PSO-based method to find electric vehicle's optimal charging schedule under dynamic electricity price

Owning to greenhouse effect and exhaustible gasoline, there is a need for the automobile industry to develop electric vehicles (EVs). EV owners' major concern is about how to minimize operating cost under dynamic market electricity price. Optimization of a charging scenario draws great attention from the researchers worldwide. This paper presents a particle swarm optimization (PSO) based optimization approach that can help EV owners achieve the most economical charging behavior.

[1]  Mohamed A. El-Sharkawi,et al.  Optimal Charging Strategies for Unidirectional Vehicle-to-Grid , 2011, IEEE Transactions on Smart Grid.

[2]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  Chengyong Si,et al.  Optimal control of an electric vehicle’s charging schedule under electricity markets , 2012, Neural Computing and Applications.

[4]  Sekyung Han,et al.  Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation , 2010, IEEE Transactions on Smart Grid.

[5]  Hans Bernhoff,et al.  Electrical Motor Drivelines in Commercial All-Electric Vehicles: A Review , 2012, IEEE Transactions on Vehicular Technology.

[6]  Lizhi Wang Potential Impacts of Plug-in Hybrid Electric Vehicles on Locational Marginal Prices , 2008, 2008 IEEE Energy 2030 Conference.

[7]  Lino Guzzella,et al.  Vehicle Propulsion Systems: Introduction to Modeling and Optimization , 2005 .

[8]  Ahmed Yousuf Saber,et al.  Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions , 2011, IEEE Transactions on Industrial Electronics.

[9]  Willett Kempton,et al.  Vehicle-to-grid power fundamentals: Calculating capacity and net revenue , 2005 .

[10]  Willett Kempton,et al.  Using fleets of electric-drive vehicles for grid support , 2007 .

[11]  Mohamed A. El-Sharkawi,et al.  Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services , 2012, IEEE Transactions on Smart Grid.

[12]  S. Grijalva,et al.  Electric vehicle-intelligent energy management system (EV-IEMS) for frequency regulation application , 2012, 2012 IEEE Transportation Electrification Conference and Expo (ITEC).

[13]  Willett Kempton,et al.  Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy , 2005 .

[14]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[15]  Nilay Shah,et al.  Effects of optimised plug-in hybrid vehicle charging strategies on electric distribution network losses , 2010, IEEE PES T&D 2010.

[16]  H. T. Mouftah,et al.  Prediction-based charging of PHEVs from the smart grid with dynamic pricing , 2010, IEEE Local Computer Network Conference.

[17]  G.B. Shrestha,et al.  Study on the optimization of charge-discharge cycle of electric vehicle batteries in the context of Singapore , 2007, 2007 Australasian Universities Power Engineering Conference.

[18]  Soo Hee Han,et al.  Design of an optimal aggregator for vehicle-to-grid regulation service , 2010, 2010 Innovative Smart Grid Technologies (ISGT).