Energy resource management in smart grids with intensive use of electric vehicles: heuristic and deterministic approaches

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Energy Resource Management in Smart

Grids with Intensive Use of Electric Vehicles:

Heuristic and Deterministic Approaches

Doctorate degree in Electrical and Computer Engineering

João André Pinto Soares

Supervisor: Professor Dr. Zita Maria Almeida do Vale – ISEP/IPP Co-supervisor: Professor Dr. José Paulo Barroso de Moura Oliveira – UTAD

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Energy Resource Management in Smart

Grids with Intensive Use of Electric Vehicles:

Heuristic and Deterministic Approaches

Doctorate degree in Electrical and Computer Engineering

João André Pinto Soares

Supervisor: Professor Dr. Zita Maria Almeida do Vale – ISEP/IPP Co-supervisor: Professor Dr. José Paulo Barroso de Moura Oliveira – UTAD

Evaluation Panel:

Rector of UTAD represented by Professor Dr. José Boaventura Ribeiro da Cunha, UTAD, Vila Real, Portugal Professor Dr. Eduardo José Solteiro Pires, UTAD, Vila Real, Portugal

Dr. Hugo Gabriel Valente Morais, EDF Lab Paris-Saclay, Paris, France

Professor Dr. José Paulo Barroso de Moura Oliveira, UTAD, Vila Real, Portugal

Professor Dr. Paulo Jorge Freitas de Oliveira Novais, University of Minho, Braga, Portugal Professor Dr. Manuel José Cabral dos Santos Reis, UTAD, Vila Real, Portugal

Professor Dr. Zita Maria Almeida do Vale, ISEP, Polytechnic of Porto, Porto, Portugal

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Este trabalho foi financiado pela FCT - Fundação para a Ciência e a Tecnologia, através do programa de financiamento QREN – POPH - Tipologia 4.1 – Formação Avançada comparticipado pelo Fundo Social Europeu e por fundos do MCTES.

Bolsa individual com a referência SFRH/BD/81809/2012 entre 2013 e 2017.

Copyright notice: parts of this thesis have been published or submitted to scientific articles. Therefore, copyright contents have been transferred to its publisher, namely IEEE and Elsevier according to their current policy.

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I dedicate this work to my beloved parents, my sister and my friends,

and in memory of my grandfather, who passed away in the sad year of 2014.

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If you wish to make an apple pie from scratch, you must first invent the universe.

— Carl Sagan

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Quero aproveitar este espaço para dedicá-lo às pessoas que contribuíram para que o trabalho desta tese chegasse a bom porto. Faço-o em português, pois é a minha língua nativa e que permite expressar da melhor forma a minha sincera gratidão.

Começo por agradecer aos meus pais, que me ajudaram em diversos momentos da minha vida, sobretudo nos piores. Reconheço claramente que superaram as suas obrigações e sem eles tudo seria mais difícil. Por isso, estar-lhes-ei eternamente grato.

Quero agradecer à Professora Zita Vale, orientadora deste trabalho de doutoramento, pela oportunidade de crescimento profissional e também pessoal que me proporcionou ao longo de vários anos no centro de investigação que também ela coordena, o GECAD. Além das várias responsabilidades que assume, aceitou coordenar e proporcionar as condições necessárias para que este trabalho tivesse sucesso, que também é seu mérito. Estou por isso bastante grato. Obrigado pela sua ajuda, paciência e pelas diversas oportunidades.

Ao Professor Paulo Oliveira, agradeço por ter aceitado a coorientação deste trabalho. O professor Paulo foi um excelente aliado, sempre disponível e o meu braço direito na UTAD. Estou agradecido por isso e reconheço também o mérito dele neste trabalho, nas ideias que trocámos e na ajuda em vários artigos científicos.

Ao Nuno Borges, agradeço o apoio constante em particularidades do trabalho de doutoramento. E não menos importante, pela ajuda na cooperação de diversas publicações científicas.

Quero agradecer ao Ali Fotouhi por ter ajudado na conquista de bons resultados científicos e pela sua ajuda em diversos problemas de índole técnica. (I am thankful to Ali Fotouhi for the

strong collaboration in our joint works and for his invaluable technical help).

Ao Bruno Canizes, um eterno agradecimento pelo que aprendemos juntos e pela sua ajuda com o método de decomposição de Benders.

Ao Pedro Faria e Tiago Pinto, agradeço por terem colaborado em diversos trabalhos académicos e científicos, assim como em vários procedimentos burocráticos.

Além da Professora Zita e Professor Paulo, quero agradecer ao Professor Sérgio Ramos, Hugo Morais e Tiago Pinto, pelas dicas e sugestões bastante relevantes para a melhoria deste documento de tese.

Obrigado Ana Soares, minha irmã, que apesar de não ser da área, contribuiu para melhorar o documento de tese.

A todos os outros colegas e amigos que de uma forma ou de outra sempre estiveram disponíveis para ajudar, o meu obrigado.

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Abstract

This thesis provides contributions to the complementary fields of power systems and computational intelligence by proposing innovative solutions for the energy resource management problem in the context of smart grids, therefore, supporting innovative business models in the field, such as energy aggregators.

The uncertainty concerning availability of fossil fuels and the rising climate effects caused by its widespread use in electricity generation has led to several policies and incentives to attenuate these problems. These measures contributed to huge investments in renewable energy sources and motivated many smart grid initiatives. Although the future panorama of modern power systems looks very promising, the large-scale integration of renewable sources of intermittent nature poses new challenges, strains and limitations in the current power industry. These problems are aggravated by the likely mass penetration of electric vehicles, which is expected to contribute significantly to the growing complexity already in place.

Virtual power players are defined as energy aggregator entities that can provide a highly flexible generation and demand as required by the smart grid paradigm. In addition, virtual power players can allow to achieve high integration of renewable energy supply and raise value for small producers and consumers that cannot negotiate directly in the wholesale market. Indeed, it is expected that virtual power players market will steadily grow to more than 5 billion dollars in the next few years, making it a highly interesting sector. However, these entities require adequate decision support tools to overcome the complex challenges and deal with the large number of energy resources. Indeed, energy resource management is crucial for virtual power players to reduce operation costs, increase profits, reduce carbon emissions and improve the system stability. The existing tools are not prepared to deal with integrated energy resource management and large-scale use of distributed energy resources. Moreover, models proposed to evaluate the financial losses from increasing minimum reserve in smart grids have been missing, which is important with the increasing penetration of intermittent generation. In addition, models have been ignoring important sources of uncertainty and demand response as a way to mitigate its risks. The evaluation of the models computational performance is another critical issue that has been largely ignored. These are just a few of the main limitations identified in current proposals.

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This thesis proposes solutions to address a significant part of the identified issues. The thesis aggregated contributions, ultimately result in an innovative decision support system, named Advanced Computational Environment for Leveraging Energy Resource mAnagement in smart grids (ACELERA). The ACELERA is composed of a reasonable set of diversified methodologies that together contribute to handle the complexity of energy resource management, including metaheuristics, hybrid and deterministic decomposition methods, which mitigate the issues of performance and scalability. Moreover, it incorporates models that deal with integrated management of distributed energy resources, including electric vehicles, energy storage systems and demand response. Besides, demand response programs shaped for electric vehicles have been developed and implemented to fully explore the capabilities of the future smart grid. Likewise, a multi-objective model to evaluate the financial losses from increasing minimum reserve in smart grids has been developed and implemented as well as a stochastic model to address the uncertainty. It is also important to remark that a multi-dimensional signaling method to improve the performance of metaheuristics in solving the optimization problems is proposed in this thesis. Lastly, the ACELERA is compatible and capable of parallel and distributed computing, making the best use of available technology and able to deliver ultimate performance.

The developed decision support methodologies have been tested and validated in realistic scenarios. The promising results achieved under realistic conditions support the hypothesis that the large-scale energy resource management with the wide use of diversified energy resources can be achieved in the context of smart grids.

Keywords: electric vehicles, metaheuristics, smart grid, stochastic optimization, uncertainty,

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Resumo

A presente tese contribui para o aumento de conhecimento na área dos sistemas elétricos de energia e da inteligência computacional ao propor soluções inovadoras para o problema da gestão de recursos energéticos em smart grids. Estas soluções ajudam a viabilizar o crescimento e a proliferação sustentável de novas entidades de negócio associadas ao setor de energia, nomeadamente os agregadores de energia.

A escassez de recursos fósseis e a crescente preocupação climática devido ao uso generalizado desses recursos para a produção de eletricidade, motivou a implementação de novas medidas políticas e incentivos para contrariar o problema. Estas medidas têm motivado inúmeras iniciativas relacionadas com smart grids e contribuído para elevados investimentos em recursos de energia renovável. Embora o futuro dos sistemas de energia pareça bastante promissor, a elevada penetração de recursos energéticos de origem renovável tem colocado novos desafios na indústria e uma crescente complexidade na operação. Este problema é agravado pela provável penetração em massa de veículos elétricos num futuro próximo.

Na gíria anglo-saxónica, o termo virtual power player refere-se a entidades de agregação de energia. Estas entidades terão um papel importante para atenuar o problema da imprevisibilidade, já que permitem uma elevada flexibilidade de operação. Além disso, tornam possível um maior uso de recursos de fonte de energia renovável integrando pequenos produtores e consumidores que não têm acesso ao mercado grossista. O modelo de negócio dos

virtual power players deverá ultrapassar um valor de mercado de 5 mil milhões de dólares nos

próximos anos, tornando-se assim uma atividade altamente atrativa. O apoio à decisão no que diz respeita à gestão de recursos energéticos é crucial para a sobrevivência destas entidades de forma a reduzirem o custo de operação, aumentaram o lucro, reduzirem o impacto climático e reduzirem os riscos. As soluções existentes não estão preparadas para lidar com uma gestão integrada de recursos energéticos, especialmente em elevado número. Além disso, não existem propostas para avaliar as perdas financeiras com o aumento da disponibilidade em smart grids, importante sobretudo com o aumento da produção intermitente. Outro aspeto importante, é que os modelos existentes tem ignorado importantes fontes de incerteza e utilização da demand

response como forma de atenuação da incerteza. A avaliação do desempenho computacional

dos modelos propostos é outra vertente que tem sido sistematicamente ignorada. Estas são algumas das limitações das propostas atuais identificadas durante o trabalho efetuado.

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A presente tese visa colmatar grande parte destas limitações relacionadas com a gestão de recursos energéticos. As contribuições de uma forma agregada resultam num inovador sistema de apoio à decisão, chamado Advanced Computational Environment for Leveraging

Energy Resource mAnagement in smart grids (ACELERA). O ACELERA é composto por um

leque diversificado de metodologias que, em conjunto, contribuem para lidar com a complexidade do problema de gestão de recursos energéticos. Estas metodologias incluem meta-heurísticas, métodos híbridos, determinísticos (ou matemáticos) baseados em decomposição, que contribuem para melhorar a escalabilidade e o desempenho do problema visado. Além da diversidade de técnicas, a tese propõe modelos de escalonamento de recursos de energia integrados, isto é, consideram veículos elétricos, sistema de armazenamento de energia e demand response. É de salientar que, programas de demand response adaptados a veículos elétricos foram propostos e implementados de forma a explorar o potencial da smart

grid. Da mesma forma, foi desenvolvido um modelo multiobjetivo para avaliar as perdas

financeiras com o aumento da disponibilidade da reserva que foi implementado na tese, assim como, um modelo de otimização estocástico que considera diversas fontes de incerteza. Importa frisar que um método denominado multi-dimensional signaling foi proposto e implementado nesta tese para melhorar o desempenho das meta-heurísticas nos modelos de gestão de recursos energéticos propostos. Por fim, é de salientar que o ACELERA foi desenvolvido a pensar em desempenho e de forma a usufruir da tecnologia disponível atualmente, por isso, é compatível com sistemas computacionais distribuídos e que funcionam com processamento paralelo.

As metodologias de apoio à decisão desenvolvidas na tese foram testadas e analisadas em cenários realísticos. Os resultados conseguidos nestas condições são promissores e validam a hipótese de que a gestão de recursos energéticos (diversificados e em grande escala) é possível e pode ser conseguida no contexto de smart grids.

Palavras-chave: incerteza, meta-heurísticas, otimização estocástica, smart grid, veículos

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List of figures

Figure 1.1 – Overview of the identified needs and resulting contributions 11

Figure 1.2 – Number of publications during the PhD work by chronological order 13

Figure 2.1 – Smart Grid bidirectional flow and communications 18

Figure 2.2 – Regular hybrid and plug-in hybrid vehicle 20

Figure 2.3 – Useful transport energy derived from renewable electricity 21

Figure 2.4 – Technology roadmap for passenger vehicles 22

Figure 2.6 – Example of TOU and RTP and flat rate pricing 26

Figure 2.7 – Smart charging and vehicle-to-grid approaches 27

Figure 2.8 – Energy aggregators: opportunities and interactions with other SG players 30 Figure 2.9 – VPP control strategies: a) centralized b) decentralized c) fully decentralized 31

Figure 2.10 – VPP central entity: ERM inputs and outputs 32

Figure 3.1 – Day-ahead energy resource scheduling diagram 46

Figure 3.2 – Scenario tree with 5 scenarios and 10 nodes 61

Figure 3.3 – Elasticity curves for distinct groups of EVs consumers 68

Figure 4.1 – Type of optimization problems 76

Figure 4.2 – Benders decomposition flowchart: hourly approach 79

Figure 4.3 – Benders decomposition flowchart: multi-period approach 79

Figure 4.4 – DSA algorithm flowchart 85

Figure 4.5 – Lagged particles and waiting phenomena in PSO and QPSO 87

Figure 4.6 – Multi-objective Particle Swarm Optimization flowchart 89

Figure 4.7 – Convergence history of the fitness in 100 runs for a toy problem in QPSO 90

Figure 4.8 – Convergence history of the diversity measure for a toy problem in DSA 91

Figure 4.9 – MD signaling procedure in metaheuristics 93

Figure 4.10 – Hybrid based approach flowchart 98

Figure 4.11 – Overview of ACELERA decision support system 103

Figure 5.1 – 33-bus single-line diagram 111

Figure 5.2 – Convergence history of the fitness over 100 runs: PSO, QPSO and DSA 114

Figure 5.3 – Convergence history of the diversity measure compared: DSA approaches 115

Figure 5.4 – Energy resources scheduling: a) DSA; b) HDSA; c) BD-MP 117

Figure 5.5 – Consumption scheduling a) DSA; b) HDSA; c) BD-MP 118

Figure 5.6 –180-bus single-line diagram 120

Figure 5.7 – Convergence history of the fitness: PSO, QPSO, and DSA (180-bus) 121

Figure 5.8 – Convergence history of the diversity measure in the 180-bus scenario 122

Figure 5.9 – Energy resource scheduling a) HDSA; b) BD-H; c) BD-MP 124

Figure 5.10 – Consumption scheduling a) HDSA; b) BD-H; c) BD-MP 125

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Figure 5.12 – 233-bus Vila Real distribution grid 130

Figure 5.13 – Pareto front for PSO, MPOSO, NSGA-II with and without MD signaling 132

Figure 5.14 – Energy resources scheduling (P) and consumption (C): NDS-L 133

Figure 5.15 – Energy resources scheduling (P) and consumption (C): NDS-R 133

Figure 5.16 – Total trip demand of the 1000 EVs’ scenario 136

Figure 5.17 – External supplier and demand response price 136

Figure 5.18 – Energy resource scheduling in single-objective scenario 138

Figure 5.19 – EVs battery state, charge and trips demand 139

Figure 5.20 – Convergence history for fitness in single-objective scenario 139

Figure 5.21 – Pareto front: a) PSO and hybrid PSO b) comparison with MILP 140

Figure 5.22 – Optimal resource scheduling in hybrid PSO for: a) NDS-L b) NDS-R 141

Figure 5.23 – Consumption scheduling in hybrid PSO: a) NDS-L b) NDS-R 142

Figure 5.24 – 201-bus MV network used in the case study 146

Figure 5.25 – Wind and solar scenarios 147

Figure 5.26 – Regular load demand scenarios 148

Figure 5.27 – Electric vehicles scenarios 148

Figure 5.28 – Market prices scenarios 148

Figure 5.29 – Stochastic energy resource scheduling in case 1 (with DR) 149

Figure 5.30 – Stochastic energy resource scheduling in case 2 (no DR) 149

Figure 5.31 – Stochastic consumption scheduling in case 1 (with DR) 150

Figure 5.32 – Stochastic consumption scheduling in case 2 (no DR) 150

Figure 5.33 – Stochastic energy resources totals in case 1 (with DR) 151

Figure 5.34 – Stochastic energy resources totals in case 2 (no DR) 151

Figure 5.35 – EVs group distribution and trip demand forecast by group in parentheses 154 Figure 5.36 – Electric vehicles trip demand forecast: 50 scenarios in MCS simulation (σEVs =15%) 154

Figure 5.37 – Variation of decision variables for the deterministic and stochastic solution 155 Figure 5.38 – Comparison of the proposed EV pricing solution using deterministic and stochastic model 156 Figure 5.39 – EV tariff vs expected EV charging by group (average) of the stochastic solution 156

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List of tables

Table 1.1 – Summary of key contributions 12

Table 2.1 – Estimated levelized cost of electricity for new energy resources 19

Table 2.2 – Targets for batteries cost for the period 2020-2030 19

Table 2.3 – Fleet penetration projections 22

Table 2.4 – Future development targets of EVs’ batteries 23

Table 2.5 – Typical charging power modes expected in Portugal/Europe 24

Table 5.1 – 33-bus grid scenario characterization 111

Table 5.2 – Metaheuristics-based approaches parameters 112

Table 5.3 – 33-bus scenario methods’ results (averaged over 100 runs) 113

Table 5.4 – ANOVA test between the algorithms in the 33-bus scenario 115

Table 5.5 – 180-bus grid scenario characterization 119

Table 5.6 – 180-bus scenario methods’ results (averaged over 100 runs) 120

Table 5.7 – ANOVA test between the algorithms in the 180-bus scenario 122

Table 5.8 – Vila Real 233-bus grid characterization 129

Table 5.9 – Metaheuristics-based approaches parameters 131

Table 5.10 – Scenario results: Profits and emissions 134

Table 5.11 – Energy resources characterization 137

Table 5.12 – Parameters used in the PSO based approaches 137

Table 5.13 – Single-objective scenario results 138

Table 5.14 – Selected non-dominated solutions from the Pareto front 141

Table 5.15 – Multi-objective scenario results (NDS-L and NDS-R) 143

Table 5.16 – NDS-L and NDS-R: Totals for ESS and EVs 143

Table 5.17 – Computational execution time 144

Table 5.18 – Zaragoza 201-bus grid stochastic scenario characterization 147

Table 5.19 – Advantage of stochastic programming approach 151

Table 5.20 – Zaragoza 2030 scenario characterization 153

Table 5.21 – Comparison of results between deterministic and stochastic solution 155

Table 5.22 – Advantage of stochastic programming approach 157

Table 5.23 – Expected operation performance under different pricing schemes 157

Table A.1 – Vehicle classes with figure 1

Table A.2 – Electric vehicles characteristics 3

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Abbreviations

AC Alternating Current

ACELERA Advanced Computational Environment to Leverage ERM in smart grids BEV Battery Electric Vehicle

CHP Combined Heat and Power

DC Direct Current

DER Distributed Energy Resources

DG Distributed Generation

DLC Direct Load Control

DR Demand Response

DSA Differential Search Algorithm DSO Distribution System Operator

DSS Decision Support System

EC European Commission

EM Electricity Market

EREV Extended Range Electric Vehicle

ERM Energy Resource Management

ESS Energy Storage System

EU European Union

EV Electric Vehicle

EVeSSi Electric Vehicle Scenario Simulator EVSE Electric Vehicle Supply Equipment FCEV Fuel Cell Electric Vehicle

GA Genetic Algorithm

HV High voltage

LP Linear Programming

LV Low Voltage

MCS Monte Carlo Simulation

MG Microgrid

MILP Mixed Integer Linear Programming (MIP is equivalent to MILP) MINLP Mixed Integer Nonlinear Programming

MOEA Multi-Objective Evolutionary Algorithm MOPSO Multi-objective Particle Swarm Optimization

MV Medium Voltage

NDS Non-Dominated Solution

NLP Nonlinear Programming

NSD Non-Supplied Demand

PHEV Plug-in Hybrid Electric Vehicle PSO Particle Swarm Optimization PV Photovoltaic solar panels

RES Renewable Energy Sources

RTP Real Time Price

SG Smart Grid

TOU Time-Of-Use

V2G Vehicle-to-Grid

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1 Introduction

This chapter presents the principal motivation that has led to the development of the work carried out in the scope of this thesis. The presented motivation leads to the set of research questions and objectives defined in section 1.2. Then, this PhD’ key contributions are outlined in section 1.3, followed by the presentation of successful publications in section 1.4. Finally, section 1.5 presents the organization of the document.

1.1 Motivation

Modern societies are highly dependent on the electricity supply. Ranging from telecommunications to water supply systems, electricity is the foundation of a safe, reliable, comfortable and prosperous living and is recognized as the pillar of the future smart cities. In spite of being essential to most infrastructures’ systems, the electricity generation poses a threat to the environment, as it is still highly dependent on fossil fuels [EUROPEAN COMMISSION,2016;

FOSTER,2014; RADHAKRISHNAN,2016].

The electricity generation mix must continue to change in the next few years in order to overcome the mentioned concerns and to achieve certain targets. For instance, European Union (EU) countries have committed to achieve at least 27% of renewable share of the total energy consumption by 2030, according to the new target, revised in 2014 [EUROPEAN COMMISSION,

2014]. To obtain this high share of renewables, several incentives have been put in practice by those involved countries [EUROPEAN COMMISSION,2016]. Huge investments have been made

in new renewable based generation plants in parallel with several Smart Grid (SG) initiatives to support this paradigm shift. In Europe, a total of 459 SG projects have been funded from 2002 to 2014 amounting to €3.15 billion investments, both public and private [JRC,2014].

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Actually, the majority of the developed countries are already reaching demonstration and deployment phase of specific SG technologies like smart metering. However, they continue to invest in research and development of other applications, such as aggregation models and vehicle-to-grid concepts [COLAK, 2015; LUO, 2016]. It is currently expected that mass

penetration of Electric Vehicles (EVs) will occur and bring more complexity to the operation and planning tasks, but also allow unique opportunities. SG enable safe integration and aggregation of more Distributed Energy Resources (DER), namely, Distributed Generation (DG), Energy Storage Systems (ESS), EVs and Demand Response (DR), while delivering comprehensive control, monitoring and self-healing capabilities, tailored to consumers’ needs and enabling more control over consumption and electricity usage [JRC,2014; WISSNER,2011].

Renewable generation brings great benefits to the environment, by providing clean ways of getting carbon-neutral energy. However, electricity generation does not follow the demand, especially in wind and solar power, which poses a great challenge to the power system. Therefore, flexible generation and demand is highly valuable in SGs. According to [ZERVOS,

2010], one of the many measures to be put in place in order to achieve a 100% renewable energy supply is with virtual power plants, which can provide flexible operation. Aggregation entities or virtual power players1_{(VPPs) are a relatively new idea emerged in SG context that can}

aggregate DERs and demand with the aim to raise their value in competitive electricity markets environment, and for instance, provide demand and generation flexibility, increase renewable use and achieve optimal network and resources operation. According to [NAVIGANT RESEARCH,

2014], it is expected that the revenue of VPPs will steadily grow to more than 5 billion dollars during the next decade, making it a very promising business sector. However, these advanced SG bodies require adequate tools to deal with the large number of energy resources. Undeniably, Energy Resource Management (ERM) is of crucial importance for VPPs, as a decision support tool to obtain reduced operation costs and/or higher profits, reduced carbon emissions and reduced risk, e.g. by avoiding supply shortage and high price fluctuations [SHI

YOU, 2009; ZAMANI, 2016]. However, the complexity of the ERM problem, the level of

uncertainty, and the curse of dimensionality and scalability (large number of energy resources, e.g. EVs), which are more relevant in larger time horizons like day-ahead, are issues that must be addressed. This thesis focuses essentially in this topic and these issues. Indeed, in the last

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few years significant research work has been done in this subject, delivering important results but also evidencing many limitations, which still require further attention.

To fully grasp the state of the art limitations in the current literature, an extensive literature review was carried out during the development of this thesis (see section 2.3). Several techniques have been proposed to address ERM in SGs, while many still focus on deterministic operation [MOTEVASEL,2013; OSÓRIO,2015; ZAKARIAZADEH, 2015], some steps have also

been made to tackle uncertainty [EAJAL,2016; MOTEVASEL,2014; SU,2014]. The existent gaps

perceived from the state of the art revision can be summarized in 7 main issues as follows: 1) the identified ERM models are usually developed for specific situations, small cases and not fully integrated with a broad set of different DER technologies;

2) the models to evaluate financial losses from increasing minimum reserve are not proposed;

3) the proposed ERM models computational performance has been systematically ignored in most of the published works;

4) currently the stochastic models ignore important sources of uncertainties, such as the market price and EVs behaviour while not considering important resources as DR; 5) the optimization methods available to solve ERM problems present reduced scalability, namely deterministic techniques and in particular metaheuristics need robust and better heuristics to solve large-scale ERM problems efficiently;

6) the decomposition based techniques to reduce solving time of ERM are scarcely developed and currently present some weaknesses (see literature review 2.3.3);

7) the DR programs adequately shaped for EVs are not well studied in the literature and lack full integration with ERM models.

The following section establishes the set of research questions and the main objectives in result of the identified issues.

1.2 Objectives

The limitations that have been identified in the current state of the art refer to the lack of integrated and adequate decision support models for the energy resource management of SG players. The need to develop enhanced software solutions, including new models and

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algorithms, is essential to improve the decision making of the involved actors and overcome the existent gap. On the one hand, it is important to have adequate models that accurately represent the system, but on the other hand, higher complexity may jeopardize the models usefulness. There is no added value in developing a very accurate model, if it requires two weeks to be solved. Hence, appropriate balance between accurate models and adequate technical solutions becomes essential. The significant innovations necessary in the scope of this thesis establish the main research question:

Can complex energy resource management (ERM) models be enhanced and adequately solved in smart grids context?

The first part of the research question refers to the ERM optimization models enhancement, i.e. their flexibility, suitability and accuracy (enhanced), while the second part refers to the usefulness and quality of the technical solutions deployed to solve these models (adequately solved). To answer this complex question, there is the need to scrutinize the problem into smaller and focused research topics. Therefore, the following specific research questions arise:

• What are the most adequate optimization models to solve the energy resource

scheduling centralized problem including EVs?

• Can EVs coexist with other DERs in the same scheduling model? o What are the advantages of an integrated model?

o How to solve such large-scale problems effectively and efficiently? • How can uncertainties, such as EVs, market price, solar and wind power generation,

be handled by the ERM problems?

• Can demand response programs be shaped to the needs of EVs’ users?

• Should the ERM problems be tackled with deterministic and/or metaheuristic

techniques?

o How to assess and improve the computational performance?

• Can metaheuristics be improved to effectively solve large-scale energy resource

scheduling?

• Do decomposition techniques bring advantages to the envisaged problem? o What are their advantages and disadvantages?

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• How can decision support tools be tested and validated with realistic data, namely

referring to EVs?

The research work carried out within this thesis has put all the efforts to achieve answers to these aforementioned specific questions. The global idea behind the research is to attest the hypothesis that large-scale ERM can be feasible and coexist with the presence of EVs, if adequate models, algorithms and intelligent implementations are deployed. Ultimately, the expected deliverable of this PhD research is a Decision Support System (DSS) that facilitates the validation of the developed methods and is capable to perform energy resource scheduling while being flexible enough to adapt to each situation, e.g. size of the grid or MicroGrid (MG), number and types of DERs, temporal horizon, etc. In order to achieve this goal, several models and algorithms are proposed and evaluated, while working together to provide the best decision making. These models and algorithms shall provide a significant contribution of this thesis.

It is important to remark that the definition of the objectives of this work has benefited from the symbiotic interaction with national and international R&D projects coordinated by or having the participation of the Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), where this thesis has been developed. The breakthrough nature of these projects has enabled this thesis to consider innovative perspectives that have help to enrich this work. The considered projects are:

• DREAM-GO – Enabling Demand Response for short and real-time Efficient And Market based smart Grid Operation – An intelligent and real-time simulation approach. European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 641794;

• ELECON – Electricity Consumption Analysis to Promote Energy Efficiency Considering Demand Response and Non-technical Losses, FP7-PEOPLE – IRSES, 318912;

• GID-MicroRede - Intelligent and Decentralized Management System for Private Microgrids, QREN, (Ref.34086);

• IMaDER – Intelligent Short Term Management of Distributed Energy Resources in a Multi-Player Competitive Environment (PTDC/SEN-ENR/122174/2010), funded by FCT;

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• SASGER-MeC – Simulation and analysis of SGs with renewable energy sources in the scope of competitive markets (NORTE-07-0162-FEDER-000101), co-funded by COMPETE under FEDER Programme;

• SEAS – Smart Energy Aware Systems, project number 12004, funded by European Union’s EUREKA – ITEA2.

The limitations found in the state of the art where complemented by real inputs received from these projects, which enabled a broader vision and helped to define the directions of this thesis, as it involved important organisations, real industry challenges and regulation aspects.

In this PhD an action research methodological approach was employed. An action research process consists mainly in four steps: plan, act, observe, and reflect with a primary focus to solve real problems. Firstly, the problem is identified and data collected for a more detailed analysis. After this step, a set of possible actions can be implemented. The results of these actions are analyzed, and the findings will tell how successful the actions were. At this point, the problem is re-assessed and the process begins another cycle. This process continues until the problem is determined while satisfying the required quality level.

Due to the fact that this thesis conclusions should be supported by experimentation on realistic or almost real cases, the work to be developed must rely on realistic inputs and adequate sources. A relevant example, is the use of an EVs’ scenarios simulator in the scope of this thesis, which is a complementary tool to the DSS, allowing to study the EVs’ behaviour and enabling the construction of realistic and complex scenarios based on real statistics. Thereby, the achieved results can be verified under realistic conditions, thus assuring the validation of the achieved solutions.

In the light of the mentioned aspects, which assure the response to the identified research questions, the following objectives are considered:

1- Study of SGs, EVs and related work:

• Analysis of the characteristics and particularities of the SG, DERs (including EVs) and the main involved players;

• Study of EVs management strategies and identification of the opportunities for demand response;

• Review of the models available for ERM including technical solutions that have been proposed.

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2- Design and development of mathematical models to solve the energy scheduling problem, consisting in:

• Integration of the day-ahead energy scheduling model with EVs, ESSs, demand response and other DERs;

• Pursuing different design goals, e.g. single or multi-objective; • Tackling the uncertainties arisen in the energy scheduling problem; • Developing of demand response programs specifically shaped for EVs. 3- Design and development of the technical solutions to tackle the developed models,

including:

• Study of optimization techniques;

• Implementation of deterministic methods to obtain results with a reference level of quality;

• Implementation of competitive metaheuristics and deterministic-based decomposition techniques to reduce execution time for large problem instances;

• Improvement of metaheuristics-based algorithms by using intelligent schemes and specific heuristics tailored to the studied problems;

• Investigation of hybrid approaches, combining deterministic and metaheuristics;

• Analysis and evaluation of results by using adequate validation methods to assess the performance of the developed metaheuristics, such as Analysis of Variance (ANOVA).

4- Development of a DSS directed to the ERM, namely:

• Development of a system that accommodates and integrates the methods and approaches developed in this PhD;

• Use of distributed computation and parallelization to improve the performance of the DSS.

5- Design and analysis of a set of case studies to validate the proposed DSS system. • Simulation of scenarios based on supportive projections and real or realistic

distribution grids;

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1.3 Key contributions

The combined contributions provided by the work developed in the scope of this PhD ultimately result in a DSS, called here Advanced Computational Environment for Leveraging Energy Resource mAnagement in smart grids (ACELERA). The DSS integrates the several modules that have been developed to answer the identified issues and fulfilling the objectives mentioned in previous section. Figure 1.1 presents an overview of the identified needs and the contributions provided by this thesis to satisfy them. The contributions include integrated ERM models which are described in detail in chapter 3. These models describe not only different design goals (e.g. single and multi-objective goals) but also different DR models for EVs. Moreover, a stochastic model approach to deal with sources of uncertainty is tackled by a stochastic programming framework and included in the ACELERA (see section 3.2).

In order to solve the developed ERM models, this thesis proposes and compares several technical solutions that are essential to mitigate the computational demand of ERM, namely: deterministic optimization (see section 4.2), enhanced metaheuristics (see section 4.3) leveraged with the developed Multi-Dimensional (MD) signaling (see section 4.4), and hybrid approaches that combine deterministic and metaheuristics methods (see section 4.5). The deterministic optimization includes Benders’ decomposition approach, which is explained in more detail in section 4.2.4. The benefits of MD signaling are higher solution quality, better constraint handling of the optimization model and lower convergence time of the metaheuristics. In addition, distributed and parallel computing has been investigated to enable the methods to run concurrently in order to achieve faster and reliable results (see section 4.7). These features have been integrated and implemented in the ACELERA, which rely on input data, such as, contracts (customers’ loads, generation units, electricity suppliers, etc.), network technical data, load demand forecast, EVs technical and forecast data, etc. A complementary tool has been used in this PhD work, namely the Electric Vehicle Scenario Simulator (EVeSSi), whose latest developments have been published in [SOARES, 2014; SOARES, 2016D]. The

EVeSSi is a support tool used to generate realistic EVs scenarios to provide accurate studies in ACELERA DSS.

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Figure 1.1 – Overview of the identified needs and resulting contributions

Table 1.1 summarizes the key contributions of this thesis and the linking chapters. The most important publications for each contribution are also indicated. The distinct optimization models concerning ERM have been partially covered and published in [SOARES, 2013A;

SOARES,2015A; SOARES,2015E; SOARES,2016B; SOARES,2016C]. The different models differ

by their design goal, context, level of resources integration, and the diversity of energy resources considered. An important contribution of this PhD is the integrated stochastic model approach, which has been submitted to journal and is under review. Furthermore, an innovative multi-objective model, developed in this PhD, concerning the minimum available reserve and solved by an effective hybrid and parallel approach, has been published in [SOARES,2016B].

The demand response models for EVs developed in the scope of this PhD have been partially published in [SOARES,2013A; SOARES,2015A]. The details of the developed models can be

seen in chapter 3 of this thesis.

The work undertaken in this thesis regarding hybridization of metaheuristics with deterministic methods has been paid attention in [SOARES,2013B; SOARES,2015B; SOARES,

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computational parallelization have been published in [SOARES, 2013D; SOARES, 2015E;

SOARES,2016B; SOARES,2016E]. The works presented in [FOTOUHI,2015; SOARES,2015C;

SOARES,2016B; SOARES,2016C] have focused in distinct technical solutions, namely stochastic

methods, that have been studied and developed in this thesis. Nonetheless, deterministic and metaheuristics methods can be realized in most of the mentioned publications that have resulted from this PhD, e.g. Benders’ decomposition approach applied to the problem under study has been published in [SOARES,2016A]. The development of specific heuristics, to improve the

performance and results of metaheuristics, has been the concern of the published works in [SOARES, 2013C; SOARES, 2015E; SOARES, 2016C; SOARES, 2016E]. The several technical

solutions and approaches adopted and developed in this thesis can be followed in chapter 4. The significance of the developed decision support methodologies can only be evaluated by means of realistic studies and simulations. It has been noted that the connection with EVeSSi is important to address the lack of realistic EV data, which enables to create realistic scenarios in a controlled simulation environment. Indeed, in the scope of this thesis, realistic case studies and simulation scenarios have been used to test and validate the proposed methodologies (see chapter 5 and e.g. paper [SOARES,2013A; SOARES,2016A; SOARES,2016B; SOARES,2016C]).

Table 1.1 – Summary of key contributions

Key contribution Chapter Related publications

ERM optimization

models 3

[SOARES,2013A; SOARES,2015A; SOARES,2015E; SOARES,2016B; SOARES,2016C]

Stochastic model 3 [SOARES,2017A] 1

Minimum reserve model 3 [SOARES,2016B]

Demand response EVs 3 [SOARES,2013A; SOARES,2015A; SOARES,2017B] Stochastic methods 4 [FOTOUHI,2015; SOARES,2015C; SOARES,2016B; SOARES,2016C]

Benders’ decomposition 4 [SOARES,2016A] 1

Specific heuristics 4 [SOARES,2013C; SOARES,2015E; SOARES,2016C; SOARES,2016E] Hybridization 4 [SOARES,2013B; SOARES,2015B; SOARES,2016B; SOARES,2016C] Parallelization 4 [SOARES,2013D; SOARES,2015E; SOARES,2016B; SOARES,2016E]

1_{In addition, there are further articles submitted for possible publication}

1.4 Publications

During this PhD a total of 43 papers have been published. These publications have been mostly published in top-level journals, conference proceedings and a book chapter of the fields of computational intelligence and power systems. From these, 15 papers have been published

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in SCI2_{indexed journals with significant impact factors in these fields. Figure 1.2 presents the}

number of publications by chronological order, which were achieved during this PhD.

Figure 1.2 – Number of publications during the PhD work by chronological order

It is important to refer that a significant part of this thesis has been published in peer-review dissemination or has been submitted for possible publication. These publications represent specific contributions from the work presented here. The link between the most important published work and the chapters of this thesis is the following:

• Chapter 2, namely the literature review has been partially published in [SOARES,2016B;

SOARES,2016C];

• Chapter 3 has resulted in several publications, namely the most important are [SOARES,

2013A; SOARES,2015A; SOARES,2015E; SOARES,2016B; SOARES,2016C];

• Chapter 4 has been peer-reviewed in several works, namely [FOTOUHI,2015; SOARES,

2013C; SOARES,2015B; SOARES,2016A; SOARES,2016B; SOARES,2016C];

• Chapter 5 covers application examples and case studies that have been partially published in several articles, namely [SOARES,2016A; SOARES,2016B; SOARES,2016C;

SOARES,2016E].

The defined objectives accomplishment and the contributions made in the scope of this thesis fully cover the objectives defined in the PhD scholarship (reference SFRH/BD/87809/2012) in the scope of the “Doutoramento e Pós-Doutoramento 2012” (PhD and post-doctoral 2012) programme of FCT (Fundação para a Ciência e a Tecnologia - Science and Technology Foundation).

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1.5 Thesis structure

The present document consists of six chapters. After this introduction, chapter 2 presents important concepts regarding SG, EVs and the ERM topic. It is also in chapter 2 that a comprehensive literature review is given along with the identified gaps and the critical research topics that are addressed by this PhD work. The effective contributions of the thesis are addressed in subsequent chapters, namely 3 to 5.

Chapter 3 presents the ERM developed models in the scope of this PhD, namely considering single and multi-objective goals as well as the problem of uncertainties and DR of EVs. This chapter presents the mathematical formulation of the optimization models and the necessary notation used to fully understand the proposed models.

Chapter 4 is complementary to the previous chapter, because it presents the technical solutions proposed in this PhD to solve the models described in chapter 3. These solutions include metaheuristics, deterministic and decomposition techniques, as well as hybrid approaches, which have been tailored to obtain optimal performance and high quality results for the envisaged problem.

Chapter 5 presents several case studies that demonstrate the value and effectiveness of the developed work. The results achieved are crucial for the recommendations and conclusions drawn in chapter 6, which is the last chapter of this thesis. Moreover, some perspectives of future work are also proposed in the last chapter.

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2 Background and related work

This chapter presents a review of the current status of Energy Resource Management (ERM) in Smart Grids (SGs). The SG concept is firstly introduced based on the current understanding from the available literature. An overview on current and future Distributed Energy Resources (DERs) technologies, including Electric Vehicles (EVs) in the scope of the SG paradigm is also presented. EVs are considered in this topic as their presence in future electricity networks is expected to increased, and thus play an important role in this field in the next years [HU,2016; SU,2012]. EVs could be able to participate as DERs and their presence

can influence the ERM in SGs [SABER,2012; SOARES,2013A]. This chapter enables to draw

important conclusions on the current limitations in the field and on future challenges that are expected in SGs. This discussion results in the identification of the critical research topics that are addressed by this PhD work.

2.1 Smart Grid

A SG basic definition could be electricity network with a brain [DOE, 2016]. It is possible to establish an analogy between the traditional electricity network with the earliest telephones and the SG with the newest smartphones. In the traditional grid the electricity was delivered in a one-way flow from the generator to the consumer. The SG grid approach enables bidirectional energy flow and two-way communication and control (see Figure 2.1). The goal of a SG is to deliver electricity efficiently, reliably, and securely [BLUMSACK,2012; HOSSAIN,

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Figure 2.1 – Smart Grid bidirectional flow and communications [DOE,2016] 2.1.1 Distributed Energy Resources

One of the SG features is the high presence of DERs. SG enable new technologies to be easily integrated and managed. Distributed technologies can be divided into two types: traditional generators (combustion engines) and non-traditional generators. Traditional generators include Combined Heat and Power (CHP), diesel generators or micro-turbines such as natural gas turbines with a 20-500 kW scale [EL-KHATTAM, 2004]. Non-traditional

technologies include EVs (discussed in section 2.2), fuel cells, photovoltaic panel systems (PV), small hydro, storage devices and wind power systems [REZAEE JORDEHI,2016; SOUSA,2012A].

The introduction of these DERs have been increasing over time with costs plummeting and technology improvements, which enabled widely acceptance of some of them, particularly wind and solar renewable generation. In fact, EU and national policies, that promote renewables through financial instruments such as feed-in-tariffs, have been driving significant penetration of renewables. This expansion has also been accompanied by improvements in the grid and new equipment for control, in the context of SGs. Among other factors, the levelized costs of generation can play a key role in what concerns investments decisions, hence they are analyzed in more detail in the following subsection.

2.1.2 Levelized costs of generation

Table 2.1 depicts estimated average values for the levelized cost of new energy resources investments regarding different technologies in 2020 and 2040, according to [EIA, 2015]. The levelized cost takes into account capital cost, capacity factor, fixed and variable operation and maintenance costs and grid investment. The cost is divided by the expected amount of energy generated during the lifetime period of the project. According to the available

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data, no significant changes are expected in the levelized cost of these technologies. Wind and geothermal technologies are the cheapest in both years, while PV technology appear as the most expensive on average. The levelized cost can have a direct relationship with the electricity price at which the owner is willing to sell the produced energy.

Table 2.1 – Estimated levelized cost3_{of electricity for new energy resources}

Energy resource 2020 ($/MWh) 2040 ($/MWh) Biomass 100.5 93.5 Wind 73.6 75.1 Solar PV 125.3 117.3 Hydro 83.5 89.9 Geothermal 47.8 60.8

Regarding ESS systems, namely battery storage devices, many efforts have been made in the last few decades by governments and industry. Europe, Japan and United States have a significant funding for research and development of advanced batteries. Japan leads the funding from industry based batteries manufacturers. China and South Korea also have been funding research labs both from industry and government initiatives. In some research programs the targets of the funding are set in terms of specific cost and performance figures [ELEMENT

ENERGY, 2012]. The large experience from the different sectors enables the industry to

extrapolate promising targets as can be seen in Table 2.2, which shows targets by battery technology for the period 2020-2030 according to [EASE,2013].

Table 2.2 – Targets for batteries cost for the period 2020-2030

Technology Energy cost (€/kWh) Lifecycles

Lead 100-150 3000-10,000

Nickel <250 6000-8000

Redox flow 120 >10,000

Li-ion <200 >10,000

According to the available data, li-ion and redox flow look like a very promising technology for energy storage, presenting the highest lifespan and reasonable energy cost. However, such targets are always in relation to specific conditions and should be further defined by grid operators to allow the battery industry to come up with concrete figures.

The declining costs will contribute to a growing share of RES and DERs in the near future [EUROPEAN COMMISSION,2016]. The flipside comes from the challenges of intermittent

generation and its integration in the grid. This can be mitigated with SG solutions, e.g. flexible demand, such as EVs, which seems a promising candidate and discussed in next section.

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2.2 Electric Vehicles

The term EV in this PhD work refers to any kind of vehicle that includes a storage system that can be charged from the grid and can be used to provide motion power, independently of the technology of the storage and the type of the electric motor.

2.2.1 Electric vehicle types

EVs can be classified according to their configuration [NIKOWITZ,2016]:

• Plug-in Hybrid Electric Vehicle (PHEV): This type of vehicle is similar to a regular hybrid that combines both an electric motor and an internal combustion engine for motion power. However, unlike standard hybrids, PHEVs can be charged using the electricity grid (usually connecting a plug to an electric socket). Their battery’s capacity is typically larger and a powerful electric motor is present (see Figure 2.2). Regenerative braking capabilities and other hybrids’ features are frequently present in PHEVs as well; • Extended Range Electric Vehicle (EREV): The EREV is a type of a PHEV in which the main energy source is the battery for daily trips. An internal combustion engine switches on to work as a range-extender by recharging battery on-board and usually this motor is not connected to the transmission system;

• Battery Electric Vehicle (BEV): In this kind of EV the only source of energy is the battery. The range is often more limited than in PHEVs and EREVs. However, this type of vehicles does not use any fuel and rely exclusively from the vehicle’s energy storage to get around. Typically, the batteries of BEVs are of larger capacity than those installed on PHEVs and EREVs, though making the vehicle expensive;

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• Fuel Cell Electric Vehicle (FCEV): It is a type of hydrogen vehicle using a fuel cell to produce electricity to power the on-board electric motor. The FCEV produces water vapour at the tailpipe. According to a study conducted by [BOSSEL,2006] a hydrogen

economy is inefficient and can never compete with electricity distribution due to substantial conversion losses (see Figure 2.3). This leaves an open question regarding FCEVs feasibility in terms of practicability and cost-effectiveness [FERNANDEZ,2016;

KEMPTON, 2005]. The energy advantages of battery-powered electric cars over

hydrogen FCEV are noticeable.

Figure 2.3 – Useful transport energy derived from renewable electricity [BOSSEL,2006] 2.2.2 Technology roadmap

The EVs market introduction is expected to be slow and gradually at early stages with the proliferation of several hybridization technologies before attaining full realization and maturity. Figure 2.4 presents a technology roadmap developed by [DOWNING, 2010], for

passenger vehicles in Europe up to 2030, that shows how the automotive industry will introduce electrification in their products to the market. Light passenger vehicles dominate most of automotive sales in Europe, i.e. M1 vehicles represent 87% of the total vehicle fleet according to [ACEA, 2010]. The grey chevrons represent the first introduction of a technology to the market. The top of the roadmap shows the CO2 legislation drivers that automotive industry

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should attain in their fleets. A continuing trend to reduce vehicle weight and improve aerodynamics is expected. Regular hybrids can be considered a mature technology (e.g. Toyota Prius) and sustained growth is foreseen within the market place. PHEVs and BEVs are expected to reach maturity over the next 15 years.

Figure 2.4 – Technology roadmap for passenger vehicles [DOWNING,2010]

Table 2.3 presents the projections for the penetration of EVs in the fleet by 2030 and 2050 in several regions of the world. The projections resulted from estimation, reading and collection of several data sources available in the literature in SASGER-MEC project (see 1.2). [ABVE,2016; GAMBHIR,2012; HASSET,2011; NEMRY,2010; RADAR NACIONAL,2015; REIS,

2011; ROCKY MOUNTAIN INSTITUTE,2014; TRIGG,2013; WORLD ENERGY COUNCIL,2011].

Table 2.3 – Fleet penetration projections

Country/region 2030 2050 Brazil 4% 9% China 14% 30% Europe 27% 49% India 10% 18% Portugal 28% 56% United States 7% 50%

According to [REIS,2011] the projected penetration of EVs in Portugal is expected to

be above 50% in 2050, which is higher than the average projected for Europe and United States. Regarding Brazil and India, a lower penetration is likely, namely 9% and 18%, respectively.

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Table 2.4 shows the roadmap’s targets for EV battery technology development in Japan [ELEMENT ENERGY, 2012] for different years. If these targets materialize, EV battery

technology is expected to achieve costs below 100 $/kWh after 2030. This indicates that a 20 kWh, suitable for 100-150 km range, is expected to cost about $1,400, when compared to $8,000 today. Furthermore, the weight of an equivalent 20 kWh battery should be reduced from 134 kg to just 29kg, while occupying 4 times less space.

Table 2.4 – Future development targets of EVs’ batteries

Metric 2015 2020 2030 Post 2030

Cost ($/kWh) 400 270 130 70

Energy density (Wh/kg) 150 250 500 700

Volumetric density (Wh/l) 400 600 1000 1500

The projections presented in this section clearly shows that promising achievements can be expected in the next decade, which will allow the intensification and proliferation of electricity-based transportation.

2.2.3 Charging modes

Electric Vehicle Supply Equipment (EVSE) typically includes a supply device, a power cord, and a connector. The supply device corresponds to the main component of the charging station. Usually, it is responsible for supplying power and providing shock protection. Additionally, sensor meters and information systems can be available for measuring the amount of energy consumed while EV is charging. The power cord is a cable that carries electrical current and communication signals from the supply device to the on-board charger in the EV through the connector. This connector is a plug on the power cord that physically connects the EVSE to the charging socket in the EV.

The charging levels (charging power) depend on the availability and type of EVSE used as well as the EV being charged, i.e. on-board or external charger specifications. A normal charging power at home, in Portugal, would be about 3 kW on a single-phase 230 VAC 16A socket-outlet connection but this can vary with the region. In U.S., at a typical home with a connection of 120 VAC 15A (known as level 1), the charging power would correspond to about 1.4 kW at 80% service [DICKERMAN,2010]. An equivalent EV would take about twice the time

to charge in level 1 when compared with charging at home in Portugal. In spite of that, the EV owner in U.S. has the possibility to upgrade to a dedicated 220-240 VAC 15A/30A outlet and circuit at home to charge faster (level 2). If the EV’s owner does not support higher charging modes, then upgrading to a level 2 connection is useless. Level 3 in U.S. corresponds to the fast

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charging mode and the connection typically can be a three-phase 480 VAC that can draw more than 50 kW [DICKERMAN,2010]. An important remark is that the real and apparent power in

EV charging is assumed to be essentially the same according to [CARLSON,2012].

In Europe it is expected that a uniform solution for EVs charging will be achieved reached. Table 2.5 presents the possible charging levels in Portugal and countries in Europe with similar electric infrastructure. Taking into account the current recommendations, here the possible charging modes are classified using a similar U.S. nomenclature, for simplicity’s sake.

Table 2.5 – Typical charging power modes expected in Portugal/Europe

Charging level Power _(kW) Voltage Current _(A) Estimated charge time _{(20 kWh battery)}

Level 1 Single-phase 3 230 VAC 16 6-7 hours

Level 2 Single-phase Three-phase 10 7 400 VAC 230 VAC 16 32 2-3 hours 2 hours

Three-phase 24 400 VAC 32 1 hour

Level 3 _{Direct current}Three-phase _62.543 _{400-500 VDC}400 VAC _100-12563 20-30 min (80%) _{15 min (80%)}

Three-phase charging provides higher charging power when compared with single-phase charging but a three-single-phase power supply is required. The use of three single-phase supply can enable the transmission of more power without increasing the voltage or current. Three-phase 400 VAC 63A can provide fast charging mode (level 3). A typical battery with 20 kWh energy rated capacity can be charged to 80% in about 30 minutes in this mode. DC fast charging can provide higher power rate than the AC mode while charging a 20 kWh battery to 80% in about 15 minutes.

Some EVs in the market already support fast charging mode at the moment like the Nissan Leaf and Tesla Model S [BONGES,2016]. In the future it is expected that most of them

will support fast charging mode. According to the findings in [DOWNING,2010], which resulted

from a survey in some countries in Europe, 80% of responders would prefer to charge their EVs at home. This evidence suggests that a significant part of the EVs users is likely to use the standard 3kW charge power which is available at home. In appendix A a list of studied EVs can be found. The created list tries to catch the most important EVs in the market but it is not exhaustive.

2.2.4 Vehicle-to-Grid concept

Large-scale energy storage enables excess produced electricity to be stored in grid sites so that it can be used later when electricity demand is greater. Grid storage can be used to balance supply and demand more efficiently during the day. A variant of grid storage is called

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Vehicle-to-Grid (V2G) in which EVs, connected to the grid, can provide power when needed

[KEMPTON,2005]. For the V2G approach some requirements need to be available, namely a

power connection to the grid, a control connection for communication with the grid operator and a meter sensor to indicate the battery state in each moment [CLEMENT-NYNS,2011]. SAE

establishes a series of requirements and specifications for communication between plug-in vehicles and the electric power grid, for energy transfer to and from the grid in the standard SAE J2847/1 "Communication between Plug-in Vehicles and the Utility Grid" [SAE,2011]. ISO and IEC are also developing a similar series of standards known as ISO/IEC 15118 "Road vehicles -- Vehicle to grid communication interface" [ISO, 2013]. It has been verified that vehicles are parked more than 90% of the time during a day, thus they can be available to serve as a storing device to the grid [MARKEL,2009; MENG,2016]. However, EVs are indicated to be

used as limited energy resources in power system [PETERSON,2010] due to their limited power

output. During off-peak period they can store energy and in peak period act as generation unit. EVs are suited for large and frequent power variations because they are designed to support driving needs [KEMPTON,2005].

2.2.5 Pricing strategies

A commonly adopted path to realize DR is to control the power charging or discharging rate of EVs. Charging without control is referred as dumb charging or uncoordinated charging, where users charge the EV whenever they want and need. The uncertainty of EVs’ charging is linked with the consumers’ convenience and the availability of an access to a plug. Hence, a considerable amount of uncertainty associated with the charging of EVs should be expected [HADLEY,2009; PENG,2012]. From the grid operator point-of-view EVs management can help

to avoid network problems when the number of EVs is significant [CLEMENT-NYNS, 2011;

HADLEY,2009]. The ideal time for electricity utilities to recharge EVs is typically at night when

demand is low and therefore the marginal cost tends to be lower. However, this reduction of cost might not be linear as EVs may increase the demand at night resulting in the increase of the price. Incentives to shift EVs demand to off-peak times are important according to [HADLEY,2009]. About 94% of drivers responded in a survey that they would recharge an EV

overnight to benefit from the lower electricity prices and 88% of drivers said that they would recharge the EV, whether overnight or otherwise, during a time of lower price [DOWNING,