Game-Theoretical Energy Management for Energy Internet With Big Data-Based Renewable Power Forecasting

IEEE Access

Jianqi Li¹  Biyao Huang¹  Zhenyu Zhou²  Runhai Jiao²  Chen Xu²  Bin Liao²  Zhongdong Yin²  Fei Xiong² 

[1] Global Energy Interconnection Research Institute, State Grid Corporation of China, Beijing, China;State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China;

关键词: Energy internet;    Stackelberg game;    microgrid energy management;    wind power forecasting;   

DOI  :  10.1109/ACCESS.2017.2658952

来源: DOAJ

【 摘 要 】

Energy Internet, as a major trend in power system, can provide an open framework for integrating equipment of energy generation, transmission, storage, consumption, and so on, so that global energy can be managed and controlled efficiently by information and communication technologies. In this paper, we focus on the coordinated management of renewable and traditional energy, which is a typical issue on energy connections. We consider a conventional power system consisting of the utility company, the energy storage company, the microgrid, and electricity users. First, we formulate the energy management problem as a three-stage Stackelberg game, and every player in the electricity market aims to maximize its individual payoff while guaranteeing the system reliability and satisfying users' electricity demands. We employ the backward induction method to solve the three-stage non-cooperative game problem, and give the closed-form expressions of the optimal strategies for each stage. Next, we study the big data-based power generation forecasting techniques, and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Furthermore, we prove the properties of the proposed energy management algorithm including the existence and uniqueness of Nash equilibrium and Stackelberg equilibrium. Simulation results show that accurate prediction results of wind power is conducive to better energy management.

【 授权许可】

Unknown