Graph-based Simulation Framework for Power Resilience Estimation and Enhancement

TL;DR

The paper presents a graph-based framework to estimate and enhance power distribution resilience under extreme weather. It combines a large-scale synthetic distribution network, a wind-based extreme weather scenario generator, fragility and recovery models, Monte Carlo resilience estimation, and a customized genetic algorithm for siting and sizing distributed energy resources. The approach enables resilience assessment via Monte Carlo simulations (with a trapezoid-based metric and gust-weighted episodes) and optimizes solar and battery deployments to maximize the minimum resilience across substation areas. Demonstrated on a Detroit-inspired topology with over 300k nodes and edges, the framework yields scalable resilience estimates and actionable DER configurations, offering utilities a practical path to community-level resilience planning.

Abstract

The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences. This paper presents a novel simulation framework for assessing and enhancing the resilience of power distribution networks under such conditions. Resilience is estimated through Monte Carlo simulations, which simulate extreme weather scenarios and evaluate the impact on infrastructure fragility. Due to the proprietary nature of power network topology, a distribution network is synthesized using publicly available data. To generate the weather scenarios, an extreme weather generation method is developed. To enhance resilience, renewable resources such as solar panels and energy storage systems (batteries in this study) are incorporated. A customized Genetic Algorithm is proposed to determine the optimal locations and capacities for solar panels and battery installations, maximizing resilience while balancing cost constraints. Experiment results demonstrate that on a large-scale synthetic distribution network with more than 300,000 nodes and 300,000 edges, the proposed framework can efficiently evaluate the resilience, and enhance the resilience through the installations of distributed energy resources (DERs), providing utilities with valuable insights for community-level power system resilience estimation and enhancement.

Figures (8)

• Figure 1: Synthetic distribution network of Detroit, MI.
• Figure 2: Wind speed distribution of gusts and sustained winds, along with the wind speed-based fragility curve.
• Figure 3: An example of the generated dense wind speed field, with wind gust locations shown in red and sustained winds in green.
• Figure 4: Fragility curve illustrating the joint influence of wind speed and tree coverage.
• Figure 5: Resilience estimation results across 10,000 episodes, where red denotes lower resilience (higher vulnerability) and green denotes higher resilience.