Reserve Service Strategy of Electric Vehicles Based on Double-incentive Mechanism

被引：0
|
作者：
Wang J. [1 ]
Jia Y. [1 ]
Mi Z. [1 ]
Chen H. [1 ]
Fan H. [2 ]
机构：
[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding
[2] State Grid Hebei Electric Power Supply Co., Ltd., Shijiazhuang
来源：
Wang, Junjie (wangjunjie0425@126.com) | 1600年 / Automation of Electric Power Systems Press卷 / 44期
基金：
国家重点研发计划;
关键词：
Double-incentive mechanism; Electric vehicle; Reserve service; Stochastic programming; User response uncertainty;
D O I：
10.7500/AEPS20190826007
中图分类号：
学科分类号：
摘要：
To improve the reserve service capability of electric vehicle (EV), this paper proposes a charging/discharging day-ahead scheduling strategy of EV based on the double-incentive mechanism. Firstly, a user-oriented double-incentive mechanism and market interaction mode considering the discharging mode of EV are designed. Then, a semi-managed user response mode is proposed. In this mode, the uncertainty of user response state is modeled and analyzed. A charging/discharging dispatching model of EV for responding the double-incentive is constructed by stochastic programming method. Based on the above models, the case study compares the reserve service capacities of EV with and without discharging incentive. Furthermore, the impact of some factors, such as charging and discharging incentive prices and dispatching time scale, on the reserve capacity of EV is analyzed through the sensitivity analysis of dispatching parameters. © 2020 Automation of Electric Power Systems Press.
引用
收藏
页码：68 / 76
页数：8
相关论文
共 24 条
  • [1] A notice issued by the state council on the development plan for energy conservation and new-energy vehicle industry (2012-2020)
  • [2] Development Report on New Energy Vehicle Industry in China (2018), (2018)
  • [3] Hoogvliet T.W., Litjens G.B.M.A., Van Sark W.G.J.H., Provision of regulating- and reserve power by electric vehicle owners in the Dutch market, Applied Energy, 190, pp. 1008-1019, (2017)
  • [4] Bessa R.J., Matos M.A., Soares F.J., Et al., Optimized bidding of a EV aggregation agent in the electricity market, IEEE Transactions on Smart Grid, 3, 1, pp. 443-452, (2012)
  • [5] Ahmad T., Michael N., Sajeeb S., Et al., Self-scheduling of a generating company with an EV load aggregator under an energy exchange strategy, IEEE Transactions on Smart Grid, 10, 4, pp. 4253-4264, (2019)
  • [6] Li Y., Zhao T., Liu C., Et al., An interactive decision-making model based on energy and reserve for electric vehicles and power grid using generalized Stackelberg game, IEEE Transactions on Industry Applications, 55, 4, pp. 3301-3309, (2019)
  • [7] Yang S., Jiang Z., Ai Q., Joint optimization of bidding and pricing strategy for electric vehicle aggregators considering reserve service, Electric Power Automation Equipment, 38, 12, pp. 25-32, (2018)
  • [8] Han B., Lu S., Xue F., Et al., Day-ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market, IET Generation, Transmission & Distribution, 13, 12, pp. 2517-2525, (2019)
  • [9] Pan Z., Gao C., Liu S., Research on charging and discharging dispatch of electric vehicles based on demand side discharge bidding, Power System Technology, 40, 4, pp. 1140-1146, (2016)
  • [10] Zhang Q., Shi L., Ren Y., Et al., The reserve trading model considering vehicle-to-grid reserve, Proceedings of the CSEE, 32, 31, pp. 59-67, (2012)
123