Energy Management System using Particle Swarm Optimization for Operating Costs Reduction in AC Microgrids with Battery Storage during Grid-Connected and Islanded Operation - Figueroa-Saavedra, Hugo Alessandro; Grisales Norena , Luis Fernando; Cortés Caicedo, Brandon (Ingeniería, 2025)
Context: This paper proposes an energy management system (EMS) for battery energy storage systems (BESS) to reduce operating costs in AC microgrids (MGs) operating in grid-connected (GON) and islanded (GOFF) mode, considering energy purchase, conventional generation, and maintenance costs while accounting for all the operational constraints of the system and its components. Method: A master-slave methodology based on particle swarm optimization (PSO) and an hourly power flow based on the successive approximations method (SAM) is used as a smart BESS operation strategy. This proposal is validated in a 33-bus AC-MG operating in GON and GOFF modes, in comparison with two methods utilizing the vortex search algorithm (VSA) and conitnuos version of the Chu & Beasley genetic algorithm (CBGA) and the same power flow. Results: The PSO-based EMS achieved the lowest costs i.e., 6897.59 USD/day (GON) and 17 527.42 USD/day (GOFF), with cost reductions of 1.45 and 0.13 %, and low standard deviation values (0.067 and 0.014 %), which confirms its efficiency, robustness, and constraint compliance. Conclusions: The EMS based on PSO/SAM delivers superior solution quality and processing times in both modes of operation. In GON mode, it reduces the mean costs by 0.0287%compared to the VSA and 0.2252%vs. the CBGA, whereas, in GOFF mode, the reductions are 0.0191 and 0.0355 %, respectively. These results reflect a more effective cost reduction than exact methods, which constitutes this paper’s main contribution.