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电力市场环境下计及V2G和多类型需求响应的综合能源系统多目标优化模型 被引量:4

A Multi-objective Optimization Model for Integrated Energy System Considering V2G and Multi Type Demand Response in Electricity Market Environment
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摘要 园区综合能源系统(Park integrated energy system,PIES)是满足用户多种用能需求的一种重要途径,但由于内部分布式能源机组出力具有不确定性,且与负荷在时间维度上存在一定的不匹配性,限制了能源利用效率和经济效益的提升.因此,本文首先在构建耦合热电的PIES的基础上,构建了多类型需求响应模型;其次,构建了考虑风光机组出力不确定性的以系统净收益、清洁能源弃能率为多目标函数的优化模型;然后,构建了"不确定性处理-多目标处理-单目标处理"的三阶段求解模型,最后,以V2G和需求响应为考虑条件,设计多情景.算例结果表明:V2G可以为清洁能源不稳定发电提供有效备用,在一定程度上提供清洁能源利用效率;引入电热需求响应,能够改变用户负荷,增加经济效益. The park integrated energy system is an important way to meet the various energy needs of users.However,due to the uncertainty of the output of internal distributed energy units and the mismatch with load in time dimension,the improvement of energy utilization efficiency and economic benefits is limited.Therefore,based on the construction of the park integrated energy system coupled with power and heating,this paper constructs a multi type demand response model;secondly,on the basis of considering the uncertainty of clean energy output,the optimization model with the system net income and clean energy abandonment rate as the multi-objective function is constructed;then,the"Uncertainty handling-multi objective handling-single objective handling"is constructed Finally,taking V2 G and demand response implementation as variable factors,multi scenario analysis is carried out.The results show that V2 G can provide effective reserve for unstable power generation of clean energy and provide clean energy utilization efficiency to a certain extent;introducing electric heating demand response can change user load and increase economic benefits.
作者 刘小敏 苟瑞欣 张坤 王铮 LIU Xiao-min;GOU Rui-xin;ZHANG Kun;WANG Zheng(State Grid Ningxia Electric Power Co.,Ltd.Economic and Technical Research Institute,Yinchuan 750000,China)
出处 《数学的实践与认识》 2021年第3期98-109,共12页 Mathematics in Practice and Theory
关键词 园区综合能源系统 清洁能源不确定性 V2G 多类型需求响应 多目标优化调度 park integrated energy system clean energy uncertainty V2G multi type demand response multi objective optimal scheduling
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