Contextual learning for unit commitment with renewable energy sources

Hyun Suk Lee; Cem Tekin; Mikaela Van Der Schaar; Jang Won Lee

doi:10.1109/GlobalSIP.2016.7905966

Contextual learning for unit commitment with renewable energy sources

Hyun Suk Lee, Cem Tekin, Mikaela Van Der Schaar, Jang Won Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	866-870
Number of pages	5
ISBN (Electronic)	9781509045457
DOIs	https://doi.org/10.1109/GlobalSIP.2016.7905966
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

Publication series

Name	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/Territory	United States
City	Washington
Period	16/12/7 → 16/12/9

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/GlobalSIP.2016.7905966

Cite this

Lee, H. S., Tekin, C., Van Der Schaar, M., & Lee, J. W. (2017). Contextual learning for unit commitment with renewable energy sources. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 866-870). Article 7905966 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7905966

Lee, Hyun Suk ; Tekin, Cem ; Van Der Schaar, Mikaela et al. / Contextual learning for unit commitment with renewable energy sources. 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 866-870 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

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title = "Contextual learning for unit commitment with renewable energy sources",

abstract = "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.",

author = "Lee, {Hyun Suk} and Cem Tekin and {Van Der Schaar}, Mikaela and Lee, {Jang Won}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 ; Conference date: 07-12-2016 Through 09-12-2016",

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series = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",

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Lee, HS, Tekin, C, Van Der Schaar, M & Lee, JW 2017, Contextual learning for unit commitment with renewable energy sources. in 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings., 7905966, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 866-870, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, Washington, United States, 16/12/7. https://doi.org/10.1109/GlobalSIP.2016.7905966

Contextual learning for unit commitment with renewable energy sources. / Lee, Hyun Suk; Tekin, Cem; Van Der Schaar, Mikaela et al.
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 866-870 7905966 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Contextual learning for unit commitment with renewable energy sources

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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Lee HS, Tekin C, Van Der Schaar M, Lee JW. Contextual learning for unit commitment with renewable energy sources. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 866-870. 7905966. (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). doi: 10.1109/GlobalSIP.2016.7905966

Contextual learning for unit commitment with renewable energy sources

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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Cite this