Reactive Power Planning in Microgrids with Renewable Energy Sources Using Starfish Optimization Algorithm

The increasing penetration of renewable energy sources (RESs) in microgrids introduces significant challenges to reactive power planning (RPP) due to their intermittent and uncertain nature. Effective reactive power management is essential to maintain voltage stability, minimize power losses, and enhance overall system performance. This paper presents a novel application of the Starfish Optimization Algorithm (SFOA), a recent bio-inspired metaheuristic, for optimal reactive power planning in microgrids with integrated renewable energy sources such as solar photovoltaic and wind generation. A comprehensive objective function is formulated to minimize real power losses and voltage deviation while improving voltage profiles across the network. The effectiveness of the proposed SFOA-based RPP method is validated on standard IEEE distribution test systems under different loading conditions and renewable penetration levels. Simulation results demonstrate that the proposed algorithm achieves superior convergence characteristics, improved voltage stability, and lower power losses compared to conventional optimization techniques The results confirm that SFOA is a robust and efficient tool for reactive power planning in modern microgrids with high renewable energy integration. Validation is performed on modified IEEE 37-bus and IEEE 85-bus distribution networks integrated with renewable energy components. Comprehensive comparisons with established optimization methods demonstrate SFOA's superior performance, achieving voltage profile enhancement and IEEE37-bus 21.4% & IEEE85-bus 19.6% power loss reduction compared to traditional approaches.