Demand-Side Management ICT for Dynamic Wireless EV Charging

Dynamic wireless charging of electric vehicles (EVs) aims at increasing EV range and reducing battery size and associated costs. Moreover, it minimizes the need for recharging stops, thus increasing travel comfort. As electrification of transport ultimately targets CO2 emissions reduction, increased utilization of renewable energy sources is likely to provide the means for reaching the decarbonization objectives. Demand-side management (DSM) enables the increased penetration of intermittent power sources by providing techniques that ensure grid stability. While complete frameworks for DSM deployment in static charging have been designed, these approaches must be re-evaluated with focus on the operational requirements for dynamic EV charging. In this paper, a review of DSM methods targeting static charging is made and one particular case of DSM is simulated in a dynamic charging environment in order to reveal critical aspects towards deployment.

[1]  Klara Nahrstedt,et al.  FADEC: Fast authentication for dynamic electric vehicle charging , 2013, 2013 IEEE Conference on Communications and Network Security (CNS).

[2]  Zhong Fan,et al.  A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[3]  Dionysios Aliprantis,et al.  Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles , 2012, IEEE Transactions on Smart Grid.

[4]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[5]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[6]  Andrew Mills,et al.  Strategies for Mitigating the Reduction in Economic Value of Variable Generation with Increasing Penetration Levels , 2014 .

[7]  Robert Shorten,et al.  A flexible distributed framework for realising electric and plug-in hybrid vehicle charging policies , 2012, Int. J. Control.

[8]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[9]  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.

[10]  Silvia Canale,et al.  Electric vehicles charging control in a smart grid: A model predictive control approach , 2014 .

[11]  Mohammad A. S. Masoum,et al.  Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile , 2011, IEEE Transactions on Smart Grid.

[12]  Mo-Yuen Chow,et al.  Performance Evaluation of an EDA-Based Large-Scale Plug-In Hybrid Electric Vehicle Charging Algorithm , 2012, IEEE Transactions on Smart Grid.

[13]  Hosam K. Fathy,et al.  Robust demand-side plug-in electric vehicle load control for renewable energy management , 2011, Proceedings of the 2011 American Control Conference.

[14]  S. Martin,et al.  Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support , 2015 .

[15]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[16]  A. Keane,et al.  Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems , 2012, IEEE Transactions on Power Systems.