Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

In electric power plant operation, Economic Environmental Dispatch (EED) of a thermal-wind is a significant chore to involve allocation of production amongst the running units so the price, NOx extraction status and SO2 extraction status are enhanced concurrently whilst gratifying each and every experimental constraint. This is an exceedingly controlled multiobjective optimizing issue concerning contradictory objectives having Primary and Secondary constraints. For the given work, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is recommended for taking care of EED issue. In simulation results that are obtained by applying the two test systems on the proposed scheme have been evaluated against Strength Pareto Evolutionary Algorithm 2 (SPEA 2).

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Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.494-495.1715 ◽

2014 ◽

Vol 494-495 ◽

pp. 1715-1718

Author(s):

Gui Li Yuan ◽

Tong Yu ◽

Juan Du

Keyword(s):

Genetic Algorithm ◽

Load Distribution ◽

Power Plants ◽

Optimization Problem ◽

Thermal Power ◽

Local Optimum ◽

Multi Objective Optimization ◽

Multi Objective ◽

Optimal Load ◽

Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

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Trigonometric Curve Fitting Based on Genetic Algorithm and the Application of Data Processing in Geography

Communications in Computer and Information Science - Computational Intelligence and Intelligent Systems ◽

10.1007/978-3-642-04962-0_12 ◽

2009 ◽

pp. 104-109 ◽

Cited By ~ 1

Author(s):

Baolei Gu

Keyword(s):

Genetic Algorithm ◽

Data Processing ◽

Curve Fitting

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Impact of the Green Light Program on haze in the North China Plain, China

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-11185-2019 ◽

2019 ◽

Vol 19 (17) ◽

pp. 11185-11197 ◽

Cited By ~ 1

Author(s):

Xin Long ◽

Xuexi Tie ◽

Jiamao Zhou ◽

Wenting Dai ◽

Xueke Li ◽

...

Keyword(s):

Power Generation ◽

Emission Reduction ◽

North China Plain ◽

North China ◽

Thermal Power ◽

Green Light ◽

Coal Consumption ◽

The North ◽

The North China Plain ◽

Pm2.5 Concentration

Abstract. As the world's largest developing country, China has undergone ever-increasing demand for electricity during the past few decades. In 1996, China launched the Green Light Program (GLP), which became a national energy conservation activity for saving lighting electricity as well as an effective reduction of the coal consumption for power generation. Despite the great success of the GLP, its effects on haze have not been investigated and well understood. This study focused on assessing the potential coal saving induced by the improvement of luminous efficacy, the core of the GLP, and on estimating the consequent effects on the haze in the North China Plain (NCP), where a large number of power plants are located and are often engulfed by severe haze. The estimated potential coal saving induced by the GLP can reach a massive value of 120–323 million tons, accounting for 6.7 %–18.0 % of the total coal consumption for thermal power generation in China. There was a massive potential emission reduction of air pollutants from thermal power generation in the NCP, which was estimated to be 20.0–53.8 Gg for NOx and 6.9–18.7 Gg for SO2 in December 2015. The potential emission reduction induced by the GLP plays important roles in the haze formation, because the NOx and SO2 are important precursors for the formation of particles. To assess the impact of the GLP on haze, sensitivity studies were conducted by applying a regional chemical–dynamical model (WRF-CHEM). The model results suggest that in the case of lower-limit emission reduction, the PM2.5 concentration decreased by 2–5 µg m−3 in large areas of the NCP. In the case of upper-limit emission reduction, there was much more remarkable decrease in PM2.5 concentration (4–10 µg m−3). This study is a good example to illustrate that scientific innovation can induce important benefits for environment issues such as haze.

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Research on Heat/Power Load Optimizing Distribution Model of Heating Units Based on Gene Sequencing Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.562 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 562-566

Author(s):

Wen Sheng Zhao ◽

Xiao Dong Ding ◽

Zhi Wang

Keyword(s):

Consumption Rate ◽

Thermal Power ◽

Gene Sequencing ◽

Distribution Model ◽

Heat Power ◽

Coal Consumption ◽

Operating Condition ◽

Sequencing Method ◽

Power Load ◽

Saving Effect

Based on the basic idea of biological gene sequencing method,“Shotgun Method”,this paper proposes a new method of heat/power load optimizing distribution,named as “Shotgun Method”.This method is mainly based on historical operating data of thermal power plant,and puts the reciprocal of fuel utilization coefficient in the physical sense—factory comprehensive standard coal consumption rate as thermal economic index.Through the calculation of thermal system of principle,every unit can get its own coal consumption characteristic equation,as the foundation operating condition library for the heat/power load distribution.“Shotgun Method” aims at minimum factory comprehensive standard coal consumption rate to establish mathematical model for seeking the best match between operating condition library of each unit.The application instance shows that this method is simple,practical,and the energy saving effect is remarkable.

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The Research and Comparison of some Methods which were Used to Account how to Distribute the Load in the same Plate

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.401-403.2319 ◽

2013 ◽

Vol 401-403 ◽

pp. 2319-2322

Author(s):

Xing Liu

Keyword(s):

Genetic Algorithms ◽

Power Plant ◽

Thermal Power Plant ◽

Load Distribution ◽

Thermal Power ◽

Unit Load ◽

Coal Consumption ◽

Swarm Optimization ◽

Incremental Method ◽

Nonlinear Programming Method

This paper discussed the advantages of genetic algorithm in the load distribution of the thermal power plant, and several other commonly used algorithms: equal incremental method, nonlinear programming method, particle swarm optimization for comparison and analysis. Compared with other methods, the results of use the genetic algorithms to calculate the load distribution between the thermal power plant units were both simple and accurate. So use the genetic algorithms to calculate the load distribution could optimizing the allocation of unit load, reduce coal consumption, improve the efficiency of the unit, responded to the call of the national energy saving.

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Modeling of coal consumption rate based on wavelet neural network

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962317500234 ◽

2017 ◽

Vol 08 (03) ◽

pp. 1750023

Author(s):

Xiaoqiang Wen ◽

Shuguang Jian

Keyword(s):

Neural Network ◽

Neural Networks ◽

Consumption Rate ◽

Orthogonal Experiment ◽

Thermal Power ◽

Morlet Wavelet ◽

Wavelet Neural Network ◽

Wavelet Function ◽

Wavelet Neural Networks ◽

Coal Consumption

In this paper, two wavelet neural network (WNN) frames which depend on Morlet wavelet function and Gaussian wavelet function were established. In order to improve the efficiency of model training, the momentum term was applied to modify the weights and thresholds, and the output of the network was summed up by function transformation of output layer nodes. When the Gaussian Wavelet Neural Networks (GWNN) and Morlet Wavelet Neural Networks (MWNN) were applied to coal consumption rate (CCR) estimation in a thermal power plant, the results confirmed their potency in function approximation. In addition, the influence of learning rate on the models was also discussed through the orthogonal experiment.

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