论文标题
可扩展的分布式非凸ADMM基于ADMM的主动分配系统服务修复
Scalable Distributed Non-Convex ADMM-based Active Distribution System Service Restoration
论文作者
论文摘要
分布式恢复可以利用分布式能源（DER）来增强主动分配网络的弹性。但是，大量的决策变量，尤其是重新配置的二进制决策变量，为制定有效的分布式分配服务恢复（DDSR）策略带来了挑战。本文提出了一种基于乘数的交替方向方法（ADMM）的可扩展分布式优化方法，以解决非凸层混合构成优化问题，并适用于开发DDSR框架。非凸ADMM方法由放松驱动 - 式阶段，1）放松的二进制变量并将凸ADVEX ADMM应用于温暖的开始； 2）通过近端运算符将解决方案驱动到布尔值； 3）将所获得的二进制变量固定到抛光连续变量以进行高质量解决方案。然后，开发了一种自主聚类策略以及共识ADMM，以实现基于分布的基于群集的恢复框架。基于非convex ADMM的DDSR可以以分布式的方式确定重新配置和负载拾取的DER调度和开关状态，从而在大规模分布网络中从本地故障或总停电中充满服务。通过对IEEE 123节点和IEEE 8500节点测试馈线进行测试，证明了所提出的DDSR框架的有效性和可扩展性。
Distributed restoration can harness distributed energy resources (DER) to enhance the resilience of active distribution networks. However, the large number of decision variables, especially the binary decision variables of reconfiguration, bring challenges on developing effective distributed distribution service restoration (DDSR) strategies. This paper proposes a scalable distributed optimization method based on the alternating direction method of multipliers (ADMM) for non-convex mixed-integer optimization problems and applies to develop the DDSR framework. The non-convex ADMM method consists of relax-drive-polish phases, 1) relaxing binary variables and applying the convex ADMM as a warm start; 2) driving the solutions toward Boolean values through a proximal operator; 3) fixing the obtained binary variables to polish continuous variables for a high-quality solution. Then, an autonomous clustering strategy together with consensus ADMM is developed to realize the distributed cluster-based framework of restoration. The nonconvex ADMM-based DDSR can determine DER scheduling and switch status for reconfiguration and load pickup in a distributed manner, energizing the out-of-service area from local faults or total blackouts in large-scale distribution networks. The effectiveness and scalability of the proposed DDSR framework are demonstrated through testing on the IEEE 123-node and IEEE 8500-node test feeders.
