An improved combined heat and power economic dispatch model for natural gas combined cycle power plants

Yu, Haiquan; Nord, Lars O.; Yu, Cong; Jian-xin, Zhou; Si, Fengqi

doi:10.1016/j.applthermaleng.2020.115939

Cited by 14 publications

(3 citation statements)

References 62 publications

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“…Using the decreased set of new features allows ML algorithms to work faster and more effectively. Therefore, it supports predicting more correctly by rising learning correctness of ML algorithms and edifying result simplicity [20]. The selection of feature subsets by giving a new feature set that involves 'n' number of I/P features.…”

Section: Ccpp Systemmentioning

confidence: 99%

Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms

Afzal

Alshahrani²,

Alrobaian³

et al. 2021

Energies

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This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

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Section: Ccpp Systemmentioning

confidence: 99%

Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms

Afzal

Alshahrani²,

Alrobaian³

et al. 2021

Energies

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“…Although natural gas-based power plant only accounts for about 5 % in China, it tends to increase in a long period. Thermal performance investigations on NGCC power plants have been carried on actively [15][16][17][18][19][20][21][22]. The ambient temperature is an important factor that has a considerable effect on the thermal performance of an NGCC power plant.…”

Section: Introductionmentioning

confidence: 99%

Performance degradation analysis of combined cycle power plant under high ambient temperature

Wan

Chen

2022

THERM SCI

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The mechanism of performance degradation of a natural gas combined cycle (NGCC) power plant under high ambient temperature is studied by analysing the performance characteristics and thermal properties of the working fluids. Real operating data under typical seasonal conditions are collected and studied. The results reveal that the power output of the NGCC system decreases by 22.6 %, and the energy efficiency decreases from 57.28 % to 56.3 % when the ambient temperature increases from 5-35?C. GT total power output and ST power output decrease by 17.0 % and 16.2 %, respectively, as ambient temperature increases from 5-35 0C. The enthalpy difference of the flue gas between the turbine inlet and outlet change slightly with varying ambient temperature. The fuel and air input decrease by 16.0 % and 16.2 %, respectively, as ambient temperature increases from 5-35?C. By analysing the calculated results, the decrement in air and fuel input d is considered as the immediate cause of system power output reduction. The proportion of power consumed by AC reaches 50.4 % at 35?C. This is considered to be caused by air compressor idle.

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“…Buildings equipped with multi-energy systems are an increasing trend due to the high energy efficiencies that could be achieved. In particular, the combined heat and power (CHP) systems that generate both electrical and thermal energy exploiting their inherent operating cycle [1,2] are vital components. There have been concerted efforts from building owners to replace single energy generators with higher efficiency CHP units.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Algorithm for Combined Heat and Power System Operation Management

et al. 2021

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This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach.

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An improved combined heat and power economic dispatch model for natural gas combined cycle power plants

Cited by 14 publications

References 62 publications

Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms

Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms

Performance degradation analysis of combined cycle power plant under high ambient temperature

A Heuristic Algorithm for Combined Heat and Power System Operation Management

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