Contextual learning for unit commitment with renewable energy sources

Date
2017
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Source Title
Proceedings of the IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
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Publisher
IEEE
Volume
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Pages
866 - 870
Language
English
Type
Conference Paper
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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.

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Keywords
Learning, Renewable energy, Uncertainty, Unit commitment, Learning algorithms, Natural resources, Operating costs, Semantics, Context information, Contextual learning, Renewable energy source, Unit commitment problem, Renewable energy resources
Citation
Published Version (Please cite this version)