Two-layer data-driven robust scheduling for industrial heat loads

Wu, Chuanshen, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714 and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2025. Two-layer data-driven robust scheduling for industrial heat loads. Journal of Modern Power Systems and Clean Energy 13 (1) , pp. 265-275. 10.35833/MPCE.2024.000105

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Abstract

This paper establishes a two-layer data-driven robust scheduling method to deal with the significant computational complexity and uncertainties in scheduling industrial heat loads. First, a two-layer deterministic scheduling model is proposed to address the computational burden of utilizing flexibility from a large number of bitumen tanks (BTs). The key feature of this model is the capability to reduce the number of control variables through analyzing and modeling the clustered temperature transfer of BTs. Second, to tackle the uncertainties in the scheduling problem, historical data regarding BTs are collected and analyzed, and a data-driven piecewise linear Kernel-based support vector clustering technique is employed to construct the uncertainty set with convex boundaries and adjustable conservatism, based on which robust optimization can be conducted. The case results indicate that the proposed method enables the utilization of flexibility in BTs, improving the level of onsite photovoltaic consumption and reducing the aggregated load fluctuation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Springer
ISSN:	2196-5625
Date of First Compliant Deposit:	2 April 2025
Date of Acceptance:	4 July 2024
Last Modified:	04 Apr 2025 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/177351

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