Optimal Management of Energy Storage System Based on Reinforcement Learning
Energy storage system consists of distributed generation,storage device,loads and some intelligent control devices in the smart grid.It enables energy flow from the storage device to the grid.An amount of balancing energy is procured to meet the load demand when there is a deficit in power generation.The excessive distributed generation power of storage device can either be sold to the grid or be used to provide frequency regulation service.Real-time pricing techniques would greatly influence the system control center in deciding when to sell power,buy power or provide regulation service.The power of distributed generation,load demand,electricity price and the frequency regulation price are independent of each other.Each of the four stochastic processes is modeled as a Markov process to reflect the dynamic characteristics.The optimal control problem of deciding when to sell power,buy power or provide regulation service is formulated as a semi-Markov decision process.The Sarsa algorithm is used to adapt the control operation in order to maximize the long-term rewards on the basis of meeting the load demand.Simulation results show a significant increase of total rewards,a faster convergence speed and good effect with the proposed algorithm.
smart grid distributed generation semi-Markov decision process Sarsa
LIU Jing TANG Hao MATSUI Masayuki TAKANOKURA Masato ZHOU Lei GAO Xueying
School of Computer and Information,Hefei University of Technology,Hefei,Anhui 230009,China School of Electrical Engineering and Automation,Hefei University of Technology,Hefei,Anhui 230009,Ch Kanagawa University,Yokohama 221-8686,Japan
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
8216-8221
2014-07-28（万方平台首次上网日期，不代表论文的发表时间）