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) andanhourlypowerflowbasedonthesuccessiveapproximations method (SAM)is used as a smart BESS operation strategy. This proposal is validated in a 33-bus AC MGoperating in GONandGOFFmodes,incomparisonwithtwomethodsutilizing the vortex search algorithm (VSA) and conitnuos version of the Chu & Beasley ge netic algorithm (CBGA) and the same power flow.

Results:

The PSO-based EMSachievedthelowestcostsi.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, robust ness, 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 by0.0287%comparedtotheVSAand0.2252%vs.the CBGA,where as,inGOFF 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.

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