Performance Of Combined Cycle Power Plant Research Articles

Predictive modeling of combined cycle power plant performance using a digital twin-based neural ODE approach

This study presents a novel digital twin-based predictive modeling approach to enhance the operation, maintenance, and management of combined cycle power plants. The research aimed to accurately forecast the combined cycle power (CCP) output, a critical performance indicator, to support intelligent decision-making for plant operators and engineers. The proposed framework leverages a neural ordinary differential equation (NODE) model integrated within a digital twin architecture to capture the complex, nonlinear dynamics of combined cycle gas turbine unit (CCGU) system. The predictive model was trained and validated using multivariate time-series data collected from a CCGU, achieving high accuracy with an R2_score of 0.993, a mean absolute percentage error of 0.325 %, and a root mean squared error of 1.518. Importantly, the NODE model demonstrated superior forecasting capabilities compared to traditional machine learning techniques. The digital twin (DT) concept enables seamless real-time data integration, physics-based models, and advanced data analytics to facilitate comprehensive CCGU lifecycle management. This novel approach provides operators with reliable, real-time insights to optimize plant performance, reduce maintenance costs, and improve overall sustainability. The generalization of the NODE model to an independent CCGU unit validated its applicability across diverse power plant configurations, highlighting the originality and practical value of the research. The key contribution of this work is the development of a digital twin-based predictive modeling framework centered on the innovative use of neural ordinary differential equations to enhance the operation and maintenance of critical energy infrastructure. This research advances the state-of-the-art in intelligent asset management for the built environment.

A simulation of diesel and biodiesel blending for energy generation

This study is to simulate bioenergy in the Saudi energy sector. The methodologies used in this study include the simulation of biodiesel blends in power plants. GIS software was used to calculate the area of Jatropha plants cultivated (228.5 km2) to produce 1.62 kg/s Jatropha oil. The results showed that the validation was done by comparing the net power output (72.3 MW) and efficiency of 30.44% of the real energy plant with the simulation net power output of 69.15 MW and efficiency of 30.67%. The efficiency was increased to 51.1% after the simple cycle was modified to the combined cycle. Biodiesel (1.534 kg/s) was blended with diesel. The blended diesel and biodiesel were examined and the effect of the blends on the combined cycle power plant performance was inspected. The overall result revealed that the energy from biodiesel can be an alternative to support the Saudi Arabia Vision 2030.

On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm

This paper focuses on the estimation of electrical power output (Pe) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy Pe. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), Kling–Gupta Efficiency (KGE), and Bland–Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE YVE−2 achieves the highest (Pe) accuracy, including R2=0.9368, MAE=3.3378, MSE=18.4800, RMSE=4.2985, MAPE=0.7354%, and KGE=0.9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper’s outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating Pe in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology.

Sustainable operations of a combined cycle power plant using artificial intelligence based power prediction

Combined Cycle Power Plants (CCPP) are an effective method for Power generation due to their high thermal efficiency, low fuel consumption, and low greenhouse emissions. However, investing millions into building a power plant without knowledge of the power generation capacity seems unproductive. With the help of AI, we have tried to eliminate this conundrum. The present study focuses on the prediction of power produced by a 747 MW Combined Cycle Power Plant (CCPP) using a Back Propagation Neural Network (BPNN) and compares its results with the actual data from CCPP. BPNN is a regression-based prediction technique that is utilized in this study to develop a predictive model and train it using the following input features: Ambient Temperature, Ambient Pressure, Mass Flow rate of fuel in Gas Turbine 1, and Mass Flow rate of fuel in Gas Turbine 2. The Predictive Model with 10 neurons in the hidden layer was found to be most effective with Mean Squared Error (MSE) value, for the validation dataset, of 0.0063237. CCPP is also analyzed through a thermodynamic model, developed using EES. A detailed energy analysis is carried out and the results were compared with predicted and actual data. It was found that the thermal efficiency and total power generation of actual, predicted, and simulated models were 27.541% & 667.32 MW, 28.238% & 683.48 MW and 28.201% & 683.16 MW, respectively. A parametric study was further carried out to investigate the significance of operating parameters on power output and it was concluded that the temperatures across the Gas turbines have a significant impact on the performance of CCPP. Finally, Methane was replaced by 3 different fuels, one by one, and the effect of each fuel was investigated thermodynamically. It was found that the Lower Heating Value (LHV) of fuel was an important parameter in achieving a higher power output. It can be summarized from this research work that predictive models do have accuracy and such data science techniques can be used as a substitute for extensive thermodynamic calculations.

Thermodynamic simulation and economic analysis of combined cycle with inlet air cooling and fuel pre-heating: Performance enhancement and emission reduction

Gas-steam combined cycle power plant (CCPP) performance suffers from sensitivity to ambient condition, higher fuel consumption rate and greenhouse gas emission. Several solutions were adapted to resolve one or two problems with expense of other. In this study, a hybrid process for compressor inlet air cooling (CIAC) and fuel pre-heating (FPH) is proposed to enhance the CCPP performance with simultaneous reduction in fuel consumption and emission. Three simulation cases have been presented for CCPP design and off-design mode of operation at a site-specific condition. The result shows that, compared to conventional cases the influence of CIAC and FPH leads to capacity increment of 10.7% to 12.7 % with load variation from 40% to 100%. The associated overall efficiency enhancement is 2.2 – 0.58% when ambient air temperature Ta cooled from 35 °C to 15 °C operating at site condition Ta = 35 °C, RH = 65%. The sensitivity analysis is performed based on monthly mean weather data. The rate of fuel consumption and CO2 emitted are considered per unit of power generated. The CO2 emitted is maximum at low Ta in the range of 26 – 28 °C and lower RH. The CCPP at its full-load, for an operating period of 24 h the fuel consumption will reduce approximately to 12226 kg and this will lead to saving in operating cost of 4307 dollars per day.

Air cooling techniques and corresponding impacts on combined cycle power plant (CCPP) performance: A review

This work aims to provide a state-of-the-art review of the performance of combined cycle power plant (CCPP) based on several proposed inlet air cooling systems. Investigators strive to meet the significant need to promote and develop inlet air cooling technologies to recover heat from the wasted energy in the exhaust gasses of the CCPP and diminish the environmental impacts. Various types of cooling systems mainly offer a boost for electric power generation during the peak load hours. The output power of the CCPP directly depends on the mass flow rate of air that flows through the air compressor. Therefore, during extremely hot weather conditions, subsequently, the air density drops, and this leads to a drastic decrease in the power output. This paper reviews available studies investigated the impacts of inlet air cooling systems on the performance of the CCPP. The fogging cooling system contributed by up to 17% in improving the total performance of the CCPP; however, with the use of the evaporative cooling, the performance was enhanced by only 4%. The energy consumption of mechanical chiller compared to that of evaporative cooling is high due to the effectiveness of the evaporative cooler which depends on the humidity of inlet airflow. Further, the mechanical cooling system can provide the CCPP with a cooling effect for around 7-hour on-peak periods. This method increases the gain of the CCPP performance by 13.6%. Ultimately, the CCPP equipped with an absorption chiller demonstrates the best solution to increase the performance by up to 23%.

Multi-Objective Evolutionary Optimization & 4E analysis of a bulky combined cycle power plant by CO2/ CO/ NOx reduction and cost controlling targets

The 4E analysis is utilized on a bulky combined cycle power plant (CCPP) with a dual pressure recovery boiler and an additional duct burner. Multi-objective evolutionary optimizations have been applied to obtain the best state of the heat recovery steam generator (HRSG), saturated temperature, cost reduction, and carbon dioxide emission, simultaneously. For the validation, the authentic data has been collected from an implemented CCPP. This comprehensive study has been performed to perceive the relation between the most significant parameters on the performance of CCPP. The main obtained results include five points. First, the thermal recovery boiler and combustion chamber have the highest exergy destruction among the power plant components. Second, the optimization based on the entire cycle at all temperatures has no economic justification, and its total exergy efficiency and cost are better than optimizations based on the recovery boiler and HRSG. Third, the value of the CCPP decision parameters is highly dependent on the ambient temperature. Therefore, it is not possible to apply the same value for CCPP at various temperatures. Fourth, the genetic algorithm improved the optimized cycle parameters by considering two objectives of the power plant costs reduction and CO2 emission. Fifth, the combined cycle with the nameplate function generates less NOx and monoxide than relative loads. Using such combined cycles with dual pressure recovery boiler and additional duct reduces the normalized Co2 emissions by 158.67kgMWh .

Performance augmentation of combined cycle power plant under the control of differing open loop cooling techniques to the gas turbine blades

ABSTRACT The present study investigates the influence of different blade cooling techniques on the combined cycle power plant (CCPP) in the perspective of performance augmentation. The influence of Compressor Pressure Ratio (CPR), Gas Turbine Inlet Temperature (GTIT) and Turbine blade temperature (Tb) are extensively studied on the performance of CCPP with six open loop cooling schemes namely Air Internal Convection Cooling (AICC), Air Film Cooling (AFC), Air Transpiration Cooling (ATC), Steam Internal Convection Cooling (SICC), Steam Film Cooling (SFC) and Steam Transpiration Cooling (STC). The performance characteristics energy efficiency, exergy efficiency, specific work output, coolant flow rate requirement, specific fuel consumption (sfc) and carbon dioxide (CO2) emissions of CCPP are evaluated under the influence of above cooling schemes and comparison is made between them. The results show that the maximum performance was obtained for Steam Transpiration Cooling (STC) scheme with a minimum coolant requirement along with reduced CO2 emissions.

Aligning Combined Cycle Power Plant Performance with Field Measurements

This paper studied a system of two gas turbine engines with inlet guide vanes followed by a steam turbine. The steam turbine recovers energy from the exhaust gas of the gas turbine engines through heat recovery steam generators. An actual combined cycle is modeled; this system model takes into consideration the variation of specific heats of air and combustion gases, the polytropic efficiencies of gas turbines and compressors, and the isentropic efficiencies of the steam turbine and pumps. Moreover, pressure drops were taken into consideration for all different heat exchangers. Model performance predictions were then aligned with the field measurements of a Jordanian combined cycle power plant. This paper presents the comparison between the model predictions and actual performance, specifically for power output and fuel consumption. The relative error between the theoretical and actual output power from the plant is approximately 8%. Ambient air temperature has a major effect on the plant-produced power and fuel consumption. Under desert conditions between summer and winter, when the ambient temperature drops from 46 to 1 $$^\circ $$ C, an extra 47.2 MW could be generated, with the corresponding increase in efficiency of approximately 6.5%, while SFC decreases by 1.1%.

Mathematical Modeling – The Impact of Cooling Water Temperature Upsurge on Combined Cycle Power Plant Performance and Operation

This paper presents the mathematical modeling analysis on cooling system in a combined cycle power plant. The objective of this study is to get the impact of cooling water upsurge on plant performance and operation, using Engineering Equation Solver (EES™) tools. Power plant installed with total power capacity of block#1 is 505.95 MWe and block#2 is 720.8 MWe, where sea water consumed as cooling media at two unit condensers. Basic principle of analysis is heat balance calculation from steam turbine and condenser, concern to vacuum condition and heat rate values. Based on the result shown graphically, there were impact the upsurge of cooling water to increase plant heat rate and vacuum pressure in condenser so ensued decreasing plant efficiency and causing possibility steam turbine trip as back pressure raised from condenser.

Effect of atmospheric condition and ammonia mass fraction on the combined cycle for power and cooling using ammonia water mixture in bottoming cycle

Gas/steam combined cycle power plants are being extensively used for power generation due to their better performance as compared to gas turbine based or steam turbine based power plants operating in isolation. These combined cycle power plants have natural aspiration in compressors where the state of ambient air entering it, significantly affects the work requirement of compressors and thus affects the overall combined cycle power plant performance. It is felt that in tropical countries with significant seasonal variations, when the atmospheric temperatures become quite high during some months in a calendar year, the cooling of ambient air before entering compressor using the energy available in combined cycle power plant may help in improving the overall plant performance. In view of this a combined cycle with ability of producing power and provision of simultaneous cooling of air entering the compressor and cooling of gas turbine blades through ammonia water mixture and steam using closed loop cooling scheme has been studied in this paper. A comparative analysis of the combined cycle's considered shows that for a cycle pressure ratio of 40 and turbine inlet temperature of 2000K maximum work of 2093 kJ/kg of compressed air is obtained for ammonia mass fraction of 0.6 for the combined cycle using only ammonia water mixture as coolant where as maximum first law efficiency and second law efficiency of 62.6% and 59.67% are being achieved for ammonia mass fraction of 0.7, for the same configuration and at an ambient temperature of 30 °C. Cooling load of 22 kW is observed to be maximum for the configuration using steam and ammonia water mixture as coolant and for an ambient temperature of 50 °C at the cycle pressure ratio of 40, turbine inlet temperature of 2000K and ammonia mass fraction of 0.6.

Combined Cycle Power Plant Performance Enhancement Based on Inlet Air Cooling Techniques: A Technical Review

Combined Cycle Power Plant Performance Enhancement Based on Inlet Air Cooling Techniques: A Technical Review

Boosting Gas Turbine Combined Cycles in Hot Regions Using Inlet Air Cooling including Solar Energy

Cooling the air before entering the compressor of a gas turbine of a combined cycle power plant is an effective method to boost the output power of both the gas and the steam turbines in hot regions. Usually, in hot area, the time at which the demand of power is highest coincides with the time of the largest drop of power generation due to the weather conditions, which shows the importance of air inlet cooling. However, fortunately, the daily peak period of electrical power demand coincides with the peak of solar radiation that is abundant in Saudi Arabia. This paper presents a novel system of cooling the gas turbine inlet air using a solar assisted absorption chiller. The study includes also a comparative analysis of a gas turbine combined cycle power plant performance when it is integrated with conventional and solar-assisted absorption chillers for inlet air cooling with the performance of the plant operating without inlet air cooling. The effect of ambient air temperature on the output power is investigated and reported. The advantage of air inlet cooling in boosting the power of the combined cycle in Dhahran is evident especially in summer. The study revealed that at the design hour under the weather conditions of Dhahran, KSA (July 17th at 1 p.m.), the net power output of the plant drops from 267878 kW to 226361 kW at 48 °C (15.5% drop). While the gross power output of the gas turbine drops from 189472 kW to 151469 KW (20.1% drop) and the steam turbine from 84798 KW to 80495 KW (5.1% drop). Integrating conventional and solar-assisted absorption chillers increased the net power output of the combined cycle by 34964 KW and 37999 KW, respectively. Average and hourly performance during typical days have been conducted and presented. The plants without air inlet cooling system show higher carbon emissions (0.73 kg CO2/KWh) compared to the plant integrated with conventional and solar-assisted absorption chillers (0.509 kg CO2/KWh) and (0.508 kg CO2/KWh), respectively. Also, integrating a conventional absorption chiller shows the lowest levelized electricity cost.

Combined cycle power plant performance evaluation using exergy and energy analysis

Performance evaluation of a combined cycle power plant (CCPP) is generally attempted using first law of thermodynamics based on energy and mass balance methodology. For detailed component wise performance analysis, exergy‐based evaluation based on second law of thermodynamics is gaining increasing importance. In this study, a CCPP consisting of triple pressure steam cycle with reheat is analyzed using both energy and exergy. Effect of ambient temperature, inlet and exhaust pressure loss, CCPP load, excess air percentage, cooling water temperature, and condenser vacuum has been investigated. Energy and exergy‐based performances are compared and presented. The simulation results show that efficiency of CCPP decreases with increase in the inlet and exhaust pressure loss and with increase in inlet air temperature to compressor. The total power output of CCPP decreases at higher inlet air temperature with increase of steam flow rate in bottoming cycle and increase in compressor power consumption. The exergy efficiency of combustion chamber, heat recovery steam generator, and condenser are found to be 77.48%, 87.20%, and 29%, respectively; and the overall exergy and energy efficiency of the unit at 100% design and operating condition is found to be 54.09%, 58.26% and 53.92%, 57.10%, respectively. The overall exergetic efficiency of the CCPP can be improved by reducing the losses in bottoming cycle. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1180–1186, 2017

Effect of external conditions and steam parameters on performance of combined-cycle power plants with back-pressure steam turbine

The cogeneration combined-cycle gas turbine plant with back-pressure steam turbine is considered. A simple circuit of the plant increases its reliability, and the use of optimal temperatures of the heating and return water allows obtaining the highest thermal efficiency of the plant taking into account the ratio of its electric and heat power. The technical and economic efficiency of the combined-cycle plant with back-pressure steam turbine is studied to estimate the capital investment. The parameters and conditions to maximize the net present value growth relative to the base version are determined.

A data envelopment analysis model with interval data and undesirable output for combined cycle power plant performance assessment

Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the return to scale (RS) problem in the presence of uncertain data and undesirable outputs. A combined cycle power plant is considered a decision making unit (DMU) which consumes fuels to produce electricity and emissions. The uncertainty of the inputs and outputs are modeled with interval data and the emissions are assumed to be undesirable outputs. The proposed DEA method determines the interval efficiency scores of the DMUs and offers a practical benchmark for enhancing the efficiency scores. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a six-year study of 17 combined cycle power plants in Iran. The main contributions of this paper are six fold: we (1) model the uncertainties in the input and output data using interval data; (2) consider undesirable outputs; (3) determine the efficiency scores of the DMUs as interval values; (4) develop a group of indices to distinguish between the efficient and inefficient DMUs; (5) determine the most economic scale size for the efficient DMUs; and (6) determine practical benchmarks for the inefficient DMUs.

Combined Cycle Power Plant Performance Analyses Based on the Single-Pressure and Multipressure Heat Recovery Steam Generator

AbstractIn this study sensitivity analysis is performed for single, double-pressure, and triple-pressure heat recovery steam generators in a combined cycle power plant. Steam pressure, evaporator pinch point, and economizer approach temperature differences are taken as design parameters. Temperature-heat (T-Q) graphics of heat recovery steam generator (HRSG) designs and the effects of design parameters to the plant efficiency, net power production, and heat transfer areas are obtained. The calculated results are useful to compare single and multipressure HRSGs in a combined cycle power plant scheme. In single-pressure, double-pressure, and triple-pressure HRSG, net power increment is approximately 0.05, 0.28, and 0.29% at every 10-bar inlet pressure increment. The net power output of the cycle decreases with increasing evaporator pinch point temperature difference. For single-pressure HRSG, this decrement is approximately 0.54%, whereas for double-pressure and triple-pressure HRSG it is approximately 0.21...

Modeling and Performance Analysis of Combined-Cycle Based Ship Power Plant

For combined-cycle power plant performance analysis, a ship power plant mathematical model is developed, including diesel engine, controllable pitch propeller, exhaust gas boiler, turbine generator and shaft generator models. The simulation performance characteristic curves of diesel engine under various loads are given. Comparison of simulation results and experimental data shows the model can well predict the performance of diesel engine in various operating conditions. The specific fuel oil consumption contours of combined-cycle power plant and the relations between engine operating conditions and steam cycle parameters are given. The influence of diesel engine operating conditions to the overall performance of combined-cycle power plant is discussed.

Influence of ambient temperature on combined-cycle power-plant performance

Thermal-power electricity are important mainly because of the need for diversified power-generation and the availability of natural gas, the main fuel used in this type of electricity-generating system. With the implementation of the priority plan for thermal-power plants in Brazil, dozens of units will be installed which will make it possible to transform the Brazilian electric system, today mainly based in hydraulic principles, into a hydro-thermal system. The operation of a combined cycle thermal-power plant is influenced by the conditions that are present at the place where it is installed, mainly ambient temperature, atmospheric pressure and the air's relative-humidity. These parameters affect the generated electric-power and the heat-rate during operation. Among these variables, the ambient temperature causes the greatest performance variation during operation. That is the reason why the influence of this variable on this type of generating unit is studied. The plant selected for this study has a multiple-shaft configuration and is composed of two Siemens AG 501F gas-turbines, coupled to three pressure levels HRSGs and re-heating with supplementary firing and a steam-turbine. The most relevant results obtained from a thermodynamic simulation, in which the Gate Cycle Software version 5.51.0.r was used, are the curves of generated power, as well as the heat rate and thermal efficiency as functions of ambient temperature and the supplementary firing.

Degradation Effects on Combined Cycle Power Plant Performance—Part III: Gas and Steam Turbine Component Degradation Effects

This paper presents an investigation of the degradation effects that gas and steam turbine cycles components have on combined cycle (CCGT) power plant performance. Gas turbine component degradation effects were assessed with TurboMatch, the Cranfield Gas Turbine simulation code. A new code was developed to assess bottoming cycle performance deterioration. The two codes were then joined to simulate the combined cycle performance deterioration as a whole unit. Areas examined were gas turbine compressor and turbine degradation, HRSG degradation, steam turbine degradation, condenser degradation, and increased gas turbine back pressure due to HRSG degradation. The procedure, assumptions made, and the results obtained are presented and discussed. The parameters that appear to have the greatest influence on degradation are the effects on the gas generator.