Hardware-in-the-loop comprehensive energy system pipe network and equipment parameter checking method and system

Abstract

The invention discloses a hardware-in-the-loop comprehensive energy system pipe network and equipment parameter checking method and system, and the method comprises the steps: 1, building a comprehensive energy system equipment and pipeline model based on a physical platform which comprises an electric water heater, a fan coil and a pipeline network, the equipment model comprises a heat source physical model and a heat load physical model; step 2, collecting operation data of each physical module of the hardware physical platform; and step 3, correcting parameters of the equipment model and the pipeline model by using the collected operation data, and realizing online checking of physical parameters under the assistance of hardware in the loop. According to the method, model parameters are corrected and improved by utilizing a data processing and model analysis method, the model precision of the comprehensive energy system is improved, huge model difference between theoretical analysis of the comprehensive energy system and a real operation scene is considered, the distance between theoretical analysis of the comprehensive energy system and actual operation is shortened, and the model accuracy is improved. And the application of the comprehensive energy system in actual industrial production is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system modeling and control, and more specifically, to a hardware-in-the-loop integrated energy system pipe network, equipment parameter verification method and system. Background Art

[0002] With the growth of population and economic development, the demand for energy is also increasing. The traditional single energy supply model can no longer meet the demand. Secondly, the supply of a single energy source has problems such as instability and price fluctuations. In order to improve the reliability and stability of energy supply, it is necessary to achieve optimal configuration and utilization of energy through the combination of multiple energy sources. In addition, the implementation of an integrated energy system can improve energy utilization efficiency, reduce energy consumption and emissions, achieve environmental protection and energy saving, and help achieve sustainable development. Therefore, the development of an integrated energy system is considered to be an important trend in future energy development, providing energy-saving and environmental protection solutions for various fields.

[0003] Due to the above advantages of the integrated energy system, its development has received widespread attention from countries around the world, including China. Replacing the traditional energy supply system and becoming the main energy supply source has become the mainstream form of development today. Since the realization of the integrated energy system requires the coordination and control of a variety of energy equipment to achieve efficient operation of the entire system, it is necessary to perform equipment and pipeline parameter verification to ensure that various equipment can work normally during operation and that the system can operate stably according to design requirements. At the same time, the integrated energy system involves many different types of energy equipment, and its parameter verification work is relatively complicated, requiring detailed calculations and analysis to ensure that the system can operate normally and has efficient energy utilization. Therefore, studying parameter verification is a key step to ensure the normal operation of the integrated energy system, and it is also an important way to achieve efficient system operation and energy conservation.

[0004] The paper (Dynamic optimal energy flow in the heat and electricity integrated energy system) discloses a model of an electric heat integrated energy system. However, the model is not accurate enough and does not consider the time-varying characteristics of the parameters during actual operation. There is a huge difference between the actual operation scenario and the model. The paper (Leak detection of long-distance district heating pipeline: Ahydraulic transient model-based approach) discloses a method for calibrating thermal system parameters. However, this method cannot calibrate the equipment of the electric heat integrated energy system. At present, there is no parameter calibration method that considers the integrated energy system and its equipment model. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a hardware-in-the-loop integrated energy system pipeline network, equipment parameter calibration method and system to improve the accuracy of the integrated energy system model, shorten the distance between the theoretical analysis and actual operation of the integrated energy system, and promote the application of the integrated energy system in actual industrial production.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention provides a hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method, comprising the following steps:

[0008] Step 1, establishing a comprehensive energy system equipment and pipeline model based on a physical platform, wherein the physical platform includes an electric water heater, a fan coil unit and a pipeline network, and the equipment model includes a heat source physical model and a heat load physical model;

[0009] Step 2, collecting the operation data of each physical module of the hardware physical platform in step 1;

[0010] Step 3: Use the operating data collected in step 2 to calibrate the equipment model and pipeline model parameters to achieve online verification of physical parameters with the assistance of hardware-in-the-loop.

[0011] Preferably, in step 1, a heat source physical model is established according to the electric water heater, which is expressed by the following formula:

[0012]

[0013] Where:

[0014] p is the heater power;

[0015] m represents mass flow rate in kg / s;

[0016] C represents specific heat capacity;

[0017] M is the mass of the liquid in the water tank;

[0018] T stands for temperature;

[0019] The subscript in represents input and out represents output.

[0020] Preferably, in step 1, a heat load physical model is established according to the fan coil unit and is expressed by the following formula:

[0021]

[0022] Q t =c·m(T s -T r )

[0023] Where:

[0024] Q t Represents the power of the fan coil unit;

[0025] m represents mass flow rate in kg / s;

[0026] T stands for temperature;

[0027] V represents the air volume, the unit is m 3 / s;

[0028] α / β is the empirical coefficient;

[0029] The subscripts si, air, and fcu represent inlet, air, and fan coil, respectively, and the subscript 0 represents the rated value.

[0030] Preferably, in step 1, a pipeline model is established, specifically including a heat transfer model and mass and energy conservation equations.

[0031] Heat transfer model equations:

[0032]

[0033] Where:

[0034] T represents temperature, in °C;

[0035] x represents the spatial position, the unit is m;

[0036] m represents mass flow rate, unit is kg / s;

[0037] c represents specific heat capacity, the unit is J / (kg℃);

[0038] R represents thermal resistance, in m·K / W;

[0039] The subscripts i, n, and j represent nodes, and A represents the environment;

[0040] Temperature fusion point mass conservation equation:

[0041]

[0042] Where:

[0043] and They represent the heat medium flow from node i to node n and the heat medium flow from node n to node j respectively;

[0044] Energy conservation equation at temperature fusion point:

[0045]

[0046] Where:

[0047] Represents the end temperature of the pipe where the heat medium of the i-th pipe flows into node n;

[0048] Represents the temperature at the beginning of the jth pipe where the heat medium flows out of node n.

[0049] Preferably, in step 2, the specific collected operation data is as follows:

[0050] For the heat source, collect the return water temperature, outlet water temperature, return water flow rate and outlet water flow rate data at the heat source;

[0051] For load, collect data on supply water temperature, return water temperature, supply water flow rate and outlet water flow rate;

[0052] For the pipe network, taking into account the installation cost of the temperature collection device, the flow rate of each pipe is estimated by the law of conservation of flow.

[0053] Preferably, in step 3, the model parameters of the electric water heater are calibrated, and the parameters that need to be calibrated are the electric power and the quality of water in the water tank. A traversal search is performed within the range of plus or minus 20% of the basic value with a step size of 0.01 times the basic value, and the calculated output is compared with the measured output, and the group with the smallest error is set as the calibrated parameters.

[0054] Preferably, in step 3, the model parameters of the fan coil unit are calibrated, and the parameters that need to be calibrated are the empirical parameter α and the comprehensive influence coefficient γ of the air volume influence and the rated parameter influence. The comprehensive influence coefficient γ is expressed by the following formula:

[0055]

[0056] Preferably, the power model of the fan coil unit in step 1 is rewritten as follows:

[0057] y=αx+c

[0058] in:

[0059] y=lnQ-ln(T si -T air )

[0060] c=lnγ

[0061] x=lnm fcu

[0062] The least squares linear fitting was used to obtain the optimal α and c.

[0063] Preferably, in step 3, the model parameters of the pipeline network are calibrated, and the parameter to be calibrated is the pipeline thermal resistance, which specifically includes:

[0064] Using genetic algorithm, set the thermal resistance of each pipeline as a parameter, and the fitness function is to minimize the difference between the calculated temperature and the measured temperature at the load node. Input the measured temperature at the heat source, use the pipe network model in step 1 and the thermal resistance of each pipeline to find the temperature at the load node, calculate the fitness, and find a set of pipeline thermal resistances with the lowest fitness value.

[0065] The fitness function is expressed as follows:

[0066]

[0067] n∈load

[0068] Where:

[0069] The superscript sim refers to the simulation result, and real refers to the measurement result;

[0070] T is temperature;

[0071] n represents the node, t represents the time, and E represents the error between simulation and measurement;

[0072] load is a collection of load nodes.

[0073] The second aspect of the present invention provides a hardware-in-the-loop integrated energy system pipe network and equipment parameter verification system, and the hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method are run, including:

[0074] Physical platform, used to simulate the integrated energy system, as the hardware link of hardware-in-the-loop, including electric water heaters, valves, fan coils, power circulation pumps, pipeline networks and meters;

[0075] PLC cabinet, used to collect the operation data of each physical module of the hardware platform;

[0076] A data processing module, used to generate equipment models and pipeline models and perform correction calculations on model parameters based on operating data;

[0077] The result output module is used to output the verification results of the model's physical parameters.

[0078] Compared with the prior art, the beneficial effects of the present invention include at least:

[0079] (1) The operating characteristics of heat sources, pipelines and heat loads in the integrated energy system were fully considered and the corresponding physical model was established. Based on the collected physical data, the model parameters were corrected and improved by using data processing and model analysis methods to improve the accuracy of the integrated energy system model.

[0080] (2) The huge model difference between the theoretical analysis of the integrated energy system and the actual operation scenarios was taken into consideration, narrowing the gap between the theoretical analysis of the integrated energy system and the actual operation, and promoting the application of the integrated energy system in actual industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic diagram of a hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method provided in accordance with an embodiment of the present invention;

[0082] Figure 2 is a physical diagram of a physical platform provided according to an embodiment of the present invention;

[0083] Figure 3 It is a physical platform topology diagram provided according to an embodiment of the present invention;

[0084] Figure 4 It is a comparison chart between the calculated results and the real data after the electric water heater parameter calibration provided in accordance with the application example of the present invention;

[0085] Figure 5 It is a comparison chart between the calculated results and the real data after the fan coil parameters are calibrated according to the application example of the present invention;

[0086] Figure 6 It is a comparison chart between the simulation calculation and the real data after the pipe network parameters are calibrated according to the application example of the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0088] like Figure 1 As shown, embodiment 1 of the present invention provides a hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method, comprising the following steps:

[0089] Step 1: Establish a comprehensive energy system equipment and pipeline model based on a physical platform, such as Figure 2 As shown in Figure 1, it includes electric water heaters, valves, fan coils, power circulation pumps and other equipment, pipeline networks and measuring meters. The thermal network topology is as follows: Figure 3 As shown;

[0090] In a preferred but non-limiting embodiment of the present invention, step 1 specifically comprises:

[0091] Step 1.1, establish the physical model of the heat source in the equipment. The heat source equipment in the integrated energy system is mainly divided into cogeneration units and electric water heater equipment;

[0092] Further preferably, the electric water heater device in the physical platform is modeled, and the physical model of the electric water heater device is expressed by the following formula:

[0093]

[0094] Where:

[0095] p(w) is the heater power;

[0096] m (kg / s) represents mass flow rate;

[0097] C (J / (kg·℃)) represents specific heat capacity;

[0098] M (kg) is the mass of the liquid in the water tank;

[0099] T(℃) represents temperature;

[0100] Subscript 1 represents the heat source number, subscript in represents input, and out represents output.

[0101] It is worth noting that the physical models for modeling other heat sources have the same or similar forms as the above formulas, and based on the spirit of the present invention, they should all fall within the protection scope of the present invention.

[0102] Step 1.2, establish a physical model of the heat load in the equipment; the fan coil unit is the heat load of this physical platform, and the power model of the fan coil unit generally adopts an empirical formula, which is mainly affected by the inlet mass flow, inlet temperature, air temperature and air volume under the rated value of the fan coil unit;

[0103] Further preferably, the physical model of the fan coil unit is expressed by the following formula:

[0104]

[0105] Q t =c·m(T s -T r )

[0106] Where:

[0107] Q t Represents the power of the fan coil unit;

[0108] m (kg / s) represents mass flow rate;

[0109] T(℃) represents temperature;

[0110] V(m 3 / s) is the air volume;

[0111] α / β is the empirical coefficient;

[0112] The subscripts si, air, and fcu represent inlet, air, and fan coil, respectively, and the subscript 0 represents the rated value.

[0113] Step 1.3, establish the physical model of the pipeline network. The pipeline model is mainly composed of the heat transfer model and mass and energy conservation equations.

[0114] Further preferably, the experimental platform uses an aluminum tube as a heat transfer pipe and a plastic tube as a connecting pipe, and the equation is as follows:

[0115] Heat transfer model equations:

[0116]

[0117] Where:

[0118] T represents temperature, in °C;

[0119] x represents the spatial position, the unit is m;

[0120] m represents mass flow rate, unit is kg / s;

[0121] c represents specific heat capacity, the unit is J / (kg℃);

[0122] R represents thermal resistance, in m·K / W;

[0123] The subscripts i, n, and j represent nodes, and A represents the environment.

[0124] Temperature fusion point mass conservation equation:

[0125]

[0126] Where:

[0127] and They represent the heat medium flow rate from node i into node n and the heat medium flow rate from node n to node j respectively.

[0128] Energy conservation equation at temperature fusion point:

[0129]

[0130] Where:

[0131] Represents the end temperature of the pipe where the heat medium of the i-th pipe flows into node n;

[0132] Represents the temperature at the beginning of the jth pipe where the heat medium flows out of node n.

[0133] Step 2, collecting the operation data of each physical module of the hardware physical platform in step 1;

[0134] Specifically, in the integrated energy system, for the heat source, the return water temperature, outlet water temperature, return water flow rate and outlet water flow rate data are collected at the heat source; for the load, the supply water temperature, return water temperature, supply water flow rate and outlet water flow rate data are collected; for the pipeline network, considering the installation cost of the temperature collection device, it is difficult to directly measure the temperature of each node in the pipeline network, and the flow rate of each pipeline can only be estimated through the law of flow conservation.

[0135] Step 3: Use the operating data collected in step 2 to calibrate the equipment model and pipeline model parameters to achieve online verification of physical parameters with the assistance of hardware-in-the-loop.

[0136] It can be understood that hardware-in-the-loop refers to the interaction between the actual physical platform and the theoretical model. In the present invention, hardware refers to the actual physical platform, and hardware-in-the-loop refers to data collection and communication.

[0137] In a preferred but non-limiting embodiment of the present invention, step 3 specifically comprises:

[0138] Step 3.1, calibrating the model parameters of the electric water heater;

[0139] Specifically, since the physical model of the electric water heater is a partial differential equation model, it is impossible to calibrate the parameters by changing the form of the equation and fitting the method. At the same time, considering that the parameters to be calibrated in the electric water heater are the electric power and the quality of the water in the water tank, these two parameters themselves have a relatively accurate basic value. Therefore, it is only necessary to perform a multi-step traversal search on the two parameters, traverse around the basic value, and compare the calculated output with the measured output in each set of values. The set with the smallest error is the calibrated parameter.

[0140] Further preferably, the traversal range is plus or minus 20% of the basic value, that is, [0.8, 1.2] times the basic value; the traversal step length depends on the equipment calibration accuracy and time requirements, and is generally 0.01 times the basic value.

[0141] Step 3.2, calibrating the model parameters of the fan coil unit;

[0142] Specifically, in the fan coil model, the mass flow rate, temperature and load of the fan coil can be measured, and the coil air volume will not change easily. Therefore, when analyzing the model, the impact of the fan coil air volume on the load is not considered. The parameters that need to be verified are the empirical parameter α and the comprehensive influence coefficient γ of the air volume influence and the rated parameter influence.

[0143] Based on the fan coil power model in step 1, the comprehensive influence coefficient γ is expressed as follows:

[0144]

[0145] Therefore, the power model of the fan coil unit can be rewritten as follows:

[0146] Q = γ(m fcu )α(T si -T air )

[0147] Taking the logarithm of both sides, we can get the following formula.

[0148] lnQ=lnγ+αlnm fcu +ln(T si -T air )

[0149] In the above formula, except for γ and α, the rest are measurable. At the same time, since γ is mainly composed of constant quantities, it can be considered that γ will not change with the change of fan coil working conditions. Let

[0150] y=lnQ-ln(T si -T air )

[0151] c=lnγ

[0152] x=lnm fcu

[0153] Therefore, there is

[0154] y=αx+c

[0155] It can be understood that x and y are measurable quantities, α and c are quantities to be fitted, and the optimal α and c can be obtained by using the least squares method or other linear fitting algorithms.

[0156] Step 3.3, calibrating the model parameters of the pipeline network;

[0157] Specifically, in the pipeline model, assuming that the length is an accurate quantity, the main parameter to be checked is the thermal resistance of the pipeline. Since it is impossible to directly measure the temperature at both ends of a single pipeline, we can only build a pipeline network model and perform an overall check on the thermal resistance of each pipeline in the pipeline network. That is, we find a set of more suitable pipeline thermal resistance sets to make the overall model of the pipeline network the most accurate.

[0158] Further preferably, to achieve this purpose, consider using a genetic algorithm, set the thermal resistance of each pipeline as a parameter, and the fitness function is to minimize the difference between the calculated temperature and the measured temperature at the load node, input the measured temperature at the heat source, and use the pipe network model in step 1 and the thermal resistance of each pipeline to find the temperature at the load node and calculate the fitness. Using the genetic algorithm, find a group of pipeline thermal resistances with the lowest fitness value, and then realize parameter verification.

[0159] More preferably, the fitness function is expressed as follows,

[0160]

[0161] n∈load

[0162] Where:

[0163] The superscript sim refers to the simulation result, and real refers to the measurement result;

[0164] T is temperature;

[0165] n represents the node, t represents the time, and E represents the error between simulation and measurement;

[0166] load is a collection of load nodes.

[0167] Embodiment 2 of the present invention provides a hardware-in-the-loop integrated energy system pipe network and equipment parameter verification system, which runs the hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method described in Embodiment 1, including:

[0168] Physical platform, used to simulate the integrated energy system, as the "hardware" link of hardware-in-the-loop, including electric water heaters, valves, fan coils, power circulation pumps and other equipment, pipeline networks and meters;

[0169] PLC cabinet, used to collect the operation data of each physical module of the hardware platform;

[0170] A data processing module, used to generate equipment models and pipeline models and perform correction calculations on model parameters based on operating data;

[0171] The result output module is used to output the verification results of the model's physical parameters.

[0172] Application examples:

[0173] In order to further clearly describe the technical solution of the present invention and the beneficial technical effects brought about by it, an application example is introduced below.

[0174] First, the parameters of the electric water heater were calibrated. A 5880s experiment was conducted and the experimental data was used for parameter calibration. The rated power of this electric water heater is 7000W, and the mass of the water in the water tank is estimated to be 197.4kg. Based on this binary value, the power estimation accuracy is 100W, that is, the traversal step length is 100, and the mass estimation accuracy is 1kg, that is, the traversal step length is 1. After simulation traversal, the output value closest to the measured value is 7500W, and the water tank mass is 204.4kg. Under this parameter, the simulation results are compared with the actual measurement results. Figure 4 shown.

[0175] Secondly, the parameters of the fan coil unit were checked by conducting a 7840s experiment and using the experimental data for parameter checking. During the experiment, the input temperature, ambient temperature and input flow of the fan coil unit were recorded in real time. The parameters were fitted based on the load and other data obtained, and the results are shown in the following table.

[0176] α c Linear fitting formula Fan coil unit 1 -0.01203 4.545 γ=-0.01203x+4.545 Fan coil unit 2 0.4027 5.139 γ=0.4027x+5.139 Fan coil unit 3 -0.243 4.158 γ=-0.243x+4.158 Fan coil unit 4 0.3733 5.077 γ=0.3733x+5.077

[0177] By inversely transforming the data in the above table, we can get the power formulas of the four fan coil units:

[0178] q1=94.1604*(T1-T a )*m -0.01203 q2=170.5451*(T2-T a )*m 0.4027

[0179] q3=63.9435*(T3-T a )*m -0.243 q4=160.2925*(T4-T a )*m 0.3733

[0180] The average error of the four fan coil formulas is 25.4174W, the maximum error is 457.0078W, and the variance is 4.3581. The fan coil simulation results and measurement results are shown in Figure 5 .

[0181] Finally, the parameters of the pipe network were calibrated and an experiment lasted for 7840 seconds. The water supply network contains nine pipes, two heat sources, and four heat loads, so the thermal resistance of each of the nine pipes needs to be calibrated. Through the genetic algorithm, the thermal resistance of the nine pipes is used as the independent variable. According to the actual situation, the upper and lower limits of the thermal resistance of the pipes, as well as the genetic population, crossover mutation rate and other parameters are set. The goal is to minimize the calculation error of the measurable points within 7840 seconds.

[0182] The algorithm results are as follows:

[0183] The thermal resistances of the heating pipes are:

[0184] R=[0.07966,0.028717,0.044621,0.329681,0.078206,0.149303,0.094201,0.51588,0.074856] The thermal resistance of the return water network pipeline is:

[0185] R=[0.0326,0.0473,0.1271,0.2044,0.1289,0.1818,0.1356,0.0274,0.0791]

[0186] After simulation, the average error of the water supply network temperature calculation is 0.2026, the maximum error is 2.2314, and the variance is 0.0959. The average error of the return network temperature calculation is 0.1438, the maximum error is 1.1486, and the variance is 0.0559. The temperature comparison of the water supply network is Figure 6 shown.

[0187] Compared with the prior art, the beneficial effects of the present invention include at least:

[0188] (1) The operating characteristics of heat sources, pipelines and heat loads in the integrated energy system were fully considered and the corresponding physical model was established. Based on the collected physical data, the model parameters were corrected and improved by using data processing and model analysis methods to improve the accuracy of the integrated energy system model.

[0189] (2) The huge model difference between the theoretical analysis of the integrated energy system and the actual operation scenarios was taken into consideration, narrowing the gap between the theoretical analysis of the integrated energy system and the actual operation, and promoting the application of the integrated energy system in actual industrial production.

[0190] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method, characterized in that: The following steps are involved: Step 1, establishing a comprehensive energy system equipment and pipeline model based on a physical platform, wherein the physical platform includes an electric water heater, a fan coil unit and a pipeline network, and the equipment model includes a heat source physical model and a heat load physical model; Step 2, collecting the operation data of each physical module of the hardware physical platform in step 1; Step 3: Use the operating data collected in step 2 to calibrate the equipment model and pipeline model parameters to achieve online verification of physical parameters with the assistance of hardware-in-the-loop.

2. A hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method as claimed in claim 1, characterized in that: In step 1, a heat source physical model is established based on the electric water heater, which is expressed by the following formula: Where: p is the heater power; m represents mass flow rate in kg / s; C represents specific heat capacity; M is the mass of the liquid in the water tank; T stands for temperature; The subscript in represents input and out represents output.

3. The hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method according to claim 1, characterized in that: In step 1, a heat load physical model is established based on the fan coil unit, which is expressed as follows: Q t =c·m(T s -T r ) Where: Q t Represents the power of the fan coil unit; m represents mass flow rate in kg / s; T stands for temperature; V represents the air volume, the unit is m 3 / s; α / β is the empirical coefficient; The subscripts si, air, and fcu represent inlet, air, and fan coil, respectively, and the subscript 0 represents the rated value.

4. The hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method according to claim 1, characterized in that: In step 1, a pipeline model is established, including a heat transfer model and mass and energy conservation equations. Heat transfer model equations: Where: T represents temperature, in °C; x represents the spatial position, the unit is m; m represents mass flow rate, unit is kg / s; c represents specific heat capacity, the unit is J / (kg℃); R represents thermal resistance, in m·K / W; The subscripts i, n, and j represent nodes, and A represents the environment; Temperature fusion point mass conservation equation: Where: and They represent the heat medium flow from node i to node n and the heat medium flow from node n to node j respectively; Energy conservation equation at temperature fusion point: Where: Represents the end temperature of the pipe where the heat medium of the i-th pipe flows into node n; Represents the temperature at the beginning of the jth pipe where the heat medium flows out of node n.

5. The hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method according to claim 1, characterized in that: In step 2, the specific collected operation data is as follows: For the heat source, collect the return water temperature, outlet water temperature, return water flow rate and outlet water flow rate data at the heat source; For load, collect data on supply water temperature, return water temperature, supply water flow rate and outlet water flow rate; For the pipe network, taking into account the installation cost of the temperature collection device, the flow rate of each pipe is estimated by the law of conservation of flow.

6. A hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method as claimed in claim 2, characterized in that: In step 3, the model parameters of the electric water heater are calibrated. The parameters that need to be calibrated are the electric power and the quality of water in the water tank. A traversal search is performed within the range of plus or minus 20% of the basic value with a step size of 0.01 times the basic value. The calculated output is compared with the measured output, and the group with the smallest error is set as the calibrated parameters.

7. A hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method as claimed in claim 3, characterized in that: In step 3, the model parameters of the fan coil unit are calibrated. The parameters that need to be calibrated are the empirical parameter α and the comprehensive influence coefficient γ of the air volume influence and the rated parameter influence. The comprehensive influence coefficient γ is expressed by the following formula:

8. A hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method as claimed in claim 7, characterized in that: Rewrite the power model of the fan coil unit in step 1 as follows: y=αx+c in: y=lnQ-ln(T si -T air ) c=lnγ x=lnm fcu The least squares linear fitting was used to obtain the optimal α and c.

9. A hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method as claimed in claim 4, characterized in that: In step 3, the model parameters of the pipeline network are calibrated. The parameters that need to be calibrated are the pipeline thermal resistance, including: Using genetic algorithm, set the thermal resistance of each pipeline as a parameter, and the fitness function is to minimize the difference between the calculated temperature and the measured temperature at the load node. Input the measured temperature at the heat source, use the pipe network model in step 1 and the thermal resistance of each pipeline to find the temperature at the load node, calculate the fitness, and find a set of pipeline thermal resistances with the lowest fitness value. The fitness function is expressed as follows: n∈load Where: The superscript sim refers to the simulation result, and real refers to the measurement result; T is temperature; n represents the node, t represents the time, and E represents the error between simulation and measurement; load is a collection of load nodes.

10. A hardware-in-the-loop integrated energy system pipe network and equipment parameter verification system, which runs the hardware-in-the-loop integrated energy system pipe network and equipment parameter verification method as described in any one of claims 1 to 9, characterized in that: include: Physical platform, used to simulate the integrated energy system, as the hardware link of hardware-in-the-loop, including electric water heaters, valves, fan coils, power circulation pumps, pipeline networks and meters; PLC cabinet, used to collect the operation data of each physical module of the hardware platform; A data processing module, used to generate equipment models and pipeline models and perform correction calculations on model parameters based on operating data; The result output module is used to output the verification results of the model's physical parameters.

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