A Novel Hybrid Meta-Heuristic Optimization Approach for Plug-in Electric Vehicles Charging Stations Placement in Smart Cities

Abstract

Plug-in electric vehicles (PEVs) feature rapid proliferation as a result of their low carbon emissions, reduced maintenance requirements and cost efficient operation. Nonetheless, increased penetration from PEVs poses significant challenges to distribution networks in terms of higher power losses, voltage stability and harmonic distortion. Moreover, the strong peak charging demand at public charging stations (CSs) causes strong stress at the power grid. Consequently, the best spatial allocation of CSs is important, as it is directly linked to PEV user travel behavior and decisions on charging infrastructure investments. The current work would suggest a new methodology to obtain the best CS positions inside smart cities. The framework devised includes PEV travel times to CSs taking into account horizontal displacement between vehicles and stations and simultaneous integration of traffic conditions for both conventional vehicles and PEVs. Moreover, the queuing time inside Cs is considered in this framework. The CS placement problem is formulated as a mixed-integer nonlinear programming (MINLP) model and solved using a hybrid heuristic optimization technique of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).