2025-04-142025-04-142025-01-24http://repositorio.bc.ufg.br/tede/handle/tede/14069This study investigates the integration of distributed generation and energy storage systems in medium-voltage electrical networks. The proposed approach involves the development of evolutionary algorithms to optimally determine connection points for various loads and provide the optimal active power injections from multiple generating units at different network locations. The allocation of Distributed Energy Resources (DERs) in Power Systems presents an intrinsic balance between benefits and challenges. Among the benefits, significant reductions in electrical losses, attraction of investments in the energy sector, diversification and expansion of the energy matrix with a focus on renewable sources, and positive socioeconomic impacts, such as job creation, stand out. However, challenges include difficulties related to system usage tariffs, the need for revisions in operational procedures, uncontrolled voltage profile elevation in cases of high penetration of arbitrarily allocated DERs, increased short-circuit levels, and compromised performance of protection systems. The optimization model formulation adopted the minimization of total active power losses in the system as its objective function. The study considered solar-based distributed generation and battery storage systems for simulations. Network operating conditions were analyzed using power flow studies of the base case. The developed algorithms were applied to two test systems: the IEEE 34-bus system and the Real Feeder System PD004. The results highlight the efficiency, robustness, and rapid convergence of the Hybrid Genetic Algorithm (HGA) compared to other implemented approaches, demonstrating its superiority in solving the problem. The developed computational tool shows significant practical potential for application in electric utility companies, serving as a strategic tool for distribution network planning. Utilizing the algorithm’s results enables the optimized connection of generating units and loads while respecting established generation limits.Acesso Abertohttp://creativecommons.org/licenses/by-nc-nd/4.0/Algoritmos genéticosEstratégias evolutivasGeração distribuídaRecursos energéticos distribuídosSistemas de armazenamento de energiaSistemas elétricos de potênciaGenetic algorithmsEvolutionary strategiesDistributed generationDistributed energy resourcesEnergy storage systemsPower systemsENGENHARIAS::ENGENHARIA ELETRICADissertação