基于自由熵理论和智能场景匹配的配电网无功优化

Reactive Power Optimization of Distribution Network Based on Free Entropy Theory and Intelligent Scene Matching

无功优化对保障电力系统安全、经济运行具有重要的意义。提出了一种基于自由熵理论和智能场景匹配的配电网无功优化方法。介绍了适用于配电网场景匹配和无功优化的自由熵指标的构建方法,基于自由熵指标进行智能场景匹配,从历史数据库中快速找出与待优化时刻最为接近的场景,并将其对应的控制方案作为待优化时刻的无功优化方案,以减小有功网损和节点电压偏移。最后,采用Matlab+Open DSS建立了IEEE37节点的主动配电网仿真模型,并增加了光伏/风电分布式发电和电动汽车等随机负荷模型,算例结果验证了所提方法的有效性和优越性。

Reactive power optimization plays an important role in ensuring the safety and economic operation of the power system.This paper proposes a reactive power optimization method based on free entropy theory and intelligent scene matching.The construction method of free entropy index suitable for scene matching and reactive power optimization in distribution network is introduced.The scene is matched based on free entropy index.The scene that closest to the time to be optimized is quickly found from the historical database,and its corresponding control scheme is used as the reactive power optimization scheme to optimize the time to reduce active power loss and node voltage offset.Finally,Matlab+OpenDSS is used to establish the IEEE-37node active distribution network simulation model,and the distributed load generation model such as photovoltaic/wind power distributed generation(DG)and electric vehicle are added.The results of the example verify the effectiveness and superiority of the proposed method.