侯慧, 何梓姻, 罗超, 张志强, 刘鹏, 侯婷婷, 郁海彬. 计及需求响应潜力的电动汽车聚合商多主体日前投标策略[J]. 全球能源互联网, 2024, 7(2): 220-227. DOI: 10.19705/j.cnki.issn2096-5125.2024.02.011
引用本文: 侯慧, 何梓姻, 罗超, 张志强, 刘鹏, 侯婷婷, 郁海彬. 计及需求响应潜力的电动汽车聚合商多主体日前投标策略[J]. 全球能源互联网, 2024, 7(2): 220-227. DOI: 10.19705/j.cnki.issn2096-5125.2024.02.011
HOU Hui, HE Ziyin, LUO Chao, ZHANG Zhiqiang, LIU Peng, HOU Tingting, YU Haibin. Multi-agent Day-ahead Bidding Strategy of Electric Vehicle Aggregators Upon Demand Response Potential[J]. Journal of Global Energy Interconnection, 2024, 7(2): 220-227. DOI: 10.19705/j.cnki.issn2096-5125.2024.02.011
Citation: HOU Hui, HE Ziyin, LUO Chao, ZHANG Zhiqiang, LIU Peng, HOU Tingting, YU Haibin. Multi-agent Day-ahead Bidding Strategy of Electric Vehicle Aggregators Upon Demand Response Potential[J]. Journal of Global Energy Interconnection, 2024, 7(2): 220-227. DOI: 10.19705/j.cnki.issn2096-5125.2024.02.011

计及需求响应潜力的电动汽车聚合商多主体日前投标策略

Multi-agent Day-ahead Bidding Strategy of Electric Vehicle Aggregators Upon Demand Response Potential

  • 摘要: 针对电动汽车这一新型储能资源大规模接入后电力市场多主体竞争格局,提出一种计及需求响应潜力的电动汽车聚合商多主体日前投标策略。以实现削峰填谷、保障电网安全稳定运行等为目标,促使电网与聚合商等多方市场主体可持续发展,探索其未来商业模式。首先,根据电动汽车用户出行特性进行电动汽车集群分类,考虑出行时间、到达时间及荷电状态等因素,模拟集群内用户随机充放电选择。结合聚合商整体充电功率上限值与电能波动区间值等主要因素,基于历史数据评估聚合商需求响应潜力。然后,考虑多个聚合商投标问题,建立日前市场投标模型,以测算电动汽车聚合商日前市场效益。为探寻多元竞争格局下最优日前市场投标策略,以日前投标净利润最大为目标函数,考虑电量、功率上限及电池容量等约束条件,利用Gurobi对优化问题进行求解。最后,通过湖北省武汉市某区实际负荷算例验证了所提策略的先进性及可行性。

     

    Abstract: Aiming at the competition of the multi-agent market under the mass access of electric vehicles, which are a new energy storage resource, a multi-agent day-ahead bidding strategy for electric vehicle aggregators with demand response potential is proposed. The proposed strategy is to explore the future business model, to achieve the goal of peak reduction and valley filling, ensure the safe and stable operation of the power grid, and promote the sustainable development of the multiagent market. First, cluster classification is carried out according to the travel characteristics of EV users, factors such as travel time, arrival time, and state of charge are used to simulate the charging and discharging choice of users in the cluster. Based on the historical data, demand response potential is evaluated by combining the power upper limit and power interval value.Second, considering multiple EVA bidding issues, a day-ahead market bidding model is established to calculate the market benefits of operators. Third, with the maximum net profit of day-ahead bidding as the objective function, we consider the constraints of electricity, power ceiling, and battery capacity.To explore the multi-agent day-ahead market strategy, Gurobi is used to solve the optimization problem. Finally, the real-world case studies based on a district in Wuhan, Hubei Province, China verify the effectiveness and merits of the proposed method.

     

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