Data-driven, topology-aware energy resilience assessment and enhancement in urban communities

This paper presents a data-driven, topology-aware planning framework for energy resilience assessment and enhancement in urban communities. The resilience assessment integrates two complementary datasets: (1) annual aggregated power outage data, (2) 15 min interval outage data. High-impact, low-probability (HILP) events are identified within these datasets to evaluate community resilience under extreme weather conditions. To improve resilience, a two-layer optimization framework based on mixed-integer linear programming is proposed. The first layer determines the optimal sizing and placement of solar photovoltaic (PV) systems and battery energy storage systems (BESS). The second layer performs topology-switching AC optimal power flow (AC-OPF) to ensure achieving the target resilience after infrastructure damage or topology changes caused by extreme weather events, while accounting for network constraints. A case study of the City of Detroit in Michigan demonstrates the effectiveness of the proposed framework through simulation and validation, achieving an average resilience index above 90% across all substations. Furthermore, the framework achieves a cost per resilience unit (CRU) of approximately $1,050 per kWh, demonstrating superior cost-effectiveness compared with existing resilience enhancement strategies. • Data-driven energy resilience assessment. • Optimal sizing and placement of PV and BESS to enhance energy resilience. • Topology-switching ACOPF to strengthen Energy Resilience after extreme events. • Validation using real-world data from the City of Detroit.