Optimization in Sustainable Energy: Methods and Applications (Sustainable Computing and Optimization)

This state-of-the-art book offers cutting-edge optimization techniques and practical decision-making frameworks essential for enhancing the efficiency and reliability of sustainable energy systems, making it an invaluable resource for researchers, policymakers, and energy professionals.

Optimization in Sustainable Energy: Methods and Applications brings together valuable knowledge, methods, and practical examples to help scholars, researchers, professionals, and policymakers address the growing challenges of optimizing sustainable energy. This volume covers a range of topics, including mathematical models, heuristic algorithms, renewable resource management, and energy storage optimization. Each chapter explores a different aspect of sustainable energy, providing both theoretical understanding and practical guidance.

The volume explores challenges and opportunities surrounding the integration of multi-criteria decision-making techniques in energy planning, highlighting insights on environmental, economic, and social factors influencing the strategic allocation of resources. The use of evolutionary algorithms, machine learning, and metaheuristics to optimize energy storage, distribution, and optimization are also discussed.

The transition towards sustainable energy is at the forefront of global priorities, driven by the urgent need to mitigate climate change, reduce carbon emissions, and enhance energy security. As countries and industries increasingly prioritize renewable sources like wind, solar, and hydroelectric power, the complexity of optimizing these systems becomes a critical challenge. Optimization in Sustainable Energy: Methods and Applications, is a comprehensive exploration of cutting-edge methodologies used to enhance the efficiency, reliability, and performance of sustainable energy systems.

Audience

Research scholars, academics, students, policymakers, and industry experts in mechanical engineering, electrical engineering, and energy science.

Prasenjit Chatterjee, PhD, is a professor and the Dean of Research and Consultancy at the MCKV Institute of Engineering. He has published 135 research papers and 43 books and serves as a lead series editor for several international book series. He is known for his work developing the MARCOS and RAFSI decision-making methods. His research interests include energy optimization, intelligent decision-making, fuzzy computing, sustainability modeling, and supply chain management.

Anita Khosla, PhD, is a professor at Manav Rachna International Institute of Research and Studies with over 27 years of teaching experience. She has published three books and over 50 papers in international journals and conferences and served as a speaker and organizer for numerous conferences and seminars. She is known for her coordination in establishing the Factory Automation Lab in conjunction with Mitsubishi Electric India.

Ashwani Kumar, PhD, is an associate professor in the Department of Electrical and Instrumentation Engineering at the Sant Longowal Institute of Engineering and Technology, Longowal, India with over 26 years of experience. He has over 70 publications in book chapters and international journals and conferences. He is the recipient of the Monbukagakusho and Quality Improvement Programme scholarships. His research interests include computer vision, artificial intelligence, and remote sensing.

Gülay Demir, PhD, is an associate professor at the School of Health Services at Sivas Cumhuriyet University with over 10 years of academic experience. She is the author of three books and 50 scientific articles, and the editor of two books. Her research interests include smart grids, renewable energy, and fuzzy logic.