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Article

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

by
Zhiyuan Zeng
1,*,
Tianyou Li
1,2,
Jun Su
1,2 and
Longyi Sun
1
1
School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
2
Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024, China
*
Author to whom correspondence should be addressed.
Energies 2023, 16(20), 7079; https://doi.org/10.3390/en16207079
Submission received: 7 August 2023 / Revised: 25 September 2023 / Accepted: 10 October 2023 / Published: 13 October 2023
(This article belongs to the Topic Distributed Generation and Storage in Power Systems)
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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 user-side; photovoltaic-energy storage system; PV power prediction; neural network; optimal scheduling

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MDPI and ACS Style

Zeng, Z.; Li, T.; Su, J.; Sun, L. Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers. Energies 2023, 16, 7079. https://doi.org/10.3390/en16207079

AMA Style

Zeng Z, Li T, Su J, Sun L. Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers. Energies. 2023; 16(20):7079. https://doi.org/10.3390/en16207079

Chicago/Turabian Style

Zeng, Zhiyuan, Tianyou Li, Jun Su, and Longyi Sun. 2023. "Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers" Energies 16, no. 20: 7079. https://doi.org/10.3390/en16207079

APA Style

Zeng, Z., Li, T., Su, J., & Sun, L. (2023). Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers. Energies, 16(20), 7079. https://doi.org/10.3390/en16207079

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