Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation

In order to minimize the peak load of electric vehicles (EVs) and enhance the resilience of fast EV charging stations, several sizing methods for deployment of the stationary energy storage system (ESS) have been proposed. However, methods for assessing the optimality of the obtained results and performance of the determined sizes under different conditions are missing. In order to address these issues, a two-step approach is proposed in this study, which comprises of optimality analysis and performance evaluation steps. In the case of optimality analysis, random sizes of battery and converter (scenarios) are generated using Monte Carlo simulations and their results are compared with the results of sizes obtained from sizing methods. In order to carry out this analysis, two performance analysis indices are proposed in this study, which are named the cost index and the power index. These indices respectively determine the performance of the determined sizes in terms of total network cost and performance ratio of power bought during peak intervals and investment cost of the ESS. During performance evaluation, the performance of the determined sizes (battery and converter) are analyzed for different seasons of the year and typical public holidays. Typical working days and holidays have been analyzed for each season of the year and suitability of the determined sizes is analyzed. Simulation results have proved that the proposed method is suitable for determining the optimality of results obtained by different sizing methods.

[1]  Mahmud Fotuhi-Firuzabad,et al.  Optimal Sizing of Storage System in a Fast Charging Station for Plug-in Hybrid Electric Vehicles , 2016, IEEE Transactions on Transportation Electrification.

[2]  Hak-Man Kim,et al.  Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter , 2019, Energies.

[3]  Andrew Cruden,et al.  Rating a Stationary Energy Storage System Within a Fast Electric Vehicle Charging Station Considering User Waiting Times , 2019, IEEE Transactions on Transportation Electrification.

[4]  C. Y. Chung,et al.  System State Estimation Considering EV Penetration With Unknown Behavior Using Quasi-Newton Method , 2016, IEEE Transactions on Power Systems.

[5]  Mariagrazia Dotoli,et al.  Cooperative Distributed Control for the Energy Scheduling of Smart Homes with Shared Energy Storage and Renewable Energy Source , 2017 .

[6]  Zechun Hu,et al.  Value of the energy storage system in an electric bus fast charging station , 2015 .

[7]  Evangelos Rikos,et al.  A Model Predictive Control Approach to Microgrid Operation Optimization , 2014, IEEE Transactions on Control Systems Technology.

[8]  Yu Luo,et al.  A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System , 2018 .

[9]  Michael Baldea,et al.  Integrating scheduling and control for economic MPC of buildings with energy storage , 2014 .

[10]  Hubert H. Girault,et al.  Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations , 2019, Energies.

[11]  Yan Bao,et al.  Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station , 2019, Energies.

[12]  Ali Ehsan,et al.  Active Distribution System Reinforcement Planning With EV Charging Stations—Part I: Uncertainty Modeling and Problem Formulation , 2020, IEEE Transactions on Sustainable Energy.

[13]  Hak-Man Kim,et al.  Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids , 2017 .

[14]  Mariagrazia Dotoli,et al.  Decentralized control for residential energy management of a smart users ʼ microgrid with renewable energy exchange , 2019, IEEE/CAA Journal of Automatica Sinica.

[15]  Azah Mohamed,et al.  Power quality impacts of high-penetration electric vehicle stations and renewable energy-based generators on power distribution systems , 2013 .

[16]  Diego Iribarren,et al.  Prospective Life Cycle Assessment of the Increased Electricity Demand Associated with the Penetration of Electric Vehicles in Spain , 2018 .

[17]  Chengke Zhou,et al.  A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load , 2012, IEEE Transactions on Power Systems.

[18]  Yue Yuan,et al.  Modeling of Load Demand Due to EV Battery Charging in Distribution Systems , 2011, IEEE Transactions on Power Systems.

[19]  Luigi Martirano,et al.  EV fast charging stations and energy storage technologies: A real implementation in the smart micro grid paradigm , 2015 .

[20]  Keechoo Choi,et al.  Analyzing changes in travel behavior in time and space using household travel surveys in Seoul Metropolitan Area over eight years , 2014 .