Optimization of Hybrid Power System Operation in the Sangihe I System Using the Grey Wolf Algorithm

Abstract

Optimizing power system operation in remote areas is challenging due to limited resources, load variability, and heterogeneous generation characteristics. This study proposes a hybrid optimization framework combining Unit Commitment (UC) and the Grey Wolf Optimizer (GWO) for the Sangihe I power system, which includes diesel generators (DG), photovoltaic (PV), and micro-hydropower (MHP). The UC model incorporates generator scheduling, power limits, and operational constraints using real system data and daily load profiles. The performance of GWO is compared with the Firefly Algorithm (FA). The results show that renewable energy integration reduces operational costs by decreasing reliance on diesel generation. GWO consistently provides more efficient generation scheduling, achieving lower daily operating costs and improved load distribution. Quantitatively, GWO outperforms FA by approximately 7% - 9% under unconstrained conditions and 3% - 6% under base unit constraints. Overall, the proposed approach enhances renewable energy utilization and provides a more robust and effective solution for economic dispatch and energy management in hybrid power systems, particularly in remote areas.