含固态变压器新型配电网动态无功多目标优化

风机和光伏电池等分布式电源(distributed generator, DG)大量接入配电网会导致电压波动和网损增大等问题，需要对动态无功进行优化。但是由于风光存在的不确定性会影响动态无功优化的效果，因此提出了一种含固态变压器新型配电网动态无功多目标优化方法。首先，通过 Weibull 分布和 Beta 分布对风速和光照强度进行曲线拟合，再采用风机和光伏电池出力公式生成 DG 出力模型。其次，通过蒙特卡洛仿真抽样法对上述模型进行抽样，生成上千个DG日出力场景，并采用k-means 聚类算法将上千个场景聚类成k个典型场景，以缩短随机潮流计算时间。再次，以IEEE33 节点系统为基础，建立含固态变压器有源配电网方案和含有载调压变压器有源配电网方案，以日内网损和电压波动最小为目标，采用改进型多目标灰狼算法对两种方案的相关参数进行优化。最后，以优化后的相关参数进行仿真和对比，证明了所提方法在降低配电网网损和维持节点电压稳定方面的优越性。

Multi-objective optimization of dynamic reactive power in a new distribution network with a solid state transformer

A large number of distributed generators (DG), such as wind power generators and photovoltaic cells, will be connected to the power distribution network, resulting in voltage fluctuation and active network loss increase. The dynamic reactive power needs to be optimized. However, wind-solar uncertainty will affect the effect of dynamic reactive power optimization. Thus this paper proposes a multi-objective intelligent optimization method for the dynamic reactive power of a distribution network with a solid state transformer (SST) considering wind-solar uncertainty. First, the wind speed and light intensity are fitted by the Weibull and Beta distributions, and then the output formulas of wind power generators and photovoltaic cells are used to generate the DG output model. Second, Monte Carlo simulation sampling is used to sample the above model to generate thousands of daily DG output scenarios, and the k-means clustering algorithm is used to cluster thousands of scenarios into several typical scenarios to shorten the calculation time of random power flow. Third, based on the IEEE33 node system, an active distribution network scheme with the SST and an active distribution network scheme with on-load tap changer transformer are established. To minimize the active power loss and node voltage fluctuation of the distribution network, the relevant parameters of the two schemes are optimized using an improved multi-objective gray wolf optimizer (MOGWO) algorithm. Finally, the optimized parameters are simulated and compared to prove the superiority of the proposed method in reducing network loss and maintaining node voltage stability.