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基于需求侧响应的配电网多源协同优化调度

Multi source Collaborative Optimization Scheduling of Distribution Network Based on Demand Side Response
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摘要 以往的配电网多源协同优化调度方法由于单一地使用了赋值法构建配电网优化调度模型,导致调度效果较差。因此,设计了基于需求侧响应的配电网多源协同优化调度方法。通过计算配电网的发电成本和运行成本,设定配电网协同调度的优先级,在此基础上,利用线性加权法和赋值法,构建配电网多源协同优化调度模型,之后在构建的模型中,利用需求侧响应代替配电网中供电成本较高的部分,实现配电网的协同优化。通过上述的设计,完成了对配电网多源协同优化调度方法的设计。在实验测试中,与以往的配电网多源协同优化调度方法相比,设计的基于需求侧响应的配电网多源协同优化调度方法在实际应用中的总成本仅为7129.5元,调度效果最优。 The previous multi source collaborative optimization and scheduling methods for distribution networks used a single assignment method to construct a distribution network optimization and scheduling model,resulting in poor scheduling effectiveness.Therefore,a multi-source collaborative optimization scheduling method for distribution networks based on demand side response was designed.By calculating the generation and operation costs of the distribution network,the priority of distribution network collaborative scheduling is set.Based on this,a multi-source collaborative optimization scheduling model for the distribution network is constructed using linear weighting and assignment methods.Then,in the constructed model,demand side response is used to replace the higher part of the power supply cost in the distribution network,achieving multi-source collaborative optimization of the distribution network.Through the above design,the design of a multi source collaborative optimization scheduling method for distribution networks has been completed.In experimental testing,compared with previous multi source collaborative optimization scheduling methods for distribution networks,the total cost of the designed multi source collaborative optimization scheduling method based on demand side response in practical applications is only 7129.5 yuan,and the scheduling effect is the best.
作者 牛聪 马曦 张静杨 NIU Cong;MA Xi;ZHANG Jingyang(State Grid Xixian New Area Power Supply Company,Xixian 712000,China)
出处 《通信电源技术》 2023年第9期101-103,共3页 Telecom Power Technology
关键词 需求侧响应 配电网 多源协同优化调度 demand side response distribution network multi source collaborative optimization scheduling
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