Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques

This paper proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power outputs, assisted by machine learning techniques. To flexibly identify the uncertainty space based on wind power forecast error data with disjunctive structures, th...

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Autores principales:	Ning Zhao, Fengqi You
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/c7f5f5a04f724a3c8f5deb83551205b6
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spelling	oai:doaj.org-article:c7f5f5a04f724a3c8f5deb83551205b62021-11-15T21:46:59ZUnit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques10.3303/CET21881992283-9216https://doaj.org/article/c7f5f5a04f724a3c8f5deb83551205b62021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11992https://doaj.org/toc/2283-9216This paper proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power outputs, assisted by machine learning techniques. To flexibly identify the uncertainty space based on wind power forecast error data with disjunctive structures, the uncertainty data are grouped using K-means and density-based spatial clustering of applications with noise following the optimal cluster number determined by the Calinski-Harabasz index. The disjunctive uncertainty sets are constructed accordingly as the union of multiple basic uncertainty sets, including conventional box and budget uncertainty sets, and data-driven uncertainty sets using Dirichlet process mixture model, principal component analysis coupled with kernel density estimation, and support vector clustering. The problem is formulated into a two-stage adaptive robust UC model with data-driven disjunctive uncertainty sets and with a multi-level optimization structure. To facilitate the solution process, a tailored decomposition-based optimization algorithm is developed. The effectiveness of the proposed framework is illustrated using an application on the IEEE 39-bus system. The proposed approach can reduce the price of robustness by 8-38 % compared to the conventional “one-set-fits-all” approaches. Benchmarking with stochastic programming indicates that the proposed framework can achieve the same or better economic performance with over 30 % less computational time.Ning ZhaoFengqi YouAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 Ning Zhao Fengqi You Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques
description	This paper proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power outputs, assisted by machine learning techniques. To flexibly identify the uncertainty space based on wind power forecast error data with disjunctive structures, the uncertainty data are grouped using K-means and density-based spatial clustering of applications with noise following the optimal cluster number determined by the Calinski-Harabasz index. The disjunctive uncertainty sets are constructed accordingly as the union of multiple basic uncertainty sets, including conventional box and budget uncertainty sets, and data-driven uncertainty sets using Dirichlet process mixture model, principal component analysis coupled with kernel density estimation, and support vector clustering. The problem is formulated into a two-stage adaptive robust UC model with data-driven disjunctive uncertainty sets and with a multi-level optimization structure. To facilitate the solution process, a tailored decomposition-based optimization algorithm is developed. The effectiveness of the proposed framework is illustrated using an application on the IEEE 39-bus system. The proposed approach can reduce the price of robustness by 8-38 % compared to the conventional “one-set-fits-all” approaches. Benchmarking with stochastic programming indicates that the proposed framework can achieve the same or better economic performance with over 30 % less computational time.
format	article
author	Ning Zhao Fengqi You
author_facet	Ning Zhao Fengqi You
author_sort	Ning Zhao
title	Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques
title_short	Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques
title_full	Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques
title_fullStr	Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques
title_full_unstemmed	Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques
title_sort	unit commitment under uncertainty using data-driven optimization with clustering techniques
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/c7f5f5a04f724a3c8f5deb83551205b6
work_keys_str_mv	AT ningzhao unitcommitmentunderuncertaintyusingdatadrivenoptimizationwithclusteringtechniques AT fengqiyou unitcommitmentunderuncertaintyusingdatadrivenoptimizationwithclusteringtechniques
_version_	1718426869430222848

Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques

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