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Optimal site selection study of wind-photovoltaic-shared energy storage power stations based on GIS and multi-criteria decision making: A two-stage framework

Author

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  • Gao, Jianwei
  • Wang, Yaping
  • Huang, Ningbo
  • Wei, Lingli
  • Zhang, Zixuan

Abstract

Wind-photovoltaic-shared energy storage system can improve the utilization efficiency of renewable energy resources while reducing the idle rate of energy storage resources. Using the geographic information system (GIS) and the multi-criteria decision-making (MCDM) method, a two-stage evaluation model is first developed for site selection of wind-photovoltaic-shared energy storage projects. In the first stage, infeasible alternatives are ruled out due to veto identification. In the second stage, the fuzzy MCDM method is applied to further merit selection. Firstly, the comprehensive criteria system is built, and evaluation data is collected using probabilistic linguistic term sets (PLTSs) to ensure the comprehensiveness and scientificity of decision. Then, the new score function based on compromise theory are proposed. Thirdly, the criteria weights are calculated using the improved Decision-Making and Trial Evaluation Laboratory (DEMATEL) approach. Finally, the improved gained and lost dominance score (GLDS) method is used to rank alternatives. To test model's feasibility, an empirical study is conducted in Chifeng City. The alternative A1 appears to be extremely prominent in the results. The model's stability and reliability are demonstrated by sensitivity and comparative analysis. This study aids in the layout of wind-photovoltaic-shared energy storage projects and broadens the application scopes of GIS and MCDM method.

Suggested Citation

  • Gao, Jianwei & Wang, Yaping & Huang, Ningbo & Wei, Lingli & Zhang, Zixuan, 2022. "Optimal site selection study of wind-photovoltaic-shared energy storage power stations based on GIS and multi-criteria decision making: A two-stage framework," Renewable Energy, Elsevier, vol. 201(P1), pages 1139-1162.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:1139-1162
    DOI: 10.1016/j.renene.2022.11.012
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