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Optimal sizing of energy storage system and its cost-benefit analysis for power grid planning with intermittent wind generation

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  • Xia, Shiwei
  • Chan, K.W.
  • Luo, Xiao
  • Bu, Siqi
  • Ding, Zhaohao
  • Zhou, Bin

Abstract

Energy storage system (ESS) is a key technology to accommodate the uncertainties of renewables. However, ESS at an improper size would result in no-reasonable installation, operation and maintenance costs. With concerns on these costs outweighing ESS operating profit, this paper establishes a stochastic model to size ESS for power grid planning with intermittent wind generation. In the model, the hourly-based marginal distributions with covariance is first derived from historical data of wind generation, and a stochastic cost-benefit analysis model with consideration of the generation fuel cost expectation and ESS amortized daily capital cost is formed. Then a hybrid solution approach combining the Point Estimated method and the parallel Branch and Bound algorithm (PE-BB) is designed to solve the model. Finally, the stochastic model and PE-BB approach are thoroughly tested on the 10-unit and 26-unit systems with uncertain wind generation. Simulation results confirmed the proposed model and PE-BB approach are effective to optimize ESS size for power grid planning with intermittent wind generation. The cost-benefit investigations on four typical ESSs also indicated that the ESS capital cost, charging/discharging efficiency and lifetime are important properties for optimizing ESS size, and it is not always economically justifiable to install ESS in power system.

Suggested Citation

  • Xia, Shiwei & Chan, K.W. & Luo, Xiao & Bu, Siqi & Ding, Zhaohao & Zhou, Bin, 2018. "Optimal sizing of energy storage system and its cost-benefit analysis for power grid planning with intermittent wind generation," Renewable Energy, Elsevier, vol. 122(C), pages 472-486.
    • Handle: RePEc:eee:renene:v:122:y:2018:i:c:p:472-486
    DOI: 10.1016/j.renene.2018.02.010
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    9. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Kotarela, F. & Kyritsis, A. & Papanikolaou, N. & Kalogirou, S.A., 2021. "Enhanced nZEB concept incorporating a sustainable Grid Support Scheme," Renewable Energy, Elsevier, vol. 169(C), pages 714-725.
    11. Terkes, Musa & Arikan, Oktay & Gokalp, Erdin, 2024. "The effect of electric vehicle charging demand variability on optimal hybrid power systems with second-life lithium-ion or fresh Na–S batteries considering power quality," Energy, Elsevier, vol. 288(C).
    12. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    13. Gu, Chenjia & Wang, Jianxue & Zhang, Yao & Li, Qingtao & Chen, Yang, 2022. "Optimal energy storage planning for stacked benefits in power distribution network," Renewable Energy, Elsevier, vol. 195(C), pages 366-380.
    14. Berrada, Asmae, 2022. "Financial and economic modeling of large-scale gravity energy storage system," Renewable Energy, Elsevier, vol. 192(C), pages 405-419.
    15. Heejung Park, 2021. "A Stochastic Planning Model for Battery Energy Storage Systems Coupled with Utility-Scale Solar Photovoltaics," Energies, MDPI, vol. 14(5), pages 1-13, February.
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    18. Yu Hu & David Soler Soneira & Mar'ia Jes'us S'anchez, 2020. "Barriers to grid-connected battery systems: Evidence from the Spanish electricity market," Papers 2007.00486, arXiv.org, revised Nov 2020.

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