Large-scale central heating system energy efficiency online test and evaluation method based on pyroproduct theory

Abstract

The invention discloses a large-scale central heating system energy efficiency online test and evaluation method based on the fire product theory. S101, a step S102; acquiring various operating data required for evaluating the current heat supply system on line; (S102) carrying out detection; preprocessing the operation data to form an operation database; (S103) carrying out heat treatment; calculating an operation energy efficiency evaluation index of each component or equipment of the current heat supply system by using the operation database, wherein the evaluation index comprises introduced hemodialysis dissipation; (S104) carrying out detection; calculating the weight of the evaluation index of each component or equipment of the current heat supply system; (S105) carrying out heat treatment; determining the weight of the evaluation index of each component or equipment of the current heat supply system; (S106) carrying out detection; and calculating the score of the overall energy efficiency level of the current heat supply system. According to the method, the quality dissipation phenomenon generated in the heat transfer process of the heat supply system is described by introducing the heuristic theory, and energy efficiency evaluation can be more comprehensive and perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of energy efficiency evaluation of heating systems, and particularly to an online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of thermal product. Background Art

[0002] The evaluation methods of heating systems are mainly divided into single-index and multi-index evaluation methods. The actual heating pipeline network is affected by various factors, and usually the multi-objective evaluation method is adopted. The national standard GB / T 50893-2013 "Technical Specification for Energy Conservation Renovation of Heating Systems" evaluates from three aspects: energy consumption, equipment energy efficiency, and parameter control. The national standard GB / T 50627-2010 "Evaluation Standard for Urban Heating Systems" conducts a comprehensive evaluation from four parts: facilities, management, energy efficiency, and environmental protection, safety, and fire protection, selects 6 indicators such as boiler thermal efficiency, power consumption heat transfer ratio, and make-up water rate, comprehensively reflects the energy efficiency level of each link of the system, and assigns weight values.

[0003] The existing energy efficiency evaluation indicators mostly focus on heat transfer and conversion efficiency, but lack the evaluation of energy quality. In the actual heating system, the irreversibility of the heat exchange process affects the overall performance of the system. The first and second laws of thermodynamics can only describe heat loss and cannot reflect the reduction of energy quality. For example, after the hot fluids at different temperatures are mixed, the energy quality will decline, and there will also be energy quality loss when the high-quality heat medium transfers heat to the low-quality heat medium.

[0004] When the existing energy efficiency evaluation methods of the national standard GB / T 50893-2013 "Technical Specification for Energy Conservation Renovation of Heating Systems" and the national standard GB / T 50627-2010 "Evaluation Standard for Urban Heating Systems" are applied to large-scale heating pipeline networks, all influencing factors are not comprehensively considered, there is no evaluation and analysis of the loss of energy quality, and the topology of the primary pipeline network is complex. It is difficult for the existing system to comprehensively evaluate the energy efficiency of the pipeline network, and it is also difficult to achieve ideal effects in the actual system. Summary of the Invention

[0005] In order to overcome the above defects existing in the prior art, the present invention provides an online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of thermal product. This method introduces the theory of thermal product to describe the quality dissipation phenomenon generated in the heat transfer process of the heating system, and can make the energy efficiency evaluation more comprehensive and perfect.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] An online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of thermal product, comprising the following steps;

[0008] S101; Online obtain various types of operation data required for the evaluation of the current heating system;

[0009] S102; Preprocess the operation data to form an operation database;

[0010] S103; Calculate the operation energy efficiency evaluation indexes of each component or device in the current heating system by using the operation database. The evaluation indexes include the introduced exergy dissipation;

[0011] S104; Calculate the weights of the evaluation indexes of each component or device in the current heating system;

[0012] S105; Determine the weights of the evaluation indexes of each component or device in the current heating system;

[0013] S106; Calculate the overall energy efficiency level score of the current heating system.

[0014] In the above S101, the operation data is the real operation data of the current heating season; the operation data includes the heat source, pipeline network, heat exchange station and heat users;

[0015] The operation data of the heat source includes the supply and return water temperatures, supply and return water flow rates, fuel consumption, and power consumption;

[0016] The operation data of the pipeline network includes the overall supply and return water flow rates and temperatures of the whole pipeline network;

[0017] The operation data of the heat exchange station includes the temperatures, pressures and flow rates at the inlets and outlets of the heat exchange units and pumps, as well as the power consumption of the equipment;

[0018] The operation data of the heat users includes the heating area and the indoor heating temperature parameters of the heat users.

[0019] In the above S102, preprocess the obtained operation data. The preprocessing is to complete the missing data, eliminate the abnormal data, and form an operation energy efficiency database;

[0020] Specifically:

[0021] Due to the interruption in the data upload process of the data acquisition equipment or the insensitivity of some collectors, some of the collected data is missing; for the data set with the missing data accounting for less than 50% of the total data and no long continuous missing data, methods such as average value supplementation, linear interpolation, taking the previous (next) valid value are used to supplement the missing data;

[0022] For the missing data at a single time point, use the average value supplementation method. The calculation method is as follows:

[0023]

[0024] Where x i represents the data with a vacancy at a certain moment, x i-1 represents the valid data 1h before it, x i+1Indicates the valid data for the subsequent 1 hour. That is, if the data at a certain moment is missing, the average value of the valid data 1 hour before and after it is used for supplementation;

[0025] If the data 1 hour before and after this moment is missing, this method cannot supplement the missing data. Therefore, this method needs to be modified as follows:

[0026]

[0027] Among them, x i-24 Indicates the valid value at the same moment of the previous day. x i+24 Indicates the valid value at the same moment of the next day. That is, if the data at a certain moment is missing, the average value of the valid data at the same moment 1 day before and after it is used for supplementation.

[0028] This method can solve the problem of data continuously missing for less than 24 hours. However, for the problem of more continuous vacancies, the method of linear interpolation needs to be adopted. Set [t1, x1] and [t k , x k to be the previous and the next valid values of the missing data with a continuous vacancy length of k respectively. Then the value of t at a certain position in the interval [t1, t k should satisfy:

[0029]

[0030] The values on the straight line obtained by this method are the supplementary values for the missing data, and data filling is performed on the missing data.

[0031] When the missing data accounts for more than 50% of the total data and there is a dataset with long continuous missing data, the analogy method is used to supplement the missing data. For heat exchange stations with a large amount of missing data, using the above method to supplement data will result in a large deviation. It is necessary to find a reference heat exchange station with the same building type, similar location, and relatively complete data as this heat exchange station and perform analogy according to the area. The calculation method is as follows:

[0032]

[0033] Among them, x0 is the corresponding parameter of the reference heat exchange station, s0 is the actual supply area of the reference heat exchange station, and s is the actual supply area of the heat exchange station with missing data.

[0034] In the S103, the operation energy efficiency evaluation indexes of the heat source, pipe network, heat exchange station, and heat user are calculated respectively using the operation energy efficiency database;

[0035] Among them, the heat source operation energy efficiency evaluation indexes include the fuel consumption per unit heat supply and the power consumption per unit heating area;

[0036] The energy efficiency evaluation indicators for the pipe network operation include the heat transmission efficiency of the primary pipe network, the hydraulic imbalance degree, the hydraulic imbalance degree of the heat exchange station, the thermal imbalance degree, the thermal imbalance degree of the heat exchange station, and the exergy dissipation of the primary pipe network;

[0037] The energy efficiency evaluation indicators for the heat exchange station operation include the heat exchange performance of the heat exchange unit and the exergy dissipation of the heat exchange unit;

[0038] The energy efficiency evaluation indicator for the heat user operation is the heat consumption per unit area of the heat user.

[0039] In a possible implementation, in S103, the specific calculation methods for the energy efficiency evaluation indicators are as follows:

[0040] Fuel consumption per unit heat supply B Q : The ratio of the fuel consumption G of the heat source to the heat supply Q of the heat source in the current period,

[0041]

[0042] wherein, the heat supply Q of the heat source in the current period = M·c p ·(T1 - T2), M is the water supply flow rate of the heat source, c p is the specific heat capacity of water, T1 is the temperature of the heat source water supply, and T2 is the temperature of the heat source return water;

[0043] Power consumption per unit heating area B W : The ratio of the total power consumption W of the heat source to the total building heating area A y served;

[0044]

[0045] Heat transmission efficiency η of the primary pipe network: The annual heating heat consumption ∑Q f of the building and the annual actual heat supply ∑Q s of the heat source,

[0046]

[0047] wherein, according to the "Residential Building Energy Efficiency Testing Standard", the transmission efficiency should not be less than 0.90;

[0048] Hydraulic imbalance degree X of the pipe network: The actual flow rate G S of the pipe network and the specified flow rate G g of the pipe network;

[0049]

[0050] Hydraulic imbalance degree X of the heat exchange station h : The ratio of the product of the actual flow rate G S of the heat exchange station and the heating area A to the specified flow rate G g of the heat exchange station,

[0051]

[0052] Among them, G S,i is the actual flow rate of the i-th heat exchange station node, m 3 / h; G g,i is the specified flow rate of the i-th heat exchange station node, m 3 / h; A i is the building heating area of the i-th heat exchange station node, m 2 ; n represents the number of heat exchange station nodes;

[0053] Pipe network thermal imbalance degree R: The ratio of the actual heat supply Q S of the pipe network to the specified heat supply Q g of the pipe network;

[0054]

[0055] Heat exchange station hydraulic imbalance degree R h : The ratio of the product of the actual heat supply Q S of the heat exchange station and the heating area A to the specified heat supply Q g of the heat exchange station,

[0056]

[0057] Among them, Q S,i is the actual heat supply of the i-th heat exchange station node, m 3 / h; Q g,i is the specified heat supply of the i-th heat exchange station node, m 3 / h; A i is the building heating area of the i-th heat exchange station node, m 2 ; n represents the number of heat exchange station nodes;

[0058] Fire product dissipation in the primary pipe network Fire product dissipation caused by the mixing of the return water in the return pipe network, the difference between the fire product in the initial state and the fire product in the end state,

[0059]

[0060] Among them, T i is the fluid temperature of the pipe segment at the i-th inflow node, m i is the fluid mass flow rate of the pipe segment at the i-th inflow node, m is the fluid mass flow rate of the pipe segment at the outflow node, and i is the return pipe segment number; At a certain return water mixing node, multiple fluids with different temperatures converge, and the mixed temperature T is:

[0061]

[0062] Heat transfer performance kF of the heat exchange unit: The ratio of the heat input Q1 to the heat station per unit time to the logarithmic mean heat transfer temperature difference of the heat exchange unit per unit time.

[0063]

[0064] Where: Δt P is the logarithmic mean heat transfer temperature difference of the heat exchange unit per unit time; Δt x is the medium temperature difference at the end with the smaller temperature difference of the heat exchange unit per unit time; Δt d is the medium temperature difference at the end with the larger temperature difference of the heat exchange unit per unit time; τ is the detection duration, h; The national standard stipulates that the heat transfer performance of the heat exchange unit shall not be less than 90% of the rated working condition.

[0065] Exergy dissipation of the heat exchange station The difference between the exergy on the primary side and the exergy on the secondary side of the heat exchanger.

[0066]

[0067] Where, t 1,in is the supply water temperature on the primary side of the heat exchanger; t 1,out is the return water temperature on the primary side of the heat exchanger; t 2,in is the return water temperature on the secondary side of the heat exchanger; t 2,out is the supply water temperature on the secondary side of the heat exchanger; g is the flow rate on the primary side of the heat exchanger.

[0068] Heat consumption per unit area Q yA : The ratio of the heat supply Q y0 at the building heat inlet during the heating period to the heating building area A y of the building.

[0069]

[0070] In S104, based on the entropy weight method, first calculate the weight values of the energy efficiency evaluation indicators of the entire heating system, and then calculate the overall heating energy efficiency level of the heating system by combining the numerical values of each indicator calculated from the actual operation data.

[0071] First, k indicators x1, x2,..., x k are given, and the calculation expression for standardizing each indicator is:

[0072]

[0073] After standardizing the data samples of all indicators, the information entropy expression of the data samples of each indicator is calculated as follows:

[0074]

[0075]

[0076] According to the calculated values of the information entropy of each index, the weights of each index can be calculated to form an index weight system:

[0077]

[0078] In S106, according to the overall heating energy efficiency level of the heating system obtained by calculation, the operating energy efficiency level of the current heating system is judged, and the parts with low operating energy efficiency level are rectified.

[0079] First, through the weight values of each index, the energy efficiency level of the overall heating system is calculated, and the calculation formula is as follows:

[0080]

[0081] Then, the value obtained by calculation is compared with 100%. If it is lower than 60%, all equipment and devices are optimized. If it is between 60% and 80%, the heating devices or equipment with low evaluation indexes need to be optimized.

[0082] In S105, the entropy weight method is used to calculate the weight values of the energy efficiency evaluation indexes of the entire heating system, and the specific index weight values are as follows:

[0083] The weight value of the fuel consumption per unit heat supply is 0.060;

[0084] The weight value of the power consumption per unit heating area is 0.017;

[0085] The weight value of the heat transfer efficiency of the primary pipe network is 0.003;

[0086] The weight value of the hydraulic imbalance degree of the pipe network is 0.023;

[0087] The weight value of the hydraulic imbalance degree of the heat exchange station is 0.039;

[0088] The weight value of the thermal imbalance degree of the pipe network is 0.095;

[0089] The weight value of the thermal imbalance degree of the heat exchange station is 0.102;

[0090] The weight value of the exergy dissipation of the primary pipe network is 0.109;

[0091] The heat exchange performance of the heat exchange unit is 0.176;

[0092] The exergy dissipation of the heat exchange unit is 0.154;

[0093] The weight value of the heat consumption per unit area of heat users is 0.222.

[0094] The beneficial effects of the present invention:

[0095] When describing a heating system using the First Law of Thermodynamics and the Second Law of Thermodynamics, only the heat loss during the heat transfer process can be reflected. However, during the heat transfer process, due to the gradual degradation of the fluid's own quality, and when a hot fluid at a certain temperature mixes with other hot fluids, fluids at different temperatures have different heat transfer capabilities, that is, fluids with different energy qualities converge together, and the energy quality of the hot fluid after convergence is lower than the sum of the two converging fluids. During this period, there is an energy quality loss. In addition, when a hot fluid exchanges heat with other heat media, in addition to the energy loss during the conversion process, there is also an energy quality loss when high-quality heat media transfer heat to low-quality heat media. These energy consumptions must be calculated in terms of the concept of "energy" for a given hot fluid in order to calculate this loss during the conversion process. Therefore, the theory of exergy dissipation is introduced to describe this process. For each energy loss process in the heating system, the exergy dissipation generated during the calculation process is used as an energy efficiency evaluation index. Using the concept of exergy dissipation to describe the quality dissipation phenomenon occurring during the heat transfer process of the heating system can make the energy efficiency evaluation more comprehensive and perfect.

[0096] Based on the First Law of Thermodynamics and the Second Law of Thermodynamics, while introducing the theory of exergy dissipation to describe the energy quality dissipation during the heat transfer process, a comprehensive energy efficiency index system is constructed according to the main influencing factors of the losses occurring during the heat transfer process of the heating system, a comprehensive energy efficiency evaluation method is proposed, and the actual heating energy efficiency operation situation is analyzed to score the energy efficiency operation level of the heating system. Description of the Drawings

[0097] Figure 1 It is a flowchart of an online test and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy dissipation provided by an embodiment of this application.

[0098] Figure 2 It is a graph of the actual operation data of the heating system.

[0099] Figure 3 It is a graph of the actual operation data of the heating system after data preprocessing.

[0100] Figure 4 It is the hourly operation data of the energy efficiency index of the Taihua Heating System during the heating season from 2020 to 2021.

[0101] Figure 5 It is a curve graph of the comprehensive index scores of the Taihua Heating System during the heating season from 2020 to 2021. Detailed Implementation Manner

[0102] The present invention will be further described in detail below with reference to the accompanying drawings.

[0103] An embodiment of this application provides an online test and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy dissipation during operation, asFigure 1 As shown, the method includes steps S101 to S106. Among them, Figure 1 This is only one execution order shown in the embodiments of this application and does not represent the only execution order of an online test and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy. Under the condition that the final result can be achieved, Figure 1 the steps shown can be executed in parallel or reversed.

[0104] S101: Obtain the operation data of the four parts of the heat source, pipe network, heat exchange station and heat users. This data describes the operation of the heating system under different working conditions and provides a data basis for subsequent evaluation and optimization;

[0105] Specifically, the heat source operation data includes the supply and return water temperatures, supply and return water flow rates, fuel consumption, and power consumption; the pipe network operation data includes the overall supply and return water flow rates and temperatures of the entire pipe network; the heat exchange station operation data includes the temperatures, pressures and flow rates at the inlets and outlets of the heat exchange units and pumps, as well as the equipment power consumption; the heat user operation data includes parameters such as the heating area and the indoor supply temperature of the heat users.

[0106] S102: Perform data preprocessing on the collected data, fill in the blank data, and form an operation energy efficiency database.

[0107] Specifically, for a data set where the missing data accounts for less than 50% of the total data and there is no long continuous missing data, due to the existence of nearby data, it is advisable to use methods such as average value supplementation, linear interpolation, and taking the previous (or next) valid value to supplement the missing data. For the missing data of a single time point, considering that the data collected does not change greatly in a short period of time and there are available data before and after the missing time point, the average value supplementation method is suitable, and the calculation method is as follows:

[0108]

[0109] Among them, x i represents the data with a vacancy at a certain moment, x i-1 represents the valid data 1h before it, x i+1 represents the valid data 1h after it, that is, if the data at a certain moment is missing, the average value of the valid data 1h before and after it is used for supplementation. If the data 1h before and after this moment is missing, this method cannot supplement the missing data, so this method needs to be changed to:

[0110]

[0111] Among them, x i-24 represents the valid value at the same moment of the previous day, x i+24Represents the effective value at the same time of the next day. That is, if the data at a certain moment is missing, the average value of the effective data at the same time one day before and after it is used for supplementation. This method can solve the problem of continuous data gaps less than 24h. However, for problems with more continuous gaps, the method of linear interpolation needs to be adopted. Set [t1, x1] and [t k , x k as the previous and next effective values of the missing data with a continuous gap length of k respectively. Then the value of t at a certain position in the interval [t1, t k should satisfy:

[0112]

[0113] The values on the straight line obtained by this method are the supplementary values for the missing data, and the missing data is filled.

[0114] Data missing will affect the integrity and effectiveness of the operation database of the heating system, reduce the data quality, and is not conducive to the subsequent research on the energy efficiency analysis and operation regulation optimization of the heating system. Scientific methods must be adopted to supplement the missing data.

[0115] Specifically, when the missing data accounts for more than 50% of the total data and there is a dataset with long continuous missing data, the analogy method is preferably used to supplement the missing data. For heat exchange stations with more missing data, using the above method to supplement the data will produce a large deviation. It is necessary to find a reference heat exchange station with the same building type, similar location, and relatively complete data as the heat exchange station with missing data, and make an analogy according to the area. The calculation method is as follows:

[0116]

[0117] Among them, x0 is the corresponding parameter of the reference heat exchange station, s0 is the actual heating area of the reference heat exchange station, and s is the actual heating area of the heat exchange station with missing data.

[0118] S103: Calculate specific energy efficiency evaluation indicators using the data in the database.

[0119] The first part is the heat source. Fuel is transported into the boiler for combustion. During the process of converting chemical energy into heat energy, there is a loss in the energy quality. At the same time, the boiler consumes a large amount of electricity during operation. Therefore, for the heat source part, the energy efficiency evaluation indicators of fuel consumption per unit heat supply and power consumption per unit heat supply are selected. The medium for transporting heat is output from the heat source plant and enters the primary network circulation pump. The pump drives the fluid to flow, and a certain amount of power consumption is generated during this period. The fluid flows into the primary network transmission pipeline. When flowing inside a large-scale primary heating network with a complex actual structure and a long transmission distance, according to the law of conservation of energy, the main losses include heat loss caused by heat exchange with the external cold fluid and water loss at the actual network leakage points. The indicators of hydraulic imbalance degree, thermal imbalance degree, and network transmission efficiency are respectively selected to describe the energy efficiency loss during the transmission process. And for the flow energy quality dissipation and return water mixing energy quality dissipation generated during the heat transport process of the heating network, the fire product dissipation of the primary network is given as an evaluation indicator.

[0120] The hot fluid is transported to the heat exchange station through the primary network and exchanges heat in the heat exchange unit in the heat exchange station. During the heat conversion process, there is energy quality dissipation. The heat exchange efficiency of the heat exchange unit and the fire product dissipation of the heat exchange unit are selected as evaluation indicators. Finally, the heat is transported to the heat users through the secondary network, resulting in power consumption and heat consumption. The power consumption to heat transmission ratio of the secondary network, the hydraulic imbalance degree of the secondary network, the network transmission efficiency, the thermal imbalance degree, and the heat dissipation per unit area of the heat users can be selected as evaluation indicators. The present invention focuses on evaluating the energy efficiency of large-scale urban primary networks. Therefore, the secondary network and the overall secondary side of the heat users are regarded as a whole, and the heat consumption per unit area of the secondary side is selected as the overall energy efficiency evaluation indicator for this part.

[0121] Specifically, the fuel consumption per unit heat supply B Q : is the ratio of the fuel consumption G of the heat source to the heat supply Q of the heat source in the current period,

[0122]

[0123] wherein, the heat supply Q of the heat source in the current period = M·c p ·(T1 - T2); M is the water supply flow of the heat source, c p is the specific heat capacity of water, T1 is the temperature of the heat source water supply, and T2 is the temperature of the heat source return water; it is collected through data acquisition equipment to ensure accurate values;

[0124] The power consumption per unit heating area B W : is the ratio of the total power consumption W of the heat source to the total building heating area A y served by the heat source;

[0125]

[0126] Primary pipeline heat transfer efficiency η: Annual heating heat consumption ∑Q of buildings f and the actual annual heat supply ∑Q of the heat source s ratio,

[0127]

[0128] Among them, according to the "Residential Building Energy Efficiency Detection Standard", the transfer efficiency should not be less than 0.90;

[0129] Hydraulic imbalance degree X of the pipeline network: Actual flow rate G of the pipeline network S and the specified flow rate G of the pipeline network g ratio;

[0130]

[0131] Hydraulic imbalance degree X of the heat exchange station h : The ratio of the product of the actual flow rate G of the heat exchange station and the heating area A to the specified flow rate G S and the specified flow rate G g ratio,

[0132]

[0133] Among them, G S,i is the actual flow rate of the i-th heat exchange station node, m 3 / h; G g,i is the specified flow rate of the i-th heat exchange station node, m 3 / h; A i is the building heating area of the i-th heat exchange station node, m 2 ; n represents the number of heat exchange station nodes;

[0134] Thermal imbalance degree R of the pipeline network: Actual heat supply Q of the pipeline network S and the specified heat supply Q of the pipeline network g ratio;

[0135]

[0136] Thermal imbalance degree R of the heat exchange station h : The ratio of the product of the actual heat supply Q of the heat exchange station and the heating area A to the specified heat supply Q S and the specified heat supply Q g ratio,

[0137]

[0138] Among them, Q S,i is the actual heat supply of the i-th heat exchange station node, m 3 / h; Q g,i is the specified heat supply of the i-th heat exchange station node, m 3 / h; A iis the building heating area of the i-th heat exchange station node, m 2 ; n represents the number of heat exchange station nodes, and the actual heat supply of the heat exchange station Q S = M S ·c p ·(T S,1 - T S,2 ); M S is the primary side water supply flow rate at the heat exchange station, c p is the specific heat capacity of water, T S,1 is the primary side water supply temperature at the heat station, T S,2 is the primary side return water temperature at the heat station;

[0139] Exergy dissipation of the primary pipe network Exergy dissipation caused by the mixing of return water in the return pipe network, the difference between the exergy at the initial state and the exergy at the end state,

[0140]

[0141] Among them, T i is the fluid temperature of the pipe segment at the i-th inflow node, m i is the fluid mass flow rate of the pipe segment at the i-th inflow node, m is the fluid mass flow rate of the pipe segment at the outflow node, and i is the return pipe segment number; at a certain return water mixing node, multiple fluids with different temperatures converge, and the mixed temperature T is:

[0142]

[0143] Heat transfer performance kF of the heat exchanger unit: the ratio of the heat input Q1 to the heat exchanger unit per unit time to the logarithmic mean temperature difference of the heat exchanger unit per unit time,

[0144]

[0145] Among them: Δt P is the logarithmic mean temperature difference of the heat exchanger unit per unit time; Δt x is the temperature difference of the medium at the smaller temperature difference end of the heat exchanger unit per unit time; Δt d is the temperature difference of the medium at the larger temperature difference end of the heat exchanger unit per unit time; τ is the detection duration, h; the national standard stipulates that the heat transfer performance of the heat exchanger unit cannot be less than 90% of the rated working condition;

[0146] Exergy dissipation of the heat exchange station The difference between the exergy of the primary side and the exergy of the secondary side of the heat exchanger,

[0147]

[0148] Among them, t 1,in is the water supply temperature of the primary side of the heat exchanger; t 1,outis the return water temperature of the primary side of the heat exchanger; t 2,in is the return water temperature of the secondary side of the heat exchanger; t 2,out is the supply water temperature of the secondary side of the heat exchanger; g is the primary side flow rate of the heat exchanger;

[0149] The heat consumption per unit area Q yA : The heat supply at the building thermal inlet during the heating period Q y0 and the heating building floor area A y ratio.

[0150]

[0151] Among them, the heat supply at the building thermal inlet Q y0 = M y · c p · (T y,1 - T y,2 ); My is the primary side supply water flow rate at the heat exchange station, c p is the specific heat capacity of water, T y,1 is the supply water temperature at the building inlet, T S,2 is the return water temperature at the building inlet;

[0152] S104: Using the entropy weight method, calculate the weight values of the energy efficiency evaluation indicators of the entire heating system.

[0153] Based on the entropy weight method, first calculate the weight values of the energy efficiency evaluation indicators of the entire heating system. Take the hourly calculation data of each indicator during the entire heating season as the data sample, which is used as the sample library for calculating the weight values by the entropy weight method, and perform weight calculation.

[0154] Specifically, first given n indicators x1, x2,..., x n , the calculation expression for standardizing each indicator is:

[0155]

[0156] Among them, x ij is the value of the i-th indicator of the j-th sample (i = 1, 2..., n; j = 1, 2,..., m); min(x i ) is the minimum value of the i-th indicator in all samples; max(x i ) is the minimum value of the i-th indicator in all samples; Y ij is the standardized value of the i-th indicator of the j-th sample (i = 1, 2..., n; j = 1, 2,..., m);

[0157] After standardizing the data samples of all indicators, the information entropy expression for calculating the data samples of each indicator is as follows:

[0158]

[0159] Among them, Y ij is the standardized value of the i-th index of the j-th sample (i = 1, 2..., n; j = 1, 2,..., m); p ij is the proportion of the j-th sample value under the i-th index in this index (i = 1, 2..., n; j = 1, 2,..., m); E i is the entropy value of the i-th index; n is the number of indexes;

[0160] According to the calculated values of the information entropy of each index, the weights of each index can be calculated to form an index weight system:

[0161]

[0162] Among them, E i is the entropy value of the i-th index; W i is the entropy weight value of the i-th index; n is the number of indexes;

[0163] S105: Using the entropy weight method, calculate the weight values of the energy efficiency evaluation indexes of the entire heating system, and obtain the specific index weight values as follows:

[0164]

[0165] S106: Calculate the energy efficiency level of the entire system and evaluate it:

[0166] Through the weight values of each index, the overall evaluation of the heating pipe network can be carried out.

[0167] Specifically, the comprehensive energy efficiency index system and index weights constitute a complete comprehensive energy efficiency evaluation method. According to this evaluation method, determine the calculation method of the comprehensive energy efficiency evaluation index η cis The expression is:

[0168]

[0169] Among them, W i is the entropy weight value of the i-th index; x i is the value of the i-th index; η cis is the comprehensive energy efficiency evaluation index;

[0170] Specifically, compare the calculated value with 100%. If it is lower than 60%, optimize all the equipment and devices. If it is between 60% - 80%, then optimize the heating devices or equipment with low evaluation indexes.

[0171] The following lists a case where a heating company uses real data to evaluate the energy efficiency of the heating system to illustrate this application.

[0172] The heat supply area covered by the Taihua heating system from this data source is extensive, with a total heat supply area of 13.3824 million square meters. A data acquisition system is installed in this system, which collects data once an hour for a total of 120 days. As Figure 2 shown, due to the instability of the heating system monitoring device, and problems such as errors or damage in the acquisition equipment, unstable data upload signals, etc., and at the same time, problems such as manual reading and writing errors or data omissions may occur during the data acquisition process, there are a large number of missing data in the operation data of the heating system obtained through research.

[0173] After supplementing the missing data using methods such as mean value supplementation, linear interpolation, taking the previous (next) valid value, and analogy method, as Figure 3 shown, the data validity has increased significantly, preparing for the next calculation.

[0174] Using the comprehensive energy efficiency evaluation system, calculate the energy efficiency indicators that have a more significant impact on energy efficiency in the heating system, and respectively draw the change curves of energy efficiency indicators based on the hourly operation data of the 2020 - 2021 heating season as Figure 4 shown. It can be observed that all indicators show the same type of change trend during the entire heating season, that is, they have lower calculated values of energy efficiency indicators in the initial cold period, and higher calculated operation values in the severe cold period. Finally, as the severe cold period transitions to the end cold period, the energy efficiency indicator values gradually decrease and tend to be flat. However, during the severe cold period, the heat loss adjustment degree and the hydraulic loss adjustment degree deviate significantly from the standard range between 0.9 - 1.2, and the power consumption and the exergy dissipation energy consumption are relatively large, affecting the operation energy efficiency level of the heating system.

[0175] According to the proposed heating system energy efficiency evaluation system, calculate the hourly operation energy efficiency level of the Taihua heating system during the 2020 - 2021 heating season, and the change curve for the entire heating season is as Figure 5 shown. The comprehensive energy efficiency score of the Taihua heating system during the 2020 - 2021 heating season is 55.19, and there is still a large room for improvement compared to the full score of 100. Among them, there is still 81.82% of the energy-saving improvement space at the time point with the lowest energy efficiency level.

[0176] According to the score comparison curve, due to the reasons of pipe network regulation, the energy efficiency level of Taihua Heating Company is relatively unstable in the middle and early stages of the heating season, with a relatively low energy efficiency level and great room for improvement. In the middle and late stages of the heating season, it has a relatively stable and relatively high energy efficiency level. Especially during the severe cold period when the temperature drops suddenly, the energy efficiency reaches the lowest value. From the change trend of the energy efficiency level score and the information obtained from the actual investigation, it can be preliminarily analyzed that the system gradually realizes the improvement and stability of the energy efficiency level through methods such as pipe network regulation, heat source quality regulation and quantity regulation during the severe cold period. However, there is still a large room for improvement, especially in the middle and early stages of the heating season. Improving the hydraulic imbalance and thermal imbalance of the pipe network can effectively improve the overall energy efficiency and improve the imbalance phenomenon of the whole network. Taking the improvement of the energy efficiency level as the optimization goal, the pipe network will be further optimized and regulated in the future to improve the energy efficiency.

[0177] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting the present application; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present application.

Claims

1. An online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of thermal accumulation, characterized in that The steps include: S101； Online access to all kinds of operating data required for current heating system evaluation; S102: Preprocess the operation data to form an operation database; S103； The operation database is used to calculate the operation energy efficiency evaluation index of each component or equipment of the current heating system, and the evaluation index includes the introduced fire accumulation dissipation; S104: Calculate the weight of the evaluation index of each component or equipment of the current heating system; S105: Determine the weight of the evaluation index of each component or equipment of the current heating system; S106: Calculate the overall energy efficiency level score of the current heating system.

2. The online test and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy accumulation according to claim 1, characterized in that, In S101, the operation data is the actual operation data of the current heating season; the operation data is the heat source, the pipe network, the heat exchange station and the heat user; Heat source operation data include supply and return water temperature, supply and return water flow, fuel consumption, and power consumption; The network operation data includes the overall supply and return water flow and temperature of the entire network; The operation data of the heat exchange station includes the temperature, pressure and flow rate of the heat exchange unit and pump inlet and outlet, as well as the power consumption of the equipment; Heat user operation data: heating area, heat user indoor heating parameters.

3. The online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy accumulation according to claim 1, characterized in that In S102, the acquired operation data is preprocessed, and the preprocessing is to fill in the missing data, remove the abnormal data, and form an operation energy efficiency database; Specifically: For data sets where the missing data account for less than 50% of the total data and there is no long period of continuous missing data, the missing data are supplemented by using the method of average value supplementation, linear interpolation, or taking the previous (next) valid value; For missing data at a single time point, the mean value supplementation method was used, and the calculation method was as follows: Among them, x i represents the data with a vacancy at a certain moment, and x i-1 represents the valid data in the previous 1 h, and x i+1 represents the valid data in the next 1 h, that is, if the data at a certain moment is missing, the average value of the valid data 1 h before and after it is used for supplementation; If the data before and after 1 hour is missing, the formula is: where x i-24 represents the effective value at the same time of the previous day, and x i+24 represents the effective value at the same time of the next day, that is, if the data at a certain moment is missing, the average value of the effective data at the same time one day before and after it is used for supplementation; For problems with many continuous vacancies, linear interpolation is used; Set [t1, x1] and [t k , x k to be the previous and next valid values of the missing data with a consecutive missing length of k respectively. Then, the value of t at a certain position in the interval [t1, t k should satisfy: The values on the straight line obtained by this method are the supplementary values for the missing data, which are used to fill in the missing data.

4. The online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy accumulation according to claim 3, characterized in that, When the missing data collected accounted for more than 50% of the total data and there were long continuous missing data sets, the analogy method was used to supplement the missing data; The calculation method is as follows: Among them, x0 is the corresponding parameter of the reference heat exchange station, s0 is the actual supply area of the reference heat exchange station, and s is the actual supply area of the heat exchange station with missing data.

5. The online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy accumulation according to claim 1, characterized in that In S103, the operating energy efficiency evaluation indexes of the heat source, the pipe network, the heat exchange station and the heat user are calculated respectively using the operating energy efficiency database; Among them, the heat source operation energy efficiency evaluation indicators include fuel consumption per unit of heat supply and power consumption per unit of heating area; The energy efficiency evaluation indicators of the pipeline network operation include the heat transfer efficiency of the primary pipeline network, hydraulic unbalance, hydraulic unbalance of the heat exchange station, thermal unbalance, thermal unbalance of the heat exchange station, and the dissipation of fire accumulation in the primary pipeline network; The energy efficiency evaluation indicators of heat exchange station operation include heat exchange performance of heat exchange unit and heat accumulation dissipation of heat exchange unit; The energy efficiency evaluation indicators for heat users include the heat consumption per unit area of heat users.

6. The online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy accumulation according to claim 5, characterized in that In S103, the specific energy efficiency evaluation index calculation method is as follows: Unit heat supply fuel consumption B Q : The ratio of the fuel consumption G of the heat source to the heat supply Q of the heat source in the current period Among them, the heat supply of the heat source in the current period Q = M·c p ·(T1 - T2), where M is the water supply flow of the heat source, cp is the specific heat capacity of water, T1 is the temperature of the heat source's water supply, and T2 is the temperature of the heat source's return water; Power consumption per unit heating area B W : The total power consumption of the heat source W and the total building heating area A served y Ratio; Primary pipeline network heat transfer efficiency η: The annual heating heat consumption ∑Q of the building f and the annual actual heat supply ∑Q of the heat source s ratio Pipe network hydraulic imbalance degree X: The ratio of the actual flow rate G of the pipe network S to the specified flow rate G of the pipe network g ; Hydraulic imbalance degree X of heat exchange station h : The product of the actual flow rate G S of the heat exchange station and the heating area A and the specified flow rate G g ratio Among them, G S,i is the actual flow rate of the i-th heat exchange station node, m 3 / h; G g,i is the specified flow rate of the i-th heat exchange station node, m 3 / h; A i is the building heating area of the i-th heat exchange station node, m 2 ; n represents the number of heat exchange station nodes; The heat loss adjustment ratio R of the pipe network: the actual heat supply Q of the pipe network S and the specified heat supply Q of the pipe network g ratio; Hydraulic imbalance degree R of heat exchange station h : The product of the actual heat supply Q S of the heat exchange station and the heating area A and the specified heat supply Q g ratio: Among them, Q S,i is the actual heat supply of the i-th heat exchange station node, m 3 / h; Q g,i is the specified heat supply of the i-th heat exchange station node, m 3 / h; A i is the building heating area of the i-th heat exchange station node, m 2 ; n represents the number of heat exchange station nodes; Fire product dissipation in the primary pipe network Fire product dissipation caused by the return water mixing in the return pipe network, the difference between the fire product in the initial state and the fire product in the terminal state: Among them, T i is the fluid temperature of the pipe segment at the i-th inflow node, m i is the mass flow rate of the fluid in the pipe segment at the i-th inflow node, m is the mass flow rate of the fluid in the pipe segment at the outflow node, and i is the return pipe segment number; at a certain return water mixing node, multiple fluids with different temperatures converge, and the mixed temperature T is: Heat exchange performance kF of heat exchanger unit: the ratio of heat input Q1 of heat station per unit time to the logarithmic average heat exchange temperature difference of heat exchanger unit per unit time: where: Δt P is the logarithmic mean heat transfer temperature difference of the heat exchange unit per unit time; Δt x is the medium temperature difference at the end with a smaller temperature difference of the heat exchange unit per unit time; Δt d is the medium temperature difference at the end with a larger temperature difference of the heat exchange unit per unit time; τ is the detection duration, h; Fire product dissipation of heat exchange station Difference between the fire product of the primary side and the fire product of the secondary side of the heat exchanger: where t 1,in is the supply water temperature on the primary side of the heat exchanger; t 1,out is the return water temperature on the primary side of the heat exchanger; t 2,in is the return water temperature on the secondary side of the heat exchanger; t 2,out is the supply water temperature on the secondary side of the heat exchanger; g is the flow rate on the primary side of the heat exchanger; Heat consumption per unit area Q yA : The heat supply at the building's heating inlet during the heating period Q y0 and the building's heating floor area A y Ratio:

7. The online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of exergy accumulation according to claim 1, characterized in that In the S104, First, n indicators x1, x2, ……, x n , and the calculation expressions for standardizing each indicator are as follows: where x ij is the value of the i-th index of the j-th sample (i = 1, 2..., n; j = 1, 2,..., m); min(x i ) is the minimum value of the i-th index among all samples; max(x i ) is the minimum value of the i-th index among all samples; Y ij is the standardized value of the i-th index of the j-th sample (i = 1, 2..., n; j = 1, 2,..., m); After standardizing the data samples of all indicators, the information entropy expression of each indicator data sample is calculated as follows: Among them, Y ij is the standardized value of the i-th index of the j-th sample (i = 1, 2..., n; j = 1, 2,..., m); p ij is the proportion of the j-th sample value under the i-th index in this index (i = 1, 2..., n; j = 1, 2,..., m); E i is the entropy value of the i-th index; n is the number of indexes; According to the calculated values of the information entropy of each index, calculate the weight of each index to form an index weight system: Among them, E i is the entropy value of the i-th index; W i is the entropy weight value of the i-th index; n is the number of indices.

8. The online testing and evaluation method for the energy efficiency of a large-scale central heating system based on the theory of thermal accumulation according to claim 1, characterized in that In S106, First, calculate the energy efficiency level of the overall heating system through the weight values of each index. The calculation formula is as follows: Among them, W i is the entropy weight value of the i-th index; x i is the value of the i-th index; η cis is the comprehensive energy efficiency evaluation index; Then, compare the calculated value with 100%. If it is lower than 60%, optimize all devices and components. If it is between 60% and 80%, optimize the heating devices or equipment with low evaluation indexes.

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