Second-order cone programming robust state estimation method and system for electric heating integrated energy system

Abstract

The application provides a second-order cone programming robust state estimation method for an electric-thermal integrated energy system, and comprises the following steps: obtaining measurement of the electric-thermal integrated energy system; inputting the measurement into a pre-constructed second-order cone programming state estimation model of the electric-thermal integrated energy system to obtain an estimated value of a node voltage amplitude of a power system, an estimated value of a node phase angle of the power system, an estimated value of a node pressure of a thermal system, an estimated value of a node heating temperature and an estimated value of a node regenerative temperature; the measurement of the electric-thermal integrated energy system comprises a power system measurement and a thermal system measurement; and the application of the method can compensate for the loss of measurement redundancy in the power grid and improve the accuracy of the state estimation of the power system in the electric-thermal integrated energy system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy state assessment, specifically involving a method and system for robust state estimation of second-order cone programming for integrated electrothermal energy systems. Background Technology

[0002] In traditional energy systems (electricity, natural gas, heat, etc.), they are planned, designed, and operated independently, leading to low overall energy efficiency. Therefore, integrated energy systems (IES) are considered the primary form of energy for future human society. As a crucial energy conversion component, CHP (Combined Heat and Power, which utilizes fossil fuels, waste energy, renewable energy, and electricity to simultaneously generate electricity and usable heat) is becoming increasingly important in IES research. Compared to independent energy systems, CHP offers greater flexibility and can offset fluctuations in renewable energy sources like wind power due to its ability to provide alternative energy sources to loads and control energy flow. Simultaneously, CHP converts fuel chemical energy into high-grade heat energy for power generation while utilizing low-grade heat energy to provide heating, significantly improving fuel efficiency. To achieve comprehensive, real-time, and accurate perception of the operating status of IEHS (Integrated Electro-Heat Systems), state estimation (SE) of IEHS must be considered.

[0003] A bilinear robust state estimation method for integrated electrothermal systems (IEHS) is provided in the prior art. First, a linear WLAV model for IEHS is constructed by introducing auxiliary variables. Then, the estimated values ​​of the state variables are obtained by nonlinear transformation and solving the quadratic programming model. This method eliminates the need for nonlinear iteration and initial value selection for the state variables. Furthermore, it has good identification capabilities for strongly correlated poorly performing data. However, it still has the following problems: i) the introduction of auxiliary variables reduces the measurement redundancy of the power grid section; ii) when constructing the measurement equations, it ignores thermal power measurements, thus reducing the overall measurement redundancy. Therefore, how to improve the accuracy of state estimation for integrated electrothermal systems is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this invention provides a robust state estimation method for a second-order cone programming model of an integrated electrothermal energy system, comprising:

[0005] Acquire quantities from the integrated electrothermal energy system;

[0006] The measured values ​​are input into a pre-constructed second-order cone programming state estimation model of the integrated electric and thermal energy system to obtain estimated values ​​of power system node voltage amplitude, power system node phase angle, thermal system node pressure, node heating temperature, and node regeneration temperature.

[0007] Preferably, the measurement of the integrated electrothermal energy system includes:

[0008] Measurement of quantities in power systems and thermal systems.

[0009] Preferably, the construction of a second-order cone programming state estimation model for an integrated electrothermal energy system includes:

[0010] Determine auxiliary state variables of the power system based on power system state variables; determine auxiliary quantity measurements of the power system based on power system quantity measurements; determine auxiliary state variables of the thermal system based on thermal system state variables; determine auxiliary quantity measurements of the thermal system based on thermal system quantity measurements.

[0011] Based on the power system auxiliary state variables and power system auxiliary quantity measurements, a linear measurement equation for the power system is constructed.

[0012] Based on the auxiliary state variables and auxiliary quantity measurements of the thermal system, a linear measurement equation for the thermal system is constructed.

[0013] Based on the aforementioned linear measurement equations of the power system, the thermal system, and the coupling mode of the coupling nodes between the power system and the thermal system, a linear measurement equation for the integrated electric and thermal energy system is constructed.

[0014] Based on the linear measurement equation of the integrated electrothermal energy system, a linear weighted minimum absolute value state estimation model for the integrated electrothermal energy system is constructed.

[0015] Based on the relationship between the linear weighted minimum absolute value state estimation model of the integrated electric and thermal energy system and the auxiliary state variables of the power system, a second-order cone programming state estimation model for the integrated electric and thermal energy system is constructed.

[0016] Preferably, the power system auxiliary state variables and power system auxiliary quantities are measured as follows:

[0017]

[0018]

[0019] in, V is an auxiliary state variable of the power system. i a , As the first auxiliary quantity introduced, For auxiliary quantity measurement of power systems, U i Let P be the voltage magnitude at node i. i The injected active power at node i, Q i The reactive power injected into node i, P ij Q is the active power of branch ij. ij Let be the reactive power of branch ij;

[0020] The formula for calculating the first auxiliary quantity is as follows:

[0021]

[0022] Among them, U i Let U be the voltage magnitude at node i. j The voltage amplitude at node j, θ ij Let be the phase angle difference between node i and node j.

[0023] The preferred linear measurement equation for the power system is as follows:

[0024]

[0025] In the formula, U i Let P be the voltage magnitude at node i. i The injected active power at node i, Q i The reactive power injected into node i, P ij Q is the active power of branch ij. ij Let V be the reactive power of branch ij, Ni be the number of nodes in the power system, and V be the reactive power of branch ij. i a , As the first auxiliary quantity introduced, g si Let b be the equivalent ground resistance of node i. si Let g be the equivalent ground reactance of node i. ij Let b be the equivalent resistance of branch ij. ij G is the equivalent reactance of branch ij. ij B is determined by the equivalent resistance of branch ij. ij It is determined by the equivalent reactance of branch ij.

[0026] Preferably, the measurements of auxiliary state variables and auxiliary quantities of the thermal system are as follows:

[0027]

[0028]

[0029] in, For auxiliary state variables of the thermal system, As the second auxiliary quantity, Auxiliary quantity measurement of thermal system Let be the water flow rate of branch ij. Inject water flow rate into the node, φ i Let T be the thermal power of node i. si Let T be the heating temperature of node i. ri Let be the regeneration temperature of node i;

[0030] The formula for calculating the second auxiliary quantity is as follows:

[0031]

[0032] Where, p ij For pipeline pressure head loss, s ij It is determined by the relationship between the pressure head at node i and the pressure head at node j.

[0033] The preferred linear measurement equation for the thermodynamic system is as follows:

[0034]

[0035] In the formula, p is the second auxiliary quantity. ij For pipeline pressure head loss, s ij It is determined by the relationship between the pressure head at node i and the pressure head at node j. Let be the water flow rate of branch ij. Inject water flow into the node, T si Let T be the heating temperature of node i. ri Let φ be the regeneration temperature of node i. i K represents the thermal power of node i. ij Let C be the pipe impedance coefficient of branch ij. p This is the specific heat capacity of water.

[0036] The preferred linear measurement model for the integrated electrothermal energy system is as follows:

[0037]

[0038] In the formula, x a z is the auxiliary state variable of the integrated electric and thermal energy system. a For auxiliary quantity measurement of integrated electric and thermal energy systems, For auxiliary state variables of the power system, For auxiliary state variables of the thermal system, For auxiliary quantity measurement of power system, For auxiliary quantity measurement of thermal systems, H a Let e ​​be the constant coefficient matrix of the integrated electrothermal energy system. a For measurement errors in the integrated electrothermal energy system, N1 represents the heat energy generated when a gas turbine or internal combustion engine is used in the coupling method, where N1 is the number of nodes in the gas turbine or internal combustion engine coupling method. For the heat energy generated by the steam turbine coupling method, c m N2 represents the ratio of output thermal power to electrical power, and N2 is the number of nodes coupled with a steam turbine. For electrical energy generated using a gas turbine or internal combustion engine coupling method, Pcon This represents the maximum electrical output of the steam turbine. This refers to the electrical energy generated using a steam turbine coupling method.

[0039] Preferably, the constant coefficient matrix of the integrated electrothermal energy system is represented as follows:

[0040] H a =[H ae ,0;0,H ah ]

[0041] Wherein, is H a The constant coefficient matrix of the integrated electrothermal energy system, H ae H is the constant coefficient matrix of the power system. ah This is the constant coefficient matrix of the thermodynamic system.

[0042] The preferred linear weighted minimum absolute value state estimation model for the integrated electric and thermal energy system is shown in the following equation:

[0043]

[0044] In the formula, w is the measurement weight matrix of the integrated electrothermal energy system, and e a For the measurement error of the integrated electrothermal energy system, z a For auxiliary quantity measurement of integrated electric and thermal energy systems, x a H is an auxiliary state variable for the integrated electric and thermal energy system. a For the constant coefficient matrix of the integrated electrothermal energy system, N1 represents the heat energy generated when a gas turbine or internal combustion engine is used in the coupling method, where N1 is the number of nodes in the gas turbine or internal combustion engine coupling method. For the heat energy generated by the steam turbine coupling method, c m N2 represents the ratio of output thermal power to electrical power, and N2 is the number of nodes coupled with a steam turbine. For electrical energy generated using a gas turbine or internal combustion engine coupling method, P con This represents the maximum electrical output of the steam turbine. This refers to the electrical energy generated using a steam turbine coupling method.

[0045] Preferably, based on the linear weighted minimum absolute value state estimation model of the integrated electric and thermal energy system and the relationship between the auxiliary state variables of the power system, a second-order cone programming state estimation model for the integrated electric and thermal energy system is constructed, including:

[0046] Based on the relationship between auxiliary state variables of the power network, a second-order cone inequality constraint is constructed.

[0047] Based on the second-order cone inequality constraint and the linear weighted minimum absolute value state estimation model of the integrated electric and thermal energy system, a second-order cone programming state estimation model for the integrated electric and thermal energy system is constructed.

[0048] Preferably, the second-order cone inequality constraint is as follows:

[0049]

[0050] In the formula, V i a V j a , This is the first auxiliary quantity introduced.

[0051] The preferred state estimation model for the integrated electric and thermal energy system using second-order cone programming is shown in the following equation:

[0052]

[0053] In the formula, w is the measurement weight matrix of the integrated electrothermal energy system, u and v are two types of non-negative variables introduced, and z a For auxiliary quantity measurement of integrated electric and thermal energy systems, H a Let V be the constant coefficient matrix of the integrated electrothermal energy system, and let V be the auxiliary state variables of the integrated electrothermal energy system. i a V j a , As the first auxiliary quantity introduced, N1 represents the heat energy generated when a gas turbine or internal combustion engine is used in the coupling method, where N1 is the number of nodes in the gas turbine or internal combustion engine coupling method. For the heat energy generated by the steam turbine coupling method, c m N2 represents the ratio of output thermal power to electrical power, and N2 is the number of nodes coupled with a steam turbine. For electrical energy generated using a gas turbine or internal combustion engine coupling method, P con This represents the maximum electrical output of the steam turbine. This refers to the electrical energy generated using a steam turbine coupling method.

[0054] Preferably, the measured inputs are used to construct a second-order cone programming state estimation model for the integrated electric and thermal energy system, yielding estimated values ​​for the voltage amplitude at power system nodes, the phase angle at power system nodes, the pressure at thermal system nodes, the heating temperature at nodes, and the regenerative temperature at nodes, including:

[0055] The measured values ​​are input into a pre-constructed second-order cone programming state estimation model of the integrated electric and thermal energy system to calculate the estimated values ​​of the auxiliary state variables of the integrated electric energy system.

[0056] The relationship between intermediate variables and auxiliary state variables of the integrated electric energy system is constructed, and the estimated value of the intermediate variables is calculated after nonlinear transformation.

[0057] Based on the estimated values ​​of the intermediate variables, after linear transformation, the estimated values ​​of the voltage amplitude of the power system nodes, the estimated values ​​of the phase angle of the power system nodes, the estimated values ​​of the pressure of the thermal system nodes, the estimated values ​​of the heating temperature of the nodes, and the estimated values ​​of the regenerative temperature of the nodes are calculated.

[0058] Based on the same concept, this invention also provides a robust state estimation system for a second-order cone programming model of an integrated electrothermal energy system, comprising:

[0059] The data acquisition module is used to acquire measurements of the integrated electric and thermal energy system.

[0060] The state estimation module is used to input the measured values ​​into a pre-built second-order cone programming state estimation model of the integrated electric and thermal energy system to obtain estimated values ​​of the voltage amplitude of the power system nodes, the phase angle of the power system nodes, the pressure of the thermal system nodes, the heating temperature of the nodes, and the regeneration temperature of the nodes.

[0061] Preferably, the module for constructing a second-order cone programming state estimation model for an integrated electric and thermal energy system includes:

[0062] The auxiliary variable construction module is used to determine auxiliary state variables of the power system based on power system state variables; determine auxiliary quantity measurements of the power system based on power system quantity measurements; determine auxiliary state variables of the thermal system based on thermal system state variables; and determine auxiliary quantity measurements of the thermal system based on thermal system quantity measurements.

[0063] A power system linear measurement equation construction module is used to construct power system linear measurement equations based on the power system auxiliary state variables and power system auxiliary measurements.

[0064] A module for constructing linear measurement equations for a thermal system is used to construct linear measurement equations for a thermal system based on the auxiliary state variables and auxiliary quantities of the thermal system.

[0065] The module for constructing linear measurement equations for an integrated electric and thermal energy system is used to construct linear measurement equations for an integrated electric and thermal energy system based on the linear measurement equations of the power system, the linear measurement equations of the thermal system, and the coupling mode of the coupling nodes of the power system and the thermal system.

[0066] The module for constructing a linear weighted minimum absolute value state estimation model for an integrated electric and thermal energy system is used to construct a linear weighted minimum absolute value state estimation model for the integrated electric and thermal energy system based on the linear measurement equation of the integrated electric and thermal energy system.

[0067] The module for constructing a second-order cone programming state estimation model for an integrated electric and thermal energy system is used to construct a second-order cone programming state estimation model for the integrated electric and thermal energy system based on the linear weighted minimum absolute value state estimation model of the integrated electric and thermal energy system and the relationship between the auxiliary state variables of the power system.

[0068] Preferably, the state estimation module includes:

[0069] Calculation module 1 is used to input the measured values ​​into a pre-built second-order cone programming state estimation model of the integrated electric and thermal energy system, and to calculate the estimated values ​​of the auxiliary state variables of the integrated electric energy system.

[0070] Calculation module 2 is used to construct the relationship between intermediate variables and auxiliary state variables of the integrated power energy system, and calculate the estimated value of intermediate variables after nonlinear transformation;

[0071] Calculation module 3 is used to calculate, based on the intermediate variable estimates and through linear transformation, the estimated values ​​of power system node voltage amplitude, power system node phase angle, thermal system node pressure, node heating temperature, and node regeneration temperature.

[0072] Compared with the closest existing technology, the present invention has the following beneficial effects:

[0073] This invention provides a robust state estimation method for a second-order cone programming model of an integrated electric and thermal energy system, comprising: acquiring measurements of the integrated electric and thermal energy system; inputting the measurements into a pre-constructed second-order cone programming state estimation model of the integrated electric and thermal energy system to obtain estimated values ​​of power system node voltage amplitude, power system node phase angle, thermal system node pressure, node heating temperature, and node regenerative temperature. The application of this invention compensates for the loss of measurement redundancy in the power grid and improves the accuracy of power system state estimation in the integrated electric and thermal energy system.

[0074] Meanwhile, this invention considers thermal power measurement when constructing the second-order cone programming state estimation model of the integrated electric and thermal energy system, thereby improving the accuracy of the thermal system state estimation in the integrated electric and thermal energy system. Attached Figure Description

[0075] Figure 1 A schematic diagram of the robust state estimation method for a second-order cone programming model of an integrated electrothermal energy system provided by this invention;

[0076] Figure 2 This is a schematic diagram of a second-order cone programming robust state estimation system for an integrated electrothermal energy system provided by the present invention.

[0077] Figure 3This is a comparison chart of the estimated and true values ​​of the coupled node measurements during the test and analysis of the three electrothermal integrated energy system state estimation methods provided in this embodiment of the invention. Detailed Implementation

[0078] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0079] Example 1:

[0080] This invention provides a robust state estimation method for a second-order cone programming model of an integrated electrothermal energy system, as illustrated in the schematic diagram below. Figure 1 As shown, the process includes: acquiring measurements of the integrated electric and thermal energy system; inputting the measurements into a pre-constructed second-order cone programming state estimation model of the integrated electric and thermal energy system to obtain estimated values ​​of the voltage amplitude of the power system nodes, the phase angle of the power system nodes, the pressure of the thermal system nodes, the heating temperature of the nodes, and the regeneration temperature of the nodes.

[0081] S1 acquires the quantity measurement of the integrated electrothermal energy system.

[0082] S2 inputs the measured values ​​into the pre-built second-order cone programming state estimation model of the integrated electric and thermal energy system to obtain the estimated values ​​of the voltage amplitude of the power system nodes, the estimated values ​​of the phase angle of the power system nodes, the estimated values ​​of the pressure of the thermal system nodes, the estimated values ​​of the heating temperature of the nodes, and the estimated values ​​of the regeneration temperature of the nodes.

[0083] S2-1 Constructs a second-order cone programming state estimation model for an integrated electric and thermal energy system, including:

[0084] S2-1-1 Constructing the Basic Model of IEHS

[0085] S2-1-1-1 Constructing the Measurement Equations of the Thermal System

[0086] A heating network generally includes a hydraulic model and a thermal model. The description of the hydraulic model consists of the following equations.

[0087]

[0088]

[0089] The coefficient of friction K is usually given by the Colebrook-White equation, expressed as:

[0090] K = 8Lf / (D) 5 ρ 2 π 2 g) (3)

[0091]

[0092] Equation (5) is implicit and cannot be solved directly. This invention uses the Haaland formula [8] to solve it, which is expressed as:

[0093]

[0094]

[0095] The equations describing the thermodynamic model mainly include:

[0096]

[0097]

[0098]

[0099] In the formula, T represents the mass flow rate (kg / s) leaving and entering the node within the pipeline. out T represents the mixing temperature (°C) at a certain node. in The temperature (°C) at the inlet pipe port represents the flow rate.

[0100] In a regional heating network, the state variable x h And measurement z h It is expressed as follows:

[0101]

[0102] The specific expression for the measurement is shown below (for simplicity, the measurement error is ignored here. The measurement equations below also follow this rule).

[0103]

[0104] In the formula: when p i >p j At that time, s ij =1; when p i <p j At that time, s ij =-1. p i ,T si ,T ri and φ i They are p and T respectively. s ,T r Elements of φ.

[0105] S2-1-1-2 Constructing Measurement Equations for Power Systems

[0106] State variables and quantities in a power grid are represented as follows:

[0107] x e =[θ i ;Ui ];z e =[U i ;P i Q i ;P ij Q ij (12)

[0108] The measurement equation is as follows:

[0109]

[0110] For simplicity, measurement noise is ignored here. Meanwhile, equation (1) uses a π-type equivalent circuit, and the parameters of the equivalent circuit are: g ij =g s / k,b ij =b s / k,g si =(1-k)g s / k 2 b si =(1-k)b s / k 2 +b c / 2, G ij =-g ij B ij =-b ij g s +jb s For series susceptance, b c The charging susceptance is given by k, which represents the branch turns ratio (for branches without transformers, k = 1, b...). c =0).

[0111] In the formula, U i U j P represents the voltage magnitudes at nodes i and j, respectively. i Q i P represents the injected active and reactive power at node i. ij Q ij These represent the active power and reactive power of branch ij, respectively, N. i Let θ be the number of nodes in the power system. ij Let g be the phase angle difference between node i and node j. s b s These are the actual resistance and actual reactance of branch ij, respectively, and g ij b ij The equivalent resistance and equivalent reactance of branch ij are respectively, g si b si These are the equivalent ground resistance and reactance of node i, respectively.

[0112] S2-1-1-3 Constructing the Coupled Component Model

[0113] 1) Gas turbines and internal combustion engines

[0114]

[0115] 2) Steam turbine

[0116]

[0117] The coupled components generate both heat and electricity. For the two operating modes of IEHS, Island and Grid-Connected (GC), the interface access of the slack node is shown in Table 1.

[0118] Table 1. Connection of relaxed nodes in CHP and IEHS under two operating modes.

[0119]

[0120] In addition to relaxed nodes, other types of nodes (such as PQ nodes and heat load nodes) can also be connected through CHP units.

[0121] In summary, the IEHS measurement model is expressed as follows:

[0122]

[0123] Where h e (x e ) and h h (x h ) is represented by (11) and (13). r e and r h The measurement errors are for the thermal and electrical components. Subscripts N1 and N2 represent two types of coupling nodes, with coupling relationships of (14) and (15), respectively.

[0124] Based on the basic model of IEHS and the relationship between auxiliary state variables of the power system, S2-1-2 constructs a second-order cone programming state estimation model for an integrated electric and thermal energy system.

[0125] In IEHS, the number of nodes in the heating network is N. h The number of branches is B h The number of nodes in the power grid is N. e The number of branches is B e Assume that all measurements in (11) and (13) are measurable.

[0126] Linearization of the measurement equation S2-1-2-1

[0127] In mathematics, the traditional SE method requires solving a nonlinear, nonconvex optimization problem. The result may be a local optimum or it may not converge. This is because the measurement equations are nonlinear. Therefore, it is necessary to linearize the measurement equations, transforming the problem into a linear, convex optimization problem.

[0128] By introducing appropriate auxiliary state variables, a set of linear measurement equations for IEHS was established.

[0129] S2-1-2-1-1 Linearization of Measurement Equations for Thermodynamic Systems

[0130] Let p ij =p i -p j In equation (11), let Pressure head p replacing the node i and p j Therefore, the measurement equation can be expressed as:

[0131]

[0132]

[0133] choose As an auxiliary variable. Therefore, a set of linearized measurement equations is obtained (note that the superscript 'a' indicates an auxiliary variable):

[0134]

[0135] S2-1-2-1-2 Linearization of Power System Measurement Equations

[0136] 1) Select the square of the node voltage amplitude As a new measurement value and replacing the node voltage amplitude U i .

[0137]

[0138] 2) Order in, Therefore, equation (13) is linearized as follows:

[0139]

[0140] Construction of the Unified Linearized Measurement Model for S2-1-2-1-3 IEHS

[0141] The unified linearized measurement model of IEHS is:

[0142]

[0143] Where H isa A constant coefficient matrix, which consists of the following parts.

[0144] H a =[H ae ,0;0,H ah ] (twenty three)

[0145]

[0146]

[0147] Among them, H UV H pα H sr and H rs This is a diagonal matrix with element 1. The expressions for the other submatrix elements are shown in Table 2.

[0148] Table 2. Element expressions for the remaining submatrices

[0149]

[0150] Construction of the S2-1-2-2WLAV model

[0151] The above linearized measurement model can be constructed as a WLAV-based SE model:

[0152]

[0153] S2-1-2-3 Construction of Constraints for Second-Order Cone Programming

[0154] For auxiliary state variables and V i a They have the following relationship:

[0155]

[0156] Transforming the quadratic equality into an inequality constraint, and relaxing (41), we get:

[0157]

[0158] To make the above inequality as close as possible to the equality constraint, we add a term to the objective function: Therefore, model (26) is transformed into:

[0159]

[0160] Because auxiliary variables were introduced in the power section, the number of state variables increased by N in (27). e-1 results in a loss of measurement redundancy in the power grid. After considering the second-order cone constraint, the number of measurements equivalently increases by N. e -1, thus compensating for the loss of measurement redundancy.

[0161] In equation (29), the objective function is continuous but not differentiable everywhere. Therefore, it can be equivalently transformed into a linear programming (LP) problem, and thus, equation (29) becomes:

[0162]

[0163] Solving the second-order cone programming state estimation model (i.e., SOCP model) for an integrated electrothermal energy system (S2-1-2-4)

[0164] The state variable x = [θ] is obtained i ;U i ;p i ;T si ;T ri The overall steps for estimating the value include solving the SOCP, performing a nonlinear transformation, and performing a linear transformation.

[0165] Solving the second-order cone programming problem S2-1-2-4-1

[0166] This invention utilizes MOSEK to solve the aforementioned SOCP model (30). This commercial solver possesses strong solving capabilities, and its accuracy has been proven even for problems with challenging conditions. Variable x a The estimated value can be obtained:

[0167]

[0168] S2-1-2-4-2 Nonlinear Transformation

[0169] Define intermediate variable x I :

[0170]

[0171] x I With x a The following relationships exist:

[0172]

[0173] To improve accuracy, for θ b I :

[0174]

[0175] Therefore, x can be obtained by transforming (36) and (37). I .

[0176] S2-1-2-4-3 Linear Transformation

[0177] Because the number of branch phase angle differences and branch pressure differences is equal to the number of node phase angles and pressure heads to be determined, the state variable x has a unique solution. The state variable x can be obtained through x... I The linear transformation yields:

[0178]

[0179] in These are the reduced-order branch node correlation matrices (excluding relaxed nodes) of the power grid and heating network, respectively, where each element (a e (k,j) and a h (k,j) is expressed as:

[0180] 1)+1, if the current / flow rate in branch k enters node i;

[0181] 2)-1, if the current / flow rate in branch k leaves node i;

[0182] 3) 0, if there is no connection between branch k and node i.

[0183] Redundancy analysis

[0184] The measurement redundancy changes from the original model (16) to SOCP (30) as follows:

[0185] 1) The original WLS measurement model (16)

[0186] In equation (16), the number of measurements for the power grid and the heating network are 3N respectively. e +2B e and 5N h +B h The corresponding number of state variables is 2N. e -1 and 2N h +B h Therefore, the initial measurement redundancy is expressed as:

[0187]

[0188] 2) Linearized measurement model (22), WLAV model (26)

[0189] Due to the introduction of auxiliary variables and changes in measurement methods, the measurement redundancy changes as follows:

[0190]

[0191] 3) SOCP model (30)

[0192] The inequality constraints of the rotated second-order cone (28) equivalently increase the number of measurements. Therefore, the measurement redundancy is expressed as:

[0193]

[0194] Generally, the topology of power distribution networks and regional heating networks is radial. Therefore, the relationship between nodes and branches follows: B e =N e -1 and B h =N h -1. The changes in measurement redundancy from the original model (16) to SOCP (30) are shown in Table 3.

[0195] Table 3 Changes in Measurement Redundancy

[0196]

[0197] Here, t represents Z. e x e Z h x h The trend of increase or decrease (compared to the previous stage). Table 3 shows:

[0198] 1) The application of constraint (28) ensures that the SOCP model does not sacrifice any measurement redundancy compared to WLS.

[0199] 2) As shown in equation (28), the measurement redundancy of SOCP is higher than that of WLAV. Therefore, theoretically, the robustness and estimation accuracy of SOCP should be better than those of WLAV.

[0200] Example 2

[0201] This invention proposes a robust state estimation method for IEHS based on SOCP by introducing auxiliary and intermediate variables. By adding auxiliary variables to IEHS and reselecting measurement values, a set of linearized measurement equations is obtained. Then, based on WLAV, these equations are transformed into a linear programming problem. Due to the presence of second-order cone constraints in the auxiliary variables, a SOCP model is finally established. A test case in a regional combined heat and power system in Bali demonstrates that this method has good estimation accuracy and robustness.

[0202] A combined heat and power (CHP) system in Bali was used as a case study. The power grid consists of 9 nodes and 8 branches. The heating network comprises 32 nodes and 32 branches. Three CHP generators are used as coupling components: a gas turbine, an extraction turbine, and a reciprocating engine. A detailed description of the topology and parameters of the case study is provided in the prior art. The algorithm was implemented in MOSEK using MATLAB and executed on an Intel(R) Core(TM) i7 PC with a 2.80 GHz processor and 8 GB of memory.

[0203] Table 4. Coupling Relationships in Case Studies

[0204]

[0205] Before performing state estimation, it is necessary to know the accurate value of the steady-state operation of the system. Here, a piecewise electro-thermal calculation method is used to calculate the accurate operating point. Note that, unlike [7], this paper adjusts the power factor of the power grid from 1 to 0.8 to obtain the value related to reactive power.

[0206] 1. Testing under normal measurement conditions

[0207] The measurements are formed by adding Gaussian noise to the true values. This section analyzes the performance of this estimation method, including estimation accuracy, measurement redundancy, and computational efficiency, and compares it with traditional nonlinear WLS and bilinear WLAV.

[0208] 1) Measurement redundancy

[0209] The measurement redundancy of WLS, WLAV, and SOCP is shown in Table 5. It can be seen that the measurement redundancy of SOCP is slightly less than that of WLS, but higher than that of WLAV. Note that in Table 5, "+3" indicates the contribution of CHP coupling to the measurement redundancy, and "+8" indicates the number of second-order cone constraints in SOCP.

[0210] Table 5 Measurement redundancy of traditional nonlinear WLS and SOCP

[0211]

[0212] 2) Accuracy of the estimate

[0213] For WLS, set the voltage amplitude to 1 and the phase angle to 0 for all nodes in the grid (except for relaxation nodes). Set the supply temperature of all nodes in the heating network (except source nodes) to 70 degrees Celsius and the regeneration temperature to 30 degrees Celsius. Note that due to the existence of… Item, which is related to p i (or p) j The partial derivative of ) is s ij (p ij ) -12Therefore, the initial value p for all nodes is... i We cannot use the same value for p. Here we take 95% of the true value of p as the initial value.

[0214] 1.1 Single State Estimation

[0215] Single-state estimation of the Barry Island test system was performed using traditional nonlinear WLS, bilinear WLAV, and SOCP, respectively.

[0216] For SOCP, the auxiliary and intermediate variables corresponding to the first two stages are shown in Table 6 (due to space limitations, only the power grid portion is shown). Here x a+ and x I+ x represents a and x I The estimated value.

[0217] Table 6 x of SOCP a and x I The estimated value

[0218]

[0219]

[0220] Tables 7 and 8 provide the estimated values ​​of state variables calculated by WLS, WLAV, and SOCP, respectively. For the state variable x of the heating network... h This mainly lists the estimated values ​​of nodes along the main route "1-2-5-11-13-14-19-22-25-28-31-7". (Assume the head of the reference node is 0, and the reference node is node 6.)

[0221] Table 7. Estimates of x obtained from WLS and SOCP e

[0222]

[0223] Table 8. Estimates of x obtained from WLS and SOCP h

[0224]

[0225] For the coupled unit, the estimated values ​​of the quantities involved are as follows: Figure 3As shown in (a) and (b), and compared with the corresponding true measurements, in (a) and (b), Nodes in electricity network are nodes in the power grid, Nodes in heat network are nodes in the heat network, True values ​​are true values, WLS is the estimated value measured by the weighted least squares model, WLAV is the estimated value measured by the weighted least absolute value model, and SOCP is the estimated value measured by the second-order cone model.

[0226] 1.2 Monte Carlo Simulation

[0227] Due to the absolute randomness of single-state estimation, Monte Carlo simulation experiments are used to statistically analyze the accuracy of state estimation. The maximum estimation error of the state variables is chosen as the measurement index, expressed as:

[0228]

[0229] Where x true Let represent the true value of the state variable, and T be the total number of Monte Carlo experiments. 1000 Monte Carlo experiments were conducted on the test system using traditional nonlinear WLS, bilinear WLAV, and SOCP, respectively. The results are shown in Tables 9 and 10. It can be seen that the estimation accuracy of SOCP is slightly lower than that of WLS, but higher than that of bilinear WLAV.

[0230] Table 9 Maximum estimation error of state variable x e

[0231]

[0232] Table 10 Maximum estimation error of state variable x h

[0233]

[0234] 3) Computational efficiency

[0235] The computation times required for single-state estimation by WLS, bilinear WLAV, and SOCP are shown in Table 11.

[0236] Table 11 Computational efficiency of WLS, WALV, and SOCP

[0237]

[0238] It can be seen that SOCP has the highest computational efficiency.

[0239] 2. Resistance test

[0240] 2.1 General Undesirable Data

[0241] Generally, bad data refers to erroneous measurements that have a weak mathematical and physical connection. It is usually obtained by inverting the sign of some measurement data, setting its value to 0, or adding or subtracting more than 20% from its value. Bad data settings and corresponding estimated measurement values ​​are shown in Tables 12 and 13.

[0242] Table 12 Power Grid Data Setting Errors

[0243]

[0244] Table 13 Heating Network Data Settings Error

[0245]

[0246] This demonstrates that SOCP can effectively identify general malfunctions in IEHS.

[0247] 2.2 Strongly correlated bad data

[0248] Highly correlated undesirable data refers to undesirable data that is highly correlated. There are three types of highly correlated undesirable data in IEHS:

[0249] a) Power grid: This strongly correlated data mainly includes (P) i ,P ij1 ,…P ijn ) and (Q i Q ij1 ,…Q ijn )wait.

[0250] b) Heating network: In the hydraulic section, (p ij ,m ij ) and (m qi ,m ij1 ,…m ijn These are two types of strongly correlated data.

[0251] c) Coupling unit: Active power and thermal power (P) at the electro-thermal coupling node i ,Φ j There is a strong correlation.

[0252] To test the algorithm's ability to identify the aforementioned types of bad data, we divided the bad data into three groups and tested them separately. (P) i ,P ij1 ,…P ijn ) and (Q i Q ij1 ,…Q ijn )

[0253] Table 15 First Group of Defective Data Settings

[0254]

[0255] (p ij ,m ij ) and (m qi ,m ij1 ,…m ijn )

[0256] Table 16 Second Group of Defective Data Settings

[0257]

[0258] (P i ,Φ j )

[0259] Table 17, Group 3, Defective Data Settings

[0260]

[0261] The above three types of tests demonstrate that SOCP has a good ability to estimate, identify, and correct the five types of strongly correlated adverse data in IEHS.

[0262] Example 3:

[0263] Based on the same concept, this invention also provides a robust state estimation system for a second-order cone programming model of an integrated electrothermal energy system, such as... Figure 2 As shown, it includes:

[0264] The data acquisition module is used to acquire measurements of the integrated electric and thermal energy system.

[0265] The state estimation module is used to input the measured values ​​into a pre-built second-order cone programming state estimation model of the integrated electric and thermal energy system to obtain estimated values ​​of power system node voltage amplitude, power system node phase angle, thermal system node pressure, node heating temperature, and node regeneration temperature.

[0266] The measurement of the integrated electrothermal energy system includes: power system measurement and thermal system measurement.

[0267] Preferably, the module for constructing a second-order cone programming state estimation model for an integrated electric and thermal energy system includes:

[0268] The auxiliary variable construction module is used to determine auxiliary state variables of the power system based on power system state variables; determine auxiliary quantity measurements of the power system based on power system quantity measurements; determine auxiliary state variables of the thermal system based on thermal system state variables; and determine auxiliary quantity measurements of the thermal system based on thermal system quantity measurements.

[0269] A power system linear measurement equation construction module is used to construct power system linear measurement equations based on the power system auxiliary state variables and power system auxiliary measurements.

[0270] A module for constructing linear measurement equations for a thermal system is used to construct linear measurement equations for a thermal system based on the auxiliary state variables and auxiliary quantities of the thermal system.

[0271] The module for constructing linear measurement equations for an integrated electric and thermal energy system is used to construct linear measurement equations for an integrated electric and thermal energy system based on the linear measurement equations of the power system, the linear measurement equations of the thermal system, and the coupling mode of the coupling nodes of the power system and the thermal system.

[0272] The module for constructing a linear weighted minimum absolute value state estimation model for an integrated electric and thermal energy system is used to construct a linear weighted minimum absolute value state estimation model for the integrated electric and thermal energy system based on the linear measurement equation of the integrated electric and thermal energy system.

[0273] The module for constructing a second-order cone programming state estimation model for an integrated electric and thermal energy system is used to construct a second-order cone programming state estimation model for the integrated electric and thermal energy system based on the linear weighted minimum absolute value state estimation model of the integrated electric and thermal energy system and the relationship between the auxiliary state variables of the power system.

[0274] Preferably, the state estimation module includes:

[0275] Calculation module 1 is used to input the measured values ​​into a pre-built second-order cone programming state estimation model of the integrated electric and thermal energy system, and to calculate the estimated values ​​of the auxiliary state variables of the integrated electric energy system.

[0276] Calculation module 2 is used to construct the relationship between intermediate variables and auxiliary state variables of the integrated power energy system, and calculate the estimated value of intermediate variables after nonlinear transformation;

[0277] Calculation module 3 is used to calculate, based on the intermediate variable estimates and through linear transformation, the estimated values ​​of power system node voltage amplitude, power system node phase angle, thermal system node pressure, node heating temperature, and node regeneration temperature.

[0278] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0279] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0280] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0281] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0282] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit its protection scope. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this application, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A robust state estimation method for electric-thermal integrated energy systems based on second-order cone programming, characterized in that, The application relates to a method for estimating the state of an electric-thermal integrated energy system. The method comprises the following steps: acquiring measurement of the electric-thermal integrated energy system; inputting the measurement into a pre-constructed second-order cone programming state estimation model of the electric-thermal integrated energy system to obtain estimated values of node voltage amplitude of a power system, estimated values of node phase angle of the power system, estimated values of node pressure of a heat supply system, estimated values of node heat supply temperature and estimated values of node heat return temperature; the construction of the second-order cone programming state estimation model of the electric-thermal integrated energy system comprises the following steps: determining auxiliary state variables of the power system based on state variables of the power system, determining auxiliary measurement of the power system based on measurement of the power system, determining auxiliary state variables of the heat supply system based on state variables of the heat supply system and determining auxiliary measurement of the heat supply system based on measurement of the heat supply system; constructing a linear measurement equation of the power system based on the auxiliary state variables of the power system and the auxiliary measurement of the power system; constructing a linear measurement equation of the heat supply system based on the auxiliary state variables of the heat supply system and the auxiliary measurement of the heat supply system; constructing a linear measurement equation of the electric-thermal integrated energy system based on the linear measurement equation of the power system, the linear measurement equation of the heat supply system and a coupling mode of a coupling node of the power system and the heat supply system; constructing a linear weighted least absolute value state estimation model of the electric-thermal integrated energy system based on the linear measurement equation of the electric-thermal integrated energy system; 2. The method of claim 1, wherein, constructing the second-order cone programming state estimation model of the electric-thermal integrated energy system based on the relationship between the linear weighted least absolute value state estimation model of the electric-thermal integrated energy system and the auxiliary state variables of the power system. The measurement of the electric-thermal integrated energy system comprises the following steps:

3. The method of claim 1, wherein, measurement of the power system and measurement of the heat supply system. where, V is the power system auxiliary state variable, i a , V is the introduced first auxiliary quantity, U is the power system auxiliary quantity measurement, i V is the voltage magnitude at node i, i P is the injected real power at node i, i Q is the injected reactive power at node i, ij P is the real power of branch ij, ij Q is the reactive power of branch ij; The auxiliary state variables of the power system and the auxiliary measurement of the power system are as follows: where U i is the voltage amplitude at node i, U j is the voltage amplitude at node j, and ij is the phase angle difference between node i and node j.

4. The method of claim 1, wherein, The calculation formula of the first auxiliary quantity is as follows: where U i is the voltage magnitude at node i, P i is the injected real power at node i, Q i is the injected reactive power at node i, P ij is the real power of branch ij, Q ij is the reactive power of branch ij, N i is the number of nodes of the power system, is the first auxiliary quantity introduced, g si is the equivalent ground resistance at node i, b si is the equivalent ground reactance at node i, g ij is the equivalent resistance of branch ij, b ij is the equivalent reactance of branch ij, G ij is determined by the equivalent resistance of branch ij, B ij is determined by the equivalent reactance of branch ij.

5. The method of claim 1, wherein, The linear measurement equation of the power system is as follows: wherein, is a thermal system auxiliary state variable, is a second auxiliary quantity, thermal system auxiliary quantity measurement, is a water flow rate of branch ij, is a node injection water flow rate, φ i is a thermal power of node i, T si is a heating temperature of node i, T ri is a back heating temperature of node i; The auxiliary state variables of the heat supply system and the auxiliary measurement of the heat supply system are as follows: where p ij is the pipe pressure head loss, s ij is determined from the relationship between the pressure head at node i and the pressure head at node j.

6. The method of claim 1, wherein, The calculation formula of the second auxiliary quantity is as follows: where is the second auxiliary quantity, p ij is the pipe pressure head loss, s ij is determined from the relationship between the pressure head at node i and the pressure head at node j, is the water flow rate of branch ij, is the node injection water flow rate, T si is the heating temperature of node i, T ri is the regenerative temperature of node i, φ i is the thermal power of node i, K ij is the pipe impedance coefficient of branch ij, C p is the specific heat capacity of water.

7. The method of claim 1, wherein, The linear measurement equation of the heat supply system is as follows: wherein x a is an auxiliary state variable of the electric-thermal integrated energy system, z a is an auxiliary measurement of the electric-thermal integrated energy system, is an auxiliary state variable of the power system, is an auxiliary state variable of the heating system, is an auxiliary measurement of the power system, is an auxiliary measurement of the heating system, H a is a constant matrix of the electric-thermal integrated energy system, e a is a measurement error of the electric-thermal integrated energy system, is thermal energy generated in a gas turbine or internal combustion engine coupling mode, N1 is the number of nodes in the gas turbine or internal combustion engine coupling mode, is thermal energy generated in a steam turbine coupling mode, c m is the ratio of output thermal power to electric power, N2 is the number of nodes in the steam turbine coupling mode, is electric energy generated in a gas turbine or internal combustion engine coupling mode, P con is the maximum output electric energy of the steam turbine, is electric energy generated in a steam turbine coupling mode.

8. The method of claim 7, wherein, The linear measurement model of the electric-thermal integrated energy system is as follows: H a = [H ae , 0; 0, H ah ] wherein H a is the constant matrix of the integrated energy system, H ae is the constant matrix of the power system, H ah is the constant matrix of the heating system.

9. The method of claim 1, wherein, The constant matrix of the electric-thermal integrated energy system is as follows: min w|e a | where w is the measurement weight matrix of the integrated energy system, e a is the measurement error of the integrated energy system, z a is the auxiliary measurement of the integrated energy system, x a is the auxiliary state variable of the integrated energy system, H a is the constant matrix of the integrated energy system, is the heat energy generated by using gas turbine or internal combustion engine coupling, N1 is the number of nodes using gas turbine or internal combustion engine coupling, is the heat energy generated by using steam turbine coupling, c m is the ratio of output heat power to electric power, N2 is the number of nodes using steam turbine coupling, is the electric energy generated by using gas turbine or internal combustion engine coupling, P con is the maximum output electric energy of the steam turbine, is the electric energy generated by using steam turbine coupling.

10. The method of claim 1, wherein, The linear weighted least absolute value state estimation model of the electric-thermal integrated energy system is as follows: The second-order cone programming state estimation model of the electric-thermal integrated energy system is constructed based on the relationship between the linear weighted least absolute value state estimation model of the electric-thermal integrated energy system and the auxiliary state variables of the power system, and the method comprises the following steps: constructing a second-order cone inequality constraint based on the relationship between the auxiliary state variables of the power network; 11. The method of claim 10, wherein, constructing the second-order cone programming state estimation model of the electric-thermal integrated energy system based on the second-order cone inequality constraint and the linear weighted least absolute value state estimation model of the electric-thermal integrated energy system. wherein is the first auxiliary quantity for introducing the branch ij.

12. The method of claim 1, wherein, The second-order cone inequality constraint is as follows: In the formula, w is the measurement weight matrix of the electric-thermal integrated energy system, u and v are two types of non-negative variables introduced, z a is the auxiliary measurement of the electric-thermal integrated energy system, H a is the constant matrix of the electric-thermal integrated energy system, is the auxiliary state variable of the electric-thermal integrated energy system, is the first auxiliary quantity of the branch ij introduced, is the heat energy generated in the coupling mode of gas turbine or internal combustion engine, N1 is the number of nodes adopting the coupling of gas turbine or internal combustion engine, is the heat energy generated in the coupling mode of steam turbine, c m is the ratio of output heat power to electric power, N2 is the number of nodes adopting the coupling of steam turbine, is the electric energy generated in the coupling mode of gas turbine or internal combustion engine, P con is the maximum output electric energy of the steam turbine, is the electric energy generated in the coupling mode of steam turbine.

13. The method of claim 1, wherein, The second-order cone programming state estimation model of the electric-thermal integrated energy system is as follows: The method comprises the following steps: inputting the measurement into a pre-constructed second-order cone programming state estimation model of the electric-thermal integrated energy system to obtain estimated values of node voltage amplitude of a power system, estimated values of node phase angle of the power system, estimated values of node pressure of a heat supply system, estimated values of node heat supply temperature and estimated values of node heat return temperature; The measurement is input into a pre-constructed second-order cone programming state estimation model of the electric-thermal integrated energy system to calculate an auxiliary state variable estimation value of the electric-thermal integrated energy system; A relationship between an intermediate variable and the auxiliary state variable of the electric-thermal integrated energy system is constructed, and an intermediate variable estimation value is calculated through nonlinear transformation; Based on the intermediate variable estimation value, a linear transformation is performed to calculate an electric power system node voltage amplitude estimation value, an electric power system node phase angle estimation value, a heat supply system node pressure estimation value, a node heat supply temperature estimation value, and a node heat return temperature estimation value.

14. The second order cone programming robust state estimation system for electric-thermal integrated energy systems, characterized in that, The method comprises: a data acquisition module configured to acquire measurements of an electric-thermal integrated energy system; a state estimation module configured to input the measurements into a pre-constructed second-order cone programming state estimation model of the electric-thermal integrated energy system to obtain an electric power system node voltage amplitude estimation value, an electric power system node phase angle estimation value, a heat supply system node pressure estimation value, a node heat supply temperature estimation value, and a node heat return temperature estimation value; The second-order cone programming state estimation model construction module comprises: an auxiliary variable construction module configured to determine an electric power system auxiliary state variable based on an electric power system state variable, determine an electric power system auxiliary measurement based on an electric power system measurement, determine a heat supply system auxiliary state variable based on a heat supply system state variable, and determine a heat supply system auxiliary measurement based on a heat supply system measurement; an electric power system linear measurement equation construction module configured to construct an electric power system linear measurement equation based on the electric power system auxiliary state variable and the electric power system auxiliary measurement; a heat supply system linear measurement equation construction module configured to construct a heat supply system linear measurement equation based on the heat supply system auxiliary state variable and the heat supply system auxiliary measurement; an electric-thermal integrated energy system linear measurement equation construction module configured to construct an electric-thermal integrated energy system linear measurement equation based on the electric power system linear measurement equation, the heat supply system linear measurement equation, and a coupling mode of a coupling node of the electric power system and the heat supply system; an electric-thermal integrated energy system linear weighted least absolute value state estimation model construction module configured to construct an electric-thermal integrated energy system linear weighted least absolute value state estimation model based on the electric-thermal integrated energy system linear measurement equation; an electric-thermal integrated energy system second-order cone programming state estimation model construction module configured to construct an electric-thermal integrated energy system second-order cone programming state estimation model based on a relationship between the electric-thermal integrated energy system linear weighted least absolute value state estimation model and the electric power system auxiliary state variable.

15. The system of claim 14, wherein, The state estimation module comprises: a calculation module 1 configured to input the measurements into a pre-constructed second-order cone programming state estimation model of the electric-thermal integrated energy system to calculate an auxiliary state variable estimation value of the electric-thermal integrated energy system; a calculation module 2 configured to construct a relationship between an intermediate variable and the auxiliary state variable of the electric-thermal integrated energy system, and calculate an intermediate variable estimation value through nonlinear transformation; a calculation module 3 configured to perform a linear transformation based on the intermediate variable estimation value to calculate an electric power system node voltage amplitude estimation value, an electric power system node phase angle estimation value, a heat supply system node pressure estimation value, a node heat supply temperature estimation value, and a node heat return temperature estimation value. The computing module 3 is configured to calculate the power system node voltage amplitude estimation value, the power system node phase angle estimation value, the thermal system node pressure estimation value, the node heat supply temperature estimation value and the node heat recovery temperature estimation value through linear transformation based on the intermediate variable estimation value.

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