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Dynamic energy storage capacity optimization based on ultra-short-term prediction in grid-connected PV system

Author

Listed:
  • Huang, Jing
  • Teng, Xiao
  • Hu, Qingyi
  • Guo, Su
  • Boland, John

Abstract

As an unstable yet inexhaustible renewable energy, solar energy has great potential to meet the world's electricity demand. Distributed photovoltaic system utilizes local solar energy resources to generate electricity nearby to reduce energy losses and alleviate supply shortages. Energy storage system plays an important role in the process of distributed photovoltaic power generation, such as in power peak shaving. This paper takes the distributed photovoltaic storage system as the research object, focusing on photovoltaic output prediction and energy storage optimization. Firstly, three scenarios of power generation and consumption are constructed to analyze the changes in energy storage efficiency by different control strategies. In the scenario of supply and demand strongly related, the required storage capacity is the smallest (12 kW h), with the highest annual power supply per kWh of storage (589 kW h). Secondly, in this scenario, the Coupled AutoRegressive and Dynamical System (CARDS) model is employed for ultra-short-term photovoltaic prediction. The Root Mean Square Error (RMSE) of the model is 6.78 W/m2, which is lower than that of LSTM (8.49 W/m2) and ARIMA (9.14 W/m2) models. Under the condition that the total annual electricity consumption is basically unchanged, the frequency of equipment use is scheduled by the prediction results, and the limited control of energy storage capacity is realized. The results show that in the Southern Hemisphere scenario, after limited planning based on the original optimization, the storage capacity is reduced by 12.5 %. The proportion of renewable energy use is increased by 5.06 %, and the energy storage utilization efficiency is increased by 107.3 %. The stability analysis of the model through 200 simulations and typical days in different seasons has revealed that the state of the model is very stable before and after control. Finally, the case study conducted in the Northern Hemisphere further validates its applicability and economic feasibility under different geographical conditions and scenarios involving multiple types of electrical equipment.

Suggested Citation

  • Huang, Jing & Teng, Xiao & Hu, Qingyi & Guo, Su & Boland, John, 2025. "Dynamic energy storage capacity optimization based on ultra-short-term prediction in grid-connected PV system," Renewable Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:renene:v:253:y:2025:i:c:s0960148125013060
    DOI: 10.1016/j.renene.2025.123644
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    References listed on IDEAS

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