Power system restoration is an essential activity for grid resilience, where grid operators restart generators, re-establish transmission paths, and restore loads after a blackout event. With a goal of restoring electric service in the shortest time, the core decisions in restoration planning are to partition the grid into sub-networks, each of which has an initial power source for black-start (called sectionalization problem), and then restart all generators in each network (called generator startup sequencing problem or GSS) as soon as possible. Due to the complexity of each problem, the sectionalization and GSS problems are usually solved separately, often resulting in a sub-optimal solution. Our paper develops models and computational methods to solve the two problems simultaneously. We first study the computational complexity of the GSS problem and develop an efficient integer linear programming formulation. We then integrate the GSS problem with the sectionalization problem and develop an integer linear programming formulation for the parallel power system restoration (PPSR) problem to find exact optimal solutions. To solve larger systems, we then develop bounding approaches that find good upper and lower bounds efficiently. Finally, to address computational challenges for very large power grids, we develop a randomized approach to find a high-quality feasible solution quickly. Our computational experiments demonstrate that the proposed approaches are able to find good solutions for PPSR in up to 2000-bus systems.
翻译:电源系统恢复是电网复原力的一项基本活动,电网操作员在电网恢复发电机,重建传输路径,并在停电事件发生后恢复负荷。为了在最短的时间内恢复电力服务,恢复规划的核心决定是将电网分割成子网络,每个网络都有初始电源源用于黑启动(所谓分解问题),然后尽快重新启动每个网络的所有发电机(所谓的发电机启动顺序问题或GSS),由于每个问题的复杂性,部门化和GSS问题通常单独解决,往往导致亚最佳的解决办法。我们的文件开发模型和计算方法,以同时解决这两个问题。我们首先研究GSS问题的计算复杂性,并开发高效的整数线性编程设计。我们随后将GSS问题与分解问题结合起来,为平行电力系统恢复(PPSR)问题制定一个整线性编程编程配制,以找到准确的最佳解决办法。为了解决更大的系统,我们随后开发了捆绑式方法,发现好的上下层和下层解决办法。最后,我们开发了模型式模型,以快速的计算方法解决高容量的电网格问题。我们提出了一套高容量的系统。