Storage Scheduling with Stochastic Uncertainties: Feasibility and Cost of Imbalances
For operators of renewable energy systems with storage, this work provides a method to guarantee dispatch feasibility under uncertainty, addressing a key operational challenge.
This paper proposes a novel scheduling method for energy storage systems that ensures dispatch feasibility with a pre-determined probability under stochastic uncertainties, using probabilistic forecasts. Simulations on real data show the method's effectiveness compared to scenario optimization.
Dispatchability of renewable energy sources and inflexible loads can be achieved using a volatility-compensating energy storage. However, as the future power outputs of the inflexible devices are uncertain, the computation of a dispatch schedule for such aggregated systems is non-trivial. In the present paper, we propose a novel scheduling method that enforces the feasibility of the dispatch schedule with a pre-determined probability based on a description of the operation of the system as a two-stage decision process. Thereby, a crucial point is the use of probabilistic forecasts, in terms of cumulative density function, of the inflexible energy consumption/production profile. Then, for the sake of comparison, we introduce a second scheduling method based on state-of-the-art scenario optimization, where, unlike the proposed method, the focus is on the minimization of the expected final cost. We draw upon simulations based on real consumption and production data to compare the methods and illustrate our findings.