This book presents a holistic approach to addressing the challenges and opportunities associated with the integration of Plug-in Electric Vehicles (PEVs) and Distributed Generators (DGs) into modern electricity markets and transportation networks. It covers a wide range of topics, from hierarchical planning and optimal sizing of PEV charging facilities to coordinated optimization of logistics scheduling and electricity dispatch. By providing practical insights and advanced methodologies, the book enables readers to develop comprehensive strategies for managing PEV charging demand, optimizing the operation of PEV charging stations, and participating effectively in electricity markets. Whether you are a researcher, policymaker, or industry professional working in the field of sustainable energy, this book will serve as an invaluable resource for understanding the complexities of PEV integration and developing innovative solutions to meet the growing demand for clean, efficient transportation.

Comprehensive coverage of PEV integration, from planning to operation and market participation Practical insights and advanced methodologies for optimizing PEV charging demand and energy dispatch Unique focus on coordinated optimization of logistics scheduling and electricity dispatch for enhanced efficiency

Autorentext

Prof. Qiang Yang received Ph.D. degree in Electronic Engineering and Computer Science from Queen Mary, University of London, London, U.K., in 2007 and worked in the Department of Electrical and Electronic Engineering at Imperial College London, U.K., from 2007 to 2010. He visited the University of British Columbia and the University of Victoria Canada as a visiting scholar in 2015 and 2016. He is currently a full Professor at the College of Electrical Engineering, Zhejiang University, China, and has published more than 380 technical papers, filed more than 100 national patents, co-authored 4 books, and edited 3 books and several book chapters. His research interests over the years include smart energy systems, large-scale complex network modeling, control and optimization, learning based optimization and control. He is a Member of National Academy of Artificial Intelligence (MNAAI), a Fellow of the British Computer Society (FBCS), the Institution of Engineering and Technology (FIET), and International Association of Advanced Materials (FIAAM), a Senior Member of IEEE and a Distinguished Member of China Computer Federation (CCF).

Yanchong Zheng received the B.S. and M.S. degrees in statistics and applied mathematics from the Wuhan University of Technology, Wuhan, China, in 2012 and 2015, respectively. He is currently pursuing the Ph.D. degree in electrical engineering with Zhejiang University, Hangzhou, China. From 2015 to 2021, he was an Assistant Research Fellow with the Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China. His research interests include coordinated charging of electric vehicles in smart grids, vehicle-to-grid, low-carbon transportation, energy economic and sustainability.

Yuanyi Chen received the B.S. degree in electrical engineering from the Beijing Jiaotong University, Beijing, China, in 2020. He is currently working toward the Ph.D. degree in electrical engineering with the College of Electrical Engineering, Zhejiang University, Hangzhou, China. His research interests include the dynamic modeling and coordinated optimization of coupled power and transportation networks.

Dr. Siyang Sun received the B.S. degree and the Ph.D. degree both in electrical engineering from Zhejiang University, Hangzhou, China, in 2014 and 2019, respectively. Now he is with China Southern Power Grid as a manager. His research interests include electric vehicle grid integration and the emerging business of power grid corporations.

Inhalt

Introduction.- State of the art.- Hierarchical planning of PEV charging facilities and DGs.- Optimal sizing of PEV fast charging stations.- Hierarchical optimal planning for PEV fast charging stations.- Temporal analysis for characterizing PEV charging demand.- Optimal temporal-spatial PEV charging demand scheduling.