Establishment method of basic unit model of integrated energy system based on statistical synthesis method

Abstract

The application provides a kind of integrated energy system basic unit model establishing method based on statistical synthesis method based on the technical field of integrated energy system research, comprising the following steps: S1, statistical arrangement integrated energy system basic unit equipment physical framework, S2, using statistical principles to select typical basic unit equipment, S3, establish the physical model of typical basic unit equipment and arrange its physical characteristic parameters, S4, establish the final basic unit model, S5, example analysis verification;The application is based on statistical synthesis method analysis selects typical basic unit equipment, extracts typical basic unit equipment feature data, establishes various basic unit models according to the energy quality attribute of basic unit, and verifies the rationality and applicability of various basic unit models proposed through example analysis.

Description

Technical Field

[0001] This invention relates to a method for establishing a basic unit model of a comprehensive energy system based on statistical synthesis, which belongs to the technical field of comprehensive energy system research. Background Technology

[0002] To coordinate the balance between economic development, energy utilization, and environmental protection, and to achieve synergistic complementarity among different energy forms, the concept of integrated energy systems has emerged. An integrated energy system is a diversified energy architecture whose primary purpose is to meet users' diverse energy needs and reduce energy consumption.

[0003] Currently, modeling research on integrated energy systems has attracted significant attention from industry professionals, with extensive studies conducted on integrated energy power system modeling and integrated energy system load modeling. These studies include the basic theories and development trends of power system modeling, the establishment of accurate dynamic load models, the applicability of various modeling methods, and the establishment of energy trading models. However, research on integrated energy system modeling has not yet focused on the specific modeling of the system's fundamental units. Because integrated energy systems involve multiple energy forms such as electricity, gas, heat, and cooling, and contain various fundamental units, each of which contains numerous fundamental unit devices, the models of these fundamental units become redundant and data measurement is difficult during the interconnection and transmission of various energy sources, failing to adequately reflect the information of the fundamental units. Summary of the Invention

[0004] To address one of the aforementioned technical shortcomings, a method for establishing a basic unit model of an integrated energy system based on statistical synthesis is provided.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by this invention is: a method for establishing a basic unit model of a comprehensive energy system based on statistical synthesis, comprising the following steps:

[0006] S1. Compile and analyze the physical structure of basic unit equipment in the integrated energy system;

[0007] S2. Select typical basic unit equipment by using statistical principles;

[0008] S3. Establish physical models of typical basic unit equipment and organize their physical characteristic parameters;

[0009] S4. Establish the final basic unit model;

[0010] S5. Case analysis and verification.

[0011] Beneficial effects:

[0012] 1) This invention categorizes each basic unit device into a type of basic unit based on their "similar" or "common" energy properties, establishing a basic unit model. The characteristics of the entire integrated energy system are reflected through the characteristics of the basic unit model. Basic unit modeling can better reflect the commonalities of each type of basic unit device in the integrated energy system, simplifying the model. At the same time, treating the basic unit as the research object of the integrated energy system can smooth out the different time scales of each basic unit device in the system, and better analyze the mechanism characteristics of the integrated energy system from the dimensions of time, space, and magnitude.

[0013] 2) This invention is based on the statistical synthesis method and uses fuzzy clustering for data processing. It clusters the characteristic parameters of basic unit equipment according to a certain membership degree to form comprehensive characteristic parameters, which are finally fed back to each type of basic unit, providing reliable data support for the construction of the basic unit model.

[0014] 3) Typical basic unit equipment selected by statistical synthesis method maintains its own unique energy quality properties in the model. There is no coupling conversion and complementary utilization between heterogeneous energy flows. It only produces, transmits and stores the corresponding energy.

[0015] 4) Compared with traditional basic unit models, the model proposed in this invention has clear physical properties and convenient feature parameter calculation;

[0016] 5) By studying the characteristics of the integrated model under static and dynamic conditions, it was found that the characteristic parameters determined by the model proposed in this invention are consistent with the characteristic parameters of the actual system. Attached Figure Description

[0017] The present invention will now be described in further detail with reference to the accompanying drawings;

[0018] Figure 1 This is a schematic diagram of the basic physical architecture of the integrated energy system in this invention;

[0019] Figure 2 This is a schematic diagram illustrating the modeling approach of the statistical synthesis method used in this invention;

[0020] Figure 3 This is a 3D diagram of FIS surface clustering in the example analysis of this invention;

[0021] Figure 4 This is a planar diagram of FIS surface clustering in the example analysis of this invention;

[0022] Figure 5 P is the basic unit in the numerical example analysis of this invention. u -P Z Feature fitting plot;

[0023] Figure 6 P is the basic unit in the numerical example analysis of this invention. u-P I Feature fitting plot;

[0024] Figure 7 P is the basic unit in the numerical example analysis of this invention. u -P P Feature fitting plot;

[0025] Figure 8 This is a fitting diagram of the dynamic characteristics of the basic unit in the numerical example analysis of this invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments; based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The present invention provides a method for establishing a basic unit model of a comprehensive energy system based on the statistical synthesis method, comprising the following steps:

[0028] S1. Compile and analyze the physical structure of basic unit equipment in the integrated energy system;

[0029] S2. Select typical basic unit equipment by using statistical principles;

[0030] S3. Establish physical models of typical basic unit equipment and organize their physical characteristic parameters;

[0031] S4. Establish the final basic unit model;

[0032] S5. Case analysis and verification.

[0033] An integrated energy system comprises multiple basic units, which in turn contain numerous basic unit devices. These basic unit devices together form the basic physical architecture of the integrated energy system.

[0034] Existing research both domestically and internationally has proposed energy internet, pan-energy networks, and other similar architectures, all of which are frameworks for integrated energy systems. At the basic physical architecture and equipment level, the forms of integrated energy systems are fundamentally consistent, encompassing energy production, transmission, storage, and consumption. Based on existing integrated energy system architectures, we can derive the following... Figure 1 The basic physical framework of the integrated energy system shown.

[0035] The specific content of step S1, which involves statistically organizing the physical architecture of the basic unit equipment of the integrated energy system, is as follows:

[0036] The basic units of an integrated energy system include energy supply units, transmission units, and energy storage units;

[0037] The energy supply unit equipment includes micro gas turbines, photovoltaic DG, wind power, fuel cells, electric boilers, heat pumps, natural gas, electric hydrogen production, electric chillers, gas boilers, and absorption chillers;

[0038] The transmission unit equipment includes power transmission and distribution lines, heating pipelines, natural gas pipelines, water pumps, and compressors;

[0039] The energy storage unit includes an energy storage battery, a thermal storage tank, and a gas storage tank.

[0040] This invention employs a widely used comprehensive statistical method to study the composition of basic unit equipment in integrated energy systems, the investigation and statistics of parameters, and the parameter clustering of physical characteristics.

[0041] First, the statistical synthesis method obtains the mathematical models of typical basic unit equipment in each basic unit through experiments and mathematical derivation. Second, the information perception of basic unit equipment at certain special moments during the specific operating time of the system is statistically analyzed, that is, the percentage of each type of typical basic unit equipment in the total equipment quantity of the system, as well as the basic unit equipment data involved in each line. Then, the obtained data is integrated step by step according to the relationship between energy consumption, transmission energy, and production capacity. Finally, the data at all levels are integrated to obtain the basic unit model of that type.

[0042] The statistical synthesis modeling method is based on the composition and physical characteristics of typical equipment. First, it determines the composition and parameters of the system's basic unit equipment. Second, based on the system's equipment composition, fuzzy clustering is used to select the most representative basic unit equipment. Finally, the physical characteristics represented by these representative basic unit equipment are attributed to the corresponding basic unit nodes, resulting in the final basic unit model representing the characteristics of each type of basic unit. The modeling approach is as follows: Figure 2 As shown.

[0043] The steps in step S2, which utilize statistical principles to select typical basic unit devices, are as follows:

[0044] S21. Select a few representative basic unit devices from each of the basic units to study, determine their composition and proportion in the system, and establish the system's domain of discourse F1 based on this. Then, use equation (1) to standardize the domain of discourse F2.

[0045] (1)

[0046] Where: M j This represents the maximum value in the j-th column;

[0047] S22. The similarity matrix of F2 is calculated using the maximum-minimum method shown in equation (2), and then the obtained similarity matrix is ​​converted into an equivalent matrix by fuzzy clustering method to obtain the membership value λ that reflects the physical characteristics of each type of basic unit equipment.

[0048] (2);

[0049] S23. Based on the λ value obtained by fuzzy clustering, establish the λ cutoff matrix, and find the λ value that can best reflect the commonality of the clustering of basic unit equipment in the system, and select the typical basic unit equipment of the system.

[0050] The steps in step S22, which involve obtaining the membership value λ through fuzzy clustering, are as follows:

[0051] Fuzzy clustering analysis is a mathematical method that classifies objective things by establishing fuzzy similarity relationships based on their characteristics, degree of similarity, and other characteristics. This method sets the classification objects as U = {u1, u2, ..., u...}. n}, and each object u i Each feature is represented by m feature data, thus establishing a feature index matrix U. * As shown below:

[0052]

[0053] However, since the dimensions and orders of magnitude of the m characteristic indicators are not necessarily the same, it is necessary to adjust U. * Format:

[0054] (3)

[0055] The data is normalized to between 0 and 1 to eliminate the influence of dimensions. The fuzzy similarity relationship of the objects is determined by multivariate analysis, and the fuzzy similarity matrix R is established. However, this matrix may not be transitive. Therefore, the squaring method is used to find the transitive closure t(R) so that it satisfies the relationship of equation (4):

[0056] (4)

[0057] After performing a finite number of operations on the above model, the transitive closure matrix can be determined, as shown in equation (5):

[0058] (5)

[0059] At this point, the parameter in the closure matrix is ​​the membership degree λ value. After dynamic clustering of different membership degree λ values, the classification at each level is obtained. Finally, the optimal membership degree λ value is determined according to actual needs.

[0060] The steps for dynamically clustering different membership degree λ values ​​are as follows:

[0061] 1) Establish fuzzy similarity relationships for basic unit devices and calculate similarity using the minimax method;

[0062] 2) Convert the similarity relationships of typical basic unit devices into equivalence relationships;

[0063] 3) Clustering is performed based on different λ cut levels. When λ decreases from 1 to 0, the resulting classifications become coarser and gradually merged, completing the cluster analysis process. Finally, the units with higher similarity are taken as typical basic units.

[0064] Step S3 establishes physical models of typical basic unit devices and organizes their physical characteristic parameters. The physical models used in this step are the basic unit devices from step S1. The physical models of some basic unit devices are as follows:

[0065] 1) Physical model of gas turbine

[0066] (6)

[0067] In the formula: P EGT (t) represents the output electrical power of the gas turbine during time period t; V EGT (t) represents the natural gas consumption of the gas turbine during time period t; L NG Indicates the lower heating value of natural gas; η EGT Δt represents the power generation efficiency of the gas turbine; Δt represents the time step.

[0068] 2) Photovoltaic DG physical model

[0069] (7)

[0070] In the formula: ζ represents the local solar radiation intensity; θ represents the incident angle of sunlight on the solar panel; η m Indicates the efficiency of the MPPT controller; A P Indicates the area of ​​the solar panel; η p Indicates the efficiency of the solar panel;

[0071] 3) Physical model of electric boiler

[0072] An electric boiler is a typical coupled equipment unit. The physical model of an electric boiler is as follows:

[0073] (8)

[0074] In the formula: Q EHB (t) represents the heat supplied by the electric boiler at time t; P EHB(t) represents the power consumption of the electric boiler at time t; η EHB Indicates electrothermal conversion efficiency; μ Loss This represents the heat loss at time t;

[0075] 4) Physical model of heat pump

[0076] Heat pumps drive compressors by inputting high-quality electricity, resulting in a high coefficient of performance (COP) and less susceptibility to load variations. The model for a heat pump is as follows:

[0077] (9)

[0078] In the formula: P HP (t), H HP (t) represents the electrical power consumed and the heating power generated by the heat pump at time t; cop HP This indicates the coefficient of performance (COP) of the heat pump.

[0079] 5) Physical model of absorption chiller

[0080] (10)

[0081] (11)

[0082] In the formula: Q AC The output cooling power of the absorption chiller is represented by COP; AC represents the coefficient of performance (COP); QH AC represents the input heat power of the absorption chiller; W s This indicates the input hot steam flow rate of the absorption chiller; h s1 and h s2 These represent the specific enthalpy of hot steam and the specific enthalpy of condensate, respectively.

[0083] 6) Physical model of gas-fired boiler

[0084] (12)

[0085] In the formula: q GHB (t) represents the heat output power of the gas-fired boiler; V GHB (t) represents the natural gas consumption of the gas-fired heating boiler during time period t; L NG Indicates the lower heating value of natural gas; η GHB This indicates the thermal efficiency of a gas-fired heating boiler.

[0086] 7) Physical model of power transmission and distribution lines

[0087] (13)

[0088] In the formula: P LIndicates the output power after flowing through the transmission and distribution lines; I represents the operating current; U represents the output power after flowing through the transmission and distribution lines. L0 Indicates the input voltage; η L This represents network losses, including line losses and substation losses.

[0089] 8) Physical model of heating pipe network

[0090] The heating network is an important component of the heating system, mainly consisting of two parts: heating pipes and circulating water pumps.

[0091] Physical model of heat pipes

[0092] (14)

[0093] (15)

[0094] (16)

[0095] In the formula: p1 and p2 are the pressures at the beginning and end of the pipeline, respectively; ω represents the average flow velocity of the pipeline; υ represents the average specific volume of the pipeline; g represents the acceleration due to gravity; D1 and D0 represent the inner and outer diameters of the pipeline, respectively; λ represents the friction coefficient; L represents the length of the pipeline network; ∑ζ represents the local resistance coefficient; H2 and H1 represent the heights at the beginning and end of the pipeline, respectively; t in and t out These represent the temperatures at the beginning and end of the pipe, respectively; Q Loss G represents the heat loss of the pipeline; L Indicates the flow rate of the pipe; c P K represents the specific heat capacity at constant pressure of hot water; R represents the equivalent length coefficient of heat loss components; t represents the thermal resistance of the pipe; and t represents the average temperature of the medium inside the pipe. a Indicates ambient temperature;

[0096] Power calculation of circulating water pump

[0097] (17)

[0098] In the formula: P wp Indicates the power of the circulating water pump; η WP H represents the efficiency of the water pump. i G represents the head of the i-th pump; i This represents the flow rate of the i-th pump;

[0099] 9) Physical model of the transmission capacity of natural gas pipeline network

[0100] (18)

[0101] (19)

[0102] Where: M d q represents the mass flow rate of natural gas; d,0 P represents the volumetric flow rate of natural gas at 101.325 kPa and 273.15 K; s Z represents the rated absolute pressure of natural gas at the pipeline inlet; s The compressibility factor of natural gas at the pipeline inlet; C B P represents the potential energy factor function of natural gas. e Z represents the absolute pressure rating of natural gas at the pipeline terminus. e Z represents the compressibility factor of natural gas at the pipeline terminus. ave For Z s and Z e The average value; d represents the inner diameter of the pipeline; λ represents the friction coefficient of the pipeline; L represents the pipeline length; R represents the gas constant of natural gas; T represents the temperature of natural gas; ρ0 represents the density of natural gas at 101.325 kPa and 273.15 K.

[0103] The compressibility factor of a gas can be expressed as:

[0104] (20)

[0105] In the formula: Z represents the gas compressibility factor. When the gas pressure is less than 1.2 MPa, the natural gas compressibility factor Z is taken as 1; p represents the natural gas pressure; Δ represents the relative density of natural gas.

[0106] The potential energy factor function of natural gas can be expressed as:

[0107] (twenty one)

[0108] In the formula: g represents the acceleration due to gravity; h represents the height difference between the start and end points of the pipe;

[0109] The formula for calculating the friction factor of a pipeline is:

[0110] (twenty two)

[0111] In the formula: K represents the equivalent absolute roughness of the inner surface of the pipe; R e Represents the Reynolds number;

[0112] The physical model for real-time flow in a natural gas pipeline is as follows:

[0113] (twenty three)

[0114] In the formula: P1 and P2 represent the natural gas pressure at the starting and ending points of the natural gas pipeline, respectively; Q represents the hourly flow rate of the natural gas pipeline; ρ represents the density of natural gas in the pipeline; T represents the temperature of the natural gas during transmission; T0 is the temperature value of 273.15K;

[0115] 10) Compressor physical model

[0116] The main parameters of a compressor include its output power, gas delivery capacity, and exhaust temperature. Since the focus is on the power of the basic unit equipment, the power calculation model for the compressor is as follows:

[0117] (twenty four)

[0118] In the formula: P dis and P suc These represent the compressor discharge pressure and intake pressure, respectively; m represents the compressor polytropic index; V r Indicates actual displacement

[0119] 11) Physical model of energy storage battery

[0120] (25)

[0121] In the formula: S oc (t) and S oc (t0) represents the remaining charge of the energy storage battery at times t and t0, respectively; δ represents the self-discharge rate of the energy storage battery, in % / h; Δt represents the time span from t0 to t; P ch and P dis These represent the charging and discharging power of the energy storage battery, respectively; η ch and η dis These represent the charge and discharge efficiencies of the energy storage battery, respectively.

[0122] 12) Physical model of thermal storage tank

[0123] (26)

[0124] In the formula: Q HS (t) represents the amount of heat stored in the thermal storage tank at time t; μ Loss Q represents the heat loss rate of the thermal storage tank; HS (t0) represents the heat storage capacity of the thermal storage tank at time t0; Qch HS(Δt) represents the heat charge of the thermal storage tank between time t0 and time t; ηch HS represents the heat charge efficiency of the thermal storage tank; Qdis HS(Δt) represents the heat release of the thermal storage tank between time t0 and time t; ηdis HS represents the heat release efficiency of the thermal storage tank.

[0125] 13) Physical model of the gas storage tank

[0126] (27)

[0127] In the formula: V GS V represents the effective gas storage volume of the gas storage tank. c P represents the geometric volume of the gas storage tank; high P low P0 represents the absolute pressure under the highest and lowest operating conditions; P0 represents the engineering standard pressure.

[0128] The step S4 of establishing the final basic unit model specifically involves, based on the energy properties of the basic unit equipment itself, summarizing the physical model of the basic unit equipment and the physical characteristic parameters of the selected typical basic unit equipment under the corresponding basic unit to establish the final basic unit model.

[0129] The following uses specific data to illustrate the specific steps of the example analysis and verification in step S5.

[0130] Case Analysis

[0131] 1. Selection of typical basic unit equipment

[0132] This invention analyzes and studies the basic unit operating conditions of an integrated energy system under normal operation. The system uses the 24-hour mixed load demand of industrial and residential areas during peak summer seasons as the basic data for the survey, and the required parameters are calculated by the mathematical model of the basic unit equipment in steps S1-2.

[0133] The system comprises 18 basic unit devices: photovoltaic DG, power transmission and distribution lines, compressors, heating networks, hydrogen production equipment, gas boilers, gas storage tanks, fuel cells, thermal storage tanks, electric boilers, natural gas networks, absorption chillers, electrolyzers, gas turbines, cryogenic thermal storage chambers, heat pumps, water pumps, and energy storage batteries. These are designated as the basic unit devices to be clustered, denoted as U = {U1, U2, U3, U4, U5, U6, U7, U8, U9, U...}. 10 U 11 U 12 U 13 U 14 U 15 U 16 U 17 U 18} consists of 18 initially selected typical basic units; there are 6 object parameters, denoted as U. i ={U i1 U i2 U i3 U i4 Ui5 U i6} = {Voltage, Current, Power, Temperature, Pressure, Volume}. The selected basic unit equipment characteristic parameters are shown in Table 1.

[0134] Table 1 Basic Unit Equipment Parameter Survey Form

[0135] Table 1 Base unit equipment parameter questionnaire

[0136] Basic unit equipment Voltage (V) Current (A) Power (kW) Temperature (°C) Pressure (MPa) <![CDATA[Volume (m 3 )]]> Photovoltaic DG 600 15 95 20 0 0 power transmission and distribution lines 380 225 116 0 0 0 compressor 0 0 11 90 1.3 0.4 heating pipe network 0 0 2800 200 2.5 100 Electric hydrogen production equipment 380 48 14 45 1.5 0 Gas boiler 24 18 3500 95 0.7 20 gas tank 0 0 220 75 1.5 1.3 fuel cells 24 6 386 80 3.5 0 thermal storage tank 0 0 10930 200 1.6 10 electric boiler 380 109 72 80 1.0 0.4 Natural gas pipeline network 0 0 2200 75 1.0 100 Absorption chiller 0 0 78 40 0.6 10 Electrolytic cell 24 90 12 90 5 0 gas turbine 0 0 20260 521 0.1 0 Low-temperature thermal storage chamber 0 0 3600 10 2.1 20 heat pump 380 24 41 80 4 0.5 water pump 380 24 55 20 1.0 0 Energy storage battery 36 7 4000 24 0 0 total 2608 566 48390 1755 27.4 262.6

[0137] Based on the physical characteristic parameters of the basic unit equipment in Table 1, calculate the percentage of each basic unit equipment parameter and establish the universe of discourse of the basic unit equipment, as shown in F1.

[0138]

[0139] The data in the domain are standardized using the standardization formula shown in Equation (1), and the result is shown in F2. Typical basic units are selected based on the fuzzy clustering method of equivalence relation, and their similarity is calculated by the minimax method shown in Equation (2).

[0140]

[0141] The established similarity matrix R is as follows:

[0142]

[0143] The transitive closure is solved using the squaring method shown in equation (5), and the equivalent matrix R is obtained. 2 as follows:

[0144]

[0145] After deriving the above model, R can be calculated. 8 =R 4 The relation is given by the following:

[0146]

[0147] Therefore, the transitive closure of matrix F is t(R) = R. 4 As shown below:

[0148]

[0149] With membership degrees {0.29, 0.3, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.45, 0.53, 0.54, 0.55, 0.58, 1}, select values ​​of λ to construct the λ-cut matrix. From the obtained transitive closure, select a set of λ values ​​in descending order, where λ∈[0, 1], to determine the corresponding λ-cut matrix. Using fuzzy clustering based on fuzzy relations in fuzzy dynamic clustering, apply fuzzy clustering to the 18*6 dimensional sample data to obtain the following results: Figure 3 and 4 The diagrams shown are the 3D and 2D plots of the FIS surface clustering.

[0150] Figure 3 , Figure 4 This reflects the clustering results of basic unit equipment of the system under different membership degrees. Within the membership degree interval of [0, 1], the distribution and proportion of membership degrees in the figure show that when λ=0.4, it reflects the commonality of the clustered samples to the greatest extent, which meets the requirements of sample parameters in statistical synthesis modeling.

[0151] Since different values ​​of λ correspond to different λ-cut matrices, when λ=0.4, the constructed matrix t(0.4) is as follows:

[0152]

[0153] The basic units of clustering can be divided into two categories: Category I {1, 2, 6, 7, 8, 9, 10, 14, 16, 17, 18} is the selected basic unit containing the most elements; Category II {3, 4, 5, 11, 12, 13, 15} is a basic unit containing fewer elements.

[0154] The purpose of cluster analysis in statistical synthesis is to increase the data differences between dissimilar groups and decrease the data differences between similar groups within a system. Therefore, this invention selects the basic unit equipment of the system after clustering with a membership degree λ=0.4 as the research object. The basic unit equipment after clustering with a membership degree λ=0.4 is shown in Table 2.

[0155] Table 2. Selected Typical Basic Unit Equipment Parameter Table

[0156] Table 3 Selected typical base unit equipment parameters table

[0157] Basic unit equipment Voltage (V) Current (A) Power (kW) Temperature (°C) Pressure (MPa) <![CDATA[Volume (m 3 )]]> Photovoltaic DG 600 15 95 20 0 0 transmission lines 380 225 116 0 0 0 Gas boiler 24 18 3500 95 0.7 20 gas tank 0 0 220 75 1.5 1.3 fuel cells 24 6 386 80 3.5 0 thermal storage tank 0 0 10930 200 1.6 10 electric boiler 380 109 72 80 1.0 0.4 gas turbine 0 0 20260 521 0.1 0 heat pump 380 24 41 80 4 0.5 water pump 380 24 55 20 1.0 0 Energy storage battery 36 7 4000 24 0 0 total 2204 428 39675 1215 13.3 32.2

[0158] The selected typical basic unit devices are classified according to their attributes and categorized into each basic unit category, as shown in Table 3.

[0159] Table 3. Classification of Selected Basic Unit Equipment

[0160] Table 3 Selected base unit equipment classification

[0161] Basic unit Basic unit equipment Energy supply unit Photovoltaic DG, gas-fired boilers, fuel cells, electric boilers, gas turbines Transmission unit Power transmission and distribution lines, heat pumps, water pumps Energy storage unit Gas storage tank, thermal storage tank, energy storage battery

[0162] 2. Characteristics of typical basic unit models

[0163] (1) Static model of typical basic unit

[0164] Based on Table 2, a typical basic unit model is established based on the characteristic parameters of typical components in the latest research reports from EPRI and IEEE in the United States, and the comprehensive static characteristic parameters of the typical basic unit can be obtained.

[0165] 1) A comprehensive physical characteristic model of typical basic unit representing the power supply condition is shown in Table 4.

[0166] Table 4 Static characteristic parameters of the power supply unit under operating conditions

[0167] Table 4 Static characteristics parameters of the working conditions of the energy supply unit

[0168] Basic unit equipment System percentage (%) <![CDATA[p u ]]> <![CDATA[p f ]]> <![CDATA[q u ]]> <![CDATA[q f ]]> Photovoltaic DG 0.49 0.08 2.9 1.6 1.8 electric boiler 18.18 0.1 0.0 0.0 0.0 fuel cells 2.01 2.3 -1.0 1.61 -1.0

[0169] 2) Typical basic unit characterization of the comprehensive physical characteristics of transmission conditions, as shown in Table 5.

[0170] Table 5. Transmission Unit Operating Condition Characteristics Parameters

[0171] Table 5 Parameters of the operating conditions of the transmission unit

[0172] Basic unit equipment System percentage (%) <![CDATA[p u ]]> <![CDATA[p f ]]> <![CDATA[q u ]]> <![CDATA[q f ]]> power transmission and distribution lines 0.6 2.0 0.0 0.0 0.0

[0173] 3) A comprehensive physical characteristic model of typical basic units representing energy storage operating conditions is shown in Table 6.

[0174] Table 6. Operating characteristic parameters of energy storage units

[0175] Table 6 Energy storage unit operating characteristic parameters

[0176] Basic unit equipment System percentage (%) <![CDATA[p u ]]> <![CDATA[p f ]]> <![CDATA[q u ]]> <![CDATA[q f ]]> gas tank 1.14 2.0 0.0 5.2 -4.6 thermal storage tank 56.79 0.2 0.0 0.0 0.0 Energy storage battery 20.78 0.08 2.9 1.6 1.8

[0177] The calculation of static model parameters is based on the static operating characteristic parameters of each basic unit equipment. The system percentages in Tables 4, 5, and 6 represent the load percentages of the power supply unit, transmission unit, and energy storage unit under the entire system's static operating conditions, respectively; p u p f q u q f These are the static characteristic coefficients of each basic unit under static operating conditions. Their magnitude depends on the actual voltage in the system, the voltage during disturbance-free operation, and the active and reactive power of the basic unit equipment.

[0178] 4) The modeling method based on the load characteristic power function model was used to calculate the parameters of the power function integrated static model. The results are shown in Table 7.

[0179] Table 7 Characteristic parameters of the power function comprehensive model

[0180] Table 7 Power functions synthesize model feature parameters

[0181] Power function comprehensive model parameters <![CDATA[p u ]]> <![CDATA[p f ]]> <![CDATA[q u ]]> <![CDATA[q f ]]> Calculation results 0.23 0.6 0.43 0.31

[0182] The static integrated model is the static characteristic coefficient of the integrated load obtained after the static characteristic coefficients and proportions of each load component in the system are statistically determined. The parameters in Table 7 reflect the integrated model parameters of the load voltage characteristics and load frequency characteristics of all basic unit equipment under static conditions. The characteristic parameters of the system integrated model are further calculated using equations (28) and (29):

[0183] (28)

[0184] (29)

[0185] By combining the power function synthesis model with the data in Table 7 and performing a Taylor series expansion around the system voltage reference point U0, the characteristic parameters of the ZIP model can be obtained. The calculation results of the characteristic parameters of the static synthesis model of the system basic unit are shown in Table 8.

[0186] Table 8 Characteristic parameters of the static integrated model of the system's basic units

[0187] Table 8 System static synthesis model feature parameters

[0188] ZIP Integrated Model Feature Parameters <![CDATA[P Z ]]> <![CDATA[P I ]]> <![CDATA[P P ]]> <![CDATA[Q Z ]]> <![CDATA[Q I ]]> <![CDATA[Q P ]]> Calculation results -0.09 0.41 0.68 -0.12 0.68 0.44

[0189] The data in Table 8 is the overall integrated model of the system under static operating conditions, reflecting the parameter characteristics of active and reactive power, which is the final static model of the system's basic units.

[0190] (2) Typical basic unit dynamic model

[0191] Using the data in Table 9, the dynamic model parameters, and the characteristic parameter calculation formula in step S3, the characteristic parameters of the system dynamic integrated model are calculated, and the calculation results are shown in Table 10.

[0192] Table 9 Characteristic parameters of dynamic operating conditions of basic unit

[0193] Table 9 Dynamic working condition characteristic parameters of the base unit

[0194] Basic unit equipment System percentage (%) <![CDATA[R s ]]> <![CDATA[X s ]]> <![CDATA[X m ]]> <![CDATA[R r ]]> <![CDATA[X r ]]> A B <![CDATA[T j ]]> <![CDATA[LF m ]]> <![CDATA[N m ]]> gas turbine 64.32 0.013 0.067 3.8 0.009 0.17 1.0 0.0 3.0 0.8 1.0 Gas boiler 17.13 0.031 0.1 3.2 0.018 0.18 1.0 0.0 1.4 0.6 1.0 heat pump 9.47 0.053 0.083 1.9 0.036 0.068 0.2 0.0 0.56 0.6 0.9 water pump 9.08 0.079 0.12 3.2 0.052 0.12 1.0 0.0 1.4 0.7 1.0

[0195] Table 10 Characteristic parameters of the dynamic integrated model of the system's basic units

[0196] Table 10 System dynamic synthesis model feature parameters

[0197] Dynamic synthesis model feature parameters <![CDATA[R s ]]> <![CDATA[X s ]]> <![CDATA[X m ]]> <![CDATA[R r ]]> <![CDATA[X r ]]> A B <![CDATA[T j ]]> <![CDATA[LF m ]]> <![CDATA[N m ]]> Calculation results 0.02 0.09 3.28 0.01 0.01 0.83 0 2.26 0.73 0.99

[0198] The dynamic mechanism model is required to reflect the essence of the comprehensive physical model while also adapting to the need for simplicity and low order. Therefore, accurate characteristic parameters are needed to meet the requirements of the dynamic mechanism model. In Table 9, the system proportion is defined only by the load proportion of each basic unit equipment under dynamic operating conditions. Based on the data in Table 9, combined with the motor model, the characteristic parameters reflecting the third-order model of the electromechanical transient motor can be obtained as shown in Table 10. Finally, the characteristic parameters of the dynamic comprehensive model can be determined.

[0199] (3) Basic unit model based on statistical synthesis method

[0200] The modeling concept of the statistical synthesis method is to recursively deduce the next higher-level model after establishing the lower-level classification model, ultimately building a unified comprehensive model with each type of basic unit as a node. Using the calculation results of typical basic unit static and dynamic models, a basic unit model based on the statistical synthesis method can be established.

[0201] 1) Establishment of the basic unit integrated static model

[0202] The static model includes three basic units, as shown in Tables 11-13.

[0203] Based on the model parameters calculated using the static ZIP model, the characteristic parameters of each basic unit are calculated as shown in Tables 11-13. The node parameters are calculated using the method proposed in this invention, and these parameters characterize the basic units of the integrated energy system.

[0204] Table 11 Integrated Static Model of System Power Supply Unit

[0205] Table 11 Integrated static model of the system energy supply unit

[0206] ZIP Integrated Model Feature Parameters <![CDATA[P Z ]]> <![CDATA[P I ]]> <![CDATA[P P ]]> <![CDATA[Q Z ]]> <![CDATA[Q I ]]> <![CDATA[Q P ]]> Calculation results -0.02 0.11 0.91 -0.02 0.08 0.94

[0207] Table 12 Integrated Static Model of System Transmission Unit

[0208] Table 12 Comprehensive static model of the system transmission unit

[0209] ZIP Integrated Model Feature Parameters <![CDATA[P Z ]]> <![CDATA[P I ]]> <![CDATA[P P ]]> <![CDATA[Q Z ]]> <![CDATA[Q I ]]> <![CDATA[Q P ]]> Calculation results -0.006 0.024 0.982 -0.08 0.14 0.94

[0210] Table 13 Integrated Static Model of System Energy Storage Unit

[0211] Table 13 Integrated static model of the system energy storage unit

[0212] ZIP Integrated Model Feature Parameters <![CDATA[P Z ]]> <![CDATA[P I ]]> <![CDATA[P P ]]> <![CDATA[Q Z ]]> <![CDATA[Q I ]]> <![CDATA[Q P ]]> Calculation results -0.06 0.27 0.79 -0.12 0.63 0.49

[0213] 2) Establishment of the integrated dynamic model of basic units

[0214] Since only the power supply unit and the transmission unit exist under the dynamic mechanism, the dynamic integrated parameter model of the power supply unit established in this invention is shown in Table 14, and the dynamic integrated parameter model of the transmission unit is shown in Table 15.

[0215] Table 14 Dynamic Model of System Power Supply Unit

[0216] Table 14 Dynamic model of the system energy supply unit

[0217] Dynamic synthesis model feature parameters <![CDATA[R s ]]> <![CDATA[X s ]]> <![CDATA[X m ]]> <![CDATA[R r ]]> <![CDATA[X r ]]> A B <![CDATA[T j ]]> <![CDATA[LF m ]]> <![CDATA[N m ]]> Calculation results 0.02 0.09 3.28 0.01 0.01 0.83 0 2.26 0.73 0.99

[0218] Table 15 Dynamic Model of System Transmission Unit

[0219] Table 15 Dynamic model of the system transmission unit

[0220] Dynamic synthesis model feature parameters <![CDATA[R s ]]> <![CDATA[X s ]]> <![CDATA[X m ]]> <![CDATA[R r ]]> <![CDATA[X r ]]> A B <![CDATA[T j ]]> <![CDATA[LF m ]]> <![CDATA[N m ]]> Calculation results 0.08 0.05 2.78 0.04 0.06 0.63 0 2.05 0.23 0.79

[0221] 3) Basic Unit Characteristic Analysis

[0222] To further verify the feasibility of the proposed method, this invention employs least squares curve fitting to perform fitting analysis on the characteristics of the basic unit model.

[0223] Figures 5-7 This is a characteristic fitting diagram between the static model of the basic unit established in this invention and the static model of the actual system, reflecting the fitting of the characteristics of the established model with the characteristics of the actual system model under different Pu levels. Under static conditions, by Figure 5 It can be seen that there is a small residual between the fitted value and the actual system value on the interval [0.2, 0.7]; from Figures 6-7 It can be seen that the characteristics of the basic unit model established by this invention are highly consistent with the characteristics of the actual model over the entire range of independent variables, with only a very small residual at certain points, which verifies the feasibility of the static model of the basic unit of the integrated energy system established based on the statistical synthesis method.

[0224] Figure 8 The dynamic characteristic fitting diagram of the basic unit reflects the characteristic parameter R of the motor. sM X sM X mM R rM X rM A M B M T JM and LF mM The fitting results under dynamic conditions are shown. By comparing the actual dynamic values ​​and the fitted dynamic values ​​in the figure, it can be seen that the basic unit dynamic model established by the statistical synthesis method not only fits the dynamic characteristics of the actual system highly, but also, within the given comprehensive independent variables, the model established by this invention can measure a wider range of characteristic parameter values, demonstrating the applicability of the model and providing a certain degree of reliability for the research of basic units.

[0225] This invention addresses the challenges of massive, heterogeneous data, complex basic unit models, and measurement difficulties in integrated energy systems. Based on the fundamental principles of statistical synthesis modeling and combined with practical modeling experience in real-world systems, it proposes a set of basic ideas, methods, and steps for modeling the parameters of basic units in integrated energy systems. Characteristic parameters for various basic unit devices are established, and based on these, integrated static and dynamic parameter models for three types of basic units are obtained. Modeling practice demonstrates the feasibility and applicability of this method, leading to the following conclusions:

[0226] 1) This invention is based on the statistical synthesis method and uses fuzzy clustering for data processing. It clusters the characteristic parameters of basic unit equipment according to a certain membership degree to form comprehensive characteristic parameters, which are finally fed back to each type of basic unit, providing reliable data support for the construction of the basic unit model.

[0227] 2) Typical basic unit equipment selected by statistical synthesis method maintains its own unique energy quality properties in the model. There is no coupling conversion and complementary utilization between heterogeneous energy flows. It only produces, transmits and stores the corresponding energy.

[0228] 3) Compared with traditional basic unit models, the model proposed in this invention has clear physical properties and convenient feature parameter calculation;

[0229] 4) By studying the characteristics of the integrated model under static and dynamic conditions, it was found that the characteristic parameters determined by the model proposed in this invention are consistent with the characteristic parameters of the actual system.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for establishing a basic unit model of a comprehensive energy system based on statistical synthesis, characterized in that... Includes the following steps: S1. Compile and analyze the physical structure of basic unit equipment in the integrated energy system; The specific content of step S1, which involves statistically organizing the physical architecture of the basic unit equipment of the integrated energy system, is as follows: The basic units of an integrated energy system include energy supply units, transmission units, and energy storage units; The energy supply unit equipment includes micro gas turbines, photovoltaic DG, wind power, fuel cells, electric boilers, heat pumps, natural gas, electric hydrogen production, electric chillers, gas boilers, and absorption chillers; The transmission unit equipment includes power transmission and distribution lines, heating pipelines, natural gas pipelines, water pumps, and compressors; The energy storage unit includes an energy storage battery, a thermal storage tank, and a gas storage tank; S2. Select typical basic unit equipment by using statistical principles; The steps in step S2, which utilize statistical principles to select typical basic unit devices, are as follows: S21. Select a few representative basic unit devices from each of the basic units to study, determine their composition and proportion in the system, and establish the system's domain of discourse F1 based on this. Then, use equation (1) to standardize the domain of discourse F2. （1）； Where: M j This represents the maximum value in the j-th column; S22. The similarity matrix of F2 is calculated using the maximum-minimum method shown in equation (2), and then the obtained similarity matrix is ​​converted into an equivalent matrix by fuzzy clustering method to obtain the membership value λ that reflects the physical characteristics of each type of basic unit equipment. （2）； S23. Based on the λ value obtained by fuzzy clustering, establish the λ cutoff matrix, and find the λ value that can best reflect the commonality of clustering of basic unit equipment in the system, and select typical basic unit equipment of the system. S3. Establish physical models of typical basic unit equipment and organize their physical characteristic parameters; Step S3 establishes physical models of typical basic unit devices and organizes their physical characteristic parameters. The physical models used in this step are the basic unit devices from step S1. The physical models of some basic unit devices are as follows: 1) Physical model of gas turbine （6）； In the formula: P EGT (t) represents the output electrical power of the gas turbine during time period t; V EGT (t) represents the natural gas consumption of the gas turbine during time period t; L NG Indicates the lower heating value of natural gas; η EGT Δt represents the power generation efficiency of the gas turbine; Δt represents the time step. 2) Photovoltaic DG physical model （7）； In the formula: ζ represents the local solar radiation intensity; θ represents the incident angle of sunlight on the solar panel; η m Indicates the efficiency of the MPPT controller; A P Indicates the area of ​​the solar panel; η p Indicates the efficiency of the solar panel; 3) Physical model of electric boiler （8）； In the formula: Q EHB (t) represents the heat supplied by the electric boiler at time t; P EHB (t) represents the power consumption of the electric boiler at time t; η EHB Indicates electrothermal conversion efficiency; μ Loss This represents the heat loss at time t; 4) Physical model of heat pump （9）； In the formula: P HP (t), H HP (t) represents the electrical power consumed and the heating power generated by the heat pump at time t; cop HP This indicates the coefficient of performance (COP) of the heat pump. 5) Physical model of absorption chiller （10）； （11）； In the formula: Q AC The output cooling power of the absorption chiller is represented by COP; AC represents the coefficient of performance (COP); QH AC represents the input heat power of the absorption chiller; W s This indicates the input hot steam flow rate of the absorption chiller; h s1 and h s2 These represent the specific enthalpy of hot steam and the specific enthalpy of condensate, respectively. 6) Physical model of gas-fired boiler （12）； In the formula: q GHB (t) represents the heat output power of the gas-fired boiler; V GHB (t) represents the natural gas consumption of the gas-fired heating boiler during time period t; L NG Indicates the lower heating value of natural gas; η GHB This indicates the thermal efficiency of a gas-fired heating boiler. 7) Physical model of power transmission and distribution lines （13）； In the formula: P L Indicates the output power after flowing through the transmission and distribution lines; I represents the operating current; U represents the output power after flowing through the transmission and distribution lines. L0 Indicates the input voltage; η L This represents network losses, including line losses and substation losses. 8) Physical model of heating pipe network The heating network is an important component of the heating system, mainly consisting of two parts: heating pipes and circulating water pumps. Physical model of heat pipes （14）； （15）； （16）； In the formula: p1 and p2 are the pressures at the beginning and end of the pipeline, respectively; ω represents the average flow velocity of the pipeline; υ represents the average specific volume of the pipeline; g represents the acceleration due to gravity; D1 and D0 represent the inner and outer diameters of the pipeline, respectively; λ represents the friction coefficient; L represents the length of the pipeline network; ∑ζ represents the local resistance coefficient; H2 and H1 represent the heights at the beginning and end of the pipeline, respectively; t in and t out These represent the temperatures at the beginning and end of the pipe, respectively; Q Loss G represents the heat loss of the pipeline; L Indicates the flow rate of the pipe; c P K represents the specific heat capacity at constant pressure of hot water; R represents the equivalent length coefficient of heat loss components; t represents the thermal resistance of the pipe; and t represents the average temperature of the medium inside the pipe. a Indicates ambient temperature; Power calculation of circulating water pump （17）； In the formula: P wp Indicates the power of the circulating water pump; η WP H represents the efficiency of the water pump. i G represents the head of the i-th pump; i This represents the flow rate of the i-th pump; 9) Physical model of the transmission capacity of natural gas pipeline network （18）； （19）； Where: M d q represents the mass flow rate of natural gas; d,0 P represents the volumetric flow rate of natural gas at 101.325 kPa and 273.15 K; s Z represents the rated absolute pressure of natural gas at the pipeline inlet; s The compressibility factor of natural gas at the pipeline inlet; C B P represents the potential energy factor function of natural gas. e Z represents the absolute pressure rating of natural gas at the pipeline terminus. e Z represents the compressibility factor of natural gas at the pipeline terminus. ave For Z s and Z e The average value; d represents the inner diameter of the pipeline; λ represents the friction coefficient of the pipeline; L represents the pipeline length; R represents the gas constant of natural gas; T represents the temperature of natural gas; ρ0 represents the density of natural gas at 101.325 kPa and 273.15 K. The compressibility factor of a gas can be expressed as: （20）； In the formula: Z represents the gas compressibility factor. When the gas pressure is less than 1.2 MPa, the natural gas compressibility factor Z is taken as 1; p represents the natural gas pressure; Δ represents the relative density of natural gas. The potential energy factor function of natural gas can be expressed as: （21）； In the formula: g represents the acceleration due to gravity; h represents the height difference between the start and end points of the pipe; The formula for calculating the friction factor of a pipeline is: （22）； In the formula: K represents the equivalent absolute roughness of the inner surface of the pipe; R e Represents the Reynolds number; The physical model for real-time flow in a natural gas pipeline is as follows: （23）； In the formula: P1 and P2 represent the natural gas pressure at the starting and ending points of the natural gas pipeline, respectively; Q represents the hourly flow rate of the natural gas pipeline; ρ represents the density of natural gas in the pipeline; T represents the temperature of the natural gas during transmission; T0 is the temperature value of 273.15K; 10) Compressor physical model The main parameters of a compressor include its output power, gas delivery capacity, and exhaust temperature. Since the focus is on the power of the basic unit equipment, the power calculation model for the compressor is as follows: （24）； In the formula: P dis and P suc These represent the compressor discharge pressure and intake pressure, respectively; m represents the compressor polytropic index; V r Indicates actual displacement 11) Physical model of energy storage battery （25）； In the formula: S oc (t) and S oc (t0) represents the remaining charge of the energy storage battery at times t and t0, respectively; δ represents the self-discharge rate of the energy storage battery, in % / h; Δt represents the time span from t0 to t; P ch and P dis These represent the charging and discharging power of the energy storage battery, respectively. η ch and η dis These represent the charge and discharge efficiencies of the energy storage battery, respectively. 12) Physical model of thermal storage tank （26）； In the formula: Q HS (t) represents the amount of heat stored in the thermal storage tank at time t; μ Loss Q represents the heat loss rate of the thermal storage tank; HS (t0) represents the heat storage capacity of the thermal storage tank at time t0; Qch HS(Δt) represents the heat charge of the thermal storage tank between time t0 and time t; ηch HS represents the heat charge efficiency of the thermal storage tank; Qdis HS(Δt) represents the heat release of the thermal storage tank between time t0 and time t; ηdis HS represents the heat release efficiency of the thermal storage tank. 13) Physical model of the gas storage tank （27）； In the formula: V GS V represents the effective gas storage volume of the gas storage tank. c P represents the geometric volume of the gas storage tank; high P low P0 represents the absolute pressure under the highest and lowest operating conditions; P0 represents the engineering standard pressure. S4. Establish the final basic unit model; S5. Case analysis and verification.

2. The method for establishing a basic unit model of a comprehensive energy system based on statistical synthesis as described in claim 1, characterized in that... The steps in step S22, which involve obtaining the membership value λ through fuzzy clustering, are as follows: Let the classification objects be U = {u1, u2, ..., u}. n }, and each object u i Each feature is represented by m feature data, thus establishing a feature index matrix U. * As shown below: ； However, since the dimensions and orders of magnitude of the m characteristic indicators are not necessarily the same, it is necessary to adjust U. * Format: （3）； The data is normalized to between 0 and 1 to eliminate the influence of dimensions. The fuzzy similarity relationship of the objects is determined by multivariate analysis, and the fuzzy similarity matrix R is established. However, this matrix may not be transitive. Therefore, the squaring method is used to find the transitive closure t(R) so that it satisfies the relationship of equation (4): （4）； After performing a finite number of operations on the above model, the transitive closure matrix can be determined, as shown in equation (5): （5）； At this point, the parameter in the closure matrix is ​​the membership degree λ value. After dynamic clustering of different membership degree λ values, the classification at each level is obtained. Finally, the optimal membership degree λ value is determined according to actual needs.

3. The method for establishing a basic unit model of a comprehensive energy system based on statistical synthesis as described in claim 2, characterized in that: The steps for dynamically clustering different membership degree λ values ​​are as follows: 1) Establish fuzzy similarity relationships for basic unit devices and calculate similarity using the minimax method; 2) Convert the similarity relationships of typical basic unit devices into equivalence relationships; 3) Clustering is performed based on different λ cut levels. When λ decreases from 1 to 0, the resulting classifications become coarser and gradually merged, completing the cluster analysis process. Finally, the units with higher similarity are taken as typical basic units.

4. The method for establishing a basic unit model of a comprehensive energy system based on statistical synthesis as described in claim 3, characterized in that... The step S4 of establishing the final basic unit model specifically involves, based on the energy properties of the basic unit equipment itself, summarizing the physical model of the basic unit equipment and the physical characteristic parameters of the selected typical basic unit equipment under the corresponding basic unit to establish the final basic unit model.

5. The method for establishing a basic unit model of a comprehensive energy system based on statistical synthesis as described in claim 4, characterized in that... The conclusion of the case analysis in step S5 is that the basic unit dynamic model established by the statistical synthesis method not only fits the dynamic characteristics of the actual system very well, but also the model can measure a wider range of characteristic parameter values ​​within the given comprehensive independent variables, which reflects the applicability of the model.

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