This book investigates in detail renewable power system optimization (RPSO) technology, exploring its potential us to accommodate intermittent, random, and fluctuating renewable energy from the aspects of power supply side, power grid side, demand side and energy storage. RPSO delves into the interdisciplinary field of sustainable energy systems, offering a comprehensive exploration of methodologies and strategies to maximize the efficiency, reliability, and resilience of renewable power systems. Studies on RPSO have attracted engineers and scientists from various disciplines, such as electrical, computer, transportation, control and management science. The book integrates theoretical frameworks, computational techniques, and practical case studies, which caters to a diverse readers including researchers, engineers, policymakers, and graduate students specializing in renewable energy, electrical engineering, environmental science, and related disciplines. It is particularly beneficial for those seeking to enhance the efficiency, reliability, and resilience of renewable power systems in the face of evolving energy transition challenges.

Integrates advanced uncertain optimization with a focus on risk aversion management for renewable power systems Provides a unique perspective on low-carbon development of new power systems with high proportion of renewable energy Introduces cutting-edge optimization models ensuring robustness and reliability under uncertain operating conditions

Autorentext

Dr. Jiajia Chen received his B.E. degree in Mathematics and Applied Mathematics from Linyi University, Linyi, China, in 2010, and the Ph.D. degree in Electrical Engineering from South China University of Technology, Guangzhou, in 2015. Since Jan. 2016, Dr. Chen has been in School of Electrical and Electronic Engineering, Shandong University of Technology, as an Associate Professor. Dr. Chen focuses on the research of renewable power system operation and control. His recent research interests include key technologies and applications of energy storage, flexibility and resilience enhancement of smart grid, demand side management and power pricing, operation and control of agricultural network, etc. Since 2024, Dr. Chen has published 50 peer-reviewed technical papers in international journals and conferences. He is a member of IEEE, Deputy Secretary-General of Shandong Electrical Technology Society and the Young Editorial Board of 'Electric Power Construction' Journal, and served as a reviewer of several international journals. He was One Best Paper Awards at the IEEE 6th International Conference on Energy, Electrical and Power Engineering in 2023. Dr. Yuanzheng Li received the M.S. degree in electrical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2011, and the Ph.D. degree in Electrical Engineering from the South China University of Technology, Guangzhou, China, in 2015. Since Sep. 2015, Dr. Li has been in School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, as an Associate Professor. Dr. Li focuses on deep learning, reinforcement learning, smart grid operations, optimal power system/microgrid scheduling and decision-making, stochastic optimization considering large-scale integration of renewable energy into the power system, and multiobjective optimization. Since 2024, Dr. Li has published more than 100 peer-reviewed technical papers in international journals and conferences. He is a Senior Member of IEEE.

Inhalt

Introduction for renewable power system optimization.- Distributionally Robust Unit Commitment with Enhanced Sisjointed Layered Ambiguity Set.- Downside Risk to Low-carbon Multi-energy System Optimization.- Multi-step Reconfiguration with Many-objective Reduction for Renewable Distribution System.- Credibility Theory Based Fuzzy Chance Constrained AC OPF for Renewable Power System.- Multi-energy Hub Optimization to Enhance Resilience of Renewable Agricultural Microgrid.- Continuous-time Optimization to Improve Demand Defense of Renewable Industrial Park.- Random Clustering and Dynamic Recognition Strategy for Energy Storage System Optimization.- Mobile Energy Storage System Optimization with Peer-to-peer for Resilience Improvement.