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

Nan Li, Haining Zhang, Xiangcheng Zhang, Xue Ma and Sen Guo
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Nan Li: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
Haining Zhang: Green Energy Development Research Institute (Qinghai), Xining 810008, Qinghai, China
Xiangcheng Zhang: Green Energy Development Research Institute (Qinghai), Xining 810008, Qinghai, China
Xue Ma: Green Energy Development Research Institute (Qinghai), Xining 810008, Qinghai, China
Sen Guo: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Energies, 2020, vol. 13, issue 4, 1-20

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.

Keywords: EES planning program; Bayesian best-worst method; grey cumulative prospect theory; entropy weighting approach; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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