CN105910169A - Urban heating system heating network regulating method and system based on mechanism model prediction control - Google Patents

Abstract

The invention relates to an urban heating system heating network regulating method and system based on mechanism model prediction control. The heating network regulating method comprises the following steps that S1, a prediction model is established; and S2, regulating schemes of all pumps and valves in the first-degree heating network are obtained through the prediction model with balanced heating as the target. According to the urban heating system heating network regulating method and system, the prediction model of the first-degree heating network serves as the core, the accurate regulating schemes of the tens of to hundreds of water pumps and the electromagnetic valves in the first-degree heating network system are obtained through prediction control, the heating network is regulated in real time, and the problems that heating network control is delayed, and heating of heat users is nonuniform in temperature are solved.

Description

District heating system regulating of heating net method and system based on mechanism model PREDICTIVE CONTROL

Technical field

The present invention relates to the wisdom control technique of district heating system, particular for central heating system first-degree heating network regulation technology based on Model Predictive Control.

Background technology

Under the background of China's energy production and consumption transition, the thermal source condition of district heating system becomes to become increasingly complex, and has the multi-form combinations such as cogeneration of heat and power factory, hot-water boiler, industrial exhaust heat, wind energy, underground heat.For supporting multi-source complementary operation, the first-degree heating network of district heating system is further to interconnection, looped structural development, meanwhile, for reducing for the restriction loss of valve between return pipe net, and increases flexible adjustment, more employing distributed variable frequency pump transmission & distribution technologies.Networking operation, reliability but add the difficulty of management and running while improving.At present heating system automatization of China, intelligent level is the most on the low side, under tradition regulative mode, hydraulic misadjustment problem is still serious, and Load Distribution is unbalanced, and heat user satisfaction is low, controls the contradiction with economic benefit and is becoming increasingly acute.

Owing to the heating network of large-and-medium size cities is a large-scale complicated thermal-hydraulic system, there is non-linear, multivariate, strong coupling, the feature of high latency, controlled plant during first-degree heating network traffic control includes large number of variable frequency pump, electromagnetic valve, the system that traditional PID control method is strong for coupling, inertia is big easily causes concussion, and then causes the hydraulic misadjustment of district heating system.In recent years, model predictive control method is owing to can pass through design by the Future Trajectory of control variable, carry out the various situations of process control in the simulating reality world to greatest extent, and there is preferable robustness and dynamically control effect, obtaining a wide range of applications in fields such as oil refining, chemical industry, food processing, aviations, the control and regulation that the method is applied to civil heat supply system first-degree heating network have potential huge advantage.

Summary of the invention

It is an object of the invention to provide a kind of regulating of heating net method and system, to solve the problem that regulating of heating net is delayed and hydraulic pipeline is lacked of proper care, heat user is uneven in temperature.

In order to solve above-mentioned technical problem, the invention provides a kind of regulating of heating net method, comprise the steps:

Step S1, sets up forecast model；

Step S2, with balanced heating as target, obtains each pump, the regulation scheme of each valve in first-degree heating network by forecast model prediction.

Further, the step setting up forecast model in described step S1 includes: step S11, and thermal hydraulic analysis solves；And step S12, it was predicted that model corrects.

Further, the method that in described step S11, thermal hydraulic analysis solves includes:

Step S111, is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure by pipe network；Wherein node represents the point that there is flow turnover, represents with set V, V=[V₁,V₂,…,V_n], in formula, n is the node number in pipe network；Section is internodal connection pipeline section, represents with set E, E=[E₁,E₂,…,E_m], in formula, m is sector number, and directed graph is expressed as G=<V, E>；

Obtain the incidence matrix A and fundamental circuit matrix B of pipe network according to network graph theory, wherein A is n × m rank matrixes, and B is s × m rank matrixes, and s is fundamental circuit number s=m-n+1；

Step S112, calculates the characteristics resistance coefficient of pipe network；

Step S113, structure forecast model, i.e. hydraulic pipeline computational mathematics model:

AG^T=0；

BΔH^T=0；

In formula, G is the row vector G=[G of each pipeline section inner volume flow in record pipe network figure₁,G₂,…,G_m]；Δ H is the column vector of each pipeline section drag overall loss in record pipe network figure, i.e.

Δ H=S* | G | * G+Z-H_b；

In formula, S is the characteristics resistance coefficient matrix of each pipeline section in first-degree heating network

S=diag{S₁,S₂,…,S_m}；

$S_i=7.02\&times;{10}^{-3}\frac{K^{0.25}}{{\mathrm{\&rho;d}}^{5.25}}(l+l_d),i=1,2,...,m$

K is the equivalent absolute roughness of tube wall, general K=0.0005m；D is internal diameter of the pipeline；l、l_dIt is respectively length and the local resistance equivalent length of pipe network run of designing；ρ is the average density of the interior hot water of pipe；

Z is the column vector Z=[Z of pipeline section two node potential energy difference₁,Z₂,…,Z_m]^T；

H_bFor pump head column vector H in heat supply network_b=[H_b1,H_b2,…,H_bm]^T；

Step S114, heat supply network thermal-hydraulic calculates, i.e.

Described hydraulic pipeline computational mathematics models coupling loop adjustment Flow-rate adjustment algorithm is calculated through successive ignition, it is thus achieved that the volume flow of hot water in each pipeline section.

In described step S12, forecast model correction includes: utilize heat supply network actual measurement service data to be modified forecast model.

Further, with balanced heating as target in described step S2, obtain the method for the regulation scheme of each pump, each valve in first-degree heating network by forecast model prediction and comprise the steps:

Step S21, is predicted the thermic load needed for each thermal substation with balanced heating for target, draws the primary side flow target value that should arrive each thermal substation；

Step S22, uses forecast model that pump, the method for operation of valve in first-degree heating network are carried out optimizing.

Further, the thermic load needed for each thermal substation is predicted for target with balanced heating by described step S21, draws the primary side flow target value that should arrive each thermal substation；I.e.

The target equation of primary side flow is:

${\left(G_{1r}\right)}_i=\frac{1}{2C\mathrm{\&rho;}(\frac{K_{const}}{{\mathrm{\&zeta;}}_i}-\frac{1}{{\left(K_1{F}_1\right)}_i})},i=1,2,...,s---\left(1\right);$

(G in formula_1r)_iRepresenting the primary side flow target value of i-th thermal substation, s is the sum of thermal substation, and C is that specific heat of water holds, and ρ is the density of hot water, ζ_iFor from radiator hot water to outdoor equivalent heat transfer factor, (K₁F₁)_iRepresent the exchange capability of heat of heat exchanger, K in i-th thermal substation_constRepresent quantitatively；And

And

${\left(K_1{F}_1\right)}_i=\frac{{\&Integral;}_{{\mathrm{\&tau;}}_0}^{{\mathrm{\&tau;}}_1}{\left(G_2\right)}_{i}C\mathrm{\&rho;}\&lsqb;t_{2g}\left(\mathrm{\&tau;}\right)-t_{2h}\left(\mathrm{\&tau;}\right)\&rsqb;d\mathrm{\&tau;}}{{\&Integral;}_{{\mathrm{\&tau;}}_0}^{{\mathrm{\&tau;}}_1}0.5\{\&lsqb;t_{1g}\left(\mathrm{\&tau;}\right)+t_{1h}\left(\mathrm{\&tau;}\right)\&rsqb;-\&lsqb;t_{2g}\left(\mathrm{\&tau;}\right)+t_{2h}\left(\mathrm{\&tau;}\right)\&rsqb;\}d\mathrm{\&tau;}};$

At ζ_i(K₁F₁)_iFormula in, (G₂)_iRepresent thermal substation i secondary heat networks recirculated water volume flow, t_2gRepresent secondary heat networks supply water temperature, t_2hRepresent secondary heat networks return water temperature, t_1gRepresent thermal substation first-degree heating network supply water temperature, t_1hRepresent thermal substation first-degree heating network return water temperature, t_wFor outdoor temperature, τ₁-τ₀≈ 3～5 days；

There is again first-degree heating network total water supply flow G_1tEqual to each thermal substation primary side flow sum, i.e.

$G_{1t}=\underset{i=1}{\overset{s}{\&Sigma;}}{\left(G_{1r}\right)}_i---\left(2\right);$

G_1tFor the total water supply flow of first-degree heating network, for known quantity.

By ζ_i、(K₁F₁)_iSubstitute into (G_1r)_iComputing formula (1), simultaneous formula (1), formula (2) i.e. solve K_constValue, and then obtain each thermal substation primary side flow target value by formula (1).

Further, using forecast model that pump, the method for operation of valve in first-degree heating network are carried out optimizing in described step S22, wherein the optimization aim of optimizing includes heat supply harmony target, is arrived the first-degree heating network flow (G of each thermal substation heat exchanger by distribution₁)_iTo realize；

The object function of optimizing is

$\min Z={\mathrm{\&lambda;}}_G\underset{i=1}{\overset{N_s}{\&Sigma;}}\mathrm{\&Delta;}{\left(G_1\right)}_i+{\mathrm{\&lambda;}}_E\underset{i=j}{\overset{N_b}{\&Sigma;}}{\left(E_b\right)}_j+{\mathrm{\&lambda;}}_l(N_v\&times;100\%-\underset{k=1}{\overset{N_v}{\&Sigma;}}{l}_k);$

Wherein, Δ (G₁)_iDeviation for first-degree heating network flow with setting flow:

$\mathrm{\&Delta;}{\left(G_1\right)}_i=\left|\frac{{\left(G_1\right)}_i-{\left(G_{1r}\right)}_i}{{\left(G_{1r}\right)}_i}\right|;$

(G₁)_iThe first-degree heating network flow rate calculation value of the arrival i-th thermal substation for being obtained by forecast model simulation；And

(E_b)_jThe electrical power consumed for water pump:

${\left(E_b\right)}_j=\frac{\mathrm{\&rho;}g{\left(G_b\right)}_j{\left(H_b\right)}_j}{1000\&times;{\left({\mathrm{\&eta;}}_b\right)}_j};$

G is acceleration of gravity, (G_b)_jFor pumping the volume flow of hot water, (H_b)_jFor pump head；(η_b)_jFor the efficiency of water pump, subscript j represents that water pump is numbered；

In the computing formula of respective objects function, Z is the aggregative indicator optimized, N_sFor thermal substation number, N_bFor the number of first-degree heating network freq uency conversion supercharging pump, N_vFor the number of first-degree heating network regulation valve, l_kFor control valve opening, subscript k represents regulation valve numbering, l_minThe minimum aperture allowed for regulation valve, λ_G、λ_E、λ_lRespectively arrive the first-degree heating network flow of each thermal substation, the electrisity consumption of water pump and the importance weight of valve opening.

Another aspect, present invention also offers a kind of regulating of heating net system, including:

Forecast model sets up unit, sets up the regulating of heating net unit that unit is connected with this forecast model.

Further, described forecast model is set up unit and is adapted to set up forecast model, i.e. includes: thermal hydraulic analysis solves module, and forecast model correction module.

Further, described thermal hydraulic analysis solves module, i.e.

Pipe network is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure；Wherein node represents the point that there is flow turnover, represents with set V, V=[V₁,V₂,…,V_n], in formula, n is the node number in pipe network；Section is internodal connection pipeline section, represents with set E, E=[E₁,E₂,…,E_m], in formula, m is sector number, and directed graph is expressed as G=<V, E>；

Obtain the incidence matrix A and fundamental circuit matrix B of pipe network according to network graph theory, wherein A is n × m rank matrixes, and B is s × m rank matrixes, and s is fundamental circuit number s=m-n+1；

Calculate the characteristics resistance coefficient of pipe network；

Build forecast model, i.e. hydraulic pipeline computational mathematics model: i.e.

AG^T=0；

BΔH^T=0；

In formula, G is the row vector G=[G of each pipeline section interior-heat water volume flow rate in record pipe network figure₁,G₂,…,G_m]；Δ H is the column vector of each pipeline section drag overall loss in record pipe network figure, i.e.

Δ H=S* | G | * G+Z-H_b；

In formula, S is the characteristics resistance coefficient matrix of each pipeline section in first-degree heating network

S=diag{S₁,S₂,…,S_m}；

$S_i=7.02\&times;{10}^{-3}\frac{K^{0.25}}{{\mathrm{\&rho;d}}^{5.25}}(l+l_d),i=1,2,...,m$

K is the equivalent absolute roughness of tube wall, general K=0.0005m；D is internal diameter of the pipeline；l、l_dIt is respectively length and the local resistance equivalent length of pipe network run of designing；ρ is the average density of the interior hot water of pipe；

Z is the column vector Z=[Z of pipeline section two node potential energy difference₁,Z₂,…,Z_m]^T；

H_bFor pump head column vector H in heat supply network_b=[H_b1,H_b2,…,H_bm]^T；And

Heat supply network thermal-hydraulic calculates, i.e.

Described hydraulic pipeline computational mathematics models coupling loop adjustment Flow-rate adjustment algorithm is calculated through successive ignition, it is thus achieved that the volume flow of hot water in each pipeline section；And

Described forecast model correction module, i.e. utilizes heat supply network actual measurement service data to be modified forecast model.

Further, described regulating of heating net unit is suitable to be predicted the thermic load needed for each thermal substation with balanced heating for target, draw the primary side flow target value that should arrive each thermal substation, and use forecast model that pump, the method for operation of valve in first-degree heating network are carried out optimizing.

The invention has the beneficial effects as follows, the present invention with the forecast model of first-degree heating network as core, PREDICTIVE CONTROL is utilized to obtain in heat supply first-degree heating network system sum tens of to hundreds of water pumps with the accurate regulation scheme of electromagnetic valve, in real time heat supply network is adjusted, solves the problem that heating network control is delayed and heat user is uneven in temperature.

Accompanying drawing explanation

The present invention is further described with embodiment below in conjunction with the accompanying drawings.

Fig. 1 is the flow chart of heating network control method of the present invention；

Fig. 2 is the flow chart of step S2 in heating network control method of the present invention.

Fig. 3 is the theory diagram of the regulating of heating net system of the present invention.

Detailed description of the invention

In conjunction with the accompanying drawings, the present invention is further detailed explanation.These accompanying drawings are the schematic diagram of simplification, and the basic structure of the present invention is described the most in a schematic way, and therefore it only shows the composition relevant with the present invention.

Embodiment 1

As it is shown in figure 1, a kind of regulating of heating net method of the present invention, comprise the steps:

Step S1, sets up forecast model；

Step S2, with balanced heating as target, obtains each pump, the regulation scheme of each valve in first-degree heating network by forecast model prediction.

Concrete, this regulating of heating net system may be used for the first-degree heating network regulation in district heating system.

The step setting up forecast model in described step S1 includes:

Step S11, thermal hydraulic analysis solves；And

Step S12, it was predicted that model corrects.

The method that in described step S11, thermal hydraulic analysis solves includes:

Step S111, is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure by pipe network；Wherein node represents the point that there is flow turnover, represents with set V, V=[V₁,V₂,…,V_n], in formula, n is the node number in pipe network；Section is internodal connection pipeline section, represents with set E, E=[E₁,E₂,…,E_m], in formula, m is sector number, and directed graph is expressed as G=<V, E>；Obtain the incidence matrix A and fundamental circuit matrix B of pipe network according to network graph theory, wherein A is n × m rank matrixes, and B is s × m rank matrixes, and s is fundamental circuit number s=m-n+1；

Step S112, calculates the characteristics resistance coefficient of pipe network；

Step S113, structure forecast model, i.e. hydraulic pipeline computational mathematics model:

AG^T=0；

BΔH^T=0；

In formula, G is the row vector G=[G of each pipeline section interior-heat water volume flow rate in record pipe network figure₁,G₂,…,G_m]；Δ H is the column vector of each pipeline section drag overall loss in record pipe network figure, i.e. by Bernoulli equation and pepeline characteristic equation and consider that pump head provides: Δ H=S* | G | * G+Z-H_b；

In formula, S is the characteristics resistance coefficient matrix of each pipeline section in first-degree heating network

S=diag{S₁,S₂,…,S_m}；

$S_i=7.02\&times;{10}^{-3}\frac{K^{0.25}}{{\mathrm{\&rho;d}}^{5.25}}(l+l_d),i=1,2,...,m$

K is the equivalent absolute roughness of tube wall, general K=0.0005m；D is internal diameter of the pipeline；l、l_dIt is respectively length and the local resistance equivalent length of pipe network run of designing；ρ is the average density of the interior hot water of pipe；

Z is the column vector Z=[Z of pipeline section two node potential energy difference₁,Z₂,…,Z_m]^T；

H_bFor pump head column vector H in heat supply network_b=[H_b1,H_b2,…,H_bm]^T；When in pipeline section containing water pump, pump head is the H of this pipeline section_b, when pipeline section does not has water pump, the H of this pipeline section_bIt is 0.

Step S114, heat supply network thermal-hydraulic calculates, i.e.

Described hydraulic pipeline computational mathematics models coupling loop adjustment Flow-rate adjustment algorithm is calculated through successive ignition, it is thus achieved that the volume flow of hot water in each pipeline section.

When i.e. using forecast model to control to be optimized calculating, first-degree heating network pipeline, thermal source, thermal substation and weather relevant parameter are fixed value, using valve opening adjustable in pipe network, pump rotary speed as the input parameter of forecast model, model is output as arriving the primary side flow value vector of each thermal substation.Described loop adjustment Flow-rate adjustment algorithm is known.

In described step S12, forecast model correction includes: utilize heat supply network actual measurement service data to be modified forecast model.

The forecast model that the present invention is set up comprises online trimming process, so that model can preferably describe heat supply network actual motion state, utilizes heat supply network actual measurement service data to be modified forecast model.In each sampling instant, by heat supply network being surveyed the comparison according to primary Calculation model calculated theory state parameter of running state parameter and heat supply network state analysis system, adjust heat supply network state analysis computation model, and revise the important experiences parameter such as pipe resistance coefficient, the heat transfer coefficient value rule with service condition Parameters variation, set up the dedicated computing model that can more preferably simulate heating network operation performance.

With balanced heating as target in described step S2, obtain the method for the regulation scheme of each pump, each valve in first-degree heating network by forecast model prediction and comprise the steps:

Step S21, is predicted the thermic load needed for each thermal substation with balanced heating for target, draws the primary side flow target value that should arrive each thermal substation；

Step S22, uses forecast model that pump, the method for operation of valve in first-degree heating network are carried out optimizing.

Concrete, first obtain multiple different first-degree heating network pump, valve regulation scheme, and simulated the primary side flow theory value of each thermal substation under different schemes by forecast model；Then according to optimization aim, flow theory value is compared with flow target value, by object function, prioritization scheme is evaluated (utilizing iteration to calculate)；Repeat this step, until obtaining the optimal solution of the object function meeting optimizing.

Thermic load needed for each thermal substation is predicted for target with balanced heating by described step S21, draws the primary side flow target value that should arrive each thermal substation；I.e.

The target equation of primary side flow is:

${\left(G_{1r}\right)}_i=\frac{1}{2C\mathrm{\&rho;}(\frac{K_{const}}{{\mathrm{\&zeta;}}_i}-\frac{1}{{\left(K_1{F}_1\right)}_i})},i=1,2,...,s---\left(1\right);$

(G in formula_1r)_iRepresenting the primary side flow target value of i-th thermal substation, s is the sum of thermal substation, and C is that specific heat of water holds, and ρ is the density of hot water, ζ_iFor from radiator hot water to outdoor equivalent heat transfer factor, (K₁F₁)_iRepresent the exchange capability of heat of heat exchanger, K in i-th thermal substation_constRepresent quantitatively；And

And

${\left(K_1{F}_1\right)}_i=\frac{{\&Integral;}_{{\mathrm{\&tau;}}_0}^{{\mathrm{\&tau;}}_1}{\left(G_2\right)}_{i}C\mathrm{\&rho;}\&lsqb;t_{2g}\left(\mathrm{\&tau;}\right)-t_{2h}\left(\mathrm{\&tau;}\right)\&rsqb;d\mathrm{\&tau;}}{{\&Integral;}_{{\mathrm{\&tau;}}_0}^{{\mathrm{\&tau;}}_1}0.5\{\&lsqb;t_{1g}\left(\mathrm{\&tau;}\right)+t_{1h}\left(\mathrm{\&tau;}\right)\&rsqb;-\&lsqb;t_{2g}\left(\mathrm{\&tau;}\right)+t_{2h}\left(\mathrm{\&tau;}\right)\&rsqb;\}d\mathrm{\&tau;}};$

At ζ_i(K₁F₁)_iFormula in, (G₂)_iRepresent thermal substation i secondary heat networks recirculated water volume flow, t_2gRepresent secondary heat networks supply water temperature, t_2hRepresent secondary heat networks return water temperature, t_1gRepresent thermal substation first-degree heating network supply water temperature, t_1hRepresent thermal substation first-degree heating network return water temperature, t_wFor outdoor temperature, τ₁-τ₀≈ 3～5 days；

There is again first-degree heating network total water supply flow G_1tEqual to each thermal substation primary side flow sum, i.e.

$G_{1t}=\underset{i=1}{\overset{s}{\&Sigma;}}{\left(G_{1r}\right)}_i---\left(2\right);$

G_1tFor the total water supply flow of first-degree heating network, for known quantity.

By ζ_i、(K₁F₁)_iSubstitute into (G_1r)_iComputing formula (1), simultaneous formula (1), formula (2) i.e. solve K_constValue, and then obtain each thermal substation primary side flow target value by formula (1)；

Using forecast model that pump, the method for operation of valve in first-degree heating network are carried out optimizing in described step S22, wherein the optimization aim of optimizing includes heat supply harmony target, is arrived the first-degree heating network flow (G of each thermal substation heat exchanger by distribution₁)_iTo realize；

The object function of optimizing is

$\min Z={\mathrm{\&lambda;}}_G\underset{i=1}{\overset{N_s}{\&Sigma;}}\mathrm{\&Delta;}{\left(G_1\right)}_i+{\mathrm{\&lambda;}}_E\underset{i=j}{\overset{N_b}{\&Sigma;}}{\left(E_b\right)}_j+{\mathrm{\&lambda;}}_l(N_v\&times;100\%-\underset{k=1}{\overset{N_v}{\&Sigma;}}{l}_k);$

Wherein, Δ (G₁)_iDeviation for first-degree heating network flow with setting flow:

$\mathrm{\&Delta;}{\left(G_1\right)}_i=\left|\frac{{\left(G_1\right)}_i-{\left(G_{1r}\right)}_i}{{\left(G_{1r}\right)}_i}\right|;$

(G₁)_iThe first-degree heating network flow rate calculation value of the arrival i-th thermal substation for being obtained by forecast model simulation；And

(E_b)_jThe electrical power consumed for water pump:

${\left(E_b\right)}_j=\frac{\mathrm{\&rho;}g{\left(G_b\right)}_j{\left(H_b\right)}_j}{1000\&times;{\left({\mathrm{\&eta;}}_b\right)}_j};$

G is acceleration of gravity, (G_b)_jFor pumping the volume flow of hot water, (H_b)_jFor pump head；(η_b)_jFor the efficiency of water pump, subscript j represents that water pump is numbered；

In the computing formula of respective objects function, Z is the aggregative indicator optimized, N_sFor thermal substation number, N_bFor the number of first-degree heating network freq uency conversion supercharging pump, N_vFor the number of first-degree heating network regulation valve, l_kFor control valve opening, subscript k represents regulation valve numbering, l_minThe minimum aperture allowed for regulation valve, λ_G、λ_E、λ_lRespectively arrive the first-degree heating network flow of each thermal substation, the electrisity consumption of water pump and the importance weight of valve opening.

Embodiment 2

On the basis of embodiment 1, present invention also offers a kind of regulating of heating net system, including: forecast model sets up unit, sets up the regulating of heating net unit that unit is connected with this forecast model.

Described forecast model is set up unit and is adapted to set up forecast model, i.e. thermal hydraulic analysis solves module, and forecast model correction module.

Described thermal hydraulic analysis solves module, i.e.

Pipe network is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure；Wherein node represents the point that there is flow turnover, represents with set V, V=[V₁,V₂,…,V_n], in formula, n is the node number in pipe network；Section is internodal connection pipeline section, represents with set E, E=[E₁,E₂,…,E_m], in formula, m is sector number, and directed graph is expressed as G=<V, E>；

Obtain the incidence matrix A and fundamental circuit matrix B of pipe network according to network graph theory, wherein A is n × m rank matrixes, and B is s × m rank matrixes, and s is fundamental circuit number s=m-n+1；

Calculate the characteristics resistance coefficient of pipe network；

Build forecast model, i.e. hydraulic pipeline computational mathematics model: i.e.

AG^T=0；

BΔH^T=0；

In formula, G is the row vector G=[G of each pipeline section interior-heat water volume flow rate in record pipe network figure₁,G₂,…,G_m]；Δ H is the column vector of each pipeline section drag overall loss, i.e. Δ H=S* | G | * G+Z-H in record pipe network figure_b；

In formula, S is the characteristics resistance coefficient matrix of each pipeline section in first-degree heating network

S=diag{S₁,S₂,…,S_m}；

$S_i=7.02\&times;{10}^{-3}\frac{K^{0.25}}{{\mathrm{\&rho;d}}^{5.25}}(l+l_d),i=1,2,...,m$

K is the equivalent absolute roughness of tube wall, general K=0.0005m；D is internal diameter of the pipeline；l、l_dIt is respectively length and the local resistance equivalent length of pipe network run of designing；ρ is the average density of the interior hot water of pipe；

Z is the column vector Z=[Z of pipeline section two node potential energy difference₁,Z₂,…,Z_m]^T；

H_bFor pump head column vector H in heat supply network_b=[H_b1,H_b2,…,H_bm]^T；And

Heat supply network thermal-hydraulic calculates, i.e.

Described hydraulic pipeline computational mathematics models coupling loop adjustment Flow-rate adjustment algorithm is calculated through successive ignition, it is thus achieved that the volume flow of hot water in each pipeline section；And

Described forecast model correction module, i.e. utilizes heat supply network actual measurement service data to be modified forecast model.

Described regulating of heating net unit is suitable to be predicted the thermic load needed for each thermal substation with balanced heating for target, draws the primary side flow target value that should arrive each thermal substation, and uses forecast model that pump, the method for operation of valve in first-degree heating network are carried out optimizing.

Concrete, the working method of regulating of heating net unit may refer to the corresponding discussion of embodiment 1.

With the above-mentioned desirable embodiment according to the present invention for enlightenment, by above-mentioned description, relevant staff can carry out various change and amendment completely in the range of without departing from this invention technological thought.The content that the technical scope of this invention is not limited in description, it is necessary to determine its technical scope according to right.

Claims (10)

1. a regulating of heating net method, it is characterised in that comprise the steps:

Step S1, sets up forecast model；

Step S2, with balanced heating as target, obtains each pump in first-degree heating network, each by forecast model prediction The regulation scheme of valve.

Regulating of heating net method the most according to claim 1, it is characterised in that

The step setting up forecast model in described step S1 includes:

Step S11, thermal hydraulic analysis solves；And

Step S12, it was predicted that model corrects.

Regulating of heating net method the most according to claim 2, it is characterised in that

The method that in described step S11, thermal hydraulic analysis solves includes:

Step S111, is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure by pipe network；Its Interior joint represents the point that there is flow turnover, represents with set V, V=[V₁,V₂,…,V_n], in formula, n is Node number in pipe network；Section is internodal connection pipeline section, represents with set E, E=[E₁,E₂,…,E_m], in formula, m is sector number, and directed graph is expressed as G=<V, E>；

Obtain the incidence matrix A and fundamental circuit matrix B of pipe network according to network graph theory, wherein A is n × m rank Matrix, B is s × m rank matrixes, and s is fundamental circuit number s=m-n+1；

Step S112, calculates the characteristics resistance coefficient of pipe network；

Step S113, structure forecast model, i.e. hydraulic pipeline computational mathematics model:

AG^T=0；

BΔH^T=0；

In formula, G is the row vector of the volume flow of hot water in each pipeline section in record pipe network figure G=[G₁,G₂,…,G_m]；Δ H is the column vector of each pipeline section drag overall loss in record pipe network figure, i.e. Δ H=S* | G | * G+Z-H_b；

In formula, S is the characteristics resistance coefficient matrix of each pipeline section in first-degree heating network

S=diag{S₁,S₂,…,S_m}；

$S_i=7.02\&times;{10}^{-3}\frac{K^{0.25}}{{\mathrm{\&rho;d}}^{5.25}}(l+l_d),i=1,2,...,m;$

K is the equivalent absolute roughness of tube wall, and d is internal diameter of the pipeline；l、l_dIt is respectively pipe network run of designing Length and local resistance equivalent length；ρ is the average density of the interior hot water of pipe；

Z is the column vector Z=[Z of pipeline section two node potential energy difference₁,Z₂,…,Z_m]^T；

H_bFor pump head column vector H in heat supply network_b=[H_b1,H_b2,…,H_bm]^T；

Step S114, heat supply network thermal-hydraulic calculates, i.e.

By described hydraulic pipeline computational mathematics models coupling loop adjustment Flow-rate adjustment algorithm through successive ignition meter Calculate, it is thus achieved that the volume flow of hot water in each pipeline section；

In described step S12, forecast model correction includes: utilize heat supply network actual measurement service data to enter forecast model Row is revised.

Regulating of heating net method the most according to claim 3, it is characterised in that in described step S2 with Balanced heating is target, obtains the regulation scheme of each pump, each valve in first-degree heating network by forecast model prediction Method comprises the steps:

Step S21, is predicted the thermic load needed for each thermal substation with balanced heating for target, and drawing should Arrive the primary side flow target value of each thermal substation；

Step S22, uses forecast model that pump, the method for operation of valve in first-degree heating network are carried out optimizing.

Regulating of heating net method the most according to claim 4, it is characterised in that

Thermic load needed for each thermal substation is predicted for target by described step S21 with balanced heating, Go out to arrive the primary side flow target value of each thermal substation；I.e.

The target equation of primary side flow is:

${\left(G_{1r}\right)}_i=\frac{1}{2C\mathrm{\&rho;}(\frac{K_{const}}{{\mathrm{\&zeta;}}_i}-\frac{1}{{\left(K_1{F}_1\right)}_i})},i=1,2,...,s---\left(1\right);$

(G in formula_1r)_iRepresenting the primary side flow target value of i-th thermal substation, s is the sum of thermal substation, and C is Specific heat of water holds, ζ_iFor from radiator hot water to outdoor equivalent heat transfer factor, (K₁F₁)_iRepresent i-th heat The exchange capability of heat of heat exchanger, K in power station_constRepresent quantitatively；And

And

${\left(K_1{F}_1\right)}_i=\frac{{\&Integral;}_{{\mathrm{\&tau;}}_0}^{{\mathrm{\&tau;}}_1}{\left(G_2\right)}_{i}C\mathrm{\&rho;}\&lsqb;t_{2g}\left(\mathrm{\&tau;}\right)-t_{2h}\left(\mathrm{\&tau;}\right)\&rsqb;d\mathrm{\&tau;}}{{\&Integral;}_{{\mathrm{\&tau;}}_0}^{{\mathrm{\&tau;}}_1}0.5\left\{\&lsqb;t_{1g}\left(\mathrm{\&tau;}\right)+t_{1h}\left(\mathrm{\&tau;}\right)\&rsqb;-\&lsqb;t_{2g}\left(\mathrm{\&tau;}\right)+t_{2h}\left(\mathrm{\&tau;}\right)\&rsqb;\right\}d\mathrm{\&tau;}};$

At ζ_i(K₁F₁)_iFormula in, (G₂)_iRepresent thermal substation i secondary heat networks recirculated water volume flow, t_2gTable Show secondary heat networks supply water temperature, t_2hRepresent secondary heat networks return water temperature, t_1gRepresent that thermal substation first-degree heating network supplies water Temperature, t_1hRepresent thermal substation first-degree heating network return water temperature, t_wFor outdoor temperature, τ₁-τ₀≈ 3～5 days；

There is again first-degree heating network total water supply flow G_1tEqual to each thermal substation primary side flow sum, i.e.

$G_{1t}=\underset{i=1}{\overset{s}{\&Sigma;}}{\left(G_{1r}\right)}_i---\left(2\right);$

G_1tFor the total water supply flow of first-degree heating network, for known quantity；

By ζ_i、(K₁F₁)_iSubstitute into (G_1r)_iComputing formula (1), simultaneous formula (1), formula (2) i.e. solve K_const Value, and then obtain each thermal substation primary side flow target value by formula (1).

Regulating of heating net method the most according to claim 5, it is characterised in that

Described step S22 use forecast model pump, the method for operation of valve in first-degree heating network are carried out optimizing, Wherein the optimization aim of optimizing includes heat supply harmony target, is arrived the one of each thermal substation heat exchanger by distribution Level heat supply network flow (G₁)_iTo realize；

The object function of optimizing is

$\min Z={\mathrm{\&lambda;}}_G\underset{i=1}{\overset{N_s}{\&Sigma;}}\mathrm{\&Delta;}{\left(G_1\right)}_i+{\mathrm{\&lambda;}}_E\underset{i=j}{\overset{N_b}{\&Sigma;}}{\left(E_b\right)}_j+{\mathrm{\&lambda;}}_l(N_v\&times;100\%-\underset{k=1}{\overset{N_v}{\mathrm{\&Sigma;}}}{l}_k);$

Wherein, Δ (G₁)_iDeviation for first-degree heating network flow with setting flow:

$\mathrm{\&Delta;}{\left(G_1\right)}_i=\left|\frac{{\left(G_1\right)}_i-{\left(G_{1r}\right)}_i}{{\left(G_{1r}\right)}_i}\right|;$

(G₁)_iThe first-degree heating network flow rate calculation of the arrival i-th thermal substation for being obtained by forecast model simulation Value；And

(E_b)_jThe electrical power consumed for water pump:

${\left(E_b\right)}_j=\frac{\mathrm{\&rho;}g{\left(G_b\right)}_j{\left(H_b\right)}_j}{1000\&times;{\left({\mathrm{\&eta;}}_b\right)}_j};$

G is acceleration of gravity, (G_b)_jFor pumping the volume flow of hot water, (H_b)_jFor pump head；(η_b)_j For the efficiency of water pump, subscript j represents that water pump is numbered；

In the computing formula of respective objects function, Z is the aggregative indicator optimized, N_sFor thermal substation number, N_bFor the number of first-degree heating network freq uency conversion supercharging pump, N_vFor the number of first-degree heating network regulation valve, l_kOpen for regulation valve Degree, subscript k represents regulation valve numbering, l_minThe minimum aperture allowed for regulation valve, λ_G、λ_E、λ_lRespectively For arriving the first-degree heating network flow of each thermal substation, the electrisity consumption of water pump and the importance weight of valve opening.

7. a regulating of heating net system, it is characterised in that including:

Forecast model sets up unit, sets up the regulating of heating net unit that unit is connected with this forecast model.

Regulating of heating net system the most according to claim 1, it is characterised in that

Described forecast model is set up unit and is adapted to set up forecast model, i.e.

Thermal hydraulic analysis solves module, and forecast model correction module.

Regulating of heating net system the most according to claim 8, it is characterised in that

Described thermal hydraulic analysis solves, i.e.

Pipe network is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure；Wherein node represents There is the point of flow turnover, represent with set V, V=[V₁,V₂,…,V_n], in formula, n is in pipe network Node number；Section is internodal connection pipeline section, represents with set E, E=[E₁,E₂,…,E_m], formula Middle m is sector number, and directed graph is expressed as G=<V, E>；

Obtain the incidence matrix A and fundamental circuit matrix B of pipe network according to network graph theory, wherein A is n × m rank Matrix, B is s × m rank matrixes, and s is fundamental circuit number s=m-n+1；

Calculate the characteristics resistance coefficient of pipe network；

Build forecast model, i.e. hydraulic pipeline computational mathematics model: i.e.

AG^T=0；

BΔH^T=0；

In formula, G is the row vector G=[G of each pipeline section inner volume flow in record pipe network figure₁,G₂,…,G_m]；

Δ H is the column vector of each pipeline section drag overall loss in record pipe network figure, i.e.

Δ H=S* | G | * G+Z-H_b；

In formula, S is the characteristics resistance coefficient matrix of each pipeline section in first-degree heating network

S=diag{S₁,S₂,…,S_m}；

$S_i=7.02\&times;{10}^{-3}\frac{K^{0.25}}{{\mathrm{\&rho;d}}^{5.25}}(l+l_d),i=1,2,...,m$

K is the equivalent absolute roughness of tube wall；D is internal diameter of the pipeline；l、l_dIt is respectively pipe network run of designing Length and local resistance equivalent length；ρ is the average density of the interior hot water of pipe；

Z is the column vector Z=[Z of pipeline section two node potential energy difference₁,Z₂,…,Z_m]^T；

H_bFor pump head column vector H in heat supply network_b=[H_b1,H_b2,…,H_bm]^T；And

Heat supply network thermal-hydraulic calculates, i.e.

By described hydraulic pipeline computational mathematics models coupling loop adjustment Flow-rate adjustment algorithm through successive ignition meter Calculate, it is thus achieved that the volume flow of hot water in each pipeline section；And

Described forecast model correction module, i.e. utilizes heat supply network actual measurement service data to be modified forecast model.

Regulating of heating net system the most according to claim 9, it is characterised in that

Described regulating of heating net unit is suitable to carry out pre-for target to the thermic load needed for each thermal substation with balanced heating Survey, draw the primary side flow target value that should arrive each thermal substation, and use forecast model to one-level heat In net, pump, the method for operation of valve carry out optimizing.