Research on clean energy power generation-energy storage-energy using virtual enterprise risk assessment based on fuzzy analytic hierarchy process in China

被引:38
作者
Liu, Jicheng [1 ]
Yin, Yu [1 ]
Yan, Suli [1 ]
机构
[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
基金
中国国家自然科学基金;
关键词
Clean energy power generation; Energy storage; Energy using virtual enterprise; Risk assessment; Fuzzy AHP; BWM; SMAA; PARTNER SELECTION PROBLEM; DECISION-MAKING; MODEL; MANAGEMENT; OPTIMIZATION; INTEGRATION; PERFORMANCE; ALGORITHM; INTERNET; PROJECTS;
D O I
10.1016/j.jclepro.2019.06.302
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With the continuous attention on clean energy and energy abandonment, clean energy power generation - energy storage-energy using virtual enterprise (PGSU VE) centered on energy storage has been highly valued. The alliance can not only effectively integrate enterprise resources, but also efficiently adapt to the change of market environment. However, the various uncertainties in the alliance become a major obstacle to PGSU development. To ensure the healthy development of the alliance, risk evaluation has become a crucial step. This paper aims to identify risk factors through questionnaire survey and literature review and calculate the overall risk levels of PGSU VEs with an integrated approach with fuzzy Analytic Hierarchy Process (fuzzy AHP). Results of factors identification consisted of market, social politics and environmental, capacity, cooperation and coordination, alliance formation and risk commitment and profit distribution risk categories. Overall risk levels of three VEs were calculated and ranked with proposed approach whose feasibility and consistency were verified through a comparative analysis with Best Worst Method (BWM) and Stochastic multi-criteria acceptability analysis (SMAA). Moreover, considering existing risks, some suggestions were put forward for future development of clean PGSU VE. The implications and limitations were presented in the conclusions. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:14
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