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基于“奖优罚劣”线性变换改进前景理论的电网建设项目多属性投资排序方法
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  • 英文篇名:Multi-attribute Investment Ranking Method for Power Grid Project Construction Based on Improved Prospect Theory of “Rewarding Good and Punishing Bad” Linear Transformation
  • 作者:王昭聪 ; 潘学萍 ; 马倩
  • 英文作者:WANG Zhaocong;PAN Xueping;MA Qian;College of Energy and Electrical Engineering, Hohai University;State Grid Jiangsu Electric Power Company;
  • 关键词:电网建设 ; 投资排序 ; 奖优罚劣线性算子 ; 前景理论 ; 最优权重
  • 英文关键词:power grid construction projects;;investment ranking;;rewarding good and punishing bad linear operators;;prospect theory;;optimal weights
  • 中文刊名:DWJS
  • 英文刊名:Power System Technology
  • 机构:河海大学能源与电气学院;国网江苏省电力有限公司;
  • 出版日期:2019-03-18 14:35
  • 出版单位:电网技术
  • 年:2019
  • 期:v.43;No.427
  • 基金:国网江苏省电力公司科技项目(JSDL-2017-ZB01)~~
  • 语种:中文;
  • 页:DWJS201906038
  • 页数:11
  • CN:06
  • ISSN:11-2410/TM
  • 分类号:313-323
摘要
提出了基于"奖优罚劣"线性变换改进前景理论的电网建设项目投资排序新方法。针对市场环境下各指标值的不确定性问题,采用区间数来描述定量指标、模糊数来描述定性指标。为计及决策者的主观风险态度,提出一种基于改进前景理论的多属性排序方法。首先提出基于"奖优罚劣"的[-1,1]线性算子将原始数据规范化,并获得项目最优方案和最劣方案。进一步定义区间数和三角模糊数与最优、最劣方案的关联系数,将其应用到前景理论中改进其价值函数和损失函数,求得项目的正负前景值矩阵。然后,构建综合前景值最大的优化模型,确定最优属性权重,根据综合前景值进行项目投资排序。以IEEE RTS-79节点系统的4个建设项目为例,将上述结果与效用理论方法、逼近理想解排序法的结果进行了对比,说明了所提方法的可行性和有效性。
        A new investment ranking method of power grid construction projects based on improved prospect theory and "rewarding good and punishing bad" linear transformation is proposed in this paper. Due to uncertainty under electricity market environment, interval number is used to express quantitative index, and fuzzy number to express determinatives. In order to take into account the risk preference of decision makers, a multi-attribute ranking method based on improved prospect theory is proposed. Firstly, the linear transformation of "rewarding good and punishing bad" is applied to normalize original data, and to obtain optimal project and the worst project. Secondly, the correlation coefficient between the interval/triangular fuzzy number and the optimal/worst project is defined. This coefficient is then applied to value function and loss function in prospect theory to obtain positive and negative prospect matrix. Thirdly, an optimization model for maximizing comprehensive prospect value is constructed to determine optimal weight, and the project investment is ranked according to the project's comprehensive prospect value. Finally, the investment ranking is applied to 4 investment projects in IEEE RTS 79-node system. The results are compared with utility method and approximate ideal solution method, and feasibility and effectiveness of the proposed method are illustrated.
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