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How to Select the Optimal Electrochemical Energy Storage Planning Program? A Hybrid MCDM Method

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  • Nan Li

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
    Green Energy Development Research Institute (Qinghai), Xining 810008, Qinghai, China
    State Grid Qinghai Economic Research Institute, Xining 810008, Qinghai, China)

  • Haining Zhang

    (Green Energy Development Research Institute (Qinghai), Xining 810008, Qinghai, China
    State Grid Qinghai Economic Research Institute, Xining 810008, Qinghai, China)

  • Xiangcheng Zhang

    (Green Energy Development Research Institute (Qinghai), Xining 810008, Qinghai, China
    State Grid Qinghai Economic Research Institute, Xining 810008, Qinghai, China)

  • Xue Ma

    (Green Energy Development Research Institute (Qinghai), Xining 810008, Qinghai, China
    State Grid Qinghai Economic Research Institute, Xining 810008, Qinghai, China)

  • Sen Guo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Electrochemical energy storage (EES) is a promising kind of energy storage and has developed rapidly in recent years in many countries. EES planning is an important topic that can impact the earnings of EES investors and sustainable industrial development. Current studies only consider the profit or cost of the EES planning program, without considering other economic criteria such as payback period and return on investment (ROI), which are also important for determining an optimal EES planning program. In this paper, a new hybrid multi-criteria decision-making (MCDM) method integrating the Bayesian best-worst method (BBWM), the entropy weighting approach, and grey cumulative prospect theory is proposed for the optimal EES planning program selection with the consideration of multiple economic criteria. The BBWM and entropy weighting approach are jointly employed for determining the weightings of criteria, and the grey cumulative prospect theory was utilized for the performance rankings of different EES planning programs. Five EES planning programs were selected for empirical analysis, including 9MW PbC battery EES, 2MW LiFePO lithium ion battery EES, 3MW LiFePO lithium ion battery EES, 2MW vanadium redox flow battery EES, and 3MW vanadium redox flow battery EES. The empirical results indicate the 2MW LiFePO lithium ion battery EES is the optimal one. The sensitivity analysis related to different risk preferences of decision-makers also shows the 2MW LiFePO lithium ion battery EES is always the optimal EES planning program. The proposed MCDM method for the optimal EES planning program selection in this paper is effective and robust, and can provide certain references for EES investors and decision-makers.

Suggested Citation

  • Nan Li & Haining Zhang & Xiangcheng Zhang & Xue Ma & Sen Guo, 2020. "How to Select the Optimal Electrochemical Energy Storage Planning Program? A Hybrid MCDM Method," Energies, MDPI, vol. 13(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:931-:d:322677
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    References listed on IDEAS

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    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Zhang, Ning & Lu, Xi & McElroy, Michael B. & Nielsen, Chris P. & Chen, Xinyu & Deng, Yu & Kang, Chongqing, 2016. "Reducing curtailment of wind electricity in China by employing electric boilers for heat and pumped hydro for energy storage," Applied Energy, Elsevier, vol. 184(C), pages 987-994.
    3. Haoran Zhao & Sen Guo & Huiru Zhao, 2018. "Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory," Energies, MDPI, vol. 11(7), pages 1-24, July.
    4. Klein, Martin & Deissenroth, Marc, 2017. "When do households invest in solar photovoltaics? An application of prospect theory," Energy Policy, Elsevier, vol. 109(C), pages 270-278.
    5. Han, Xiaojuan & Ji, Tianming & Zhao, Zekun & Zhang, Hao, 2015. "Economic evaluation of batteries planning in energy storage power stations for load shifting," Renewable Energy, Elsevier, vol. 78(C), pages 643-647.
    6. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    7. Noah Kittner & Felix Lill & Daniel M. Kammen, 2017. "Energy storage deployment and innovation for the clean energy transition," Nature Energy, Nature, vol. 2(9), pages 1-6, September.
    8. Haoran Zhao & Sen Guo & Huiru Zhao, 2018. "Comprehensive benefit evaluation of eco-industrial parks by employing the best-worst method based on circular economy and sustainability," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(3), pages 1229-1253, June.
    9. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    10. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    11. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Comprehensive assessment for battery energy storage systems based on fuzzy-MCDM considering risk preferences," Energy, Elsevier, vol. 168(C), pages 450-461.
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