Adaptive Power Demand Prediction Model of Energy Storage Based on Markov Chain
He Junqiang1,2,3, Shi Changli1,2, Wei Tongzhen1,2
1. Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190 China; 2. School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing 100049 China; 3. School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan 030024 China
Abstract Due to the uncertainty of power demand of energy storage system (ESS) when ESS participates in automatic generation control (AGC) with thermal generators, an adaptive ESS power demand prediction model based on Markov chain is proposed. Firstly, according to the uncertainty of the output power of thermal generators in response to the AGC command, the Markov chain is used to model the ESS power demand in prediction horizon, and a posteriori information is used to adapt to the fluctuations of the AGC command. Secondly, to reasonably select random scenarios of power demand, a scenario tree generation approach with variable prediction horizon is presented. The approach can select scenarios more effectively when the number of nodes is fixed. A simulation was implemented to validate the effectiveness of the prediction model. The results show that compared with the Markov model without adaptive adjustment, the presented adaptive prediction model can improve the prediction accuracy by 8.28%. The prediction accuracy of the presented scenario tree approach is improved by 6.67% compared with the fixed scenario tree structure method, and 4.65% higher than the maximum likelihood estimate method.
He Junqiang,Shi Changli,Wei Tongzhen. Adaptive Power Demand Prediction Model of Energy Storage Based on Markov Chain[J]. Transactions of China Electrotechnical Society, 2021, 36(zk2): 563-571.
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2021, Vol. 36 
