Application of Jellyfish Search Algorithm for Reactive Power Planning-based Power Losses Minimization in Electrical Power Networks

This study investigates the application of the Jellyfish Search Optimization (JFSO) algorithm for Reactive Power Planning (RPP) to minimize power losses in electrical power networks. The RPP problem is formulated as a multiobjective optimization task that seeks to reduce active power losses and minimize the investment costs of reactive power compensators. The performance of the JFSO algorithm is benchmarked against Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms on the IEEE 30-bus test system under two case studies: (1) minimizing active power losses alone and (2) minimizing both losses and investment costs. For minimizing the losses in Case 1, JFSO reduced power losses from an initial 5.6 MW with 18.2% reduction, compared to 16.8% for DE and 11.0% for PSO. In Case 2, JFSO minimized the total costs to 2,391,466/year,outperformingDEandPSO,whichresultedin 2,435,863/year and $2,629,826/year, respectively. The findings demonstrate that JFSO offers superior optimization performance with enhanced convergence properties. By effectively balancing investment and operational costs, the proposed algorithm provides a robust solution for efficient RPP.