Energy Management for Microgrid Systems : Algorithms and Applications (Power Systems) (2025. 250 S. Approx. 250 p. 235 mm)

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Energy Management for Microgrid Systems : Algorithms and Applications (Power Systems) (2025. 250 S. Approx. 250 p. 235 mm)

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  • 製本 Hardcover:ハードカバー版/ページ数 250 p.
  • 言語 ENG
  • 商品コード 9783031785634

Full Description

This book provides an in-depth discussion of the significance of energy management in microgrids, focusing on three key areas: multi-energy cooperative management in single and multi-microgrid systems, and energy management issues related to the Internet of Vehicles (IoV) in microgrids.

In Part 1, the book emphasizes the collaborative management of multiple energy sources, considering electric, gas, hydrogen, and renewable energy for a single microgrid system. In Part 2, it addresses the challenges in multi-microgrid systems and proposes a scheduling scheme for multi-energy cooperative management. Finally, Part 3 investigates the energy management challenges posed by the Internet of Vehicles in microgrids, and proposes a management scheme for electric vehicles (EVs) within microgrids using deep reinforcement learning.

Overall, the algorithms discussed in this book are essential for addressing the energy management challenges in microgrids.

Contents

Part I. Multi-Energy Coordinated Management in Single Microgrid System.- Chapter 1. Online Energy Management for Microgrids with CHP Co-generation and Energy Storage.- Chapter 2. Optimal Energy Management for Microgrids with Combined Heat and Power (CHP) Generation, Energy Storages, and Renewable Energy Source.- Chapter 3. Intelligent Energy Scheduling in Renewable Integrated Microgrid with Bidirectional Electricity-to-Hydrogen Conversion.- Part II. Multi-Energy Coordinated Management in Multi-Microgrid System.- Chapter 4. Energy Scheduling for Networked Microgrids with Co-Generation and Energy Storage.- Chapter 5. Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy.- Chapter 6. Combined Carbon Capture and Utilization with Peer-to-Peer Energy Trading for Multi-Microgrids Using Multi-Agent Proximal Policy Optimization.- Part III. Vehicle-to-Grid based Energy Management in Microgrid System.- Chapter 7. Real-Time Battery Thermal Management for Electric Vehicles Based on Deep Reinforcement Learning.- Chapter 8. Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning.- Chapter 9. Real-time Energy Management for Microgrid with EV Station and CHP Generation.

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