Integration of Electric Vehicles into the Power Distribution Network with a Modified Capacity Allocation Mechanism

The growing penetration of electric vehicles (EVs) represents an operational challenge to system operators, mainly at the distribution level by introducing congestion and voltage drop problems. To solve these potential problems, a two-level coordination approach is proposed in this study. An aggregation entity, i.e., an EV virtual power plant (EV-VPP), is used to facilitate the interaction between the distribution system operator (DSO) and EV owners considering the decentralized electricity market structure. In level I, to prevent the line congestion and voltage drop problems, the EV-VPP internally respects the line and voltage constraints when making optimal charging schedules. In level II, to avoid power transformer congestion problems, this paper investigates three different coordination mechanisms, or power transformer capacity allocation mechanisms, between the DSO and the EV-VPPs, considering the case of EVs charging and discharging. The three mechanisms include: (1) a market-based approach; (2) a pro-rata approach; and (3) a newly-proposed constrained market-based approach. A case study considering a 37-bus distribution network and high penetration of electric vehicles is presented to demonstrate the effectiveness of the proposed coordination mechanism, comparing with the existing ones.

[1]  Mohsen Guizani,et al.  Battery Status-aware Authentication Scheme for V2G Networks in Smart Grid , 2013, IEEE Transactions on Smart Grid.

[2]  Francois Bouffard,et al.  Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement , 2013, 2013 IEEE Power & Energy Society General Meeting.

[3]  Ian A. Hiskens,et al.  Decentralized charging control for large populations of plug-in electric vehicles , 2010, 49th IEEE Conference on Decision and Control (CDC).

[4]  Morten Lind,et al.  Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects , 2016 .

[5]  Lei Chen,et al.  A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles , 2016 .

[6]  Bei Han,et al.  Market-Based Control in Emerging Distribution System Operation , 2013, IEEE Transactions on Power Delivery.

[7]  Jay F. Whitacre,et al.  The economics of using plug-in hybrid electric vehicle battery packs for grid storage , 2010 .

[8]  Shi You,et al.  Coordinated Charging of Electric Vehicles for Congestion Prevention in the Distribution Grid , 2014, IEEE Transactions on Smart Grid.

[9]  Josep M. Guerrero,et al.  Microgrids in active network management—Part I: Hierarchical control, energy storage, virtual power plants, and market participation , 2014 .

[10]  Zita A. Vale,et al.  Evaluation of different initial solution algorithms to be used in the heuristics optimization to solve the energy resource scheduling in smart grids , 2016, Appl. Soft Comput..

[11]  Zofia Lukszo,et al.  Renewable Energy Sources and Responsive Demand. Do We Need Congestion Management in the Distribution Grid? , 2014, IEEE Transactions on Power Systems.

[12]  Zita Vale,et al.  Electric Vehicle Scenario Simulator Tool for Smart Grid Operators , 2012 .

[13]  S. Martin,et al.  V2G strategies for congestion management in microgrids with high penetration of electric vehicles , 2013 .

[14]  Daniel Esteban Morales Bondy,et al.  A clearinghouse concept for distribution-level flexibility services , 2013, IEEE PES ISGT Europe 2013.

[15]  Nikos D. Hatziargyriou,et al.  A Multi-Agent System for Controlled Charging of a Large Population of Electric Vehicles , 2013, IEEE Transactions on Power Systems.

[16]  Davy Janssens,et al.  Decentralized coordinated charging of electric vehicles considering locational and temporal flexibility , 2015 .

[17]  Nouredine Hadjsaid,et al.  On the concept and the interest of virtual power plant: Some results from the European project Fenix , 2009, 2009 IEEE Power & Energy Society General Meeting.

[18]  Roy Billinton,et al.  A reliability test system for educational purposes-basic distribution system data and results , 1991 .

[19]  A. Conejo,et al.  Transmission Loss Allocation: A Comparison of Different Practical Algorithms , 2002, IEEE Power Engineering Review.

[20]  Ivan BEL,et al.  INNOVATIVE OPERATION WITH AGGREGATED DISTRIBUTED GENERATION , .

[21]  João Luiz Afonso,et al.  Vehicle-to-Anything Application (V2Anything App) for Electric Vehicles , 2014, IEEE Transactions on Industrial Informatics.

[22]  Francois Bouffard,et al.  Electric Vehicle Aggregator/System Operator Coordination for Charging Scheduling and Services Procurement , 2013, IEEE Transactions on Power Systems.

[23]  Victor Sreeram,et al.  The investigation of the major factors influencing plug-in electric vehicle driving patterns and charging behaviour , 2015 .

[24]  Zita Vale,et al.  Distributed energy resources management using plug-in hybrid electric vehicles as a fuel-shifting demand response resource , 2015 .

[25]  Hadi Saadat,et al.  Power System Analysis , 1998 .

[26]  Olle Sundström,et al.  Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints , 2012, IEEE Transactions on Smart Grid.

[27]  J. Apt,et al.  Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization , 2010 .

[28]  Turan Gonen,et al.  Electric Power Distribution Engineering , 2014 .