Power Generation Optimization of the Combined Cycle Power-Plant System Comprising Turbo Expander Generator and Trigen in Conjunction with the Reinforcement Learning Technique

Hyoung Tae Kim, Gen Soo Song and Sangwook Han
Additional contact information
Hyoung Tae Kim: Department of Innovation Laboratory, Korea Gas Corporation Research Institute, Gyeonggi-do 15328, Korea
Gen Soo Song: Department of R&D Center, Kum Young ENG, Daejeon 34051, Korea
Sangwook Han: Department of Electrical Information and Control, Dong Seoul University, Gyeonggi-do 13117, Korea

Sustainability, 2020, vol. 12, issue 20, 1-14

Abstract: In this paper, a method that utilizes the reinforcement learning (RL) technique is proposed to establish an optimal operation plan to obtain maximum power output from a trigen generator. Trigen is a type of combined heat and power system (CHP) that provides chilling, heating, and power generation, and the turbo expander generator (TEG) is a generator that uses the decompression energy of gas to generate electricity. If the two are combined to form a power source, a power generation system with higher efficiency can be created. However, it is very difficult to control the heat and power generation amount of TEG and trigen according to the flow rate of natural gas that changes every moment. Accordingly, a method is proposed to utilize the RL technique to determine the operation process to attain an even higher efficiency. When the TEG and trigen are configured using the RL technique, the power output can be maximized, and the power output variability can be reduced to obtain high-quality power. When using the RL technique, it was confirmed that the overall efficiency was improved by an additional 3%.

Keywords: reinforcement learning; trigen generator; power generation optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/20/8379/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/20/8379/ (text/html)

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:gam:jsusta:v:12:y:2020:i:20:p:8379-:d:426630

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().