Optimizing Electric Vehicle Charging Stations and Distributed Generators in Smart Grids: A Multi-Objective Meta-Heuristic Approach

Abstract

The global transition towards electric mobility has significantly increased the demand for efficient and consumer-friendly Electric Vehicle Charging Stations (EVCSs). As electric vehicles (EVs) continue to penetrate transportation systems, optimal integration of EVCSs within power distribution infrastructure becomes critical, not only to ensure seamless user experience but also to maintain the reliability and efficiency of electrical networks. Traditionally, EVCS planning has been carried out solely within the context of Radial Distribution Networks (RDNs), neglecting key consumer-centric factors such as travel comfort and accessibility within the road network (RN). This paper proposes a novel, consumer-aware methodology for optimally placing EVCSs and Distributed Generators (DGs) in a combined RDN-RN framework. The objective is to minimize active power loss, voltage variation, and EV consumer cost, measured through two proposed indices, while accounting for realistic travel behavior and preferences. The proposed approach utilizes a Modified Weighted Teaching Learning Based - Particle Swarm Optimization Algorithm (MWTLB-PSA) and proceeds in three stages: EVCS site selection based on road network considerations, DG placement using predetermined EVCS locations, and a final stage of simultaneous optimization of both elements. To validate the approach, a standard IEEE 33-bus RDN integrated with a 25-node RN is employed as the test system. Results demonstrate that the joint optimization of DGs and EVCSs via the proposed method significantly enhances network performance and consumer convenience. Notably, the solution achieves a reduced active power loss of 57.75 kW and an EVCCI value of 0.3958, indicating a substantial improvement over existing hybrid TLBO and PSO-based techniques. Furthermore, the proposed method leads to installation cost savings ranging from 2.51% to 18.21% compared to earlier strategies, underscoring its practical value in smart grid planning and deployment.