项顶, 胡泽春, 宋永华, 丁华杰. 通过电动汽车与电网互动减少弃风的商业模式与日前优化调度策略[J]. 中国电机工程学报, 2015, 35(24): 6293-6303. DOI: 10.13334/j.0258-8013.pcsee.2015.24.004
引用本文: 项顶, 胡泽春, 宋永华, 丁华杰. 通过电动汽车与电网互动减少弃风的商业模式与日前优化调度策略[J]. 中国电机工程学报, 2015, 35(24): 6293-6303. DOI: 10.13334/j.0258-8013.pcsee.2015.24.004
XIANG Ding, HU Zechun, SONG Yonghua, DING Huajie. Business Model and Day-ahead Dispatch Strategy to Reduce Wind Power Curtailment Through Vehicle-to-Grid[J]. Proceedings of the CSEE, 2015, 35(24): 6293-6303. DOI: 10.13334/j.0258-8013.pcsee.2015.24.004
Citation: XIANG Ding, HU Zechun, SONG Yonghua, DING Huajie. Business Model and Day-ahead Dispatch Strategy to Reduce Wind Power Curtailment Through Vehicle-to-Grid[J]. Proceedings of the CSEE, 2015, 35(24): 6293-6303. DOI: 10.13334/j.0258-8013.pcsee.2015.24.004

通过电动汽车与电网互动减少弃风的商业模式与日前优化调度策略

Business Model and Day-ahead Dispatch Strategy to Reduce Wind Power Curtailment Through Vehicle-to-Grid

  • 摘要: 电动汽车接受运营商调控参与车网互动(vehicle-togrid,V2G)能够更好地与间歇性新能源发电相配合,从而创造巨大的经济效益和社会效益,而这种V2G模式需要相应的商业模式作为支撑。通过分析受控电动汽车的特征,提出电动汽车用户、运营商(Aggregator)、电网公司和弃风电场合作的V2G商业合同模式。建立以运营商期望收益最大化为目标,以满足用户个体需求和电费补偿约束,考虑弃风功率限制、机组调节速率限制的运营商日前优化调度模型。提出了针对所建非线性混合整数规划问题的求解算法。以京津唐电网为例,验证了所提合同模式、调度策略及求解算法的有效性和准确性。

     

    Abstract: Vehicle-to-Grid(V2G) under the management of electric vehicle(EV) aggregators has great potential to interact with renewable energy generation. This interactive mode could create enormous economic and social benefits, however, it lacks appropriate business model as a support. In this paper, a business model based on cooperation among EV users, aggregators, electric companies and wind farms was proposed. Under constraints of EV users’ charging demand and discharging compensation, curtailed wind power profile and unit ramp rate, a day-ahead optimal scheduling formulation for aggregators to maximize their expected revenue was established. An algorithm which can solve this kind of nonlinear mixed integer programming problem was also proposed. Simulation based on data from the BeijingTianjin-Tangshan power grid is conducted and the results prove the effectiveness of the proposed business and scheduling models and the accuracy of proposed algorithm.

     

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