<![CDATA[Nowadays, growing interest in sustainable energy solutions, hybrid AC/DC microgrids are becoming more and more recognized as a reliable and efficient option. In order to improve the stability of such microgrids advanced solutions for ESS placement are required due to the unpredictable nature of renewable energy sources and the complexity of load needs. The precision needed to maximize microgrid stability in the face of these obstacles is lacking. In this paper, an artificial neural networks (ANN)-based framework for the strategic allocation and sizing of ESS is proposed. This study uses ANN and the process is to determine the best locations and capacities for energy storage systems (ESS) to minimize system losses while accounting for variations in renewable generating and demand profiles. Simulation is carried on IEEE 12 bus system for studying the usefulness of the proposed method and stability is determined. The power flow datasets generated through simulation are utilized to train the ANN in order to determine the most appropriate placements for ESS. Furthermore, a series of simulations were performed to examine the impact of ESS characteristics on the performance of system loss under various circumstances.]]>
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