Multi-agent System Based Energy Management Strategies for Microgrid by using Renewable Energy Source and Load Forecasting

Abstract

This article focuses on multi-agent system based hierarchical energy management strategies for maximum economic and environmental benefits for microgrids. First, a two-level multi-agent based energy management system is constructed, which consists of an upper-level EMA in the view of whole system, multiple lower-level unit agents in a distributed manner, and their interactions based on communication. Second, in the upper-level agent, the energy management strategies are mainly designed by constructing multi-objective functions and by using a particle swarm optimization method based on hybrid probabilistic forecasting of renewable energy sources and loads. Third, in lower-level renewable energy source and load agents, the forecasting approach regarding renewable energy sources and loasd is mainly researched by means of the ensemble empirical mode decomposition combined with sparse Bayesian learning, called the hybrid probabilistic forecast approach. Moreover, in lower-level schedulable generation unit agents, local control strategies are also presented to regulate the output power to satisfy the reference power that is set by the upper-level agent. Finally, the validity of the proposed multi-agent based energy management strategies is demonstrated by means of simulation results.

Keywords:

• microgrid
• energy management strategies
• multi-agent system
• probabilistic forecasting
• renewable energy source
• ensemble empirical mode decomposition
• sparse Bayesian learning
• multi-objective optimization
• load forecasting
• power dispatch

Additional information

Notes on contributors

Chun-Xia Dou

Chun-xia Dou received her B.S. and M.S. in automation from the Northeast Heavy Machinery Institute, Qiqihaer, China, in 1989 and 1994, respectively, and her Ph.D. in the Institute of Electrical Engineering from Yanshan University, Qinhuangdao, China, in 2005. In 2010, she joined the Department of Engineering, Peking University, Beijing, China, where she was a postdoctoral fellow for 2 years. Since 2005, she has been a professor in the Institute of Electrical Engineering, Yanshan University. Her current research interests include multi-agent based control, event-triggered hybrid control, distributed coordinated control, and multi-mode switching control and their applications in power systems, MGs, and smart grids.

Xiao-Gang An

Xiao-gang An received his B.S. and M.S. in Computer Science in North China Electric Power University, Heibei, China, in 2003 and 2007, respectively. He has been a doctoral student in Control Science and Engineering in Yanshan University, Qinhuangdao, China, since 2013. In 2007, he joined the China Waterborne Transport Research Institute, Beijing, China, as a senior engineer. His current research interests include multi-agent-based energy management strategies for MGs and smart grids.

Dong Yue

Dong Yue his Ph.D. from the South China University of Technology, Guangzhou, China, in 1995. He is currently a professor and dean of the Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, and also a Changjiang Professor with the Department of Control Science and Engineering, Huazhong University of Science and Technology. He is currently an associate editor of both the IEEE Control Systems Society Conference Editorial Board and IEEE Transactions on Neural Networks and Learning Systems, Journal of the Franklin Institute, and International Journal of Systems Science. He has published more than 100 papers in international journals, domestic journals, and international conferences. His research interests include analysis and synthesis of networked control systems, MASs, optimal control of power systems, and the internet of things.