Electric energy generation from renewable energy sources is generally non-dispatchable due to its intrinsic volatility. Therefore, its integration into electricity markets and in power system operation is often based on volatility-compensating energy storage systems. Scheduling and control of this kind of coupled systems is usually based on hierarchical control and optimization. On the upper level, one solves an optimization problem to compute a dispatch schedule and a coherent allocation of energy reserves. On the lower level, one performs online adjustments of the dispatch schedule using, for example, model predictive control. In the present paper, we propose a formulation of the upper level optimization based on data-driven probabilistic forecasts of the power and energy output of the uncontrollable loads and generators dependent on renewable energy sources. Specifically, relying on probabilistic forecasts of both power and energy profiles of the uncertain demand/generation, we propose a novel framework to ensure the online feasibility of the dispatch schedule with a given security level. The efficacy of the proposed scheme is illustrated by simulations based on real household production and consumption data.

由于其固有的波动性，从可再生能源产生的电能通常是不可分配的。因此，其集成到电力市场和电力系统运行中通常是基于波动补偿的储能系统。这种耦合系统的调度和控制通常基于分层控制和优化。在较高的层次上，解决了一个优化问题，以计算调度计划和能量储备的一致分配。在较低的级别上，可以使用模型预测控制等对调度表进行在线调整。在本文中，我们基于数据驱动的对可再生能源依赖的不可控负载和发电机的功率和能量输出的概率预测，提出了上层优化的公式。具体来说，依赖于两者的概率预测不确定需求/发电的电力和能源状况，我们提出了一个新颖的框架，以确保在给定安全级别下调度时间表的在线可行性。通过基于实际家庭生产和消费数据的模拟，说明了该方案的有效性。