Modified Orca Algorithm Based on the Navigation Behavior for Optimal Unit Commitment in Power Systems - Widayanti, Lilis; Afandi, Arif Nur; Herwanto, Heru Wahyu (Buletin Ilmiah Sarjana Teknik Elektro, 2025)
This study presents the Novel Navigation Orca Algorithm (NNOA), an innovative optimization algorithm derived from Orca Algorithm (OA). NNOA addresses the unit commitment (UC), a complex issue in power systems that focuses on scheduling generator units to meet power demand while taking into account each generator's limitations, with the goal of lowering operating costs and gas emissions. NNOA exhibits orca hunting behavior through echolocation, utilizing the Doppler effect principle to promote adaptive movement and circumvent local optima, as in contrast to OA's wave-based exploration. The algorithm was evaluated utilizing IEEE 30-bus system data, focused on the Integrated Economic and Emission Dispatch (IEED) objective. The performance was evaluated against OA and Particle Swarm Optimization (PSO) through convergence analysis over 10 and 30 trials, each consisting of 100 iterations. NNOA decreased the IEED value by 1.33% in regard to OA and 1.51% in regard to PSO. NNOA achieved convergence in 10 iterations, whereas OA required 35, indicating 71.4% faster convergence rate. Wilcoxon rank-sum tests demonstrated significant differences between NNOA, OA, and PSO pairings. NNOA's per-iteration computation time exceeds the time needed by PSO, but it remains economical and profitable. Significantly, NNOA contributes minimizing the fuel consumption and emissions cost, which has a positive environmental impact. It effectively adheres to the required constraints, which include the hourly power demand and generator output limits. Future research is encouraged to apply NNOA to larger-scale power systems and explore its hybridization with PSO to enhance computational efficiency, result consistency, and robustness in practical grid operations.