Browsing by Subject "Unit commitment"

Now showing 1 - 3 of 3

Open Access
Adaptive contextual learning for unit commitment in microgrids with renewable energy sources
(Institute of Electrical and Electronics Engineers, 2018) Lee, H. -S.; Tekin, Cem; van der, Schaar, M.; Lee, J. -W.
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.
Open Access
Contextual learning for unit commitment with renewable energy sources
(IEEE, 2017) Lee, H. -S.; Tekin, Cem; Schaar, M.; Lee, J. -W.
In this paper, we study a unit commitment (UC) problem minimizing operating costs of the power system with renewable energy sources. We develop a contextual learning algorithm for UC (CLUC) which learns which UC schedule to choose based on the context information such as past load demand and weather condition. CLUC does not require any prior knowledge on the uncertainties such as the load demand and the renewable power outputs, and learns them over time using the context information. We characterize the performance of CLUC analytically, and prove its optimality in terms of the long-term average cost. Through the simulation results, we show the performance of CLUC and the effectiveness of utilizing the context information in the UC problem.
Open Access
The value of multi-stage stochastic programming in risk-averse unit commitment under uncertainty
(IEEE, 2019) Mahmutoğulları, Ali İrfan; Ahmed, S.; Çavuş, Özlem; Aktürk, M. Selim
Day-ahead scheduling of electricity generation or unit commitment is an important and challenging optimization problem in power systems. Variability in net load arising from the increasing penetration of renewable technologies has motivated study of various classes of stochastic unit commitment models. In two-stage models, the generation schedule for the entire day is fixed while the dispatch is adapted to the uncertainty, whereas in multi-stage models the generation schedule is also allowed to dynamically adapt to the uncertainty realization. Multi-stage models provide more flexibility in the generation schedule; however, they require significantly higher computational effort than two-stage models. To justify this additional computational effort, we provide theoretical and empirical analyses of the value of multi-stage solution for risk-averse multi-stage stochastic unit commitment models. The value of multi-stage solution measures the relative advantage of multi-stage solutions over their two-stage counterparts. Our results indicate that, for unit commitment models, the value of multi-stage solution increases with the level of uncertainty and number of periods, and decreases with the degree of risk aversion of the decision maker.