Coordinated System Intelligent Optimization for an Ultra-Supercritical Power Unit Based on Improved Simplex Method and Condensate Throttling

Liangyu MA; Qianqian LI; Fan LI

doi:10.12096/j.2096-4528.pgt.18182

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Coordinated System Intelligent Optimization for an Ultra-Supercritical Power Unit Based on Improved Simplex Method and Condensate Throttling

Power Generation and Environmental Protection | 更新时间：2025-07-04

- Coordinated System Intelligent Optimization for an Ultra-Supercritical Power Unit Based on Improved Simplex Method and Condensate Throttling
- Power Generation Technology Vol. 39, Issue 6, Pages: 512-519(2018)
- 作者机构：
  
  华北电力大学控制与计算机工程学院, 河北省保定市 071003
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61174111)
- DOI：10.12096/j.2096-4528.pgt.18182
  CLC：
- Received：19 September 2018，
  
  Published：31 December 2018
- 稿件说明：
移动端阅览
Liangyu MA, Qianqian LI, Fan LI. Coordinated System Intelligent Optimization for an Ultra-Supercritical Power Unit Based on Improved Simplex Method and Condensate Throttling[J]. Power Generation Technology, 2018, 39(6): 512-519.
DOI：

Liangyu MA, Qianqian LI, Fan LI. Coordinated System Intelligent Optimization for an Ultra-Supercritical Power Unit Based on Improved Simplex Method and Condensate Throttling[J]. Power Generation Technology, 2018, 39(6): 512-519. DOI： 10.12096/j.2096-4528.pgt.18182.

摘要

风能、太阳等新能源的规模化并网对电网的安全稳定性造成较大影响。为提高新能源的消纳能力，电网对燃煤机组的运行灵活性、负荷快速响应能力、深度调峰能力等提出了很高的要求。采用先进的控制策略提升协调控制的品质，具有重要的现实意义。基于近年来发展起来的"凝结水节流"快速变负荷方法，采用人工神经网络建立了考虑凝结水节流特性的负荷预测模型及主汽压力预测模型。在此基础上，利用经典优化理论中的改进单纯形法，设计了协调系统智能优化控制策略，编制了实时优化程序。借助1000 MW超超临界机组仿真机开展了详细的优化控制仿真实验，结果表明文中方法可有效提高机组变工况负荷响应的快速性，减少主汽压力波动，提升协调控制品质。

Abstract

With large-scale wind and solar energy power units put into operation

it brings great influence on the stability and safety of the power network due to the high fluctuation and randomness features of the wind and solar resources. In order to absorb more new energy power

regional load dispatch center puts up very high requirement for the operation flexibility

load fast-following and deep peak load regulation ability. Therefore

it is very significant to adopt advanced control strategies to optimize the coordinated control system (CCS) to improve its control quality. In this work

the load prediction neural network model considering "condensate throttling" and the main steam pressure prediction model were developed by taking a 1000 MW ultra-supercritical power unit as the object investigated. Based on the established models

a classic optimization theory-improved simplex method was selected as the CCS optimization algorithm. An intelligent coordination optimization scheme with condensate throttling was designed

and the real-time optimization program was developed with MATLAB software. Detailed simulation tests were carried out in the full-scope simulator of the 1000 MW power unit. Simulation results indicate that the proposed scheme can effectively improve the coordinated control quality with faster load response

less main steam pressure fluctuation during dynamic load-changing process.

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references

丁明, 王伟胜, 王秀丽, 等.大规模光伏发电对电力系统影响综述[J].中国电机工程学报, 2014, 34(1):1-14.

刘吉臻, 曾德良, 田亮, 等.新能源电力消纳与燃煤电厂弹性运行控制策略[J].中国电机工程学报, 2015, 35(21):5385-5394.

刘晓光, 吕渤林.弃风条件下考虑机群电价差异的风电场控制策略[J].发电技术, 2018, 39(1):49-52.

王立军, 李晓敏. "两个细则"条件下的超临界机组AGC协调控制优化[J].现代电力, 2011, 28(2):84-89.

王淼婺.火电机组协调控制对AGC的适应性分析[J].中国电力, 1999, 32(6):45-47.

郑飞, 金丰.大中型火电机组AGC快速响应控制策略优化的研究[J].东北电力技术, 2013, 34(5):21-23.

龙辉, 黄晶晶. "十三五"燃煤发电设计技术发展方向分析[J].发电技术, 2018, 39(1):13-17.

马良玉, 高志元.超临界机组协调控制系统的预测优化控制[J].热力发电, 2014, 43(9):54-59.

Mortensen J H, Moelbak T, Andersen P, et al.Optimization of boiler control to improve the loadfollowing capability of power-plant units[J].Control Engineering Practice, 1998, 6(12):1531-1539.

Fetterer B, Lausterer G K, Leibbrandt S R.Improved unit dynamic response using condensate throttling[J]IFAC Proceedings Volumes, 1992, 25(1):129-134.

郑卫东, 马浩, 李肖肖, 等.凝结水节流技术在1000 MW机组的应用[J].浙江电力, 2015(6):39-43.

马良玉, 宁福军, 宋胜男.凝结水节流对机组负荷影响的仿真研究[J].热力发电, 2015, 44(3):109-114.

刘吉臻, 刘彧昕, 王玮.基于汽水分布方程的热力发电机组凝结水节流静态负荷响应特性[J].动力工程学报, 2015, 35(4):318-324.

王玮, 刘吉臻, 曾德良, 等.凝结水节流变负荷能力静态建模与分析[J].动力工程学报, 2015, 35(9):740-745, 759.

马良玉, 成蕾.考虑回热循环的超超临界机组负荷预测神经网络模型[J].热力发电, 2016, 45(4):19-27, 34.

马良玉, 高志元.基于神经网络的超临界机组数学模型[J].动力工程学报, 2013, 33(7):517-522.

高林, 李二堪, 薛建忠, 等.基于凝结水变负荷技术的深度滑压节能控制[J].热力发电, 2016, 45(4):121-124.

马良玉, 成蕾, 彭钢, 等.基于神经网络预测模型和凝结水节流的超超临界机组协调系统智能优化控制[J].动力工程学报, 2017, 37(8):640-648.

刘欢培, 黄建华.改进单纯形法寻优的MATLAB实现[J].浙江工业大学学报, 2003, 31(4):377-381.

李占利.最优化理论与方法[M].徐州:中国矿业大学出版社, 2012.

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