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Robust Optimal Operation of Active Distribution Network Based on Minimum Confidence Interval of Distributed Energy Beta Distribution

机译:基于分布式能量β分布的最小置信区分的主动分配网络的鲁棒优化

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摘要

With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.
机译:随着分布式能源普及率的逐渐提高,销售网络的传统优化模型不再能保证配电网的稳定,高效运行。为了解决分布式能源的必然不确定性,一个新的强大的最佳操作方法,提出了基于本文分布式能源Beta分布的置信区间的最短主动分发网络(ADN)。首先,ADN模型与二阶锥建立包括能量存储装置,电容器组,静态无功补偿器,有载抽头变换器,风力涡轮机和光伏。然后,相关的分布式能源的历史数据进行分析,并通过概率密度函数来描述,并且由间隔搜索所获得的最小置信区间。此外,通过采取这一最小置信区间为不确定区间,不太保守的两阶段稳健优化模型的建立和求解ADN。仿真结果为IEEE 33节点配电网络已经验证,该方法可以实现与传统的鲁棒优化方法相比,销售网络更稳定,高效运行。

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