论文标题
PowerModelSrestoration.jl：探索电源网络恢复算法的开源框架
PowerModelsRestoration.jl: An Open-Source Framework for Exploring Power Network Restoration Algorithms
论文作者
论文摘要
随着极端网格干扰的频率不断增加，例如自然灾害，对有效恢复计划的需求越来越多。最佳功率恢复的算法在制定此类计划中起着重要作用，但也引起了挑战性的混合企业非线性优化问题，在此问题尚未可用的情况下。为了协助研究这种解决方案方法，这项工作提出了PowerModelSrestoration，这是一种灵活的开源软件框架，用于快速设计和测试电源恢复算法。 PowerModelSrestoration构建了一个数学建模层，用于形式化核心恢复任务，该任务可以合并以开发复杂的工作流程和高性能启发式方法。概念证明的研究对文献中的三个既定病例进行了概念验证研究，重点是单相正序列网络模型，证明了所提出框架的功效。结果表明，PowerModelSrestoration再现了已建立的文献，并且首次通过非线性功率流模型对恢复进行分析，非线性功率流模型先前尚未考虑。
With the escalating frequency of extreme grid disturbances, such as natural disasters, comes an increasing need for efficient recovery plans. Algorithms for optimal power restoration play an important role in developing such plans, but also give rise to challenging mixed-integer nonlinear optimization problems, where tractable solution methods are not yet available. To assist in research on such solution methods, this work proposes PowerModelsRestoration, a flexible, open-source software framework for rapidly designing and testing power restoration algorithms. PowerModelsRestoration constructs a mathematical modeling layer for formalizing core restoration tasks that can be combined to develop complex workflows and high performance heuristics. The efficacy of the proposed framework is demonstrated by proof-of-concept studies on three established cases from the literature, focusing on single-phase positive sequence network models. The results demonstrate that PowerModelsRestoration reproduces the established literature, and for the first time provide an analysis of restoration with nonlinear power flow models, which have not been previously considered.
