Hydrology-Aware Maintenance Scheduling for a Large Run-of-River Hydropower Plant Using FID-Guided Large Neighborhood Search

Maintenance scheduling in large run-of-river hydropower plants requires balancing maintenance urgency, asset condition, hydrological opportunity, and operational constraints. This study proposes an integrated decision-support framework for the Santo Antônio Hydropower Plant, a 50-unit facility in Brazil. The methodology combines condition-informed maintenance inputs, multi-criteria prioritization of generating-unit maintenance needs, and a Forced Impact of Downtime (FID)-guided Large Neighborhood Search (LNS) algorithm with problem-specific destroy-and-repair operators. The optimization reallocates eligible maintenance activities from high-impact hydrological periods while enforcing resource, electrical, hydraulic, and synchronization constraints. The proposed approach was compared with Genetic Algorithm (GA) and Ant Colony Optimization (ACO) benchmarks over 1000 independent runs using real operational data. LNS achieved the lowest mean fitness value (3587.68), compared with 3939.45 for ACO and 3964.54 for GA, and the shortest average execution time. Relative to the baseline schedule prepared by plant specialists, the optimized schedule reduced FID from 452 to 302 equivalent unavailability hours, corresponding to a 33.2% reduction. These results demonstrate the potential of hydrology-aware, condition-informed optimization to support practical maintenance planning in large run-of-river hydropower plants.