Heat supply pipe network hydraulic optimization method and device and medium

Abstract

The invention discloses a heat supply pipe network hydraulic optimization method and device and a medium. The heat supply pipe network hydraulic optimization method comprises the steps that a pipe network topological structure is utilized to construct a hydraulic simulation model, and a pipe network balance equation is obtained; constructing a pipe network hydraulic optimization model by taking a pipe network balance equation, a heat source flow, a water pump frequency and a heating station valve opening degree as constraint conditions and taking the minimum total power of a heat supply pipe network water pump as a target function; and based on the pipe network hydraulic optimization model, performing iterative solution by using a sequential quadratic programming algorithm to obtain an optimal control strategy including optimal heat source flow distribution, optimal heating station valve opening and optimal water pump frequency, and outputting the optimal control strategy. According to the method, based on the established pipe network hydraulic optimization model, the sequential quadratic programming algorithm is used for optimizing heat source flow distribution, the valve opening degree of the heating station and the operation frequency of the water pump, hydraulic optimization of the complex heat supply pipe network is achieved, and the operation power consumption of the water pump of the system reaches the minimum on the premise of meeting user requirements and hydraulic balance.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic optimization of urban heating pipe networks, and in particular to a method, device and medium for hydraulic optimization of heating pipe networks. Background Art

[0002] As cities gradually expand, the scope of urban heating is also gradually increasing, and urban heating systems are also developing from simple single heat sources to complex multi-heat source ring networks, which puts higher requirements on operation and regulation. For centralized heating systems, it is still difficult to achieve complete renewable energy substitution. Therefore, in order to achieve energy conservation and carbon reduction as much as possible, it is necessary to optimize the operating conditions and reduce heat consumption and pump work as much as possible.

[0003] Among them, the problem of hydraulic optimization scheduling of the heating network needs to be solved to save pump work. Compared with the single-heat-source branch network, the multi-heat-source ring network has a complex network topology. The coupling between each heat source and the thermal power station is high. The phenomenon of one gaining and the other losing often occurs during the regulation process, making it difficult to truly achieve precise heating.

[0004] Therefore, how to achieve real-time optimization of the pipeline network and process it at a faster speed based on a full understanding of the hydraulic and thermal conditions of the pipeline network, and obtain the optimal heat source output distribution, the optimal thermal station valve opening and the pump group operation strategy that minimizes the power consumption of the water pump operation, must be the technical problem that needs to be solved at present. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings and defects of the prior art and to provide a method, device and medium for hydraulic optimization of a heating pipe network.

[0006] A first aspect of the present invention provides a method for hydraulic optimization of a heating network, comprising the steps of:

[0007] Use the pipe network topology to build a hydraulic simulation model and obtain the pipe network balance equation;

[0008] The network hydraulic optimization model is constructed with the network balance equation, heat source flow, water pump frequency, and thermal station valve opening as constraints, and the minimum total power of the heating network water pump as the objective function.

[0009] Based on the pipe network hydraulic optimization model, the sequential quadratic programming algorithm is used to iteratively solve the optimal control strategy including the optimal heat source flow distribution, the optimal thermal power station valve opening and the optimal water pump frequency, and then output.

[0010] Wherein, the pipe network balance equation includes the pipe network flow mass balance equation and the pipe network loop pressure balance equation;

[0011] The network flow mass balance equation is: :

[0012] The pipe network loop pressure balance equation is: ;

[0013] In the formula, represents the node traffic matrix, represents the basic association matrix of the pipe network established based on graph theory, represents the pipeline flow matrix, The basic loop matrix representing the ring network, represents the pipeline impedance matrix, Indicates the valve pressure drop, Indicates the pump head.

[0014] The simulation model of the pump head and valve pressure drop is as follows:

[0015] ,

[0016] ,

[0017] In the formula, , , Indicates the three characteristic coefficients of water pump flow-head, Indicates the actual frequency of the pump. Indicates the rated frequency of the pump, Indicates the valve adjustable ratio, Indicates the valve flow capacity, Indicates valve opening.

[0018] Wherein, the objective function is expressed as follows: ,

[0019] In the formula, Indicates the total power of the pump, represents the fluid density, represents the acceleration due to gravity, Indicates the pump efficiency, Indicates the number of pumps.

[0020] The constraints include:

[0021] ;

[0022] In the formula, Indicates thermal power station flow, Indicates thermal power station Demand flow, Indicates heat source of traffic, Indicates heat source The minimum flow rate, Indicates heat source Maximum flow rate; Indicates thermal power station The valve opening, Indicates thermal power station The minimum valve opening, Indicates thermal power station The maximum opening of the valve, Indicates water pump The frequency, Indicates water pump The minimum frequency, Indicates water pump The maximum frequency, Indicates the number of heat sources, Indicates the number of thermal power stations, Indicates the number of pumps.

[0023] Among them, in the iterative solution using the sequential quadratic programming algorithm, it is judged whether the convergence condition is met. If so, the iterative solution is stopped, and the current flow distribution of each heat source, the valve opening of each thermal power station and the frequency of each water pump are obtained as the optimal control strategy output; otherwise, the iteration continues.

[0024] Among them, when using the sequential quadratic programming algorithm to iteratively solve the problem, the KKT condition equation of the quadratic programming problem is constructed and initialized based on the Lagrangian function, and then the gradient of the Lagrangian function is calculated, and whether the convergence condition is met is judged according to the gradient value.

[0025] Among them, when the sequential quadratic programming algorithm is used for iterative solution, the quadratic programming problem is solved in each iteration to obtain the next search direction, and the positive definite matrix in the quadratic programming problem is corrected by the quasi-Newton method, and then the step of finding the gradient of the Lagrangian function is returned. Based on the corrected positive definite matrix, the gradient of the Lagrangian function is found to achieve iteration.

[0026] A second aspect of the present invention provides a heating network hydraulic optimization device, comprising a memory and a processor, wherein the memory is used to store a method program supporting execution of the processor, and the processor is configured to execute the program stored in the memory to implement the heating network hydraulic optimization method.

[0027] In a third aspect of the present invention, a computer readable storage medium is provided, on which a computer program or instruction is stored, and when the computer program or instruction is executed by a processor, it is used to implement the hydraulic optimization method of the heating network.

[0028] The heating pipe network hydraulic optimization method of the present invention is based on the established pipe network hydraulic optimization model, and utilizes the sequential quadratic programming algorithm to optimize the heat source flow distribution, the valve opening of the thermal power station and the water pump operation frequency, thereby realizing the hydraulic optimization of the complex heating pipe network. On the premise of meeting user needs and hydraulic balance, the system's water pump operation power consumption is minimized, thereby achieving precise heating, energy saving and carbon reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for hydraulic optimization of a heating network according to an embodiment of the present invention.

[0030] Figure 2 It is a flowchart of iterative solution of the sequential quadratic programming algorithm according to an embodiment of the present invention.

[0031] Figure 3 It is a flow chart of a sequential quadratic programming algorithm iteratively solving a quadratic programming problem according to an embodiment of the present invention.

[0032] Figure 4 It is a schematic diagram of a heating pipe network hydraulic optimization device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] The present invention firstly constructs a hydraulic simulation model using the pipe network topology structure, establishes the pipe network balance equation of the multi-heat source annular pipe network, and then establishes a hydraulic optimization model for heat source flow distribution, thermal station valve opening and water pump operation frequency with the pipe network balance equation including the pipe network flow mass balance equation and the pipe network loop pressure balance equation, the upper and lower limits of the heat source flow, the valve opening range of the thermal power station and the frequency of the water pump as constraint conditions, and then based on the hydraulic optimization model, the sequential quadratic programming algorithm is used to iteratively solve and output the optimal control strategy. Among them, the sequential quadratic programming algorithm has the advantages of good convergence and fast calculation speed, and uses the Lagrangian function and the quasi-Newton method to solve the first-order derivative of the constraint equation and the second-order derivative of the objective function, and continuously iterates to finally output the optimal control strategy, so that the total power consumption of the system water pump is minimized.

[0035] See also Figure 1 As shown, in a first aspect of an embodiment of the present invention, a method for hydraulic optimization of a heating network is provided, comprising the steps of:

[0036] Use the pipe network topology to build a hydraulic simulation model and obtain the pipe network balance equation;

[0037] The network hydraulic optimization model is constructed with the network balance equation, heat source flow, water pump frequency, and thermal station valve opening as constraints, and the minimum total power of the heating network water pump as the objective function.

[0038] Based on the pipe network hydraulic optimization model, the sequential quadratic programming algorithm is used to iteratively solve the optimal control strategy including the optimal heat source flow distribution, the optimal thermal power station valve opening and the optimal water pump frequency, and then output.

[0039] The basic loop method is used to describe the network flow mass balance equation representing the relationship between the network node and the pipeline flow and the network loop pressure balance equation representing the loop pressure drop relationship. Therefore, the network balance equation includes the network flow mass balance equation and the network loop pressure balance equation.

[0040] The network flow mass balance equation is: :

[0041] The pipe network loop pressure balance equation is: ;

[0042] In the formula, represents the node traffic matrix (t / h), represents the basic association matrix of the pipe network established based on graph theory, represents the pipeline flow matrix (t / h), The basic loop matrix representing the ring network, Represents pipeline impedance matrix (Pa / (t / h) 2 ), Indicates the valve pressure drop, Indicates the pump head.

[0043] The simulation model of the water pump head and valve pressure drop is as follows:

[0044] ,

[0045] ,

[0046] In the formula, , , Indicates the three characteristic coefficients of water pump flow-head, Indicates the actual frequency of the pump. Indicates the rated frequency of the pump. Indicates the valve adjustable ratio, Indicates the valve flow capacity, Indicates valve opening.

[0047] It is generally believed that the outflow flow rate at any node in the pipe network under steady-state conditions is equal to the inflow flow rate. Therefore, for a closed pipe network system without leakage and water replenishment, the flow rate at each node can be taken as 0 in hydraulic calculation. For the hydraulic simulation model, the energy balance equation is the pressure balance equation. For any loop in the pipe network, the sum of the loop pressure drops under steady-state conditions should be 0, that is, ;

[0048] The heating network generally regulates hydraulic conditions through water pumps and valves. It can be considered that the water pump is set on the heat source or the network relay pump station, and the valve is set on the heating station. When it participates in hydraulic calculations, the simulation model of the water pump and valve should be added to the pressure balance equation, so we get: :

[0049] Wherein, the objective function is expressed as follows: ,

[0050] In the formula, Indicates the total power of the pump, represents the fluid density, represents the acceleration due to gravity, represents the pump efficiency, Indicates the number of pumps.

[0051] The constraints include:

[0052] ;

[0053] In the formula, represents the flow rate of each thermal power station, represents the demand flow, Indicates heat source of traffic, Indicates heat source The minimum flow rate, Indicates heat source Maximum flow rate; Indicates thermal power station The valve opening, Indicates thermal power station The minimum valve opening, Indicates thermal power station The maximum opening of the valve, Indicates water pump The frequency, Indicates water pump The minimum frequency, Indicates water pump The maximum frequency, Indicates the number of heat sources, Indicates the number of thermal power stations.

[0054] Among them, in the iterative solution using the sequential quadratic programming algorithm, based on the Lagrangian function and according to the pipe network hydraulic optimization model, the KKT condition equation that meets the quadratic programming principle is established, the KKT condition equation is initialized, and after iteratively solving the quadratic programming problem, it is determined whether the convergence condition is met. If so, the iterative solution is stopped, and the current heat source flow distribution, the valve opening of each thermal station, and the frequency of each water pump are obtained as the optimal control strategy output, that is, the output control parameters; otherwise, the iteration parameters are updated and the iteration continues. The iteration parameters include the search direction. For the specific process, please refer to Figure 2 shown.

[0055] Among them, when using the sequential quadratic programming algorithm to iteratively solve, in each iteration, the quadratic programming problem is solved, and the iteration parameters are updated to obtain the next search direction. Then, the positive definite matrix in the quadratic programming problem is corrected by the quasi-Newton method, and then the step of finding the gradient of the Lagrangian function is returned. Based on the corrected positive definite matrix, the gradient of the Lagrangian function is found to implement iteration. For the specific process, please refer to Figure 3 shown.

[0056] The convergence condition is determined by the gradient value obtained by calculating the gradient of the Lagrangian function of the quadratic programming problem. Figure 3 shown.

[0057] Step 1: Set the number of iterations , given the initial point , , , given an initial positive definite matrix ; , , They represent the initially given strategy parameters, the Lagrange multipliers of the inequality constraints, and the Lagrange multipliers of the equality constraints respectively;

[0058] Step 2: For the Lagrangian function Find the gradient and calculate ;

[0059] Step 3: Determine whether the convergence condition is met, that is , if it is reached, stop the calculation and output the current solution The optimal solution , otherwise continue to step 4;

[0060] Step 4: Solve the quadratic programming problem to obtain the next search direction , and update and ;

[0061] Step 5: Order ;

[0062] Step 6: Use the quasi-Newton method to correct the calculation and obtain the corrected positive definite matrix ;

[0063] ;

[0064] In the formula, ; represents the solution function;

[0065] Step 7: Order , and return to step 2 to continue iterative calculation.

[0066] Among them, the Lagrangian function of the nonlinear optimization problem can be written as:

[0067] ;

[0068] In the formula, represents the solution function, Inequality constraints The Lagrange multiplier of Representing equality constraints The Lagrange multiplier of .

[0069] Construct the KKT condition equation of the optimization problem based on the Lagrangian function:

[0070] ;

[0071] In the formula, represents the optimal solution to a nonlinear optimization problem.

[0072] Based on the above process, when using quadratic programming to solve, the original nonlinear optimization problem can be further transformed into the following form through Newton iteration, and the optimal solution of the nonlinear optimization problem satisfies the KKT condition equation:

[0073] ;

[0074] ;

[0075] In the formula, represents the iterative search direction, It is an approximation based on the quasi-Newton method. The symmetric positive definite matrix of represents transpose, Represents the gradient.

[0076] According to the heating pipe network hydraulic optimization method of the embodiment of the present invention, based on the established pipe network hydraulic optimization model, the sequential quadratic programming algorithm is used to optimize the heat source flow distribution, the valve opening of the thermal power station and the water pump operation frequency, thereby realizing the hydraulic optimization of the complex heating pipe network. On the premise of meeting user needs and hydraulic balance, the system's water pump operation power consumption is minimized, thereby achieving precise heating, energy saving and carbon reduction.

[0077] See also Figure 4 As shown, a second aspect of an embodiment of the present invention provides a heating pipe network hydraulic optimization device, including a memory and a processor, the memory is used to store a method program supporting the execution of the processor, and the processor is configured to execute the program stored in the memory to implement the heating pipe network hydraulic optimization method.

[0078] Furthermore, the heating network hydraulic optimization device also includes: a communication interface for communication between the memory and the processor, a memory for storing a computer program that can be run on the processor, and the memory may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. If the memory, processor and communication interface are implemented independently, the communication interface, memory and processor can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0079] Optionally, in a specific implementation, if the memory, processor and communication interface are integrated on a chip, the memory, processor and communication interface can communicate with each other through an internal interface.

[0080] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0081] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, it is used to implement the hydraulic optimization method of the heating network.

[0082] The embodiment of the present application also provides a computer program product, including a computer program or instructions, which, when executed, implements the above-mentioned method for hydraulic optimization of a heating network.

[0083] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0084] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0085] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0086] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A method for hydraulic optimization of a heating network, characterized in that: Includes steps: Use the pipe network topology to build a hydraulic simulation model and obtain the pipe network balance equation; The network hydraulic optimization model is constructed with the network balance equation, heat source flow, water pump frequency, and thermal station valve opening as constraints, and the minimum total power of the heating network water pump as the objective function. Based on the pipe network hydraulic optimization model, the sequential quadratic programming algorithm is used to iteratively solve the optimal control strategy including the optimal heat source flow distribution, the optimal thermal power station valve opening and the optimal water pump frequency, and then output.

2. The method for hydraulic optimization of a heating network according to claim 1, characterized in that: The pipe network balance equation includes a pipe network flow mass balance equation and a pipe network loop pressure balance equation; The network flow mass balance equation is: : The pipe network loop pressure balance equation is: ; In the formula, represents the node traffic matrix, represents the basic association matrix of the pipe network established based on graph theory, represents the pipeline flow matrix, The basic loop matrix representing the ring network, represents the pipeline impedance matrix, Indicates the valve pressure drop, Indicates the pump head.

3. The method for hydraulic optimization of a heating network according to claim 2, characterized in that: The simulation model of the pump head and valve pressure drop is as follows: ， ， In the formula, , , Indicates the three characteristic coefficients of water pump flow-head, Indicates the actual frequency of the pump. Indicates the rated frequency of the pump, Indicates the valve adjustable ratio, Indicates the valve flow capacity, Indicates valve opening.

4. The method for hydraulic optimization of a heating network according to claim 3, characterized in that: The objective function is expressed as follows: , In the formula, Indicates the total power of the pump, represents the fluid density, represents the acceleration due to gravity, represents the pump efficiency, Indicates the number of pumps.

5. The method for hydraulic optimization of a heating network according to claim 4, characterized in that: The constraints include: ； In the formula, Indicates thermal power station flow, Indicates thermal power station Demand flow, Indicates heat source flow, Indicates heat source Minimum flow rate, Indicates heat source Maximum flow rate; Indicates thermal power station The valve opening, Indicates thermal power station The minimum valve opening, Indicates thermal power station The maximum opening of the valve, Indicates water pump The frequency, Indicates water pump The minimum frequency, Indicates water pump The maximum frequency, Indicates the number of heat sources, Indicates the number of thermal power stations.

6. The method for hydraulic optimization of a heating network according to claim 1, characterized in that: In the iterative solution using the sequential quadratic programming algorithm, it is determined whether the convergence condition is met. If so, the iterative solution is stopped, and the current heat source flow distribution, the valve opening of each thermal power station and the frequency of each water pump are obtained as the optimal control strategy output; otherwise, the iteration is continued.

7. The method for hydraulic optimization of a heating network according to claim 6, characterized in that: When using the sequential quadratic programming algorithm to iteratively solve the problem, the KKT condition equation of the quadratic programming problem is constructed and initialized based on the Lagrangian function, and then the gradient of the Lagrangian function is calculated, and whether the convergence condition is met is determined based on the gradient value.

8. The method for hydraulic optimization of a heating network according to claim 7, characterized in that: When using the sequential quadratic programming algorithm to iteratively solve the problem, the quadratic programming problem is solved in each iteration to obtain the next search direction, and the positive definite matrix in the quadratic programming problem is corrected by the quasi-Newton method. Then, the step of finding the gradient of the Lagrangian function is returned, and the gradient of the Lagrangian function is found based on the corrected positive definite matrix to achieve iteration.

9. A heating network hydraulic optimization device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a method program supporting execution of the processor, and the processor is configured to execute the program stored in the memory to implement the hydraulic optimization method for a heating pipe network as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, it is used to implement the hydraulic optimization method for a heating network as described in any one of claims 1 to 8.

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