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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.
With the increasing penetration of renewable energy and flexible loads in smart grids, a more complicated power system with high uncertainty is gradually formed, which accordingly brings great challenges to smart grid forecast and dispatch. Traditional methods usually require knowing accurate mathematical models, and they cannot well deal with the growing complexity and uncertainty. Fortunately, the widespread popularity of advanced meters makes it possible for smart grids to collect massive data, which offers opportunities for data-driven Artificial Intelligence (AI) methods to address the forecast and dispatch issues. In fact, Big Data and AI-enabled computational methods are widely deployed nowadays. People from different industries
try to apply AI-enabled techniques to solve practical yet challenging problems. The power and energy industry is no exception. AI-enabled computational methods can be utilized to fully explore the value behind these historical data and enhance electric services such as power forecast and dispatch.
This book explores and discusses the applications of AI-enabled forecast and dispatch techniques in smart grids. The contents are divided into three parts. The first part (Chaps. 1–3) provides a comprehensive review of recent developments in smart grid forecast and dispatch, respectively. Then, the second part (Chaps. 4–7) investigates the AI-enabled forecast approaches for smart grid applications, such as load forecast, electricity price forecast and charging power forecast of electric vehicle charging station. On this basis, the smart grid dispatch issues are introduced in the third part (Chaps. 8–11). This part introduces the application of extreme learning machine, data-driven Bayesian assisted optimization algorithm, multi-objective optimization approach, Deep Reinforcement Learning as well as the Federated Learning, etc. Finally, the future research directions of smart grid forecast and dispatch (Chap. 12) are presented. This book presents model formulations, novel algorithms, in-depth discussions and comprehensive case studies.
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.
1 Introduction for Smart Grid Forecast and Dispatch
2 Review for Smart Grid Forecast
3 Review for Smart Grid Dispatch
4 Deep Learning-Based Densely Connected Network for Load Forecast
5 Reinforcement Learning Assisted Deep Learning for Probabilistic Charging Power Forecasting of EVCS
6 Dense Skip Attention-Based Deep Learning for Day-Ahead Electricity Price Forecasting with a Drop-Connected Structure
7 Uncertainty Characterization of Power Grid Net Load of Dirichlet Process Mixture Model Based on Relevant Data
8 Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power
9 Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems
10 Many-Objective Distribution Network Reconfiguration Using Deep Reinforcement Learning-Assisted Optimization Algorithm
11 Federated Multi-agent Deep Reinforcement Learning for Multi-microgrid Energy Management
12 Prospects of Future Research Issues