A Surrogate Model Approach to Predict Operations and Maintenance Cost for Combined Cycle Power Plant

Abstract In the transition toward carbon neutrality by 2050, renewable energy sources are gaining prominence. The variability of renewable energy poses challenges for power grid stability. Gas turbine combined cycle (GTCC) power plants play a crucial role in grid stabilization, especially during peak loads. In GTCC power generation, operations and maintenance (O&M) cost is the next most important after fuel and initial investment cost, and it is necessary to predict O&M cost according to changes in the operating environment for efficient and stable operation of GTCC power plant. However, various uncertainties exist when estimating O&M costs because actual design information is limitedly disclosed due to maintenance contracts between operators and manufacturers, and it is difficult to obtain accurate variables according to the operation condition. In this paper, the correlation was analyzed considering the uncertainty of the factors affecting the O&M cost of the GTCC power plant through sensitivity analysis, and the main factors were defined. In addition, a surrogate model was proposed that can quickly calculate fixed and variable O&M cost according to changing operating scenarios such as shortening startup time, enhancing ramp rate, or lower minimum load. Through the case study, short-term and long-term O&M costs can be estimated in the operating environment of the GTCC power plant currently being operated. Additionally, the model facilitates cost projections for new hybrid GTCC facilities (combining gas turbines with energy storage systems), aiding long-term investment planning.

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Publication Details

Journal
Journal of Engineering for Gas Turbines and Power
Published
2024-08-19
DOI
https://doi.org/10.1115/1.4066252
Primary Topic
Advanced Power Generation Technologies
Type
article
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article

A Surrogate Model Approach to Predict Operations and Maintenance Cost for Combined Cycle Power Plant

Woosung Choi, David R. Noble, Bobby M. Webb
Journal of Engineering for Gas Turbines and Power
Advanced Power Generation Technologies
article

A Surrogate Model Approach to Predict Operations and Maintenance Cost for Combined Cycle Power Plant

Woosung Choi, David R. Noble, Bobby M. Webb
article en

Abstract

Abstract In the transition toward carbon neutrality by 2050, renewable energy sources are gaining prominence. The variability of renewable energy poses challenges for power grid stability. Gas turbine combined cycle (GTCC) power plants play a crucial role in grid stabilization, especially during peak loads. In GTCC power generation, operations and maintenance (O&M) cost is the next most important after fuel and initial investment cost, and it is necessary to predict O&M cost according to changes in the operating environment for efficient and stable operation of GTCC power plant. However, various uncertainties exist when estimating O&M costs because actual design information is limitedly disclosed due to maintenance contracts between operators and manufacturers, and it is difficult to obtain accurate variables according to the operation condition. In this paper, the correlation was analyzed considering the uncertainty of the factors affecting the O&M cost of the GTCC power plant through sensitivity analysis, and the main factors were defined. In addition, a surrogate model was proposed that can quickly calculate fixed and variable O&M cost according to changing operating scenarios such as shortening startup time, enhancing ramp rate, or lower minimum load. Through the case study, short-term and long-term O&M costs can be estimated in the operating environment of the GTCC power plant currently being operated. Additionally, the model facilitates cost projections for new hybrid GTCC facilities (combining gas turbines with energy storage systems), aiding long-term investment planning.

Journal of Engineering for Gas Turbines and PowerVol. 147(2)
Korea Electric Power Corporation (South Korea) (KR)
Affordable and clean energy
Openalex Percentile: Top 89%
Advanced Power Generation Technologies
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