Integrating Resilience Into Transmission and Generation Expansion Planning Via Probability-Free, Multi-Criteria, Multi-Hazard Stress Tests

Integrating resilience into system expansion planning models requires reckoning with extreme event risks, which are multifaceted, uncertain, and irreducible to expected costs. We propose a unified resilience and capacity expansion planning model that optimizes generation, transmission, and hardening investments by treating events as probability-free, multi-criteria stress tests that prevent unacceptable unmet demand and damage outcomes in extreme conditions while operating costs are optimized over ordinary conditions. Each event can contain multiple hazards that can disrupt operations, damage assets, and increase demand. Resilience can be improved with various hardening options that protect against some hazard impacts and by changing the timing, location, or technology of conventional investments. The model's solution is the least-cost, long-term expansion plan that meets all resilience and conventional planning criteria, such as emissions reduction targets. Resilience criteria are then varied to find Pareto-optimal cost and resilience tradeoffs. We execute the method on a large-scale 4,894-node synthetic Texas system with extreme heat, winter storm, and hurricane events, modeled as a linear program. The representative case study results show that a mix of hardening options and changes to capacity decisions relative to the baseline plan can improve system resilience while limiting cost increases to about 9%. The optimal adaptations de pend on both emissions targets and resilience criteria thresholds, which underscores the need for integrated models to meet the real-world requirements of planners and policy makers.