The increasing number of photovoltaic (PV) generation and electric vehicles (EVs) on the load side has necessitated an aggregator (Agg) in power system operation. In this paper, an Agg is used to manage the energy profiles of PV generation and EVs. However, the daily management of the Agg is challenged by uncertain PV fluctuations. To address this problem, a robust multi-time scale energy management strategy for the Agg is proposed. In a day-ahead phase, robust optimization is developed to determine the power schedule. In a real-time phase, a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs. A case study indicates a good scheduling performance under an uncertain PV output. Through the convexification, the solving efficiency of the real-time operation model is improved, and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent. Moreover, the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient. The strategy can ensure the flexibility of the Agg for real-time operation.

中文翻译：

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以光伏发电和电动汽车为特征的聚集器的多尺度规模能源管理策略

负载侧的光伏（PV）发电和电动汽车（EV）的数量不断增加，因此需要在电力系统运行中使用聚合器（Agg）。在本文中，Agg用于管理光伏发电和电动汽车的能源分布。但是，Agg的日常管理面临着不确定的PV波动的挑战。为了解决这个问题，提出了一种鲁棒的Agg多时标能量管理策略。在日前阶段，开发了强大的优化功能来确定电源计划。在实时阶段，基于电动汽车的灵活性，建立了基于滚动视野的凸优化模型，以跟踪日前用电计划。案例研究表明，在不确定的PV输出下，良好的调度性能。通过凸化 提高了实时运行模型的求解效率，可以在一定程度上抑制电动汽车的过充过放问题。而且，当EV调度能力足够时，提前调度与实时调度之间的功率偏差是可控制的。该策略可以确保Agg实时操作的灵活性。