Akademik Veri Yönetim Sistemi
COMPUTERS & INDUSTRIAL ENGINEERING, cilt.213, ss.1-19, 2026 (SCI-Expanded, Scopus)
An effective day-ahead planning strategy is pivotal for ensuring the economic, secure, and balanced operation of modern electricity grids. To address this challenge, various metaheuristic methods have been proposed for multi-objective day-ahead energy management, yet many suffer from scalability and convergence issues under realistic operating constraints. This study presents an efficient multi-objective optimization framework for day-ahead hourly optimal energy scheduling (DAHOES) in renewable-integrated distribution systems. The proposed framework employs the Fast Non-Dominated Sorting Multi-Objective Symbiotic Organism Search (FNSMOSOS) algorithm to minimize both active power losses and total operating costs. Following the optimization process, a fuzzy decision-making method is utilized to select a balanced solution from the generated Pareto front, ensuring that the final operation plan aligns with practical performance criteria. To reflect actual distribution system behavior, a modified five-bus distribution network comprising photovoltaic (PV) units, wind energy systems (WES), energy storage systems (ESS), and grid supply is modelled. In addition, realistic hourly demand profiles, renewable generation forecasts, and grid price signals are incorporated to ensure both theoretical optimality and practical feasibility. The proposed algorithm is compared with several other methods, and simulation results show that FNSMOSOS outperforms NSMOCS by 24.1% in HV and surpasses MOGWO, MOWOA, and MONNA by 56%, 117%, and 790%, respectively, demonstrating superior Pareto convergence and diversity. Overall, the results confirm that the proposed framework offers a scalable and effective decision-support tool for distribution system operators facing multi-criteria scheduling challenges in complex and uncertain power systems.