Enhancing resilience in Los Angeles energy systems

Wildfires, exacerbated by climate change, pose significant challenges to the resilience and operational stability of energy systems, particularly in regions like Los Angeles (LA). These extreme events disrupt critical infrastructure, hinder renewable energy generation, and amplify uncertainties in energy supply and demand. This paper proposes a novel quantum computing-based Distributionally Robust Optimization (DRO) framework to enhance the resilience of Virtual Power Plant (VPP)-enabled microgrids during wildfire scenarios. The framework integrates advanced quantum principles with dynamic wildfire impact modeling to optimize energy resource allocation under high-dimensional uncertainty. Key features of the proposed framework include a quantum-enhanced DRO model for energy dispatch, a wildfire propagation model to capture cascading impacts on grid operations, and a robust energy allocation strategy prioritizing critical loads. The study employs real-world data from recent LA wildfires, incorporating geospatial fire spread dynamics, wind patterns, and renewable generation profiles. A case study on a representative LA microgrid demonstrates the framework's scalability and effectiveness. The proposed method achieves approximately a 25.3% reduction in operational costs, improves the resilience score by up to 18.7%, and ensures uninterrupted support to over 98% of critical loads during high-intensity wildfire scenarios. This research advances the state of the art in energy system resilience by combining quantum computing with robust optimization techniques tailored to wildfire-induced challenges. The findings offer actionable insights for designing adaptive, sustainable, and resilient energy systems in fire-prone regions.