Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

The Bi-directional Three-Phase PWM Rectifier Inverter for Electric Vehicle Charging Station

Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G （vehicle to grid） mode, electric vehicles can be used as energy storage units and transfer power to the grid. The electric vehicles charge at night to reduce the cost and the grid load, simultaneously to fill the valley. When grid load increases, electric vehicles＇ batteries discharge to the grid to improve the stability of the grid. As distributed storage units, electric vehicles are important components of the smart grid. In this paper, the three-phase PWM （pulse width modulation） rectifier used for smart charging and discharging system of electric vehicles are analyzed and designed. This paper includes the principle of PWM rectifier-inverter and direct current control strategy. Also, the SVPWM （space vector pulse width modulation） and system design of three-phase PWM rectifiers are analyzed. A 10 kW prototype is developed. Simulation and experiment results show that the three-phase PWM rectifiers reach the unit power factor. From the experimental results, PWM rectifier implements the sinusoidal grid current and achieves the unit power factor.

Modelling of electric vehicle charging station and controlled by fuzzy logic controller with different modes of operation

The main objective of implementing charging stations is to ensure the good charging to theElectric Vehicles by using a solar PV array which is interconnected to the battery energy storagesystems. The charging station regulates the supply voltage and frequency without the use of amechanical speed governor. It also assures that energy gained from grid or by the DG set willhave the unity power factor (UPF) when the load is nonlinear. Besides this, the Point of CommonCoupling (PCC) voltage is synchronised with the grid/generator voltage in order to providecontinuous charging. In order to increase the optimal efficiency of the charging stations, thecharging stations will perform the active/reactive power transfer from the vehicle to grid, vehicleto house and vehicle to vehicle (V2V) power transfer. The operational experiment of the chargingstation is simulated and verified by using MATLAB/SIMULINK.

Impact of EV load uncertainty on optimal planning for electric vehicle charging station

With the development of electric vehicles(EV), there is a huge demand for electric vehicle charging stations(EVCS). The utilization of renewable energy sources(RES) in EVCS can not only decrease the energy fluctuation by participating in peakload reduction of the grid, but also reduce the pollution to the environment by cutting down the use of fossil fuels. In this paper,the optimal planning for grid-connected EVCS with RES is studied by considering EV load uncertainty. Nine scenarios are set based on a different characteristic of EV load to reveal the impact of EV load on net present cost(NPC) and to express the relationship between the optimal capacity and energy flow. Moreover, since electricity price also plays an important role in EVCS planning, an economic comparison between different cases with different electricity prices for peak-valley-flat period is carried out. The results reveal the economic benefits of applying RES in EVCS, and demonstrate that EV load with different characteristics would influence the capacity of each device(PV, battery, converter) in the EVCS optimal planning.

Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability

The popularity of electric vehicles(EVs)has sparked a greater awareness of carbon emissions and climate impact.Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations(EVCSs),impacting radial distribution networks(RDNs).To reduce the impact of voltage drop,the increased power loss(PL),lower system interruption costs,and proper allocation and positioning of the EVCSs and capacitors are necessary.This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value(NPV),considering the reduction in energy losses and interruption costs.As a part of the analysis considering reliability,several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV.To locate the best nodes for EVCSs and capacitors,the hybrid of grey wolf optimization(GWO)and particle swarm optimization(PSO)(HGWO_PSO)and the hybrid of PSO and Cuckoo search(CS)(HPSO_CS)algorithms are proposed,forming a combination of GWO,PSO,and CS optimizations.The impact of EVCSs on NPV is also investigated in this paper.The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.