考虑DG不确定性的主动配电网两阶段无功机会 约束优化方法

随着高比例的分布式电源(Distributed generation，DG)并网，传统的确定性无功优化方法难以应对DG和负荷的双侧不确定性，提出了主动配电网的两阶段无功机会约束优化方法。首先将配电网潮流方程线性化和松弛，建立基于混合整数二阶锥的配电网两阶段无功优化模型，分别在第一阶段优化有载调压变压器、并联电容器等离散控制装置，在第二阶段考虑DG的不确定性优化DG的无功出力。然后，通过场景缩减法来减少机会约束优化方法的场景数。在95节点系统的仿真结果表明，与确定性性无功优化方法相比，所提出的两阶段无功机会约束优化方法能有效地消除安全约束越限问题，在略微增加网损的情况下获得了较高的鲁棒性。

Two-stage reactive power chance-constrained optimization method for an active distribution network considering DG uncertainties

With the high proportion of Distributed Generation (DG) connected to the grid, traditional deterministic reactive power optimization methods cannot deal with the double-sided uncertainties of DGs and load. This paper proposes a two-stage chance-constrained reactive power optimization method for an active distribution network. First, the power flow equation of the network is linearized and relaxed, and a two-stage reactive power optimization model based on a mixed integer second-order cone is established. In the first stage, discrete control devices such as the on-load tap changer and shunt capacitor are optimized. In the second stage, the uncertainties of DGs are considered to optimize the reactive power output of the DG. Then, a scenario reduction method is used to reduce the number of scenarios of the chance-constrained optimization method. Smulation results of a 95-bus system show that the proposed method can effectively eliminate the problem of security constraint violations, and achieve higher robustness with slightly increased network loss compared with the deterministic reactive power optimization method. This work is supported by the National Key Research and Development Program of China (No. 2017YFB0903705).