Sizing Of Electric Vehicle Charging Stations Research Articles

Data-driven equitable placement for electric vehicle charging stations: Case study San Francisco

California electric vehicle (EV) regulation on phasing out new gasoline vehicles by 2035 and government subsidies are important steps toward the wide adoption of EVs. However, the lack of EV charging infrastructure with their inequitable distribution is still the most severe obstacle to widespread EV uptake while the charging infrastructure continues to expand across the US. This paper proposes a novel data-driven placement and sizing of charging stations in San Francisco, by quantifying social equity access, EV charging demand coverage, and site development costs including construction, operating, and installation costs. To this end, first 200 possible EV charging stations, as an initial generation, are located in San Francisco. Then, the optimal size, charging type, and locations of charging stations are adjusted/identified resulting from the trade-off between minimized site development cost, maximized social equity access, and EV charge demand fulfillment. The placement, charging type, and sizing of EV charging stations are formulated as a multi-objective optimization (MOO) problem and solved by the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Ultimately, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied to obtain the optimal solution from the Pareto-optimal front. According to the balance between minimizing site development costs, maximizing EV demand coverage, and ensuring the highest level of social equity access, energy service providers and charging station owners can strategically decide to place charging stations in San Francisco's middle, west, and northwest regions.

OPTIMAL PLACEMENT AND SIZING OF ELECTRIC VEHICLE CHARGING INFRASTRUCTURE USING DC POWER FLOW MODEL

With the burgeoning interest in electric vehicles (EVs) due to their sustainable attributes, concerns arise regarding the electrical grid’s capacity to handle the consequent rise in electricity demand from charging stations. Ontario’s aspiration to ensure that 5% of all vehicle sales are electric by 2020, driven by the province’s Climate Change Action Plan, accentuates these concerns, particularly with the potential rise in fossil fuel power generation. This study delves into the optimization of generator outputs and the strategic placement and sizing of EV charging stations in Ontario. The goal is to curtail overall generation costs, adhering to the demand, generation, and transmission constraints. Through the utilization of a representative system, modeled after the IEEE 34-node test feeder due to data unavailability, the research explores Ontario’s power dynamics over a 24-hour period in 2020. The findings provide insights into ideal locations and dimensions for charging stations, while also quantifying the environmental ramifications of the increased electrical grid load. This paper offers a comprehensive strategy to mitigate grid stress while bolstering EV infrastructure efficiently.

Stochastic Second-Order Conic Programming for Optimal Sizing of Distributed Generator Units and Electric Vehicle Charging Stations

The increased penetration of electric vehicles (EVs) and distributed generator (DG) units has led to uncertainty in distribution systems. These uncertainties—which have not been adequately considered in the literature—can entail risks in the optimal sizing of EV charging stations (EVCSs) and DG units in active distribution network planning. This paper proposes a method for obtaining the optimal sizing of DG units and EVCSs (considering uncertainty), to achieve exact power system analysis and ensure EV driver satisfaction. To model uncertainties in optimal sizing planning, this study first generates scenarios for each system asset using a probability distribution that considers the asset characteristics. In this step, the wind-turbine (WT), PV, and EVCS are modeled applying the Weibull, exponential, and kernel density estimation (KDE), and scenarios for each asset are generated using random sampling. Then, the k-means clustering is carried out for scenario reduction and the representative scenario abstract. The probability of occurrence for each representative scenario is assigned depending on the number of observations within each cluster. The representative scenarios for each asset are integrated into the scenario for all assets through the joint probability. The integrated scenarios are applied in the optimization problem for optimal sizing of the system asset framework. The optimal sizing of the system assets problem is proposed (to minimize the line loss and voltage deviation) and formulated via stochastic second-order conic programming, to reflect the uncertainty under an AC power flow; this is a convex problem that can be solved in polynomial time. The proposed method is tested on a modified IEEE 15 bus system, and the simulation is performed with various objective functions. The simulation results demonstrate the effectiveness of the proposed method.

Review of recent trends in charging infrastructure planning for electric vehicles

The exhaustive nature of fossil fuels and environmental concerns associated with greenhouse gases are the major causes of the paradigm shift from conventional vehicles to electric vehicles (EVs). The electrification of the transportation sector and the increasing popularity of the EVs have driven scientists and researchers to delve into charging stations. Underdeveloped charging infrastructure, optimal placement of charging stations, and charge scheduling in the charging stations are the major concerns for the large‐scale deployment of EVs. Even few questions related to EVs, such as the vehicle price and driving range, can be partially solved with a well‐developed charging infrastructure. Existing old infrastructure may bring severe limitations in the realization of the optimal placement of charging stations as EVs have not existed when the road and grid infrastructure have been developed. For the past few years, the studies associated with the optimal placement and sizing of EV charging stations have drawn the consideration of the researchers. The researchers have used different approaches, objective functions, and applied different optimization algorithms while dealing with the problem of charging station placement. The complex and dynamic nature of the problem has led researchers to apply different optimization algorithms for the solution of the problem. In this particular review study, the research works bearing on the charging infrastructure planning for the EVs are considered. Thus, this review work will endow the research community with the latest developments and research findings in the paradigm of charging infrastructure planning for EVs.This article is categorized under: Energy Infrastructure > Systems and Infrastructure Energy and Urban Design > Systems and Infrastructure Energy and Transport > Science and Materials Energy Systems Analysis > Science and Materials

Optimal planning of plug‐in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties

This study presents a mathematical model to determine the optimal site and the size of plug-in hybrid electric vehicles charging stations (CSs) in the distribution networks. The presented optimal planning is done along with considering the rate of customers' participation in demand response programs (DRPs) at the presence of some uncertainties associated with the load values and electricity market price. In order to model the load, time-of-use DRP is used. The proposed model involves distribution system manager (DSM) benefit maximising derived from the appropriate use of CS for charging and discharging vehicle batteries, reliability improvement and supplying network's load demand at peak times as the objective function, technical limitation as constraint and the siting and sizing of electric vehicles CS as optimisation variables. The genetic algorithm method with embedded Monte Carlo simulation is used in order to solve the optimisation problem and is applied to the 9-bus and 33-bus networks. The test results indicate that not only does the appropriate planning have an economic benefit for DSM, but also voltage profile and power supply reliability for customers can be improved and active power losses can also be reduced.

Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems

With the progressive exhaustion of fossil energy and the enhanced awareness of environmental protection, more attention is being paid to electric vehicles (EVs). Inappropriate siting and sizing of EV charging stations could have negative effects on the development of EVs, the layout of the city traffic network, and the convenience of EVs' drivers, and lead to an increase in network losses and a degradation in voltage profiles at some nodes. Given this background, the optimal sites of EV charging stations are first identified by a two-step screening method with environmental factors and service radius of EV charging stations considered. Then, a mathematical model for the optimal sizing of EV charging stations is developed with the minimization of total cost associated with EV charging stations to be planned as the objective function and solved by a modified primal-dual interior point algorithm (MPDIPA). Finally, simulation results of the IEEE 123-node test feeder have demonstrated that the developed model and method cannot only attain the reasonable planning scheme of EV charging stations, but also reduce the network loss and improve the voltage profile.