论文标题
通过应急资源实现灾难性分配系统：一种实用方法
Achieving Disaster-Resilient Distribution Systems via Emergency Response Resources: A Practical Approach
论文作者
论文摘要
本文提出了一种实用的方法，用于利用紧急响应资源（ERR）和污水爆炸后可用的分布式能源资源（PDA-DERS），以提高对自然灾害的发电机分配系统的弹性。所提出的方法由两个顺序步骤组成：首先，在策略前计划模型中确定了最小数量的错误量；其次，提出了一个污点后的恢复模型，以优化派遣预先计划的错误和PDA-DIS，以最大程度地减少灾害对客户的影响，即整个修复窗口中未得到的能量。 Compared with existing restoration strategies using ERRs, the proposed approach is more tractable since 1) in the pre-disaster stage, the needed EERs are determined based on the prediction of energy shortage and disaster-induced damages using machine learning-based algorithms (i.e., cost-sensitive-RFQRF for prediction of outage customers, random forest for prediction of outage duration, and CART for prediction of disaster-induced damages); 2）在灾后阶段，引入了分配网络的超节点近似（SNA）和凸船体松弛（CHR），以实现计算负担和准确性之间的最佳权衡。对IEEE测试馈线的拟议方法的测试表明，SNA和CHR的组合大大降低了污水剂后恢复模型的溶液时间。
This paper presents a practical approach to utilizing emergency response resources (ERRs) and post-disaster available distributed energy resources (PDA-DERs) to improve the resilience of power distribution systems against natural disasters. The proposed approach consists of two sequential steps: first, the minimum amount of ERRs is determined in a pre-disaster planning model; second, a post-disaster restoration model is proposed to co-optimize the dispatch of pre-planned ERRs and PDA-DERs to minimize the impact of disasters on customers, i.e., unserved energy for the entire restoration window. Compared with existing restoration strategies using ERRs, the proposed approach is more tractable since 1) in the pre-disaster stage, the needed EERs are determined based on the prediction of energy shortage and disaster-induced damages using machine learning-based algorithms (i.e., cost-sensitive-RFQRF for prediction of outage customers, random forest for prediction of outage duration, and CART for prediction of disaster-induced damages); 2) in the post-disaster stage, the super-node approximation (SNA) and the convex hull relaxation (CHR) of distribution networks are introduced to achieve the best trade-off between computational burden and accuracy. Tests of the proposed approach on IEEE test feeders demonstrated that a combination of SNA and CHR remarkably reduces the solution time of the post-disaster restoration model.
