Optimized placement of distributed generators, capacitors, and EV charging stations in reconfigured radial distribution networks using enhanced artificial hummingbird algorithm - Sahay, Sumeet; Biswal, Saubhagya Ranjan; Shankar, Gauri; Jha, Amitkumar V.; Appasani, Bhargav; Srinivasulu, Avireni; Nsengiyumva, Philibert (Scientific reports, 2025)
This study presents an assessment of concurrently identifying the best location and size of distributed generators (DGs), shunt capacitors (SCs), and electric vehicle charging stations (EVCSs) in optimally reconfigured radial distribution networks (RDNs). A comprehensive literature review indicates that this multi-unit combination has the potential to enhance RDN performance significantly, but it remains an underexplored area of research. Therefore, further in-depth investigation is necessary to understand and fully maximize the benefits of this method. The optimal placement and sizing (OPS) of the mentioned multi-unit in RDNs is realized by employing a metaheuristic optimization technique subject to the fulfillment of a well-defined fuzzified-objective function comprising of line losses reduction, power factor improvement, voltage deviation reduction, and DG penetration limit. Employing the concept of centroid-based oppositional learning (COL), an improved version of the artificial hummingbird algorithm (AHA), named COLAHA, is proposed to decipher the adopted issue. The results achieved utilizing the offered approach are matched with those of the additional innovative algorithms such as the basic AHA, arithmetic optimization algorithm, genetic algorithm, and whale optimization algorithm. By evaluating it against several benchmark functions, the effectiveness of the proposed COLAHA is established. The performance of the aforementioned studied algorithms is further tested to find the OPS of DGs, SCs and EVCSs in the standard IEEE 69- and 118-bus RDNs. Results obtained conclude that the COLAHA has offered quick convergence and the best results over the others for all the studied combinations of the multi-unit model.