Libin Yang, Tingxiang Liu, Zhengxi Li, Wanpeng Zhou, Zhengxi Li, and Na An
Particle filtering; Markov chain; microgrid; energy storage; energy management
Microgrid energy storage systems play a crucial role in power systems, but their state estimation and energy management face challenges due to the uncertainty and complexity of system state and energy changes. A particle filter-based method for estimating the charge state and predicting the lithium-ion batteries lifespan was proposed to address the issues of state estimation and energy management in microgrid energy storage systems. At the same time, a microgrid energy storage system energy management model was constructed by integrating Markov chain and Monte Carlo methods. The research results indicated that the SOC estimation results using the open circuit voltage definition were more accurate than those using the ampere-hour integral definition, showing an approximately linear change in voltage within the SOC range of 20%–100%. In terms of microgrid energy management, the total energy capacity of energy storage batteries and electric vehicle (EV) batteries was 8 kWh and 16 kWh, respectively. By considering the prediction of EV and optimising energy management during electricity usage time, operating costs were reduced. From the above experimental results, both proposed methods have achieved good results in state prediction and energy management of microgrid energy storage systems, providing an effective theoretical basis and empirical support for the estimation and prediction of lithium-ion batteries and energy management of microgrids.
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