文章摘要
王永利,刘 晨,马子奔,许苗苗,王晓辉,徐 楠,王 蕾.基于双阶段变异差分进化算法的电网业务组合投资决策优化[J].电力需求侧管理,2023,25(1):20-26
基于双阶段变异差分进化算法的电网业务组合投资决策优化
Optimization of power grid business portfolio investment decision based on two-stage mutation differential evolution algorithm
投稿时间:2022-10-01  修订日期:2022-11-02
DOI:10.3969/j.issn.1009-1831.2023.01.004
中文关键词: 电网新兴业务  业务投资决策  CRITIC-序关系分析集成赋权法  差分进化算法
英文关键词: emerging business in power grids  business port⁃folio investment decisions  CRITIC-sequential relationship analysis integrated empowerment method  differential evolutionary algorithm
基金项目:国家电网有限公司管理科技项目(1300- 202056133A-0-0-00 )
作者单位
王永利 华北电力大学 经济与管理学院北京 102206 
刘 晨 华北电力大学 经济与管理学院北京 102206 
马子奔 华北电力大学 经济与管理学院北京 102206 
许苗苗 华北电力大学 经济与管理学院北京 102206 
王晓辉 国网经济技术研究院有限公司北京 102200 
徐 楠 国网河北省电力有限公司 经济技术研究院石家庄 050081 
王 蕾 中国社会科学院 工业经济研究所北京 100037 
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中文摘要:
      “十四五”规划中提出发展战略新兴产业,加快建设现代产业体系的国家重大战略部署。为此,电网企业亟需拓展新兴业务以提升电网企业自身效益。首先从社会、政策、经济、技术、环境多个维度构建了基于社会网络的电网新兴业务投资价值评价体系,并采取CRITIC-序关系分析集成赋权法结合主客观赋权法优势对指标进行权重设置,然后以业务组合贡献度为目标函数,构建了电网新兴业务组合投资决策优化模型,并基于双阶段变异差分进化算法求解投资决策优化模型。通过算例分析验证了所提出的业务组合投资决策优化模型的适用性和双阶段变异差分进化算法的优越性,并在保证电网效益最大化的同时考虑其他综合效益,为电网企业新兴业务投资决策提供一定参考。
英文摘要:
      The“14th Five-Year Plan”puts forward the development of strategic new industries, accelerating the construction of a modern industrial system of national major strategic deployment.Therefore, power grid enterprises need to expand their emerging business to enhance their own benefits. Firstly, a social network based on investment value evaluation system for emerging businesses in power grids is constructed from social, policy, economic,technological and environmental dimensions, and the CRITIC-sequential relationship analysis integrated weighting method is adopted to combine the advantages of subjective and objective weighting method to set the weights of indicators. Then, an investment decision optimisation model for emerging business portfolios in power grids is constructed with the business portfolio contribution degree as the objective function, and the investment decision optimisation model is based on a two-stage variation. The investment decision optimization model is solved based on a two-stage variational evolutionary algorithm. The applicability of the proposed business portfolio investment decision optimization model and the superiority of the two-stage variational differential evolutionary algorithm are verified through case studies.
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