Dandelion Algorithm for Optimal Location and Sizing of Battery Energy Storage Systems in Electrical Distribution Networks

This paper describes a new way to improve the performance of an EDN by integrating distributed battery energy storage systems (BESs) in the best way possible. This method is based on the Dandelion Algorithm (DA). The search space for BES’ locations is first predetermined using loss sensitivity factors (LSFs), and then DA is used to determine the optimal locations and sizes. The reduction of real power distribution loss is regarded as the primary objective function, and the impact of BESs is extended to examine the network voltage profile, voltage stability, and GHG emissions. IEEE 33-bus EDN is  used to calculate the computational efficiency of LSF-DA. Results show that DA is more efficient than Archimedes optimization (AOA), future search algorithm (FSA), pathfinder algorithm (PFA), and butterfly optimization algorithm (BOA) algorithms. Furthermore, the results show that the proposed DA enhances all technological and environmental factors and RDN performance.