Buy Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch | COMPUTER & IT BOOK

ABOUT THE BOOK

With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch.  Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts.

(1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch.

(2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast.

(3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch.             

The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.

TABLE OF CONTENTS

1. Introduction for Smart Grid Forecast and Dispatch

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 1-11

2. Review for Smart Grid Forecast

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 13-29

3. Review for Smart Grid Dispatch

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 31-53

4. Deep Learning-Based Densely Connected Network for Load Forecast

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 55-73

5. Reinforcement Learning Assisted Deep Learning for Probabilistic Charging Power Forecasting of EVCS

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 75-100

6. Dense Skip Attention-Based Deep Learning for Day-Ahead Electricity Price Forecasting with a Drop-Connected Structure

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 101-129

7. Uncertainty Characterization of Power Grid Net Load of Dirichlet Process Mixture Model Based on Relevant Data

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 131-156

8. Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 157-182

9. Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems

   • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

   Pages 183-208

10. Many-Objective Distribution Network Reconfiguration Using Deep Reinforcement Learning-Assisted Optimization Algorithm

    • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

    Pages 209-229

11. Federated Multi-agent Deep Reinforcement Learning for Multi-microgrid Energy Management

    • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

    Pages 231-253

12. Prospects of Future Research Issues

    • Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng

    Pages 255-260