Adaptive contextual learning for unit commitment in microgrids with renewable energy sources

Abstract

In this paper, we study a unit commitment (UC) problem where the goal is to minimize the operating costs of a microgrid that involves renewable energy sources. Since traditional UC algorithms use a priori information about uncertainties such as the load demand and the renewable power outputs, their performances highly depend on the accuracy of the a priori information, especially in microgrids due to their limited scale and size. This makes the algorithms impractical in settings where the past data are not sufficient to construct an accurate prior of the uncertainties. To resolve this issue, we develop an adaptively partitioned contextual learning algorithm for UC (AP-CLUC) that learns the best UC schedule and minimizes the total cost over time in an online manner without requiring any a priori information. AP-CLUC effectively learns the effects of the uncertainties on the cost by adaptively considering context information strongly correlated with the uncertainties, such as the past load demand and weather conditions. For AP-CLUC, we first prove an analytical bound on the performance, which shows that its average total cost converges to that of the optimal policy with perfect a priori information. Then, we show via simulations that AP-CLUC achieves competitive performance with respect to the traditional UC algorithms with perfect a priori information, and it achieves better performance than them even with small errors on the information. These results demonstrate the effectiveness of utilizing the context information and the adaptive management of the past data for the UC problem.