Impact of Priority Criteria on Electric Vehicle Charge Scheduling

Coordinated charging is the superior charging method for integrating electric vehicles (EVs) smoothly and efficiently into the existing power system. Coordinated EV charging can be further classified into two types, namely time coordinated charging (TCC) and power coordinated charging (PCC). Scheduling of EV charging is the vital component in both types of coordinated EV charging methods. Generally, priority criteria such as battery state of charge (SOC) and slack time are used for scheduling EV charging. In this paper, the impact of different priority criteria on the chargeability of EVs and charging fairness is studied for both types of coordinated EV charging methods. The overall performance of the coordinated EV charging methods employing various combinations of different priority criteria is evaluated using data-driven simulations. In addition, this paper evaluates the need for using weighted priority criteria for scheduling EV charging along with appropriate weights required for different cases.

[1]  Zhigang Cao,et al.  Charging Scheduling of Electric Vehicles With Local Renewable Energy Under Uncertain Electric Vehicle Arrival and Grid Power Price , 2013, IEEE Transactions on Vehicular Technology.

[2]  Xiaosong Hu,et al.  Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .

[3]  Yalchin Efendiev,et al.  A dynamic data-driven application simulation framework for contaminant transport problems , 2006, Comput. Math. Appl..

[4]  Gooi Hoay Beng,et al.  Charging of electric vehicles and demand response management in a Singaporean car park , 2014, 2014 49th International Universities Power Engineering Conference (UPEC).

[5]  Paul S. Moses,et al.  Distribution transformer losses and performance in smart grids with residential Plug-In Electric Vehicles , 2011, ISGT 2011.

[6]  Lang Tong,et al.  Optimal deadline scheduling with commitment , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[7]  P. L. So,et al.  V2G Capacity Estimation Using Dynamic EV Scheduling , 2014, IEEE Transactions on Smart Grid.

[8]  Dirk Uwe Sauer,et al.  Influence of plug-in hybrid electric vehicle charging strategies on charging and battery degradation costs , 2012 .

[9]  Shangtai Jin,et al.  A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems , 2011, IEEE Transactions on Control Systems Technology.

[10]  Ward Jewell,et al.  Controlled Electric Vehicle Charging for Mitigating Impacts on Distribution Assets , 2015, IEEE Transactions on Smart Grid.

[11]  Zhe Chen,et al.  Optimal Operation of Plug-In Electric Vehicles in Power Systems With High Wind Power Penetrations , 2013, IEEE Transactions on Sustainable Energy.

[12]  Gonzalo Seco-Granados,et al.  Fair Design of Plug-in Electric Vehicles Aggregator for V2G Regulation , 2012, IEEE Transactions on Vehicular Technology.

[13]  B. Egardt,et al.  Enhanced Sample Entropy-based Health Management of Li-ion Battery for Electrified Vehicles , 2014 .

[14]  Chris Marnay,et al.  Sustainable Campus with PEV and Microgrid , 2012 .

[15]  Tom Molinski,et al.  PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data , 2012, IEEE Transactions on Smart Grid.

[16]  Ufuk Topcu,et al.  Optimal decentralized protocol for electric vehicle charging , 2011, IEEE Transactions on Power Systems.

[17]  P. L. So,et al.  Electric vehicle charging profile prediction for efficient energy management in buildings , 2012, 2012 10th International Power & Energy Conference (IPEC).

[18]  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.

[19]  Hitoshi Yano,et al.  A novel charging-time control method for numerous EVs based on a period weighted prescheduling for power supply and demand balancing , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[20]  Ebrahim Farjah,et al.  Probabilistic Analysis of Plug-In Electric Vehicles Impact on Electrical Grid Through Homes and Parking Lots , 2013, IEEE Transactions on Sustainable Energy.

[21]  Charbel Farhat,et al.  On a data-driven environment for multiphysics applications , 2005, Future Gener. Comput. Syst..

[22]  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.

[23]  Xiaosong Hu,et al.  Charging time and loss optimization for LiNMC and LiFePO4 batteries based on equivalent circuit models , 2013 .

[24]  Ka Wing Chan,et al.  Real-time scheduling of electric vehicles charging in low-voltage residential distribution systems to minimise power losses and improve voltage profile , 2014 .

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

[26]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[27]  William G. Temple,et al.  Intelligent electric vehicle charging: Rethinking the valley-fill , 2011 .

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

[29]  Zhuo Wang,et al.  From model-based control to data-driven control: Survey, classification and perspective , 2013, Inf. Sci..