This book provides a comprehensive exploration of cutting-edge research in electric vehicles (EVs) integrated smart energy systems with a main focus on the application of artificial intelligence (AI). This book offers a wide and comprehensive practical approach with the applications of AI to address the challenges and opportunities of modern hybrid energy systems for developing advanced hybrid intelligent methodologies for forecasting and scheduling variable power output from renewable energy sources (RESs) and EVs. This will enhance system flexibility and facilitate the integration of RESs and EVs efficiently, which is a step towards a sustainable future. The chapters cover diverse topics offering valuable knowledge and methodologies including an introduction to Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Cybersecurity, and their applications in modern power and energy systems, intelligent control of power electronics for RESs and EVs, intelligent charging management of EVs, etc.

This book aims to provide insights into various suitable solutions to increase the security, reliability, and interoperability of the grid under high penetration of renewable energy, storage systems, and electric transport in the context of the modern smart grid. The multi-objective optimization problems such as economic and emission dispatch problems; flexibility and reliability problems; and economic and reliability problems are solved to determine the trade-off solutions using efficient evolutionary algorithms. The chapters cover diverse topics offering valuable knowledge and methodologies including an introduction to Artificial Intelligence (AI), Machine Learning (ML), IoT, Cybersecurity, and their applications in modern power and energy systems, intelligent control of power electronics for RESs and EVs, intelligent charging management of EVs, etc.

Explores cutting-edge research in electric vehicles integrated smart energy systems with a focus on application of AI Offers a practical approach to AI's applications to address challenges and opportunities of modern hybrid energy systems Presents a computational platform for the performance analysis and optimization of future hybrid energy systems

Autorentext

Surender Reddy Salkuti has been working with Woosong University, Daejeon, Republic of Korea as an Associate Professor in the Department of Railroad and Electrical Engineering since April 2014. He received a Ph.D. degree in Electrical Engineering from the Indian Institute of Technology Delhi (IITD), New Delhi, India, in 2013. He was a Postdoctoral Researcher at Howard University, Washington, DC, USA, from 2013 to 2014. His research interests include power system restructuring issues, smart grid development with the integration of wind and solar photovoltaic energy sources, battery storage, and electric vehicles, demand response, power system analysis and optimization, artificial intelligence and soft computing techniques application in power systems, and renewable energy. He has published three edited volumes with Springer (LNEE and GREEN) and over 300 research articles in peer-reviewed international journals and conference proceedings. He served as Guest Editor for various international journals. He is also an editorial board member for many journals. He is the recipient of the 2016 Distinguished Researcher Award from Woosong University Educational Foundation, Republic of Korea, and the POSOCO Power System Award (PPSA) 2013, India. He is listed in the top 2% of scientists published in a study conducted by researchers of ICSR Lab, Elsevier BV & Stanford University, USA. He has Google Scholar citations of 6839 with an h-index of 41 and an i10-index of 150. He is a Member of IEEE and IEEE Power and Energy Society.

Inhalt

.- Artificial Intelligence in Electric Vehicles for Sustainable Driving.- Modernization of Electric Grids for Charging of Electric Vehicles.- Mitigating Impacts of Electric Vehicle Charging Stations to the Distribution Systems by Optimal Operation of Soft Open Point.- Performance Evaluation of Artificial Neural Networks for Electric Vehicle State of Charge Estimation across Different Driving Cycles.- **** GJO-Pattern Search Algorithm based DG and Capacitor Placement in Distribution Network with Zone based Installation of EVCSs, etc.