Evaluation of Achievable Vehicle-to-Grid Capacity Using Aggregate PEV Model

Large-scale plug-in electric vehicles (PEVs) utilizing vehicle-to-grid (V2G) technology can collectively behave as a storage system under the control of an aggregator, e.g., arbitraging in the energy market and providing ancillary services to the grid. Quantitatively evaluating V2G capacity, i.e., c...

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Bibliographic Details
Published in:IEEE transactions on power systems 2017-01, Vol.32 (1), p.784-794
Main Authors: Zhang, Hongcai, Hu, Zechun, Xu, Zhiwei, Song, Yonghua
Format: Article
Language:English
Subjects:
Aggregates
Ancillary services
Batteries
Capacity evaluation
Computer simulation
Discharges (electric)
Electric vehicle charging
Electric vehicles
Energy management
Forecasting
Mathematical models
plug-in electric vehicle (PEV)
Real time operation
Real-time systems
reserve
Scale (ratio)
Schedules
Scheduling
smart charging
Technology utilization
Vehicle-to-grid
vehicle-to-grid (V2G)
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Summary:Large-scale plug-in electric vehicles (PEVs) utilizing vehicle-to-grid (V2G) technology can collectively behave as a storage system under the control of an aggregator, e.g., arbitraging in the energy market and providing ancillary services to the grid. Quantitatively evaluating V2G capacity, i.e., charging and discharging power ranges, for a PEV fleet utilizing V2G technology (which is referred to as a V2G fleet in this paper) ahead of time is of fundamental importance for V2G implementation. However, because of the stochastic characteristics of PEV driving behaviors, charging demands are difficult to forecast, which makes evaluating V2G capacity technically difficult. This paper first establishes an aggregate model of a V2G fleet that employs aggregated parameters to represent energy and power constraints of the entire V2G fleet and, therefore, reduces the difficulty of forecasting. Then, an evaluation method for V2G capacity of large-scale PEVs is developed based on the proposed aggregate model. To make the V2G capacity evaluated in advance achievable while guaranteeing charging demands during real-time operation, a heuristic smart charging strategy is designed. The application of the evaluation method in optimal charge and discharge scheduling for a V2G fleet providing power reserves is illustrated. Numerical simulations are conducted to validate the proposed method.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2016.2561296