曾鸣, 潘婷, 贺薪颖, 龚传正, 董厚琦. 基于贝叶斯反馈修正云模型的虚拟电厂运营风险综合评价方法[J]. 现代电力, 2023, 40(5): 715-723. DOI: 10.19725/j.cnki.1007-2322.2022.0098
引用本文: 曾鸣, 潘婷, 贺薪颖, 龚传正, 董厚琦. 基于贝叶斯反馈修正云模型的虚拟电厂运营风险综合评价方法[J]. 现代电力, 2023, 40(5): 715-723. DOI: 10.19725/j.cnki.1007-2322.2022.0098
ZENG Ming, PAN Ting, HE Xinying, GONG Chuanzheng, DONG Houqi. A Comprehensive Evaluation Method for Operational Risk of Virtual Power Plants Based on Bayesian Feedback Modified Cloud Model[J]. Modern Electric Power, 2023, 40(5): 715-723. DOI: 10.19725/j.cnki.1007-2322.2022.0098
Citation: ZENG Ming, PAN Ting, HE Xinying, GONG Chuanzheng, DONG Houqi. A Comprehensive Evaluation Method for Operational Risk of Virtual Power Plants Based on Bayesian Feedback Modified Cloud Model[J]. Modern Electric Power, 2023, 40(5): 715-723. DOI: 10.19725/j.cnki.1007-2322.2022.0098

基于贝叶斯反馈修正云模型的虚拟电厂运营风险综合评价方法

A Comprehensive Evaluation Method for Operational Risk of Virtual Power Plants Based on Bayesian Feedback Modified Cloud Model

  • 摘要: 研究了虚拟电厂运营风险综合评价体系,为虚拟电厂建设运营提供支撑与指导。首先以运行风险、经济风险、安全风险和管理风险为一级指标构建了虚拟电厂风险综合评价指标库,为虚拟电厂安全可靠运行评价提供了支撑。其次提出了虚拟电厂风险综合评价模型,选用有序加权平均(ordered weighted average,OWA)—改进的层次分析法(analytic hierarchy process,AHP)对指标进行赋权,进而通过基于贝叶斯反馈修正的云模型评价方法得出各级指标的评价分数,用以评价虚拟电厂的风险程度,为虚拟电厂运行改进与优化提供判断依据。最后选取某虚拟电厂进行算例分析验证了所提基于贝叶斯改进云模型的虚拟电厂风险评价指标体系的有效性及优越性。

     

    Abstract: Considering that the risk evaluation system and model of virtual power plant operation in China need to be established and improved, this paper mainly studies the comprehensive risk evaluation system of virtual power plant operation to provide support and guidance for the construction and operation of virtual power plant. Firstly, the comprehensive risk evaluation index library of virtual power plant is constructed by taking operation risk, economic risk, safety risk and management risk as first-level indexes, which provides support for safe and reliable operation evaluation of virtual power plant. Secondly, the comprehensive risk evaluation model of virtual power plant is proposed. AHP-OWA is used to assign weights to the indicators, and then the evaluation scores of all indicators are obtained through the cloud model evaluation method based on Bayesian feedback correction, which is used to evaluate the risk degree of virtual power plant and provide judgment basis for the operation improvement and optimization of virtual power plant. Finally, a virtual power plant is selected for example analysis to verify the effectiveness and superiority of the proposed virtual power plant risk evaluation index system based on Bayesian improved cloud model.

     

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