Abstract
Photovoltaic (PV) power generation exhibits stochastic and uncertain characteristics. In order to improve the economy and reliability of a photovoltaic-energy storage system (PV-ESS), it is crucial to optimize both the energy storage capacity size and the charging and discharging strategies of the ESS. An optimal scheduling model for PV-ESS is proposed in this paper, comprehensively considering factors in terms of energy cost and charging/discharging constraints of the PV-ESS. Moreover, the model employs a particle swarm optimization-backpropagation (PSO-BP) neural network to predict the PV power using historical generation data from a factory in Xiamen. The proposed two PV-ESS scheduling strategies are compared under three weather conditions. In the demand management strategy, the ESS can flexibly respond to different weather conditions and load demand changes, and effectively reduce the electricity cost for users.
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