基于时序注意力机制的电动汽车灵活性概率建模

电动汽车是一种可以向电力系统提供灵活性的柔性负荷。现有研究对电动汽车灵活性进行建模时，多数仅考虑了充电行为的不确定性以及分时电价的影响，忽略了日前电价与实时电价的偏差，缺少对实时电价、充电负荷多时间尺度时序特征的建模。针对此问题，文中总结了电动汽车灵活性的表现形式与影响因素，考虑面向电价的响应不确定性以及充电行为不确定性，提出基于时序注意力机制的电动汽车灵活性概率建模方法。通过时序注意力机制提取不同时序权重，设计基于时序卷积网络的多时间尺度特征提取网络学习充电行为、电价等不确定性，提取多时间尺度灵活性波动特征。算例表明，所提模型能够有效学习充电行为不确定性与面向电价的响应不确定性，其概率建模效果具有更高的可靠性与精度。

Probabilistic Modeling of Electric Vehicle Flexibility Based on Temporal Attention Mechanism

Electric vehicle (EV) is the flexible load that can provide flexibility to the power system. Most of the existing studies modeling the flexibility of EVs only consider the uncertainty of charging behavior and the impact of time-of-use tariffs. The deviation between the day-ahead tariff and real-time tariff is ignored, and the modeling of real-time tariff and charging load multi-timescale time-series characteristics is neglected. Aiming at this problem, this paper summarizes the manifestations and influencing factors of the flexibility of EVs, and proposes a probabilistic modeling method of the flexibility of EVs based on the temporal attention mechanism by considering the uncertainty of tariff-oriented response and the uncertainty of charging behavior. The different timescale weights are extracted by the time-series attention mechanism. A multi-timescale feature extraction network based on the temporal convolutional network is designed to learn the uncertainty of charging behavior and electricity price, and extract multi-timescale flexibility fluctuation features. The cases show that the proposed model can effectively learn charging behavior uncertainty and tariff-oriented response uncertainty, and its probabilistic modeling effect has higher reliability and accuracy.