Electric Vehicles In Smart Grid Research Articles

Preface

The purpose of the three-day 2023 2nd International Conference on Energy Internet and Power Systems (ICEIPS 2023), held via virtual form on August 4th to 6th, 2023 in Chengdu, China, was to create a timely forum for multi-disciplinary discussions related to the recent developments on energy Internet and power systems.As usual, the program of the meeting mainly consisted of keynote speeches, oral presentations and poster presentations, with the former including speeches delivered by three sophisticated professors from different areas. The speeches covered three main topics: i) Challenges and Opportunities of Large Scale Electric Vehicles in Smart Grids with Renewable Generation, by Professor Jahangir Hossain from University of Technology Sydney, Australia; ii) Power System Security and Load Shedding, by Professor Mohamed EL-Shimy from Ain Shams University, Egypt; iii) Load-transfer Strategy for Urban High-Voltage Distribution Network based on Link-Path Model, by Professor Hongwei Li from Southwest Petroleum University, China.In the framework of experimental and theoretical approaches, the Conference gathered about 150 delegates from all over the world and addressed a number of highly relevant aspects of energy Internet and power systems. Covering Green Energy Development Technologies, Energy Internet Technology, Electric Motors and Drives, Smart Grid Power Transmission and Distribution, Intelligent Systems and Methods, etc., the selected contributions of this special issue provide guidance for future interdisciplinary developments. In this way, the multi-scale aspects of energy Internet and power systems should be considered.We received various manuscripts of invited papers and contributions presented at the Conference. All of these have been peer reviewed and are collected in this volume. We would like to thank all the anonymous colleagues who have acted as referees to assess the suitability of the various articles for publication in Journal of Physics: Conference Series. We are confident that the high quality of both invited and contributed papers contained in this Proceedings will be appreciated by the energy and power community.We would like to express our thanks to all the authors for their time and genuine efforts, and to the reviewers for their fruitful comments during the preparation of this volume. We also acknowledge the support provided in various ways by Northeastern University.List of Committee Member is available in the pdf.

Optimal placement of parking of electric vehicles in smart grids, considering their active capacity

Optimal placement of parking of electric vehicles in smart grids, considering their active capacity

Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electric vehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency has brought EV technology to the forefront of the electric power distribution systems due to their ability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoption of EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillary service-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenario by incorporating demand-side management (DSM) through EV integration. The integration of EVs with DSM techniques is hurdled with various issues and challenges addressed throughout this literature review. The various research conducted on EV-DSM programs has been surveyed. This review article focuses on the issues, solutions, and challenges, with suggestions on modeling the charging infrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separately to enhance the EV-DSM operation. Gaps in current research and possible research directions have been discussed extensively to present a comprehensive insight into the current status of DSM programs employed with EV integration. This extensive review of EV-DSM will facilitate all the researchers to initiate research for superior and efficient energy management and EV scheduling strategies and mitigate the issues faced by system uncertainty modeling, variations, and constraints.

Blockchain Smart Contract-Enabled Secure Energy Trading for Electric Vehicles

In this paper, a blockchain-enabled energy trading method is proposed to deal with the inefficiency and security issues in energy trading for electric vehicles in smart grids. It includes the design of a smart contract and the excitation mechanism of energy sharing. The credit points of each vehicle as a node are considered in the design of the smart contract, which is used to supervise the process of energy trading. A strategy to estimate the credit points of each node and describe the excitation mechanism is illustrated. The connection between the credit points and the probability that a node would be accepted for energy trading has been established. To control the energy trading access, a double auction method is used to choose the part of the nodes participating in energy trading. Only selected nodes with winning bids can supply or request energy from the blockchain-based platform. Then it reaches the conclusion that the higher the credit points they have, the more trading initiative they would have during the energy transaction and transmission. The smart contract design and the excitation mechanism proposed in this paper would reward the vehicles that perform well and punish the beguiling vehicles for regulating the trading process.

Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management

In this paper, a survey is presented on the use of optimization models for the integration of electric vehicles (EVs) and charging stations (CSs) in the energy system, paying particular attention both to planning problems (i.e., those problems related to long term decisions such as the siting and sizing of CSs), and operational management problems (i.e., the optimal scheduling of EVs in smart grids, microgrids and buildings taking into account vehicle-to-grid (V2G) capabilities). Moreover, specific attention was dedicated to decision problems that couple transportation and electrical networks, such as the energy demand assessment for a vehicle over a path and routing and charging decision problems for goods and people transportation. Finally, an effort was dedicated to highlighting the integration and the use of EVs in very recent regulation frameworks, with specific reference to the participation in the balancing market through the figure of an aggregator and the inclusion in the management of Energy Communities (ECs) and sustainable districts.

A comprehensive survey on charge scheduling of electric vehicles in smart grids in the 2010s

Electric vehicles (EVs) are being introduced by different manufacturers as an eco-friendly alternative to vehicles with internal combustion engines, provided with several benefits. In the upcoming years, the count of EVs is expected to expand. However, the uncoordinated charging of these vehicles can put severe stress on the power grid. The problem of charge scheduling of EVs is an important issue and has seen significant research activity in the past few years. Thus, this survey paper intents to perform a review of 65 research works about this scenario that operate with different approaches under a diverse period of time. Particularly, this paper describes about the power flow model, scheduling control mechanism and type of charging followed in EVs. The performance measures recorded in each of the works are described here. Finally, a compact description on research gaps and challenges relying on this aspect is made that aids the researchers in doing future works.

Event-Triggered Mechanism for Multiple Frequency Services of Electric Vehicles in Smart Grids

This paper considers an event-triggered mechanism (ETM) for multiple frequency services of electric vehicles (EVs) in smart grids (SGs). A new model of a SG with an ETM and multiple agents of EVs is introduced. ETM is deployed to reduce the load on network bandwidth. EVs are incorporated in both secondary and primary frequency services to suppress frequency fluctuations in the system. The behaviours of EV owners are also considered by using batteries SOC controls. Then, a mathematical model of the system is derived which contains parametric uncertainties due to the incorporation of SOC control, an ETM variable arising from ETM control, and a time-varying delay in both the state and control input vectors. We derive new Lyapunov stability and stabilizability conditions to ensure robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance of the closed-loop system. The conditions are derived based on various mathematical tools including relaxed Jensen inequality, free weighting matrix technique, reciprocally convex approach, and matrix inequality for uncertainties. An effective procedure is proposed based on an approach using the state transformation and tractable linear matrix inequalities to synthesis robust static output feedback controller gains. Extensive simulations of a SG with three agents of EVs are undertaken to validate of our proposed control scheme.

A novel underfill-SOC based charging pricing for electric vehicles in smart grid

With the advanced communication, computation, control and manufacturing technologies, electric vehicles have been extensively developed to improve the utilization of clean energy and reduce emissions. Despite the tremendous advantages, the rapidly growing of deploying electric vehicles will bring new insecurities, such as disorderly charging behaviors, which affect the stability of the smart grid. Although considerable efforts on charging schedules have been developed to improve energy utilization and reduce load fluctuation in the smart grid, the profits of utility company have not been investigated. In addition, most of these schemes also lead to inefficient battery utilization, i.e., electric vehicles usually charge while their batteries still have a lot of energy left. To address these issues, in this paper, a novel underfill-SOC based charging pricing (USoCP) scheme is proposed for electric vehicles in smart grid, which can achieve great battery utilization, as well as guarantee great profits of utility company and load balance of the whole grid. Particularly, via introducing the Logistic function into charging pricing determination, the proposed USoCP scheme can effectively improve the underfill state of charge (underfill-SOC) for battery charging, thereby improving the battery utilization. Meanwhile, to reduce the load fluctuation and guarantee great profits of utility company, a charging price based demand-response model is conducted to determine the effective charging power quantity, and a charging pricing determination model is formalized as an optimization problem to reduce the peak-to-average ratio of power load and guarantee great electricity selling profits of utility company. Finally, a Particle Swarm Optimization (PSO) based solution is proposed to solve the optimization problem and determine the effective charging price for electric vehicles. Via extensive evaluations, the results show that the proposed USoCP scheme can effectively stimulate electric vehicles to charge more energy in each battery charging (i.e., improving the battery utilization), reduce peak power load, as well as guarantee great profits of utility company.

Electric vehicles in smart grid and smart city for Petroşani case

The success of the development of electric vehicles and the increase in demand for their purchase amid greater autonomy of power systems and the development of charging infrastructure also highlights the possibility of using these vehicles as an integral part of a Smart Grid system in the context of sustainable urban development around the extended ideas of the Smart City concept in which other innovative concepts can be integrated. This paper aims to present new research developed around the idea of Smart City in Petroşani, starting from electric vehicles that can serve both as a means of transport and monitoring system of environmental parameters but can also be integrated into the electricity network so that in periods of high consumption it can supply electricity when needed and in periods of over-production to charge their batteries. Although at present at the level of Petroşani there are not many electric vehicles that can become part of the electricity network, this idea is applicable especially in a public or private facility with the possibility of scaling in the future.

Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

This study introduces a type of solid-state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic-assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real-time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real-time power allocation which improves the efficiency of the photovoltaic-based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power.

Optimal integration of electric vehicles in smart grids with renewables and battery storage systems under uncertainty

There has been a huge trend to integrate Renewable Energies (REs) and Electric Vehicles (EVs) into energy networks (Figure 1). This is mostly due to the shrinking price of their application and the increasingly strict emission policy. However, the integration of REs and EVs brings new challenges to the network operation [1]. For instance, a considerable amount of REs cannot be accommodated in the network and thus has to be curtailed due to technical limitations [2-5]. For overcoming this problem, Battery Storage Systems (BSSs) can be used to store the surplus energy and consequently increase economic benefits [4].

RETRACTED ARTICLE: A Decentralized Dynamic Marketing-Based Demand Response Using Electric Vehicles in Smart Grid

Due to different energy storage capabilities of electric vehicles and their unpredicted charging/discharging patterns, the load uncertainty has significantly increased on the utility grid. This load uncertainty has also enabled the elasticity in the power production through intermittent energy resources. This emerging electricity generation elasticity could be used to optimize aggregator load profile by altering real-time demand of a power network, particularly in the premises of a massively EVs surrounded grid. In this paper, we have proposed a demand response algorithm to optimize vehicle to grid (V2G) aggregation, by enabling EVs scheduled charging/discharging, in quest to minimize the energy cost for the retailer, as well as satisfying the electricity market, i.e., day-ahead and instantaneous real-time markets. We have employed a decentralized approach in our proposed scheme, which is accessible, allows fast convergence, and assures consumers’ privacy. The simulation results show that our proposed technique significantly reduces the energy cost, explicitly for a retailer who tends to optimize the power network by penetrating an efficient algorithm. In addition, our algorithm shows more relevance when the grid leniently manages several intermittent energy generation resources. Practically the real-time load variations are uncertain, and are difficult to control, which increases the power and cost variations, and some time ends up as complete system failure. By using this algorithm the energy retailers can offer reliable service and suitable tariffs to consumers, to prove relevancy by restricting such constraints.

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

Electric vehicles (EVs) are an eco-friendly alternative to vehicles with internal combustion engines. Despite their environmental benefits, the massive electricity demand imposed by the anticipated proliferation of EVs could jeopardize the secure and economic operation of the power grid. Hence, proper strategies for charging coordination will be indispensable to the future power grid. Coordinated EV charging schemes can be implemented as centralized, decentralized, and hierarchical systems, with the last two, referred to as distributed charging control systems. This paper reviews the recent literature of distributed charging control schemes, where the computations are distributed across multiple EVs and/or aggregators. First, we categorize optimization problems for EV charging in terms of operational aspects and cost aspects. Then under each category, we provide a comprehensive discussion on algorithms for distributed EV charge scheduling, considering the perspectives of the grid operator, the aggregator, and the EV user. We also discuss how certain algorithms proposed in the literature cope with various uncertainties inherent to distributed EV charging control problems. Finally, we outline several research directions that require further attention.

Real-time frequency regulation using aggregated electric vehicles in smart grid

The electric vehicle (EV) market has witnessed a continuous and steady increase in the past few years. The benefits of lower energy costs and less greenhouse gas (GHG) emission have been widely recognized by customers. In addition to these benefits for the transportation sector, EVs are also considered a critical supplementary resource for building a sustainable energy system in a smart grid environment. The applications of EVs in a smart grid have attracted wide attention in recent years. One appealing application is to use aggregated EVs as either energy sources or sinks to provide a service of frequency regulation for the request signals from the grid. A real-time decision-making model is proposed in this paper for the EV aggregator to dynamically control the energy flow between the grid and each individual EV in the aggregated group as an effective response to the signals of frequency regulation issued by the grid using Markov Decision Process. The aggregator’s benefit is maximized through the identification of a set of optimal charging/discharging decisions for the aggregated EVs. A semi-online solution strategy is also proposed to find the near-optimal decisions on a real-time basis. A numerical case study is used to illustrate the effectiveness of the proposed model.

Joint routing and scheduling for electric vehicles in smart grids with V2G

Modern distribution systems with high penetration of Electric Vehicles (EVs) are the focus of increasing attention. EVs charging strategies impact on power networks operation and they can even affect driving patterns when considering Vehicle-to-Grid (V2G) market-driven scenarios. This paper proposes a joint EV routing and charging/discharging scheduling strategy to operate an EV fleet. Particularly, we propose a mathematical framework based on a mixed-integer linear programming problem with the goal of maximizing the revenue of EV users. This approach is illustrated and tested using the IEEE 37-node test feeder. The results show that slight changes in driving patterns can provide benefits to EV users and improve the network operation. The correct design of the price dynamics is concluded to be key for promoting V2G participation.

Electric vehicles in smart grid: a survey on charging load modelling

Electric vehicles (EVs) have been rapidly developed during the last few years due to the low-carbon industry and smart grid initiatives. Meanwhile, the impact of large-scale EVs' integration on the reliability and safety of power grids is becoming increasingly prominent. To address and solve these problems, challenges on EV charging control have been presented. Besides, the EV charging load modelling with improved accuracy and rationality is required. To investigate the influencing factors of EV charging load, this survey summarises the existing EV charging load modelling methods. In addition, a new research framework for a scale EV evolution model of charging load is proposed, with an emphasis on reducing the deficiencies of the existing research in dealing with the EV scale development. Moreover, the future research prospect of EV charging load modelling on power system planning, operation, and market design has also been discussed.

Energy Flows Management of Multiple Electric Vehicles in Smart Grid

This paper presents energy flows management of multiple electric vehicles charging and its impact on a microgrid. The studied low voltage grid comprises also two photovoltaic generators and non-dispatchable loads. Three concepts using electric vehicles batteries as a distributed storage and bidirectional energy transfer in the Smart Grid are considered: Vehicle to Home, Vehicle to Vehicle and Vehicle to Grid. The potential for implementation of flexible microgrid energy flow management strategy for reduction of peaks in power consumption, absorption or production of reactive power (when required) and improving the power quality in the studied microgrid are estimated. For this purpose a set of usual load profiles of electric vehicle charging modes are studied and presented. Simulation results demonstrate the potential of electric vehicles as a flexible load and energy storage for peak load shaving in microgrids.DOI: 10.5755/j01.eie.25.1.22730

Charging Control of Electric Vehicles in Smart Grid: a Stackelberg Differential Game Based Approach

In this paper, we investigate the charging control problems of the electrical vehicles in smart grid, where electricity transactions exist between the aggregation and the electrical vehicles. We use the Stackelberg differential game to formulate the charging/discharging interactions between the aggregation and the electrical vehicles, where the dynamic behavior of the energy levels of the aggregation and the electrical vehicles are formulated as a differential game, and the charging/discharging interactions between the aggregation and the electrical vehicles are formulated as a Stackelberg game. The aggregation acts as the leader, and control the electricity price for to maximize the payoff. The electrical vehicles acts as the followers, and control their charging/discharging power to minimize the energy cost. Two kinds of equilibrium solutions to the Stackelberg differential game will be analyzed, which are the open loop equilibriums and the feedback equilibriums, respectively. The obtained equilibrium solutions can be considered as the optimal control for the aggregations and the electrical vehicles. Numerical simulations and results show the effectiveness and advantages of the proposed algorithms.

Optimal Charging Schemes for Electric Vehicles in Smart Grid: A Contract Theoretic Approach

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.

A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

As the number of Electric Vehicle (EV) increases, the uncontrolled EV charging behaviors may cause the grid load fluctuations and other passive effects. In order to balance the EV charging load in the smart grid, an Electric Vehicle Charging and Discharging Strategy (EVCDS) which is based on a Charging Decision Function (CDF) as well as a Discharging Decision Function (DDF) is proposed. In the CDF and DDF, there are three sub-functions related to the residual energy of battery, EV’s charging habits, and the charging efficiency of charging station respectively. The residual energy of battery is used to calculate the excepted probability that satisfies the user’s mileage requirement, which is an important factor. The charging habits are used to calculate the second sub-function value, which stands for the user’s comfort. The charging efficiency and distance are combined together to calculate the third sub-function. All the sub-functions are weighted and combined into the CDF and DDF to decide whether to charge, discharge or do nothing. In the numerical results, we set up a scenario for the commercial vehicles and private vehicles. After compared with other strategies, EVCDS performs well in terms of reducing the charging demand fluctuations and improving the charging demand balance among charging stations.