论文标题
智能配电系统的多阶段弹性管理：随机强大的优化模型
Multi-stage Resilience Management of Smart Power Distribution Systems: A Stochastic Robust Optimization Model
论文作者
论文摘要
天气和极端气候的大量停电强调了以弹性为中心的电网风险管理的迫切需求。本文提出了一个多阶段随机鲁棒优化（SRO）模型，该模型在两个主要方面的现有规划框架上发展。首先，它捕获了与决策的操作措施相互作用。其次，它适当地处理了计划中的许多不确定性。 SRO模型协调配备分布式生成单元和开关的智能配电系统的硬化和系统操作措施。为了捕获极端事件造成的损害的不确定性，通过将飓风的概率信息与高架结构的性能相结合来开发不确定性集。一个新的概率模型用于损坏线的修复时间，以解释恢复过程中的不确定性。基于差分进化算法和混合材料求解器的集成的解决方案策略旨在解决弹性最大化模型。所提出的方法应用于具有485个实用杆的改良IEEE 33-BUS系统和具有1841杆的118总线系统。该系统在美国德克萨斯州哈里斯县绘制的映射，美国的结果表明，最佳的硬化决策可能会受到弹性运营措施的显着影响。
Significant outages from weather and climate extremes have highlighted the critical need for resilience-centered risk management of the grid. This paper proposes a multi-stage stochastic robust optimization (SRO) model that advances the existing planning frameworks on two main fronts. First, it captures interactions of operational measures with hardening decisions. Second, it properly treats the multitude of uncertainties in planning. The SRO model coordinates hardening and system operational measures for smart power distribution systems equipped with distributed generation units and switches. To capture the uncertainty in the incurred damage by extreme events, an uncertainty set is developed by integrating probabilistic information of hurricanes with the performance of overhead structures. A novel probabilistic model for the repair time of damaged lines is derived to account for the uncertainty in the recovery process. A solution strategy based on the integration of a differential evolution algorithm and a mixed-integer solver is designed to solve the resilience maximization model. The proposed approach is applied to a modified IEEE 33-bus system with 485 utility poles and a 118-bus system with 1841 poles. The systems are mapped on the Harris County, TX, U.S. Results reveal that optimal hardening decisions can be significantly influenced by resilience operational measures.
