• Development of a tiered outage modeling structure that enables controlled and progressive load shedding, thereby reducing the severity of uncontrolled service interruptions. • Integrated scheduling of power plant maintenance and planned outages within the allocation and distribution decision process, improving operational feasibility and system reliability. • Optimization of electricity distribution across regions and time periods to enhance access to critical loads and improve resilience under constrained and disruptive operating conditions. Amid rising electricity demand, constrained generation capacity, and increasing complexity of power distribution networks, effective outage management and resource allocation have become critical engineering challenges. This study proposes a mathematical optimization framework based on a Hybrid Evolutionary Annealing algorithm that combines Genetic Algorithm and Simulated Annealing to jointly optimize power plant allocation, generation site selection, and electricity distribution scheduling under outage conditions. The model explicitly incorporates real-world parameters, including plant locations, transmission capacities, geographic distances, and regional load profiles. Key contributions include the introduction of tiered outage levels for progressive crisis control, priority-based service allocation reflecting operational criticality, and integrated spatial–temporal optimization of network operations. A case study based on data from an Iranian province demonstrates that the proposed approach can significantly reduce outage-induced system shocks, improve electricity access in critical zones, and lower long-term infrastructure costs compared with current operational practices. Beyond numerical performance, the framework enhances grid resilience and supports informed decision-making during energy crises. The results indicate that carefully designed, non-economic optimization mechanisms can substantially contribute to sustainable and resilient power system operation in modern electricity networks.
Hejazi et al. (Sun,) studied this question.