Abstract
In the context of digital transformation within the bureaucratic system, namely in West Kalimantan, the effect of The fast use of electric vehicles (EVs) presents substantial difficulties to active distribution systems (ADS), specifically higher power losses and network performance deterioration. Optimal location of EVCS is therefore critical to ensure the efficient and stable operation of distribution networks. This work offers a combined Genetic-Dragonfly Algorithm (CGDA) for optimally allocating EVCS in ADSs, having the purpose of lowering actual energy losses. The proposed algorithm combines the Genetic Algorithm (GA)’s global exploration capabilities with the Dragonfly Algorithm’s efficient exploitation and swarm intelligence properties, resulting in faster convergence and higher solution quality. The proposed GADA’s performance is validated using two test systems. A standard IEEE 15-bus considered as Test System – 1 and PG and E 69-bus distribution test system is considered as Test System – 2 in live network settings. In this optimization-based study, line flow limits and voltage limits are considered as inequality constraints. The simulation results show that the proposed GADA consistently outperforms traditional GA, PSO, and DA in terms of power loss reduction and convergence characteristics. The results show that the suggested hybrid optimization framework is a reliable and effective approach for arranging EVCS in current active distribution networks. The proposed GADA method is also validated in stochastic environment.
Keywords: Active Distribution System, Dragonfly Algorithm, Electric Vehicle Charging Stations, Genetic Algorithm, Hybrid Optimization, Power Loss Minimization.