Dynamic Control Method of Thermoelectric System

Abstract

The present invention provides a method for dynamic control of a thermal power system, comprising the following steps: S1. collecting thermal power system parameters; S2. determining the thermal work coefficient of the steam turbine unit in the thermal power system, and allocating the steam extraction amount for each steam turbine unit; S3. performing curve fitting on the steam consumption, power generation and steam extraction amount of each steam turbine unit; S4. constructing an optimal scheduling function model; S5. using a particle swarm optimization algorithm to solve the optimal scheduling function model to obtain an optimized scheduling result; through the above method, various energy equipment and energy media in a self-contained power plant can be effectively modeled and optimized and controlled, and at the same time, a technical solution for optimal scheduling of power generation, steam production and steam extraction loads is given from the perspective of global economic optimization of the thermal power system, which can provide accurate guidance for the production of steel enterprises.

Description

Technical Field

[0001] The invention relates to a control method, and in particular to a dynamic control method for a thermoelectric system. Background Art

[0002] The production of steel is accompanied by the generation and consumption of a large amount of energy. These energies are not easy to manage and store. Therefore, there are a lot of situations such as gas emission and steam emission in steel mills, which not only wastes a lot of energy, but also pollutes the environment. Therefore, in order to rationally utilize the energy within the enterprise, steel enterprises have established their own power plants to consume excess gas. In the thermal power system of the power plant, the thermal power units can generate electricity to meet the electricity needs of the enterprise to a certain extent. At the same time, various types of steam extraction from turbines and steam production from boilers can also be used for the steam needs of the enterprise's steel production process.

[0003] The operating conditions in the production process of steel enterprises change in a complex manner, so the energy demand fluctuates frequently. At the same time, the working efficiency of energy equipment in power plants, such as turbines and boilers, will also change with the workload of the equipment. This will have a great impact on the energy allocation strategy of the thermal power system in the power plant of the steel enterprise. How to make each device operate under more economical conditions while meeting the energy load demand has become a technical problem for the operation of the thermal power system.

[0004] Therefore, in order to solve the above technical problems, it is urgent to propose a new technical means. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a dynamic control method for a thermal power system, which can effectively model and optimize the control of various energy equipment and energy media in a self-contained power plant, and at the same time, provide a technical solution for optimizing the scheduling of power generation, steam production and steam extraction loads from the perspective of global economic optimization of the thermal power system, which can provide accurate guidance for the production of steel enterprises.

[0006] The present invention provides a method for dynamic control of a thermoelectric system, comprising the following steps:

[0007] S1. Collecting thermoelectric system parameters;

[0008] S2. Determine the thermal work coefficient of the steam turbine unit in the thermal power system and allocate the steam extraction amount to each steam turbine unit;

[0009] S3. Perform curve fitting on the steam consumption, power generation and steam extraction of each steam turbine unit;

[0010] S4. Build an optimized scheduling function model:

[0011]

[0012] Among them, f0 represents the objective function, C u represents the unit cost of fuel u, E t represents the electricity purchase price at time t, T represents the scheduling time set, U represents the boiler fuel set, B represents the boiler equipment set, and F b,u,t represents the fuel u supplied to boiler b at time t;

[0013] S5. Use the particle swarm optimization algorithm to solve the optimal scheduling function model and obtain the optimal scheduling result.

[0014] Further, in step S2, the thermalization work coefficient of each steam turbine unit is determined by the following method:

[0015]

[0016] Where: K g,j represents the thermalization work coefficient of steam j in steam turbine unit g, represents the inlet steam enthalpy of the steam turbine unit g, represents the extraction enthalpy of steam j in steam turbine unit g, h p represents the extraction enthalpy of the pth stage extraction, α p It represents the extraction ratio coefficient of the pth extraction stage, and m represents the total number of extraction stages from the 1st extraction stage to the current extraction stage.

[0017] Furthermore, the steam extraction amount allocation for each steam turbine unit specifically includes:

[0018] S201. Divide the steam extraction of the steam turbine unit into different types according to different types of steam pressure;

[0019] S201. In any n rounds of allocation, select the steam turbine unit with the largest thermalization work coefficient from the steam turbine units that produce steam j and have not completed steam j allocation, and determine the steam j load to be allocated at this time. Is it less than the difference between the maximum extraction load and the minimum extraction load allowed by the steam j of the steam turbine unit? If so, then at time t, the extraction load of the steam j on the current steam unit g is set to And the load is set to 0 in the n+1th round of extraction load distribution. If not, the extraction load of steam j on the current extraction unit g is set to the maximum extraction load.

[0020] S202. Continue to screen out the remaining steam turbine units that produce steam j and have not completed steam j distribution, select the steam turbine unit with the largest thermalization work coefficient, and distribute the steam extraction load of steam j according to step S201 until the load of steam j to be distributed is 0.

[0021] Further, step S3 specifically includes:

[0022] S301. Construct the fitting formula:

[0023]

[0024]

[0025]

[0026] in: Indicates the steam consumption of steam turbine unit g, P g represents the power generation of steam turbine unit i, represents the extraction load of steam j on unit g, a g 、b g and c g It represents the characteristic coefficient of unit g in the fitting formula;

[0027] S302. Construct a boiler energy conversion efficiency calculation formula to determine the energy conversion efficiency of each boiler:

[0028] Where: γ b,s represents the energy efficiency of boiler b producing steam s, H s represents the enthalpy of steam s, L b,s represents the production load of steam s produced on boiler b, F b,u represents the fuel u supplied to boiler b, and U represents the type set of fuel;

[0029] S303. Convert the energy conversion efficiency of the boiler into a quadratic curve:

[0030]

[0031] in: η i,k represents the energy efficiency of boiler b producing steam s, X b,s represents the normalized load of boiler b producing steam s, L b,s represents the production load of steam s produced on boiler b, Represents the rated load of boiler b producing steam s.

[0032] Further, in step S4, the optimization scheduling function includes the following constraints:

[0033] Steam consumption constraints of steam turbine units:

[0034]

[0035] Steam consumption of steam turbine unit g; Indicates the lower limit of steam consumption of steam turbine unit g, Indicates the upper limit of steam consumption of steam turbine unit g;

[0036] Steam extraction capacity constraints of steam turbine units:

[0037]

[0038] represents the steam extraction amount of steam j on steam turbine unit i, represents the lower limit of steam extraction of steam j on steam turbine unit g, represents the upper limit of steam extraction capacity of steam j on steam turbine unit g;

[0039] The upper and lower limits of steam turbine unit power generation:

[0040]

[0041] P g,t represents the steam extraction amount of steam j on steam turbine unit g at time t, represents the lower limit of power generation of steam turbine unit g, represents the upper limit of power generation at steam turbine unit g;

[0042] Steam turbine unit climbing constraints:

[0043]

[0044]

[0045] P g,t represents the power generation of steam turbine unit g at time t, Indicates the upward climbing limit of the power generation of the steam turbine unit. Indicates the downward climbing limit of the power generation of the steam turbine unit g;

[0046] Steam consumption demand constraints of steam turbine units:

[0047]

[0048] represents the steam flow rate of steam s on steam turbine unit g at time t, G represents the equipment set of steam turbine unit, L b,s,t represents the steam production of steam s on boiler b at time t;

[0049] Boiler fuel usage constraints:

[0050]

[0051] F b,u,t represents the usage of fuel u on boiler b at time t, represents the lower limit of the usage of fuel u on boiler b, It represents the upper limit of the usage of fuel u by boiler b;

[0052] Boiler steam load constraints:

[0053]

[0054] L b,s represents the load for producing steam s on boiler b, represents the lower load limit of boiler b producing steam s, represents the upper limit of the load of boiler b producing steam s;

[0055] Boiler fuel calorific value constraints:

[0056]

[0057]

[0058] H u represents the calorific value of fuel u, and They represent the lower and upper limits of the fuel calorific value of boiler b respectively;

[0059] Boiler efficiency constraints:

[0060]

[0061] Energy conversion constraints for boilers:

[0062]

[0063] H s represents the enthalpy of steam s;

[0064] Steam load demand constraints:

[0065]

[0066] The demand for steam j at time t is:

[0067] Power load demand constraints:

[0068]

[0069] P t dem represents the electricity demand at time t.

[0070] Further, step S4 specifically includes:

[0071] S401. Rewrite all constraint inequalities of the optimization scheduling function into the following standard form:

[0072] gi (x)≤0,i=1,2,…,l

[0073] Among them, x represents the model variable to be solved, and l represents the number of inequality constraints;

[0074] Rewrite the equations for all constraints of the optimization scheduling function into the following standard form:

[0075] h i (x) = 0, i = l + 1, l + 2, ..., m;

[0076] x represents the model variable to be solved, and m represents the number of inequality constraints;

[0077] S402. Set penalty terms for inequality and equality constraints:

[0078] Where: e i (x) represents the penalty term of the i-th constraint, and ε represents the tolerance of the equality constraint;

[0079] S403. Construct a new optimization scheduling function:

[0080] Where: f 0 (x) represents the optimization function of the original model, and σ represents the penalty factor;

[0081] S404. Initialize the particles: set the number of particles to be solved at each time t to N, set the treatment of each solution variable in each particle to a random value between the upper and lower limits of the solution variable, and set the solution parameters, which include: the maximum number of iterations, the inertia weight values ​​at the beginning and end of the iteration, the individual learning factor and the group learning factor values, the iteration tolerance, and the penalty factor;

[0082] S405. Perform iterative calculations on all particles and update the solved positions and movement speeds:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] represents the velocity vector of particle i in the kth iteration, w k represents the inertia weight at the kth iteration, c 1 represents the individual learning factor, c 2 represents the group learning factor, r 1 and r 2 Represents a random number with equal probability between (0,1); represents the value of the dth dimension in the optimal position of particle i in the previous k iterations; represents the optimal position of particle i in the first k iterations, represents the optimal position of all particles in the first k iterations; w k represents the inertia weight at the kth iteration, K max Indicates the maximum number of iterations set during optimization, w ini and w en d represents the inertia weight set at the beginning and end of the iteration respectively;

[0091] S406. When the number of iterative calculations reaches the maximum number of iterations or the difference in the objective function of the global optimal position between two adjacent iterations is less than the iteration tolerance, the iteration is stopped and the result of the model optimization scheduling is output.

[0092] The beneficial effects of the present invention are as follows: Through the present invention, various energy equipment and energy media in a self-contained power plant can be effectively modeled, optimized and regulated. At the same time, a technical solution for optimizing the scheduling of power generation, steam production and steam extraction loads is provided from the perspective of global economic optimization of the thermal power system, which can provide accurate guidance for the production of steel enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0094] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0095] The present invention is further described in detail below:

[0096] The present invention provides a method for dynamic control of a thermoelectric system, comprising the following steps:

[0097] S1. Collecting thermoelectric system parameters;

[0098] S2. Determine the thermal work coefficient of the steam turbine unit in the thermal power system and allocate the steam extraction amount to each steam turbine unit;

[0099] S3. Perform curve fitting on the steam consumption, power generation and steam extraction of each steam turbine unit;

[0100] S4. Build an optimized scheduling function model:

[0101]

[0102] Among them, f 0 represents the objective function, C u represents the unit cost of fuel u, E t represents the electricity purchase price at time t, T represents the scheduling time set, U represents the boiler fuel set, B represents the boiler equipment set, and F b,u,t represents the fuel u supplied to boiler b at time t;

[0103] S5. The particle swarm optimization algorithm is used to solve the optimization scheduling function model and obtain the optimization scheduling result. Through the above method, various energy equipment and energy media in the self-contained power plant can be effectively modeled and optimized. At the same time, the technical solution for the optimal scheduling of power generation, steam production and steam extraction loads is given from the perspective of the global economic optimization of the thermal power system, which can provide accurate guidance for the production of steel enterprises.

[0104] In this embodiment, in step S2, the thermal work coefficient of each steam turbine unit is determined by the following method:

[0105]

[0106] Where: K g,j represents the thermalization work coefficient of steam j in steam turbine unit g, represents the inlet steam enthalpy of the steam turbine unit g, represents the extraction enthalpy of steam j in steam turbine unit g, h p represents the extraction enthalpy of the pth stage extraction, α p It represents the extraction ratio coefficient of the pth extraction stage, and m represents the total number of extraction stages from the 1st extraction stage to the current extraction stage.

[0107] In this embodiment, allocating the steam extraction amount for each steam turbine unit specifically includes:

[0108] S201. Divide the steam extraction of the steam turbine unit into different types according to different types of steam pressure;

[0109] S201. In any n rounds of allocation, select the steam turbine unit with the largest thermalization work coefficient from the steam turbine units that produce steam j and have not completed steam j allocation, and determine the steam j load to be allocated at this time. Is it less than the difference between the maximum extraction load and the minimum extraction load allowed by the steam j of the steam turbine unit? If so, then at time t, the extraction load of the steam j on the current steam unit g is set to And the load is set to 0 in the n+1th round of extraction load distribution. If not, the extraction load of steam j on the current extraction unit g is set to the maximum extraction load.

[0110] S202. Continue to screen out the remaining steam turbine units that produce steam j and have not completed steam j distribution, select the steam turbine unit with the largest thermalization work coefficient, and distribute the steam extraction load of steam j according to step S201 until the load of steam j to be distributed is 0; optimize on the basis of steam extraction distribution;

[0111] The above process can be expressed by the following formula:

[0112]

[0113] represents the extraction load of steam j on unit g at time t, D j,t represents the extraction load of steam j to be distributed at time t, represents the minimum extraction load of steam j on unit g, represents the maximum extraction load of steam j on unit g;

[0114]

[0115] Among them: The update formula of the extraction load to be allocated in the process of steam extraction load allocation is: represents the extraction load demand of steam j at time t.

[0116] In this embodiment, step S3 specifically includes:

[0117] S301. Construct the fitting formula:

[0118]

[0119]

[0120]

[0121] in: Indicates the steam consumption of steam turbine unit g, P g represents the power generation of steam turbine unit i, represents the extraction load of steam j on unit g, a g 、b g and c g It represents the characteristic coefficient of unit g in the fitting formula;

[0122] S302. Construct a boiler energy conversion efficiency calculation formula to determine the energy conversion efficiency of each boiler:

[0123] Where: γ b,s represents the energy efficiency of boiler b producing steam s, H s represents the enthalpy of steam s, L b,s represents the production load of steam s produced on boiler b, F b,u represents the fuel u supplied to boiler b, and U represents the type set of fuel;

[0124] S303. Convert the energy conversion efficiency of the boiler into a quadratic curve:

[0125]

[0126] in: η i,k represents the energy efficiency of boiler b producing steam s, X b,s represents the normalized load of boiler b producing steam s, L b,s represents the production load of steam s produced on boiler b, Indicates the rated load of boiler b producing steam s. The above method can ensure the accuracy of the final result while avoiding the complexity of the algorithm.

[0127] In this embodiment, in step S4, the optimization scheduling function includes the following constraints:

[0128] Steam consumption constraints of steam turbine units:

[0129]

[0130] Steam consumption of steam turbine unit g; Indicates the lower limit of steam consumption of steam turbine unit g, Indicates the upper limit of steam consumption of steam turbine unit g;

[0131] Steam extraction capacity constraints of steam turbine units:

[0132]

[0133] represents the steam extraction amount of steam j on steam turbine unit i, represents the lower limit of steam extraction of steam j on steam turbine unit g, represents the upper limit of steam extraction capacity of steam j on steam turbine unit g;

[0134] The upper and lower limits of steam turbine unit power generation:

[0135]

[0136] P g,t represents the steam extraction amount of steam j on steam turbine unit g at time t, represents the lower limit of power generation of steam turbine unit g, represents the upper limit of power generation at steam turbine unit g;

[0137] Steam turbine unit climbing constraints:

[0138]

[0139]

[0140] P g,t represents the power generation of steam turbine unit g at time t, Indicates the upward climbing limit of the power generation of the steam turbine unit. Indicates the downward climbing limit of the power generation of the steam turbine unit g;

[0141] Steam consumption demand constraints of steam turbine units:

[0142]

[0143] represents the steam flow rate of steam s on steam turbine unit g at time t, G represents the equipment set of steam turbine unit, L b,s,t represents the steam production of steam s on boiler b at time t;

[0144] Boiler fuel usage constraints:

[0145]

[0146] F b,u,t represents the usage of fuel u on boiler b at time t, represents the lower limit of the usage of fuel u on boiler b, It represents the upper limit of the usage of fuel u by boiler b;

[0147] Boiler steam load constraints:

[0148]

[0149] L b,s represents the load for producing steam s on boiler b, represents the lower load limit of boiler b producing steam s, represents the upper limit of the load of boiler b producing steam s;

[0150] Boiler fuel calorific value constraints:

[0151]

[0152]

[0153] H u represents the calorific value of fuel u, and They represent the lower and upper limits of the fuel calorific value of boiler b respectively;

[0154] Boiler efficiency constraints:

[0155]

[0156] Energy conversion constraints for boilers:

[0157]

[0158] H s represents the enthalpy of steam s;

[0159] Steam load demand constraints:

[0160]

[0161] The demand for steam j at time t is:

[0162] Power load demand constraints:

[0163]

[0164] P t dem represents the electricity demand at time t.

[0165] In this embodiment, step S4 specifically includes:

[0166] S401. Rewrite all constraint inequalities of the optimization scheduling function into the following standard form:

[0167] g i (x)≤0,i=1,2,…,l

[0168] Among them, x represents the model variable to be solved, and l represents the number of inequality constraints;

[0169] Rewrite the equations for all constraints of the optimization scheduling function into the following standard form:

[0170] h i (x) = 0, i = l + 1, l + 2, ..., m;

[0171] x represents the model variable to be solved, and m represents the number of inequality constraints;

[0172] S402. Set penalty terms for inequality and equality constraints:

[0173] Where: e i(x) represents the penalty term of the i-th constraint, and ε represents the tolerance of the equality constraint;

[0174] S403. Construct a new optimization scheduling function:

[0175] Where: f 0 (x) represents the optimization function of the original model, and σ represents the penalty factor;

[0176] S404. Initialize the particles: set the number of particles to be solved at each time t to N, set the treatment of each solution variable in each particle to a random value between the upper and lower limits of the solution variable, and set the solution parameters, which include: the maximum number of iterations, the inertia weight values ​​at the beginning and end of the iteration, the individual learning factor and the group learning factor values, the iteration tolerance, and the penalty factor;

[0177] S405. Perform iterative calculations on all particles and update the solved positions and movement speeds:

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185] represents the velocity vector of particle i in the kth iteration, w k represents the inertia weight at the kth iteration, c 1 represents the individual learning factor, c 2 represents the group learning factor, r 1 and r 2 Represents a random number with equal probability between (0,1); represents the value of the dth dimension in the optimal position of particle i in the previous k iterations; represents the optimal position of particle i in the first k iterations, represents the optimal position of all particles in the first k iterations; w k represents the inertia weight at the kth iteration, K max Indicates the maximum number of iterations set during optimization, w ini and w endRespectively represent the inertia weights set at the beginning and end of the iteration;

[0186] S406. When the number of iterative calculations reaches the maximum number of iterations or the difference in the objective function of the global optimal position between two adjacent iterations is less than the iteration tolerance, the iteration is stopped and the result of the model optimization scheduling is output.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for dynamic control of a thermoelectric system, characterized in that: The following steps are involved: S1. Collecting thermoelectric system parameters; S2. Determine the thermal work coefficient of the steam turbine unit in the thermal power system and allocate the steam extraction amount to each steam turbine unit; S3. Perform curve fitting on the steam consumption, power generation and steam extraction of each steam turbine unit; S4. Build an optimized scheduling function model: Among them, f0 represents the objective function, C u represents the unit cost of fuel u, E t represents the electricity purchase price at time t, T represents the scheduling time set, U represents the boiler fuel set, B represents the boiler equipment set, and F b,u,t represents the fuel u supplied to boiler b at time t; S5. Use particle swarm optimization algorithm to solve the optimal scheduling function model and obtain the optimal scheduling result; In step S2, the thermalization work coefficient of each steam turbine unit is determined by the following method: Where: K g,j represents the thermalization work coefficient of steam j in steam turbine unit g, represents the inlet steam enthalpy of the steam turbine unit g, represents the extraction enthalpy of steam j in steam turbine unit g, h p represents the extraction enthalpy of the pth stage extraction, α p represents the steam extraction ratio coefficient of the pth steam extraction stage, m represents the total number of steam extraction stages from the first steam extraction stage to the current steam extraction stage; The extraction steam allocation for each steam turbine unit specifically includes: S201. Divide the steam extraction of the steam turbine unit into different types according to different types of steam pressure; S202. In any n rounds of allocation, select the steam turbine unit with the largest thermalization work coefficient from the steam turbine units that produce steam j and have not completed steam j allocation, and determine the steam j load to be allocated at this time. Is it less than the difference between the maximum extraction load and the minimum extraction load allowed by the steam j of the steam turbine unit? If so, then at time t, the extraction load of the steam j on the current steam unit g is set to And the load is set to 0 in the n+1th round of extraction load distribution. If not, the extraction load of steam j on the current extraction unit g is set to the maximum extraction load. S203. Continue to screen out the remaining steam turbine units that produce steam j and have not completed steam j distribution, and select the steam turbine unit with the largest thermalization work coefficient to distribute the steam j extraction load according to step S201 until the steam j load to be distributed is 0; Step S3 specifically includes: S301. Construct the fitting formula: in: represents the steam consumption of steam turbine unit g, P g represents the power generation of steam turbine unit i, represents the extraction load of steam j on unit g, a g , b g and c g Indicates the characteristic coefficient of unit g in the fitting formula; S302. Construct a boiler energy conversion efficiency calculation formula to determine the energy conversion efficiency of each boiler: Where: γ b,s represents the energy efficiency of boiler b producing steam s, H s represents the enthalpy of steam s, L b,s represents the production load of steam s produced on boiler b, F b,u represents the fuel u supplied to boiler b, and U represents the type set of fuel; S303. Convert the energy conversion efficiency of the boiler into a quadratic curve: in: η i,k represents the energy efficiency of boiler b producing steam s, X b,s represents the normalized load of boiler b producing steam s, L b,s represents the production load of steam s produced on boiler b, Represents the rated load of boiler b producing steam s.

2. The method for dynamic control of a thermoelectric system according to claim 1, characterized in that: In step S4, the optimization scheduling function includes the following constraints: Steam consumption constraints of steam turbine units: Steam consumption of steam turbine unit g; Indicates the lower limit of steam consumption of steam turbine unit g, Indicates the upper limit of steam consumption of steam turbine unit g; Steam extraction capacity constraints of steam turbine units: represents the steam extraction amount of steam j on steam turbine unit i, represents the lower limit of steam extraction of steam j on steam turbine unit g, represents the upper limit of steam extraction capacity of steam j on steam turbine unit g; The upper and lower limits of steam turbine unit power generation: P g,t represents the steam extraction amount of steam j on steam turbine unit g at time t, represents the lower limit of power generation of steam turbine unit g, represents the upper limit of power generation at steam turbine unit g; Steam turbine unit climbing constraints: P g,t represents the power generation of steam turbine unit g at time t, Indicates the upward climbing limit of the power generation of the steam turbine unit. Indicates the downward climbing limit of the power generation of the steam turbine unit g; Steam consumption demand constraints of steam turbine units: represents the steam flow rate of steam s on steam turbine unit g at time t, G represents the equipment set of steam turbine unit, L b,s,t represents the steam production of steam s on boiler b at time t; Boiler fuel usage constraints: F b,u,t represents the usage of fuel u on boiler b at time t, represents the lower limit of the usage of fuel u on boiler b, It represents the upper limit of the usage of fuel u by boiler b; Boiler steam load constraints: L b,s represents the load for producing steam s on boiler b, represents the lower load limit of boiler b producing steam s, represents the upper limit of the load of boiler b producing steam s; Boiler fuel calorific value constraints: H u represents the calorific value of fuel u, and They represent the lower and upper limits of the fuel calorific value of boiler b respectively; Boiler efficiency constraints: Energy conversion constraints for boilers: H s represents the enthalpy of steam s; Steam load demand constraints: The demand for steam j at time t is: Power load demand constraints: P t dem represents the electricity demand at time t.

3. The method for dynamic control of a thermoelectric system according to claim 2, characterized in that: Step S4 specifically includes: S401. Rewrite all constraint inequalities of the optimization scheduling function into the following standard form: g i (x)≤0,i=1,2,…,l Among them, x represents the model variable to be solved, and l represents the number of inequality constraints; Rewrite the equations for all constraints of the optimization scheduling function into the following standard form: h i (x)=0,i=l+1,l+2,…,m; x represents the model variable to be solved, and m represents the number of inequality constraints; S402. Set penalty terms for inequality and equality constraints: Where: e i (x) represents the penalty term of the i-th constraint, and e represents the tolerance of the equality constraint; S403. Construct a new optimization scheduling function: Where: f0(x) represents the optimization function of the original model, σ represents the penalty factor; S404. Initialize the particles: set the number of particles to be solved at each time t to N, set the treatment of each solution variable in each particle to a random value between the upper and lower limits of the solution variable, and set the solution parameters, which include: the maximum number of iterations, the inertia weight values ​​at the beginning and end of the iteration, the individual learning factor and the group learning factor values, the iteration tolerance, and the penalty factor; S405. Perform iterative calculations on all particles and update the solved positions and movement speeds: represents the velocity vector of particle i in the kth iteration, w k represents the inertia weight at the kth iteration, c1 represents the individual learning factor, c2 represents the group learning factor, r1 and r2 represent random numbers with equal probability between (0,1); represents the value of the dth dimension in the optimal position of particle i in the previous k iterations; represents the optimal position of particle i in the first k iterations, represents the optimal position of all particles in the first k iterations; w k represents the inertia weight at the kth iteration, K max Indicates the maximum number of iterations set during optimization, w ini and w end Respectively represent the inertia weights set at the beginning and end of the iteration; S406. When the number of iterative calculations reaches the maximum number of iterations or the difference in the objective function of the global optimal position between two adjacent iterations is less than the iteration tolerance, the iteration is stopped and the result of the model optimization scheduling is output.

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