CN112084631A - Heat supply pipe network steam back supply scheduling method and system based on simulation model - Google Patents
Heat supply pipe network steam back supply scheduling method and system based on simulation model Download PDFInfo
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Abstract
The invention discloses a heat supply pipe network steam back supply scheduling method based on a simulation model, which comprises the following steps: s1, constructing a heat supply pipe network simulation mechanism model for the returning users, and calibrating the simulation model according to historical operation data; step S2, establishing a feedback steam access decision evaluation model; and step S3, determining a scheduling scheme of the feedback steam through a real-time online simulation result of the heat supply pipe network simulation mechanism model, and realizing the real-time optimization of the whole network. The invention provides a simulation model-based heat supply pipe network steam back supply scheduling method and system, and aims to solve the problem of heat supply network safety caused by unreasonable back supply user steam access strategy.
Description
Technical Field
The invention relates to a simulation model-based heat supply pipe network steam back supply scheduling method and system, and belongs to the field of intelligent regulation and control of industrial steam pipe networks.
Background
At present, with the development of energy internet in the field of heat supply, the traditional heat supply industry can interconnect heat supply network energy nodes represented by heat sources and heat users by means of advanced control technology, information technology and intelligent management technology, so that the purposes of effectively integrating upstream and downstream parties of an industrial chain and forming supply and demand interactive transactions are achieved, the energy peer-to-peer exchange of bidirectional flow is realized, and the roles of producers and consumers are effectively converted.
In an industrial steam heating network, a special type of heat users exist, and the heat users are used as consumers to use steam under the conditions of capacity reduction, equipment failure and the like; when the capacity is sufficient, the steam can be byproduct through the produced waste heat and can be used as a producer to convey part of the steam to the heat network. This type of heat consumer is called a resupply consumer, and the steam delivered to the heat grid is called resupply steam.
When a hot user who can supply steam back exists in the industrial park, the heat supply network usually selects low-grade industrial waste heat to be consumed as heat source supplement so as to improve the overall energy utilization efficiency of the park. However, the back-supply steam has the characteristics of intermittency and fluctuation, the input of the back-supply steam can interfere the flow velocity and the flow direction of the pipeline steam, and in addition, the complex running state of multi-heat-source heat supply easily causes the potential safety hazards of steam stagnation, backflow and the like of partial pipe sections of the heat supply network.
Therefore, the unreasonable feedback steam access strategy cannot realize the efficient utilization of energy, but can cause the reduction of the steam quality and even harm the safe operation of a heat supply network. It can be seen that optimal scheduling of the back-supplied steam is critical to safe and efficient operation of the heat grid. Therefore, a method for online real-time evaluation of the influence of the returned steam on the whole network after being accessed and providing an optimal decision for the steam return is needed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a heat supply network steam back supply scheduling method and system based on a simulation model so as to solve the problem of heat supply network safety caused by unreasonable back supply user steam access strategy.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a heat supply pipe network steam back supply scheduling method based on a simulation model comprises the following steps:
s1, constructing a heat supply pipe network simulation mechanism model for the returning users, and calibrating the simulation model according to historical operation data;
step S2, establishing a feedback steam access decision evaluation model;
and step S3, determining a scheduling scheme of the feedback steam through a real-time online simulation result of the heat supply pipe network simulation mechanism model, and realizing the real-time optimization of the whole network.
Further, the step S1 of constructing a heat supply pipe network simulation mechanism model for the users to be returned to, and the step of calibrating the model according to the historical operation data includes:
step S11, establishing a thermodynamic calculation basic model;
step S12, performing supplementary correction on the thermodynamic calculation basic model according to the characteristics of the returned users;
and step S13, carrying out static calibration on the thermodynamic calculation basic model according to the historical operation data of the heat supply pipe network.
Further, the step of establishing a thermodynamic calculation correction model in step S11 includes:
step S111, abstracting an actual heat supply pipe network structure into a directed flow chart model formed by nodes and pipe sections;
step S112, constructing an incidence matrix representing the connection relation between the nodes and the pipe sections;
step S113, writing a fluid physical balance equation into the directed flow chart model, and establishing a computational topological network model according with the actual heat supply pipe network characteristics, wherein the energy conservation constraint is in the form of:
for the pressure of the node, the following should be satisfied:
ATP＝-diag2(Q)RP；
in the above formula, the first and second carbon atoms are,
P＝[P1,P2,...,Pi,...,PI]Tis the node steam pressure column vector;
diag () is a diagonal matrix function;
RP＝[RP1,RP2,...,RPj,...,RPJ]Tis the fluid resistance coefficient column vector of the pipe section;
for the temperature of the node, the following should be satisfied:
ATT＝-KTΒT[T-(Tsur)I×1]；
in the above formula, the first and second carbon atoms are,
T＝[T1,T2,...,Ti,...,TI]Tis a node steam temperature column vector;
KT＝[KT1,KT2,...,KTj,...,KTJ]the heat exchange coefficient row vector of the working medium at the inlet of the pipe section and the environment is taken as the heat exchange coefficient row vector of the working medium at the inlet of the pipe section and the environment;
the beta is a node outflow matrix and represents the corresponding connection relation between the nodes and the node outflow pipe section, and the mathematical expression is as follows:
further, the step of performing supplementary modification on the model according to the characteristics of the feedback user in step S12 includes:
step S121, adding a heat user node net mass flow constraint which reflects the heat utilization characteristic of the heat user which actually generates heat to the heat user with energy recovery capacity on the basis of the thermodynamic calculation basic model;
when returning steam for use by the user as a normal hot user, qreNot less than 0; q when the returning user transmits steam to the heat supply pipe networkreThe following constraints should be satisfied:
in the above formula, the first and second carbon atoms are,
step S122, distinguishing the hot user nodes from other nodes, further supplementing the basic model of the thermal hydraulic calculation, and defining column vectors P of the pressure and the temperature of the hot user nodesu＝[P1,P2,...,Pu,...,PU]TAnd Tu＝[T1,T2,...,Tu,...,TU]TIt is expressed mathematically as follows:
Pu＝SP，
Tu＝ST；
in the above formula, the first and second carbon atoms are,
S＝(sui)U×Iselecting a matrix for the node;
u represents the number of hot users currently consuming steam.
Further, in the step S13, historical operation data of some typical days of the target heat supply network is selected, the thermodynamic and hydraulic calculation correction model is statically calibrated, a prediction residual error is calculated, and a second-order dynamic correction model is established:
Fa,t＝Ff,t+a2dFf,t-2+a1dFf,t-1+a0+e(t)；
in the above formula, the first and second carbon atoms are,
f is steam temperature, pressure and flow;
Fforecast,tis at presentThe model predicted value of (2);
Fadjust,tpredicting a value for the corrected model;
dFf,tis the prediction residual for time period t;
ai(i is 0,1,2) is a residual autoregressive coefficient;
e (t) is model intrinsic white noise.
Further, the step of establishing a back-supply steam access decision evaluation model in step S2 includes:
step S21, establishing a feedback steam dispatching objective function;
and step S22, constraining the objective function to ensure that the conditions of reverse flow and stagnation do not occur in the supply process and the steam parameters reach the contract value or above all the time.
Further, in step S21, the two factors of the back-supply steam that is consumed as much as possible and the change of the full-grid steam parameter after the back-supply steam is consumed are comprehensively considered, and the following back-supply steam scheduling objective function is established:
in the above formula, the first and second carbon atoms are,
w1、w2respectively is a back supply steam quantity coefficient and a heat supply pipe network steam parameter variation coefficient;
Pu,t、Tu,trespectively representing the steam pressure and the temperature monitoring value of the hot user u at the current moment;
Pu,t+1、Tu,t+1respectively a steam pressure and a temperature predicted value of a thermal user u in a simulation time step.
Further, in the step S22, a solution domain reflecting the practical operation feasibility of the heat supply pipe network is defined for the objective function of the step S21, including the constraint that the pipe section does not have the stagnation flow, the reverse flow and the steam parameters reaching the contract value:
(1) safety constraint:
in the above formula, the first and second carbon atoms are,
Qj,t、Qj,t+1respectively a mass flow monitoring value at the current moment of the pipe section j and a mass flow predicted value in a simulation time step;
Dj、ρjthe diameters of the pipeline and the working medium density of the pipeline section j are respectively;
uminminimum steam flow rate to ensure no stagnation in the pipe section;
(2) steam parameter constraint:
in the above formula, the first and second carbon atoms are,
contract values for steam pressure and temperature for hot user u, respectively.
Further, in step S3, determining a scheduling scheme of the feedback steam according to the online simulation result of the heat supply pipe network simulation mechanism model, and implementing real-time optimization of the whole network includes:
step S31, monitoring the state of the feedback users in real time, and correcting the parameters of the heat supply pipe network simulation mechanism model according to the user state;
step S32, resolving the parameter input and the heat supply pipe network simulation mechanism model, and generating a preliminary scheduling scheme of the return supply steam according to an online simulation result;
and S33, repeating the step S32 according to the set optimization time step, and realizing the real-time optimization of the steam back-supply dispatching until the back-supply users do not have the requirement of delivering the steam to the heat supply pipe network.
A heat supply pipe network steam back supply scheduling system based on simulation model includes:
the sensing unit is used for monitoring steam parameters of each heat user and a heat source in the heat supply pipe network in real time and providing input for model calculation and target optimization;
the model calibration unit is used for carrying out static calibration on the model and providing more accurate decision basis for the real-time optimization unit;
the real-time optimization unit is used for judging whether a steam back supply demand exists in the heat supply pipe network, carrying out real-time optimization on the back supply steam access amount according to a predicted value in a unit prediction time step length and giving a scheduling scheme of the back supply steam;
and the execution unit is used for calculating the valve opening of the back-supply steam according to the decision value given by the real-time optimization unit and controlling the flow of the back-supply steam.
By adopting the technical scheme, the invention carries out online quantitative evaluation on the influence of the returned steam after the returned steam is connected into the heat supply pipe network by monitoring the steam parameters of each heat user and a heat source in the heat supply pipe network in real time and combining a heat supply pipe network simulation mechanism model, a returned steam connection decision evaluation system and a real-time optimization technology, carries out online optimization on the real-time connection quantity of the returned steam on the basis, and adjusts the flow quantity of the returned steam according to the action of an optimization value so as to fulfill the aim of consuming as much returned steam as possible on the premise of safe operation of the whole network, thereby taking the efficient utilization of energy in the steam return supply process and the safe operation of the heat supply network into consideration.
Drawings
FIG. 1 is a flow chart of an embodiment of a heat supply pipe network steam back supply scheduling method based on a simulation model according to the present invention;
FIG. 2 is a modular flow diagram of FIG. 1;
FIG. 3 is a schematic structural view of a heat supply pipe network according to the present invention;
FIG. 4 is a flow chart of a feedback steam scheduling optimization algorithm based on real-time calibration techniques and model predictive control in accordance with the present invention;
FIG. 5 is a flow chart of the model prediction and decision evaluation algorithm of the present invention;
FIG. 6 is a flowchart of an algorithm for calculating a predicted value of a network wide parameter according to the present invention;
fig. 7 is a schematic block diagram of the simulation model-based heat supply network steam back supply scheduling system of the present invention.
Detailed Description
In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
Example 1
As shown in fig. 1, the invention provides a heat supply pipe network steam back supply scheduling method based on a simulation model, comprising the following steps:
step S1, constructing a heat supply pipe network simulation mechanism model for the returning users, and calibrating the model according to historical operation data; step S2, establishing a feedback steam access decision evaluation model by taking the safety operation of the safety network as a precondition and taking the feedback steam as much as possible to be consumed as a target; and step S3, determining the optimal scheduling scheme of the feedback steam according to the online simulation result of the heat supply pipe network simulation mechanism model, and realizing the real-time optimization of the whole network.
Describing the scheduling method with reference to the modular process of fig. 2, specifically, in step S1, according to module 1 in fig. 2, a basic model of a heat supply pipe network is constructed according to a thermodynamic principle, a hydromechanical principle, and a system engineering related theory, and according to characteristics that a back-supply user can use steam or produce steam, corresponding constraints are added to the basic model to correct, so as to distinguish a common heat user from a back-supply user, and the basic model is calibrated based on historical operating data; in step S2, according to module 2 in fig. 2, a dimensionless objective function is introduced to quantitatively balance the maximum consumption capacity of the heat supply network on the feedback steam and the influence of the feedback steam access on the parameters of the whole heat supply network, and safety and economic constraints, so as to establish a feedback steam access decision evaluation system; in step S3, according to module 3 and module 4 in fig. 2, the established model is input into real-time operation data of the heat supply pipe network, the whole network steam parameters of the next time period after a certain mass flow rate of steam is accessed are predicted, the linear optimization solver is combined to solve the optimal steam amount of steam to be returned, an optimal scheduling scheme is generated and an instruction is issued to an execution mechanism, and the opening of a steam valve to be returned is adjusted in real time by an automatic control system, so that the safety and the efficiency of the steam to be returned are ensured.
Constructing a heat supply pipe network simulation mechanism model with return users in the step S1, and calibrating the model according to historical operation data, wherein the step S comprises the following steps: step S11, establishing a thermodynamic calculation basic model; step S12, the thermodynamic calculation basic model is supplemented and corrected according to the characteristics of the returned users; and step S13, carrying out static calibration on the thermodynamic calculation basic model according to the historical operation data of the heat supply pipe network.
The step of establishing the thermodynamic calculation basic model in the step S11 is as follows:
and step S111, abstracting the actual heat supply network structure into a directed flow chart model formed by nodes and pipe sections. Node N ═ N1,N2,...,Ni,...,NIThe node number is represented by i; section E ═ E1,E2,...,Ej,...,EJAnd j represents a pipe section number.
Step S112, constructing an incidence matrix A representing the connection relation between the nodes and the pipe sections:
wherein a isijThe connection relation between the ith node and the jth pipe section is represented, and the following rules are satisfied:
in actual practice, aijThe numerical value of (A) is determined in the stage of establishing the physical model of the heat supply network, and represents the topological relation of specific nodes of the heat supply network and pipe sections connected with the specific nodes. In principle, a corresponding to terminal nodes such as heat source and heat userijHave values of-1 and 1, respectively, but aijThe positive and negative values of (A) do not affect the subsequent calculation process, but have values different from aboutIs known as symbol representation.
And step S113, writing the fluid physical balance equation into the directed graph, and establishing a calculation topological network model according with the actual heat supply network characteristics. When steam is used as the heat transfer medium, the model should satisfy the following equation:
(1) conservation of mass:
AQ＝q
wherein the content of the first and second substances,
Q＝[Q1,Q2,...,Qj,...,QJ]Tis the mass flow column vector of the pipe section; q ═ q1,q2,...,qi,...,qI]TIs the net mass flow column vector, q, of the nodei> 0 denotes NiWith net mass flow in, qi< 0 means NiWith net mass flow out, q i0 represents NiThere is no net mass flow in or out. In general, for nodes with only net mass flow out, q, of heat sources or the likeiIs constantly less than 0; for nodes with only net mass outflow, e.g. hot users, qiIs constantly greater than 0; for nodes with no net mass flow in or out, such as tees, qiIs constantly equal to 0. In the mass conservation equation of the invention, because the flow of the terminal nodes such as a heat source, a heat user and the like are all obtained by the flow meter to obtain real-time data, and the flow value of the middle node such as a tee joint and the like is constantly equal to 0, the mass flow of each pipe section which is difficult to be monitored comprehensively in practice can be solved by taking the net mass flow column vector q of the node as the input of a model and combining the incidence matrix A determined by the heat network topological structure.
(2) Conservation of energy:
for the node pressure, it should satisfy:
ATP＝-diag2(Q)RP
wherein the content of the first and second substances,
P＝[P1,P2,...,Pi,...,PI]Trepresenting a nodal steam pressure column vector; diag () is a diagonal matrix function; rP＝[RP1,RP2,...,RPj,...,RPJ]TArray of fluid resistance coefficients representing tube segmentsThe vector, the value of the coefficient vector is related to parameters such as steam density, pipe section diameter, friction factor, pipe section length and flow resisting element (such as valve) characteristics, can be regarded as a constant vector in a decision time interval of accessing back supply steam, and the specific calculation method of the pipe section fluid resistance coefficient is as follows:
wherein rho is the density of the working medium; d is the inner diameter of the pipe section; f is the darcy friction factor; l is the length of the pipe section; knIs the loss factor of the flow-impeding element.
For the node temperature, it should satisfy:
ATT＝-KTΒT[T-(Tsur)I×1]
wherein T ═ T1,T2,...,Ti,...,TI]TIs a node steam temperature column vector; kT＝[KT1,KT2,...,KTj,...,KTJ]The heat transfer coefficient row vector of the working medium at the inlet of the pipe section and the environment is taken as a constant vector in a decision time interval of accessing the return steam; the beta is a node outflow matrix and represents the corresponding connection relation between the network nodes and the node outflow pipe sections, and the mathematical expression of the matrix is as follows:
the method for calculating the heat exchange coefficient between the specific pipe section and the environment comprises the following steps:
wherein, cpIs the constant pressure specific heat capacity of the working medium; k is a pipe sectionThe calculation method of the heat exchange coefficient is as follows:
wherein h isj、hsurThe heat convection coefficient of the working medium of the pipe section j and the pipe wall and the heat convection coefficient of the pipe section and the environment are respectively; djIs the inner diameter of tube section j; lambda [ alpha ]1、Dj,1The thermal conductivity and the outer diameter of the pipeline are respectively; lambda [ alpha ]2,λ3,...,λm、Dj,2,Dj,3,...,Dj,mThe thermal conductivity and the outer diameter of each heat-insulating layer are respectively.
In step S12, in consideration of the characteristics that the feedback users have the requirements for using steam and the self-generated steam can be delivered to the heat supply network, the basic model of thermodynamic calculation established in step S11 is subjected to supplementary correction to obtain a correction model of heat supply network hydraulic calculation for the feedback users, and the specific steps are as follows:
and step S121, adding node net mass flow constraint which reflects the heat utilization characteristic of the heat actually generated to a heat user with energy recovery capacity on the basis of the original thermodynamic and hydraulic calculation basic model. When returning steam for use by the user as a normal hot user, qreNot less than 0; when the feedback user transmits steam to the heat supply network, qreThe following constraints should be satisfied:
wherein
The maximum available steam amount of the current resupply user is obtained.
Step S122, in order to meet the requirement of heat supply network state evaluation, the heat user nodes are distinguished from other nodes (including the return supply user nodes which are supplying steam), the original model is further supplemented, and the column vector P of the pressure and the temperature of the heat user nodes is definedu＝[P1,P2,...,Pu,...,PU]TAnd Tu＝[T1,T2,...,Tu,...,TU]TIt is expressed mathematically as follows:
Pu＝SP
Tu＝ST
wherein S ═ Sui)U×IA matrix is selected for the nodes, U representing the number of hot users currently consuming steam. And under the given working condition, S is a constant matrix and is used for selecting corresponding hot user node parameters from the node pressure column vector and the node temperature column vector. The implementation method of S is as follows:
the steam pipe network structure shown in fig. 3 has the corresponding correlation matrix:
at this time, for the hot user, the node selection matrix is:
the implementation method comprises the steps of firstly defining a hollow matrix S, screening out a row which has only one element 1 in the matrix A, and when the ith row of the matrix A has the characteristics, inserting a full zero row into the matrix S, wherein the number of columns is the number of nodes, and setting the ith element of the row as 1. The process is readily implemented in computer language.
In step S13, historical operating data of some typical days of the target heat supply network are selected, and static calibration is performed on model parameters such as pipe section resistance coefficients and pipe section heat exchange coefficients of the heat supply network hydraulic calculation correction model, so that a reference model capable of reflecting the operating characteristics of the target heat supply network is provided for subsequent online model prediction control, the online optimized prediction residual error is reduced, and the reliability of heat supply network regulation and control is improved. The calibration process adopts an error autoregressive method, combines the prediction result of the steam heat network model with historical operation data, calculates the prediction residual error, and establishes a second-order dynamic correction model:
Fa,t＝Ff,t+a2dFf,t-2+a1dFf,t-1+a0+e(t)
wherein F refers to a certain concerned parameter, and is a model parameter such as a pipe section resistance coefficient, a pipe section heat exchange coefficient and the like in the invention; fforecast,tPredicting a current model value; fadjust,tPredicting a value for the corrected model; dFf,tIs the prediction residual for time period t; a isi(i ═ 0,1,2) are residual autoregressive coefficients, are fixed values related to heat network characteristics and prediction models, and can be determined by least squares recursion; e (t) is model intrinsic white noise.
In step S2, the step of establishing a back-supply steam access decision evaluation model with the goal of consuming as much back-supply steam as possible under the precondition of safe operation of the security network includes: step S21, establishing a feedback steam dispatching objective function; and step S22, constraining the objective function to ensure that the conditions of reverse flow and stagnation do not occur in the supply process and the steam parameters reach the contract value or above all the time.
In step S21, the two factors of the steam back-supply as much as possible and the parameter change of the full-grid steam after the steam back-supply is consumed are considered comprehensively, and the following back-supply steam scheduling objective function is established:
wherein w1、w2The two coefficients are respectively the importance coefficients reflecting the back supply steam quantity and the parameter change of the whole network steam, both the two coefficients are more than 0, and the coefficients can be flexibly selected by a decision maker according to experience; pu,t、Tu,tRespectively representing the steam pressure and the temperature monitoring value of the hot user u at the current moment; pu,t+1、Tu,t+1Respectively a steam pressure and a temperature predicted value of a thermal user u in a simulation time step.
In step S22, a solution domain reflecting the practical operation feasibility of the heat supply network is defined for the objective function in step S21, including the constraint that no stagnation, reverse flow and steam parameters of the pipe section reach the contract value:
(1) safety constraint:
wherein Qj,t、Qj,t+1Respectively a mass flow monitoring value at the current moment of the pipe section j and a mass flow predicted value in a simulation time step; dj、ρjThe diameters of the pipeline and the working medium density of the pipeline section j are respectively; u. ofminMinimum steam flow rate to ensure no stagnation in the tube sections.
(2) Steam parameter constraint:
wherein
Contract values for steam pressure and temperature for hot user u, respectively.
Step S3, determining the optimal scheduling scheme of the feedback steam through the online simulation result of the heat supply pipe network simulation mechanism model, and realizing the real-time optimization of the whole network comprises the following steps: step S31, monitoring the state of the feedback user in real time, and correcting the model parameters according to the state of the user; step S32, inputting parameters and resolving a model, and generating a preliminary scheduling scheme of the steam return supply according to an online simulation result; and step S33, repeating the step S32 according to the set optimization time step, and realizing the real-time optimization of the steam back-supply dispatching until the back-supply users do not have the requirement of delivering the steam to the heat supply network. The specific algorithm flow of step S3 is shown in fig. 4, where Δ t is a simulation time step;
the maximum amount of the steam which can be supplied back to the back-supplying user at the moment t is greater than 0, which indicates that the back-supplying user has the demand of the back-supplying steam at the current moment.
In step S32, according to the corrected heat supply network mechanism model, real-time monitoring parameter values of current time, such as steam flow, temperature, pressure and the like, obtained by monitoring of a common heat user and a heat source side are used as input, the feedback steam flow of a feedback user is used as a decision variable, and a calculation strategy of sequential solution is adopted to simulate on-line theoretical steam parameters of all parts of the whole network after accessing a specific mass flow feedback steam. And optimizing the back-supply steam admission amount by combining the simulation predicted value obtained by calculation and the back-supply steam admission decision evaluation model established in the step S2. The specific implementation of the algorithm is shown in fig. 5, wherein e (0,1) in the graph is an attenuation coefficient, which can be set manually or determined by using a gradient descent method for an objective function; to set the accuracy. The specific method for calculating the predicted value of the whole network parameter in fig. 5 is as follows:
(1) establishing an incidence matrix capable of reflecting the current connection state of the heat supply network nodes according to a network topological structure, and setting an indicator i whether static calibration is needed, wherein if the indicator i is needed, the i is 1; otherwise i defaults to 0.
(2) And reading the flow, temperature and pressure parameters of a heat source in the system and the flow parameters of a heat user in real time.
(3) Initializing a node net mass flow vector q, filling actually measured flow parameters into heat sources and heat user nodes, and setting the net mass flow to be 0 for an intermediate node.
(4) And calculating the mass flow of each pipe section according to the mass conservation law to obtain the value of the vector Q.
(5) According to energy conservation, the pressure and the temperature of each node are calculated from the temperature and the pressure parameters of the heat source node, and the value of the vector P, T is obtained.
(6) And outputting the temperature and pressure parameters of the heat user node and the flow parameters of each pipe section.
(7) Judging a static calibration indicator, and if the static calibration indicator needs to be corrected, entering the step (8); otherwise, outputting the prediction calculation parameters and finishing the calculation.
(8) And calculating the network error according to the actual steam temperature and pressure parameters of the hot user.
(9) Judging whether the network error meets the precision requirement, if so, returning the static calibration indicator to 0, and skipping to the step (6); otherwise, calibrating the heat exchange coefficient and the resistance coefficient of the pipe section by using the back propagation error parameters, and skipping to the step (5).
The detailed flow of the calculation process is shown in fig. 2:
and generating an optimal scheduling scheme according to the optimizing result, and sending the optimal scheduling scheme to an execution mechanism for regulating the flow of the back-supplied steam.
Example 2
On the basis of embodiment 1, the invention also provides a heat supply pipe network steam back supply scheduling system based on a simulation model, as shown in fig. 7, the system mainly comprises the following parts:
a sensing unit: the system is used for monitoring steam parameters such as steam temperature, pressure, flow and the like of each heat user and a heat source in a heat supply pipe network in real time and providing input for model calculation and target optimization;
a model calibration unit: the model is used for carrying out static calibration on the model, and more accurate decision basis is provided for the real-time optimization unit;
a real-time optimization unit: the system comprises a heat supply pipe network mechanism correction module and a feedback steam access decision evaluation module, wherein the heat supply pipe network mechanism correction module and the feedback steam access decision evaluation module are used for judging whether a steam feedback demand exists in a heat supply pipe network, carrying out real-time optimization on the access amount of feedback steam according to a predicted value in a unit prediction time step length and providing a scheduling scheme of the feedback steam;
an execution unit: and calculating the opening of the steam return supply valve according to the decision value given by the real-time optimization unit, and controlling the flow of the steam return supply.
The above embodiments are described in further detail to solve the technical problems, technical solutions and advantages of the present invention, and it should be understood that the above embodiments are only examples of the present invention and are not intended to limit the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A heat supply pipe network steam back supply scheduling method based on a simulation model is characterized by comprising the following steps:
s1, constructing a heat supply pipe network simulation mechanism model for the returning users, and calibrating the simulation model according to historical operation data;
step S2, establishing a feedback steam access decision evaluation model;
and step S3, determining a scheduling scheme of the feedback steam through a real-time online simulation result of the heat supply pipe network simulation mechanism model, and realizing the real-time optimization of the whole network.
2. The heat supply network steam back supply scheduling method based on the simulation model according to claim 1, wherein the heat supply network simulation mechanism model with the back supply users is constructed in the step S1, and the step of performing model calibration according to the historical operation data comprises:
step S11, establishing a thermodynamic calculation basic model;
step S12, performing supplementary correction on the thermodynamic calculation basic model according to the characteristics of the returned users;
and step S13, carrying out static calibration on the thermodynamic calculation basic model according to the historical operation data of the heat supply pipe network.
3. The simulation model-based heat supply network steam back-supply scheduling method of claim 2, wherein the step of establishing the thermodynamic calculation correction model in the step S11 comprises:
step S111, abstracting an actual heat supply pipe network structure into a directed flow chart model formed by nodes and pipe sections;
step S112, constructing an incidence matrix representing the connection relation between the nodes and the pipe sections;
step S113, writing a fluid physical balance equation into the directed flow chart model, and establishing a computational topological network model according with the actual heat supply pipe network characteristics, wherein the energy conservation constraint is in the form of:
for the pressure of the node, the following should be satisfied:
ATP＝-diag2(Q)RP；
in the above formula, the first and second carbon atoms are,
P＝[P1,P2,...,Pi,...,PI]Tis the node steam pressure column vector;
diag () is a diagonal matrix function;
RP＝[RP1,RP2,...,RPj,...,RPJ]Tis the fluid resistance coefficient column vector of the pipe section;
for the temperature of the node, the following should be satisfied:
ATT＝-KTΒT[T-(Tsur)I×1]；
in the above formula, the first and second carbon atoms are,
T＝[T1,T2,...,Ti,...,TI]Tis a node steam temperature column vector;
KT＝[KT1,KT2,...,KTj,...,KTJ]the heat exchange coefficient row vector of the working medium at the inlet of the pipe section and the environment is taken as the heat exchange coefficient row vector of the working medium at the inlet of the pipe section and the environment;
the beta is a node outflow matrix and represents the corresponding connection relation between the nodes and the node outflow pipe section, and the mathematical expression is as follows:
4. the heat supply network steam supply back scheduling method based on simulation model according to claim 3, wherein the step of performing supplementary modification on the model according to the characteristics of the supply back user in step S12 comprises:
step S121, adding a heat user node net mass flow constraint which reflects the heat utilization characteristic of the heat user which actually generates heat to the heat user with energy recovery capacity on the basis of the thermodynamic calculation basic model;
when returning steam for use by the user as a normal hot user, qreNot less than 0; q when the returning user transmits steam to the heat supply pipe networkreThe following constraints should be satisfied:
in the above formula, the first and second carbon atoms are,
step S122, distinguishing the hot user nodes from other nodes, further supplementing the basic model of the thermal hydraulic calculation, and defining column vectors P of the pressure and the temperature of the hot user nodesu＝[P1,P2,...,Pu,...,PU]TAnd Tu＝[T1,T2,...,Tu,...,TU]TIt is expressed mathematically as follows:
Pu＝SP，
Tu＝ST；
in the above formula, the first and second carbon atoms are,
S＝(sui)U×Iselecting a matrix for the node;
u represents the number of hot users currently consuming steam.
5. The simulation model-based heat supply network steam back-supply scheduling method of claim 4, wherein in the step S13, historical operating data of some typical days of the target heat supply network are selected, the thermodynamic and hydraulic calculation correction model is statically calibrated, a prediction residual error is calculated, and a second-order dynamic correction model is established:
Fa,t＝Ff,t+a2dFf,t-2+a1dFf,t-1+a0+e(t)；
in the above formula, the first and second carbon atoms are,
f is steam temperature, pressure and flow;
Fforecast,tpredicting a current model value;
Fadjust,tpredicting a value for the corrected model;
dFf,tis the prediction residual for time period t;
ai(i is 0,1,2) is a residual autoregressive coefficient;
e (t) is model intrinsic white noise.
6. The simulation model-based heating network steam feedback scheduling method of claim 1, wherein the step of establishing a feedback steam access decision evaluation model in step S2 comprises:
step S21, establishing a feedback steam dispatching objective function;
and step S22, constraining the objective function to ensure that the conditions of reverse flow and stagnation do not occur in the supply process and the steam parameters reach the contract value or above all the time.
7. The simulation model-based heat supply pipe network steam feedback scheduling method of claim 6, wherein the heat supply pipe network steam feedback scheduling method comprehensively considers two factors of the feedback steam consumption as much as possible and the whole network steam parameter change after the feedback steam consumption in the step S21, and establishes the following feedback steam scheduling objective function:
in the above formula, the first and second carbon atoms are,
w1、w2respectively is a back supply steam quantity coefficient and a heat supply pipe network steam parameter variation coefficient;
Pu,t、Tu,trespectively representing the steam pressure and the temperature monitoring value of the hot user u at the current moment;
Pu,t+1、Tu,t+1respectively a steam pressure and a temperature predicted value of a thermal user u in a simulation time step.
8. The simulation model-based steam supply back scheduling method for the heat supply network according to claim 7, wherein the objective function of step S21 in step S22 defines a solution domain reflecting the feasibility of the actual operation of the heat supply network, including the constraint that the pipe sections do not have the stagnation flow, the reverse flow and the steam parameters reach the contract values:
(1) safety constraint:
in the above formula, the first and second carbon atoms are,
Qj,t、Qj,t+1respectively a mass flow monitoring value at the current moment of the pipe section j and a mass flow predicted value in a simulation time step;
Dj、ρjthe diameters of the pipeline and the working medium density of the pipeline section j are respectively;
uminminimum steam flow rate to ensure no stagnation in the pipe section;
(2) steam parameter constraint:
in the above formula, the first and second carbon atoms are,
contract values for steam pressure and temperature for hot user u, respectively.
9. The heat supply network steam feedback scheduling method based on the simulation model as claimed in claim 1, wherein the step S3 of determining the scheduling scheme of the feedback steam according to the online simulation result of the heat supply network simulation mechanism model, and the step of implementing the real-time optimization of the whole network includes:
step S31, monitoring the state of the feedback users in real time, and correcting the parameters of the heat supply pipe network simulation mechanism model according to the user state;
step S32, resolving the parameter input and the heat supply pipe network simulation mechanism model, and generating a preliminary scheduling scheme of the return supply steam according to an online simulation result;
and S33, repeating the step S32 according to the set optimization time step, and realizing the real-time optimization of the steam back-supply dispatching until the back-supply users do not have the requirement of delivering the steam to the heat supply pipe network.
10. The utility model provides a heat supply pipe network steam returns and supplies dispatch system based on simulation model which characterized in that includes:
the sensing unit is used for monitoring steam parameters of each heat user and a heat source in the heat supply pipe network in real time and providing input for model calculation and target optimization;
the model calibration unit is used for carrying out static calibration on the model and providing more accurate decision basis for the real-time optimization unit;
the real-time optimization unit is used for judging whether a steam back supply demand exists in the heat supply pipe network, carrying out real-time optimization on the back supply steam access amount according to a predicted value in a unit prediction time step length and giving a scheduling scheme of the back supply steam;
and the execution unit is used for calculating the valve opening of the back-supply steam according to the decision value given by the real-time optimization unit and controlling the flow of the back-supply steam.
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