Title :
Assessment of Impacts of PHEV Charging Patterns on Wind-Thermal Scheduling by Stochastic Unit Commitment
Author :
Liu, Cong ; Wang, Jianhui ; Botterud, Audun ; Zhou, Yan ; Vyas, Anantray
Author_Institution :
Decision & Inf. Sci. Div. & Energy Syst. Div., Argonne Nat. Lab., Argonne, IL, USA
fDate :
6/1/2012 12:00:00 AM
Abstract :
Light duty plug-in hybrid electric vehicle (PHEV) technology holds a promising future due to its “friendliness” to the environment and potential to reduce dependence on fossil fuels. However, the likely significant growth of PHEVs will bring new challenges and opportunities for power system infrastructures. This paper studies the impacts of PHEV charging patterns on power system operations and scheduling. The stochastic unit commitment model described in this paper considers coordination of thermal generating units and PHEV charging loads, as well as the penetration of large-scale wind power. The proposed model also addresses ancillary services provided by vehicle-to-grid techniques. Daily electricity demands by various types of PHEVs are estimated on the basis of a PHEV population projection and transportation survey. The stochastic unit commitment model is used to simulate power system scheduling with different charging patterns for PHEVs. The results show that a smart charging pattern can reduce the operating costs of a power system and compensate for the fluctuation in wind power. The proposed model also can serve as a foundation and tool to perform long-term cost-benefit analysis and to assist policy making.
Keywords :
hybrid electric vehicles; power generation scheduling; power grids; thermal power stations; wind power plants; PHEV charging pattern; electricity demands; fossil fuels; large-scale wind power; light duty plug-in hybrid electric vehicle technology; long-term cost-benefít analysis; power system; stochastic unit commitment; vehicle-to-grid techniques; wind-thermal scheduling; Electricity; Load modeling; Power systems; Stochastic processes; Vehicles; Wind forecasting; Wind power generation; Mixed integer programming; plug-in hybrid electric vehicle; stochastic; unit commitment; wind power;
Journal_Title :
Smart Grid, IEEE Transactions on
DOI :
10.1109/TSG.2012.2187687