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Identification of transformer overload and new energy planning for enterprises based on load forecasting

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  • Longjin Lv
  • Yuxian Han

Abstract

The new energy system constructed by energy storage and photovoltaic power generation system can effectively solve the problem of transformer overload operation in some enterprises. It can not only reduce the cost of electricity, but also realize low-carbon emission reduction. However, due to its low return on investment, the willingness of enterprises to install new energy is not high. In this paper, we first establish a load forecasting model to users whose transformers are overloaded or about to be overloaded, which are potential customers with new energy installation needs. Then, Optimal configuration models of PV and energy storage systems based on nonlinear programming are developed for these potential customers. The optimal installed capacity of the PV energy storage and the optimal charging and discharging strategy for the energy storage system can be obtained. This optimization strategy ensures that the electricity consumption of the enterprise does not exceed the rated capacity, and effectively reduces the enterprise’s basic tariff and electricity price to achieve cost reduction and efficiency. Finally, taking a building materials production factory as an example, we obtain the optimal plan for the new energy capacity, as well as the economic benefits of the plan and the specific strategy of energy storage charging and discharging for this factory.

Suggested Citation

  • Longjin Lv & Yuxian Han, 2024. "Identification of transformer overload and new energy planning for enterprises based on load forecasting," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0311354
    DOI: 10.1371/journal.pone.0311354
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    References listed on IDEAS

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    1. Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
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