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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received December 16, 2020
Accepted March 16, 2021
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Multiscale modeling and integration of a combined cycle power plant and a two-tank thermal energy storage system with gPROMS and SimCentral

Department of Chemical Engineering, Hanbat National University, San 16-1, Dukmyung-dong, Yuseong-gu, Daejeon 34158, Korea 1PENTECH Engineering, 803, JEI PLATZ, 186, Gasan Digital 1-ro, Geumcheon-gu, Seoul 08502, Korea 2Institute for Materials and Processes, School of Engineering, The University of Edinburgh, Robert Stevenson Road, Edinburgh, EH9 3FB, UK 3AVEVA, 13F, Kbiz DMC Tower, 189, Seongam-ro, Mapo-gu, Seoul 03929, Korea
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Korean Journal of Chemical Engineering, July 2021, 38(7), 1333-1347(15), 10.1007/s11814-021-0789-1
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Abstract

With different computational tools, simulations ranging from detailed and rigorous mathematical models to overall process plant of black box models can be carried out. Whereas most of these computational tools cannot practically execute different scales of models at the same time, it becomes relevant to devise strategies in coupling two or more of them for better analysis of processes. In this light, this study proposes Excel as an interactive scale bridge of data exchange to aid the multiscale modeling and dynamic simulation of combined cycle (CC) power plant integration with two-tank thermal energy storage (TES) system using gPROMS and SimCentral. This is relevant to analyze not only the performance of TES, but the feasibility of its integration with CC in augmenting energy production to meet daily power demand. The integrated system modeled in four operational modes of CC increased in power generation by 7.3MW at an efficiency of 98.30%. The study validated the usefulness of the TES integration of 99.66% efficiency. The research results provide a communication strategy for different computational tools and an approach to effectively increase CC power production to meet varying daily demand.

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