Optimal location of EV charging stations in a neighborhood considering a multi-objective approach

Abstract Despite the environmental and economic benefits of Electric Vehicles (EVs), distribution network operators will need to understand the location where the charging infrastructure will be placed to ensure EV users’ needs are met. In this sense, this work proposes a methodology to define the optimal location of EV semi-fast charging stations (CS) at a neighborhood level, through a multi-objective approach. It applies a hierarchical clustering method to define CS service zones, considering both technical and mobility aspects. Besides, it considers uncertainties related to the EV load profile to determine the CS capacity, based on the user's charging behavior. A Pareto Frontier method is deployed to support the decision-making process on CS optimal location, considering utility and EV users’ preferences. The results indicate that the best CS locations for mid-term EV penetration can also fit into long-term planning, with higher EV charging demand. Thus, these locations would be good candidates for the power utility to make initial investments, regarding both planning horizons. A real distribution system case is used to demonstrate the applicability of the results.

[1]  Luis F. Ochoa,et al.  Control of EV Charging Points for Thermal and Voltage Management of LV Networks , 2016, IEEE Transactions on Power Systems.

[2]  Victor O. K. Li,et al.  Classification of electric vehicle charging time series with selective clustering , 2020 .

[3]  Francisco de Paula García-López,et al.  Grid-friendly integration of electric vehicle fast charging station based on multiterminal DC link , 2020 .

[4]  Ram Rajagopal,et al.  Design and Planning of a Multiple-Charger Multiple-Port Charging System for PEV Charging Station , 2019, IEEE Transactions on Smart Grid.

[5]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[6]  Yugong Luo,et al.  Optimal location planning method of fast charging station for electric vehicles considering operators, drivers, vehicles, traffic flow and power grid , 2019, Energy.

[7]  Seyedmohsen Hosseini,et al.  Development of a Bayesian network model for optimal site selection of electric vehicle charging station , 2019, International Journal of Electrical Power & Energy Systems.

[8]  Xiaobo Dou,et al.  Optimal planning of electric vehicle charging stations comprising multi-types of charging facilities , 2018, Applied Energy.

[9]  Wei Gu,et al.  Coordinated allocation of distributed generation resources and electric vehicle charging stations in distribution systems with vehicle-to-grid interaction , 2020 .

[11]  Xiaowen Chu,et al.  Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions , 2013, IEEE Transactions on Smart Grid.

[12]  Vahid Vahidinasab,et al.  Probabilistic planning of electric vehicles charging stations in an integrated electricity-transport system , 2020 .

[13]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[14]  Siyuan Chen,et al.  GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles , 2019, Energy.

[15]  Om P. Malik,et al.  Placement and sizing of battery energy storage for primary frequency control in an isolated section of the Mexican power system , 2018, Electric Power Systems Research.

[16]  Ndaona Chokani,et al.  User behaviour and electric vehicle charging infrastructure: An agent-based model assessment , 2019, Applied Energy.

[17]  Manuel A. Matos,et al.  Optimization models for an EV aggregator selling secondary reserve in the electricity market , 2014 .

[18]  Mehdi Ehsan,et al.  A risk-constrained decision support tool for EV aggregators participating in energy and frequency regulation markets , 2020 .

[19]  Juncheng Lu,et al.  Comparison of SiC MOSFETs and GaN HEMTs based high-efficiency high-power-density 7.2kW EV battery chargers , 2017, 2017 IEEE 5th Workshop on Wide Bandgap Power Devices and Applications (WiPDA).

[20]  Bertrand Travacca,et al.  Inducing Human Behavior to Maximize Operation Performance at PEV Charging Station , 2021, IEEE Transactions on Smart Grid.

[21]  W. Dorner,et al.  A review of spatial localization methodologies for the electric vehicle charging infrastructure , 2018, International Journal of Sustainable Transportation.

[22]  Ali Ghiasian,et al.  Long term profit maximization strategy for charging scheduling of electric vehicle charging station , 2018, IET Generation, Transmission & Distribution.

[23]  Cesar H. Comin,et al.  Clustering algorithms: A comparative approach , 2016, PloS one.

[24]  Jianwei Huang,et al.  Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling , 2017, IEEE Transactions on Sustainable Energy.

[25]  Xu Wang,et al.  Coordinated Planning Strategy for Electric Vehicle Charging Stations and Coupled Traffic-Electric Networks , 2019, IEEE Transactions on Power Systems.

[26]  Xiangxiang Zeng,et al.  An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization , 2019, IEEE Transactions on Cybernetics.

[27]  Bruno Dias,et al.  Electric Vehicles Charging Optimization Considering EVs and Load Uncertainties , 2019, 2019 IEEE Milan PowerTech.

[28]  Csaba Csiszár,et al.  Urban public charging station locating method for electric vehicles based on land use approach , 2019, Journal of Transport Geography.

[29]  Mostafa Sedighizadeh,et al.  Optimal siting and sizing of distribution system operator owned EV parking lots , 2016 .

[30]  Liying Wang,et al.  Capacity planning and pricing design of charging station considering the uncertainty of user behavior , 2021 .

[31]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.