Multi-scenario data-driven robust optimisation for industrial steam power systems under uncertainty

Yulin Han, Jingyuan Zheng, Xiaoyan Luo, Yu Qian and Siyu Yang

Energy, 2023, vol. 263, issue PD

Abstract: In actual industrial production, the deterministic optimisation of the steam power system cannot meet most production scenarios due to the influence of uncertain factors such as product demand and environmental conditions. This paper proposes an operational optimisation method for SPS under uncertainty by combining multi-scenario partition and data-driven adaptive robust optimisation algorithm. A hybrid equipment model was developed to modify the critical equipment models based on industrial data and process mechanisms. Considering that demand uncertainty varies with the different steam quality, the clustering method divides the entire time horizon into several periods, and the uncertainty set is constructed by variable robust kernel density estimation for each period. A multi-scenario data-driven robust optimisation model is developed by incorporating uncertainty sets into deterministic optimisation, and the counterpart model is obtained through the affine decision rules. Furthermore, the proposed framework is applied to the SPS of a coal chemical plant to verify the feasibility. The annual operating costs before and after optimisation are 125 million USD and 123 million USD, respectively, and the system's energy efficiency can be improved by more than 5%.

Keywords: Steam power system; Multi-scenario partition; Uncertainty sets; Robust optimisation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222029188
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222029188

DOI: 10.1016/j.energy.2022.126032

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().