 random years, case I has an average satisfaction ratio of 97.4% (with a standard deviation of 0.27%). System performance does not vary strongly depending on climate pattern. If the 10 years pattern is considered, case 1 as an average satisfaction ratio of 96.9%. Regarding financial balance over 10 years, this system could have generated a theoreti-

147 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

Figure 4.12 – Satisfaction ratio density (case I)

Figure 4.13 – Mean satisfaction ratio density for 100 random years (case I) cal injection profit of 272.9 ke (if commitments were fully respected). Actual power injection generated 267.2 ke with a penalty charged by the aggregator valued at 36 ke. BESS cost is estimated at 23.5 ke and the grid power cost to fill the battery is estimated at 1.7 ke. Actual gross profit deducted from all operation costs detailed above is estimated at 201.4 ke 1. Besides, it is possible to consider the same model without battery degradation in order to assess its impact on global profitability and satisfaction ratio. Thus, battery stays at nominal capacity over 10 years, this implies that battery cost per stored kWh tends to 0 because such storage could be used for an unlimited amount of time.

1. A Table presenting a synthesis of every results is available at the end of case II.

148 4.4. Results and discussion

Case II

CommunityMG in case II has a 50 kWh BESS and uses RLS-based predictor-corrector method. Figure 4.14 illustrates the residentialMG power ﬂow in case II for the same day as case I. Usage of BESS helped the MG to deliver to amount of power contracted with the aggregator.

150

100

Power (kW) 0

S-PER Forecast RLS Forecast -50 Real Solar Power Contract Power Injected Power Residential Load -100 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time (h) Apr 30, 2025 Figure 4.14 – Residential microgrid power ﬂow for a given day (case II)

Difference of tracking performance can be noted between S-PER and RLS method in Figure 4.14 at hour 6, 9 and 13. It can be noted that at hour 10 injected power equals contract power, which is not the case for the system using S-PER forecast method (case I).

149 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

Figure 4.15 represents power ﬂow towards the utility grid during injection events and shows 4 curves: contract (bidden) and injected power curves for a system using S-PER forecast method (case I) and same curves for case II. At hour 8 and 9 both systems satisfy contract power and thanks to RLS method, the second system is able to deliver extra power to the grid. At hour 10, performance improvement from proposed forecast method and extra energy still available in the storage allowed theMG to satisfy contract power compared to theMG which uses smart-persistence.

0 Contract Power (S-PER) -10 Injected Power (S-PER) Contract Power (RLS) Injected Power (RLS) -20

-30

-40

-50 Power (kW)

-60

-70

-80

-90 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time (h) Apr 30, 2025 Figure 4.15 – Residential microgrid power injection ﬂow for a given day (case II)

Indeed, at hour 10, S-PER and RLS systems bid same amount of power but S-PER system provided less power due to the fact that this system required more energy from the storage during previous hours. At hour 13, both systems satisﬁed the aggregator but RLS system provided more power thanks to a better forecast accuracy.

150 4.4. Results and discussion

Figure 4.16 shows battery SOC over the same day. In this case, compared to case I there was no need to charge the battery because battery SOC was superior to 60% at the beginning of this day. Then, extra surplus energy was used to charge the battery in the morning (hour 7). At hour 8, 9 and 10, energy was extracted from the storage to ensure that injected power matches with aggregator’s power expectations.

100

40 State of Charge (%) 20 00:00 04:00 08:00 12:00 16:00 20:00 Time (h) Apr 30, 2025 Figure 4.16 – State of charge for a given day (case II)

Figure 4.17 depicts the simulated satisfaction ratio density for a random ﬁrst year in case II. Mean satisfaction ratio is equal to 98%. It can be noted that satisfaction performance is slightly increased compared to case I (97.4%).

Figure 4.17 – Example of satisfaction ratio density (case II)

In the same way as in case I, a statistical study was conducted over 100 random years to assess that performance presented in Figure 4.17 does not depend on a speciﬁc solar pattern. Statistical study results are displayed in Figure 4.18. Mean satisfaction ratio is equal to 97.6% (with a standard deviation equal to 0.27%). As it can be noted, mean satisfaction ratio over 100 random years is higher in case II compared to case I.

151 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

Figure 4.18 – Mean satisfaction ratio density for 100 random years (case II)

Regarding battery power flow evolution and SOH, these patterns are similar compared to case I. The remaining SOH at the end of the studied period (10 years) is 70.55%. It represents a small improvement of storage life expectancy compared to case I. Considering financial balance over the studied period (10 years), theoretical power injection could have generated 271.1 ke the actual power injection yielded 266.3 ke with a penalty charged by the aggregator valued at 27.9 ke. BESS cost is estimated at 18.9 ke and the grid power cost to fill the battery is estimated at 1.4 ke. Global profit is estimated at 217.9 ke. Financial results introduced above are summed up in Table 4.4 in order to compare both cases. Case I I* II II* Theoretical Injection Profit (ke) 272.9 272.9 271.1 271.1 Actual Injection Profit (ke) 267.2 268.2 266.3 267.3 Injection Penalty (ke) 36 29.3 27.9 21.3 BESS Cost (50 kWh) (ke) 19.1 0 18.9 0 Grid Power Cost (SOC setting) (ke) 1.7 1.8 1.4 1.5 Actual Profit (ke) 210.4 237.1 217.9 244.5

SKPI (%) 96.9 97.4 97.2 97.7 Table 4.4 – Proﬁtability and aggregator satisfaction over 10 years

Finally, it can be noted that proposed method (based on RLS), provide significant profitability improvement compared to the method based on S-PER. Values from Table 4.4 show that global profitability is increased by 3.5%. If injection penalty alone is considered, penalties are decreased by 22.5%. Cases I* and II* do not involve battery SOH degradation model. Global profitability without SOH implication is increased by 12.7% compared to case I using SOH and global profitability without SOH implication (case II*) is increased by 12.2% compared to case II using SOH

152 4.4. Results and discussion

4.4.2 Sensitivity Analysis

Impact of various parameters such as BESS capacity and aggregator penalty coefﬁcient are conducted for cases I and II. Aggregator penalty is a way to incentiveMGs to provide best possible power injection service. Here, it incentives the respects of commitments regarding power injection bids. Figure 4.19 depicts the evolution of practical balance in function of penalty values for both cases.

Penalty function pagg is applied by the aggregator to modify fees with a quadratic function of relative error between bidden and injected power multiplied by a penalty coefficient. For instance, a penalty coefficient kagg equal to 10 will result in a quadratic fee where 100% of the missing energy price is charged to theMG if the bidding error is equal to 31.5%, a penalty coefficient equal to 50 will result in a quadratic fee where 100% of the missing energy price is charged to the MG if the bidding error is equal to 14.25%.

250 Practical balance (case I) Practical balance (case II) 200

150

100 10 20 30 40 50 60 70 80 Penalty coefficient Figure 4.19 – Evolution of practical balance (actual proﬁt) in function of penalty coefﬁcient (kagg)

Practical balance in case I goes from 228 ke to 102 ke (a drop of 126 ke). Case IIMG’s practical balance goes from 230 ke to 134 ke (a drop of 96 ke). For low fees (kagg = 10), there is a difference in practical balance of less than 1% between case I and II. For bigger fees

(kagg = 50), case II is 12.8% more proﬁtable than case I.

153 Chapter 4 – Residential microgrid energy management considering ﬂexibility services opportunities and forecast uncertainties

Figure 4.20 illustrates the impact of BESS size over the satisfaction ratio for cases I and II. Various BESS size were tested from 0 to 200 kWh using a step size of 25 kWh. For each BESS size, the system was simulated over the complete studied period (10 years). In both cases, increasing the storage size improves the satisfaction ratio towards the aggregator. For case I, the satisfaction ratio varies from 87.43% without BESS to 99.83% using a 200 kWh storage system. For case II, the satisfaction ratio varies from 87.77% without BESS to 99.87% using a 200 kWh storage system.

100 260

98 240

220 96

200 94 180 92 160 Satisfaction ratio (%) 90 140 Satisfaction ratio (case I) 88 Satisfaction ratio (case II) Practical balance (case I) 120 Practical balance (case II) 86 100 0 20 40 60 80 100 120 140 160 180 200 Storage size (kWh) Figure 4.20 – Evolution of mean satisfaction ratio and practical balance in function of battery size (both cases)

Regarding practical balances, for case I proﬁts varies from 115.7 ke without BESS to 252.6 ke using a 200 kWh storage system, for case II proﬁts varies from 122.8 ke without BESS to 252.9 ke using a 200 kWh storage system.

154 4.4. Results and discussion

Figure 4.21 displays both relative gains (satisfaction and proﬁtability) provided by proposed method over the reference. This is another way to represent results from Figure 4.20.

0.5 8 Relative satisfaction gain 0.4 Relative profitability gain 6 0.3 4 0.2 2 0.1

0 0 Relative profitability gain (%) Relative satisfaction gain (%) 0 50 100 150 200 Storage size (kWh) Figure 4.21 – Relative improvements of proﬁtability and satisfaction provided by proposed method in function of battery size

Best relative improvement of both profitability and satisfaction are obtained by proposed method associated with a 25 kWh BESS. Profitability for the considered system with proposed method and a 25 kWh BESS is equal to 7.6% and satisfaction is improved by 0.46%. Second best performance is obtained with a system without BESS: profitability is improved by 6.10% and satisfaction by 0.38%. Proposed system (with 50 kWh of storage capacity) obtains the following results: 0.38% increase of satisfaction and 3.63% increase of profitability. Finally, it can be noted that both relative profitability and satisfaction gains decrease according to the BESS size for sizes superior to 25 kWh.

4.4.3 Discussion

This part discusses results presented above. Two cases were compared, the first case represents aMG with BESS and S-PER system and the second one represents aMG with BESS and RLS system. In the scope of this work, RLS predictor-corrector system provided a significant increase in hour-ahead forecast prediction (9.75% skill for proposed method regarding nRMSE). This improvement is caused by the fact that proposed method keeps in memory past errors and takes them into account to compute future irradiation value. Proposed algorithm has an adaptive gain that varies in function of past forecast errors. Fine tuning of a weight coefficient applied to past errors (namely the "forgetting factor") helps to improve forecast accuracy. Regarding the satisfaction ratio and profitability, proposed system (case II) outperformed the reference (case I) by 0.38% for the satisfaction and increased profitability by 3.63%. Best

155 Chapter 4 – Residential microgrid energy management considering flexibility services opportunities and forecast uncertainties performance improvements are achieved forMGs with 25 kWh of storage capacity. This is due to the fact that storage capacity is able to buffer major forecast errors of both methods. Because of its better accuracy 2, proposed method in association with a small storage provides a significant performance boost. In this case (25 kWh storage), proposed system outperformed the reference by 0.46% for the satisfaction ratio and improved profitability by 7.6%. With storage up to 100 kWh, the proposed method still visibly outperforms the reference in terms of satisfaction ratio and financial balance. For bigger storage sizes, the reference model equals the proposed model. This is due to the fact that, combined with enough available energy in the storage, the reference MG was able to inject energy in the same way as proposed system. So, performance differences between both forecast algorithms are mitigated by the storage. Regarding the penalty coefficient values, theMG is more profitable in case II. This is due to the fact that high fees sanctions case I profitability due to its forecast inaccuracy compared to case II. This is particularly true for kagg = 50 where there is a profitability difference of 12.8% in favour of RLS-basedMG. Thanks to its forecast accuracy improvements, proposed method helped theMG to avoid extra aggregator penalties compared to smart persistence method. Moreover, it can be noted that profitability does not vary linearly in function of battery size. Up to sizes of 100 kWh, profitability and satisfaction ratio are increased thanks to bigger storage. For bigger sizes, profitability and satisfaction ratio are still increased but not significantly. In order to reach the last percent of satisfaction, a huge storage capacity should be installed. This is due to the fact that remaining errors, despite being very uncommon, require an important quantity of energy to be mitigated. Finally, proposed method is particularly suitable forMGs which encounter significant weather variations and equipped with small storage capacity (or even without storage capacity) because proposed solution outperforms reference solution whatever the battery size is.

2. Performance of proposed method is justiﬁed by the relative good performance improvements noted in Figure 4.21 for aMG without storage

156 4.5. Conclusion

4.5 Conclusion

In this Chapter, an energy management strategy for peninsular communityMG operation under solar irradiation uncertainties was proposed. This energy management framework involves weather forecast technique using Markov chains and a predictor-corrector method based on recursive least-squares. This framework aims to ensure that power injection commitments to the aggregator are respected as much as possible. The predictor-corrector method based on recursive least-squares as well as BESS size helps to increase the satisfaction ratio. Extensive simulation for bothMGs using smart persistence and predictor-corrector models demonstrated the effectiveness of the proposed technique. Numerical results prove that using predictor-corrector model with local measurements brings signiﬁcant improvements regarding penalty charged by the aggregator and improvements regarding the satisfaction ratio in particular forMGs with small-sized storage. Main results are listed below:

— For aMG with a 25 kWh BESS and taking into account the uncertainty, the proposed framework ensures a 0.46% increase of the satisfaction coefficient value and a 7.6% increase in profits when the predictor-corrector method is applied. Performance improvements for systems without a BESS is close to the best with a 0.38% increase of the satisfaction and 6.10% increase of the profitability. — Despite the fact that usage of BESS tends to mitigate reference forecast model under performance because of its buffer effect, proposed model is still able to outperform by 0.38% regarding satisfaction ratio and increase profitability by 3.63% when a 50 kWh storage is involved. — Nevertheless, for 100 kWh BESS (or bigger), differences in terms satisfaction ratio and profitability are arguable due to the fact that accuracy errors of both models are corrected by energy coming from the storage.

In results presented above, it can be noted that proﬁtability can be easily improved using proposed method compared to reference method. Besides, thanks to the fact that improvements comes from the software side, no extra investments in physical assets are required to use proposed method. Depending ofMG sizing, this study certiﬁes that a practicalMG is worthwhile in the context of real-time electricity tariffs and bid-based power injection policies provided by aggregators in order to increase grid stability. Nevertheless, in this context, only short-term forecasting (hour- ahead horizon) was taken into account. Further work could assess the impact of uncertainties on a wider time frame.

157

CONCLUSIONANDPERSPECTIVES

The growing share of RES in the global energy mix offers new possibilities for energy generation in a context where low-emission generation is a trending topic. In this thesis, focus has been put on residentialMG based in France. Besides, presented methods can be easily adapted for other countries and other strategies. Research focused on both energy management andMG sizing techniques suitable to such MG taking into account local regulations. Regarding energy management, two ﬂexibility services were presented: energy arbitrage and respect of injected power commitments. Regarding sizing, an optimal sizing methodology was adapted for residential end users. This methodology takes into account: current regulations related to energy generation and energy retail price trends with the aim to minimize operation costs, facilitate RES integration and satisfy the load. These ﬂexibility services are performed to help the grid to cope with problems generated by the massive integration of RES and to ensureMG sustainability. Several contributions can be highlighted regarding current literature and various topics presented in this thesis.

— Sizing methodology applied to single-family homes and communityMGs subject to French regulations. Several scenarios were tested (self-consumption, partial and total injection as well as off-grid operation) and KPIs were presented as objectives: — Proﬁtability. — RnE penetration. — Load satisfaction. — An energy arbitrage strategy applied to aMG that is able to take part in energy spot markets was presented. The key point was the assessment of proﬁtability improvement through the usage of a 48h time-horizon for energy trading instead of a 24h horizon traditionally used in the literature for such application. — An energy management strategy based on power injection commitments to help the grid to mitigate imbalances was presented. Accuracy of power commitments is based on accurate solar irradiation forecasts. Thus, a statistical auto regressive method adapted for short-term horizons (1h) and strong climatic variations (peninsular weather of Saint- Nazaire) was presented and compared to the reference: smart persistence method to assess its performance improvement.

159 In Chapter 1, a review of residential sector energy consumption trends and residentialMG context was presented. Then, current limitations (technical, economic, policy) that apply to the residential sector and perspectives to overcome such limitations were discussed. Finally, a review of sizing and energy management methods suitable for residential applications was introduced. Models used in this thesis forPV panels, storage, loads and aggregator interactions were presented in Chapter 2. Despite their apparent simplicity, these models were able to assess accurately generated/consumed power regarding the considered time step (1h). Moreover, data and parameters presented in this Chapter reflect current technical and legal possibilities for the residential sector to participate in the European energy transition with a realist economical approach. Results from optimal sizing showed that: (1) considering single-family houses, the installation of a rooftopPV system was profitable only considering partial and full injection; considering the communityMG every scenario was profitable thanks to load sharing and savings on system cost due to its larger scale; (2)PV array and BESS size in order to achieve complete autonomy are very substantial. Moreover, if the objective is to ensure complete autonomy, results demonstrated that using recycled batteries fromEV is a good solution to significantly decrease overall system cost. Besides, Pareto front showed that only a significant rise of BESS capacity allow the single-family home or the residentialMG to satisfy its load in an off-grid context. Nevertheless, in this Chapter, BESS degradation was based only on calendar degradation method. Battery energy management strategies were limited and focused only on off-grid operation. In Chapter 3, an optimized energy management strategy was proposed in order to perform a flexibility service. In this Chapter, this strategy is a battery energy management framework based on time horizons. Two time horizons (24 and 48h) were introduced in this work. Both time horizon-based methods aim to maximize communityMGs profitability by using energy arbitrage method while taking into account energy storage degradation. Proposed method has a profitability improvement that equals respectively to 3.66%, 3.94% and 3.16% for studied communityMGs where 48h horizon-based algorithm was applied over reference horizon (24h). Optimization related to profit and RnE penetration rate maximization showed that with proposed MG sizing and operation, setting more than 80% of battery SOC to store solar surplus power does not increase significantly RnE penetration rate. This result means that used battery sizing method is suitable to store a large majority of solar surplus provided in this residential configuration. Sensitivity analysis also showed that relation between profitability and aggregator margin is not linear. This is due to the fact that the algorithm focuses on self-consumption for high margin values thus allowing theMG to be less dependant from spot prices. Finally, Chapter 4 presented an another energy management strategy (that also belong to

160 the field of flexibility services) focused in weather solar irradiation forecast. In this Chapter, the energy management framework involves weather forecast technique which uses a predictor- corrector method based on recursive least-squares. This framework aims to ensure that power injection commitments to the aggregator are respected as much as possible. The predictor- corrector method based on recursive least-squares as well as BESS size helps to increase the satisfaction ratio. For aMG with a 25 kWh BESS and taking into account the uncertainty, the proposed framework ensures a 0.46% increase of the satisfaction coefficient value and a 7.6% increase of profits when the predictor-corrector method is applied. Despite the fact that usage of more storage capacity tends to mitigate reference forecast model under performance because of its buffer effect, proposed model is still able to outperform by 0.38% regarding satisfaction ratio and increase profitability by 3.63% when a 50 kWh storage is involved. It is possible to assess gains provided by the proposed algorithm alone by assessing results of a system without storage. In this case, satisfaction and profitability gains are respectively equal to 0.38% and 6.10%. Nevertheless, for 100 kWh of storage capacity (or bigger), differences in terms satisfaction ratio and profitability are arguable due to the fact that accuracy errors of both models are corrected by energy coming from said storage capacity. Results obtained in this thesis must be considered taking into account hypothesis and boundaries presented along this work. Thus, it is possible to highlight several limitations listed thereafter:

— Usage of high-level models and hour step time: simpliﬁed models were used in this work forPV panels, BESS and loads in order to keep computation time within reasonable limits. Besides, a time step of one hour was considered in this study. Thus, numerous phenomenon were not taken into account such as primary frequency/voltage control, harmonics and so on... Transients that are generally observed with shorter time steps cannot be analysed with such models. — BESS technology: lithium-ion batteries were selected thanks to their commercial availability nowadays and their characteristics compared to other batteries for residential applications (energy density, charge/discharge power, setup and maintenance costs, life expectancy). This may not be the best choice for residentialMG applications. Thus other storage alternatives proposed in the perspective part could be considered. — Studied location: only one location was considered in this thesis due to various constraints that had to be respected during the data gathering phase (availability, period, reliability). Thus, proposed methodologies for both sizing and energy management was only applied to a single location. Results should be considered taking into account spe- ciﬁc characteristics of considered location (climate data, local regulations and policies, energy prices...).

161 — Uncertainties: in Chapter 3, uncertainties were not taken into account for energy arbitrage which is a strong assumption. For both 24 and 48h horizons data was considered perfectly reliable. In reality, there are two major kind of uncertainties, solar irradiation uncertainties (for surplus power storage) and day-ahead energy price uncertainties. Thus, technically, errors generated by day-ahead uncertainties could mitigate gains generated by using a 48h time-horizon.

Several perspectives can be proposed considering results and limits presented above. These perspective are sorted in two categories: short-term and long-term perspectives. Short- term perspectives stand for improvements that could be carried out directly using models and results presented in this thesis whereas long-term perspectives could be integrated in a new thesis. Short-term perspectives are presented below.

— Uncertainties: uncertainties are particularly useful for energy arbitrage applications, thus for instance, usage of forecast data (provided or generated) would allow to compare proposed time horizons more realistically. Moreover, usage of forecast data would allow to assess the length of a relevant time horizon. According to the reliability of forecast data, a dynamic time-horizon could be considered. Therefore, a global algorithm that take into account "long" (24-48h) and "short" (1h or less) rolling horizons presented respectively in Chapters 3 and 4 to perform both DER ofﬂine planning and online forecast corrections in order to optimize said planning can be considered for future work.

— Integration of DSM techniques in proposed system: DSM were not considered in this thesis. In order to increase RES penetration without increasing amount of RnE generators, control of Heating, Ventilation and Air-Conditioning (HVAC) systems can be added in simulations. To be more realistic, controllable loads and critical loads groups should be considered as well as user comfort. To get maximum efﬁciency, real-time load data should be communicated from end users to the EMS. However, gathering of real time data requires smart meters and social acceptation of data acquisition which is not yet the case in every location.

— Impact assessment ofMG clusters : this study was carried out in only one location. If integration ofMG clusters is considered, it would be relevant to perform a correlation analysis between various energy sources and loads over a wider area. This analysis may provide relevant results to help cluster designers during the topology process.

— Integration of new storage and generation technologies: various kind of storage technologies may be more suitable for proposed applications such as hydrogen fuel cells in addition of lithium batteries. Nevertheless, integration of new storage technologies re-

162 quires to adapt proposed energy management algorithm. Focus should be put on priority of charge/discharge to respect each storage internal dynamics. Regarding generation, due to regulations and technical limitations, only solar panels were considered in this thesis. IfMG clusters are considered and some of them are industrialMG, wind power (in studied location particularly offshore wind power) could be added in simulations. Long-term perspectives are presented below. — Integration of control methods: frequency/voltage control is a key point forMG stability and reliability. Secondary and primary control methods must be carried out if the deployment of suchMG especially if off-grid operation is considered. Nevertheless, power quality must be taken care of even in grid operation. A certain level of power quality is required for power injected into the grid. It is not possible to perform such study with models used in this thesis. Moreover, control strategies could be tested on Hardware-In-the-Loop (HIL) test beds to assess the stability and the performance of proposed strategies. — Consideration of Environmental, Social, and Governance (ESG) criteria: before deployment of such communityMG, it is necessary to carry out a social and environmental study to verify how this concept is accepted by involved people. This study will allow to decide which are the best strategies to use taking into account end-users and various stakeholders needs and expectations. Moreover, in this thesis, the major KPI was profitability. Further work could put focus on other KPI such as carbon foot print in order to decide which RES to integrate and provide a relevantMG topology. Various perspectives can be considered to develop this work. Regarding low-level control, it may not really relevant to mix both levels because considered objectives seems to be too different. Various energy related regulations all over the world and even withinEU seems to be a signiﬁcant limitation for the rise of (residential)MG and their relevance to help the main grid to support the rising share of RnE generation in the global energy mix.

163 APPENDIX A EXTRA INFORMATION RELATED TO RENEWABLEENERGYANDMICROGRIDS

A.1 Current data for energy consumption and production

In 2019, eurostats published a document named "Energy, transport and environment statistics" [67] where data about energy consumption and production forEU-28 are summed up. Figure A.1 showsEU-28 gross inland energy consumption in 2017. Renewables: 13.6% Solid fossil fuels: 14.5%

Nuclear: 12.3%

Petroleum products: 36.4% Gas products: 23.2%

Figure A.1 – Gross inland energy consumption by source (2017)

Gross inland energy consumption covers energy sector self-consumption, transformation and distribution losses and final energy consumption by end users. In 2017, theEU gross energy consumption was around 1675 Megatonnes of oil equivalent (Mtoe) of energy which is roughly equivalent to 19480 TWh. The main part of consumed energy comes from non- renewable sources. Petroleum products involves oil, petroleum products (except bio fuels), oil shale and sands. Solid fossil fuels involve coal and peat as well as their respective products. Besides, the EU produced around 45% of its consumed energy while 55% was imported. [67] noted thatEU is still dependent from other countries regarding energy supply. For instance, regarding crude oil,EU imports 30% of needed crude oil (and 40% of needed natural gas) from Russia.EU objectives focus on gross final energy consumption which is different from gross inland energy consumption. Gross final energy consumption involves energy sector self- consumption in order to produce heat or electricity, transmission and distribution losses and

164 final available energy to end users. It excludes energy consumption for transformation processes (electricity power plants, oil refineries, blast furnaces). In 2017, according to [67], 17.5% of gross final energy consumption comes from renewable sources which is not far from 2020 objectives. Regarding global energy production, Figure A.2 displaysEU-28 energy production by source in 2017. Petroleum products: 9.7%

Nuclear: 27.8% Gas products: 13.6%

Solid fossil fuels: 19%

Renewables: 29.9% Figure A.2 – Energy production by source (2017)

In 2017, 758 Mtoe (8815 TWh) of energy were produced in theEU. It can be noted that a bit less than third of global production is ensured by RES. In the context of this work, a focus will be made on electricity generation in theEU. Elec- tricity generation is often regarded as gross electricity generation (with power station’s auxiliary services consumption, imports and exports as well as storage included). In 2017, 3294 TWh of electricity have been generated in theEU-28 [67, 147]. Figure A.3 shows electricity generation by source for year 2017.

Petroleum products: 2.15% Solid fossil fuels: 20.25% Renewables: 30.5%

Gas products: 21.9%

Nuclear: 25.2% Figure A.3 – Electricity generation by source (2017)

165 Electricity generated from RES represent 30.5% (1005 TWh) of gross electricity generation in theEU-28 for 2017 [67]. RES involve several domains such as wind power (36% of RnE generation), hydro power (32.9%), biofuels (biogases, bioliquids and biomass: 16.3%), solar power (PV and thermal: 11.8%), renewable wastes (2.2%) as well as geothermal power (0.7%). Tide, wave and ocean power represent a small share of RnE generation in the mix (below 0.1%).

RegardingEU objectives, eachEU member have a fixed share of RES for its gross final energy consumption. As written before, the average share for theEU-28 was fixed at 20% for 2020. Figure A.4 shows share of energy from renewable sources for year 2017 [67].

2017 2020 target 50

% 30

10 UK Italy Malta Spain Latvia EU -28 France Ireland Poland Austria Cyprus Croatia Finland Greece Estonia Czechia Sweden Belgium Bulgaria Portugal Slovakia Slovenia Hungary Romania Lithuania Denmark Germany Netherlands Luxembourg Figure A.4 – Share of energy from renewable sources in the European Union (2017)

It can be noted on Figure A.4 that current average penetration of RES in gross energy consumption is equal to 17.5%. This value is not far from 2020 objectives. Some countries such as Sweden, Finland, Denmark and Estonia already have outperformed their respective goals. Nevertheless some countries such as Ireland, United Kingdom, Cyprus and Malta are quite far from their respective goals. This is mainly due to the fact that insular countries rely more on conventional power sources to supply energy. Moreover, some speciﬁc insular constraints such as lack of suitable area for RES implantation projects, speciﬁc insular weather, social protest movements against some RnE technologies do not help the deployment of RES.

166 A.2 Feed-in Tariffs in France

In France, FIT policy related toPV was enacted in February 2000 (law n° 2000-108). First tariffs were enacted in 2002 [105]. In this law, there was no premium for building integration. Peak power limitations were 5 kWp for single-family houses, 1000 kWp for collective dwellings or buildings used for professional purposes, 150 kWp in other cases. In the end of 2004, power limits were abrogated and were reenacted in 2010. For projects above 100 kWp, France chose to use a FIP model where future projects have to bid prices in a call for tenders.

60 Homeland Overseas Integration 50 premium RIAB9 40 RIAB36 RISB36 /kWh)

e RISB100 30

20 Incentive (c

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year Figure A.5 – Incentives evolution for photovoltaic-based projects in France (2002-2017)

Figure A.5 shows several incentive programs. They started in 2002 with "homeland" and "overseas". Tariffs were set according to the location (in homeland or overseas in remote french territories). In 2006, a speciﬁc premium appeared for projects that integrated solar panels on buildings’/houses’ roofs. "Integration premium" represents "homeland" or "overseas" incentives taking into account premium tariff. In 2010, in order to face a project boom and foreseeable speculation, new laws were enacted to restrict project deployment by adding new constraints [104]. "Homeland" became the new tariff for projects which were not related to single-family houses, healthcare buildings or without any building integration. Then, tariffs were adapted according to projects’ peak power and building integration type. RIAB9 stands for 0-9 kWp residential panel array with building integration and RIAB36 stands 9-36 kWp residential panel ar-

167 ray with building integration. RISB36 stands for 0-36 kWp residential panel array with simplified building integration and RISB100 stands for 36-100 kWp residential panel array with simplified building integration. There are few differences between building integration and simplified building integration: building integration implies that solar panels arrays "become" buildings’/houses’ roofs (they have to ensure a sealing function) whereas simplified integration can be summed up as solar panel arrays installed on top of existing roofs. In reality tariffs were revised every trimester since 2011, but for the sake of readability, tariffs are presented on a yearly basis. Figure A.6 shows the amount of availablePV power in France. Data was gathered from french government-backed open data platform for 2001-2017 time period [120].

8,000 PV power

6,000

4,000

2,000 Available power (MW)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year Figure A.6 – Evolution of available photovoltaic power in France (2001-2017)

Thanks to high FIT between 2006 and 2010, a available solar power in France skyrocketed from 4 MW in 2006 to MW in 2584 MW in 2011. Then, between 2012 and 2017 available power was increased by around 800 MW/year. This increase can be explained by (still) high FIT values and decreasing setup cost thanks to market competition between solar panel suppliers and other stakeholders related toPV markets. Moreover, new regulations towards RES integration are not enacted only in theEU. In May 2018, California Energy Commission (CEC) enacted a new set of regulations regarding new buildings. These regulations will enter into application in January 2020. Adopted standards requirePV systems to be integrated to new home building

168 projects in order to reduce GHG[58]. This a further step towards massive RnE penetration.

A.3 Experimentel smart grid projects in Europe

Kergrid is a concept of smart microgrid integrated inside a building (offices). This building is connected to the distribution grid and has its own local RnE source (PV and wind-based sources), storage system andEV that can be seen either as a load or a source. The aim of the project is to allow the building to be in islanded mode during two hours per day during peak hours. The building is located in Brittany, this region is far away from main power plants and is subject to power outages during peak hours. Indeed Brittany produces only 9.5% of its consumed energy so the risk of power outage is even stronger. GreenLys is a smart grid experiment made in France. The experiment was launched in 2012 and has shown good results. G2ELab contributed to the project providing its knowledge and simulation tools related to smart-grids. The objective was to adapt load demand with future grids’ constraints (i.e quality and reliability problems caused by RnE integration). To reach this objective, the project asked 400 customers to perform load management. They were helped with free load management software and encouraged to optimize their needs with special electricity tariff (i.e.: electricity price was varying along the day as DSO or TSO are used to) [77]. SEAS is an ITEA 2 project which aims to overcome the problem of inefficient energy consumption due to a lack of control means. It will allow collaboration between the electrical part and the ICT part where energy the consumed and deploy dynamic control solutions to monitor and estimate energy consumption [140]. The project will also aim to propose business models that will take into account micro grids and prosumers inside the energy market [90]. FUSE-IT is also an ITEA 2 project which aims to provide sustainable, reliable, user friendly, efficient, safe and secure BMS in the context of smart critical sites such as hospitals, military facilities or even data centers [91]. Demonstrators will be presented to illustrate reached goals. There are 4 demonstrators: an hospital in Portugal, a techno-park in Turkey, a HomeLab (smart house built as a testbed) in Belgium and a facility managed by Cassadian Cyber Security (an Airbus subsidiary company) in France. As written in the Grid4EU report [78], the project aimed at testing innovative concepts and technologies in real-size environments, in order to highlight and help remove barriers to the deployment of Smart Grids in Europe. It focused on how DSOs can dynamically manage electricity supply and demand. The main topics addressed by the project are:

— the improvement ofMV and LV network automation technologies to face the constraints introduced by the increased amount of DERs and new usages (for instanceEVs, heat pumps) to reduce energy losses and maintain or increase quality of supply;

169 — the optimized and smooth integration of an increased number of small and medium-sized DERs (PV, wind, CHP, heat pump and direct or indirect storage);

— The balancing of intermittent energy sources (including better prediction) withDR, and different storage technologies and services;

— the assessment of islanding as a solution to increase the grid reliability;

— the increasing use of active demand including the potential future developments of new usages and evolving customers’ behaviours.

DREAM-GO is a project between Europe and the USA scientific and non-scientific communities, its aim is to enable demand response for short and real-time efficient and market-based smart grid operation. It will reach its objective thanks to an intelligent and real-time simulation approach. To sum it up, DREAM-GO project aims to provide:

— reliable business models for both short and real-timeDR in the context of smart grids.

— models to simulateDR in smart grids;

— communication tools, devices and methods speciﬁcations to enable direct load control (which is an essential key feature forDR).

The BestRES project aims to provide European aggregators’ best practices in order to make them become important facilitators of system ﬂexibility. In [27] several business models are reviewed in order to highlight the emergence of a new market role. Real use cases were reviewed such as: supplying mid-scale customers with variable tariffs and optimize their peak load (Next Kraftwerke, Germany), trading aggregated RnE on spot markets (Next Kraftwerke, Belgium) and valorisingDG of customers in apartment buildings (oekostrom, Austria).

A.4 Flexibility services

Frequency regulation: voltage regulation is realized by modifying alternator excitation voltage and the frequency regulation by modifying the turbine speed. InSG context, many other grid components may offer these services: controllable loads, electric storage (static or from EVs) and so on. Thus, actors offering regulation services appeared in the market.

— Californian energy commission presented storage as a twice more efﬁcient solution compared to turbines for frequency regulation [98]. Califorina Independant System Opera- tor (CAISO) proposed resources such as ﬂywheels and batteries to provide such grid services. Enbala Power networks located in Ontario proposes to help the grid through load management.

170 — Studies requested by the European parliament presented storage as an opportunity to improve RnE integration through their participation in grid balancing needs [66]. Another example with Ameresco. This company is an energy supplier in the United Kingdom which provides grid services (load management for Demand Side Response (DSR)) to the local grid operator: National Grid.

Besides, there is ongoing research on integration ofEVs in the scope of frequency regulation. Authors in [17] aimed to to highlight various parameters variation depending on the usage ofEVs (driving patterns) and grid context as well as their effect on the proﬁtability of the frequency regulation service. Moreover, in [18], another study was performed with the aim to propose an algorithm in view of dynamic simulation taking into account grid requests, vehicles driving patterns and the electricity prices taking into account battery wear. Indeed, thanks to realEVs and grid data as well as simulated ones, the proposed algorithm allows to compute proﬁtability for various markets situations, mobility patterns and charging schedules. Voltage regulation: this is an important operation in order to maintain stability inside aMG and as an ancillary service. As presented in [2], there are 3 levels of voltage regulation. They are very similar to the frequency levels regarding their operating modes (automatic, manual...).

— Primary voltage control is a local automatic regulation which aim to maintain the voltage set point at a given bus (stator for generating machines). Local automatic regulation is provided by an Automatic Voltage Regulator (AVR). A Static Voltage Controller (SVC) can also take part in the process of primary regulation. — Secondary voltage control is a centralized control that manage local devices in order to proceed to the injection of the right amount of reactive power within a local zone. — Tertiary voltage control is a manual optimization which aims to manage reactive power ﬂows across the grid.

Because of voltage magnitude and reactive power are related, stakeholders should use devices that can produce or absorb reactive power (in this case, induction engines are not suitable because of their lack of reactive power control). Voltage regulation requires massive capacity, thus is not generally possible for small entities to perform such service. Nevertheless, it would be possible to provide this service thanks to aggregation mechanisms. DR/DSR: demand response is a change in the power consumption of an electric utility customer to better match the demand for power with the supply. Utilities may signal demand requests to their customers in a variety of ways, including simple off-peak metering, in which power is cheaper at certain times of the day, and smart metering, in which explicit requests or changes in price can be communicated to customers. It is also possible to be remunerated for availability and power with Vehicle to Grid (V2G) and Battery to Grid (B2G)[137] in order to

171 help the grid when prices rise (peak shaving) or when there is a grid instability. Market operators send requests to groups of end-users in order to make them change their consumption profile temporarily. If the request is accepted, end-users are remunerated for the provided service. If it is not (for financial or physical reasons), the prosumer or the group of prosumers can send back a message to ask for a request that will fit them.DR is a “bottom-up” action (prosumers have the final decision) and Demand Side Response is a “top-down” action where prosumers agreed that an aggregator is able to manage their loads. So to sum it up, inDR signals are received by prosumers and they decide to operate according to them or not whereas in DSR, network aggregators directly manage flexible loads. DR/DSR integration encounters difficulties in some countries, especially in France. It is mainly due because of the lack of infrastructures (technical and financial platforms) and regulations [165]. Nevertheless, there is a research effort thanks to various experiments and from VPP to provide such systems. For instance, Open Automated Demand Response (OpenADR) is a software used to send information and signals to cause electrical devices to be turned off during peak periods. Black start: this service is procured from generator that have the capability to start main blocks of generation from an auxiliary generator, without depending on external site supplies. In case of black start, a storage can inject power to restart the grid. In this case, all the loads are disconnected. The amount of power has to be relevant to ensure the service. The grid is the customer of this service through the intermediates. The MG disconnects all its loads to relieve the grid or to be ready to provide enough power to restart the production. When the storage is injecting power to the grid, the prosumer becomes a supplier. Real time information exchanges involve an effective communication between stakeholders. The contract between the micro-grid and the utility grid can include a remuneration according to the service [128]. If theMG provides a fast response during a crisis and relieves the main grid, after restor- ing power, the remuneration of the supplied energy will be significant. Besides,MGs demonstrated through simulations that they are able to support the utility grid in case of blackouts and also to reduce the restoration time with the help ofDG. A procedure to help aMV network in case of black start was presented in [25]. Islanding: in case of grid weakness or if the system operator asked for it, a MG can switch into islanded mode thanks to itsDG units and its storage that are able to provide power. The storage may supply some critical loads in the micro grid and during this operation the function- ing will be islanded as long as grid failure is present or the requested time. Stationary batteries can a good alternative in case of failure system especially in critical sites like hospitals or government buildings. Islanding operation require special attention from the MG operator because the system is faced with several issues discussed in [135] and listed

172 below. — It needs to ensure good power quality, especially voltage and frequency to keep the MG in normal operating condition. If values do not stay within acceptable limits, the power consumption controllable loads should be decreased or they could be disconnected to ensure a recovery to normal operating values. — It also needs to ensure the balance between supply and demand through the use of DR/DSR. — It has to deal withDG issues in term of predictability, and response time. Traditional generation units are dispatchable therefore predictable. They have an appropriate response time compared to mostDG units that need to be coupled with fast response storage like supercapacitors to ensure a good response during for instance, fast power ﬂow changes in the MG. — All of points above cannot be correctly implemented if there is a lack of good communication in theMG. Indeed, good control cannot work without good communication system. The communication delay and the process time to treat feedback should not be relevant within theMG. In any case, DERs should work in cooperation to ensure the stability of the islandedMG.

A.5 Microgrid challenges

A.5.1 Technical challenges

Additional infrastructure is required in order to deploy the MG/SG concept correctly. For instance: smart sensors and electronic devices, advanced metering systems, advanced EMS, well deﬁned communication architecture and cyber security devices are key parts ofSG integration which need to be added to current power systems [84, 167]. Because of intermittency in some DER, stand-by capacity might be required to back up energy generation for times where intermittent DER cease to produce energy. Among numerous challenges, some of important technical challenges are presented above. According to [162], technological issues are still experienced inMGs such as: dual-mode switching between grid-connected and off-grid modes, general operation to ensure power quality and overall protection. The switch from grid-connected to islanded mode can be a complicated task because the transition should occur quickly after an external fault or because of a blackout. The other transition state of reconnecting with the main grid can also generate challenges. Resynchronisation requires to carefully compute the speciﬁc moment when the breaker is closed and may require

173 specific voltage/frequency control modes due to the fact that such events are likely to generate large imbalances between local generation and loads. Moreover, due to the fact thatMG network profiles are often LV, management of instan- taneous active and reactive power can become difficult (high resistance to reactance ratio). Besides, a large amount of DER(PV, wind, gas engines...) with different requirements will stress the management of harmonics. Protection and safety also suffer from technical issues,MGs need protection system for both internal and external faults. Major issues occur during off-grid operation because of fault currents in inverters may not ensure sufficient current rates in order to use classic protection techniques that rely on high values of fault currents to trigger the protection mechanisms. EV are developed thanks to a strongEU policy towards GHG emission levels reduction in the transport sector [33]. European Commission foresees that 1990 GHG emission levels need to be decreased by 60% in 2050. There are two technical mains barriers forEV deployment: (1) lack of charging points, (2) fast charging stations that will represent a challenge from grid management point of view and may lead to a need for grid reinforcement and size extra capacity in order to cover power drawn byEVs. Moreover, there is currently a lack of standardization and interoperability between regardingEV service and operation protocols as well as lack of standardDC charging system in order to improve charging access and scalable charging infrastructures. Strong communication abilities to help coordination will helpSG deployment [112]. There are several challenges to deal with in order to reach strong communication abilities: (1) standard interoperability, (2) access to unlicensed radio spectra and (3) cyber security. For instance, in order to enhanceMG capacity and reduce its non-RnE sources dependency, exchange of information with a common meaning should be operated between large scale power producers (as well as large scale renewable generation), consumers andMG should be considered.

A.5.2 Regulatory/Social challenges

Regarding injection tariffs for residential applications, some European countries such as France enforced FIT which rates are ﬁxed. Besides, tariffs decrease over time (revised each trimester). In countries like Spain, regulation Royal Decree-Law 1663/2000 prevents generation and local loads to be connected on the same metered circuit. Moreover, regulation Royal Decree-Law 1699/2011 prevents the emergence ofMG since the integration of generation and storage capacity in the same system as well as the fact that islanding is forbidden. Furthermore, Spanish law 24/2013 on self-consumption requires from entities who perform self-consumption to pay system and operation fees like classic customers (also known as "sun tax"). Thus a self-consumed kWh of energy is subject to the same fee as one bought from the utility grid

174 (around 6cC/kWh). These kind of regulations generate a strong barrier for the development of MG and RnE penetration in the residential sector [162]. Recently, Royal Decree-Law 15/2018, eliminated the so-called "sun tax" and enforced a series of regulation to allow Spanish people to installPV for self-consumption purposes, thus, decreasing current regulatory issues for the development ofMGs in Spain.

A.5.3 Financial challenges

800 ) 600 MWh / e 400

Spot price ( 200

Jul-01 Jan-01 Jan-31 Mar-01 Apr-01 May-01 Jun-01 Aug-01 Aug-31 Oct-01 Oct-31 Nov-30 Dec-31 Time Figure A.7 – Spot prices (EPEX SPOT market, french area) for year 2016

Figure A.7 shows electricity spot price evolution in France for year 2016. Data was gathered from ENTSO-E transparency platform [139]. Spot price surge in November 2016 is explained by RTE in [146]. Multiples reasons caused such price peaks. Firstly, this was due to the historically low availability of nuclear facilities in France and Switzerland as well as shutdown of 4 GW of coal-ﬁred power capacity in Great Britain. It matched with coal and gas prices rise in Europe. In November 2016, prices peaked at 874 e/MWh on the spot market. Lack of large amount of conventional dispatchable power sources led to price volatility over a short period of time. Thus, enhanced control methods or energy storage abilities could be deployed in order to avoid such problem. As it can be noted in section 1.1.2,EU policies were revised several times across the last

175 decade. Therefore, due to the lack of long-term stable policies and regulations regarding RES tariffs and high upfront investment costs (especially related to ESS such as batteries or even EVs for active participation in load management orDR) to set upMG, investors are not inter- ested to take risks until standards and ROI are foreseeable.

A.5.4 Technical perspectives

Regarding the switching mechanism between grid-connected and off-grid operation, extra research on developing a series of inverters with a built-in switching mechanism could be carried out. Besides, enhanced droop control methods for current inverters are a considered solution in the present literature [162]. Power quality issues belongs to lower levels of control, thus in order to deal with the intermittent behaviour ofPV and wind sources, the usage of current ramp rate limitation techniques applied in BESS units will help to mitigate voltage flicker. Regarding voltage variations, author in [166] focused its research on a building-integrated DCMG satisfying the power balance at the local level and supplyingDC loads during both, grid- connected and isolated operation modes. Its main objective was to define energy management strategies in order to keep the bus voltage stable as well as to reduce the energy cost to the end users and the negative impact to the main grid. Moreover, in [177], the objective was to study the power losses in an urbainDCMG in order to improve its overall energy efficiency. Power losses are quite variable in particular with RES such asPV panels. In the literature, converter efficiency is often treated as a constant, but experimental tests were carried out to show that it was not the case. Thus a simple and fast estimation method of power losses was created. Thanks to its simplicity, the proposed method can be implemented in the real-time system. Thus the energy management strategies are proposed to interact with the variable efficiency on the high and low level control. Finally, authors from [177] demonstrated that these strategies are capable of shedding power in theMG with flexibility and accelerating the convergence speed of control through the knowledge of power losses of each converter. Besides modulation of power losses, it is also possible to consider reconfigure the topology of a network to maximize its efficiency. In [51], author focused its research on key points that causedPV systems to fail and to make them more reliable and more efficient than current devices. In this work, authors carried out simulations to reproduce real solar irradiation pattern and managed to separate losses caused by the environment and losses caused by electrical mismatches. Moreover, in [81], author focused its work on the study of partially shadedPV panel arrays and flexible power architectures in order to decrease their impact onMGs. Besides, control strategies were assessed in normal and fault operation. This work aimed to propose a new approach to model and simulate complexPV systems taking into account modularity,

176 scalability and non-standard working condition properties. Protection perspectives are also considered, they are several potential solutions considered in the literature such as: adaptive protection that tweaks relay settings, coordination of generation and non-critical loads to avoid safety relays to trip out. IEEE 1547 series for and IEEE Standard 2030 are two sets of standards proposed by IEEE towards DER integration and towardsSG interoperability reference model. IEEE 1547 series provide requirements relevant to interconnection and interoperability performance, testing and operation as well as safety, maintenance and security considerations. 1547 series address distribution-level connected DER and specially: microgrids, very high penetration of renewables, intermittency and uncertainty of renewable generation,DR and load effects as well as storage (including storage as a load). IEEE 2030 emphasis on functional interfaces, logical connections and data flows. It involves electric-sourced transportation infrastructures (EV), interoperability of ESS and the rest of the power infrastructure as well as test procedures related to ESS for electric power systems applications. Regarding ESS, perspectives and research are related to performance and cost of storage optimization. There are several technical perspectives in order to integrateEVs. European Commission opened a laboratory dedicated toEV standards [33] and will ensure full interoperability between EV andSG. The Alternative Fuels Infrastructure Directive recommends at least one public charging point for every 10 vehicles [35]. Another perspective would be to enable participation ofEV in demand-side services and to link RnE generation to fast charging operations. Author in [151], aimed to study and reduce the impacts of theEVs on the distribution grid thanks to V2G technology.EVs are able to supply the grid depending on requests and thus to offer a flexibility service. The objective of presented work is to ensure an energy management of EVs by choosing the adequate mode of charge/discharge and also taking into consideration the request of local demand and the presence of renewable production ofPV and wind in the distribution grid. ActiveEV participation through smart charging strategies [30] as well as primary frequency control [29] has been considered in the literature. Standards for communication inSG are proposed in the literature. For instance, International Electrotechnical Commission (IEC) 61850 series stands for communications within transmission and distribution sectors. American National Standards Institute (ANSI) C12.20 deal with revenue metering accuracy specification and IEEE 1588 focuses towards time management and clock synchronization of equipment acrossSG. Regarding protocols, several candidates have been identified to allow communication between devices and stakeholders: Zigbee, WiMax, 4G/LTE, 5G are potential communication protocols.

177 NOMENCLATURE

Indices

∆t Difference between two consecutive time steps (one hour) t Time step, t ∈ T y Year

Parameters

β Tilt angle (rad)

βfir Finite Impulse Response (FIR) forgetting factor

ηch, ηdch Charging and discharging efﬁciencies

ηPV Solar panel efﬁciency

ref ηPV Reference solar panel efﬁciency

ηrt Round-trip efﬁciency

µfir Reciprocal of βfir

ρground Ground albedo

Brated Rated battery capacity (kWh)

−1 CT Cell temperature coefﬁcient (°K )

CESS Battery installation and maintenance cost (e/kWh) kagg Aggregator penalty coefﬁcient

Katm Atmosphere correction factor

MaxDistQuick Particle swarm optimizer stopping criterion

Npanel Amount of solar panels

NOCT Normal Operating Cell Temperature (°K) pagg Aggregator penalty function

Pmax Maximum charge/discharge power

178 PP V,peak Solar panel peak power (Wp) ragg Aggregator margin rate

Rbuy Injection tariff repricing rate

Rb Battery degradation rate

Rd Discount rate

Rgrid Grid price inﬂation rate

Ri Repricing rate

Rsdr Solar system degradation rate

Spanel Surface of a solar panel

SOCeod Battery SOC at the end of each day

SOCinit Initial battery SOC

SOCmax Maximum battery SOC

SOCmin Minimum battery SOC

SOHinit Initial battery SOH

SOHmin Minimum battery SOH xloss Battery internal losses coefﬁcient

Sets

T Set of hours, {1, 2, ··· , 87600} (for 10 years)

Y Set of years, {1, 2, ··· , }

Variables and functions

ηPV Solar panel efﬁciency

ω Hour angle (°)

t π hDay Theoretical trading proﬁtability for a given day (e/kWh)

t Bcap Battery capacity at time t (Wh)

t Crate Battery (dis)charging rate at time t

t Cspot Spot price at time t (e/Wh)

179 y Ce,c Cost of charged energy during year y (e) y Cinc Incentives for self-consumption for year y (e) y CO&M Operation and maintenance costs for year y (e) y Csave Cost of saved energy from grid during year y (e) Day CBatt Cost of a full round-trip for a given day (e/kWh) Day Day Day Day Day Day Day Day Day CBuy ,CSell ,CIntraday,CLate,CNight,CMixSell,CMixLate,CMixEarly,CBuy,report Cost of energy on spot markets for a given time block (e/kWh)

Cch,Cdch Cost of charge/discharge (e)

Cproject Project cost (e)

t dsoh(Crate) SOH degradation function y Ebatt,d Amount of energy discharged from battery during year y (kWh) y EPV Expected energy yield for year y (kWh)

Ei Cloud cover state i (from 1 to 10: 10 equals clear-sky condition)

−2 et,eˆt+i|t Error at time t (W m ), estimated prediction error for time t + i generated at time t (W m−2) f(Kt) Function of variable Kt fi(e1, ··· , et) Finite Impulse Response (FIR) ﬁlter

−2 I0h Extraterrestrial horizontal irradiation (W m )

−2 IBT Beam irradiation on a tilted plane(W m )

−2 IDT Diffuse irradiation on a tilted plane(W m )

−2 IGH Global irradiation on horizontal surface (W m )

−2 IGT Global irradiation on tilted plane (W m )

−2 IRT Reﬂected irradiation on a tilted plane (W m )

Kt Clear-sky index at time t

Khis Clear-sky index history

Ksky Clear-sky index (general notation)

−2 −2 mt,m¯ t,mˆ t+i|t Irradiance measurement at time t (W m ), irradiance prediction for time t (W m ), corrected irradiance prediction for time t + i generated at time t (W m−2)

180 p(i, j) Transition probability to move from sky state Ei to Ej

t Pgrid Power from grid at time t (W)

t Pinj Power injected at time t (W)

t Pmiss Power not injected at time t (W) t|t−1 Pbid Power bidden at time t − 1 to be delivered at time t (W)

Pt Power ﬂow at time t (W)

PPV Output solar power (W)

RB Ratio between irradiation incidence angle and zenith angle

SKPI Satisfaction ratio

SOCbegin Battery SOC at the beginning of each day

SOCt Battery SOC at time t

SOHt Battery SOH at time t

StateDay Battery state for a given day (trading strategy)

Tambient Ambient air temperature (°K)

Tcell Cell operating temperature (°K)

181 NOMENCLATURE

Indices

∆t Difference between two consecutive time steps (one hour) t Time step, t ∈ T y Year

Parameters

β Tilt angle (rad)

βfir Finite Impulse Response (FIR) forgetting factor

ηch, ηdch Charging and discharging efﬁciencies

ηPV Solar panel efﬁciency

ref ηPV Reference solar panel efﬁciency

ηrt Round-trip efﬁciency

µfir Reciprocal of βfir

ρground Ground albedo

Brated Rated battery capacity (kWh)

−1 CT Cell temperature coefﬁcient (°K )

CESS Battery installation and maintenance cost (e/kWh) kagg Aggregator penalty coefﬁcient

Katm Atmosphere correction factor

MaxDistQuick Particle swarm optimizer stopping criterion

Npanel Amount of solar panels

NOCT Normal Operating Cell Temperature (°K) pagg Aggregator penalty function

Pmax Maximum charge/discharge power

182 PP V,peak Solar panel peak power (Wp) ragg Aggregator margin rate

Rbuy Injection tariff repricing rate

Rb Battery degradation rate

Rd Discount rate

Rgrid Grid price inﬂation rate

Ri Repricing rate

Rsdr Solar system degradation rate

Spanel Surface of a solar panel

SOCeod Battery SOC at the end of each day

SOCinit Initial battery SOC

SOCmax Maximum battery SOC

SOCmin Minimum battery SOC

SOHinit Initial battery SOH

SOHmin Minimum battery SOH xloss Battery internal losses coefﬁcient

Sets

T Set of hours, {1, 2, ··· , 87600} (for 10 years)

Y Set of years, {1, 2, ··· , }

Variables and functions

ηPV Solar panel efﬁciency

ω Hour angle (°)

t π hDay Theoretical trading proﬁtability for a given day (e/kWh)

t Bcap Battery capacity at time t (Wh)

t Crate Battery (dis)charging rate at time t

t Cspot Spot price at time t (e/Wh)

183 y Ce,c Cost of charged energy during year y (e) y Cinc Incentives for self-consumption for year y (e) y CO&M Operation and maintenance costs for year y (e) y Csave Cost of saved energy from grid during year y (e) Day CBatt Cost of a full round-trip for a given day (e/kWh) Day Day Day Day Day Day Day Day Day CBuy ,CSell ,CIntraday,CLate,CNight,CMixSell,CMixLate,CMixEarly,CBuy,report Cost of energy on spot markets for a given time block (e/kWh)

Cch,Cdch Cost of charge/discharge (e)

Cproject Project cost (e)

t dsoh(Crate) SOH degradation function y Ebatt,d Amount of energy discharged from battery during year y (kWh) y EPV Expected energy yield for year y (kWh)

Ei Cloud cover state i (from 1 to 10: 10 equals clear-sky condition)

−2 I0h Extraterrestrial horizontal irradiation (W m )

−2 IBT Beam irradiation on a tilted plane(W m )

−2 IDT Diffuse irradiation on a tilted plane(W m )

−2 IGH Global irradiation on horizontal surface (W m )

−2 IGT Global irradiation on tilted plane (W m )

−2 IRT Reﬂected irradiation on a tilted plane (W m )

Kt Clear-sky index at time t

Khis Clear-sky index history

Ksky Clear-sky index (general notation)

−2 −2 mt,m¯ t,mˆ t+i|t Irradiance measurement at time t (W m ), irradiance prediction for time t (W m ), corrected irradiance prediction for time t + i generated at time t (W m−2)

184 p(i, j) Transition probability to move from sky state Ei to Ej

t Pgrid Power from grid at time t (W)

t Pinj Power injected at time t (W)

t Pmiss Power not injected at time t (W) t|t−1 Pbid Power bidden at time t − 1 to be delivered at time t (W)

Pt Power ﬂow at time t (W)

PPV Output solar power (W)

RB Ratio between irradiation incidence angle and zenith angle

SKPI Satisfaction ratio

SOCbegin Battery SOC at the beginning of each day

SOCt Battery SOC at time t

SOHt Battery SOH at time t

StateDay Battery state for a given day (trading strategy)

Tambient Ambient air temperature (°K)

Tcell Cell operating temperature (°K)

185

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RÉSUMÉENFRANÇAIS

L’émergence des futures villes dites "smart cities" qui reposent sur les réseaux intelligents pour l’approvisionnement en énergie, l’intégration des sources d’énergie renouvelable et le respect des politiques liées à la transition énergétique vont demander un long travail de transformation des infrastructures et des systèmes de communication actuels. Ce travail doit intégrer des paramètres technico-économiques, sociaux et environnementaux. Le point principal est de réussir à démonter que les citoyens (et par extension la société entière) bénéficient de ce nouveau paradigme et profitent d’une planète plus propre, d’une économie bas carbone, et de sys- tèmes énergétiques économes. Ainsi, étendre le concept du microréseau au secteur résiden- tiel va permettre d’atténuer les problèmes causés par les énergies renouvelables et d’aider le réseau principal au travers de divers services de flexibilité. Par ailleurs, les réseaux actuels sont radiaux et la transmission d’énergie est majoritairement unidirectionnelle: l’énergie est délivrée via un bus d’alimentation. Les flux de puissance des futurs réseaux seront multidirectionnels. Ainsi, les réseaux intelligents basés sur une topologie de maille sont envisagés comme solution pour l’intégration de diverses sources d’énergie renouvelable et dans un but de minimisation des pertes. Pour contrôler ce genre de topologie et la multitude des sources, des méthodes de gestion intelligente doivent être développées. En effet, plusieurs problèmes ont pu être mis en évidence au sein de l’état de l’art réalisé dans cette thèse. Parmi ceux-ci, ces travaux se sont concentrés sur les problématiques de gestion des capacités de stockage dans le cadre d’une stratégie d’arbitrage sur le réseau global et sur celles de prévision d’ensoleillement à court terme (1h) sur site péninsulaire (changements météorologiques fréquents). Concernant la gestion du stockage, la littérature utilise majoritairement un horizon de 24h pour l’optimisation au sein des études à long terme qui prennent en compte le vieillissement des éléments de stockage et des unités de production. Vis-à-vis de la prévision d’ensoleillement, les méthodes utilisées dans la littérature sont var- iées et celles qui réussissent à battre la méthode de référence "smart-persistence" ont recours à l’imagerie satellite, à l’analyse du ciel (photographie) ou aux prévisions météorologiques futures. Ces données étant ensuite traitées le plus souvent à l’aide d’un réseau de neurones afin d’affiner la prévision. De plus, les méthodes proposées n’incluent que très rarement un système de correction en ligne.

206 Ces travaux ont abordé les aspects de dimensionnement d’un microréseau résidentiel en tenant compte des spécificités locales en matière de climat, de coût des installations et des lois afin de proposer un dimensionnement réaliste pour une maison individuelle ainsi qu’un quartier. Ce dimensionnement sert de base d’étude sur les aspects de gestion d’énergie à l’aide de stockage et de prévision d’ensoleillement. Concernant la gestion de l’énergie , ces travaux se positionnent sur l’étude de l’impact d’un horizon glissant de planification plus élevé (48h au lieu de 24h) afin de vérifier s’il y a un intérêt économique pour le service d’arbitrage à l’aide de capacité de stockage; concernant la prévision de l’ensoleillement, ces travaux se positionnent sur l’aspect prévision à court terme avec correction des erreurs passées en proposant une méthode autorégressive basée sur les moindres carrés récursifs simple à mettre en œuvre afin d’améliorer la qualité des prévisions et de diminuer les pénalités subies par le microréseau en cas d’erreur. Au sein des travaux menés dans ce mémoire, comme brièvement énoncé auparavant, trois volets sont abordés : la modélisation et le dimensionnement d’une maison individuelle sur le territoire français (modélisation étendue à un quartier); l’optimisation de la gestion énergétique dans le cadre d’une économie de marché grâce à l’intégration d’un horizon temporel étendu pour l’arbitrage d’énergie sur le réseau et finalement l’élaboration d’une méthode de prévision d’ensoleillement à court terme adaptée au climat péninsulaire dans le contexte d’un contrat de fourniture d’énergie avec un agrégateur. Les travaux présentés dans le premier chapitre sont issus de la recherche bibliographique sur les points suivants: l’état actuel des politiques menées en Union Européenne et particulière- ment en France concernant les énergies renouvelables appliquées au secteur résidentiel; les verrous techniques et les perspectives envisageables afin de rendre l’intégration de la production résidentielle compétitive dans le mix global et finalement l’étude des méthodes et stratégies de dimensionnement et de gestion d’un microréseau afin d’avoir plusieurs levier qui permettent d’assurer ladite compétitivité. Dans le début de ce chapitre, un état actuel du mix énergétique européen est présenté ainsi que les objectifs votés par la commission européenne. A cet effet, des incitations financières via des programmes de déploiement sont actuellement en vigueur afin d’atteindre les objectifs eu- ropéens présentés ci-dessus. Afin de promouvoir l’intégration des énergies renouvelables dans le secteur résidentiel, plusieurs pays européens ont voté des subventions pour l’installation de panneaux solaires sur les toitures des particuliers. Des tarifs d’injection ont été introduits pour limiter l’incertitude concernant le retour sur investissement. Ces tarifs sur revus périodiquement pour éviter la spéculation. En France, les marchés de l’énergie ont été libéralisés en 2007 pour les particuliers. Ils peuvent donc choisir leur fournisseur d’énergie et souscrire à un tarif régle- menté d’achat d’énergie injectée chez un fournisseur tiers mais l’éventail des possibilités se

207 restreint à ces options ce qui limite les possibilités de valorisation de la production et amoindrit la compétitivité. A cause de leur intermittence intrinsèque, les énergies renouvelables (principalement solaire et vent) sont classifiées comme étant non contrôlables et leur production est difficile à prévoir. Cela est particulièrement vrai face à des sources conventionnelles. Bien que l’instauration d’une politique de développement des énergies renouvelables dans le secteur résidentiel soit une bonne idée pour augmenter la part desdites énergies dans le mix global, une part signi- ficative de sources non contrôlables dans la production globale et un accès limité au marché de l’énergie vont générer les problèmes suivants.

— Une allocation non optimale de l’énergie. Les marchés de l’énergie ne sont actuellement pas accessibles aux particuliers à cause de leur taille en tant que consomma- teur et producteur. Il serait techniquement possible pour une association de consomma- teurs/producteurs (sous la forme d’un microréseau résidentiel) d’accéder à ces marchés via un agrégateur et d’être capable d’aider le réseau global en fournissant des services de ﬂexibilité. Dans ce contexte, plusieurs modèles économiques potentiels ont été en- visagés tels que: la fourniture d’énergie à tarif variable avec optimisation de la pointe, l’arbitrage classique d’énergie renouvelable sur le marché spot ou la valorisation de la production locale au sein d’immeubles résidentiels.

— Des problèmes d’approvisionnement de la charge de base causés par le faible facteur de charge des énergies renouvelables et des erreurs de prévision de production.

— Le déconsidération du stockage au sein des mécanismes de subvention. Le stockage est souvent rejeté dans le secteur résidentiel à cause de son coût actuel prohibitif et du manque de subventions à son égard. Il pourrait fournir un degré de liberté supplé- mentaire aﬁn de développer des méthodes de dimensionnement et de gestion d’énergie compétitives.

En ce qui concerne les erreurs de prévision, les gestionnaires de réseaux vont devoir tenir compte de la quantité de panneaux solaires installés qui sont non contrôlables et dont la prévi- sion de production est rendue difﬁcile à cause des variation locales d’ensoleillement. Ainsi les politiques d’incitation envers le photovoltaïque résidentiel pourraient être néfastes pour le réseau global. Comme écrit auparavant, l’intégration massive des énergies renouvelables implique pour toutes les parties prenantes de passer d’un paradigme à l’autre en ce qui concerne le dimensionnement et la gestion des réseaux électriques. Pour cela, ce chapitre s’est focalisé sur l’intégration des énergies renouvelables (photovoltaïque) dans le secteur résidentiel ainsi que les services de ﬂexibilité qu’un potentiel microréseau résidentiel pourraient fournir. Les

208 limitations actuelles (techniques, économiques ou législatives) qui s’appliquent sur le secteur résidentiel ainsi que les perspectives pour les surmonter sont présentées. Pour faire face à ces problèmes, une revue des stratégies de dimensionnement (méthodes graphiques, probabilistes, analytiques, itératives, basées sur l’intelligence artificielle/les algo- rithmes évolutionnistes ou encore basées sur la co-optimisation) et de gestion d’énergie adap- tées aux applications résidentielles (gestion des incertitudes climatiques locales, gestion du stockage) est détaillée dans ce chapitre. Dans cette étude, les limites des différentes stratégies de dimensionnement et de gestion étant discutées, le choix des domaines d’étude se portera l’amélioration des performances dans le domaine de l’arbitrage et des capacités à prévoir la production photovoltaïque à court terme sous un climat péninsulaire (considéré comme très variable). Le deuxième chapitre introduit les modèles utilisés tout au long de ces travaux. Il s’agit des modèles suivants: panneaux photovoltaïques, stockage (batteries), charge résidentielle et agrégateur. Les radiations solaires sont basées sur le modèle HDKR très répandu dans la littérature. La représentation climatique est issue des données de la décennie passée. Le modèle du panneau est basé sur une équation de rendement tenant compte de la température d’opération des cellules solaires. Le modèle des batteries se base sur l’état de charge associé à un vieillissement calendaire (ce vieillissement calendaire passera à un calcul précis de l’état de santé des batteries en fonction de l’agressivité et de la profondeur de charge/décharge dans les chapitres suivants). La charge résidentielle est issue des résultats de simulations menées avec le logiciel LoadProfileGenerator déjà utilisé dans plusieurs travaux scientifiques. Ce logiciel permet de travailler avec des courbes de charges réalistes représentant l’activité d’une famille dans des maisons récentes et anciennes. Ces courbes ont servi de base pour l’élaboration de la courbe du quartier étudié dans cette étude. Concernant l’agrégateur, celui- ci est modélisé par: l’obligation d’achat dans le chapitre 2; l’achat au prix spot avec marge d’intermédiaire dans le chapitre 3 et finalement, dans le chapitre 4 l’achat spot avec pénalité sur la part d’énergie injectée ne correspondant pas aux engagements pris entre le microréseau étudié et l’agrégateur. Malgré leur apparente simplicité, ces modèles permettent d’évaluer fidèlement la puissance générée/consommée en considérant un pas de temps horaire (1h). Par ailleurs, les données et les paramètres présentés dans ce chapitre reflètent les possibilités techniques et législatives actuelles pour la participation du secteur résidentiel dans la transition énergétique européenne avec une approche économique réaliste. Pour cela, ce chapitre propose des méthodes d’optimisation du dimensionnement des panneaux solaires et des batteries au sein d’une maison individuelle et d’un quartier. Cette optimisation est mono ou multiobjectif selon le scénario envisagé. Les scénarios considérés dans

209 ce chapitre sont les suivants : autoconsommation sans stockage, injection du surplus de production solaire, injection de la totalité de la production et autoconsommation avec stockage (possibilité d’îlotage). L’optimisation proposée se base sur l’algorithme d’optimisation par essaim de particules et tient compte des points suivants:

— le coût futur de l’énergie aﬁn d’assurer une sécurité ﬁnancière au ménage;

— la satisfaction de la charge (également appelé confort utilisateur);

— le taux de pénétration des énergies renouvelables.

L’étude s’est d’abord faite sur une maison individuelle puis a été étendue à un quartier. Les ré- sultats obtenus dans ce chapitre ont montré que: (1) l’installation de panneaux photovoltaïques sur la toiture d’une maison individuelle (dans les conditions présentées) n’était profitable que pour les scénarios d’injection du surplus ou d’injection totale; concernant le quartier, tous les scénarios présentés génèrent des gains grâces aux économies d’échelle réalisées; (2) le nombre de panneaux photovoltaïques ainsi que la taille du stockage demandés pour atteindre l’autonomie est conséquent. De plus, si l’objectif est d’assurer une autonomie totale, les ré- sultats ont démontré la pertinence d’utiliser des batteries recyclées (de véhicules électriques par exemple) comme alternative plus abordable. Néanmoins, dans ce chapitre, la dégradation de la durée du vie du stockage n’a été con- sidérée que de manière calendaire. Aussi, les résultats sont susceptibles de varier en con- sidérant une dégradation en fonction du profil d’utilisation des batteries. Par ailleurs, dans ce chapitre, les stratégies de gestion des batteries furent limitées au stockage du surplus solaire et à l’approvisionnement de la charge pour les cas d’îlotage. Le chapitre 3 propose d’évaluer la pertinence d’une stratégie de gestion de l’énergie basée sur un horizon temporel de 48h comparé aux 24h classiquement utilisés dans la littérature afin de réaliser des arbitrages d’énergie. Cette étude s’applique sur un microréseau résidentiel (connecté au réseau principal) basé sur la génération photovoltaïque et sur un dispositif de stockage d’énergie. Les trois cas d’étude proposés utilisent tous les résultats de l’algorithme de dimensionnement présenté dans le chapitre 2. Dans ce chapitre, contrairement au chapitre précédent, le choix de représentation des aléas climatiques s’est porté de une représentation par chaînes de Markov, ainsi la robustesse de la stratégie de gestion proposée peut être testée sur un grand nombre d’années générées à l’aide du profil climatique (matrice des transitions de Markov) local. Dans la suite de ce chapitre, la stratégie classique d’arbitrage d’énergie à l’aide de capacité de stockage est présentée. Dans la littérature, les travaux faisant appel à l’optimisation des flux entrants et sortants des unités de stockages établissent leur programme de gestion sur une journée (24h). La stratégie proposée utilise un horizon de 48h, ce qui fournit d’avantage

210 de possibilités de gestion comme la capacité de reporter des arbitrages (la batterie peut être chargée un jour donné et déchargée le lendemain) et d’avoir une plus grande étendue d’heures pour optimiser le profil de charge/décharge. L’optimisation par essaim de particules est utilisée pour solutionner les fonctions coût. De plus, une analyse de sensibilité est menée pour évaluer l’impact économique d’un stockage forcé du surplus solaire dans les batteries et l’impact de la taille de ces dernières. La méthode proposée est appliquée sur 3 quartiers résidentiels différents fonctionnant en microréseau. Ces quartiers sont respectivement composés de: — 25 maisons dites "anciennes" et 25 maisons dites "RT2012" ainsi que de d’une capacité de production photovoltaïque et de stockage permettant un fonctionnement assurant 30% des besoins en énergie. — La même répartition en ce qui concerne les habitations mais une capacité de production photovoltaïque et de stockage permettant un fonctionnement assurant 50% des besoins en énergie. — 50 maisons dites "RT2012" et une capacité de production photovoltaïque et de stockage permettant également un fonctionnement assurant 50% des besoins en énergie. Elle améliore leur profitabilité respective de 3,66%, 3,84% et de 3,16% comparativement à la méthode de référence utilisant un horizon de 24h. En ce qui concerne l’analyse de sensibil- ité, la méthode visant à maximiser le taux de pénétration de la production solaire à l’aide de capacité de stockage a mis en évidence la présence d’un palier de performance. En effet, il ap- paraît qu’au sein du dimensionnement considéré, l’allocation d’un taux supérieur ou égal à 80% des capacités de stockage en vue de récupérer le surplus solaire pour l’utiliser ultérieurement n’influe plus significativement sur l’amélioration du taux de pénétration du solaire. Ces résultats montrent que la méthode de dimensionnement de la production solaire et des capacités de stockage associées est adaptée pour récupérer une très large majorité du surplus généré et ainsi pouvoir en disposer ultérieurement. L’analyse de sensibilité à aussi montré que la relation entre la profitabilité du microréseau et la marge de l’agrégateur n’est pas linéaire. Ceci est du au fait que l’algorithme se focalise sur l’autoconsommation lorsque les marges pratiquées par l’agrégateur sont élevées, ce qui rend le système moins sensible aux prix de marché. Le chapitre 4 propose une autre stratégie de gestion d’énergie (appartenant au domaine des services de flexibilité) qui se focalise sur la prévision météorologique et plus particulière- ment sur la prévision à court terme (1h) de l’ensoleillement local afin d’assurer la fourniture en énergie promise au partenaire (l’agrégateur) lors de l’heure précédente. Dans ce chapitre, l’objectif est d’assurer que les prévisions d’injection communiquées à l’agrégateur soient respectées autant que possible, dans le cas contraire, le microréseau étudié

211 sera pénalisé. La stratégie de gestion énergétique proposée repose sur une méthode prédicteur- correcteur basée sur les moindre carrés récursifs. Celle-ci se confronte à la méthode "smart- persistence". Cette méthode est couramment utilisée dans la littérature pour la prévision à court terme de l’ensoleillement. Il est important pour chaque méthode de prévision de fournir les résultats les plus pertinents possible car le choix du modèle de pénalité financière est basé sur une équation quadratique, ce qui impacte fortement les grands écarts avec l’ensoleillement réel. La méthode proposée dans ce chapitre, basée sur les moindres carrés ainsi que la présence de capacité de stockage permet d’augmenter le ratio de satisfaction de l’agrégateur (rapport entre la quantité d’énergie réellement injectée sur la quantité d’énergie annoncée). Dans le cas d’un microréseau sans capacité de stockage prenant en compte les incertitudes climatiques, la méthode proposée améliore la ratio de satisfaction de 0,38% et la profitabilité économique de 6,10%. Avec une petite capacité de stockage de 25 kWh, la satisfaction est améliorée de 0.46% et la profitabilité de 7,6% par rapport au même système (avec la même capacité de stockage) qui utilise la méthode de référence. En dépit du fait qu’une capacité de stockage importante tende à atténuer la sous-performance du modèle de référence (appelé "smart persistence"), la méthode proposée est toujours capable de générer une amélioration de 0,38% pour la ratio de satisfaction et une amélioré de la profitabilité de 3,5% avec une capacité de stockage de 50 kWh. Néanmoins, à partir de 100 kWh de capacité de stockage, les différences de performances entre la méthode proposée et celle de référence deviennent négligeables. Cela est du au fait que les erreurs commises par les deux méthodes sont compensées par la grande réserve d’énergie à disposition dans les batteries. Toutefois, il est important de noter que l’amélioration de la profitabilité dépend intégrale- ment de l’application de l’algorithme proposé dans ces travaux. Cela implique qu’il n’y a pas d’investissements supplémentaires requis pour améliorer la compétitivité du microréseau dans cette étude. En conclusion, ces travaux ont permis de proposer des améliorations par rapport à l’état actuel de la recherche dans le domaine des microréseaux résidentiels et des services de flexi- bilité. En premier lieu, une méthode de dimensionnement de panneaux solaires et de capacité de stockage d’énergie appliquée aux maisons individuelles et par extension aux quartiers a été présentée. Elle se distingue par l’étude de plusieurs scénarios tels que: l’autoconsommation, l’injection partielle et totale ainsi que la capacité d’îlotage. A cela s’ajoute la présence de plusieurs indicateurs clés comme la profitabilité, le taux de pénétration des énergies renouvelables ainsi que le confort utilisateur. Ces indicateurs servant d’objectifs pour l’optimisation

212 multiobjectif introduite dans cette étude. Par la suite, une stratégie de gestion d’énergie appliquée à un microréseau résidentiel qui souhaite devenir un acteur au sein des marchés de l’énergie est abordée. Le point clé de cette stratégie est l’utilisation d’un horizon de temps de 48h en lieu et place de l’horizon de 24h couramment utilisé dans la littérature pour l’arbitrage. Ce changement d’horizon étend le champ des possibilités d’utilisation des capacités de stockage, ce qui a permis d’améliorer la compétitivité globale du microréseau étudié. Finalement, une autre stratégie de gestion d’énergie fut également présentée. Cette fois, elle se concentre sur le respect de la quantité d’énergie injectée par bloc horaire afin d’aider le réseau à réduire les déséquilibres entre l’offre et la demande. Cette stratégie repose sur l’utilisation d’une méthode statistique autorégressive adaptée pour la prévision d’ensoleillement à court terme. Cette méthode, plus performante, est utilisée à la place de celle nommée "smart persistence" qui sert d’étalon dans la littérature. Néanmoins, les résultats obtenus dans ces travaux tiennent compte de nombreuses hy- pothèses et sont soumis au cadre imposé dans l’ensemble de l’étude. Ainsi, plusieurs perspectives sont envisageables pour améliorer les méthodes et stratégies présentées dans cette thèse. L’amélioration majeure serait d’inclure l’incertitude des prévisions d’ensoleillement au sein de la stratégie d’arbitrage présentée dans le chapitre 3 afin de réaliser un algorithme tra- vaillant sur plusieurs horizons de temps et qui possède la capacité de corriger la planification du microréseau au cours de la journée. Il serait également possible d’ajouter un degré de liberté en incluant les techniques de DSM (gestion de la charge non-critique) ou encore de considérer une grappe de microréseaux interagissant entre eux ainsi que de nouvelles technologies de stockage d’énergie. Par ailleurs, la validation de ces stratégies sur banc d’essai ou démon- strateur pourrait être envisagée. Dans ces travaux, plusieurs indicateurs de performance clé ont été utilisés, néanmoins, l’indicateur de profitabilité/compétitivité est prépondérant; d’autres indicateurs basés sur des critères sociaux, environnementaux ou de gouvernance pourraient être pris en considération dans les fonctions d’optimisation utilisées dans ces travaux.

213 Titre : Stratégies de gestion d’énergie appliqués aux microréseaux résidentiels orientés aﬁn de fournir des services de ﬂexibilité.

Mot clés : Microréseaux ; services de ﬂexibilité ; optimisation de l’énergie ; incertitudes

Résumé : Le développement des énergies renou- nouveau modèle autorégressif de prévision d’en- velables, de la consommation résidentielle ainsi soleillement à court terme adapté aux conditions que les politiques liées à la transition énergétique péninsulaires est proposé puis comparé au modèle réclament de nouvelles techniques de gestion de de référence. l’énergie afin de gérer les problèmes d’incertitudes Les résultats du dimensionnement montrent et de fournir des services réseaux compétitifs. que la méthode proposée génère des économies Cette thèse se focalise sur des services de substantielles pour les ménages. Néanmoins, le flexibilité appliqués au microréseaux résidentiels. prix des batteries rend actuellement l’îlotage non Le dimensionnement (mono/multiobjectif) optimise viable économiquement. Les stratégies de ges- la production solaire et la taille de la batterie en tion proposées sont également plus profitables que tenant compte du coût, du confort utilisateur, de la leurs références respectives. pénétration des énergies renouvelables et de la ré- Les stratégies présentées permettent un fonc- glementation. L’approche de gestion n°1 compare tionnement compétitif et capable d’atténuer les des fenêtres de 24 et 48h pour l’arbitrage d’éner- déséquilibres entre l’offre et la demande. L’asso- gie et évalue les possibilités supplémentaires de ciation de l’énergie renouvelable et des capaci- celle de 48h. L’approche n°2 se focalise sur le res- tés des microréseaux pour les citoyens est une pect des injections d’énergie sur le réseau au sein excellente opportunité d’obtenir une énergie plus d’un contrat de marché avec un agrégateur. Un propre, plus fiable et abordable.

Title: Energy management strategies applied to photovoltaic-based residential microgrids for ﬂexibility services purposes

Keywords: microgrids; ﬂexibility services; energy management optimization; uncertainties

Abstract: The rising share of renewable sources, bid-based market context. A novel auto-regressive residential consumers as well as novel energy tran- short term solar irradiation forecast method suit- sition policies call for new energy management able for peninsular weather is proposed and com- strategies to deal with renewable energy sources pared with a reference method. uncertainty issues and to provide cost-competitive Regarding the sizing optimization, results flexibility services. showed that proposed optimization can generate This thesis focuses on flexibility related use- bill savings for households. Nevertheless, due to cases applied to residential microgrids. Presented current storage cost, off-grid operation is still an sizing approach uses both mono and multi- unreliable option regarding cost-competitiveness. objective particle swarm optimization to optimize Both presented energy management strategies both solar generation and storage taking into ac- showed profitability gains compared to their re- count cost competitiveness, user comfort and respective reference. newable energy penetration while respecting local To conclude, strategies showed cost- regulations. First energy management approach competitive operation and ability to mitigate supply compares 24 and 48h time-horizons for energy ar- and demand imbalances. Association of renewable bitrage and assess extended possibilities provided energy and microgrids abilities for communities is by a wider horizon. Second energy arbitrage ap- an excellent opportunity for cleaner, more reliable proach focuses on energy injection accuracy in a and cheaper energy.