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考虑风光不确定性的综合能源系统规划设计方法 被引量:18

Integrated Energy System Planning and Design Method Considering Uncertainty of Wind Power and Photovoltaic System
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摘要 在能源互联网飞速发展的背景下,综合能源系统逐渐成为能源低碳高效运行的重要实现方式。为了实现综合能源系统配置的合理规划,提出了一种考虑风光不确定性的综合能源系统多目标规划模型。通过结合序列分布概率的典型曲线挖掘方法获得风光的典型出力曲线,减小可再生能源出力描述不确定性对配置的影响。以综合能源系统运行成本最低、系统电网供电率最低为优化目标,采用基于混合博弈决策的改进型多目标粒子群算法对规划模型进行求解和Pareto解集决策,生成满足决策者的不同偏好需求的配置结果。并对影响系统规划的关键参数进行敏感性分析。基于中国某综合能源示范工程的实际数据,给出了规划方案,仿真结果验证了该方法的有效性。 In the context of the rapid development of energy internet,integrated energy system has gradually become an important way to realize multi-energy low-carbon and efficient operation.In order to realize the rational planning of the integrated energy system configuration,a multi-objective integrated energy system planning model considering the uncertainty of landscape was proposed.The typical output curve of landscape was obtained by the method of typical curve mining combined with the probability of sequence distribution.Aiming at the lowest system operating cost and power supply rate,an improved multi-objective particle swarm optimization algorithm was adopted to solve the problem.The generated Pareto solution set was screened by hierarchical composition class mixed strategy game,so as to meet the different preferences of decision makers.The sensitivity analysis of the key parameters affecting the system planning scheme was carried out.Based on the actual data of a comprehensive energy demonstration project in China,the planning scheme was presented,and the simulation results verify the effectiveness of the proposed method.
作者 郑亚锋 魏振华 刘思渠 ZHENG Ya-feng;WEI Zhen-hua;LIU Si-qu(State Nuclear Electric Power Planning Design & Research Institute Co. Ltd., Beijing 100095,China;School of Control and Computer Engineering, North China Electric Power University, Beijing 102206,China)
出处 《科学技术与工程》 北大核心 2021年第31期13342-13348,共7页 Science Technology and Engineering
基金 国家电力投资集团资助项目(KYTC2020ZH07)。
关键词 综合能源系统 风光不确定性 系统规划 可再生能源 多目标优化 integrated energy systems landscape uncertainty system planning renewable energy multi-objective optimization
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