Analytics and Optimization for Renewable Energy Integration

This book covers modeling and forecasting renewable energy, operation and planning of power systems with renewable energy integration, and handling uncertainty with advanced stochastic unit commitment models, algorithms, and operation strategies.

Format: Paperback / softback
Length: 394 pages
Publication date: 29 August 2022
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the realm of modeling and forecasting renewable energy, as well as the operation and planning of power systems with the integration of renewable energy sources. In the first part, it explores the foundational mathematical theories of stochastic mathematics, providing a solid foundation for the subsequent chapters. The second part showcases innovative modeling and analytic techniques for the generation of renewable energy, including solar, wind, and hydroelectric power. It discusses advanced methodologies for simulating and analyzing these energy sources, enabling accurate predictions and optimal utilization.

The third part of the book focuses on addressing the challenges posed by the uncertainty associated with renewable energy in power system operations. It introduces advanced stochastic unit commitment models to optimize generation schedules under uncertain conditions, leveraging advanced optimization algorithms to calculate probabilistic power values. Additionally, the book proposes an efficient operation strategy for renewable power plants participating in electricity markets, ensuring their effective integration and contribution to the grid.

By encompassing a wide range of topics and employing cutting-edge methodologies, this book serves as a valuable resource for researchers, professionals, and policymakers involved in the field of renewable energy and power system operations. It equips readers with the knowledge and tools necessary to navigate the complexities of integrating renewable energy into power systems, maximizing their benefits while minimizing associated risks.

Weight: 604g
Dimension: 156 x 233 x 28 (mm)
ISBN-13: 9781032401638