CN107609680B - Hydraulic working condition optimization scheduling method for multi-heat-source annular centralized heat supply pipe network - Google Patents
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Abstract
The invention discloses a method for optimizing and scheduling hydraulic working conditions of a multi-heat-source annular centralized heat supply pipe network, which comprises the following steps: (1) user flow demand G for each heating power stationsub,n(ii) a (2) Setting the initial iteration point f of the heat source pump frequency0Calculating the flow G of each branch0And opening x of valve of heating power station0Setting a step length relaxation factor E; (3) calculating total power consumption W of heat source pump under k working conditionsTGeneralized gradient of
Improving heat source pump frequency f in the direction of the gradientk+1(ii) a (4) Newton iteration is utilized to calculate when the frequency of the heat source pump is fk+1The user traffic demand is Gsub,nOpening x of valve of each thermal stationk+1(ii) a (5) Calculating total power of heat source pump
And judging whether the condition is satisfied. The invention optimizes the water pump rotating speed and the valve opening of the multi-heat-source annular heat supply network on line in real time, can solve the problems of hydraulic imbalance and transmission and distribution energy consumption optimization in the scheduling process of the multi-heat-source annular network, and fully exerts the transmission and distribution capacity of the multi-heat-source annular network, so that the multi-heat-source annular network runs more reliably, efficiently and saves energy.
Description
Technical Field
The invention relates to a centralized heating technology, in particular to a hydraulic working condition optimized dispatching method for a multi-heat-source annular centralized heating pipe network.
Background
In order to enhance the reliability of the heat supply network, the urban central heating system is gradually developing to a complex ring topology structure with multiple heat sources and large scale. For example, a multi-heat source ring network in Tianjin has 4 thermal power plants and several gas peak shaving boiler rooms, and the total heat supply area reaches nearly two hundred million square meters (as shown in figure 1). The multiple heat source circular central heating system of the Shijiazhuang has three heat sources, the number of the heat stations reaches 1138, and the total heating area also reaches about 7000 ten thousand square meters (as shown in figure 2).
However, the multiple heat sources of the multi-heat source annular centralized heat supply pipe network have the disadvantages of large number of heat sources, complex pipe network topological structure and large space span, so that the system operation scheduling is difficult, the hydraulic and thermal imbalance phenomena are obvious, and the energy consumption of the heat supply network is high. Although many cities have realized a multi-heat source annular centralized heat supply pipe network covering the whole urban area, the networking operation is not actually realized, but the areas supplied by each heat source are isolated by valves to carry out the operation of removing the network. Although some cities try to operate in a network, the regulation effect is not ideal due to the complex characteristics of the multi-heat-source ring network.
At present, many scholars develop research aiming at the scheduling of a multi-heat-source annular centralized heat supply pipe network, and the operation adjusting method can solve the operation adjusting problem of the multi-heat-source annular network, but when a plurality of heat sources are put into operation simultaneously, the flow distribution scheme of each heat source still has greater subjectivity, and the flow configuration of each heat source in operation still has greater freedom degree to optimize. When the heat supply network is accessed with more heat sources, the larger number of the heat sources brings huge challenges to the formulation of a heat source scheduling scheme and the optimal scheduling of heat source flow, for example, a multi-heat-source ring network in a central urban area of Tianjin city has up to 30 heat sources (including 4 thermal power plants and 20 gas peak shaving boiler rooms). With the continuous development of renewable energy technology, more renewable energy sources such as solar energy, industrial waste heat, geothermal energy, wind energy and the like are connected to a future centralized heat supply network as heat sources. The heat supply of the new energy is uncertain, and the new energy is used as a passive heat source to be treated, and an active heat source with adjustable load, such as a thermal power plant, a gas boiler and the like, is used for assisting in the adjustment.
In addition, with the development of the intelligent power grid technology in China, large-scale grid connection of new energy such as wind power and the like requires a power system to have larger peak regulation capacity, and cogeneration is an effective way for expanding the peak regulation capacity of the power grid. The traditional mode of 'fixing electricity with heat' urgently needs to be converted into a 'heat and electricity cooperation' mode under the condition that the peak shaving capacity of a power grid is urgently required to be expanded. When the heat supply unit participates in electric power peak shaving, the heat supply amount of the heat supply unit is not controlled by a heat supply network dispatching system any more, and is converted into a passive heat source, and other active heat sources are needed for auxiliary heat supply. There is a need for a more flexible heat supply network scheduling technique to perform real-time online optimal configuration of the flow of each active heat source in the heat supply network to supplement the load of the passive heat source, or to timely withdraw part of the active heat sources or reduce the output. However, the existing heat supply network adjusting technology cannot realize the online optimal scheduling of a plurality of heat source flows.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a method for optimizing and scheduling hydraulic working conditions of a multi-heat-source annular centralized heat supply pipe network based on a nonlinear optimization technology.
The technical scheme adopted by the invention is as follows: a multi-heat source annular centralized heat supply pipe network hydraulic working condition optimization scheduling method comprises the following steps:
step A: user flow demand G for each heating power stationsub,n；
And B: setting the initial iteration point f of the heat source pump frequency0Calculating the flow G of each branch by Newton iteration0And opening x of valve of heating power station0Let the working condition k equal to 0;
and C: setting a step length relaxation factor E;
step D: calculating total power consumption W of heat source pump under k working conditionsTGeneralized gradient of
Improving heat source pump frequency f in the direction of an approximate gradientk+1The calculation formula is as follows:
step E: newton iteration is utilized to calculate when the frequency of the heat source pump is fk+1The user traffic demand is Gsub,nOpening x of valve of each thermal stationk+1；
Step F: if Newton iteration converges, performing the next step; if the Newton iteration exceeds the set times and is not converged, making the value of the step length relaxation factor e be half of the value of the original step length relaxation factor, and repeating the steps D to F;
step G: calculating total power of heat source pump
And judging the conditions:
whether the difference is satisfied, wherein the difference is a residual error; if satisfied, outputting an improved heat source pump frequency fk+1Total power of heat source pump
End of optimization calculation, f*＝fk+1The optimal solution is obtained; if not, let k equal to k +1, and repeat steps D to G.
In step D, i.e. about the gradient
The calculation formula of (a) is as follows:
wherein, WTThe total power of the heat source pump; h is a column vector formed by the pump lifts of the water pumps in the heat supply network; f is the heat source pump frequency; gscIs a heat source flow vector;
Stis the flow vector, S, corresponding to the branchrIs the flow vector corresponding to the remaining branches,
and
respectively G at the differentialt-1、Gsc-1、GrmAnd gsc,1Flow rate, Gt-1Column vectors, G, corresponding to the flow of the rest of the branches after removal of the heat source branch at the reference nodesc-1Column vectors corresponding to the flow of other heat source branches after the heat source branch at the reference node is removed; grmFlow vectors, g, corresponding to the remaining branches of the ringsc,1The 1 st heat source pump flow rate;
and I is an identity matrix.
In step G, the total power W of the heat source pumpTThe calculation formula of (a) is as follows:
wherein, gsc,jThe jth heat source pump flow rate; f. ofjIs the jth heat source pump frequency; hj(gsc,j,fj) A head characteristic curve of the jth heat source pump; etaj(gsc,j,fj) The j-th heat source pump efficiency characteristic curve is shown.
The invention has the beneficial effects that: the invention relates to a hydraulic working condition optimization scheduling method for a multi-heat-source annular centralized heat supply pipe network, which is based on a nonlinear optimization technology, can optimize the water pump rotating speed and the valve opening of the multi-heat-source annular heat supply network on line in real time, can solve the problems of hydraulic imbalance and transmission and distribution energy consumption optimization in the scheduling process of the multi-heat-source annular network, and fully exerts the transmission and distribution capacity of the multi-heat-source annular network, so that the multi-heat-source annular network can run more reliably, efficiently and energy-efficiently.
Drawings
FIG. 1: a schematic diagram of a multi-heat source annular centralized heat supply pipe network in Tianjin city;
FIG. 2: a schematic diagram of a multi-heat source annular centralized heat supply pipe network in Shizhuang city;
FIG. 3: the invention discloses a flow schematic diagram of a hydraulic working condition optimization scheduling method of a multi-heat-source annular centralized heat supply pipe network.
Detailed Description
The heat source flow optimization scheduling problem of the multi-heat source ring network can be summarized as the following optimization problems: under the condition that the flow demand of each tail end is known and the flow and the lift of each heat source are unknown under a certain water supply temperature, the optimal rotating speed of each heat source pump and the valve opening of a tail end user are optimized to meet the requirements of the users by taking the lowest transmission and distribution energy consumption as a target. The optimization problem can be described as:
an objective function:
constraint conditions are as follows:
A·G＝0 (2)
AT·P＝S·Ga·G+H (3)
Gsub＝Gsub,n (4)
Ssub,a≥Ssub,n (5)
30Hz≤fj≤50Hz (6)
wherein, WTThe total power of the heat source pump; gsc,jThe jth heat source pump flow rate; f. ofjIs the jth heat source pump frequency; hj(gsc,j,fj) A head characteristic curve of the jth heat source pump; etaj(gsc,j,fj) An efficiency characteristic curve of the jth heat source pump; a is a basic adjacency matrix of the heat supply network; p is a node pressure vector of the heat supply network; g is a column vector formed by the flow of all branches in the heat supply network; s is a diagonal matrix formed by the impedance of each pipeline of the heat supply network; gaIs a diagonal matrix of G, and is formed by taking absolute values of vector G and then diagonalizing; h is a column vector formed by the pump lifts of the water pumps in the heat supply network; ssub,aThe actual impedance of each thermal station; ssub,nImpedance when the valves of each heating power station are fully opened; gsubA column vector formed by actual flow of all heating power stations; gsub,nA column vector formed for the demand flows of all thermal power stations. In the optimization calculation process, Gsub,nIs given.
The method is a large-scale nonlinear optimization problem, an objective function is nonlinear, constraint conditions are also nonlinear, and the method can be solved by adopting nonlinear optimization algorithms such as a generalized gradient method, a sequence quadratic programming method, an internal point penalty function method and the like. The optimal scheduling method can calculate the rotating speed of each operating heat source pump and the opening of each tail end valve in real time, and further obtain the optimal flow distribution of each heat source.
The embodiment adopts a generalized approximation gradient method for solving.
The generalized gradient method needs to calculate the total power consumption W of the heat source pump in each iteration stepTGeneralized mean gradient of
To calculate
The following changes to the constraint equations are required. Partitioning the matrix A into blocks
A＝[At Ar] (6)
For having NscA heat source, NsubN are respectively arranged on a supply and return water dry net of each heating power stationrA multi-heat source ring network of rings, wherein N can besc-1 heat source, Nsub2N on branch and main lines of heating power stationrmThe surplus branches are treated as surplus branches of the whole heat supply pipe network. In the formula (6), AtA sub-array of basic adjacency matrices corresponding to branches of the entire pipe network, ArAnd the sub-arrays are sub-arrays of basic adjacent matrixes corresponding to the remaining branches of the whole pipe network. According to the graph theory, the matrix AtIs non-singular, such that equations (2) and (3) may be varied to the following forms:
wherein G istIs the flow vector corresponding to the branch, GrIs the flow vector corresponding to the remaining branches, StIs the impedance vector, S, corresponding to the branchrIs the impedance vector corresponding to the remaining branches. Combining formula (8) and formula (9) yields:
wherein I is an identity matrix.
A is to berThe further partitioning is as follows:
Ar＝[Ar,sub Ar,sc-1 Ar,rm] (11)
wherein A isr,subFor the division into blocks corresponding to thermal power stations, Ar,sc-1To correspond toRemoving one heat source as a reference node and then dividing blocks corresponding to other heat sources into blocks Ar,rmIs the block corresponding to the rest branches on the pipe network ring. Equation (7) is:
wherein G issc-1Column vectors corresponding to the flow of other heat source branches after the heat source branch at the reference node is removed; grmAnd flow vectors corresponding to the remaining branches on the ring. And (3) obtaining the micro increment of the total transmission and distribution energy consumption of the heat supply network by fully differentiating the formula (1):
wherein f is the heat source pump frequency; gscIs the heat source flow vector.
Due to the equality constraints of equations (2) to (4), df and dGscThere is a dependency relationship between these two micro increments, and the relationship between these two micro increments is derived below. The two sides of equation (12) are differentiated to obtain:
wherein G ist-1Column vectors, g, corresponding to the flow of the rest branches after the heat source branch at the reference node is removedsc,1The 1 st heat source pump flow rate.
Equation (14) can be:
wherein, It-1Is an identity matrix. Thereby to obtain
Wherein,
the equation (10) is differentiated on both sides and is reduced to the following form:
wherein,
and
respectively G at the differentialt-1、Gsc-1、GrmAnd gsc,1And (4) flow rate. Order to
Equation (17) can be:
partitioning U and performing an initial row transform (multiplying by a row transform matrix) on equation (18) yields the following form:
U＝[V1 V2] (19)
so that equation (18) can be:
substituting equation (16) into equation (20) yields:
equation (21) can be written as:
wherein,
formula (22) gives df and dGscThe method has a dependency relationship, and the micro-increment combination formula (22) of the transmission and distribution energy consumption of the whole heat supply network can be reduced to:
so that the approximate gradient of the transmission and distribution energy consumption of the heat supply network is as follows:
the approximation gradient derived below in conjunction with the above derivation
The invention is further described by combining the calculation formula of (a) and the accompanying drawing.
As shown in fig. 3, the method for optimizing and scheduling the hydraulic working conditions of the multi-heat-source annular centralized heat supply pipe network comprises the following steps:
step A: user flow demand G for each heating power stationsub,n；
And B: setting the initial iteration point f of the heat source pump frequency0Calculating the flow G of each branch by Newton iteration0And opening x of valve of heating power station0Let the working condition k equal to 0;
and C: setting a step size relaxation factor epsilon (which can be 0.5);
step D: calculating total power consumption W of heat source pump under k working conditionsTGeneralized gradient of
Improving heat source pump frequency f in the direction of an approximate gradientk+1Wherein is about a gradient
Calculated using equation (24), heat source pump frequency fk+1The following formula is adopted for calculation:
step E: newton iteration is utilized to calculate when the frequency of the heat source pump is fk+1The user traffic demand is Gsub,nOpening x of valve of each thermal stationk+1；
Step F: if Newton iteration converges, performing the next step; if Newton iteration is not converged after the set number of times (usually 50 times), making the value of the step length relaxation factor epsilon be half of the value of the original step length relaxation factor, and repeating the steps D to F;
step G: calculating the total power of the heat source pump by adopting the formula (1)
And judging the conditions:
is satisfied, wherein, is residual error (may be 10)-6) (ii) a If satisfied, outputting an improved heat source pump frequency fk+1Total power of heat source pump
End of optimization calculation, f*＝fk+1The optimal solution is obtained; if not, let k equal to k +1, and repeat steps D to G.
Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and those skilled in the art can make many modifications without departing from the spirit and scope of the present invention as defined in the appended claims.
Claims (2)
1. A hydraulic working condition optimization scheduling method for a multi-heat-source annular centralized heat supply pipe network is characterized by comprising the following steps:
step A: user flow demand G for each heating power stationsub,n；
And B: setting the initial iteration point f of the heat source pump frequency0Calculating the flow G of each branch by Newton iteration0And opening x of valve of heating power station0Let the working condition k equal to 0;
and C: setting a step length relaxation factor E;
step D: calculating total power consumption W of heat source pump under k working conditionsTGeneralized gradient of
Improving heat source pump frequency f in the direction of an approximate gradientk+1The calculation formula is as follows:
therein, i.e. about the gradient
The calculation formula of (a) is as follows:
wherein, WTThe total power of the heat source pump; h is in the heat supply networkA row vector formed by the pump lifts of the water pumps; f is the heat source pump frequency; gscIs a heat source flow vector;
Stis the flow vector, S, corresponding to the branchrIs the flow vector corresponding to the remaining branches,
and
respectively G at the differentialt-1、Gsc-1、GrmAnd gsc,1Flow rate, Gt-1Column vectors, G, corresponding to the flow of the rest of the branches after removal of the heat source branch at the reference nodesc-1Column vectors corresponding to the flow of other heat source branches after the heat source branch at the reference node is removed; grmFlow vectors, g, corresponding to the remaining branches of the ringsc,1The 1 st heat source pump flow rate;
i is an identity matrix;
step E: newton iteration is utilized to calculate when the frequency of the heat source pump is fk+1The user traffic demand is Gsub,nOpening x of valve of each thermal stationk+1；
Step F: if Newton iteration converges, performing the next step; if the Newton iteration exceeds the set times and is not converged, making the value of the step length relaxation factor epsilon be half of the value of the original step length relaxation factor, and repeating the steps D to F;
step G: calculating total power of heat source pump
And judging the conditions:
whether the difference is satisfied, wherein the difference is a residual error; if satisfied, outputting an improved heat source pump frequency fk+1Total power of heat source pump
End of optimization calculation, f*＝fk+1The optimal solution is obtained; if not, let k equal to k +1, and repeat steps D to G.
2. The method for optimizing and scheduling hydraulic working conditions of the multi-heat-source annular centralized heating pipe network according to claim 1, wherein in the step G, the total power W of heat source pumpsTThe calculation formula of (a) is as follows:
wherein, gsc,jThe jth heat source pump flow rate; f. ofjIs the jth heat source pump frequency; hj(gsc,j,fj) A head characteristic curve of the jth heat source pump; etaj(gsc,j,fj) The j-th heat source pump efficiency characteristic curve is shown.
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| CN109140581A (en) * | 2018-08-10 | 2019-01-04 | 天津六百光年智能科技有限公司 | A kind of method and automatic energy saving system of thermodynamic transport pipe network Optimization of Energy Saving |
| CN109636037B (en) * | 2018-12-12 | 2023-02-14 | 石家庄华电供热集团有限公司 | Multi-heat-source annular heat supply network hydraulic optimization scheduling method based on PSO algorithm |
| CN109611949B (en) * | 2019-01-04 | 2020-11-17 | 清华四川能源互联网研究院 | Pipeline thermal calculation method and monitoring and heating system adopting same |
| CN110473118B (en) * | 2019-08-14 | 2023-04-14 | 华北电力大学（保定） | Generation method of multi-heat-source dendritic heat supply pipe network incidence matrix |
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| CN112417662B (en) * | 2020-11-13 | 2022-07-15 | 天津大学 | Method for realizing dynamic hydraulic optimization of centralized heat supply pipe network system |
| CN115143517B (en) * | 2021-03-29 | 2023-09-22 | 北京华大智宝电子系统有限公司 | Balance adjustment method and system for secondary network of central heating pipe network |
| CN115143516B (en) * | 2021-03-29 | 2023-09-22 | 北京华大智宝电子系统有限公司 | Balance adjustment method and system for primary network of dendritic heat supply pipe network |
| CN113836780B (en) * | 2021-05-28 | 2024-06-07 | 重庆大学 | Water distribution network hydraulic calculation method based on improved bovine method |
| CN115031284B (en) * | 2022-06-27 | 2024-04-09 | 唐山市热力集团有限公司 | Geothermal and ground source heat pump comprehensive composite heat supply control system |
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