Techno-Economic Analysis and Optimization of a Compressed-Air Energy Storage System Integrated with a Natural Gas Combined-Cycle Plant

Pavitra Senthamilselvan Sengalani, Md Emdadul Haque, Manali S. Zantye, Akhilesh Gandhi, Mengdi Li, M. M. Faruque Hasan and Debangsu Bhattacharyya ()
Additional contact information
Pavitra Senthamilselvan Sengalani: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
Md Emdadul Haque: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
Manali S. Zantye: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
Akhilesh Gandhi: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
Mengdi Li: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
M. M. Faruque Hasan: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
Debangsu Bhattacharyya: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA

Energies, 2023, vol. 16, issue 13, 1-23

Abstract: To address the rising electricity demand and greenhouse gas concentration in the environment, considerable effort is being carried out across the globe on installing and operating renewable energy sources. However, the renewable energy production is affected by diurnal and seasonal variability. To ensure that the electric grid remains reliable and resilient even for the high penetration of renewables into the grid, various types of energy storage systems are being investigated. In this paper, a compressed-air energy storage (CAES) system integrated with a natural gas combined-cycle (NGCC) power plant is investigated where air is extracted from the gas turbine compressor or injected back into the gas turbine combustor when it is optimal to do so. First-principles dynamic models of the NGCC plant and CAES are developed along with the development of an economic model. The dynamic optimization of the integrated system is undertaken in the Python/Pyomo platform for maximizing the net present value (NPV). NPV optimization is undertaken for 14 regions/cases considering year-long locational marginal price (LMP) data with a 1 h interval. Design variables such as the storage capacity and storage pressure, as well as the operating variables such as the power plant load, air injection rate, and air extraction rate, are optimized. Results show that the integrated CAES system has a higher NPV than the NGCC-only system for all 14 regions, thus indicating the potential deployment of the integrated system under the assumption of the availability of caverns in close proximity to the NGCC plant. The levelized cost of storage is found to be in the range of 136–145 $/MWh. Roundtrip efficiency is found to be between 74.6–82.5%. A sensitivity study with respect to LMP shows that the LMP profile has a significant impact on the extent of air injection/extraction while capital expenditure reduction has a negligible effect.

Keywords: air energy storage; power plant; net present value; dynamic optimization; levelized cost of storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/13/4867/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/13/4867/ (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:jeners:v:16:y:2023:i:13:p:4867-:d:1176756

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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