基于马尔科夫链的自适应储能需求功率预测模型

doi:10.19595/j.cnki.1000-6753.tces.L90136

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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.

Key words： Automatic generation control (AGC) Markov chain prediction model scenario tree adaptive adjustment

Received: 30 June 2020

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TM761

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	Articles by authors
	He Junqiang
	Shi Changli
	Wei Tongzhen

Cite this article:

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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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90136 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/Izk2/563

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