A novel-practicable method for improving power plant benefit based on CCWS operation optimization

Huijie Wang, Baoyun Qiu, Tianxu Yan, Fangling Zhao, Guipeng Qi and Chen Li

Energy, 2025, vol. 316, issue C

Abstract: With the change in global electricity production structure, the optimal matching operation between a circulating cooling water system (CCWS) and a thermodynamic system has become a challenge due to frequent peak-shaving in Rankine cycle power plants. This paper advances a method to maximize the period net income of power plants through CCWS operation optimization. First, a calculation method for CCWS transient operation water temperature is developed using deep learning. Then, based on the coupling relationship between CCWS and the thermodynamic system, a two-layer optimization integral model is constructed to solve CCWS optimal operation and cleaning schemes in a forthcoming operation period and evaluate year-round optimal operation effects. The model contains the costs of pump unit operation, water replenishment, pump regulation, and condenser cleaning. After conducting CCWS operation optimization for a 2 × 330 MW power plant, the year-round electricity generation could increase by 4.73–10.71 million kWh, illustrating significant effects. The method is convenient to implement and could realize an electricity generation increase of 27.1 billion kWh and a water-saving of 12.6 billion tons in China, ultimately achieving a net income increase of 12.7 billion yuan. The method effectiveness is also examined for thirteen climate types worldwide.

Keywords: Rankine cycle power plant; Maximum period net income; Circulating cooling water system; Operation optimization; Deep learning; Atomic search optimization algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225002300
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:316:y:2025:i:c:s0360544225002300

DOI: 10.1016/j.energy.2025.134588

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 ().