NEXTGENERATION ARTIFICIAL INTELLIGENCE DRIVEN SMART AND RENEWABLE ENERGY (HB 2025). Questo articolo non è disponibile.NEXTGENERATION ARTIFICIAL INTELLIGENCE DRIVEN SMART AND RENEWABLE ENERGY (HB 2025)

To provide for a sustainable future, the potential synergies at the dynamic intersection of renewable energy (RE) incorporated with smart energy and artificial intelligence (AI) must be exploited. RE is crucial to preserve the environment. Energy involving various systems must be optimized and assessed to provide better performance. However, the design and development of RE systems remains a challenge. Advanced optimization techniques, AI, and machine learning (ML) plays a crucial role in implementing the latest innovative research in the field of renewable energy-integrated electrical systems. This book also describes the practical challenges encountered, and the solutions and future scope to be adopted. Applications of a variety of advanced optimization and AI techniques in the design and development of RE-integrated systems are discussed to provide new solutions in the RE domain.

Key features:

• Discusses modern modeling/control approaches for improving renewable energy integrating artificial intelligence-driven power systems
• Describes the principles and methods of renewable energy generation technologies, and an analysis of their implementation, management, and optimization, and related economic advantages
• Presents critical information on the technological design and policy issues that must be taken into considered while implementing a smart grid
• Explains of the metaheuristic optimization algorithm for complex electrical systems, and the whale optimization algorithm-based multi-objective hydrothermal scheduling
• Covers the electric vehicle charging station in the distribution network, and transient stability constraint optimal power flow problem using chaotic quasi-oppositional chemical reaction optimization

The topics covered including microgrids, wind power, solar photo voltaic (PV), optimal power flow (OPF), grid connected inverter, electric vehicle, combined heat and power economic dispatch, FACTS tools for smart energy, harmonic impedance of a salient pole synchronous generator (HI), maximum power point tracking (MPPT) and advanced optimization techniques. Next Generation Artificial Intelligence-Driven Smart and Renewable Energy is ideal for academicians, practitioners, teachers, engineers, industry professionals, researchers, and students in diverse fields, including electrical engineering, electronics and communications engineering, energy, and environmental engineering.

Provas Kumar Roy received the BE degree in Electrical Engineering from R.E. College, Durgapur, Burdwan, India in 1997; ME degree in Electrical Machine from Jadavpur University, Kolkata, India in 2001 and Ph.D. from NIT Durgapur in 2011. Presently he is working as Professor at the department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, India. He has published more than 140 research papers in international journals, 65 conferences paper, 15 book chapters and Scopus citation is nearly 4700 with H-index of 40. His field of research interest includes Economic Load Dispatch, Optimal Power flow, FACTS, Unit Commitment, Radial Distribution System, State Estimation, Automatic Generation Control, Power System Stabilizer and Evolutionary computing techniques. He has supervised 12 Ph.D. scholars in the domain of the power system analysis with high penetration of renewable energy, image processing. He has been placed among “World Ranking of top 2% Scientists” for last three consecutive years in the field of Energy by Stanford University scientists. He has been awarded many times with outstanding reviewer awards, top scientist awards, top peer reviewer awards, best paper awards. He has also contributed as committee member of several national and international conferences. He is a member of the Institute of Engineers and Indian Society of Technical Education (ISTE). He is a regular reviewer of leading journals including Elsevier, IET and IEEE Transactions/journals.

Sunanda Hazra received the Ph.D. degree in Electrical Engineering. He is presently associated with Electrical Engineering Department of Haldia Institute of Technology, Haldia, W.B, India. He has ten years of teaching experience. He has published around 20 research papers in International Journals and conference records. His research interest includes load dispatch, hydrothermal scheduling, renewable energy, optimization techniques, etc. He has been awarded with Young Scientist from VD Good Technology Factory in 2021. He has reviewed few research works submitted to National/International Journals. He has attended & organized several short-term courses/faculty development programmes. He is a member of IEI.

Chandan Paul received B. Tech degree in Electrical Engineering from Dr. B. C. Roy Engineering College, Durgapur (under West Bengal University of Technology), India, in 2006 and an M. Tech degree from National Institute of Technology, Durgapur, West Bengal, India in Electrical Engineering (specialisation of Electrical System) in 2012. He obtained PhD in Electrical Engineering from IIT (ISM) Dhanbad in 2022. He has eight Journals published in reputed SCI and Scopus indexed Journals. His area of research includes hydro-thermal scheduling, optimal power flow, combined heat and power dispatch and evolutionary algorithms. He has three international journals. He is working as an Assistant Professor in the Department of Electrical Engineering, Dr. B.C. Roy Engineering College, Durgapur, India