IEEE Transactions on Power Delivery · 2013 · 393 citations · 22 references
EngineeringElectrical EngineeringElectric Vehicle EngineeringElectric Vehicle TechnologyEnergy SystemsCharging InfrastructureCharging SystemsDistributed Energy ResourcesDistribution Center LocationDistribution Network PlanningEnergy Management StrategiesBattery Capacity PlanningCharging OptimizationCharging Station DesignData Envelopment AnalysisDistribution SystemsElectric Vehicle Charging StationsElectric Vehicle SchedulingMulti-objective Charging Scheduling
Smart‑grid development requires effective solutions such as electric vehicles, and optimal locating and sizing of charging stations are essential for large‑scale EV deployment. The study proposes a multiobjective EV charging station planning method that ensures charging service while reducing power losses and voltage deviations. The method models battery‑capacity‑constrained EV flow to maximize chargeable traffic, uses data‑envelopment analysis to identify optimal solutions, and applies the cross‑entropy method to solve the planning problem. Simulation on a 33‑node distribution system and a 25‑node traffic network demonstrated the method’s effectiveness.
Smart-grid development calls for effective solutions, such as electric vehicles (EVs), to meet the energy and environmental challenges. To facilitate large-scale EV applications, optimal locating and sizing of charging stations in smart grids have become essential. This paper proposes a multiobjective EV charging station planning method which can ensure charging service while reducing power losses and voltage deviations of distribution systems. A battery capacity-constrained EV flow capturing location model is proposed to maximize the EV traffic flow that can be charged given a candidate construction plan of EV charging stations. The data-envelopment analysis method is employed to obtain the final optimal solution. Subsequently, the well-established cross-entropy method is utilized to solve the planning problem. The simulation results have demonstrated the effectiveness of the proposed method based on a case study consisting of a 33-node distribution system and a 25-node traffic network system.
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