Electric vehicle as a pathway for deep decarbonization

Electric Vehicle as a Pathway for Deep Decarbonization

Thèse

Ali Hajebrahimi

Doctorat en génie électrique

Philosophiæ doctor (Ph. D.)

Electric Vehicle As A Pathway for Deep

Decarbonization

Thèse

Ali Hajebrahimi

Sous la direction de: Hoang-Le Huy Innocent Kamwa

Résumé

Cette thèse étudie l’impact du véhicule électrique en tant que voie de décarbonisation profonde dans les études de planification et d’exploitation. L’étape de planification comprend trois phases diffé-rentes : Phase I) Un modèle de planification pour un véhicule électrique enfichable est proposé dans cette phase. De plus, un contrôle de charge à deux niveaux pour le contrôle de charge et de décharge qui est capable de prendre en compte à la fois les intérêts des clients et de l’opérateur de système indépendant (ISO) est proposé dans cette phase. Phase II) Au cours de cette phase, on étudie l’impact du véhicule électrique autonome en tant que technologie de rupture sur la transition optimale vers les véhicules électriques rechargeables. Dans les deux phases précédentes, il n’y a aucun nouvel investis-sement dans le secteur de l’électricité et une méthode de de décomposition de benders est également appliquée pour linéariser la non-linéarité générée dans le problème d’optimisation par le schéma de contrôle des cycles de charge et décharge. Phase III) Ici, un problème de planification est proposé à la fois pour le secteur de l’électricité et celui des transports électrifiés afin de maximiser la pénétration des véhicules électriques rechargeables dans le réseau. Pour résoudre ce problème, on mets en oeuvre une optimisation robuste du point de vue de la distribution, basée l’analyse de scénario et appliquant une règle de décision affine pour gérer les incertitudes associées.

Dans la deuxième étape de la thèse qui porte sur la programmation de l’exploitation, il y a deux phases : Phase I) Au cours de cette phase, une stratégie d’appel d’offres collaborative est proposée pour les agrégateurs de véhicules électriques participant au marché de l’électricité. Une optimisation robuste du point de vue des scénarios, capable de prendre en compte à la fois les informations concer-nant la distribution statistique distribution et les informations métriques de distance dans un ensemble d’ambiguïtés. Phase II) Un modèle de fiabilité pour le parc de véhicules électriques participant aux programmes de réponse à la demande est proposé. De plus, l’impact de l’IMA (infrastructure de me-surage avancée) réparable sur le modèle de fiabilité du parc de véhicules électriques est étudié dans cette phase.

Abstract

This thesis investigates the impact of electric vehicle as a pathway for deep decarbonization in both planning and operation studies. The planning study stage include three different phases: Phase I) A planning model for plug-in electric vehicle is proposed in this phase. Moreover, a bi-level charging control for charging and discharging control which is able to consider both interests of customers and independent system operator (ISO) is proposed in this phase. Phase II) In this phase, the impact of autonomous electric vehicle as a clean disruptive technology is investigated on optimal transition to plug-in electric vehicles. In the two previous phases, there is no new investment in the electricity sector and also a benders decomposition method is applied to linearize the non linearity arising from the by charging and discharging control schemes in the optimization problem. Phase III) Herein, a planning problem is proposed for both electricity sector and electrified transportation sector in order to maximize the penetration of plug-in electric vehicles in the grid. A scenario based distributionally robust optimization applying affine decision rule to handle the associated uncertainties is proposed. In the second stage of the thesis which is the operation study, there are two phases as: Phase I) In this phase a collaborative bidding strategy is proposed for electric vehicles aggregator participating in electricity market. A scenario wise distributionally robust optimization which is able to consider both distributional information and distance metric information inside an ambiguity set is developed. Phase II) Herein, a reliability model for electric vehicle fleet participating in demand response programs is proposed. Moreover, the impact of repairable AMI on reliability model of electric vehicle fleet is investigated in this phase.

Table des matières

Résumé iii

Abstract iv

Table des matières v

Liste des tableaux ix

Liste des figures x

Nomenclature xii

Acknowledgements xviii

Foreword xix

Problem Statement xxii

Original Contribution xxiii

Introduction 1

0.1 C . . . 1

0.2 Deep Decarbonization Pathway Projects in Canada . . . 1

0.3 Role of Transportation Sector in DDPP . . . 2

0.4 Role of Electricity Sector in DDPP . . . 3

0.5 Thesis Outline. . . 4

1 Relevant State of the Art 6 1.1 Overview of TEP . . . 6

1.1.1 TEP problem evaluation . . . 6

1.1.2 TEP from viewpoint of modelling . . . 7

1.1.3 TEP from viewpoint of power system structure . . . 7

1.1.4 TEP in viewpoint of uncertainty in power system . . . 7

1.1.5 TEP in viewpoint of time horizon . . . 8

1.1.6 TEP and environmental impact . . . 8

1.1.7 Coordinated TEP and GEP . . . 8

1.2 Overview of Electric Vehicles Charge Scheduling . . . 8

1.2.1 Decentralized Versus Centralized . . . 9

1.2.3 Static versus mobility-aware charging . . . 9

1.2.4 Integration of renewable energies . . . 10

1.2.5 Providing ancillary service . . . 10

1.2.6 Objectives of optimization algorithm for charging and discharging sche-dule . . . 10

1.2.7 Methods for optimization of charging schedule . . . 10

1.3 Disruptive technology. . . 10

1.4 Bidding strategy. . . 12

1.4.1 Single GenCo optimization. . . 12

1.4.2 Game theory based models . . . 13

1.4.3 Bertrand competition . . . 13

1.4.4 Cournot competition . . . 13

1.4.5 Supply Function Equilibrium. . . 13

1.4.6 Agent-based Model. . . 13

1.4.7 Hybrid Model. . . 13

2 Plug-in Electric Vehicle Planning model 14 2.1 Résumé . . . 14

2.2 abstract . . . 14

2.3 Literature Review and Contribution . . . 15

2.4 Formulation . . . 16

2.4.1 Input Random Variables . . . 16

2.4.2 Planning Problem (Master Problem) . . . 17

2.4.3 Home-Based charging/Discharging management . . . 17

2.4.4 Bi-level Charging Control . . . 18

2.4.5 Program A . . . 18

2.4.6 Program B . . . 19

2.4.7 ISO Feasibility Check Subproblem . . . 19

2.4.8 ISO Operation Subproblem. . . 20

2.5 Test Result . . . 20

2.5.1 Optimal Transition to EVs without V2G technology . . . 21

2.5.2 Optimal Transition to EVs with V2G technology . . . 23

2.6 Conclusion . . . 24

3 Impact of Autonomous Electric Vehicle On Plug-in electric Vehicle Planning 25 3.1 Résumé . . . 25

3.2 Abstract . . . 25

3.3 Literature review and contribution . . . 26

3.3.1 Disruptive technology . . . 27

3.3.2 Problem Description . . . 27

3.4 Solution Strategy . . . 29

3.5 Formulation . . . 31

3.5.1 Master Problem. . . 32

3.5.2 Subproblem I : Charging Management . . . 33

3.5.3 Fixed home-based full charging/discharging management . . . 33

3.5.4 Program A : Flexible home-based full charging/discharging management 34 3.5.5 Program B : Home-based partial charging/discharging management . . . 34

3.5.7 Subproblem III : Operation Cost . . . 36

3.5.8 Generating Bender Cuts . . . 36

3.6 Test Results . . . 37

3.6.1 Optimal Transition to Plug-in Electric Vehicle With Disruptive Technology 39 3.6.2 Optimal Transition to Plug-in Electric Vehicle With Vehicle to Grid Tech-nology . . . 41

3.6.3 Test case II : 6 bus test system . . . 41

3.6.4 Discussion . . . 43

3.7 Conclusion . . . 44

4 Incorporating Plug-in Electric Vehicle Planning With Transmission and Genera-tion Expansion Planning 46 4.1 Résumé . . . 46

4.2 Abstract . . . 46

4.3 Introduction . . . 46

4.3.1 Power System Planning Literature review . . . 46

4.3.2 Power System Planning Incorporating PEVs . . . 48

4.3.3 Distributionally Robust Optimization (DRO) . . . 49

4.3.4 Contribution . . . 50

4.4 TGPP Formulation . . . 51

4.4.1 Initialization Level . . . 51

4.4.2 Formulation Of TGPP Under Uncertainty . . . 52

4.4.3 Uncertainty Approaches Review . . . 55

4.4.4 Distributionally Robust Optimization . . . 55

4.4.5 Matrix Form of TGPP Model . . . 57

4.4.6 Scenario-Based Ambiguity Set Construction . . . 57

4.4.7 DR-TGPP Model Proposition . . . 58

4.5 Test Result . . . 61

4.5.1 Test System I : . . . 62

4.5.2 Test System II : 118 bus test system . . . 67

4.5.3 Evaluation of proposed model under different methodology . . . 69

4.6 Conclusion . . . 71

5 Plug-in Electric Vehicle Bidding Strategy in Electricity Market 72 5.1 Résumé . . . 72

5.2 Abstract . . . 72

5.3 Introduction . . . 72

5.4 Electricity energy market . . . 73

5.5 Bidding strategy. . . 74

5.6 Deterministic Formulation . . . 76

5.6.1 Bi-level Model for Independent Offering Strategy . . . 77

5.6.2 Single Level Model . . . 79

5.6.3 MILP model . . . 80

5.7 DRO Model for offering strategy . . . 82

5.7.1 Scenario- Wise Ambiguity Set Construction . . . 83

5.7.2 Scenario Wise Affine Recourse Approximation . . . 84

5.8 Test Result . . . 87

5.8.2 Test System II : . . . 88

5.8.3 Out of sample performance. . . 89

5.9 Conclusion . . . 91

6 A Reliability Model for Plug-in Electric Vehicle Fleet 93 6.1 Résumé . . . 93

6.2 Abstract . . . 93

6.3 Literature review and contribution . . . 94

6.4 Frequency and Duration Technique . . . 95

6.5 Frequency and Duration Concept of an EVF . . . 96

6.5.1 Multi Sate Markov Model of PEV in DRPs . . . 96

6.5.2 Reliability Model of Repairable AMI . . . 97

6.5.3 Reliability Model of EVF with Flexible EVs . . . 99

6.6 Reliability Assessment of Generation System with EVFs . . . 100

6.7 Test Result . . . 100

6.7.1 Reliability Assessment Considering Inflexible EVs . . . 100

6.7.2 Power System Reliability Assessment without Contemplating AMIs Failure 102 6.7.3 Power System Reliability Assessment Considering AMIs Failure. . . 104

6.7.4 Reliability Assessment Considering flexible Load and AMIs Failure . . . 106

6.8 Conclusion . . . 107

General conclusion and future perspectives 108

Appendix 110

Liste des tableaux

1.1 Summary of literature for single GenCo bidding strategy . . . 12

2.1 Comparison of Proposed Model and Previous Model . . . 24

3.1 Property Level Versus Vehicle Type . . . 31

3.2 Sectors Load Curtailments and PEV Penetration Versus Different Control Strategy in The End of Planning Horizon. . . 39

3.3 CO2Emission Reduction With or Without V2G in The End of Planning Horizon. . . 41

3.4 Transmission Line Data for Modified 6-Bus System. . . 43

3.5 Generators Date for Modified 6-Bus System.. . . 44

3.6 Sectors Load Curtailments and PEV Penetration Versus Different Control Strategy in Presence of AEV. . . 44

4.1 Summary of the Literature . . . 50

4.2 Summaries of uncertainty modeling attributes. . . 56

4.3 Transmission data Ontario’s system . . . 65

4.4 Transmission Lines Installation in Ontario . . . 66

4.5 Transmission Line Data for IEEE 118 bus . . . 67

4.6 Transmission Line Installation In 118 Bus IEEE Test System . . . 69

4.7 Computation Time . . . 71

5.1 Computation Time in Minutes . . . 91

6.1 COPTEV Ffor An EVF in Bus 2 Without Considering AMI Failure . . . 103

6.2 COPTSY ST EMIncluding A 2-MW EVF Without Considering AMI Failure . . . 103

6.3 BUS Category And Related MAP . . . 103

6.4 Probability Distribution of The EVF Output Power Versus AMI FOR . . . 105

6.5 EENS, LOLE, AND LOLF Versus Different MAP with Repairable AMIS . . . 105

6.6 Amount of Enrollment in DRPS . . . 107

Liste des figures

0.1 PEV sales portion of total vehicle sales (1). . . 2

0.2 CO2emission for Canada in transportation and electricity sectors. . . 3

1.1 The self-driving car schema. Source : Google . . . 11

2.1 Distribution of arrival time and required energy of EVs. . . 16

2.2 Simplified Ontario’s network.. . . 21

2.3 Maximum potential of Ontario’s grid for EVs penetration versus emission reduction as reported in (2). . . 22

2.4 Maximum emission reduction with optimal transition to EVs without V2G technology. 22 2.5 Charging profiles of EVs versus different control strategy.. . . 23

2.6 Maximum emission reduction with optimal transition to Evs incorporating V2G tech-nology. . . 23

2.7 Load curtailment versus EVs penetration for all control strategies. . . 24

3.1 Hierarchy of decomposed planning problem.. . . 30

3.2 Simplified Ontario’s network.. . . 38

3.3 Vehicle ownership projection versus AEV membership. . . 39

3.4 Average trip request and accepted trip request versus different charging control in the last year of planning. . . 40

3.5 Installed charging station capacity for AEVs fleets versus different charging control in Bus 1. . . 40

3.6 Maximum CO2emission reduction without disruptive technology incorporating V2G technology. . . 41

3.7 Electricity Load curtailment versus PEVs penetration for all control strategy.. . . 42

3.8 Six bus test system. . . 42

3.9 Convergence of Monte Carlo simulation combined by scenario reduction. . . 43

4.1 Uncertainty approaches in power system. . . 55

4.2 Example of Ξ for two scenarios. . . 59

4.3 Distribution of wind speed for three scenarios. . . 62

4.4 Distribution of solar radiation for three scenarios. . . 63

4.5 Distribution of time arrival for three scenarios. . . 63

4.6 Distribution of time arrival for three scenarios. . . 64

4.7 Simplified Ontario grid. . . 64

4.8 Charging profiles of EVs versus different control strategy.. . . 65

4.9 Installed generation capacity over the planning horizon for different charging strate-gies in Ontario test case. . . 66

4.11 Installed generation capacity over the planning horizon for different charging

strate-gies in 118 bus IEEE test system. . . 68

4.12 PEV penetration versus different charging control in 118 bus IEEE test system. . . . 69

4.13 Emission reduction in both test cases applying charging control strategy in Case III. . 70

4.14 Out-of-sample performance versus different size of training sample. . . 70

4.15 Quality of the approximation by the proposed affine decision rule. . . 71

5.1 Market elements. . . 74

5.2 Bidding strategy approaches. . . 75

5.3 (a) Actual wind power generation and balancing power. (b) Actual Hydro generation and balancing power. (c) Actual PEV demand and balancing power. . . 88

5.4 Offered wind power in different strategies. . . 89

5.5 Offered price of wind farms in different strategies.. . . 90

5.6 Offered hydro generation in different strategies. . . 90

5.7 Offered PEV aggregator in different strategies.. . . 91

5.8 Out-of-sample performance of the Wasserstein, Chebyshev and SAA methods. The solid lines represents the mean, and the error bars expresses the 20% and 80% quan-tiles of the optimality gaps, respectively. . . 92

6.1 Electric vehicle fleet overview. . . 96

6.2 Markov model for a PEV’s participation in DRPs. . . 97

6.3 The EVF’s state space diagram without considering multi state PEV . . . 98

6.4 Modified single diagram of the RBTS in presence of EVFs. . . 101

6.5 Customer participation time series in DRPs for Connecticut in RTO in eligible per-iods (3). . . 101

6.6 PEV load reduction sequence for 178 hours. . . 102

6.7 The LOLE and LOLF versus MAP in Scenario ?? . . . 103

6.8 RBTS peak load carrying capability using LOLE. . . 104

6.9 LOLE and LOLF versus AMI forced outage rate. . . 105

Nomenclature

EL Set of transmission lines.

c Subscript index of charging station for AEVs. f Subscript index for PEVs or AEVs fleet. g Subscript index for generating units. i Subscript index for nodes.

j Subscript index for lines. l Subscript index for load blocks. m, n Starting and ending nodes of line j. s Subscript index for scenarios. t Subscript index for hours of a day. y Subscript index for planning year. Ψ Set of peak load hours.

wi Set of low wind generation hours.

AET Average energy consumption of AEV per trip. CC Number of candidate charging stations. CP Capacity of charging stations.

CS Capital investment of charging stations. DT Load block duration factor.

Ere he required energy of PEVs.

EFG, Sco2 _{Emission factor and cost of emission.}

FL Number of candidate PEV fleets.

I Discount rate.

IM Incidence matrix. INC Incentive cost for PEVs. LB Number of load blocks.

LFM,U L Capacity and status of transmission lines. NG Total number of generating units.

NS Number of scenarios. PEN Penalty factor.

PPH Period of planning horizon.

Pc, Pc Minimum and maximum charging capacity. Pcd∗, Pdd∗ Desired charging and discharging profiles. Pd, Pd Minimum and maximum discharging capacity. PM,U P Capacity and status of generating units. Re, d Hourly trip request and electricity demand.

Sbase, rbase Base values for curtailments. TC Total capacity of charging stations. TCLE Reliability criterion for electricity sector. TCLT Reliability criterion for transportation sector. T D Average duration of a trip.

T OU Time of use pricing for charge and discharge. TV Total number of vehicles.

V H,V S Capacity of PEV and AEV fleets, respectively.

Z Number of nodes.

ta,td _{Arrival and departure times of PEVs.} ∆t Time slot length in hour for charging.

η Charging efficiency.

ω1, ω2 Importance factors.

π , π∗ Electricity and transportation service price. γ Susceptance of transmission line.

AR Accepted trip request.

EP Charging profile of charging station.

LF Line flow.

P Real power generation.

Pcd, Pdd _{Charging and discharging profiles of PEV.} X H Binary variable indicating status of PEV fleet.

Y Binary variable indicating installation status of charging station. r1, r2 Slack variable vectors.

S1, S2 Slack variable vectors.

scd, sdd Binary variables indicating charging and discharging status. ζ , ϑ , Γ Positive variables.

θ Bus angle.

b_\B Subscript index and set of load blocks.

c\e Subscript index of candidate and existing elements. d_\D Subscript index and set of random variables.

f\Ff Subscript index and set of PEV fleets. g\Gg Subscript index and set of generating units. k\K Subscript index and set of samples.

l\Ll Subscript index and set of lines. m, n\N Subscript index and set of nodes.

re\Gre Subscript index and set of renewable generating units. s\S Subscript index and set of scenarios.

t\T Subscript index and set of hours in a day. to(l)\ f r(l) Set of ending and starting nodes.

y_\Y Subscript index and set of planning year. Jcd _{Maximum charging capacity.}

∆t Duration of charging.

ICG Investment cost of generating unit. ICL Investment cost of transmission line. ICF Incentive cost of PEV fleet.

CI Load curtailment cost.

EF Emission factor of generating unit.

PP Penalty cost of charging profile modifications. DT Load block duration factor.

f

RE The required energy of PEVs. NV Number of PEVs in a fleet.

p\σ Probability and interest rate.

PE, PC Capacities of existing and candidate generating units, respectively. e

ug,ute Status of generating unit and transmission line.

LE, LC Capacities of existing and candidate transmission lines. e

D Hourly electricity demand. ∆D Maximum curtailment.

Jcd∗ _{Desired charging profiles of PEV.} γ Susceptance of transmission line. µ , ˜v Random variable and its mean. η Efficiency of charging.

M Sufficiently large constant value. ˜ta_\˜td _{Arrival and departure time of the PEV.} θ \le Bus angle.

pe Real power generation. Jcd _{Charging profiles of the PEV.}

X Binary variable indicating status of charging.

xg,yl,z f Binary variables indicating status of the generating units, the transmission lines and the PEV fleet. ∆D Load curtailment.

T, L, π, π0 Dual multiplier.

a\Ωa Subscript index and set of PEV aggregators. b\Ωb Subscript index and set of bids.

c\Ωc Subscript index and set of integrated resources. d\ΩD Subscript index and set of load.

h\Ωh Subscript index and set of hydro units. t\Ωh Subscript index and set of hours in a day. w\Ωw Subscript index and set of wind farms. imb Subscript index of imbalancing unit. Pa Maximum charging capacity. Pw Maximum capacity of wind farm. Ph Maximum capacity of hydro unit. RU P, RDO limits of regulation service. ∆t Duration of charging.

λ Operation cost.

RE The required energy of PEVs.

p Probability.

D Maximum hourly electricity demand. ηa\ηh Charging and hydro efficiency. M Sufficiently large constant value. W I Maximum water-inflow.

Ph Real power generation of hydro unit.

PC Real power generation of integrated resource. APw Actual power generation of wind farm. AP Actual charging power of PEV aggregator.

IP Buy/sell power from balancing market. PCO Offered power by integrated resource.

PO Offered power.

W S Dispatched capacity of reservoir. ϖ Offered price in clearing market. β , ϕ Locational marginal price. ϒ, ψ Dual multiplier.

ω , ξ , α Dual multiplier.

ϑ Auxiliary binary variable. ˜

v, ˜u Random and auxiliary variables. µ Mean of the random variable. θ Wasserstein distance.

X ,Y First and second stage decision variables. EP Expectation with respect to distribution P.

_

FΘ Lifted scenario-wise ambiguity set. Wn Extended set for joint distribution of ˜v, ˜u. S ,U Dimension of random variable ˜v, ˜u.

I Number of required constraints to build extended setW . M1_,M2 _{Number of first and second stage problems constraints.} N 1_,N2 _{Number of first and second stage problems variables.}

Q Joint probability distribution of random variable ˜vand auxiliary random variable ˜u. P Distribution of random variable ˜v.

di_Θ Wasserstein distance of two distributions. ρ Wasserstein distance metric.

Yr(υ, u, n) rth recourse decision approximated by scenario wise affine decision rule. Y0n

r Constant term ofYr(v, u, n). Yυ n

rs Coefficient of random variable υ inYr(υ, u, n). Yun

ru Coefficient of auxiliary random variable u inYr(υ, u, n). NS The total number of states for PEV.

M The number of states in an EVF. EN The amount of enrollment in DRPs. NA The total number of AMIs in an EVF. N The total number of PEVs.

λC, RC Failure and repair rates of component C in an AMI system. nc The total number of components in an AMI.

P_SAMI The probability of success state for AMI. PM The probability of state M in an EVF. PΩ

S Steady state probability of success state in an EVF. Λ The number of approximated states in an EVF. NST EP The total number of approximated states for an EVF. CEV F The capacity of an EVF.

GΛ _{Output power of state λ in an EVF.} GREAL The real output power of an EVF. PH The probability of identical state H.

P_ϒ The probability of the new combined state ϒ. NSS The total number of identical states.

α , β Two states in an EVF which have mutual transition. A_ϒ States that result the identical state ϒ.

Dedicated to my beloved parents for their love, endless support, encouragement sacrifices

You can never cross the ocean unless you have the courage to lose sight of the shore.

Acknowledgement

Firstly, I would like to gratefully and sincerely thank my supervisor, Prof. Innocent Kamwa, for his guidance, patience, and most importantly, his kind support and help during my Ph.D. I also thank my co-supervisor, Prof. Hoang Le-Huy, for the continuous support of my Ph.D. study.

I would like to express my deep and sincere gratitude to my father, my mother, and my family for supporting me spiritually in the whole period of my life.

Finally, I acknowledge the generous support and scholarship provided by Hydro-Quebec and the grant of the Natural Sciences and Engineering Research Council of Canada (NSERC). I also appreciate the partial scholarship support for me which was provided by Université Laval.

Foreword

This thesis consists of different chapters. First, the introduction and the relevant state of the art are presented in Chapters 0-1 which have been originally written by Ali Hajebrahimi and have never been published before. Chapters 2-6 include the extended version of submitted, accepted or published version of articles in international scientific journals and conferences as follows :

Chapter 1 :

This chapter presents a general discussion on transmission expansion planning problem, generation expansion planning problem, autonomous electric vehicle disruption, PEV charging control and bid-ding strategy.

Chapter 2 : [1] A. Hajebrahimi, I. kamwa, Plug-in electric Vehicle Planning Toward DDPP Constrai-ned by elec-tricity Grid Limitation. IEEE, Denver, USA, Apr 2018.

Author : Ali Hajebrahimi

Contributions : Proposed the planning model of Plug-in Electric Vehicle, implemented the model, performed the simulation and prepared this paper.

Co-Author : Innocent Kamwa

Contributions : Supervised the overall flow of the project, and provided important insight on simula-tions and interpretation of the results. Aided in the preparation of the manuscript.

In this chapter we introduce a planning model for electric vehicle incorporating a charging control to maximize their penetration in the grid. It is shown that the penetration is increased by applying the proposed charging control.

Chapter 3 : [2] A. Hajebrahimi, I. kamwa, M. Henualt, A Novel Approach for Plug-in Electric Vehicle Planning and Electricity Load Management in Presence of a Clean Disruptive Technology. Published in Energy journal, September 2018.

Author : Ali Hajebrahimi

Contributions : Proposed the planning model of Plug-in Electric Vehicle in presence of autonomous electric vehicle, implemented the model, performed the simulation and prepared this paper

Co-Author : Innocent Kamwa

Contributions : Supervised the overall flow of the project, and provided important insight on simula-tions and interpretation of the results. Aided in the preparation of the manuscript.

Co-Author : Maurice Henualt

Contribution : Reviewed the paper and polished it in terms of English language and paper structure. In this chapter we introduce a planning model for electric vehicle considering the impact of autono-mous electric vehicle on transition to PEVs. It is shown that the transportation load is satisfied by less number of PEVs when the penetration of autonomous electric vehicles are increased.

Chapter 4 :

In this chapter we introduce a scenario based distributionally robust optimization for electricity and electrified transportation sectors planning. A scenario based distributionally robust optimization ap-plying affine decision rules is proposed in this chapter. The proposed model is applied on Ontario grid and IEEE 118 bus test system. A major result of this chapter is submitted as the following paper : [3] A. Hajebrahimi, I. kamwa, E. Delage, M. Abdelaziz, Adaptive Distributionally Robust Optimi-zation for Electricity and Electrified Transportation Planning. Awaiting for second revision in IEEE Transaction on Smart Grid, Submitted in Sep 2019.

Author : Ali hajebrahimi

Contribution : Proposed the planning model, Developed DRO model, implemented the model, perfor-med the simulation and prepared this paper.

Author : Innocent Kamwa

Contribution : Supervised the overall flow of the project, and provided important insight on simula-tions and interpretation of the results. Aided in the preparation of the manuscript.

Author : Morad abdelaziz

Contribution : Reviewed the paper and polished it in terms of English language and paper structure. Author :Erick delage

Contribution : Developed the idea of DRO model, reviewed the paper and modified the paper in terms of mathematics and proof section.

Chapter 5 : In this chapter we investigate the impact of plug-in electric vehicle in electricity market. We introduce a bidding strategy model for electric vehicle aggregator in mix with other generating units. Moreover, we introduce a scenario based DRO model based on Wasserstein distance. The pro-posed model is able to consider both collaborative and non collaborative bidding strategy where it is

shown that collaborative bidding strategy has a better performance. A major result of this chapter is submitted as the following paper :

[4] A. Hajebrahimi, I. kamwa, M. Abdelaziz, A. Moeini, Scenario-Wise Distributionally Robust Optimization for Collaborative Hydro, Wind and Electric Vehicle Aggregator Bidding Strategy in Day-ahead Market. Awaiting for second revision in IEEE Transaction on Power System, Submitted in Jul 2019.

Author : Ali hajebrahimi

Contribution : Proposed the bidding strategy model, proposed the scenario based DRO model, imple-mented the model, performed the simulation and wrote this paper.

Author : Innocent Kamwa

Contribution : Supervised the overall flow of the project, and provided important insight on simula-tions and interpretation of the results. Aided in the preparation of the manuscript.

Author : Morad abdelaziz

Contribution : Reviewed the paper and polished it in terms of English language and paper structure. Author : Ali Moeini

Contribution : Reviewed the paper and polished it in terms of English language and paper structure. Chapter 6 :[6] A. Hajebrahimi, I. kamwa, Toward A Reliability Model for Electric Vehicle Fleet Incorporating Repairable AMIs. IEEE, Montreal, Canada, OCT , 2019.

Author : Ali hajebrahimi

Contribution : Proposed the reliability model, implemented the model, performed the simulation and wrote this paper.

Author : Innocent Kamwa

Contribution : Supervised the overall flow of the project, and provided important insight on simula-tions and interpretation of the results. Aided in the preparation of the manuscript. In this chapter we develop a reliability model for electric vehicle fleets participating in demand response programs or electricity market.

Finally, the general finding and recommendations are highlighted for future studies. This thesis was supervised by Prof. Innocent Kamwa (my supervisor)

Problem Statement

Different problems are investigated in this thesis regarding the penetration of PEVs in transportation sector. Here, these problems are categorized in terms of power system planning and operation.

— Planning horizon : Since planning in only one sector is not an efficient approach, it is required to develop a planning model which integrates both the electricity and the electrified transporta-tion sectors for transitransporta-tion to the electric vehicles. The planning model should be able to consider the impact of the AEV as a clean disruptive technology for the PEV to increase the environ-mental benefits of the electric vehicles as a pathway for DDPP. These problems are covered in Chapters 3, 4 and 5.

— Operation horizon : The imminent penetration of PEVs in the near future implicates serious research attention aiming at their efficient integration in power system. In the recent studies, it is shown that a mass integration of uncontrolled PEVs can cause load curtailment, voltage deviation, transformer overloading and loss increment in distribution networks. A comprehen-sive review of the PEV integration impact is studied in (4). On the other hand, the PEV battery suggests a flexible tool as distributed energy storage and demand response for active load parti-cipation in electricity markets if their charging profiles are managed, efficiently. Hence, further studies should be conducted in the area of operation studies, particularly in power market stu-dies. A model should be developed for PEV bidding strategy to increase the share of PEVs in power market considering uncertainties of PEV e-mobility. Moreover, a reliability model for these resources participating in power market should be developed to design a reliable power market. These problems are covered in Chapters 6 and 7, respectively.

Original Contribution

Generall contribution of this thesis are categorized as follow :

— A planning model for maximizing PEVs in the grid without any investment in transmission and generation is proposed. A Benders decomposition is applied here for solving the two stage optimization problem.

— A bi-level charging control is proposed in this project which is able to consider both interests of the customers and independent system operator for charging the batteries

— The impact of autonomous electric vehicle on optimal transition to electric vehicles are investi-gated in this project.

— An adaptive distributionally robust optimization for integrated transmission, generation expan-sion learning incorporating electric vehicle planning problem is proposed in this thesis. A sce-nario based ambiguity set applying affine decision rule is proposed in this project to consider high number of random variables in the power system uncertainty analysis.

— A distributionally robust offering strategy for mix of generating unit incorporating electric ve-hicle aggregator bidding strategy is proposed in this paper. A scenario based ambiguity set considering Wasserstein distance metric is proposed in this project.

— A reliability model of electric vehicle fleet for participating in demand response programs is developed in this thesis. The impact of repairable AMIs on reliability modelling of electric vehicle fleet is investigated in this project.

Introduction

0.1

C

Climate alteration caused by greenhouse gas (GHG) is posing a massive threat to human welfare (5). Amongst all kinds of GHGs, CO contributes to 77% of the GHG impacts on global warming. The-refore, developing a zero-carbon society has gradually become a universal and an inevitable issue in energy planning study.

In this context, Deep Decarbonization Pathways Projects (DDPPs) comprise all collaborative global efforts trying to find out a "pathway" for every country to transition to a free-carbon society on a social techno-economic framework (6). This project is toward an international consented target for limiting global warming to less than 2◦C.

0.2

Deep Decarbonization Pathway Projects in Canada

Canada as one of the aforementioned committed countries needs to decarbonize its society to attain its goal of carbon mitigation which is emission reduction of 80 percent by 2050 compared to 2005 level (7). In order to achieve this goal, six pathways under three principal topics are proposed in (8) :

— Reinforce Current Trends

— Pathway 1 : Decarbonize electrification. — Pathway 2 : Improve energy productivity. — Pathway 3 : Reduce non-energy emissions. — Pushing Towards Next Generation Technologies

— Pathway 4 : Move to zero emission transport fuels. — Pathway 5 : Decarbonize industrial processes. — Pathways of Structural Economic Change (Pathway 6)

Amongst all these pathways, pathways 1 and 4 raise different challenges from the viewpoint of the po-wer system planners. The first pathway facilitates end use sectors to alleviate their produced emissions by switching from fossil fuel products and natural gas to clean electricity ones. The fourth pathway

FIGURE0.1 – PEV sales portion of total vehicle sales (1).

emphasizes on shifting from the fossil fuels to electric batteries, non-food crop biofuels, and hydrogen to power up light-duty vehicles to completely decarbonize the transportation sector. As noted in (9), the transportation sector accounts for 30% of the total GHG emissions in Canada, the majority of which comes from light-duty vehicles making personal travels. It is worthwhile to mention that the electrification of the light duty vehicles and rail transport is a promising carbon reduction option using existing technology (8).

0.3

Role of Transportation Sector in DDPP

The Canadian federal and Quebec governments have committed to reduce their GHG emissions by 17% and 20% (below 2005 levels) by 2020, respectively (10). In this context, Quebec has commit-ted to reduce the GHG emissions by 37.5% (below 1990) by 2030 (10). The transportation sector will be certainly affected by taking the aforementioned GHG reduction target into account. As men-tioned in (9), 35% of Canadian energy utilization is related to the transportation sector, while the transportation accounts for 30% of the total GHG emissions and the majority of which comes from the light-duty vehicles (LDV) making personal travels. As shown in Fig0.2b, the LDV transportation contributes 54% to the total emissions produced in the transportation sector (1). The PEVs can si-gnificantly reduce the transportation emissions if they are joint with a decarbonized electricity supply combination (9;11;12). The total EV sales across Canada is demonstrated in Fig0.1where EVS made up 2.2% of total vehicle sales in 2018. Almost 44,000 vehicles were sold in 2018, more than double the sales in 2017. EVs sales are highest in the provinces of Quebec, Ontario and British Columbia. As a projection for province of Ontario1, the penetration target for the PEVs is proposed to be 5% by 2020. The province of Quebec2has also proposed a penetration target of 18% by 2030 supplementing

1. “One out of every 20 vehicles driven in Ontario” should “be electrically powered by 2020” ; see : http ://www.mto.gov.on.ca/english/dandv/vehicle/electric/index.shtml.,

2. 25% of new light passengers vehicles sold in Quebec by 2020 should be EVs ; see : http ://vehiculeselectriques.gouv.qc.ca/plan-action.asp.

(a) Electricity sector GHG emission for Canada (1).

(b) Transportation sector GHG emission for Canada (1).

FIGURE0.2 – CO2emission for Canada in transportation and electricity sectors.

the previous policy (9). It should be noted that there has been a very little correlation between the transportation and the electricity sectors due to limited penetration of the PEVs, so far. However, this small correlation would no longer exist with the emergence of a clean disruptive technology such as autonomous electric vehicles (AEVs) in the transportation sector and the aforementioned changes in Quebec and other provinces of Canada. Through this transition to the PEVs and the AEVs, an addi-tional electricity demand will be imposed to the electricity sector. Hence, further investments seem to be necessary for the electricity grid to support potentially millions of the PEVs and the AEVs.

0.4

Role of Electricity Sector in DDPP

As stated in (1), the electricity sector was one of the largest source of the GHG emissions accounting for 18% of the total emission in 2017. Since then, GHG emissions have declined to less than 75 megatonnes in 2017. As depicted in Figure 0.2a, greenhouse gas emissions (GHG) from electricity generation were stable around almost 130 megatonnes in 2001. Since then, GHG emissions have declined to less than 75 megatonnes in 2017 where almost 82% of electricity in Canada came from non-GHG emitting sources (1). The growing share of the electricity generated from clean sources such as hydro, nuclear and other renewables is the main reason for this reduction. Hydro made up 60%, nuclear 15%, and other renewables the remaining 7%. However, the GHG emissions produced by

coal-based generation has the most share of the GHG emitted in the electricity sector. Being a major GHG emission source and encountering with the challenges of the DDPP implicate that decarbonization in the electricity sector is a vital and essential issue. Indeed, increasing the share of the PEVs and the AEVs in the transportation sector and on the other side, renewable energy resources in the electricity sector are inevitable indications that Canada is moving in this direction. There are outlooks that this movement will require a substantial evolution in the electric transmission and the generation expansion planning over the next decays.

0.5

Thesis Outline

The rest of this thesis is organized as follow :

— Chapters 1 represents a general discussion on the materials applied in this thesis.

— Chapters 3-7 represent the contribution of this thesis containing the extended versions of publi-shed, accepted or submitted articles in international scientific journals or conferences as

— Chapter 2 : In this chapter we introduce a planning model for electric vehicles in order to increase their penetration considering limitations in investment cap. A bi-level charging control is developed here which is able to increase the penetration of the PEVs in planning horizon, significantly. The proposed model is applied on simplified Ontario grid.

— Chapter 3 : In this chapter, the impact of autonomous electric vehicle on optimal transi-tion to plug-in electric vehicles is investigated here. A Bayesian decision-based conjoint method for developing a heterogeneous transportation demand model is applied in this chapter. It is shown that we can meet the transportation load with lees PEVs when the penetration of autonomous electric vehicles are increased in the grid.

— Chapter 4 : In this chapter a planning model for both electricity sectors and electrified transportation sectors is proposed to increase the maximum penetration of electric ve-hicles in the grid. An adaptive distributionally robust optimization is developed in this chapter to handle the uncertainties of load, renewable, PEV e-mobility and availabilities of transmission and generating units. The proposed model is applied on simplified Ontario grid and IEEE 118 bus test system.

— Chapter 5 : In this chapter the impact of PEVs in operation horizon is investigated. A bidding strategy schema for PEVs aggregator participating in demand response programs, renewable energies and other types of generating units is addressed in this chapter. Mo-reover, a scenario based DRO model considering Wasserstein distance is proposed in this paper to handle the uncertainties. It is shown that in cooperative bidding strategy we have better performance for market participant than non-cooperative bidding strategy.

— Chapter 6 : Herein, a reliability model for electric vehicle fleet participating in demand response programs is proposed in this chapter. Moreover, the impact of reparable AMI on reliability modelling of electric vehicle fleets is investigated here.

Chapitre 1

Relevant State of the Art

1.1

Overview of TEP

Transmission lines are one of the important components of power system utilities. Therefore, trans-mission expansion planning (TEP) problem plays an important rule in power system planning studies. The TEP problem specifies an optimal and secure configuration of the transmission system based on the load growth and the future generation mix for a specific period of the time (13). Generally, the TEP is supposed to answer the following questions :

— Where the new lines are supposed to be installed ? — When the new transmission lines should be installed ? — What type of transmission lines should be installed ?

Similar to the classical optimization problems, different objective functions and constraints have been applied in the TEP problem. In this context, the objective functions such as transmission investment cost, operation cost, reliability cost and also congestion cost, etc. are the most popular objectives function which have been used in the TEP problem. The constraints of the TEP can be classified into two main categories :

— Obligatory : Such as limits of generating units, line flows, voltage level and other operational constraints.

— Optional : Such as investment limits, reliability requirement and environmental limits, etc. 1.1.1 TEP problem evaluation

Basically, all types of the TEP approaches can be classified into the following categories : — In terms of modelling :

— DC model (14). — AC model (15).

— Deregulated environment (16). — Traditional environment (17). — In terms of uncertainty of power system :

— Deterministic approaches (18). — Stochastic approaches (14). — In terms of time horizon :

— Dynamic planning (18). — Static planning (14).

— TEP and environmental impact (19).

— Coordinated TEP and generation expansion planning (GEP) problem (20). 1.1.2 TEP from viewpoint of modelling

Generally, two modeling approaches for TEP problem are proposed in the literature. The first model-ling approach is AC model which is a complete model for the TEP problem. The AC model consi-ders the reactive power and the related topics such as power loss and FACTS devices in planning studies (21;22). The second approach is DC model which includes simplifications (20;14). In our project, a DC model for the TEP problem is utilized.

1.1.3 TEP from viewpoint of power system structure

In the traditional power system, the TEP problem was formulated as a single-objective problem mi-nimizing investment costs of new lines while maintaining power system reliability. In the traditional environment, certain required information was accessible for planners where the uncertainties were neglected. However, new objectives were introduced in the field of the TEP problem by deregulation in power systems. The major contribution of deregulation was to introduce a competitive market en-vironment maximizing the overall social welfare. In a deregulated electricity industry, a transmission network must provide a fair environment for increasing the merits of market participants. It should be noted that the traditional TEP may not handle the market-based power flow patterns and stockhol-der’s interests. Thus, any model for the TEP problem in deregulated environment should be capable of supporting interests of stockholders and should also be based on a social welfare study rather than a classical least cost approach.

1.1.4 TEP in viewpoint of uncertainty in power system

Uncertainty should be considered in any power system planning such as the TEP problem. A per-formance comparison between deterministic and stochastic models in the TEP problem is provided in (23). This paper indicates that the TEP incorporating uncertainties have better performance compa-red to deterministic ones. In the following, some of the general uncertainties are provided :

— Transmission and generating unit availability (20). — Load and electricity price forecast error (20;24).

— Renewable energy generation such as wind and solar. etc. (25;26;14).

A comprehensive review on uncertainty techniques in the power systems is conducted in (27). 1.1.5 TEP in viewpoint of time horizon

In terms of time horizon, the transmission expansion planning problem can be classified into dynamic and static models. In the static planning model, the time horizon is not considered and the optimal plan is determined for a single year. However, in the dynamic transmission expansion planning problem, the amount of investment would be obtained for each period of time horizon. It should be noted that the dynamic TEP leads to a better and cheaper optimal plan, although it is very complex and time consuming (28).

1.1.6 TEP and environmental impact

As discussed in Section0.4, substantial evolutions in the transmission and the generation sectors are required to achieve the goal of deep decarbonization. Hence, the planners are encouraged to install the renewable energy resources and provide their clean energies through the new transmission lines to the end user sectors. Specifically, the transmission and the generation expansion planning are able to help the electric vehicles to decarbonize the society by providing their required energies from clean energy resources.

1.1.7 Coordinated TEP and GEP

Transmission and generation systems are coupled to each other in the real power system. Therefore, the TEP problem can be solved simultaneously with the GEP problem. Coordinated TEP and GEP have been carried out in previous studies (29; 30; 31; 20). The proposed model in reference (20) has applied a Benders decomposition to solve a market based transmission and generation expansion planning. We have modified this modelling approach incorporating the electrified transportation sector planning.

1.2

Overview of Electric Vehicles Charge Scheduling

Plug-in electric vehicles (PEVs) integration to distribution grid would eventually introduce different concerns such as increment in feeders loading, appearance of harmonics in the distribution system, augmentation of network loss (32). In this context, multifarious charging control strategies have been proposed which can be categorized into

— Decentralized versus centralized charging control. — Unidirectional versus bidirectional charging control.

— Static versus mobility-aware Charging. — Integration of renewable energy resources. — Providing ancillary services.

— Objectives of optimization algorithm for charging and discharging schedule. — Methods for optimization of charging schedule.

1.2.1 Decentralized Versus Centralized

In centralized mode, optimization of the PEV charging pattern would be performed at the aggregator level when the information about required energy for charging the PEVs are collected from customers. The informations communicated between the PEVs and the aggregator may include state of the charge (SOC), maximum capacity of the PEV’s battery and charging rate. Afterward, the ISO and each aggre-gator would make a contract based on an aggregated energy requirement. Hence, the ISO dispatches the capacity for each aggregator considering operational constraints. Then, each aggregator performs an optimization to obtain the optimal charging schedule of the PEVs so that their required energy are met. Generally, applying this charging control with high penetration of the PEVs is computatio-nally overwhelming and majority of previous studies were focused on this control strategy (33;34). In decentralized charging control, the PEVs who collaborate with the aggregator are able to decide if they want to charge, discharge or nothing. In order to apply this control strategy, the PEVs should be equipped with the communication infrastructures. In this case, the communicated informations bet-ween the PEVs and the aggregator include the required energy of the PEVs to be fully charged and the PEVs may choose if they are going to participate in various programs offered by the ISO or not. It should be noted that this control strategy is applicable for the large number of electric vehicles. The decentralized control strategy were applied in (35;36;37).

1.2.2 G2V versus V2G charging control

G2V model indicates the charging control where the power only flows from grid to charge the electric vehicles (38;32). While, V2G mode offers a charging control scheme in which the energy flows in both directions enabling charging and discharging mods (39;40). However, simultaneous charge and discharge of the PEVs is not authorized in V2G mode. The V2G model is capable of providing more flexibility in charging/discharging compared to the G2V model.

1.2.3 Static versus mobility-aware charging

Static charging control is assigned to a charging management in which the PEV’s mobility is not taken into consideration. In fact, the PEV is modeled as a static and typical load without any movement or temporal characteristics (38; 41). In contrast, several mobility characteristics of the PEVs such as arrival times, departure times, travel histories and unexpected departures of the PEVs are considered in the mobility-aware charging control (32;42). It should be noted that the uncertainties of the PEV

mobility make the problem of the optimal charging control more realistic. While, the static charging of the PEVs offers a simplified formulation of the problem to consider the impact of other parameters on the grid.

1.2.4 Integration of renewable energies

As mentioned in sections 0.3 et0.4, the PEVs as well as the AEVs in the transportation sector and the renewable energies on the other hand in the electricity sector can significantly decarbonize the society. Thus, it is required to develop a charging schedule tool to handle the integration of the electric vehicles incorporating renewable energies. Minority of the literature have explored the integration of the electric vehicles with the renewable energy in their studies (32;42;43).

1.2.5 Providing ancillary service

the electric vehicles in V2G mode not only can provide supplying energy, but also they can offer some ancillary service such as frequency control or spinning reserve through modifying their charge and discharge patterns. Some of the existing works on charge scheduling also consider the PEVs providing such an ancillary service for the grid (38;44).

1.2.6 Objectives of optimization algorithm for charging and discharging schedule The problem of charge/discharge planning is an optimization problem. Obviously, various objective functions depending on the type of problem may be applied in the problem of charge scheduling. Some popular objectives which are considered in the previous literature are provided here :

— Loss minimization (42). — Charging cost (42;45).

— Minimizing Co2emission (41). — Operation cost (40;46).

1.2.7 Methods for optimization of charging schedule

The precise formulation for the charge/discharge planning problem may change regarding the constraints and the objectives of optimization problem. Therefore, multifarious methodologies and optimization techniques were proposed in the literature which can be classified into two main groups. Some of the previous studies have applied mathematical technique (40;46;44), while some of them have imple-mented heuristic approaches (32;42).

1.3

Disruptive technology

Disruptive technology is defined as a technology which constructs a new market and cost network and finally disrupts an existing market and cost network (47). In 2009, Milan Zeleny defined the high

technology as a disruptive technology and propounded the question of what is being disrupted and its answer was the support network (48). For instance, the author has argued that the PEV as a high technology disrupts the support network for internal combustion vehicles (ICV) (47). In the same manner, a disruptive technology for the PEV is the self-driving technology which will likely overlap the PEV disruption in the future. Taking Tesla as an example which intends to make 90% of their cars in self-driving mode for the public, the same scenario as the Internet and mobile communications will occur for the PEV with self-driving technology, creating the technology of automotive electric vehicles (AEV). Fig. 1.1demonstrates a vehicle which is equipped with the technology of self driving. It is announced by Google that its AEVs will be released for the public by no later than 2018 (49). It is projected that most cars will be autonomous and will be being operated completely independent from human control by 2035 (50).

FIGURE1.1 – The self-driving car schema. Source : Google

As stated in (47), self-driving car is disruptive because they will profoundly change the very nature of car ownership. It means that most of the people will not be interested to buy a car in the near future and mobility on demand will be what they will be looking for. In fact, the people need the capability to move from point A to point B whenever they desire and this is guaranteed by owning a car. However, this mentality takes no longer when the autonomous electric vehicles are able to pick up and drop off passenger at any time in anywhere. Keeping this fact in the mind, the AEVs are more preferable to car ownership. For this purpose, companies such as Zipcar and Uber are investing on the AEVs in order to meet the transportation demand in the near future (47). Taking the impact of the AEVs into account, it is required to carried out more research in power system operation and planning to maximize the environmental and economic benefits of the electric vehicles.

1.4

Bidding strategy

Different approaches for bidding strategy can be classified into the following approaches : 1.4.1 Single GenCo optimization

In this approach, all electricity market is modelled through a simple model. The interaction amongst different components of the market is not related to this kind of approach and the only important output for Genco is the final price of the market. This approaches normally utilize price forecasting and they bids according to the estimated price. In this approach, generation constraints can be modelled very precisely, while the only error is out of price prediction. This is the reason why some literature applies different risk aversion approaches in their studies. In this approach, market participants are classified into two groups : price takers and price setters. Table 1.1represents a literature review for this approach.

TABLE1.1 – Summary of literature for single GenCo bidding strategy

Ref Method Auction Markets

Rules Assumptions Applications

(51) SNLP/GA California ISO

Sealed bids on 24 linear supply functions w/o price caps Market power analysis for DAMs

(52) News-Vendor General DAM Normally distributed MCPs

Risk analysis and profit maximization in a PAB PoolCo for DAMs

(53) NLP ISO New England

Quadratic bid function with rivals’ being available as distributions

Cost minimization of bidding in DAMs

(54) MDP Hybrid Different imbalance

price assumptions GenCo imbalance cost minimization in the hybrid (55) SILP/MILP Hybrid Rational bidders ; risk-neutral case ; no penalty for balancing market bidder Decreasing WenCos’ risk of profit variability (56) NLP British type PX Perfect competition for GenCos ; individual bids do not affect MCP Multi-period auction rule

(57) Statistical Model NYISO

Perfectly inelastic and exogenously given load profile

Demand side management

1.4.2 Game theory based models

Game theory approach is an efficient method to analysis the power each participant on final price for the markets with incomplete information (58). In this approach a game is executed amongst different players, then the Nash equilibrium is determined according to associated important factors impacting on participant decision. It can be claimed that decision of one single participant can not set the final price, but the interaction with other participants may impact its profit and consequently the final price. It should be noted that in this type of approach, the players can not gain a profit more than Nash equilibrium by changing their strategy. It means that, if a player violates from the Nash points, other participant can change their decisions so that they can reduce the profit of violated participant. These methods can be classified into three main approaches.

1.4.3 Bertrand competition

In this type of approach (59), the only factor that players compete with each other is the final price. It should be noted that there is no limitation on unit generation. In this approach, the players who offers the lowest price, can take the control of the market since there is no limitation on generation capacity. 1.4.4 Cournot competition

This approach is more realistic than previous approach (60). In this approach, the players compete together considerring the price as a components of the market. Here, the final price is obtained when the market is clear and Nash equilibrium is obtained.

1.4.5 Supply Function Equilibrium

This approach is more realistic than the two previous approaches (61). In this approach, some pro-blems associated with Cournot model are solved and it is more close to a complete market.

1.4.6 Agent-based Model

With regard to the problem related to game theory approach, agent based model is proposed in which the target is not to reach the Nash equilibrium any more. The market participant not only are able to learn from the historical data, they are able to make decision by mistake (62).

1.4.7 Hybrid Model

This method combines three previous methods (63).

It should be noted that energy market is categorized into day-ahead market and real time market in united state. In day-ahead market, transactions are executed in a day before the operation day, while the real time market is in fact a balancing market allowing market participant to trade energy in the operation day.

Chapitre 2

Plug-in Electric Vehicle Planning model

A. Hajebrahimi, I. Kamwa, Plug-in electric Vehicle Planning Toward DDPP Constrained by Elec-tricity Grid Limitation. IEEE, Denver, USA, Apr 2018.

2.1

Résumé

éhicule électrique (EV) a fait allusion à une solution pour CO2. Toutefois, une pénétration incontrôlée de véhicules électriques dans le cas d’un réseau électrique entraînera une augmentation des émissions de CO2dans le secteur de l’électricité. Par conséquent, dans cet article, un modèle décomposé de véhi-cules électriques est proposé pour obtenir la pénétration optimale des véhivéhi-cules électriques en fonction des incertitudes associées. En outre, cet article aborde une nouvelle commande de chargement / dé-chargement à deux niveaux qui prend en compte les désirs des PEV et de l’opérateur du système. Les résultats démontrent qu’il est possible d’augmenter le nombre de véhicules électriques jusqu’à 30% d’ici 2025 tout en réduisant la charge totale de 37% et les émissions totales de 28% dans le scénario de référence sans contrôle de charge / décharge du véhicule électrique. Le problème de planification proposé est appliqué au réseau de l’Ontario et aux plans existants de développement du transport et de la production.

2.2

abstract

Electric vehicle (EV) has alluded as a solution for CO2emission reduction in the transportation sector. However, uncontrolled penetration of EVs considering power grid limitation will increase CO2 emis-sion in the electricity sector. Hence, in this paper, a decomposed model of EVs planning is proposed to obtain the optimal penetration of EVs considering associated uncertainties. Moreover, a new bi-level charging/discharging control which considers both desires of the PEVs and the system operator is addressed in this paper. The results demonstrate that it is possible to increase the penetration of EVs up to 30% by 2025 while reducing the total load curtailment by 37% and the total emission by 28% compared to the baseline case with no supervisory EV charging/discharging control. The proposed planning problem is applied to Ontario’s grid considering existing and projected plans of transmission and generation expansion.

2.3

Literature Review and Contribution

Deep Decarbonization Pathway Projects (DPPP) are structured on three pillars (64) : 1. Energy efficiency and conservation.

2. Low carbon electricity.

3. Electrification of end-use sector .

It is not possible to reach deep decarbonization targets if any of these pillars aren’t accomplished at sufficient level (64). Amongst the three mentioned pillars, the electrification of the end-use sector includes the efforts for shifting end-use energy consumption from fossil fuels to zero-carbon fuels. In summary (64) :

— Shifting from coal to natural gas.

— In the longer run, shifting to decarbonized energy carriers. — Adoption of electric, biofuel or hydrogen vehicles.

Therefore, the electric vehicle (EV) in transportation sector can be considered as a solution to reduce the CO2emission in this sector (65;66). However, it is necessary to mention that the required energy of EVs should be provided through a clean electricity sector (65). Otherwise, increasing the penetration of EVs would cause a shift from a decentralized emission production to a centralized one. Therefore, the optimal penetration of electric vehicle in the electricity grid has turned to a controversial issue in power system studies (67;68;2).

The majority of previous studies have disregarded the impact of transmission constraints on PEVs penetration (67;68). In this context, the authors in (2;69) have investigated the optimal transition to hybrid EVs considering electricity grid limitation. However, the uncertainties related to EVs mobility and renewable generations are overlooked. In (2), the EVs are considered as simple loads which appear only on weekends, and there is no charging/discharging control policy. In order to obtain the maximum penetration of PEVs in the electricity sector in this project, a Monte Carlo simulation is applied to investigate the impact of EVs uncertainties as well as the renewable generation on the optimal transition to the electric vehicle. The required energy of EVs to be fully charged, arrival and departure time and wind generation are considered as uncertain variables in this project. Herein, the EVs can participate in different charging programs proposed by the independent system operator (ISO). Three charging control strategies are investigated here. In the first control strategy, the EVs start to charge and discharge as soon as they arrive homes and there is no control policy for charging. At the second charging control strategy, a home-based charging/discharging control which obtains the desired charging profile of EVs is applied (42). A bi-level charging control as the third control strategy is proposed in this paper in which the EVs participate in different programs satisfying both interests of end-user and the ISO for charging EVs. Moreover, a benders decomposition is developed here for EVs planning such that the optimal number of electric vehicle fleets are determined through

the designated feasibility and infeasibility benders cuts. The proposed planning problem is applied on simplified Ontario’s electricity grid. It is shown that the proposed model increases the penetration of EVs up to 30% by 2025.

2.4

Formulation

The planning problem is decomposed into one master problem and three subproblems. Monte Carlo simulation is implemented here to generate the scenarios corresponding to required energy of EVs (Ere_{), arrival time (t}a_{), departure time (t}d_{) and wind farm generation. Afterward, a scenario reduction} using SCENRED tool in GAMS is applied to extract the most probable scenarios. Then, the generated scenarios are submitted to home-based charging management and two subproblems. In the next step, the master problem specifies the number of the electric vehicle fleet and submits the binary vector (Xh₎ representing EV fleets statuses into the feasibility check subproblem. Afterward, the home-based char-ging/discharging management determines the desired charging and discharging profiles of the EVs for each scenario. Hence, Xh, Pcd∗and Pdd∗are submitted to infeasibility check subproblem. At this stage, the infeasibility check subproblem determines the load curtailment according to each control strategy. Then, the infeasibility Benders cut is generated if the reliability criterion is not met by the proposed penetration of the EVs. If the decision variables ( Xh, Pcd∗and Pdd∗) satisfy the reliability criterion, they will be submitted to operation subproblem. The operation subproblem updates the Lagrangian multiplier and generates the feasibility Benders cut until the convergence criterion is satisfied. 2.4.1 Input Random Variables

All the historical data related to the required energy of EVs to be fully charged, arrival and departure times can be found in (70).

FIGURE2.1 – Distribution of arrival time and required energy of EVs.

Distribution of EVs’ home arrival time and their required energy to be fully charged are exhibited in Fig.2.1. Herein, the uncertainties corresponding to wind generation are modeled by means of hourly

capacity factors (71).

2.4.2 Planning Problem (Master Problem)

The optimal number of EVs fleet in each year is obtained through the following optimization :

max NY

∑

∑

∑

∑

X_{i f}h_(y−1)≤ X_{i f y}h ∀i, ∀y (2.3)

where, X_{i f y}h and V_{i f y}h represent the status of candidate electric vehicle fleet f at bus i in year y and its corresponding capacity, respectively. The incentive cost for each EV fleet and discount rate are indica-ted by INCi f yand I, respectively. TVi f yh denotes the total number of EVs, NY presents the duration of planning period and FL indicates the total number of EVs fleets. Constraint3.7limits the penetration of the PEVs and constraint2.3maintain the the status of PEV fleet during the planning period. 2.4.3 Home-Based charging/Discharging management

In this control strategy, there is no modification on desired charging and discharging profiles of EVs. At the first stage of charging control, the following optimization is executed to determine the desired charging and discharging profiles of EVs and the desired charging profiles will be submitted to the fleet aggregator. min Z

∑

∑

∑

E_sre= td s−1

∑

The objective function3.11indicates the minimum cost of charging EVs. Constraints3.12and 3.13

limit the charge (Pcd

iltys) and the discharge values (P dd

iltys) at scenario s, hour t and load block l, respec-tively. Constraint3.14denotes that EV’s battery can neither charge nor discharge in the period which EV is not available. Constraint2.8ensures that the EV’s battery can’t charge and discharge, simulta-neously. The energy balance equation in EV’s battery is represented by constraint3.16. Finally, the desired charging profiles will be submitted to ISO by fleet aggregator at the second stage of home-based charging control. Hence, the required energy of EVs without any modification will be provided by ISO.

2.4.4 Bi-level Charging Control

A decentralized charging control which considers the interests of both end users and the ISO is deve-loped in this project. In this control strategy, the customers can participate in various programs offered by the ISO. Hence, this control strategy is explained for each program, separately.

2.4.5 Program A

The EVs in this program can modify their charging profiles according to the optimal charging profile which is proposed by the ISO. However, they are guaranteed to be provided by 100% of their required energy. At the first level of this charging control, the desired charging profiles of EVs are determined through the home based charging management which is explained in Section 2.4.3. Afterward, the optimal charging profiles of EVs are identified in the feasibility check and the operation subproblems considering the following objective function and its corresponding constraints.

DCys= min Z

∑

∑

∑

∑

∑

∑

∑

∑

∑

∑

∑

∑

P_iltcd∗− P_iltco= ζiltcd− ϑ co

ilt (2.11)

P_iltdd∗− P_iltdo= ζiltdd− ϑ do