电力系统自动化2016，Vol.40Issue(14)：8-14,7.DOI:10.7500/AEPS20160124002
基于前景理论的电网建设项目组合多属性决策方法
Prospect Theory Based Multiple-attribute Decision-making Method for Determining Portfolio of Construction Projects in Power Systems
摘要
Abstract
Determining the most desirable portfolio of construction projects in a power system is a multiple‐attribute decision‐making problem . In traditional portfolio decision‐making methods , the attribute values of indices are described as deterministic ones , and the optimal portfolio is selected from single projects ranked with the attribute values of the indices . However , it is not appropriate to represent some indices as deterministic quantities , and the optimal portfolio should not be selected based on the evaluations of single projects because of the possibility of having correlations among some single projects . Given this background , a method for computing the gains/losses and their probabilities of the indices with deterministic , interval , probabilistic , fuzzy types of data is first developed for the portfolio of construction projects . In order to deal with the impacts of psychology and risk preference of the decision‐makers , the well‐established prospect theory is next introduced to the portfolio decision‐making problem . Then , the differentiations of weights are calculated for the indices and the optimal portfolio determined by employing the VIKOR ( VIseKriterijumska Optimizacija I Kompromisno Resenje) method , which can avoid the disadvantage of compensating the bad indices with good ones in traditional weighting methods . Finally , a sample example for a provincial power system is employed to demonstrate the feasibility and efficiency of the proposed method .关键词
电力建设/项目组合/多属性决策/前景理论/差异化权重/VIKOR法Key words
electric construction/portfolio of construction projects/multiple-attribute decision-making/prospect theory/differentiation of weights/VIKOR method引用本文复制引用
张华一,文福拴,张璨,田春筝..基于前景理论的电网建设项目组合多属性决策方法[J].电力系统自动化,2016,40(14):8-14,7.基金项目
国家自然科学基金资助项目（51477151）。This work is supported by National Natural Science Foundation of China （ No ．51477151）． （）
