In the last decade, there has been an increase in environmental awareness and the responsibility of the energy industries to minimize environmental changes has been clear. Consequently, production levels using renewable energy sources have increased. The incessant growth of energy and environmental awareness leads to the search for a safe and stable electrical network, with high quality and reliability in the service provided to consumers. Renewable Energy Sources (RES) have been a common objective in the development of electrical energy at a global level. This study analyzes the issue of maximizing the production of active power in an electrical energy network. Specifically, it aims to determine which fuzzy rules can potentially maximize the value of this power. An algorithm known as Fuzzy-Particle Swarm Optimization (FL-PSO) was modified and encoded to apply to the proposed scenario across a variety of networks. Our experimental results, demonstrated in the attached figures, show significant improvements when comparing FL-PSO with state-of-the-art algorithms such as Greedy, FID, and traditional fuzzy logic applications (FuzzyN). For instance, after optimization, the total improvement across all households was striking, as illustrated by the reduction in annual average costs from €785 to €760, highlighting the efficiency of FL-PSO. Additionally, the ‘Best Setting’ figure clearly demonstrates a peak in performance at the third setting of our algorithm, emphasizing its optimal configuration, making this study a pioneering approach to integrating fuzzy logic with swarm intelligence for power optimization in electrical networks. Future work will focus on refining these fuzzy decision rules and expanding the adaptability of FL-PSO to further enhance its application to more diverse network configurations.