Optimal Power Flow-Assisted Unit Commitment with Multi-Level Load Variation Analysis in Renewable-Based Power Systems

High penetration of distributed photovoltaic (PV) generation introduces operational challenges for thermal power plants, including increased cycling, higher losses, and reduced system flexibility. This study proposes an integrated optimization framework that combines Mixed Integer Nonlinear Programming (MINLP)-based Unit Commitment (UC) with a Particle Swarm Optimization (PSO)-assisted Optimal Power Flow (OPF) solved using the Newton–Raphson method. Applied to the IEEE 30-bus system for a 24-h horizon, the UC stage schedules 3717.8 MW of thermal generation at a cost of $8771.14. Load flow validation indicates a required supply of 3793.7 MW due to network losses, increasing the cost to $9031.64 and causing several constraint violations. The PSO-assisted OPF resolves all violations and produces an adjusted total generation of 3778.5 MW, reducing losses and lowering the overall operating cost to $8912.47 through optimal redispatch and voltage regulation. To further evaluate system robustness, multiple load scenarios—including reduced, nominal, and increased demand—are analyzed. Across all scenarios, the OPF stage is able to eliminate operational violations, decrease real power losses, and maintain voltage profiles within acceptable limits, demonstrating consistent performance under varying system stress levels. Overall, the integrated UC–OPF framework enhances economic efficiency, operational reliability, and resilience under renewable variability and shifting load conditions.