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基于正态云的DR不确定性建模及配电网优化调度策略 被引量:8

Uncertainty Modeling and Optimal Scheduling Strategy for Demand Response in Distribution Network Based on Normal Cloud
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摘要 针对需求侧响应(DR)不确定性对配电网优化调度的影响,提出了基于正态云的DR不确定性建模及优化调度方法。首先,根据消费者心理学模型,分步建立了同时考虑响应量及响应边界不确定性的DR特性曲线。进而引入正态云概念,采用正态云模型对DR特性进行了统一数学描述。利用该模型,可同时反映DR误差的随机性及模糊性。在此基础上,建立了计及DR不确定性的配电网优化调度模型。针对该模型难以直接求解的问题,利用正向云发生器产生随机场景,并通过场景削减技术得到典型场景,将原模型转化为确定性模型并采用CPLEX软件进行求解。最后,采用算例仿真验证了所提调度策略的可行性及有效性。 In view of the influence of demand response(DR)uncertainty on optimal scheduling for distribution network,the paper proposes an uncertainty modeling and optimal scheduling strategy for the demand response based on normal cloud.Firstly,according to the consumer psychology model,a demand side response characteristic curve is constructed step by step based on the uncertainties of both the response magnitude and response boundary.Then normal cloud model is introduced to give a unified mathematical description of the demand response characteristics.This model can simultaneously reflect the randomness and ambiguity of the response deviation.On this basis,an optimal scheduling model considering the demand response uncertainty is established.As the model is difficult to solve directly,the forward cloud generator is used to generate stochastic scenario.In addition,several typical scenes is obtained by scene reduction technique,based on which the optimal scheduling model is solved with CPLEX.Finally,the simulation results verify the availability and effectiveness of the proposed strategy.
作者 黄利军 张彦龙 叶畅 王志成 苗世洪 HUANG Lijun;ZHANG Yanlong;YE Chang;WANG Zhicheng;MIAO Shihong(XJ Group Corporation,Xuchang461000,China;State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)
出处 《智慧电力》 北大核心 2020年第12期71-78,共8页 Smart Power
基金 国家自然科学基金资助项目(51777088) 国家电网公司科技项目(B492C0180016)。
关键词 正态云模型 需求侧响应 不确定性建模 随机场景技术 优化调度 normal cloud model demand response uncertainty modeling stochastic scenario technology optimal scheduling
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