A Risk Assessment Framework of Hybrid Offshore Wind–Solar PV Power Plants under a Probabilistic Linguistic Environment

Qinghua Mao, Mengxin Guo, Jian Lv, Jinjin Chen, Pengzhen Xie and Meng Li
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Qinghua Mao: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
Mengxin Guo: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
Jian Lv: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
Jinjin Chen: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
Pengzhen Xie: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
Meng Li: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China

Sustainability, 2022, vol. 14, issue 7, 1-29

Abstract: Hybrid offshore wind–solar PV power plants have attracted much attention in recent years due to its advantages of saving land resources, high energy efficiency, high power generation efficiency, and stable power output. However, due to the project still being in its infancy, investors will face a series of risks. Hence, a multi-criteria group decision-making framework for hybrid offshore wind–solar PV power plants risk assessment is constructed in this paper. Firstly, 19 risk indicators are identified and divided into five groups. Secondly, probabilistic linguistic term sets are then introduced to evaluate the criteria values to depict uncertainty and fuzziness. Thirdly, the expert weight determination model is built by combining subjective and objective weights based on expert information, the entropy and interaction-entropy measures of probabilistic linguistic term sets. Fourthly, the expert evaluation information is aggregated by transforming probabilistic linguistic term sets into triangular fuzzy numbers based on generalized weighted ordered weighted averaging operator. Additionally, the risk level is determined using the fuzzy synthetic evaluation method. Finally, the proposed method is applied to a case study and the risk level is slightly high with the similarity measure result of 0.938. Then, the risk indicator system and corresponding countermeasures can provide scientific reference for investment decisions and risk prevention.

Keywords: hybrid offshore wind–solar PV power generation; risk assessment; probabilistic linguistic term sets; triangular fuzzy numbers; fuzzy synthetic evaluation method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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