Optimal power flow analysis under photovoltaic and wind power uncertainties using the blood-sucking leech optimizer

• Date: 2025
• Main Authors: Bouhadouza, B., Sadaoui, F.
• Format: Article
• Language: English
• Published: National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2025
• Subjects: blood-sucking leech optimizer; optimal power flow; stochastic renewable energy sources; power systems
• Online Access: http://eie.khpi.edu.ua/article/view/334732
• Journal Title: Electrical Engineering & Electromechanics

Introduction. Optimal power flow (OPF) is a fundamental task in modern power systems, aiming to ensure cost-effective generation dispatch and efficient energy distribution. The increasing integration of renewable energy sources such as photovoltaic (PV) and wind turbines (WT), alongside conventional thermal units, introduces significant variability and uncertainty into system operations. Problem. The OPF problem is nonlinear, constrained by complex technical limits, and further complicated by the stochastic nature of PV and WT power generation. Efficiently addressing these uncertainties while maintaining system optimality remains a major challenge. The goal of this study is to solve the OPF problem in power networks that integrate PV and WT systems, while accounting for the uncertainty in their power outputs. Methodology. The stochastic behavior of PV and WT units is modeled using probability distribution functions. A novel bio-inspired metaheuristic, the Blood-Sucking Leech Optimizer (BSLO), is proposed and benchmarked against two well-established algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). Simulations are conducted on both the IEEE 30-bus test system and a real Algerian transmission network. Results. The BSLO algorithm consistently outperforms PSO and GWO in minimizing generation cost, power losses, and voltage deviation across all tested scenarios. Scientific novelty. This work considers both single and multi-objective OPF formulations, whereas most previous studies focus solely on single-objective approaches. It integrates renewable generation uncertainty through probabilistic modeling and introduces a novel metaheuristic (BSLO). Validation on a real Algerian power grid confirms the method’s robustness and practical relevance. Practical value. The results confirm the BSLO algorithm as a promising and effective tool for solving complex, renewable-integrated OPF problems in real-world power systems, contributing to more reliable, economical, and flexible grid operation. References 48, tables 13, figures 17.