WO2019075871A1 - 一种基于双侧等效模型的热网稳态运行状态估计方法 - Google Patents

一种基于双侧等效模型的热网稳态运行状态估计方法 Download PDF

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WO2019075871A1
WO2019075871A1 PCT/CN2017/114463 CN2017114463W WO2019075871A1 WO 2019075871 A1 WO2019075871 A1 WO 2019075871A1 CN 2017114463 W CN2017114463 W CN 2017114463W WO 2019075871 A1 WO2019075871 A1 WO 2019075871A1
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branch
network
node
heat
temperature
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French (fr)
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孙宏斌
郭庆来
王彬
盛同天
张伯明
吴文传
张明晔
董今妮
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Tsinghua University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D19/00—Details
    • F24D19/10—Arrangement or mounting of control or safety devices
    • F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/76—Devices for measuring mass flow of a fluid or a fluent solid material
    • G01F1/86—Indirect mass flowmeters, e.g. measuring volume flow and density, temperature or pressure
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor

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  • the invention relates to a method for estimating a steady state operation state of a heating network based on a two-sided equivalent model, and belongs to the technical field of operation and control of an integrated energy system.
  • the heat network can be divided into two types: hot water heating pipe network and steam heating pipe network according to different heating media.
  • China's industrial heating is mostly medium and high pressure steam heating pipe network
  • civil heating is mostly hot water heating pipe network.
  • the heat network can be divided into an open network and a closed network.
  • the hot network only needs to study the water supply network, while for a closed network, it is necessary to study both the water supply network and the backwater network.
  • the water supply network and the backwater network have the same topological structure.
  • the flow of each pipe in the water supply network and the backwater network is approximately the same, only the hydraulic power of the water supply network.
  • the working conditions are analyzed, and on this basis, the thermal conditions of the water supply network and the backwater network are analyzed.
  • this method cannot deal with the asymmetry of the water supply network and the backwater network.
  • the purpose of state estimation is to monitor the operation of the entire network. If the symmetric processing is simply performed, the operating conditions of the backwater network cannot be monitored.
  • the object of the present invention is to propose a method for estimating the steady state operation state of a heating network based on a two-sided equivalent model, and establish a two-sided equivalent model of the heating network superior to the existing one-sided model, and based on the two-sided equivalent model
  • the state of the steady state operation of the heating network is estimated to effectively monitor the operation status of the heating network, and the measurement is completed under the non-quantity measurement configuration to identify the bad data.
  • the method for estimating the steady state of a heat network based on the two-sided equivalent model of the heat network proposed by the invention comprises the following steps:
  • the connecting branch forms a matrix representing the relationship between the node and the branch for the heat network composed of N nodes and B branches:
  • the node-branch correlation matrix A represents the topological relationship between nodes and branches in the network.
  • the matrix A consists of three elements: 0, 1, and 1.
  • the elements in A are defined as follows:
  • i is any node in the hot network, and j is any branch in the hot network;
  • a ij ⁇ 0 ⁇ , A t the elements defined by the following formula:
  • the branch pressure loss equation represents the pressure difference between the nodes at one end of the branch.
  • the matrix form of the branch pressure loss equation is as follows:
  • H is the column vector composed of the pressures of the nodes of the heat network in the above step (2-2)
  • a T is the transposition of the node-branch correlation matrix A in the above step (1-1)
  • H p is the branch pump
  • K is the friction coefficient of the branch in the heating network, and the value is 10 to 500 Pa / (kg / sec) 2
  • m is the flow of any branch in the heating network
  • the thermal power equation represents the temperature relationship at the head end of the connecting branch q, expressed as follows:
  • the superscript q represents the connecting branch
  • ⁇ q is the thermal power used to connect the branch
  • the thermal power at the thermal load is positive
  • the thermal power at the heat source is negative
  • C p is the specific heat capacity of the heating medium
  • the physical property parameter table of the fluid is obtained
  • m q is the connected branch flow, In order to connect the temperature at the end of the branch, To connect the end temperature of the branch;
  • the objective function of establishing a steady state operation state estimation of the heating network is as follows:
  • M is the column vector composed of the flow of each branch of the heating network, and the water supply branch and the return water branch are unified as the common branch, which is represented by the superscript p, and the connecting branch is equivalent to a special branch.
  • the superscript q indicates that M is expressed as:
  • M p represents a sub-vector composed of a water supply branch and a return water branch, that is, a common branch flow
  • M q represents a sub-vector composed of a connected branch flow
  • m out is the heat medium flowing out of the flow branch node, m in the heating medium to flow into the branch node, T n is the temperature of the heating medium after mixing node, T in different branches heating medium The temperature before mixing at the node;
  • the node temperature mixing constraint is expressed as the following matrix form:
  • a f and A t are the positive node-branch correlation matrix in the above step (1-2) and the negative node-branch correlation matrix in the above step (1-3), and diag( ⁇ ) represents the diagonal Array
  • T a is the ambient temperature
  • L is the length of the ordinary branch
  • ⁇ is the heat dissipation coefficient of the common branch in the heat network. Obtained from the corresponding data sheet, e is the natural logarithm, C p is the specific heat capacity of the heat medium, M p denotes a sub-vector of the water supply branch and the return water branch, that is, the flow of the ordinary branch;
  • J(x h ) is the objective function of the above step (3)
  • ⁇ is the Lagrangian multiplier
  • c(x h ) is the constraint condition of the steady state operation of the heating network established by the above step (4)
  • T is a matrix transposition
  • the current state estimation result is used as the t-time based on the two-side equivalent model for the steady state estimation of the heat network;
  • the convergence of the state estimation result is further determined according to the accuracy ⁇ of the state of the heat network state: if the state in the nearest two state estimation results is changed x
  • the difference between a and x a-1 is smaller than the state estimation precision ⁇ , that is, max
  • State estimation result if the difference between the state variable estimates x a and x a-1 in the last two state estimation results is greater than or equal to the state estimation precision ⁇ , that is, max
  • ⁇ ⁇ , then The state variable is updated, and the node pressure in the heating network and the temperature at the head end of the branch are updated according to the temperature value estimated by the current state, and a a+1 is simultaneously made, and the process returns to step 4 to continue the state estimation process.
  • the method of the invention establishes a two-sided equivalent model of the heat network which is superior to the existing one-side model, and considers the water supply and return network and the heat transfer process, and can well solve the problem of steady state estimation of the heat network.
  • the invention can accurately track the change of the state variable such as the system temperature, and has better convergence.
  • the state estimation of the steady state operation of the heating network can effectively monitor the operation status of the heating network, complete the measurement under the non-quantity measurement configuration, identify the bad data, and be the energy management system and the dispatch management system. Provide detailed data support.
  • Figure 1 is a schematic view showing the structure of a heat network according to the method of the present invention.
  • FIG. 2 is a schematic view showing the structure of the heat-network element involved in the method of the present invention after equivalent treatment.
  • the method for estimating the steady state of a heat network based on the two-sided equivalent model of the heat network proposed by the invention comprises the following steps:
  • the structure of the heat network involved in the method of the present invention is as shown in FIG. 1 , wherein the solid line represents the water supply branch in the heat network, the dotted line represents the return water branch in the heat network, and the heat is simultaneously considered in the two-side equivalent model of the heat network.
  • the network water supply branch and the return water branch, and the heat source and the heat load are equivalent to the connection branch shown by the dotted line shown in Fig. 2.
  • the following representation nodes are formed.
  • the node-branch correlation matrix A represents the topological relationship between nodes and branches in the network.
  • the matrix A consists of three elements: 0, 1, and 1.
  • the elements in A are defined as follows:
  • i is any node in the hot network
  • j is any branch in the hot network; as shown in FIG. 2, where n is a node and b is a branch.
  • a ij ⁇ 0 ⁇ , A t the elements defined by the following formula:
  • the branch pressure loss equation represents the pressure difference between the nodes at one end of the branch.
  • the matrix form of the branch pressure loss equation is as follows:
  • H is the column vector composed of the pressures of the nodes of the heat network in the above step (2-2)
  • a T is the transposition of the node-branch correlation matrix A in the above step (1-1)
  • H p is the branch pump
  • the column vector consisting of the head, a, b, c are pump parameters, which can be found on the product nameplate of the pump, m p is the flow of the branch where the pump is located, ⁇ H is the column vector composed of the pressure loss of each branch in the heating network, and the branch pressure loss ⁇ H
  • the frictional resistance of the branch when the heating medium flows through the branch is calculated by the following formula:
  • K is the friction coefficient of the branch in the heating network, and the value is 10 to 500 Pa / (kg / sec) 2
  • m is the flow of any branch in the heating network
  • the thermal power equation represents the temperature relationship at the head end of the connecting branch q, expressed as follows:
  • the superscript q represents the connecting branch
  • ⁇ q is the thermal power used to connect the branch
  • the thermal power at the thermal load is positive
  • the thermal power at the heat source is negative
  • C p is the specific heat capacity of the heating medium
  • the physical property parameter table of the fluid is obtained
  • m q is the connected branch flow, In order to connect the temperature at the end of the branch, To connect the end temperature of the branch;
  • the objective function of establishing a steady state operation state estimation of the heating network is as follows:
  • M is a column vector formed by the flow of each branch of the heat network.
  • the water supply branch and the return water branch are uniformly equivalent to an ordinary branch, which is represented by a superscript p, and the connecting branch is equivalent to a special branch. Road, indicated by the superscript q, then M is expressed as:
  • M p represents a sub-vector composed of a water supply branch and a return water branch, that is, a common branch flow
  • M q represents a sub-vector composed of a connected branch flow
  • m out is the heat medium flowing out of the flow branch node
  • T n is the temperature of the medium heating the heating medium in different branches after mixing node, i.e. node Temperature
  • T in is the temperature of the heating medium of different branches before mixing at the node
  • the node temperature mixing constraint is expressed as the following matrix form:
  • a f and A t are the positive node-branch correlation matrix in the above step (1-2) and the negative node-branch correlation matrix in the above step (1-3), and diag( ⁇ ) represents the diagonal Array
  • T a is the ambient temperature
  • L is the length of the ordinary branch
  • ⁇ is the heat dissipation coefficient of the common branch in the heat network, and the heat dissipation coefficient of different materials is different, the value is between several and several hundred, which can be obtained from the corresponding data manual.
  • e is the natural logarithm
  • C p is the specific heat capacity of the heating medium
  • M p is the sub-vector of the water supply branch and the return water branch, that is, the flow of the ordinary branch;
  • J(x h ) is the objective function of the above step (3)
  • ⁇ is the Lagrangian multiplier
  • c(x h ) is the constraint condition of the steady state operation of the heating network established by the above step (4)
  • T is a matrix transposition
  • the current state estimation result is used as the t-time based on the two-side equivalent model for the steady state estimation of the heat network;
  • the convergence of the state estimation result is further determined according to the accuracy ⁇ of the state of the heat network state: if the state in the nearest two state estimation results is changed x
  • the difference between a and x a-1 is smaller than the state estimation precision ⁇ , that is, max
  • State estimation result if the difference between the state variable estimates x a and x a-1 in the last two state estimation results is greater than or equal to the state estimation precision ⁇ , that is, max
  • ⁇ ⁇ , then The state variable is updated, and the node pressure in the heating network and the temperature at the head end of the branch are updated according to the temperature value estimated by the current state, and a a+1 is simultaneously made, and the process returns to step 4 to continue the state estimation process.

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Abstract

一种基于双侧等效模型的热网稳态运行状态估计方法，属于综合能源系统的运行和控制技术领域。该方法包括：建立热网双侧等效模型、基于热网双侧等效模型对稳态运行的热网进行状态估计、建立状态估计的目标函数、建立热网稳态运行的约束条件、利用拉格朗日乘数法和牛顿-拉夫逊法进行状态估计计算并对状态估计结果进行收敛性判断。同时考虑了供回水网络，并且考虑了传热过程，可以很好地解决热网稳态状态估计问题，在量测配置不全的情下补全量测，存在坏数据时辨识坏数据。

Description

一种基于双侧等效模型的热网稳态运行状态估计方法
相关申请的交叉引用
本申请要求清华大学于2017年10月16日提交的、发明名称为“一种基于双侧等效模型的热网稳态运行状态估计方法”的、中国专利申请号“CN201710957867.X”的优先权。
技术领域
本发明涉及一种基于双侧等效模型的热网稳态运行状态估计方法，属于综合能源系统的运行和控制技术领域。
背景技术
热网根据供热介质不同可以分为热水供热管网和蒸汽供热管网两类。目前，我国的工业供热多为中高压蒸汽供热管网，民用供热多为热水供热管网。在本发明中我们以热水供热管网进行分析，根据是否存在回水网络，可以将热网分为开口网络和闭口网络两类。
对于开口网络而言，热网只需研究供水网络，而对于闭口网络而言，需要同时研究供水网络和回水网络。通常而言，对于闭口网络，其供水网络和回水网络拓扑结构一致，目前的研究中，将其拆分后，认为供水网络和回水网络中各管道流量近似一致，仅对供水网络的水力工况进行分析，并在该基础上分析供水网络和回水网络的热力工况。然而，这种方法不能处理供水网络和回水网络不对称的情况，当供水网络或回水网络中某条线路出现故障或正在检修时，必将导致供水网络和回水网络的不对称。另一方面，状态估计的目的是监测整个网络的运行情况，如果简单地进行对称处理，就不能对回水网络的运行工况进行监测。
发明内容
本发明的目的是提出一种基于双侧等效模型的热网稳态运行状态估计方法，建立优于已有的单侧模型的热网双侧等效模型，并基于双侧等效模型的热网稳态运行的状态估计，以有效的监测热网的运行状况，在非全量测配置下补全量测，辨识坏数据。
本发明提出的基于热网双侧等效模型的热网稳态状态估计方法，包括以下步骤：
(1)建立热网双侧等效模型的节点-支路关联矩阵，包括：
在热网双侧等效模型中同时考虑热网供水支路和回水支路，并将热源和热负荷等效为 连接支路，对于由N个节点、B条支路构成的热网，形成以下表示节点和支路关系的矩阵：
(1-1)一个节点-支路关联矩阵A，
节点-支路关联矩阵A表示网络中节点和支路的拓扑关系，矩阵A由0、1、-1三个元素组成，A中元素定义如下：
Figure PCTCN2017114463-appb-000001
其中，i为热网中的任意一个节点，j为热网中任意一条支路；
(1-2)一个正节点-支路关联矩阵Af：
正节点-支路关联矩阵Af表示各支路的首端节点与支路的关系，Af＝{A│Aij>0}，Af中元素定义如下：
Figure PCTCN2017114463-appb-000002
(1-3)一个负节点-支路关联矩阵At：
负节点-支路关联矩阵At表示各支路的末端节点与支路的关系，At＝{-A|Aij<0}，At中元素定义如下式：
Figure PCTCN2017114463-appb-000003
(2)基于热网双侧等效模型对稳态运行的热网进行状态估计：
(2-1)设定热网状态估计的收敛精度δ和最大循环次数d，初始化时设循环次数a为0；
(2-2)从热网的数据采集与监视控制系统中获取实时测量的t时刻热网的运行数据，包括热网中各节点压力H，任意两个节点间支路的流量m，两个节点间支路的首端温度Tf和末端温度Tt，热源和热负荷等效成的连接支路，热功率φq，其中上标q表示连接支路，上述运行数据构成一个测量值列向量zh；
(2-3)将热网所有待估计的状态量构成一个列向量xh，其中包括热网中各节点压力
Figure PCTCN2017114463-appb-000004
以及任两个节点间支路的首端温度
Figure PCTCN2017114463-appb-000005
和末端温度
Figure PCTCN2017114463-appb-000006
(2-4)建立一个描述热网状态量与测量值之间关系的量测函数f(x)，f(x)＝f(xh)，f(xh)为热力系统潮流方程组，热力系统潮流方程组包括以下方程：
(2-4-1)一个支路压力损失方程：
支路压力损失方程表示一条支路两端节点的压力差，支路压力损失方程的矩阵形式如下：
ATH＝ΔH-Hp
其中H为上述步骤(2-2)中的热网各节点压力组成的列向量，AT为上述步骤(1-1)中节点-支路关联矩阵A的转置，Hp为支路上泵的扬程组成的列向量，
Figure PCTCN2017114463-appb-000007
a、b、c为泵参数，从泵的产品铭牌上获取，mp为泵所在支路的流量，ΔH为热网中每条支路压力损失组成的列向量，支路压力损失ΔH通过下式计算得到：
ΔH＝K·m·|m|
其中，K为热网中支路的摩阻系数，取值为10～500帕/(千克/秒)2，m为热网中任一支路流量；
(2-4-2)一个连接支路热功率方程：
热功率方程表示连接支路q的首末端温度关系，表示为如下形式：
Figure PCTCN2017114463-appb-000008
其中，上标q表示连接支路，φq为连接支路的用热功率，热负荷处的用热功率为正，热源处的用热功率为负，Cp为供热介质的比热容，由流体的物性参数表获取，mq为连接支路流量，
Figure PCTCN2017114463-appb-000009
为连接支路首端温度，
Figure PCTCN2017114463-appb-000010
为连接支路末端温度；
(3)根据上述步骤(2-2)的测量值，建立一个热网稳态运行状态估计的目标函数如下：
minJ(xh)＝min{[zh-f(xh)]TW[zh-f(xh)]}
其中W为测量值的协方差矩阵，上标T表示矩阵转置，J(xh)表示目标函数表达式；
(4)建立热网稳态运行的约束条件c(xh)，包括：
(4-1)对所有节点建立流量连续性约束，流量连续性约束表示为如下矩阵形式：
AM＝0
其中，M为热网每条支路流量构成的列向量，将供水支路和回水支路统一等效为普通支路，用上标p表示，连接支路等效为特殊支路，用上标q表示，则M表示为：
Figure PCTCN2017114463-appb-000011
其中，Mp表示供水支路和回水支路即普通支路流量组成的子向量，Mq表示连接支路流量组成的子向量；
(4-2)对热网中的所有节点建立温度混合约束：
(∑mout)Tn＝∑(minTin)
其中，mout为供热介质流出节点的支路流量，min为供热介质流入节点的支路流量，Tn为节点处供热介质混合后的温度，Tin为不同支路供热介质在节点处混合前的温度；
用不同支路末端温度Tt代替支路供热介质在节点处混合前的温度Tin，则节点温度混合约束表示为如下矩阵形式：
diag(AfM)Tn＝Atdiag(M)Tt
其中，Af、At分别为上述步骤(1-2)中的正节点-支路关联矩阵和上述步骤(1-3)中的负节点-支路关联矩阵，diag(·)表示对角阵；
(4-3)对热网中的所有普通支路建立支路温降约束，支路温降约束的矩阵形式如下：
Figure PCTCN2017114463-appb-000012
其中，Ta为环境温度，
Figure PCTCN2017114463-appb-000013
为普通支路末端温度，
Figure PCTCN2017114463-appb-000014
为普通支路首端温度，L为普通支路长度，λ为热网中普通支路的散热系数，从相应数据手册中获取，e是自然对数，Cp为供热介质的比热容，Mp表示供水支路和回水支路即普通支路流量组成的子向量，；
(5)利用拉格朗日乘数法，将上述步骤(3)的目标函数和上述步骤(4)的约束条件构成一个拉格朗日函数如下：
L(xh,ω)＝J(xh)+ωTc(xh)
其中，J(xh)为上述步骤(3)的目标函数，ω为拉格朗日乘子，c(xh)为上述步骤(4)建立的热网稳态运行的约束条件，上标T是矩阵转置；
利用最优化理论中的牛顿-拉夫逊法，求解上述热网稳态运行时的拉格朗日函数，得到热网稳态运行时的状态估计结果；
(6)对上述步骤5的状态估计结果进行收敛性判断：
若循环次数a达到预设循环次数d，即a≥d，则将本次状态估计结果作为t时刻基于双侧等效模型的热网稳态状态估计结果；
若循环次数a未达到预设循环次数d，即a<d，则进一步根据热网状态估计的精度δ对状态估计结果收敛性进行判断：若最近相邻两次状态估计结果中的状态变xa和xa-1的差值小于状态估计精度δ，即max|xa-xa-1|<δ，则将本次状态估计结果作为t时刻基于双侧等效模型的热网稳态状态估计结果，若最近两次状态估计结果中的状态变量估计值xa和xa-1的差值大于或等于状态估计精度δ，即max|xa-xa-1|≥δ，则更新状态变量，并根据本次状态估计所得的温度值更新热网中节点压力和支路首末端温度，同时使 a＝a+1，并返回步骤4，继续本次状态估计过程。
本发明提出的基于双侧等效模型的热网稳态运行状态估计方法，其优点是：
本发明方法建立了优于已有的单侧模型的热网双侧等效模型，同时考虑了供回水网络和传热过程，可以很好地解决热网稳态状态估计问题。当热力系统受调度控制而改变运行方式时，本发明能够较为精确地追踪系统温度等状态变量的变化，且有较好的收敛性。基于双侧等效模型进行热网稳态运行的状态估计，可以有效的监测热网的运行状况，在非全量测配置下补全量测，辨识坏数据，为能量管理系统和调度管理系统提供详实的数据支撑。
附图说明
图1是本发明方法涉及的热网的结构示意图。
图2是本发明方法涉及的热网元件进行等效处理后的结构示意图。
具体实施方式
本发明提出的基于热网双侧等效模型的热网稳态状态估计方法，包括以下步骤：
(1)建立热网双侧等效模型的节点-支路关联矩阵，包括：
本发明方法涉及的热网的结构如图1所示，其中实线表示热网中的供水支路，点线表示热网中回水支路，在热网双侧等效模型中同时考虑热网供水支路和回水支路，并将热源和热负荷等效为图2所示虚线所示的连接支路，对于由N个节点、B条支路构成的热网，形成以下表示节点和支路关系的矩阵：
(1-1)一个节点-支路关联矩阵A，
节点-支路关联矩阵A表示网络中节点和支路的拓扑关系，矩阵A由0、1、-1三个元素组成，A中元素定义如下：
Figure PCTCN2017114463-appb-000015
其中，i为热网中的任意一个节点，j为热网中任意一条支路；如图2中所示，其中n是节点，b是支路。
(1-2)一个正节点-支路关联矩阵Af：
正节点-支路关联矩阵Af表示各支路的首端节点与支路的关系，Af＝{A│Aij>0}，Af中元素定义如下：
Figure PCTCN2017114463-appb-000016
(1-3)一个负节点-支路关联矩阵At：
负节点-支路关联矩阵At表示各支路的末端节点与支路的关系，At＝{-A|Aij<0}，At中元素定义如下式：
Figure PCTCN2017114463-appb-000017
(2)基于热网双侧等效模型对稳态运行的热网进行状态估计：
(2-1)设定热网状态估计的收敛精度δ和最大循环次数d，初始化时设循环次数a为0；
(2-2)从热网的数据采集与监视控制系统中获取实时测量的t时刻热网的运行数据，包括热网中各节点压力H，任意两个节点间支路的流量m，两个节点间支路的首端温度Tf和末端温度Tt，热源和热负荷等效成的连接支路(如图2中的虚线所示)热功率φq，其中上标q表示连接支路，上述运行数据构成一个测量值列向量zh；
(2-3)将热网所有待估计的状态量构成一个列向量xh，其中包括热网中各节点压力
Figure PCTCN2017114463-appb-000018
以及任两个节点间支路的首端温度
Figure PCTCN2017114463-appb-000019
和末端温度
Figure PCTCN2017114463-appb-000020
(2-4)建立一个描述热网状态量与测量值之间关系的量测函数f(x)，f(x)＝f(xh)，f(xh)为热力系统潮流方程组，热力系统潮流方程组包括以下方程：
(2-4-1)一个支路压力损失方程：
支路压力损失方程表示一条支路两端节点的压力差，支路压力损失方程的矩阵形式如下：
ATH＝ΔH-Hp
其中H为上述步骤(2-2)中的热网各节点压力组成的列向量，AT为上述步骤(1-1)中节点-支路关联矩阵A的转置，Hp为支路上泵的扬程组成的列向量，
Figure PCTCN2017114463-appb-000021
a、b、c为泵参数，可从泵的产品铭牌上查得，mp为泵所在支路的流量，ΔH为热网中每条支路压力损失组成的列向量，支路压力损失ΔH由供热介质流过支路时支路的摩擦阻力导致的，通过下式计算得到：
ΔH＝K·m·|m|
其中，K为热网中支路的摩阻系数，取值为10～500帕/(千克/秒)2，m为热网中任一支路流量；
(2-4-2)一个连接支路热功率方程：
热功率方程表示连接支路q的首末端温度关系，表示为如下形式：
Figure PCTCN2017114463-appb-000022
其中，上标q表示连接支路，φq为连接支路的用热功率，热负荷处的用热功率为正，热源处的用热功率为负，Cp为供热介质的比热容，由流体的物性参数表获取，mq为连接支路流量，
Figure PCTCN2017114463-appb-000023
为连接支路首端温度，
Figure PCTCN2017114463-appb-000024
为连接支路末端温度；
(3)根据上述步骤(2-2)的测量值，建立一个热网稳态运行状态估计的目标函数如下：
minJ(xh)＝min{[zh-f(xh)]TW[zh-f(xh)]}
其中W为测量值的协方差矩阵，上标T表示矩阵转置，J(xh)表示目标函数表达式；
(4)建立热网稳态运行的约束条件c(xh)，包括：
(4-1)对所有节点建立流量连续性约束，流量连续性约束表示为如下矩阵形式：
AM＝0
其中，M为热网每条支路流量构成的列向量，本发明中将供水支路和回水支路统一等效为普通支路，用上标p表示，连接支路等效为特殊支路，用上标q表示，则M表示为：
Figure PCTCN2017114463-appb-000025
其中，Mp表示供水支路和回水支路即普通支路流量组成的子向量，Mq表示连接支路流量组成的子向量；
(4-2)对热网中的所有节点建立温度混合约束：
(∑mout)Tn＝∑(minTin)
其中，mout为供热介质流出节点的支路流量，min为供热介质流入节点的支路流量，Tn为不同支路供热介质在节点处混合后的温度即节点处供热介质的温度，Tin为不同支路供热介质在节点处混合前的温度；
用不同支路末端温度Tt代替支路供热介质在节点处混合前的温度Tin，则节点温度混合约束表示为如下矩阵形式：
diag(AfM)Tn＝Atdiag(M)Tt
其中，Af、At分别为上述步骤(1-2)中的正节点-支路关联矩阵和上述步骤(1-3)中的负节点-支路关联矩阵，diag(·)表示对角阵；
(4-3)对热网中的所有普通支路建立支路温降约束，支路温降约束的矩阵形式如下：
Figure PCTCN2017114463-appb-000026
其中，Ta为环境温度，
Figure PCTCN2017114463-appb-000027
为普通支路末端温度，
Figure PCTCN2017114463-appb-000028
为普通支路首端温度，L为普通支路长度，λ为热网中普通支路的散热系数，不同材料散热系数不同，数值在几到几百之间，可从相应数据手册中获取，e是自然对数，Cp为供热介质的比热容，Mp表示供水支路和回水支路即普通支路流量组成的子向量，；
(5)利用拉格朗日乘数法，将上述步骤(3)的目标函数和上述步骤(4)的约束条件构成一个拉格朗日函数如下：
L(xh,ω)＝J(xh)+ωTc(xh)
其中，J(xh)为上述步骤(3)的目标函数，ω为拉格朗日乘子，c(xh)为上述步骤(4)建立的热网稳态运行的约束条件，上标T是矩阵转置；
利用最优化理论中的牛顿-拉夫逊法，求解上述热网稳态运行时的拉格朗日函数，得到热网稳态运行时的状态估计结果；
(6)对上述步骤5的状态估计结果进行收敛性判断：
若循环次数a达到预设循环次数d，即a≥d，则将本次状态估计结果作为t时刻基于双侧等效模型的热网稳态状态估计结果；
若循环次数a未达到预设循环次数d，即a<d，则进一步根据热网状态估计的精度δ对状态估计结果收敛性进行判断：若最近相邻两次状态估计结果中的状态变xa和xa-1的差值小于状态估计精度δ，即max|xa-xa-1|<δ，则将本次状态估计结果作为t时刻基于双侧等效模型的热网稳态状态估计结果，若最近两次状态估计结果中的状态变量估计值xa和xa-1的差值大于或等于状态估计精度δ，即max|xa-xa-1|≥δ，则更新状态变量，并根据本次状态估计所得的温度值更新热网中节点压力和支路首末端温度，同时使a＝a+1，并返回步骤4，继续本次状态估计过程。

Claims (1)

  1. 一种基于热网双侧等效模型的热网稳态状态估计方法，其特征在于该方法包括以下步骤：
    (1)建立热网双侧等效模型的节点-支路关联矩阵，包括：
    在热网双侧等效模型中同时考虑热网供水支路和回水支路，并将热源和热负荷等效为连接支路，对于由N个节点、B条支路构成的热网，形成以下表示节点和支路关系的矩阵：
    (1-1)一个节点-支路关联矩阵A，
    节点-支路关联矩阵A表示网络中节点和支路的拓扑关系，矩阵A由0、1、-1三个元素组成，A中元素定义如下：
    Figure PCTCN2017114463-appb-100001
    其中，i为热网中的任意一个节点，j为热网中任意一条支路；
    (1-2)一个正节点-支路关联矩阵Af：
    正节点-支路关联矩阵Af表示各支路的首端节点与支路的关系，Af＝{A│Aij>0}，Af中元素定义如下：
    Figure PCTCN2017114463-appb-100002
    (1-3)一个负节点-支路关联矩阵At：
    负节点-支路关联矩阵At表示各支路的末端节点与支路的关系，At＝{-A|Aij<0}，At中元素定义如下式：
    Figure PCTCN2017114463-appb-100003
    (2)基于热网双侧等效模型对稳态运行的热网进行状态估计：
    (2-1)设定热网状态估计的收敛精度δ和最大循环次数d，初始化时设循环次数a为0；
    (2-2)从热网的数据采集与监视控制系统中获取实时测量的t时刻热网的运行数据，包括热网中各节点压力H，任意两个节点间支路的流量m，两个节点间支路的首端温度Tf和末端温度Tt，热源和热负荷等效成的连接支路，热功率φq，其中上标q表示连接支路，上 述运行数据构成一个测量值列向量zh；
    (2-3)将热网所有待估计的状态量构成一个列向量xh，其中包括热网中各节点压力
    Figure PCTCN2017114463-appb-100004
    以及任两个节点间支路的首端温度
    Figure PCTCN2017114463-appb-100005
    和末端温度
    Figure PCTCN2017114463-appb-100006
    (2-4)建立一个描述热网状态量与测量值之间关系的量测函数f(x)，f(x)＝f(xh)，f(xh)为热力系统潮流方程组，热力系统潮流方程组包括以下方程：
    (2-4-1)一个支路压力损失方程：
    支路压力损失方程表示一条支路两端节点的压力差，支路压力损失方程的矩阵形式如下：
    ATH＝ΔH-Hp
    其中H为上述步骤(2-2)中的热网各节点压力组成的列向量，AT为上述步骤(1-1)中节点-支路关联矩阵A的转置，Hp为支路上泵的扬程组成的列向量，
    Figure PCTCN2017114463-appb-100007
    a、b、c为泵参数，从泵的产品铭牌上获取，mp为泵所在支路的流量，ΔH为热网中每条支路压力损失组成的列向量，支路压力损失ΔH通过下式计算得到：
    ΔH＝K·m·|m|
    其中，K为热网中支路的摩阻系数，取值为10～500帕/(千克/秒)2，m为热网中任一支路流量；
    (2-4-2)一个连接支路热功率方程：
    热功率方程表示连接支路q的首末端温度关系，表示为如下形式：
    Figure PCTCN2017114463-appb-100008
    其中，上标q表示连接支路，φq为连接支路的用热功率，热负荷处的用热功率为正，热源处的用热功率为负，Cp为供热介质的比热容，由流体的物性参数表获取，mq为连接支路流量，
    Figure PCTCN2017114463-appb-100009
    为连接支路首端温度，
    Figure PCTCN2017114463-appb-100010
    为连接支路末端温度；
    (3)根据上述步骤(2-2)的测量值，建立一个热网稳态运行状态估计的目标函数如下：
    minJ(xh)＝min{[zh-f(xh)]TW[zh-f(xh)]}
    其中W为测量值的协方差矩阵，上标T表示矩阵转置，J(xh)表示目标函数表达式；
    (4)建立热网稳态运行的约束条件c(xh)，包括：
    (4-1)对所有节点建立流量连续性约束，流量连续性约束表示为如下矩阵形式：
    AM＝0
    其中，M为热网每条支路流量构成的列向量，将供水支路和回水支路统一等效为普通 支路，用上标p表示，连接支路等效为特殊支路，用上标q表示，则M表示为：
    Figure PCTCN2017114463-appb-100011
    其中，Mp表示供水支路和回水支路即普通支路流量组成的子向量，Mq表示连接支路流量组成的子向量；
    (4-2)对热网中的所有节点建立温度混合约束：
    Figure PCTCN2017114463-appb-100012
    其中，mout为供热介质流出节点的支路流量，min为供热介质流入节点的支路流量，Tn为节点处供热介质混合后的温度，Tin为不同支路供热介质在节点处混合前的温度；
    用不同支路末端温度Tt代替支路供热介质在节点处混合前的温度Tin，则节点温度混合约束表示为如下矩阵形式：
    diag(AfM)Tn＝Atdiag(M)Tt
    其中，Af、At分别为上述步骤(1-2)中的正节点-支路关联矩阵和上述步骤(1-3)中的负节点-支路关联矩阵，diag(·)表示对角阵；
    (4-3)对热网中的所有普通支路建立支路温降约束，支路温降约束的矩阵形式如下：
    Figure PCTCN2017114463-appb-100013
    其中，Ta为环境温度，
    Figure PCTCN2017114463-appb-100014
    为普通支路末端温度，
    Figure PCTCN2017114463-appb-100015
    为普通支路首端温度，L为普通支路长度，λ为热网中普通支路的散热系数，从相应数据手册中获取，e是自然对数，Cp为供热介质的比热容，Mp表示供水支路和回水支路即普通支路流量组成的子向量，；
    (5)利用拉格朗日乘数法，将上述步骤(3)的目标函数和上述步骤(4)的约束条件构成一个拉格朗日函数如下：
    L(xh,ω)＝J(xh)+ωTc(xh)
    其中，J(xh)为上述步骤(3)的目标函数，ω为拉格朗日乘子，c(xh)为上述步骤(4)建立的热网稳态运行的约束条件，上标T是矩阵转置；
    利用最优化理论中的牛顿-拉夫逊法，求解上述热网稳态运行时的拉格朗日函数，得到热网稳态运行时的状态估计结果；
    (6)对上述步骤5的状态估计结果进行收敛性判断：
    若循环次数a达到预设循环次数d，即a≥d，则将本次状态估计结果作为t时刻基于双侧等效模型的热网稳态状态估计结果；
    若循环次数a未达到预设循环次数d，即a<d，则进一步根据热网状态估计的精度δ对状态估计结果收敛性进行判断：若最近相邻两次状态估计结果中的状态变xa和xa-1的差值小于状态估计精度δ，即max|xa-xa-1|<δ，则将本次状态估计结果作为t时刻基于双侧等效模型的热网稳态状态估计结果，若最近两次状态估计结果中的状态变量估计值xa和xa-1的差值大于或等于状态估计精度δ，即max|xa-xa-1|≥δ，则更新状态变量，并根据本次状态估计所得的温度值更新热网中节点压力和支路首末端温度，同时使a＝a+1，并返回步骤4，继续本次状态估计过程。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115062521A (zh) * 2022-07-21 2022-09-16 东南大学 一种质调节热水供热网络快速动态仿真方法

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108920866B (zh) * 2018-07-20 2019-07-26 清华大学 基于滚动时域估计理论的热网动态调节运行参数估计方法
CN109063292B (zh) * 2018-07-20 2022-12-23 清华大学 一种考虑散热系数区间的热网区间潮流计算方法
CN109255466A (zh) * 2018-07-20 2019-01-22 清华大学 一种基于多工况量测的热网稳态运行参数估计方法
CN108876066A (zh) * 2018-09-04 2018-11-23 常州英集动力科技有限公司 热网解列运行方案实时优化方法及其系统
CN109636148B (zh) * 2018-11-29 2022-09-13 华南理工大学 基于能量网络方程的多能流系统的工作状态评估方法
CN110688744A (zh) * 2019-09-16 2020-01-14 华南理工大学 一种应用于热电耦合网络的异步分布式状态估计方法
CN111222213B (zh) * 2020-01-15 2021-08-03 许继集团有限公司 一种热力网络动态仿真方法及装置
CN111310343B (zh) * 2020-02-22 2021-10-15 清华大学 一种用于综合能源系统调度的供热网络热路建模方法
CN111414721B (zh) * 2020-02-22 2021-10-15 清华大学 一种用于综合能源系统调度的供热网络水路建模方法
CN111414675B (zh) * 2020-02-27 2025-09-16 中国电力科学研究院有限公司 一种电热综合能源系统的双层抗差状态估计方法和系统
CN111625913B (zh) * 2020-05-25 2021-10-15 清华大学 考虑天然气管道动态特性的天然气网动态状态估计方法
CN112036003B (zh) * 2020-07-06 2024-01-12 东南大学 一种考虑不完全量测的质调节热力系统静态状态估计方法
CN112417698B (zh) * 2020-11-25 2024-04-19 东南大学 一种基于质调节的动态两端口热力系统模型

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106056478A (zh) * 2016-06-12 2016-10-26 清华大学 一种电‑热耦合系统中热网的区间潮流计算方法
CN106056251A (zh) * 2016-06-12 2016-10-26 清华大学 一种电‑热耦合多能流系统的优化调度方法
CN106067677A (zh) * 2016-05-27 2016-11-02 清华大学 一种电‑热耦合多能流系统静态安全分析方法
CN106447113A (zh) * 2016-10-08 2017-02-22 东南大学 一种基于运行优化模型的多区域综合能源系统运行方法
CN106682369A (zh) * 2017-02-27 2017-05-17 常州英集动力科技有限公司 供热管网水力仿真模型辨识修正方法及系统、操作方法
CN106998079A (zh) * 2017-04-28 2017-08-01 东南大学 一种热电联合优化调度模型的建模方法

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6095426A (en) * 1997-11-07 2000-08-01 Siemens Building Technologies Room temperature control apparatus having feedforward and feedback control and method
JP4743944B2 (ja) * 2000-08-25 2011-08-10 鎮男 角田 シミュレーションモデル作成方法及びそのシステムと記憶媒体
US8346712B2 (en) * 2009-11-24 2013-01-01 King Fahd University Of Petroleum And Minerals Method for identifying hammerstein models
CN102592024B (zh) * 2012-01-06 2013-04-03 北京航空航天大学 一种确定径向热传导稳态温度极大值的热网络建模方法
US9740214B2 (en) * 2012-07-23 2017-08-22 General Electric Technology Gmbh Nonlinear model predictive control for chemical looping process
CN105071388B (zh) * 2015-08-14 2017-06-13 贵州电网公司信息通信分公司 一种基于极大似然估计的配电网状态估计方法
CN106022624B (zh) * 2016-05-27 2019-07-26 清华大学 一种电-热耦合多能流网络状态估计方法
CN106253350B (zh) * 2016-08-11 2019-03-05 清华大学 基于供热管网储热效益的热-电联合机组组合方法
CN106339772B (zh) * 2016-08-11 2019-06-18 清华大学 基于供热管网储热效益的热-电联合优化调度方法
CN106339794A (zh) * 2016-08-16 2017-01-18 清华大学 一种电‑热耦合多能流网络节点能价计算方法
CN106356840B (zh) * 2016-09-08 2018-12-28 国网浙江省电力公司杭州供电公司 基于同步相量量测的地区电力系统状态估计方法及系统
CN106329535B (zh) * 2016-09-09 2018-11-09 广东电网有限责任公司电力调度控制中心 电网电压稳定性的评估控制方法及评估控制装置
CN107046285A (zh) * 2017-04-12 2017-08-15 国家电网公司 一种基于混合量测的配电网状态评估方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106067677A (zh) * 2016-05-27 2016-11-02 清华大学 一种电‑热耦合多能流系统静态安全分析方法
CN106056478A (zh) * 2016-06-12 2016-10-26 清华大学 一种电‑热耦合系统中热网的区间潮流计算方法
CN106056251A (zh) * 2016-06-12 2016-10-26 清华大学 一种电‑热耦合多能流系统的优化调度方法
CN106447113A (zh) * 2016-10-08 2017-02-22 东南大学 一种基于运行优化模型的多区域综合能源系统运行方法
CN106682369A (zh) * 2017-02-27 2017-05-17 常州英集动力科技有限公司 供热管网水力仿真模型辨识修正方法及系统、操作方法
CN106998079A (zh) * 2017-04-28 2017-08-01 东南大学 一种热电联合优化调度模型的建模方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115062521A (zh) * 2022-07-21 2022-09-16 东南大学 一种质调节热水供热网络快速动态仿真方法

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