论文标题
改进了随机分布网络重新配置的连续分支减少
Improved Successive Branch Reduction for Stochastic Distribution Network Reconfiguration
论文作者
论文摘要
我们提出了一种改进的连续分支减少（SBR）方法来解决随机分布网络重新配置（SDNR），这是一个混合企业，已知在计算上具有挑战性。首先，对于具有单个冗余分支的特殊分销网络，我们为文献中的单阶段SBR算法进行了改进的设计，以结合不确定的可再生能源后代和负载。基于求解随机最佳功率流，改进的算法通过一小部分候选分支来识别和搜索，从中确定最佳分支可以打开并获得具有最低预期运营成本的径向网络。然后，对于具有多个冗余分支的通用网络，我们基于一个近距离开放的过程设计了启发式的两阶段SBR算法，该过程迭代地运行了所提出的一阶段SBR算法。 IEEE 33-BUS和123 BUS分布网络模型的数值结果以最佳和计算效率来验证所提出的方法。
We propose an improved successive branch reduction (SBR) method to solve stochastic distribution network reconfiguration (SDNR), a mixed-integer program that is known to be computationally challenging. First, for a special distribution network with a single redundant branch, we propose an improved design for a one-stage SBR algorithm in the literature to incorporate uncertain renewable generations and loads. Based on solving stochastic optimal power flow, the improved algorithm identifies and searches through a small set of candidate branches, from which it determines the optimal branch to open and obtains a radial network with the minimum expected operational cost. Then, for a general network with multiple redundant branches, we design a heuristic two-stage SBR algorithm based on a close-and-open procedure that iteratively runs the proposed one-stage SBR algorithm. Numerical results on the IEEE 33-bus and 123-bus distribution network models verify the proposed method in terms of optimality and computational efficiency.
