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Peak shaving benefit assessment considering the joint operation of nuclear and battery energy storage power stations: Hainan case study

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  • Chen, Xiaojiao
  • Huang, Liansheng
  • Liu, Junbo
  • Song, Dongran
  • Yang, Sheng

Abstract

The rapid development of battery energy storage technology provides a potential way to solve the grid stability problem caused by the large-scale construction of nuclear power. Based on the case of Hainan, this study analyses the economic feasibility for the joint operation of battery energy storage and nuclear power for peak shaving, and provides an effective solution framework for construction scale and battery type determination. Under the proposed framework, a novel cost model for the large-scale battery energy storage power station is proposed. Then, economic analysis is conducted to get the most economical battery type and construction scale by considering the comprehensive economic benefit of the joint operation under the limit of the load factor. On this basis, sensitivity analysis of economic indicators to control parameters and economic parameters is performed to further demonstrate the economic feasibility of the joint operation. Comparative analysis shows that 270 MW lithium iron phosphate battery energy storage power station has the best and stable comprehensive performance in terms of the IRR, PBP and LCOE, which are 16.27%, 6.27 year and 0.464¥/kWh, respectively. In comparison with the pumped storage, the battery energy storage has lower initial investment, faster capital recovery and smaller floor area under the joint operation mode. Moreover, sensitivity analysis illustrates that the large-scale application of battery energy storage still depends on the trade-off between the cost performance of battery and the real-time electricity price.

Suggested Citation

  • Chen, Xiaojiao & Huang, Liansheng & Liu, Junbo & Song, Dongran & Yang, Sheng, 2022. "Peak shaving benefit assessment considering the joint operation of nuclear and battery energy storage power stations: Hainan case study," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221021459
    DOI: 10.1016/j.energy.2021.121897
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    4. Zhang, Wen-Yi & Chen, Yue & Wang, Yi & Xu, Yunjian, 2023. "Equilibrium analysis of a peer-to-peer energy trading market with shared energy storage in a power transmission grid," Energy, Elsevier, vol. 274(C).
    5. Wang, Sen & Li, Fengting & Zhang, Gaohang & Yin, Chunya, 2023. "Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy," Energy, Elsevier, vol. 267(C).
    6. Yang, Sheng & Zhang, Lu & Song, Dongran, 2022. "Conceptual design, optimization and thermodynamic analysis of a CO2 capture process based on Rectisol," Energy, Elsevier, vol. 244(PA).
    7. Yanfang Chen & Young-Hoon Joo & Dongran Song, 2021. "Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach," Energies, MDPI, vol. 14(21), pages 1-24, November.

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