Grid Optimal Integration Of Electric Vehicles Examples With Matlab Implementation

This book is a compilation of recent research on distributed optimization algorithms for the integral load management of plug-in electric vehicle (PEV) fleets and their potential services to the electricity system. It also includes detailed developed Matlab scripts. These algorithms can be implemented and extended to diverse applications where energy management is required (smart buildings, railways systems, task sharing in micro-grids, etc.). The proposed methodologies optimally manage PEV fleets’ charge and discharge schedules by applying classical optimization, game theory, and evolutionary game theory techniques. Taking owner’s requirements into consideration, these approaches provide services like load shifting, load balancing among phases of the system, reactive power supply, and task sharing among PEVs. The book is intended for use in graduate optimization and energy management courses, and readers are encouraged to test and adapt the scripts to their specific applications.

This book brings together important new contributions covering electric vehicle smart charging (EVSC) from a multidisciplinary group of global experts, providing a comprehensive look at EVSC and its role in meeting long-term goals for decarbonization of electricity generation and transportation. This multidisciplinary reference presents practical aspects and approaches to the technology, along with evidence from its applications to real-world energy systems. Electric Vehicle Integration via Smart Charging is suitable for practitioners and industry stakeholders working on EVSC, as well as researchers and developers from different branches of engineering, energy, transportation, economic, and operation research fields.

This authoritative new resource provides a comprehensive introduction to plug-in electric vehicles (PEVs), including critical discussions on energy storage and converter technology. The architecture and models for sustainable charging infrastructures and capacity planning of small scale fast charging stations are presented. This book considers PEVs as mobile storage units and explains how PEVS can provide services to the grid. Enabling technologies are explored, including energy storage, converter, and charger technologies for home and park charging. The adoption of EV is discussed and examples are given from the individual battery level to the city level. This book provides guidance on how to build and design sustainable transportation systems. Optimal arrival rates, optimal service rates, facility location problems, load balancing, and demand forecasts are covered in this book. Time-saving MATLAB code and background tables are included in this resource to help engineers with their projects in the field.

Energy efficiency touches all parts of the economy and lies at the heart of all plausible strategies for addressing climate change. A fascinating range of new technologies and new business models have emerged in the past few years and are rapidly reshaping the field and driving efficiency improvements — many of them completely unexpected.This book provides a fresh look at energy efficiency written in a way that can be interesting to experts and serve as an entry point for novices. With chapters written by recognized experts in their fields of expertise, the book provides readers with a clear perspective on the state-of-the art developments of both new technologies and new approaches to system design and operations in buildings, industry, transportation, and urban design. Strategies for electrification and optimization based on data and powerful algorithms are also explored in depth. The discussion includes new mobility systems, smart buildings, reimagined industrial processes, new materials, and smart grid integration.Related Link(s)

With the increase in electric vehicle (EV) adoption in recent years, the impact of EV charging activity to the power grid has become increasingly significant. Although an EV is considered beneficial to the environment by reducing greenhouse gases, large amounts of un-coordinated EV charging could be detrimental to the power grid and thereby degrade power quality. Recent developments in Vehicle to Grid (V2G) technology has converted an EV to a distributed energy resource (DER). A modern smart grid with intelligent IoT devices, solar generation and battery storage provides additional opportunities but also additional challenges to the grid operator. To alleviate the negative effects of massive EV charging load and turn them into grid assets, the current dissertation performs research in designing and developing optimal EV charging strategies to integrate EVs into the smart power grid. Using the UCLA Smart Grid Energy Research Center (SMERC) smart EV charging network infrastructure as the testbed, data has been collected regarding EV driver charging behavior for five years. Based on historical charging records, both deterministic and generative EV user behavior models are proposed to combine statistical analysis and machine learning to predict day-ahead EV driver itinerary and energy demand. Optimal Vehicle Grid Integration strategy is designed to realize different objectives including EV charging cost minimization, power grid stabilization, computational burden decentralization, increasing convergence speed, mitigating solar over-generation, etc. A distributed optimal bi-directional charging scheduling algorithm with asynchronous converging feature has been designed for load curve flattening; A two-stage optimization and a distributed water-filling algorithm have been developed for aggregating EVs to participate in energy market and demand response program. Both large-scale simulation and real-world implementation are conducted to validate and evaluate the performance of these algorithms. Results show that the proposed distributed optimal bi-directional charging scheduling algorithm is able to flatten power peak load by 35% when implemented in a test-bed located within the parking structure 9 in UCLA. A daily energy cost saving of 18% is achieved when the two-stage optimization algorithm is performed to control the EVs in a parking structure in the Civic Center Garage of the City of Santa Monica to participate in wholesale energy markets. Smart meter data collected in the Santa Monica parking lot shows the proposed charging control algorithm is able to mitigate the solar over-generation in the building by 50% on a daily basis. It can be concluded that our Vehicle Grid Integration strategy is effective in stabilizing power grid load, reducing charging cost and solving solar power over-generation problem. In addition to the development of EV user behavior models and Vehicle Grid Integration strategy, this dissertation also solves practical engineering problems for a scalable, reliable and safe EV bi-directional smart charging infrastructure.

This book introduces the optimal online charging control of electric vehicles (EVs) and battery energy storage systems (BESSs) in smart grids. The ultimate goal is to minimize the total energy cost as well as reduce the fluctuation of the total power flow caused by the integration of the EVs and renewable energy generators. Using both theoretic analysis and data-driven numerical results, the authors reveal the effectiveness and efficiency of the proposed control techniques. A major benefit of these control techniques is their practicality, since they do not rely on any non-causal knowledge of future information. Researchers, operators of power grids, and EV users will find this to be an exceptional resource. It is also suitable for advanced-level students of computer science interested in networks, electric vehicles, and energy systems.

Electric Vehicle Integration in a Smart Microgrid Environment The growing demand for energy in today’s world, especially in the Middle East and Southeast Asia, has been met with massive exploitation of fossil fuels, resulting in an increase in environmental pollutants. In order to mitigate the issues arising from conventional internal combustion engine-powered vehicles, there has been a considerable acceleration in the adoption of electric vehicles (EVs). Research has shown that the impact of fossil fuel use in transportation and surging demand in power owing to the growing EV charging infrastructure can potentially be minimalized by smart microgrids. As EVs find wider acceptance with major advancements in high efficiency drivetrain and vehicle design, it has become clear that there is a need for a system-level understanding of energy storage and management in a microgrid environment. Practical issues, such as fleet management, coordinated operation, repurposing of batteries, and environmental impact of recycling and disposal, need to be carefully studied in the context of an ageing grid infrastructure. This book explores such a perspective with contributions from leading experts on planning, analysis, optimization, and management of electrified transportation and the transportation infrastructure. The primary purpose of this book is to capture state-of-the-art development in smart microgrid management with EV integration and their applications. It also aims to identify potential research directions and technologies that will facilitate insight generation in various domains, from smart homes to smart cities, and within industry, business, and consumer applications. We expect the book to serve as a reference for a larger audience, including power system architects, practitioners, developers, new researchers, and graduate-level students, especially for emerging clean energy and transportation electrification sectors in the Middle East and Southeast Asia.

Abstract : The rapid development of plug-in electric vehicles (PEV) brings both challenges and opportunities to the power system. This dissertation studies the modeling and optimal control of vehicle-grid integration (VGI) system with the consideration of both grid-side and PEV owners' benefits. First of all, a phasor mode single-phase microgrid with PEVs and PV farm is modeled to study the cost effective optimal control of PEV charging / discharging, and cope with the power balancing problem. The linear programming (LP) and genetic algorithm (GA) are used to obtain optimized charging schedules. The time-of-use (TOU) price and discharge incentive are considered for cost minimization. Secondly, the impacts of the bi-directional power flow on battery degradation are investigated. An energy-throughput battery degradation model is applied to simulate the daily lifetime degradation. Case studies with different battery capacities, depth of discharge, temperature, and charging regime are investigated. Thirdly, a grid-tied charging system that enables both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) is designed using SimPowerSystems in Matlab/Simulink. The bi-directional AC-DC and DC-DC converters are designed with PWM-driven bridges. A predictive current control (PCC) is designed to control the AC-DC converter. The phase-lock loop (PLL) technology and band-stop filter is applied to reduce the total harmonic distortion (THD) on grid current. In the previous studies, the control algorithms are designed base on phasor mode model and validated via offline simulation. To further investigate the optimal power management algorithm for real-time simulation, a detailed power electronics level VGI system is modeled with RT-Lab RTE-drive blocks. The real-time simulations with a rule-based algorithm are conducted in Hardware-In-The-Loop (HIL) enabled OPAL-RT simulator. To deal with a large-scale of PEVs charging, a piecewise consensus-based distributed control is presented to minimize the charging power loss and maximize the utilization of PEVs for V2G services. The graph theory is applied to represent the communication network among neighboring PEVs. A metropolis stochastic matrix is applied to specify the communication topology. Case studies with ten PEVs and one hundred PEVs are conducted and analyzed respectively. In the last, due to the rapid response of PEV batteries to the power change demand, PEVs are coordinated with a dual-level consensus-based frequency control method to support the primary frequency control (PFC). The upper-level control aims to minimize the frequency deviation of multi-area power system, and the lower-level control aims to minimize the frequency regulation cost and battery degradation cost for individual PEVs. The consensus-algorithm is applied to specify the information updating and exchange among neighboring individuals. The simulation with different load change are conducted and studied.

This comprehensive reference text discusses simulation with case studies and realworld applications related to energy system models, the large-scale integration of renewable energy systems, electric vehicles, and energy storage systems. The text covers analysis and modeling of the large-scale integration of renewable energy systems, electric vehicles, and energy storage systems. It further discusses economic aspects useful for policy makers and industrial professionals. It covers important topics, including smart grids architectures, wide-area situational awareness (WASA), energy management systems (EMS), demand response (DR), smart grid standardization exertions, virtual power plants, battery degradation modeling, optimization approaches in modeling, and smart metering infrastructure. The book: Discusses the analysis and modeling of the large-scale integration of renewable energy systems, electric vehicles, and energy storage systems Covers issues and challenges encountered in the large-scale integration of electric vehicles, energy storage systems and renewable energy systems into future smart grid design Provides simulation with case studies and real-world applications related to energy system models, electric vehicles, and energy storage systems Discusses the integration of large renewable energy systems, with the presence of a large number of electric vehicles and storage devices/systems Discussing concepts of smart grids, together with the deployment of electric vehicles, energy storage systems and renewable energy systems, this text will be useful as a reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies. It further discusses topics, including electric grid infrastructure, architecture, interfacing, standardization, protocols, security, reliability, communication, and optimal control.

This book covers the recent research advancements in the area of charging strategies that can be employed to accommodate the anticipated high deployment of Plug-in Electric Vehicles (PEVs) in smart grids. Recent literature has focused on various potential issues of uncoordinated charging of PEVs and methods of overcoming such challenges. After an introduction to charging coordination paradigms of PEVs, this book will present various ways the coordinated control can be accomplished. These innovative approaches include hierarchical coordinated control, model predictive control, optimal control strategies to minimize load variance, smart PEV load management based on load forecasting, integrating renewable energy sources such as photovoltaic arrays to supplement grid power, using wireless communication networks to coordinate the charging load of a smart grid and using market price of electricity and customers payment to coordinate the charging load. Hence, this book proposes many new strategies proposed recently by the researchers around the world to address the issues related to coordination of charging load of PEVs in a future smart grid.