Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers

Zhiyuan Zeng (), Tianyou Li, Jun Su and Longyi Sun
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Zhiyuan Zeng: School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
Tianyou Li: School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
Jun Su: School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
Longyi Sun: School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China

Energies, 2023, vol. 16, issue 20, 1-21

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.

Keywords: user-side; photovoltaic-energy storage system; PV power prediction; neural network; optimal scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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