Thermoelectric coupling system distributed scheduling method considering electric boiler and thermal inertia

Abstract

The invention discloses a thermoelectric coupling system distributed scheduling method considering an electric boiler and thermal inertia. The method comprises the following steps: constructing a regional heat supply network temperature dynamic model; constructing a thermoelectric coupling system optimization scheduling model considering the electric boiler and thermodynamics; the thermoelectric coupling system optimization scheduling model is converted into a distributed optimization problem, the optimization scheduling problem required to be solved is decomposed into a power grid sub-optimization problem and a heat supply network sub-optimization problem, the two sub-optimization problems are iteratively solved, and finally a distributed scheduling strategy is obtained; and solving a power grid sub-optimization problem and a heat supply network sub-optimization problem by adopting an improved alternating direction algorithm, and finally solving a distributed optimal scheduling strategy. According to the method, the electric boiler and the thermal inertia can be utilized at the same time, the characteristics of the electric boiler and the thermal dynamics are coordinated, the distributed scheduling strategy with the maximum system wind power consumption is finally obtained, the potential of wind power consumption of the thermoelectric coupling system is utilized to the maximum degree, and the calculation speed is high.

Description

Technical Field

[0001] The invention belongs to the field of wind power dispatching, and in particular relates to a distributed dispatching method for a thermoelectric coupling system taking into account electric boilers and thermal inertia. Background Art

[0002] Combined heat and power (CHP) units provide heat for more than 60% of the heat load demand of urban residents and industries in northern my country. CHP units make the regional heating network and the power supply network form a coupled system. On the other hand, by the end of 2022, the total installed capacity of wind power in my country's "Three Norths" region was 213.7 million kilowatts. Due to the low demand for electricity load and high heat load at night, the proportion of wind abandonment during the heating season is about 20%, and the problem of wind power absorption is very serious. Therefore, it is of great significance to optimize the dispatch of the heat and power coupling system to improve the economy of the system and the wind power absorption capacity.

[0003] The existing optimization scheduling technology of thermoelectric coupling system mainly has the following problems and shortcomings:

[0004] Some existing heat-electric coupling system optimization scheduling models do not consider the thermal inertia of the district heating network, but only consider the electric boiler. This type of model only optimizes the output of the electric boiler and CHP unit, reduces the thermal power and electric power output of the CHP unit during the peak period of wind power output, and increases the thermal power output of the electric boiler to meet the heat load demand. The disadvantage of this type of model is that it ignores the model of the district heating pipeline system, and therefore does not consider the inertia of heat transfer in the pipeline. Ignoring the pipeline system model will cause the scheduling results to be inconsistent with reality;

[0005] There are also some thermal-electric coupling system optimization scheduling models that do not consider electric boilers, but only consider the thermal inertia of the regional heating network, and use the node method to model it. Thermal inertia refers to the fact that the heat energy transfer in the pipeline has a certain time delay. Therefore, the heat source output does not have to be consistent with the heat load demand at all times. This type of model uses the thermal inertia of the pipeline system to preheat the water in the pipeline when the wind power output is low; when the wind power output is high, the heat energy stored in the water in the pipeline is used to meet the heat load demand, so as to reduce the CHP power output and thus reduce the amount of wind power abandoned. In addition, thermal inertia modeling based on the node method refers to the use of pipeline outlet temperature and historical ambient temperature without considering heat loss to represent the dynamics of pipeline temperature. This type of model has two main disadvantages: first, only considering thermal inertia but not considering that electric boilers cannot maximize the consumption of wind power. Thermal inertia can provide certain flexibility for the optimal scheduling of the power grid, but it cannot change the constraints between thermal power output and electric power output in the CHP unit; second, the modeling method based on the node method will introduce a large number of auxiliary variables in the optimal scheduling model of the thermal-electric coupling system, causing the problem of difficulty in solving the model. Summary of the invention

[0006] In order to address the deficiencies of the prior art, the present invention aims to provide a distributed scheduling method for a thermoelectric coupling system taking into account electric boilers and thermal inertia. The method can simultaneously utilize electric boilers and thermal inertia, coordinate electric boilers and thermodynamic characteristics, and ultimately obtain a distributed scheduling strategy that maximizes the system's wind power consumption, maximize the potential of the thermoelectric coupling system to consume wind power, and have a high calculation speed.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A distributed scheduling method for a thermoelectric coupling system taking into account electric boilers and thermal inertia comprises the following steps:

[0009] S1: Constructing a temperature dynamics model for the district heating network;

[0010] S2: Construct an optimal scheduling model for the thermal-electric coupling system taking into account electric boilers and thermal dynamics;

[0011] S3: Convert the optimal scheduling model of the thermal-electric coupling system into a distributed optimization problem, decompose the optimal scheduling problem to be solved into a power grid sub-optimization problem and a thermal grid sub-optimization problem, and iteratively solve these two sub-optimization problems to finally obtain a distributed scheduling strategy;

[0012] S4: An improved alternating direction algorithm is used to solve the power grid sub-optimization problem and the heat grid sub-optimization problem, and finally the distributed optimal scheduling strategy is obtained.

[0013] Wherein, step S1 specifically includes:

[0014] The following differential equations are used to describe the temperature dynamics model of the district heating network to be built:

[0015]

[0016]

[0017] Where: subscript i represents the pipe number; subscripts s and r represent the supply pipe and return pipe respectively; z represents a discrete time point; vector T represents the temperature of a discrete position point x along the pipe direction; T in Indicates the temperature at the pipeline inlet location; Δz represents the distance between adjacent discrete time points; Δx represents the distance between adjacent discrete position points; ρ and C p represents the density and heat capacity of water respectively; m represents the flow rate of water; A and R represent the cross-sectional area and thermal conductivity of the pipe respectively; T a Indicates the ambient temperature.

[0018] Step S2 specifically includes:

[0019] Based on the temperature dynamics model described in step S1, the distributed scheduling method of the thermoelectric coupling system is described by the following optimization problem:

[0020]

[0021] Where: Decision variable p gt and p chp Represent the power output of the traditional generator set and the CHP unit respectively; the decision variable h eb Indicates the thermal power output of the electric boiler; C E (·) represents the operating cost index of traditional generator sets, CHP units and electric boilers, which is represented by a quadratic function. The specific form can be expressed as follows:

[0022]

[0023] Among them, N gt , N chp and N eb Respectively represent the number of traditional generator sets, CHP units and electric boilers; and Indicates the jth gt The operating cost coefficient of a traditional generator set; Indicates the jth chp Thermal power output of each electric boiler; Indicates the jth eb The operating cost coefficient of each electric boiler; and Indicates the jth chp The operating cost factor of each CHP unit; Indicates the jth chp Thermal power output of each CHP unit; It represents the correlation coefficient between the electrical power output and the thermal power output of the CHP unit;

[0024] H eq (h chp ,h eb )=0 (3)

[0025] Where: h chp and h eb Respectively represent the thermal power output of CHP unit and electric boiler; H eq (·) represents the thermal power balance constraint. Here, the model in equation (1) is used, and the heat generation equation h chp =C p m chp (T s,i (z)-T r,i (z)) and h eb =C p m eb(T s,i (z)-T r,i (z)), and the node temperature mixing equation express;

[0026] Among them, m chp and m eb Respectively represent the flow rate of water passing through the CHP unit and the electric boiler, Indicates that the flow rate in the vth water supply pipe is m v The temperature of the water flowing into the node, represents the temperature of water flowing out of the node; V in represents the set of pipes connecting the outlets to the nodes, V out A collection of pipes representing the connections between inlets and nodes;

[0027] H ineq (h chp ,h eb )≤0 (4)

[0028] Where: H ineq (·) represents the inequality constraint related to thermal power output, where the thermal power output limit constraint of the electric boiler is used. in, and They represent the lower and upper limits of the thermal power output of the electric boiler respectively;

[0029] E eq (p gt ,p chp ,p eb ,p wind )=0 (5)

[0030] Where: p eb represents the electric power demand of the electric boiler; p wind Indicates wind power output; E eq (·) represents the electric power balance constraint, which can be expressed as:

[0031]

[0032] in, and Represent the electric power output of the traditional generator set and the CHP unit, Indicates the electrical power demand of the electric boiler;

[0033] E ineq (p gt ,p chp )≤0 (6)

[0034] Where: E ineq(·) represents the inequality constraint related to the electric power output, using the CHP unit electric power output constraint and p chp ≥β(h chp -γ), the power output constraint of traditional generator sets And the output ramp constraints of CHP units and traditional units; and They represent the lower and upper limits of the CHP unit’s electrical power output, respectively. Constants κ, β, and γ represent the constraint relationship between the CHP unit’s electrical power output and thermal power output. and They respectively represent the lower and upper limits of the electric power output of traditional generator sets.

[0035] Step S3 specifically includes: describing the distributed scheduling method of the thermoelectric coupling system as the following two sub-optimization problems:

[0036] Among them, the power grid sub-optimization problem is:

[0037]

[0038] st type (5), (6)

[0039] and hotnet optimization problem

[0040]

[0041] st type (3), (4)

[0042] Where: Indicates the jth eb The efficiency of an electric boiler; and represent auxiliary variables related to the heat output of the electric boiler and CHP unit respectively; and represents the Lagrange multiplier in the power grid subproblem; and represents the Lagrange multiplier in the thermal network subproblem; Indicates the thermal power of the CHP unit in the heating network.

[0043] Step S4 specifically includes:

[0044] 1) In formula (7), set The initial value of , and solve the minimum value of equation (7) under constraints (5) and (6), we get The updated value of

[0045] 2) Set in equation (8) The initial value of , and solve the minimum value of equation (7) under constraints (5) and (6), we get The updated value of

[0046] 3) According to and The updated value of The updated value of is calculated by formula (9)

[0047] The minimum value of The updated value of

[0048] 4) According to formula (10)

[0049]

[0050] Calculate the updated value and

[0051] 5) According to formula (11)

[0052]

[0053] Calculate the updated value and According to formula (12)

[0054]

[0055] Calculate the updated value and

[0056] 6) Verification conditions and Is it satisfied: If it is satisfied, the distributed scheduling strategy is obtained

[0057] and Otherwise, return to step 2).

[0058] The symbols in the above formula mean: α represents the relaxation parameter; ε represents the calculation convergence accuracy; γ represents the weight parameter.

[0059] The present invention has the following beneficial effects:

[0060] 1. The existing thermal power coupling system scheduling technology uses the thermal inertia of electric boilers or heating networks to improve the system's wind power absorption capacity, but cannot coordinate electric boilers and thermal inertia at the same time, and cannot maximize the potential of the system to absorb wind power. The method proposed in the present invention can simultaneously utilize electric boilers and thermal inertia, coordinate electric boilers and thermodynamic characteristics, and ultimately obtain a distributed scheduling strategy that maximizes the system's wind power absorption.

[0061] 2. The existing scheduling technology of thermoelectric coupling system taking into account thermal inertia adopts the node method to model the temperature dynamic characteristics of the pipeline. This method requires the introduction of additional auxiliary variables. Considering the large number of pipelines in the heating network, the thermodynamic model using the node method is prone to make it difficult to solve the optimization scheduling model. The temperature dynamic model adopted by the present invention does not need to introduce additional auxiliary variables, thus alleviating the difficulty of solving the optimization scheduling model; in addition, the model is based on the physical process of temperature propagation in the medium, which is easy to understand and apply in engineering.

[0062] 3. The existing thermal-electric coupling system scheduling technology uses the traditional alternating direction method to solve the model, which has convergence problems. The present invention uses an improved alternating direction method, adds relaxation parameters α and variable estimation values The update strategy can significantly improve the convergence of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a design flow chart of the present invention;

[0064] Figure 2 Design a flow chart for the distributed scheduling strategy of the present invention;

[0065] Figure 3 This is the electrothermal coupling test system diagram of the present invention:

[0066] Figure 4 It is a prediction diagram of electric load, heat load and wind power output of the test system of the present invention;

[0067] Figure 5(a)-5(e) This is a comparison chart of technical performance under different methods;

[0068] Figure 6 It is a convergence line graph of the method proposed in the present invention. DETAILED DESCRIPTION

[0069] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not intended to limit the scope of use of the present invention.

[0070] like Figure 1 As shown, the present invention proposes a distributed scheduling method for a thermoelectric coupling system taking into account electric boilers and thermal inertia, so as to maximize the system wind power absorption capacity by utilizing electric boilers and thermal inertia, and solve the problem that the complexity of thermodynamic representation causes the difficulty of model solution. The implementation process includes the following detailed steps:

[0071] Step 1: Construct a temperature dynamics model of the district heating network.

[0072] The following differential equations are used to describe the temperature dynamics model of the district heating network constructed in the present invention:

[0073]

[0074]

[0075] Where: subscript i represents the pipe number; subscripts s and r represent the supply pipe and return pipe respectively; z represents a discrete time point; vector T represents the temperature of a discrete position point x along the pipe direction; T in Indicates the temperature at the pipeline inlet location; Δz represents the distance between adjacent discrete time points; Δx represents the distance between adjacent discrete position points; ρ and C p represents the density and heat capacity of water respectively; m represents the flow rate of water; A and R represent the cross-sectional area and thermal conductivity of the pipe respectively; T a Indicates the ambient temperature.

[0076] Step 2: Construct an optimal scheduling model for the thermal-electric coupling system taking into account electric boilers and thermal dynamics.

[0077] Based on the above temperature dynamics model, the present invention proposes an optimization scheduling method for a thermoelectric coupling system taking into account thermal inertia and electric boilers. The method can be described by the following optimization problem:

[0078]

[0079] Where: Decision variable p gt and p chp Represent the power output of the traditional generator set and the CHP unit respectively; the decision variable h eb Indicates the thermal power output of the electric boiler; C E (·) represents the operating cost index of traditional generator sets, CHP units and electric boilers, which is represented by a quadratic function. The specific form can be expressed as

[0080]

[0081] Among them, N gt , N chp and N eb Respectively represent the number of traditional generator sets, CHP units and electric boilers, and Indicates the jth gt The operating cost coefficient of a traditional generator set; Indicates the jth chp Thermal power output of each electric boiler; Indicates the jth eb The operating cost coefficient of each electric boiler; and Indicates the jth chp The operating cost factor of each CHP unit; Indicates the jth chp Thermal power output of each CHP unit; It represents the correlation coefficient between the electrical power output and thermal power output of the CHP unit.

[0082] H eq (h chp ,h eb )=0 (3)

[0083] Where: h chp and h eb Respectively represent the thermal power output of CHP unit and electric boiler; H eq (·) represents the thermal power balance constraint. Here, the model in equation (1) is used, and the heat generation equation h chp＝ C p m chp (T s,i (z)-T r,i (z)) and h eb =C p m eb (T s,i (z)-T r,i (z)), and the node temperature mixing equation Indicates. chp and m eb Respectively represent the flow rate of water passing through the CHP unit and the electric boiler, Indicates that the flow rate in the vth water supply pipe is m v The temperature of the water flowing into the node, represents the temperature of water flowing out of the node; V in represents the set of pipes connecting the outlets to the nodes, V out A collection of pipes representing inlets connected to nodes.

[0084] H ineq (h chp ,h eb )≤0 (4)

[0085] Where: h chp and h eb Respectively represent the thermal power output of CHP unit and electric boiler; H ineq (·) represents the inequality constraint related to thermal power output, where the thermal power output limit constraint of the electric boiler is used. here and They respectively represent the lower and upper limits of the thermal power output of the electric boiler.

[0086] E eq (p gt ,p chp ,p eb ,p wind )=0 (5)

[0087] Where: p gt and p chp Represent the electric power output of the traditional generator set and the CHP unit respectively; p eb represents the electric power demand of the electric boiler; p wind Indicates wind power output; E eq (·) represents the power balance constraint, which can be expressed as

[0088]

[0089] Among them, N gt , N chp and N eb Respectively represent the number of traditional generator sets, CHP units and electric boilers, and Represent the electric power output of the traditional generator set and the CHP unit, Indicates the electrical power demand of the electric boiler.

[0090] E ineq (p gt ,p chp )≤0 (6)

[0091] Where: p gt and p chp Represent the electric power output of the traditional generator set and the CHP unit respectively; E ineq (·) represents the inequality constraint related to the electric power output. Here, the electric power output constraint of the CHP unit is adopted. and p chp ≥β(h chp -γ), the power output constraint of traditional generator sets As well as the output ramp constraints of CHP units and traditional units, here and They represent the lower and upper limits of the CHP unit’s electrical power output, respectively. Constants κ, β, and γ represent the constraint relationship between the CHP unit’s electrical power output and thermal power output. and They respectively represent the lower and upper limits of the electric power output of traditional generator sets.

[0092] Step 3: Convert the optimization scheduling model into a distributed optimization problem.

[0093] The present invention adopts a distributed method to solve the optimization scheduling problem described by equations (2)-(6) including the thermodynamic model (1) and the electric boiler model. The present invention decomposes the optimization scheduling problem of the thermoelectric coupling system to be solved into a power grid sub-optimization problem and a heat grid sub-optimization problem, and then iteratively solves the two sub-optimization problems to finally obtain a distributed scheduling strategy.

[0094] In the present invention, the distributed scheduling method of the thermoelectric coupling system is described as the following two sub-optimization problems:

[0095] Power grid sub-optimization problem

[0096]

[0097] st type (5), (6)

[0098] and hotnet optimization problem

[0099]

[0100] st type (3), (4)

[0101] Where: Indicates the jth eb The efficiency of an electric boiler; and represent auxiliary variables related to the heat output of the electric boiler and CHP unit respectively; and represents the Lagrange multiplier in the power grid subproblem; and represents the Lagrange multiplier in the thermal network subproblem; Represents the thermal power of the CHP unit in the heating network; the meanings of other symbols are the same as those of the parameters in equations (2)-(6).

[0102] Step 4: Use the improved alternating direction method to solve the distributed optimization model.

[0103] The present invention adopts an improved alternating direction algorithm to effectively solve the power grid sub-problem (7) and the heat grid sub-problem (8), and finally obtains the distributed optimal scheduling strategy. Figure 2 As shown in Figure 2, the process of using the improved alternating direction algorithm to solve the sub-optimization problem described in equations (7) and (8) is as follows:

[0104] 1) In formula (7), set The initial value of , and solve the minimum value of equation (7) under constraints (5) and (6), we get The updated value of

[0105] 2) Set in equation (8) The initial value of , and solve the minimum value of equation (7) under constraints (5) and (6), we get The updated value of

[0106] 3) According to and The updated value of The updated value of is calculated by formula (9)

[0107] The minimum value of The updated value of

[0108] 4) According to formula (10)

[0109]

[0110] Calculate the updated value and

[0111] 5) According to formula (11)

[0112]

[0113] Calculate the updated value and According to formula (12)

[0114]

[0115] Calculate the updated value and

[0116] 6) Verification conditions and Is it satisfied: If it is satisfied, the distributed scheduling strategy is obtained and Otherwise, return to step 2).

[0117] The meanings of the symbols in the above formulas are: α represents the relaxation parameter; ε represents the calculation convergence accuracy; γ represents the weight parameter; the meanings of the remaining symbols are the same as those in formulas (1)-(8).

[0118] The existing thermal power coupling system scheduling technology uses the thermal inertia of electric boilers or heating networks to improve the system's wind power absorption capacity, but cannot coordinate electric boilers and thermal inertia at the same time, and thus cannot maximize the potential of the system to absorb wind power. The method proposed in the present invention can simultaneously utilize electric boilers and thermal inertia, coordinate electric boilers and thermodynamic characteristics, and ultimately obtain a distributed scheduling strategy that maximizes the system's wind power absorption;

[0119] Secondly, the existing scheduling technology of thermoelectric coupling system taking into account thermal inertia adopts the node method to model the temperature dynamic characteristics of the pipeline. This method requires the introduction of additional auxiliary variables. Considering the large number of pipelines in the heating network, the thermodynamic model using the node method is prone to make it difficult to solve the optimization scheduling model. The present invention adopts the temperature dynamic model described by formula (1), which does not require the introduction of additional auxiliary variables, thereby alleviating the difficulty of solving the optimization scheduling model; in addition, the model is based on the physical process of temperature propagation in the medium, which is easy to understand and apply in engineering.

[0120] In addition, the existing thermal-electric coupling system scheduling technology uses the traditional alternating direction method to solve the model, which has convergence problems. The present invention uses an improved alternating direction method, adds relaxation parameters α and variable estimation values The update strategy (such as Figure 2 As shown in the figure, it can improve the convergence of the algorithm to a limited extent, maximize the potential of the thermal-electric coupling system to absorb wind power, and has a good calculation speed.

[0121] Case Study:

[0122] In order to further illustrate the technical effect of the present invention, Figure 3 The described electrothermal coupling system is used as an example to explain the practical application of the present invention.

[0123] The system consists of a 6-node power supply network and a 6-node heating network. The power supply network and the heating network are connected to each other through a combined heat and power (CHP) unit and an electric boiler (EB). G1 and G2 represent conventional generators. D1-D3 represent electrical loads. W1 represents a wind farm. The predicted values ​​of electrical load, thermal load and wind power output are shown in Figure 4 shown.

[0124] (1) Performance comparison

[0125] Figure 5 compares the wind curtailment under different methods and the output of traditional generators, CHP units and electric boilers. As shown in Figure 5(a), the method proposed in the present invention can maximize the reduction of wind curtailment.

[0126]

[0127] Table 1 Economic comparison

[0128] Table 1 compares the economic efficiency of different methods. As can be seen from Table 1, the method without considering thermal inertia has the highest operating cost. This is because this type of method ignores thermal inertia and cannot optimize the coordination of electric boiler operation and thermodynamic characteristics. Although the method without considering the electric boiler has the lowest operating cost, it can be seen from Figure 5(a) that this type of method will cause a large amount of wind abandonment. The operating cost of the method proposed in the present invention is between the two. This shows that the method proposed in the present invention can maximize the absorption of wind power while taking into account the economy of system operation. Therefore, the method proposed in the present invention is significantly better than the existing method.

[0129] (2) Economic Comparison

[0130] Figure 6 The calculation error of the CHP unit power output is given. It can be seen from the figure that the calculation error converges after 10 iterations, which shows that the method proposed in the present invention has a good calculation speed.

[0131] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A distributed scheduling method for a thermoelectric coupling system taking into account electric boilers and thermal inertia, characterized in that: The following steps are involved: S1: Constructing a temperature dynamics model for the district heating network; S2: Construct an optimal scheduling model for the thermal-electric coupling system taking into account electric boilers and thermal dynamics; S3: Convert the optimal scheduling model of the thermal-electric coupling system into a distributed optimization problem, decompose the optimal scheduling problem to be solved into a power grid sub-optimization problem and a thermal grid sub-optimization problem, and iteratively solve these two sub-optimization problems to finally obtain a distributed scheduling strategy; S4: An improved alternating direction algorithm is used to solve the power grid sub-optimization problem and the heat grid sub-optimization problem, and finally the distributed optimal scheduling strategy is obtained.

2. The distributed scheduling method for thermoelectric coupling system taking into account electric boilers and thermal inertia according to claim 1 is characterized in that: Step S1 specifically includes: The following differential equations are used to describe the temperature dynamics model of the district heating network to be built: Where: subscript i represents the pipe number; subscripts s and r represent the supply pipe and return pipe respectively; z represents a discrete time point; vector T represents the temperature of a discrete position point x along the pipe direction; T in Indicates the temperature at the pipeline inlet point; Δz represents the distance between adjacent discrete time points; Δx represents the distance between adjacent discrete position points; ρ and C p represents the density and heat capacity of water respectively; m represents the flow rate of water; A and R represent the cross-sectional area and thermal conductivity of the pipe respectively; T a Indicates the ambient temperature.

3. The distributed scheduling method for thermoelectric coupling system taking into account electric boilers and thermal inertia according to claim 1 is characterized in that: Step S2 specifically includes: Based on the temperature dynamics model described in step S1, the distributed scheduling method of the thermoelectric coupling system is described by the following optimization problem: Where: Decision variable p gt and p chp Represent the power output of the traditional generator set and the CHP unit respectively; the decision variable h eb Indicates the thermal power output of the electric boiler; C E (·) represents the operating cost index of traditional generator sets, CHP units and electric boilers, which is represented by a quadratic function. The specific form can be expressed as follows: Among them, N gt , N chp and N eb Respectively represent the number of traditional generator sets, CHP units and electric boilers; and Indicates the jth gt The operating cost coefficient of a traditional generator set; Indicates the jth chp Thermal power output of each electric boiler; Indicates the jth eb The operating cost coefficient of each electric boiler; and Indicates the jth chp The operating cost factor of each CHP unit; Indicates the jth chp Thermal power output of each CHP unit; It represents the correlation coefficient between the electrical power output and the thermal power output of the CHP unit; s.t.H eq (h chp ,h eb )=0 (3) Where: h chp and h eb Respectively represent the thermal power output of CHP unit and electric boiler; H eq (·) represents the thermal power balance constraint. Here, the model in equation (1) is used, and the heat generation equation h chp =C p m chp (T s,i (z)-T r,i (z)) and h eb =C p m eb (T s,i (z)-T r,i (z)), and the node temperature mixing equation express; Among them, m chp and m eb Respectively represent the flow rate of water passing through the CHP unit and the electric boiler, Indicates that the flow rate in the vth water supply pipe is m v The temperature of the water flowing into the node, represents the temperature of water flowing out of the node; V in represents the set of pipes connecting the outlets to the nodes, V out A collection of pipes representing the connections between inlets and nodes; H ineq (h chp ,h eb )≤0 (4) Where: H ineq (·) represents the inequality constraint related to thermal power output, where the thermal power output limit constraint of the electric boiler is used. in, and They represent the lower and upper limits of the thermal power output of the electric boiler respectively; E eq (p gt ,p chp ,p eb ,p wind )=0 (5) Where: p eb represents the electric power demand of the electric boiler; p wind Indicates wind power output; E eq (·) represents the electric power balance constraint, which can be expressed as: in, and Represent the electric power output of the traditional generator set and the CHP unit, Indicates the electrical power demand of the electric boiler; E ineq (p gt ,p chp )≤0 (6) Where: E ineq (·) represents the inequality constraint related to the electric power output, using the CHP unit electric power output constraint and p chp ≥β(h chp -γ), the power output constraint of traditional generator sets And the output ramp constraints of CHP units and traditional units; and They represent the lower and upper limits of the CHP unit’s electrical power output, respectively. Constants κ, β, and γ represent the constraint relationship between the CHP unit’s electrical power output and thermal power output. and They respectively represent the lower and upper limits of the electric power output of traditional generator sets.

4. The distributed scheduling method for thermoelectric coupling system taking into account electric boilers and thermal inertia according to claim 3 is characterized in that: Step S3 specifically includes: The distributed scheduling method of the thermal power coupling system is described as the following two sub-optimization problems: Power grid sub-optimization problem: st type (5), (6) and hotnet optimization problem st type (3), (4) Where: η jeb Indicates the jth eb The efficiency of an electric boiler; and represent auxiliary variables related to the heat output of the electric boiler and CHP unit respectively; and represents the Lagrange multiplier in the power grid subproblem; and represents the Lagrange multiplier in the thermal network subproblem; Indicates the thermal power of the CHP unit in the heating network.

5. The distributed scheduling method for thermoelectric coupling system taking into account electric boilers and thermal inertia according to claim 4 is characterized in that: Step S4 specifically includes: 1) In formula (7), set The initial value of , and solve the minimum value of equation (7) under constraints (5) and (6), we get The updated value of 2) Set in formula (8) The initial value of , and solve the minimum value of equation (7) under constraints (5) and (6), we get The updated value of 3) According to and The updated value of The updated value of is calculated by formula (9) The minimum value of The updated value of 4) According to formula (10) Calculate the updated value and 5) According to formula (11) Calculate the updated value and According to formula (12) Calculate the updated value and 6) Verification conditions and Is it satisfied: If it is satisfied, the distributed scheduling strategy is obtained and Otherwise, return to step 2). In the formula, α represents the relaxation parameter; ε represents the calculation convergence accuracy; γ represents the weight parameter.

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