Hybrid Swarm Intelligence Algorithms for Enhanced Optimal Power Flow in Renewable Energy Networks

This research delves into the synthesis of hybrid swarm intelligence algorithms for optimal power flow (OPF) solutions in renewable energy networks. Traditional optimization techniques often face challenges such as local minima entrapment and slow convergence rates when applied to the complex and non-linear nature of renewable energy systems. By harnessing the strengths of multiple swarm intelligence algorithms, the proposed hybrid approach mitigates these limitations and achieves enhanced efficiency. Detailed simulations were conducted on standard benchmark systems to validate the effectiveness and superiority of the hybrid methodology over standalone algorithms. The results distinctly showcase improved convergence rates and higher solution accuracy. Furthermore, robustness against varying network conditions and contingencies was examined, confirming the algorithm's adaptability and resilience. This research underscores the immense potential of hybrid swarm intelligence approaches in navigating the intricate landscape of OPF problems in renewable energy networks, paving the way for more sustainable and efficient power system operations.