Optimum placement of distributed generation resources, capacitors and charging stations with a developed competitive algorithm.

This study presents a novel approach for the optimal placement of distributed generation (DG) resources, electric vehicle (EV) charging stations, and shunt capacitors (SC) in power distribution systems. The primary objective is to improve power efficiency and voltage profiles while considering practical and nonlinear constraints. The proposed model combines competitive search optimization (CSO) with fuzzy and chaotic theory to develop an efficient and effective solution. The use of fuzzy theory in the model enables the identification of optimal locations for DG sources and SCs, leading to significant enhancements in power index, generation, power losses, and system voltage. Moreover, the proposed fuzzy method is employed to determine the best locations for EV charging stations, further optimizing the overall system performance. The theoretical analysis demonstrates substantial improvements in both accuracy and convergence speed, highlighting the robustness of the proposed approach. In addition, the utilization of chaos theory enhances the local search optimization process, making the proposed method more efficient in finding high-quality solutions. To validate the performance of the model, extensive simulations are conducted on a 69-bus distribution system and various test functions. The results consistently reveal the superiority of the proposed method compared to other conventional optimization techniques. The key contribution of this study lies in its development of a comprehensive and efficient approach for the optimal placement of DG, EV charging stations, and SCs in power distribution systems. The integration of CSO, fuzzy theory, and chaotic theory enables the simultaneous consideration of multiple objectives and constraints, resulting in enhanced power dissipation reduction and voltage profile improvement. The obtained results demonstrate the practical applicability and superiority of the proposed method, which can significantly benefit power system planners and operators in real-world scenarios.