Hongcai Zhang, Yonghua Song, Ge Chen, Peipei Yu

Reliable Non-Parametric Techniques for Energy System Operation and Control

Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods

• Broschiertes Buch

Produktbeschreibung

Reliable Non-Parametric Techniques for Energy System Operation and Control: Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods offers a comprehensive guide to cutting-edge smart methods in energy system operation and control. This book begins by covering fundamentals, applications in deterministic and uncertain environments, accuracy in imbalanced datasets, and overcoming measurement limitations. It also delves into mathematical insights and computationally-efficient implementations. Part II addresses energy system control using safe reinforcement learning, exploring training-efficient intrinsic-motivated reinforcement learning, physical layer-based control, barrier function-based control, and CVaR-based control for systems without hard operation constraints. Designed for graduate students, researchers, and engineers, Reliable Non-Parametric Techniques for Energy System Operation and Control stands out for its practical approach to advanced methods in energy system control, enabling sustainable developments in real-world conditions.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 350
• Erscheinungstermin: 1. Juli 2025
• Englisch
• ISBN-13: 9780443364921
• ISBN-10: 0443364923
• Artikelnr.: 73291720

Autorenporträt

Hongcai Zhang is currently an Assistant Professor with the State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering at the University of Macau, China. Prior to this, he was a postdoctoral scholar with the University of California, USA, from 2018-2019. His current research interests include Internet of Things for smart energy, optimal operation and optimization of power and transportation systems, and grid integration of distributed energy resources. He has published over 70 JCR Q1/Q2 journal papers with 3 identified as ESI highly cited papers, and is an Associate Editor for IEEE Transactions on Power Systems and the Journal of Modern Power Systems and Clean Energy.

Inhaltsangabe

1. Introduction
PART I: ENERGY SYSTEM OPERATION BASED ON CONSTRAINT LEARNING
2. Fundamentals of Constraint Learning and Its Application in Deterministic
Energy System Operation Problems
3. Extending Constraint Learning to Energy System Operations under
Uncertain Environments
4. Ensuring Accuracy of Constraint learning in the Face of Imbalanced
Operational Datasets
5. Overcoming Measurement Limitations by Combining Constraint Learning with
Measurement Recovery
6. Mathematical Insights and Computationally-efficient Implementations of
Constraint Learning
PART II: ENERGY SYSTEM CONTROL BASED ON SAFE-REINFORCEMENT LEARNING
7. Training-efficient Intrinsic-motived Reinforcement Learning Control for
Energy Systems with Soft Operation Constraint
8. Physical Layer-based Safe Reinforcement Learning Control for Energy
Systems with Accurate Formula of Hard Operation Constraint
9. Barrier Function-based Safe Reinforcement Learning Control for Energy
Systems with Partially Formulable Hard Operation Constraint
10. CVaR-based Safe Reinforcement Learning Control for Energy Systems
without Formula of Hard Operation Constraint
11. Conclusion