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.
|Title of host publication||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2017 Apr 19|
|Event||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States|
Duration: 2016 Dec 7 → 2016 Dec 9
|Name||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings|
|Other||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016|
|Period||16/12/7 → 16/12/9|
Bibliographical noteFunding Information:
The work of H.-S. Lee and J.-W. Lee was supported in part by Midcareer Researcher Program through NRF grant funded by the MSIP, Korea (2013R1A2A2A01069053).
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Networks and Communications