This paper examines the coordinated charging and discharging of plug‑in electric vehicles (PEVs) in distribution networks with the dual aim of improving techno‑economic performance and mitigating environmental impacts. Rapid growth in electric vehicle adoption is transforming traditional power systems, creating new and highly stochastic demand patterns that challenge distribution network operation, protection, and planning. Uncontrolled or poorly coordinated charging can lead to feeder overloading, voltage violations, increased power losses, and protection miscoordination, while also raising system operating costs and emissions. At the same time, the inherent flexibility and distributed storage capability of PEV batteries offer significant opportunities for demand response, loss reduction, and provision of ancillary services if they are optimally managed.​
Motivated by these challenges and opportunities, the paper develops an optimization framework that explicitly models PEV charging and discharging decisions alongside distributed generation (DG) units within a distribution system. The problem is formulated as a multi‑objective optimization model that simultaneously minimizes power losses, energy not supplied (or interruption‑related costs), and the total operational cost associated with PEV participation and network operation. The framework incorporates locational marginal pricing (LMP) signals, grid constraints, and PEV owner preferences to determine spatio‑temporal charging patterns that are both system‑feasible and economically attractive. In addition, the model accounts for battery degradation costs through depth‑of‑discharge (DoD)‑based expressions, ensuring that intensive use of vehicle‑to‑grid (V2G) services does not impose unrealistic or uneconomic wear on PEV batteries.​
The proposed method is solved using a population‑based metaheuristic algorithm inspired by particle swarm optimization (PSO), which is well suited to handling the nonlinear, nonconvex, and mixed‑integer structure of the underlying problem. Decision variables include unit commitment of DGs, charging/discharging schedules of PEV fleets, and several network operation parameters over a 24‑hour horizon. The optimization evaluates different operating scenarios with varying levels of PEV penetration and DG integration, allowing the authors to compare uncoordinated, partially coordinated, and fully coordinated charging strategies. Performance indices such as total cost, energy losses, reliability‑related costs, and emission indices (e.g., CO2 , NOx , and SO2 ) are used to assess the effectiveness of the scheduling framework.​
Simulation studies are carried out on a test distribution network to demonstrate the applicability and benefits of the proposed scheme. Results show that coordinated PEV charging and discharging can significantly reduce active power losses and distribution network operating costs compared with conventional uncoordinated charging. The model also achieves noticeable improvements in reliability indices by lowering expected interruption costs through better utilization of PEV storage during operation periods. Furthermore, by shaping the net load profile and enabling higher penetration of clean distributed resources, the framework contributes to emission reductions relative to baseline operation. Sensitivity analyses on the level of PEV penetration, DG capacity, and pricing structures highlight how economic signals and technical constraints jointly influence optimal scheduling outcomes.​