集中供热系统运行调节优化及热负荷预测方法研究
|
摘要
目前城市集中供热已成为我国北方地区冬季供热的一种主要形式,并且发展较快。城市集中供热系统热能耗费数量很大,品味较低,通常是120℃以下的低位热能,却主要以高品位的一次能源来供应,故具有较大的节能潜力。尤其是在供热系统运行过程中,如何通过实施运行调节,使系统在最优状态下运行,提高供热系统的经济效益和节能水平,是供热领域亟须解决的问题,而与之相关的理论和应用研究已经得到关注。
针对城市集中供热系统一次热网多采用质、量并调这种调节方式的现状,以运行能耗费用最低为优化目标,以供热管网的运行特性为约束条件,建立以供水流量和供回水温度为变量的集中供热系统一次热网运行能耗费用方程,通过求解该方程得到指导一次热网进行质、量调节的最优运行参数。使用非线性规划方法提供的分析思路和数值方法分析和求解方程,并通过在求解方程的不同阶段选择合适的方法建立了用于求解该方程的软件系统。通过对计算结果的分析,证明课题提出的运行能耗费用方程在指导集中供热系统一次热网的质、量调节时,比集中质调节具备更好的节能效益,同时证实用非线性规划方法开发的软件系统求解一次热网运行能耗费用方程比使用Matlab工具箱和步长法效率更高,结果更加准确。
针对城市集中供热系统热负荷预测成为集中供热系统运行调节前提和基础的实际情况,考虑到供热系统在实际运行过程中供热热负荷与诸多影响因素之间具有非线性和动态性关系的特点,使用人工神经网络技术实现了某小区二级换热站热负荷的预测,提出了确立BP神经网络结构和参数的方法,并使用开发的软件系统对工程实例热负荷进行了预测,通过对预测结果的分析表明BP神经网络用于热负荷预测能够获得较高的精度,满足一般的应用需求。
本文选择Visual C#程序设计语言和Microsoft Visual studio 2008程序设计平台实现上述算法和程序,它们所具备的显著特点,如面向对象技术、图形用户界面和软件部署等,为以后软件系统的后续研发提供了较好的基础框架。
At present, district heating system is very popular in northern part of China and develops rapidly. The system consumes large quantity thermal energy which is of low level. The low level thermal energy which is usually 120℃is mainly supported by high level thermal energy, so it has much energy saving potential in district heating system operation, how to carry out the regulation to make the system run in best condition is a hot topic which is urgently to be solved, the relative theory and research have been focused.
The primary heating networks are generally regulated by quality and quantity method. The optimization objective is the minize operation cost. The constraints conditions are dynamic characteristic of heating networks. The supply temperature and return temperature are variable value. The operation cost equation is set up and to be solved. The corresponding software program is also developed. Through the comparison, it is proved that the equation presented in this paper is of validity. Tt is also pointed out that the quality and quantity regulation is prior to the central heating quality regulation. And the non-linear optimize method is proved more efficient than toolbox of MATLAB and step method.
For the heat load prediction is basis for the regulation of district heating system, considering the nonlinear characteristic and dynamic characteristic of the heat load which may be related to many factors, the neural works technology is adopted to implement the heat load prediction of secondary heat station. The method of confirm the Bp neural work structure and parameters is presented in this paper and the software is used to predict the real data. By the analysis of the prediction result, it is proved that he BP neural applied in the heat load prediction can get high accuracy and meet the common demand.
Visual C# and Microsoft Visual studio 2008 Program platform are applied in this paper to implement the above algorithm and program. The excellent basic frame is provided for the following research and development of software.
针对城市集中供热系统一次热网多采用质、量并调这种调节方式的现状,以运行能耗费用最低为优化目标,以供热管网的运行特性为约束条件,建立以供水流量和供回水温度为变量的集中供热系统一次热网运行能耗费用方程,通过求解该方程得到指导一次热网进行质、量调节的最优运行参数。使用非线性规划方法提供的分析思路和数值方法分析和求解方程,并通过在求解方程的不同阶段选择合适的方法建立了用于求解该方程的软件系统。通过对计算结果的分析,证明课题提出的运行能耗费用方程在指导集中供热系统一次热网的质、量调节时,比集中质调节具备更好的节能效益,同时证实用非线性规划方法开发的软件系统求解一次热网运行能耗费用方程比使用Matlab工具箱和步长法效率更高,结果更加准确。
针对城市集中供热系统热负荷预测成为集中供热系统运行调节前提和基础的实际情况,考虑到供热系统在实际运行过程中供热热负荷与诸多影响因素之间具有非线性和动态性关系的特点,使用人工神经网络技术实现了某小区二级换热站热负荷的预测,提出了确立BP神经网络结构和参数的方法,并使用开发的软件系统对工程实例热负荷进行了预测,通过对预测结果的分析表明BP神经网络用于热负荷预测能够获得较高的精度,满足一般的应用需求。
本文选择Visual C#程序设计语言和Microsoft Visual studio 2008程序设计平台实现上述算法和程序,它们所具备的显著特点,如面向对象技术、图形用户界面和软件部署等,为以后软件系统的后续研发提供了较好的基础框架。
At present, district heating system is very popular in northern part of China and develops rapidly. The system consumes large quantity thermal energy which is of low level. The low level thermal energy which is usually 120℃is mainly supported by high level thermal energy, so it has much energy saving potential in district heating system operation, how to carry out the regulation to make the system run in best condition is a hot topic which is urgently to be solved, the relative theory and research have been focused.
The primary heating networks are generally regulated by quality and quantity method. The optimization objective is the minize operation cost. The constraints conditions are dynamic characteristic of heating networks. The supply temperature and return temperature are variable value. The operation cost equation is set up and to be solved. The corresponding software program is also developed. Through the comparison, it is proved that the equation presented in this paper is of validity. Tt is also pointed out that the quality and quantity regulation is prior to the central heating quality regulation. And the non-linear optimize method is proved more efficient than toolbox of MATLAB and step method.
For the heat load prediction is basis for the regulation of district heating system, considering the nonlinear characteristic and dynamic characteristic of the heat load which may be related to many factors, the neural works technology is adopted to implement the heat load prediction of secondary heat station. The method of confirm the Bp neural work structure and parameters is presented in this paper and the software is used to predict the real data. By the analysis of the prediction result, it is proved that he BP neural applied in the heat load prediction can get high accuracy and meet the common demand.
Visual C# and Microsoft Visual studio 2008 Program platform are applied in this paper to implement the above algorithm and program. The excellent basic frame is provided for the following research and development of software.
引文
[1]贺平,孙刚.供热工程.中国建筑工业出版社,2008.2
[2]冯旭.集中供热与节能.资源节约与环保,2007.2
[3]李天智,孙海波.城市集中供热系统控制策略.河北省科学院学报,2000.4
[4]Erik Dotzauer. Simple model for prediction of loads in district-heating systems. Applied Energy,2002.8
[5]康艳兵,张建国,张扬.我国热电联产集中供热的发展现状、问题与建议.中国能源,2008.10
[6]赵保安.可持续发展与集中供热行业的科技创新.学术论丛,2009.4
[7]D.K.Baker,S.A.Sherif. Heat transfer optimization of a district heating system using search methods. Int.J.Energy Res,1997.
[8]Jacob Kaminsky. District Heating and Cooling Technology Research and Development Overview of the U.S Department of Energy Program. Office of Building and Community, U.S.DOE
[9]韦新东,尹军,全贞花.日本集中供热(冷)系统的发展现状.吉林建筑工程学院学报,2001.3
[10]邹平华.借鉴俄罗斯经验积极发展我国集中供热事业.暖通与空调,2000.4
[11]张沈生,孙晓兵,傅卓林.国外供暖方式现状与发展趋势.工业技术经济,2006.7
[12]黄文,管昌生.城市集中供热研究现状及发展趋势.国外建材科技,2004.5
[13]任淇,崔铭俊.城市集中供热管网的设计.中国高新技术企业,2008.8
[14]朴东虎.供热系统设计运行及优化.民营科技,2008.11
[15]郝雪梅.城市集中供热管网的设计优化探讨.洁净煤技术,2007.3
[16]雷杰兰.集中供热管网的优化设计策略.科技创新导报,2008
[17]郑雪晶,由世俊,张欢,姜南.二级网燃气调峰集中供热系统优化设计.天津大学学报,2007.8
[18]薛宏文,张帆.供热运行中参数调节的优化.暖通空调,2001.2
[19]秦绪忠,江亿.多热源并网供热的水力优化调度研究.暖通空调,2001.1
[20]Lianzhong, L. Zaheeruddin, M. Dynamic modeling and simulation of a room with hot water baseboard heater. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2006.
[21]胡钦华,李奎山,苏廷弼.基于ANN的空气处理单元换热器的动态性能预测.计算机工程与应用,2008.3
[22]顿雷,李巴津.城市集中供热网参数预测研究.微计算机信息,2007.11
[23]郝有志；李德英；郝斌.基于神经网络的供热计量系统热负荷短期预测.暖通空调,2003.6
[24]王东亚,张琳,赵国材.神经网络遗传算法在供热负荷预测中应用.辽宁工程技术大学学报,2005.
[25]Ferrano F J, Wong K V. Prediction of thermal storage loads using a neural network. ASHRAE Transactions,1990
[26]Sidhu T S, Singh H, Sachdev M S. Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines. IEEE Transactions on Power Delivery,1995
[27]Kawashima M, Dorgan C E, Mitchell J W. Hourly thermal load prediction for the next 24 hours by Arima, Ewma L R and an artificial neural network. ASHRAE Transactions, 1995.
[28]郝有志,李德英.热负荷预测方法评析.建筑热能通风空调,2003.1
[29]王会斌.计算机应用技术.山东大学出版社,2009.6
[30]刘建军.计算机技术在供热管网中的应用.电脑与电信,2007.8
[31]范刚龙.供热管网设计计算模型优化研究.郑州大学学报,2005.9
[32]Karli Watson, Christian Nagel. Beginning Visual C# 2005,2006.5
[33]秦斌,曾斌.C#应用程序开发标准教程.上海科学普及出版社,2004.3
[34]黄聪明.C#面向对象程序设计.北京科学出版社,2004
[35]宋波,刘杰,杜庆东.UML面向对象技术与实践.北京科学出版社,2006
[36]田茂诚.热力系统.山东大学.2006.10
[37]杜楣年.泵与风机.山东科学技术出版社,1996.8
[38]杨世铭,陶文铨.传热学.高等教育出版社,1998.12
[39]石兆玉.供热系统运行调节与控制.清华大学出版社,1994.1
[40]陈开明.非线性规划.复旦大学出版社,1991.8
[41]席少霖.非线性最优化方法.高等教育出版社,1992.6
[42]粟塔山.最优化计算原理与算法程序设计.国防科技大学出版社,2001.1
[43]Mokhtar S.Bazaraa C.M.Shetty. Nonlinear Programming Theory and Algorithms. Mokhtar S.Bazaraa C.M.Shetty,1979.
[44]Bazaraa M.S., Sherali H.D., Shetty C.M. Nonlinear Programming:Theory and Algorithhms. New York:John Wiley\& Sons Inc..1993.
[45]David G. Luenberger. Linear and Nonlinear Programming. Springer,2008.
[46]周长发.C#数值计算算法编程.电子工业出版社,2007.1
[47]应玖茜,魏权龄. 非线性规划及其理论.中国人民大学出版社,1994.9
[48]李素文.非线性共轭梯度法的研究.南京理工大学硕士学位论文,2008.
[49]李政.非线性规划中的两种罚函数.上海大学硕士学位论文,2007.
[50]袁亚湘.非线性规划数值方法.上海科学技术出版社,1993.8
[51]黄聪明.C#面向对象程序设计.科学出版社,2004.1
[52]孙长玉,袁军.供热运行管理与技能技术.机械工业出版社,2008.
[53]晁阳.基于特征识别的指针式仪表自动识别研究.山东大学硕士学位论文,2008.5
[54]杨建刚.人工神经网络实用教程.浙江大学出版社.2001,1.
[55]袁曾任.人工神经网络及其应用.清华大学出版社.1999,10.
[2]冯旭.集中供热与节能.资源节约与环保,2007.2
[3]李天智,孙海波.城市集中供热系统控制策略.河北省科学院学报,2000.4
[4]Erik Dotzauer. Simple model for prediction of loads in district-heating systems. Applied Energy,2002.8
[5]康艳兵,张建国,张扬.我国热电联产集中供热的发展现状、问题与建议.中国能源,2008.10
[6]赵保安.可持续发展与集中供热行业的科技创新.学术论丛,2009.4
[7]D.K.Baker,S.A.Sherif. Heat transfer optimization of a district heating system using search methods. Int.J.Energy Res,1997.
[8]Jacob Kaminsky. District Heating and Cooling Technology Research and Development Overview of the U.S Department of Energy Program. Office of Building and Community, U.S.DOE
[9]韦新东,尹军,全贞花.日本集中供热(冷)系统的发展现状.吉林建筑工程学院学报,2001.3
[10]邹平华.借鉴俄罗斯经验积极发展我国集中供热事业.暖通与空调,2000.4
[11]张沈生,孙晓兵,傅卓林.国外供暖方式现状与发展趋势.工业技术经济,2006.7
[12]黄文,管昌生.城市集中供热研究现状及发展趋势.国外建材科技,2004.5
[13]任淇,崔铭俊.城市集中供热管网的设计.中国高新技术企业,2008.8
[14]朴东虎.供热系统设计运行及优化.民营科技,2008.11
[15]郝雪梅.城市集中供热管网的设计优化探讨.洁净煤技术,2007.3
[16]雷杰兰.集中供热管网的优化设计策略.科技创新导报,2008
[17]郑雪晶,由世俊,张欢,姜南.二级网燃气调峰集中供热系统优化设计.天津大学学报,2007.8
[18]薛宏文,张帆.供热运行中参数调节的优化.暖通空调,2001.2
[19]秦绪忠,江亿.多热源并网供热的水力优化调度研究.暖通空调,2001.1
[20]Lianzhong, L. Zaheeruddin, M. Dynamic modeling and simulation of a room with hot water baseboard heater. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2006.
[21]胡钦华,李奎山,苏廷弼.基于ANN的空气处理单元换热器的动态性能预测.计算机工程与应用,2008.3
[22]顿雷,李巴津.城市集中供热网参数预测研究.微计算机信息,2007.11
[23]郝有志；李德英；郝斌.基于神经网络的供热计量系统热负荷短期预测.暖通空调,2003.6
[24]王东亚,张琳,赵国材.神经网络遗传算法在供热负荷预测中应用.辽宁工程技术大学学报,2005.
[25]Ferrano F J, Wong K V. Prediction of thermal storage loads using a neural network. ASHRAE Transactions,1990
[26]Sidhu T S, Singh H, Sachdev M S. Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines. IEEE Transactions on Power Delivery,1995
[27]Kawashima M, Dorgan C E, Mitchell J W. Hourly thermal load prediction for the next 24 hours by Arima, Ewma L R and an artificial neural network. ASHRAE Transactions, 1995.
[28]郝有志,李德英.热负荷预测方法评析.建筑热能通风空调,2003.1
[29]王会斌.计算机应用技术.山东大学出版社,2009.6
[30]刘建军.计算机技术在供热管网中的应用.电脑与电信,2007.8
[31]范刚龙.供热管网设计计算模型优化研究.郑州大学学报,2005.9
[32]Karli Watson, Christian Nagel. Beginning Visual C# 2005,2006.5
[33]秦斌,曾斌.C#应用程序开发标准教程.上海科学普及出版社,2004.3
[34]黄聪明.C#面向对象程序设计.北京科学出版社,2004
[35]宋波,刘杰,杜庆东.UML面向对象技术与实践.北京科学出版社,2006
[36]田茂诚.热力系统.山东大学.2006.10
[37]杜楣年.泵与风机.山东科学技术出版社,1996.8
[38]杨世铭,陶文铨.传热学.高等教育出版社,1998.12
[39]石兆玉.供热系统运行调节与控制.清华大学出版社,1994.1
[40]陈开明.非线性规划.复旦大学出版社,1991.8
[41]席少霖.非线性最优化方法.高等教育出版社,1992.6
[42]粟塔山.最优化计算原理与算法程序设计.国防科技大学出版社,2001.1
[43]Mokhtar S.Bazaraa C.M.Shetty. Nonlinear Programming Theory and Algorithms. Mokhtar S.Bazaraa C.M.Shetty,1979.
[44]Bazaraa M.S., Sherali H.D., Shetty C.M. Nonlinear Programming:Theory and Algorithhms. New York:John Wiley\& Sons Inc..1993.
[45]David G. Luenberger. Linear and Nonlinear Programming. Springer,2008.
[46]周长发.C#数值计算算法编程.电子工业出版社,2007.1
[47]应玖茜,魏权龄. 非线性规划及其理论.中国人民大学出版社,1994.9
[48]李素文.非线性共轭梯度法的研究.南京理工大学硕士学位论文,2008.
[49]李政.非线性规划中的两种罚函数.上海大学硕士学位论文,2007.
[50]袁亚湘.非线性规划数值方法.上海科学技术出版社,1993.8
[51]黄聪明.C#面向对象程序设计.科学出版社,2004.1
[52]孙长玉,袁军.供热运行管理与技能技术.机械工业出版社,2008.
[53]晁阳.基于特征识别的指针式仪表自动识别研究.山东大学硕士学位论文,2008.5
[54]杨建刚.人工神经网络实用教程.浙江大学出版社.2001,1.
[55]袁曾任.人工神经网络及其应用.清华大学出版社.1999,10.