Optimizing Service Restoration in Distribution Systems With Uncertain Repair Time and Demand

Abstract

This paper proposes a novel method to co-optimize distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed. The first stage is to dispatch the repair crews to the damaged components. The second stage is distribution system restoration using distributed generators, and reconfiguration. We consider demand uncertainty in terms of a truncated normal forecast error distribution, and model the uncertainty of the repair time using a lognormal distribution. A new decomposition approach, combined with the Progressive Hedging algorithm, is developed for solving largescale outage management problems in an effective and timely manner. The proposed method is validated on modified IEEE 34-and 8500-bus distribution test systems.

Repair and Resource Scheduling in Unbalanced Distribution Systems Using Neighborhood Search

“…The model was solved using a cluster-first route-second approach. Also, we developed a stochastic mixed integer linear program (SMIP) in [16] to solve the same problem with uncertainty. The problem was decomposed into two subproblems and solved using parallel progressive hedging.…”

“…In this paper, we improve our previous work in [15] and [16] by considering the 3-phase operation of the distribution network and modeling fault isolation constraints, coordinating tree and line crews, and resource logistics in the distribution system repair and restoration problem (DSRRP). Furthermore, a new framework for modeling different types of PV systems is developed.…”

Dynamic business continuity assessment using condition monitoring data

“…L are calculated considering the revenue losses during the recovery process, which depends on the recovery time and electricity price. Due to the common use of lognormal distribution for modeling the repair process [58][59][60], we also assume that the recovery time follows a lognormal distribution with the parameters summarized in Table 3, where  and  are parameters of the lognormal distribution, whose PDF is Then, the value of in L is calculated by Monte Carlo simulation. The value of the seasonal component parameters are shown in Table 4.…”

Assessing impacts of energy storage on resilience of distribution systems against hurricanes

“…Multiple literature has studied the service restoration of distribution systems in the hours following a catastrophic event. The service restoration methods proposed in the literature inlcude optimal crew assignment problem [2,3] and deployments of mobile generators [4]. In [5], the service restoration of distribution network with transportable energy storage is also investigated wherein transportable energy storage is proposed to be transported over railway networks to support different islanded areas of a degraded distribution network.…”