Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis

Electric vehicles (EVs) have received significant attention in recent years. The high penetration of EVs increased the charging load in the distribution network, which has an enormous impact on “transportation and power grid” coupled energy networks. This paper developed the models and methods to evaluate the capacity of electrical energy supply based on voltage stability in the worst charging case scenario, under the existing coupled network. An optimization method with transportation network constraints is proposed to find the worst charging case scenario. In addition, the mobility of EVs is considered to calculate the load margin and the maximum number of EVs charging that a given grid can support in the critical situation. The method and model are simulated by test cases, which provide a perspective for realizing the EVs integration capacity limitation in urban areas considering the coupling relationship between transportation and power grids. Finally, the impact of charging location and route choice in the voltage margin limitation is emphasized.

[1]  Hongjie Jia,et al.  A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model , 2017 .

[2]  Danny H. K. Tsang,et al.  Optimal Scheduling for Electric Vehicle Charging With Discrete Charging Levels in Distribution Grid , 2018, IEEE Transactions on Smart Grid.

[3]  Yu Cheng,et al.  Configuration and operation combined optimization for EV battery swapping station considering PV consumption bundling , 2017 .

[4]  Mariana Kamel,et al.  Development and Application of a New Voltage Stability Index for On-Line Monitoring and Shedding , 2018, IEEE Transactions on Power Systems.

[5]  Anthony Faris,et al.  An Optimal Thevenin Equivalent Estimation Method and its Application to the Voltage Stability Analysis of a Wind Hub , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[6]  H. Chiang,et al.  Nonlinear predictors and hybrid corrector for fast continuation power flow , 2008 .

[7]  Zechun Hu,et al.  Cost/Benefit Assessment of a Smart Distribution System With Intelligent Electric Vehicle Charging , 2014, IEEE Transactions on Smart Grid.

[8]  Tao Chen,et al.  Optimal Routing and Charging of an Electric Vehicle Fleet for High-Efficiency Dynamic Transit Systems , 2018, IEEE Transactions on Smart Grid.

[9]  Furong Li,et al.  Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates , 2016 .

[10]  Alberto Sangiovanni-Vincentelli,et al.  Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach , 2019, IEEE Transactions on Industrial Electronics.

[11]  L. Dickerman,et al.  A New Car, a New Grid , 2010, IEEE Power and Energy Magazine.

[12]  Jonn Axsen,et al.  Batteries for Plug-in Hybrid Electric Vehicles (PHEVs): Goals and the State of Technology circa 2008 , 2008 .

[13]  Zhang Jianhua.  A Probabilistic Load Flow Calculation Method with Latin Hypercube Sampling , 2009 .

[14]  Wei Hu,et al.  A Novel Method to Monitor and Predict Voltage Collapse: The Critical Transitions Approach , 2018, IEEE Transactions on Power Systems.

[15]  Volker Pickert,et al.  Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array , 2016 .

[16]  Chen Song-qiao.  Improvement of Warshall Algorithm Based on Transitive Closure , 2005 .

[17]  A. Owen.  Controlling correlations in latin hypercube samples , 1994 .

[18]  Mehrdad Abedi,et al.  Short-term interaction between electric vehicles and microgrid in decentralized vehicle-to-grid control methods , 2019 .

[19]  Yalian Yang,et al.  Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization , 2016 .

[20]  Hsiao-Dong Chiang,et al.  CPFLOW: a practical tool for tracing power system steady-state stationary behavior due to load and generation variations , 1995 .

[21]  Kai Zhang,et al.  A Reliability Assessment Approach for Integrated Transportation and Electrical Power Systems Incorporating Electric Vehicles , 2018, IEEE Transactions on Smart Grid.

[22]  L Chen,et al.  A model for electric vehicle charging load forecasting based on trip chains , 2015 .

[23]  Bala Venkatesh,et al.  Line-Wise Power Flow and Voltage Collapse , 2018, IEEE Transactions on Power Systems.

[24]  Liana Cipcigan,et al.  Probabilistic analysis of electric vehicles charging load impact on residential Distributions Networks , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[25]  Shan Gao,et al.  A Multi-Stage Optimization Approach for Active Distribution Network Scheduling Considering Coordinated Electrical Vehicle Charging Strategy , 2018, IEEE Access.

[26]  Andrea J. Goldsmith,et al.  Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks , 2015, IEEE Transactions on Control of Network Systems.

[27]  Zechun Hu,et al.  PEV Fast-Charging Station Siting and Sizing on Coupled Transportation and Power Networks , 2018, IEEE Transactions on Smart Grid.

[28]  Hong Wang,et al.  Cyber-Physical Control for Energy-Saving Vehicle Following With Connectivity , 2017, IEEE Transactions on Industrial Electronics.

[29]  Qi Huang,et al.  A coordinated charging strategy for electric vehicles based on multi-objective optimization , 2017, 2017 2nd International Conference on Power and Renewable Energy (ICPRE).

[30]  Olivier Deblecker,et al.  An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes , 2020 .

[31]  Honggang Zhang,et al.  Coordinated charging strategies for electric bus fast charging stations , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).