A real-time dispatching method for electric-thermal integrated energy system with guaranteed feasibility

Abstract

The present invention discloses a real-time scheduling method for an electric-thermal integrated energy system with guaranteed feasibility, comprising the following steps: 1) obtaining system parameters of the electric-thermal integrated energy system; 2) establishing a real-time electric-thermal joint scheduling model; 3) designing a modified Bendes decomposition algorithm that strictly guarantees feasibility and applying it to solving the distributed real-time scheduling problem of the electric-thermal integrated energy system; 4) outputting a distributed real-time scheduling result of the electric-thermal integrated energy system with guaranteed feasibility. The present invention strictly guarantees the safety of the electric-thermal integrated energy system through a new feasible cut before the cyclic iteration, greatly reducing the number of iterations required for the algorithm to converge, and the temporary scheduling schemes in the iterative process can be applied to the actual system in real time and safely. Therefore, the real-time scheduling method can meet the efficiency and safety requirements of real-time scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling and optimization of electric-thermal integrated energy systems, and in particular to a real-time scheduling method for electric-thermal integrated energy systems that ensures feasibility. Background Art

[0002] Traditional distributed scheduling methods cannot effectively guarantee the safety of real-time operation of actual electric-thermal integrated energy systems. The relaxation mechanism of the dual decomposition algorithm causes the relaxed regional coupling constraints to be unable to be strictly satisfied even after the algorithm converges, resulting in errors; while the original decomposition algorithm needs to continuously superimpose feasible cuts on the power grid scheduling sub-problem, and a feasible solution can only be obtained after a large number of iterations. Whether the scheduling scheme can strictly meet all the constraints of the scheduling model in real time has long been a problem that distributed optimization theory has not been able to effectively solve. Therefore, in order to solve the problem of low efficiency and difficulty in ensuring system safety in traditional distributed scheduling algorithms for the intraday scheduling problem of electric-thermal integrated energy systems, it is urgent to propose a new feasible cut generation method, design a modified Benders decomposition algorithm that strictly guarantees feasibility, and apply it to the solution of distributed real-time scheduling problems in electric-thermal integrated energy systems. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned defects in the prior art and to provide a real-time scheduling method for an electric-thermal integrated energy system that ensures feasibility. The real-time scheduling method strictly ensures the safety of the electric-thermal integrated energy system through a new feasible cut before the loop iteration, greatly reducing the number of iterations required for algorithm convergence, and the temporary scheduling schemes in the iterative process can be applied to the actual electric-thermal integrated energy system in real time and safely. Therefore, the real-time scheduling method can meet the efficiency and safety requirements of real-time scheduling.

[0004] The purpose of the present invention can be achieved by adopting the following technical solutions:

[0005] A real-time dispatching method for an electric-thermal integrated energy system that ensures feasibility, the real-time dispatching method comprising the following steps:

[0006] S1. Input system parameters of the electric and thermal integrated energy system;

[0007] S2. Establish a real-time electric and thermal joint scheduling model;

[0008] S3, applying the modified Benders decomposition algorithm to solve the distributed real-time scheduling problem of electric and thermal integrated energy system;

[0009] S4. Output the distributed real-time dispatch results of the electric-thermal integrated energy system with feasibility guarantee.

[0010] Furthermore, the system parameters of the electric-thermal integrated energy system include the complex power of the injection node, the line flow, the node voltage, the wind abandonment penalty cost, the specific heat capacity of the water on the heat network side, the pipeline water supply flow rate, the node temperature of the water supply network and the return network, the inlet and outlet temperature of the water supply network and the return network, the power of the heat load, and the length of the pipeline. The input of the system parameters provides a data basis for modeling, and other data can be calculated through the existing parameters.

[0011] Furthermore, a mathematical model is established for the real-time electric-heat joint scheduling model by using the obtained system parameters of the electric-heat integrated energy system. The formula of the real-time electric-heat joint scheduling model established in step S2 is as follows:

[0012]

[0013] sD E x E +B E h E ≤ b E (A)

[0014] D H x H +F E h E +F H h H =f H (B)

[0015]

[0016]

[0017] Among them, x E represents the internal variables of the power grid, including the vector consisting of power generation, up / down rotation reserve capacity of the generator unit, bus phase angle and power generation of the wind farm, x H Represents the internal variables of the heat network, including the node temperature of the supply / return side of the pipeline node, the supply / return side temperature of the heat source, the measured temperature of the supply / return pipe inlet, the measured temperature of the supply / return pipe outlet, and the vector composed of the supply / return side temperature of the heat load, B E and D E are the coefficient matrices of boundary variables and state variables, b E is the right-hand term vector of the power grid inequality constraint, D H is the coefficient matrix of the internal variables of the heating network, F E is the coefficient matrix before the heat source output, F H is the coefficient matrix before the heat source heat output, f H is the right-hand term of the equality constraint, D H 、F E 、F H、f H All are related to the parameters of the heating network and the power grid. x H and are the lower and upper bounds of the internal state variables of the heating network, h E is the heat source electrical output, h H Heat output for heat source, h H and is the lower and upper bounds of the heat output of the heat source. In the objective function formula (1), C H (h H ) is the cost of heat source generation within the heating network, expressed as:

[0018] C H (h H )=c H T h H (2)

[0019] Among them, c H T is the heat source heat cost coefficient vector, C in the objective function formula (1) E (x E ,h E ) is a quadratic function that combines the wind curtailment penalty cost, coupled heat sources (such as cogeneration units), and the power generation cost of thermal units, and is expressed as:

[0020] C E (x E ,h E )=h E T G E +c E T h E (3)

[0021] Where G is the coefficient matrix heat source electricity cost coefficient matrix, c E T is the heat source electricity cost coefficient vector.

[0022] Furthermore, in order to protect the privacy of the power grid and the heat network, a distributed method is used to solve the optimization scheduling problem of the electric and thermal integrated energy system, so that the power grid control center and the heat network control center only need to exchange a small amount of boundary information to achieve almost the same result as the centralized solution, while protecting the privacy of different subjects without losing the solution accuracy. The process of step S3 is as follows:

[0023] S31. Since a distributed method is used to solve the optimization scheduling problem of the electric-thermal integrated energy system, this problem must be decomposed and solved. The original optimization scheduling problem of the electric-thermal integrated energy system is decomposed into the main problem of the power grid and the sub-problem of the thermal network through the feasible cut generation method; the economic scheduling problem of the power grid is taken as the main problem of the power grid and modeled as follows:

[0024]

[0025] sD H x H +F E h E +F H h H =f H (B)

[0026] in, represents the thermal output of the fixed CHP unit, which is a boundary variable determined by the grid control center, h' E is the heat source electrical output h E Virtual copy of heat source electrical output h E As a decision variable in the operation of the heating network, inf means finding the lower bound of the function;

[0027] fixed The heating network operation problem is optimized as a heating network sub-problem:

[0028]

[0029]

[0030] D H x H +F E h' E +F H h H =f H (F)

[0031]

[0032]

[0033] Therefore, the feasible cut generated by the heating network control center will be added to the main power grid problem:

[0034]

[0035] where h' H is the heat output of the heat source h H Virtual copy of heat source heat output h H As a decision variable in power grid operation, are the first and second coefficient matrices of the feasible cut, g FC is the coefficient vector of the feasible cut, and its expression is as follows:

[0036]

[0037] Where I is the identity matrix, e H is a unit vector;

[0038] The modified main grid problem is as follows:

[0039]

[0040]

[0041]

[0042] in, is the optimal solution of the internal variables of the power grid, is a constant determined by the heat network control center, argmin means finding the minimum value of the function and giving the objective function;

[0043] S32. Solve the optimization problem of the electric-thermal integrated energy system by using the Bendes decomposition;

[0044] Bendes decomposition has excellent performance in solving the structure of mathematical programming problems with complex variables, so Bendes decomposition is used for iterative solution. In each iteration of Bendes decomposition, the power grid control center solves the main power grid problem and sends the boundary state to the heating network control center; then, the heating network control center determines whether the heating network subproblem determined by the constraint formulas (E), (F), (C) and (D) is feasible. If feasible, an optimal cut will be generated; otherwise, a feasible cut will be generated; additional constraints are imposed on the boundary variables and added to the main power grid problem;

[0045] Corresponding to different The optimal cut is the cutting plane of the boundary variables. The optimal cut provides the lower bound of the optimal value of the heating network operation cost. The constraint formula (E) is expressed by the Lagrange multiplier vector λ * The hot network subproblem is expressed as follows:

[0046]

[0047]

[0048]

[0049] D H x H +F E h' E +FH h H =f H (F)

[0050] Therefore, for any heat source power output h that satisfies the constraint formula (F), E , we get:

[0051]

[0052] η H +G OC h E ≥g OC (10)

[0053] Formula (17) is the optimal cut obtained from formula (16), where G OC is the coefficient matrix of the optimal cut, g OC is the coefficient vector of the optimal cut, G OC =-(λ * ) T , is the optimal solution to the thermal network problem, η H is an estimate of the optimal cost of operating the heating network on the grid side;

[0054] S33. Implementing a modified Benders decomposition strategy to ensure feasibility of solving the problem of electric-thermal integrated energy system:

[0055] The heating network control center determines the feasible cut and sends the constraint conditions to the power grid control center; secondly, the power grid control center sets the iteration index k = 1 and the target value f (0) = +∞, use the current objective function and constraints to solve the modified master problem, and obtain the optimal solution and and will The data is sent to the heating network, and the heating network control center solves the following heating network sub-problems:

[0056]

[0057]

[0058] D H x H +F E h' E +F H h H =f H (F)

[0059]

[0060]

[0061] get The optimal Lagrange multiplier vector λ corresponding to the constraint (M) k , and send the corresponding parameters of the optimal cut to the power grid, and then make a convergence judgment. The power grid control center calculates the kth iteration objective function If the absolute value of the difference between two adjacent objective function values ​​during the iteration process is less than the pre-specified judgment threshold constant ε, the iteration process ends. Otherwise, the power grid control center will solve the following enhanced modified power grid main problem:

[0062]

[0063] sD E x E +B E h E ≤ b E (A)

[0064]

[0065]

[0066] in, are the estimated values ​​of the optimal cost of the heat network operation for the power grid internal variables, power grid boundary variables, heat network boundary variables and the power grid side after the k+1th iteration, and then update the iteration index, set k equal to k+1, and It is sent to the heating network, and the heating network control center solves equation (12) again, and the iterative process ends until the convergence condition is met;

[0067] S34. Apply the feasibility-guaranteed modified Bendes decomposition to the electric-thermal integrated energy system:

[0068] The proposed modified Bendes algorithm with feasibility guarantee is applied to the scheduling problem of a robust electric-thermal integrated energy system. The uncertainty caused by renewable energy sources (such as wind farms) is well considered. The output power of the wind farm should satisfy the following relationship:

[0069]

[0070]

[0071] in, Upload the predicted available wind power range to the grid control center for each wind farm. represents the lower bound of the available wind power predicted for the g-th wind farm at the t-th time, The grid control center determines and informs each wind farm of the upper limit of the wind power output range allowed. It represents the lower limit of wind power output allowed by the power grid to the g-th wind farm at the t-th time. It represents the upper limit of wind power output allowed by the power grid to the g-th wind farm at the t-th time. is the benchmark wind power output of the g-th wind farm at the t-th time, is the actual wind power output of the g-th wind farm at the t-th time. Formula (13) indicates that the output power interval allowed by the wind farm is a subset of the predicted power interval;

[0072] Since the deviation of the output wind power from the reference point is proportionally balanced by the thermal unit, p as a deterministic variable g,t and Use the uncertainty variables of both in the constraints and Substituting, we get formula (22):

[0073]

[0074] Where Kg is the coefficient that makes the sum of all heat units equal to 1, and p g,t is the benchmark output thermal power of the g-th wind farm at the t-th time, is the actual thermal power output of the g-th thermal power unit at the t-th time, The g'th wind farm outputs actual wind power at time t. For thermal power generators, For wind farm collection, is the time collection;

[0075] The cost of wind abandonment is further obtained:

[0076]

[0077] Therefore, a robust model can be used to characterize the uncertainty brought by wind farms:

[0078]

[0079]

[0080]

[0081]

[0082] in, is an uncertain set, is the lower bound of the uncertain set, The upper bound of an uncertain set is defined as is the wind curtailment cost function, σg The wind curtailment penalty factor, W, is a matrix determined by the parameters of the electric-thermal integrated energy system considering renewable energy;

[0083] The above robust model is equivalent to the following:

[0084]

[0085]

[0086]

[0087]

[0088] D H x H +F E h' E +F H h H =f H (F)

[0089] In the above formula and The specific expression is as follows:

[0090]

[0091] The matrix W + and W - It is composed of the positive and negative elements of W, that is, W + =max(W,0),W - =min(W,0), max(W,0) means taking out the elements greater than 0 in the matrix W, and min(W,0) means taking out the elements less than 0 in the matrix W;

[0092] This problem can be solved directly in a distributed manner using the proposed feasibility-guaranteed modified Bende's decomposition. Therefore, the feasibility-guaranteed modified Bende's decomposition is fully compatible with the robust model.

[0093] Furthermore, the process of step S4 is as follows:

[0094] Solve the distributed dispatch results of the electric and thermal integrated energy system, output the output of each device in the power grid and the heat network, the operating cost of the electric and thermal integrated energy system, the power purchase cost, and the amount of abandoned wind. The solution results are used to provide dispatch reference plans for the power grid control center and the heat network control center.

[0095] Compared with the prior art, the present invention has the following advantages and effects:

[0096] (1) Reliability: All feasible constraints are generated once before the iteration and sent from the heating network control center to the power grid control center, which ensures the feasibility of the heating network control center and the power grid control center from the beginning.

[0097] (2) Efficiency: An improved distributed solution, namely the modified Benders decomposition that ensures feasibility, is proposed and applied to the distributed scheduling of electric and thermal integrated energy, which significantly reduces the solution speed and the number of iterations.

[0098] (3) Scalability: The problem modeling in this method is all in matrix form, which can solve the scheduling problems of electric and thermal integrated energy systems of different scales.

[0099] (4) Real-time: The safety of the electric-thermal integrated energy system is strictly guaranteed by the new feasible cut before the loop iteration, so all scheduling schemes in the iteration process can be applied. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0101] Figure 1 It is a flow chart of a real-time dispatching method for an electric-thermal integrated energy system that ensures feasibility disclosed by the present invention;

[0102] Figure 2 is a diagram of an electric and thermal integrated energy system in an embodiment of the present invention;

[0103] Figure 3 It is a simulation system diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0104] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0105] Example

[0106] This embodiment discloses a real-time scheduling method for an electric-thermal integrated energy system that ensures feasibility. The real-time scheduling method includes the following steps:

[0107] S1. Input system parameters of the electric and thermal integrated energy system;

[0108] In this embodiment, the system parameters of the electric-thermal integrated energy system include the complex power of the injection node, the line flow, the node voltage, the wind abandonment penalty cost, the specific heat capacity of the water on the heat network side, the pipeline water supply flow rate, the node temperature of the water supply network and the return network, the inlet and outlet temperature of the water supply network and the return network, the power of the heat load, and the length of the pipeline. The input of the system parameters provides a data basis for modeling, and other data can be calculated through the existing parameters.

[0109] S2. Establish a real-time electric and thermal joint scheduling model;

[0110] In this embodiment, the formula of the real-time electric-heat joint scheduling model established in step S2 is expressed as follows:

[0111]

[0112] sD E x E +B E h E ≤ b E (A)

[0113] D H x H +F E h E +F H h H =f H (B)

[0114]

[0115]

[0116] Among them, x E represents the internal variables of the power grid, including the vector consisting of power generation, up / down rotation reserve capacity of the generator unit, bus phase angle and power generation of the wind farm, x H Represents the internal variables of the heat network, including the node temperature of the supply / return side of the pipeline node, the supply / return side temperature of the heat source, the measured temperature of the supply / return pipe inlet, the measured temperature of the supply / return pipe outlet, and the vector composed of the supply / return side temperature of the heat load, B E and D E are the coefficient matrices of boundary variables and state variables, b E is the right-hand term vector of the power grid inequality constraint, D H is the coefficient matrix of the internal variables of the heating network, F E is the coefficient matrix before the heat source output, F H is the coefficient matrix before the heat source heat output, f H is the right-hand term of the equality constraint, D H 、F E 、FH 、f H All are related to the parameters of the heating network and the power grid. x H and are the lower and upper bounds of the internal state variables of the heating network, h E is the heat source electrical output, h H Heat output for heat source, h H and is the lower and upper bounds of the heat output of the heat source. In the objective function formula (1), C H (h H ) is the cost of heat source generation within the heating network, expressed as:

[0117] C H (h H )=c H T h H (2)

[0118] Among them, c H T is the heat source heat cost coefficient vector, C in the objective function formula (1) E (x E ,h E ) is a quadratic function that combines the wind curtailment penalty cost, coupled heat sources (such as cogeneration units), and the power generation cost of thermal units, and is expressed as:

[0119] C E (x E ,h E )=h E T G E +c E T h E (3)

[0120] Among them, G is the coefficient matrix heat source electricity cost coefficient matrix, c E T is the heat source electricity cost coefficient vector.

[0121] S3, applying the modified Benders decomposition algorithm to solve the distributed real-time scheduling problem of electric and thermal integrated energy system;

[0122] In this embodiment, the process of step S3 is as follows:

[0123] S31. The original optimization dispatching problem of the electric-thermal integrated energy system is decomposed into the main problem of the power grid and the sub-problem of the thermal grid through the feasible cut generation method; the economic dispatching problem of the power grid is taken as the main problem of the power grid and modeled as follows:

[0124]

[0125] sD H x H +F E h E +F H h H =f H (B)

[0126] in, represents the thermal output of the fixed CHP unit, which is a boundary variable determined by the grid control center, h' E is the heat source electrical output h E Virtual copy of heat source electrical output h E As a decision variable in the operation of the heating network, inf means finding the lower bound of the function;

[0127] fixed The heating network operation problem is optimized as a heating network sub-problem:

[0128]

[0129]

[0130] D H x H +F E h' E +F H h H =f H (F)

[0131]

[0132]

[0133] Therefore, the feasible cut generated by the heating network control center will be added to the main power grid problem:

[0134]

[0135] Among them, h' H is the heat output of the heat source h H Virtual copy of heat source heat output h H As a decision variable in power grid operation, are the first and second coefficient matrices of the feasible cut, g FC is the coefficient vector of the feasible cut, and its expression is as follows:

[0136]

[0137] Where I is the identity matrix, e H is a unit vector;

[0138] The modified main grid problem is as follows:

[0139]

[0140]

[0141]

[0142] in, is the optimal solution of the internal variables of the power grid, is a constant determined by the heat network control center, argmin means finding the minimum value of the function and giving the objective function;

[0143] S32. Solve the optimization problem of the electric-thermal integrated energy system by using the Bendes decomposition;

[0144] In each iteration of the Bendes decomposition, the power grid control center solves the power grid main problem and sends the boundary state to the heating network control center; then, the heating network control center determines whether the heating network subproblem determined by the constraint formulas (E), (F), (C) and (D) is feasible. If feasible, an optimal cut will be generated; otherwise, a feasible cut will be generated; additional constraints are imposed on the boundary variables and added to the power grid main problem;

[0145] Corresponding to different The optimal cut is the cutting plane of the boundary variables. The optimal cut provides the lower bound of the optimal value of the heating network operation cost. The constraint formula (E) is expressed by the Lagrange multiplier vector λ * The hot network subproblem is expressed as follows:

[0146]

[0147]

[0148]

[0149] D H x H +F E h' E +F H h H =f H (F)

[0150] Therefore, for any heat source power output h that satisfies the constraint formula (F), E , we get:

[0151]

[0152] η H +G OC hE ≥g OC (10)

[0153] Formula (17) is the optimal cut obtained from formula (16), where G OC is the coefficient matrix of the optimal cut, g OC is the coefficient vector of the optimal cut, G OC =-(λ * ) T , is the optimal solution to the thermal network problem, η H is an estimate of the optimal cost of operating the heating network on the grid side;

[0154] S33. Implementing a modified Benders decomposition strategy to ensure feasibility of solving the problem of electric-thermal integrated energy system:

[0155] First, the heating network control center determines the feasible cut and sends the constraint conditions to the power grid control center; secondly, the power grid control center sets the iteration index k = 1 and the target value f (0) = +∞, use the current objective function and constraints to solve the modified master problem, and obtain the optimal solution and and will The data is sent to the heating network, and the heating network control center solves the following heating network sub-problems:

[0156]

[0157]

[0158] D H x H +F E h' E +F H h H =f H (F)

[0159]

[0160]

[0161] get The optimal Lagrange multiplier vector λ corresponding to the constraint (M) k , and send the corresponding parameters of the optimal cut to the power grid, and then make a convergence judgment. The power grid control center calculates the kth iteration objective function If the absolute value of the difference between two adjacent objective function values ​​during the iteration process is less than the pre-specified judgment threshold constant ε, the iteration process ends. Otherwise, the power grid control center will solve the following enhanced modified power grid main problem:

[0162]

[0163] sD E x E +B E h E ≤ b E (A)

[0164]

[0165]

[0166] in, are the estimated values ​​of the optimal cost of the heat network operation for the power grid internal variables, power grid boundary variables, heat network boundary variables and the power grid side after the k+1th iteration, and then update the iteration index, set k equal to k+1, and It is sent to the heating network, and the heating network control center solves equation (12) again, and the iterative process ends until the convergence condition is met;

[0167] S34. Apply the modified Bendes decomposition to ensure feasibility to the electric-thermal integrated energy system;

[0168] The proposed modified Bendes algorithm with feasibility guarantee is applied to the dispatch of a robust electric-thermal integrated energy system, in which the uncertainty caused by renewable energy sources (such as wind farms) is well considered. The output power of the wind farm should satisfy the following relationship:

[0169]

[0170]

[0171] in, Upload the predicted available wind power range to the grid control center for each wind farm. represents the lower bound of the available wind power predicted for the g-th wind farm at the t-th time, The grid control center determines and informs each wind farm of the upper limit of the wind power output range allowed. It represents the lower limit of wind power output allowed by the power grid to the g-th wind farm at the t-th time. It represents the upper limit of wind power output allowed by the power grid to the g-th wind farm at the t-th time. is the benchmark wind power output of the g-th wind farm at the t-th time, is the actual wind power output of the g-th wind farm at the t-th time. Formula (13) indicates that the output power interval allowed by the wind farm is a subset of the predicted power interval;

[0172] Since the deviation of the output wind power from the reference point is proportionally balanced by the thermal unit, p as a deterministic variable g,t and Use the uncertainty variables of both in the constraints and Substituting, we get formula (22):

[0173]

[0174] Where Kg is the coefficient that makes the sum of all heat units equal to 1, and p g,t is the benchmark output thermal power of the g-th wind farm at the t-th time, is the actual thermal power output of the g-th thermal power unit at the t-th time, The g'th wind farm outputs actual wind power at time t. For thermal power generators, For wind farm collection, is the time collection;

[0175] The cost of wind abandonment is further obtained:

[0176]

[0177] The robust model is:

[0178]

[0179]

[0180]

[0181]

[0182] in, is an uncertain set, is the lower bound of the uncertain set, The upper bound of an uncertain set is defined as is the wind curtailment cost function, σ g The wind curtailment penalty factor, W, is a matrix determined by system parameters;

[0183] The above robust model is equivalent to the following:

[0184]

[0185]

[0186]

[0187]

[0188] D H x H +F E h' E +F H h H =f H (F)

[0189] In the above formula and The specific expression is as follows:

[0190]

[0191] The matrix W + and W - It is composed of the positive and negative elements of W, that is, W + =max(W,0),W - =min(W,0), max(W,0) means taking out the elements greater than 0 in the matrix W, and min(W,0) means taking out the elements less than 0 in the matrix W;

[0192] This problem can be solved directly in a distributed manner using the proposed feasibility-guaranteed modified Bende's decomposition. Therefore, the feasibility-guaranteed modified Bende's decomposition is fully compatible with the robust model.

[0193] S4. Output the distributed real-time dispatch results of the electric-thermal integrated energy system with feasibility guarantee.

[0194] Solve the distributed dispatch results of the electric-thermal integrated energy system, and output the output of each device in the power grid and heat network, the operating cost of the electric-thermal integrated energy system, the electricity purchase cost, and the amount of abandoned wind.

[0195] The simulation system consists of Figure 3 As shown, it is a small electric-thermal integrated energy system consisting of a 6-node power grid and a 6-node heat network, in which the thermoelectric coupling device electric pump and CHP are at node 6 of the power grid, and the wind farm is also at node 6 of the power grid.

[0196] Table 1 is the simulation result. According to Table 1, compared with individual dispatch, centralized dispatch reduces the wind volume by 74.5%, thereby reducing the total cost by 15.7%. The comprehensive dispatch efficiency of the wind farm reaches 90.45%.

[0197] Table 1. Economic performance of different scheduling strategies

[0198] Isolated Scheduling Centralized dispatch Proposed method <![CDATA[Wind curtailment penalty cost (10 4 $)]]> 1.5870 0.3875 0.3875 <![CDATA[Total scheduling cost (10 4 $)]]> 1.4225 6.1875 6.1875

[0199] Centralized scheduling and a real-time scheduling method for an electric-thermal integrated energy system disclosed in Example 1 that ensures feasibility ultimately obtain an accurate optimal solution to the integrated scheduling model, but the distributed solution of this method can protect the privacy of the power grid and the heat network to a great extent.

[0200] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A real-time dispatching method for an electric-thermal integrated energy system to ensure feasibility, characterized in that: The real-time scheduling method comprises the following steps: S1. Input system parameters of the electric and thermal integrated energy system; S2, establish a real-time electric-heat joint scheduling model; the formula of the real-time electric-heat joint scheduling model established in step S2 is as follows: (1) s.t. (A) (B) (C) (D) in, x E Represents the internal variables of the power grid, including the vector consisting of power generation, up / down spin reserve capacity of the generator unit, bus phase angle and power generation of the wind farm, x H Represents the internal variables of the heat network, including the node temperature of the supply / return side of the pipeline node, the supply / return side temperature of the heat source, the measured temperature of the supply / return pipe inlet, the measured temperature of the supply / return pipe outlet, and the vector composed of the supply / return side temperature of the heat load. B E and D E are the coefficient matrices of boundary variables and state variables, respectively, b E is the right-hand term vector of the power grid inequality constraint, D H is the coefficient matrix of the internal variables of the heating network, F E is the coefficient matrix before the heat source electric output, F H is the coefficient matrix before the heat source heat output, f H is the right-hand term of the equality constraint, D H , F E , F H 、f H All are related to the parameters of the heating network and the power grid. and are the lower and upper bounds of the internal state variables of the heating network, h E For heat source electricity output, h H Heat output for heat source, and are the lower and upper bounds of the heat output of the heat source, and the objective function formula (1) C H ( h H ) is the cost of heat source generation within the heating network, expressed as: C H ( h H )= c H T h H (2) in, c H T is the heat source heat cost coefficient vector, and the objective function formula (1) C E ( x E , h E ) is a quadratic function that combines the wind curtailment penalty cost, coupled heat source, and the power generation cost of thermal units, expressed as: C E ( x E , h E )= h E T G h E + c E T h E (3) Among them, G is the coefficient matrix heat source electricity cost coefficient matrix, c E T is the heat source electricity cost coefficient vector; S3, applying the modified Benders decomposition algorithm to solve the distributed real-time scheduling problem of electric and thermal integrated energy system; S4. Output the distributed real-time dispatch results of the electric-thermal integrated energy system with feasibility guarantee.

2. A real-time dispatching method for an electric-thermal integrated energy system with guaranteed feasibility according to claim 1, characterized in that: The system parameters of the electric-thermal integrated energy system include the complex power injected into the node, the line flow, the node voltage, the wind abandonment penalty cost, the specific heat capacity of the water on the heat network side, the pipeline water supply flow rate, the node temperature of the water supply network and the return network, the inlet and outlet temperatures of the water supply network and the return network, the power of the heat load, and the pipeline length.

3. A real-time dispatching method for an electric-thermal integrated energy system with guaranteed feasibility according to claim 1, characterized in that: The process of step S4 is as follows: Solve the distributed dispatch results of the electric-thermal integrated energy system, and output the output of each device in the power grid and heat network, the operating cost of the electric-thermal integrated energy system, the electricity purchase cost, and the amount of abandoned wind.

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