Review of Process Modeling of Solid-Fuel Thermal Power Plants for Flexible and Off-Design Operation

Avagianos, Ioannis; Rakopoulos, Dimitrios C.; Karellas, Sotiriοs; Kakaras, E.

doi:10.3390/en13246587

Cited by 16 publications

(4 citation statements)

References 78 publications

(90 reference statements)

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“…The basic genetic algorithm and adaptive genetic algorithm have the problems of slow search speed and easily falling into the local optimal solution. The operation rules of the genetic algorithm are probabilistic rather than deterministic [11]. When the crossover and mutation operation of the traditional genetic algorithm is performed with a certain probability, the intersection and mutation location change randomly, and the next generation of individuals also changes randomly.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Tao

Song²,

Zhu

et al. 2023

Journal of Sensors

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Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal-blending system has some defects, such as fuzzy ash content of coal-blending warehouse products and excessive error of the coal-blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal-blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal-blending research, then establishes an optimized dynamic coal-blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal-blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal-blending method, the average error is 3.33%, and the accuracy is improved by 4.82%.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Tao

Song²,

Zhu

et al. 2023

Journal of Sensors

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“…The validation was performed in the load-descending section, as shown in Figure 1 (b). (14) and are the external and internal temperatures of the tube, respectively, is the thermal conductivity k divided by the thickness D, and are the reciprocal of the convective film coefficients and , which follow Eqs. ( 12) and ( 13), respectively.…”

Section: Validation For Thermo-structural Fe Modelmentioning

confidence: 99%

Effect of Flexible Operation on Residual Life of High-Temperature Components of Power Plants

Heo¹,

Park²,

Kim³

et al. 2023

Preprint

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Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep-fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determine an analytical solution for the straightened superheater tube under thermal conditions and compare it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep-fatigue life of the reheater header is estimated by considering flexible operation factors and employing the Coffin-Manson and Larson-Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermal-structure analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors.

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“…Avagianos et al [16] have published a review of the solid-fuel flexible thermal power plants, discussing the effect of the penetration of non-dispatchable power plants upon wind and solar, for example, and analysing flexible operation requirements that influence their economic viability: fast start-up process, low minimum load, and ramping.…”

Section: Literature Review and Paper Contributionsmentioning

confidence: 99%

Load Frequency Model Predictive Control of a Large-Scale Multi-Source Power System

2022

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With increased interests in affordable energy resources, a cleaner environment, and sustainability, more objectives and operational obligations have been introduced to recent power plant control systems. This paper presents a verified load frequency model predictive control (MPC) that aims to satisfy the load demand of three practical generation technologies, which are wind energy systems, clean coal supercritical (SC) power plants, and dual-fuel gas turbines (GTs). Simplified state-space models for the two thermal units were constructed by concepts of subspace identification, whereas the individual wind turbine integration was implicated by the Hammerstein–Wiener (HW) model and then augmented from the output to simulate the effect of a wind farm, assuming similar power harvesting from all turbines in the farm. A practical strategy of control was then suggested, which was as follows: with a changing load demand, the available harvested wind energy must be fully admitted to the network to cover part of the load demand with the free energy, and the resultant load signal will then be instructed to the MPCs designed for the coal and gas units for the coordination of generation. The load signal, after being penetrated by wind, has more transients and faster changes, and needs a more sophisticated control in order to follow the load demand of the flexible coal and gas units. Furthermore, as the level of wind penetration increases, the power system frequency excursions are higher. The simulation results show an acceptable performance for linear MPCs embedded to the GT and coal units, with around a 90 MW share of wind without exceeding the safe restrictions of the plants and allowable reasonable frequency excursions. The complete simulation framework can be used to facilitate wind energy penetration in such power systems and train the operators and future engineers with subsequent power system frequency simulation studies.

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Review of Process Modeling of Solid-Fuel Thermal Power Plants for Flexible and Off-Design Operation

Cited by 16 publications

References 78 publications

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Effect of Flexible Operation on Residual Life of High-Temperature Components of Power Plants

Load Frequency Model Predictive Control of a Large-Scale Multi-Source Power System

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