Stochastic Pre-Event Preparation for Enhancing Resilience of Distribution Systems with High DER Penetration

TL;DR

This paper introduces a stochastic optimization approach for pre-event resource allocation in distribution systems with high DER penetration, improving resilience and restoration efficiency during extreme weather events.

Contribution

It develops a two-stage stochastic mixed-integer linear programming model with a scenario decomposition method to optimize pre-event preparations considering high PV penetration.

Findings

The method enhances system resilience during outages.

PV integration significantly improves post-event restoration.

Scalability demonstrated on large distribution feeders.

Abstract

This paper proposes a stochastic optimal preparation and resource allocation method for upcoming extreme weather events in distribution systems, which can assist utilities to achieve faster and more efficient post-event restoration. With the objective of maximizing served load and minimizing operation cost, this paper develops a two-stage stochastic mixed-integer linear programming (SMILP) model. The first-stage determines the optimal positions and numbers of mobile resources, fuel resources, and labor resources. The second-stage considers network operational constraints and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, we consider three types of PV systems during power outage and compare the resilience improvements with different PV penetration levels. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the scalability and effectiveness of the proposed method.