Unit commitment accelerated optimization method and system for eliminating redundant constraint based on machine learning, storage medium, electronic device, chip, program product, and program

Abstract

Disclosed in the present invention are a unit commitment accelerated optimization method and system for eliminating a redundant constraint based on machine learning. The method comprises: converting an original unit commitment problem into a line power flow constraint-free problem, and using variables 0-1 to control the establishment of the line power flow constraint, so as to eliminate dense and complex security constraints. Moreover, the change frequency range of each unit in each time period is learned by means of a machine learning method, and a redundant minimum continuous on / off time constraint identification rule is formed, so that a redundant minimum continuous on / off time constraint is eliminated, thereby reducing the number of problem constraints and the problem scale, and improving the unit commitment optimization efficiency.

Description

Method and system for accelerating optimization of unit combination based on machine learning to eliminate redundant constraints, storage medium, electronic device, chip, program product, program

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the Chinese patent application with application number 202410314469.6, application date March 19, 2024, and invention name “Method and system for accelerating optimization of unit combination based on machine learning to eliminate redundant constraints”, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference. Technical Field

[0003] The present invention belongs to the field of machine learning and relates to a method and system for accelerating the optimization of unit combination based on machine learning to eliminate redundant constraints, a storage medium, an electronic device, a chip, a program product, and a program. Background Art

[0004] Complex and dense safety constraints and minimum consecutive start-stop time constraints are key factors affecting the efficiency of large-scale unit commitment solutions. In practice, some of these constraints are invalid or redundant. While removing them from the original model does not affect the overall solution domain, adding them makes the problem difficult to solve. Reducing safety power flow constraints and minimum consecutive start-stop time constraints can significantly improve the overall model solution efficiency. However, accurately identifying the characteristics of these constraints and eliminating redundant constraints are challenging.

[0005] Summary of the Invention

[0006] The purpose of the present invention is to solve the problem in the prior art that it is impossible to accurately identify the characteristics of safety flow constraints and minimum continuous start-stop time constraints and to eliminate redundant constraints, and to provide a unit combination accelerated optimization method and system based on machine learning to eliminate redundant constraints, storage medium, electronic equipment, chip, program product, and program.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The unit commitment optimization method based on machine learning to eliminate redundant constraints includes:

[0009] Obtain the semi-continuous constraints and line power flow constraints of the upper and lower limits of the SCUC model unit output;

[0010] If the semi-continuous constraints of the upper and lower limits of unit output and the line power flow constraints in the SCUC model are both met, the upper and lower limits of the two constraints are very close, and then a subproblem containing only 0-1 variables is introduced and solved;

[0011] The solution of the subproblem containing only 0-1 variables is added to the objective function of the original unit combination problem in a penalty-based manner, so that the solution of the subproblem is as close as possible to the optimal solution of the original unit combination problem;

[0012] Based on the solutions to the subproblems and the optimal solution to the original unit combination problem, the unit state variable offsets for each unit in each time period are obtained. The characteristic demand D of the original unit combination problem is changed to obtain multiple optimal solutions. The relationship between demand D and offset is obtained and learned into a predictive regression model.

[0013] Given a new unit commitment problem, the learning-based prediction model predicts the unit state variable offset of each unit in each time period of the new unit commitment problem to be solved, identifies and removes redundant constraints in the minimum continuous start-stop time constraint, and realizes accelerated optimization of unit commitment.

[0014] A further improvement of the present invention is:

[0015] Furthermore, the semi-continuous constraints of the upper and lower limits of the SCUC model unit output are as follows:

[0016] Among them, I i,t is the start and stop state variable of the unit, and are the minimum and maximum output of the unit respectively.

[0017] Furthermore, the line power flow constraints are as follows:

[0018] Among them, GSF i,l is the transfer distribution factor, which represents the proportion of power transferred from unit i to line l.

[0019] Furthermore, a subproblem containing only 0-1 variables is introduced and solved. Specifically, if the line power flow constraint holds, the upper and lower bounds of the line power flow constraint are very close to the upper and lower bounds of the semi-continuous constraint transformation form. A subproblem containing only 0-1 variables is constructed to solve the value of the corresponding 0-1 variable. The goal of this 0-1 variable subproblem is to minimize the distance between the upper and lower bounds of the semi-continuous constraint and the upper and lower bounds of the line power flow constraint, and the closer the upper and lower bounds of the semi-continuous constraint are, the better.

[0020] The semi-continuous constraint is transformed into the same form as the line power flow constraint, as shown in formula (3):

[0021] Construct a subproblem that only contains 0-1 variables:

[0022] Among them, the constraint item is the unit commitment problem containing only Ii,t 、W i,t and Y i,t The constraint H(I)≥0; W i,t and Y i,t are the decision variables for whether to start or shut down the unit;

[0023] The above objective function is transformed into a function containing only I i,t The linear expression L(I i,t ), solve such sub-problems and obtain the solution S of 0-1 variables;

[0024] Furthermore, the solution of the subproblem containing only 0-1 variables is added to the objective function of the original unit commitment problem in a penalty-based manner, so that the solution of the subproblem is as close as possible to the optimal solution of the unit commitment problem. Specifically:

[0025] Among them, the constraints are G(I,P,W)≥0, H(I)≥0, which expresses the unit combination problem as the unit start-stop decision variable I, unit output variable P and other variables w; G(I,P,W) is a constraint set without line flow constraints; H(I)≥0 is the unit combination problem containing only I i,t , W i,t , Y i,t 0-1 variable constraints.

[0026] Furthermore, the minimum continuous start-stop time constraint is as follows:

[0027] Furthermore, based on the solution of the sub-problem and the optimal solution of the original unit combination problem, the unit state variable offset of each unit in each time period is obtained; the characteristic demand D of the original unit combination problem is changed, multiple optimal solutions are obtained, the relationship between demand D and offset is obtained, and a prediction regression model is learned. Specifically, the demand D is changed, the unit combination problem is solved, and several optimal solutions I are obtained. * , calculate each I * The distance from the solution S of the subproblem containing only 0-1 variables; the deviation degree of each unit in each time period is counted to form the relationship between the demand D and the deviation vector v; the SCUC model determined by the demand d∈D has a functional relationship Make

[0028] Among them, v j is each component of the deviation degree v of each unit in each time period.

[0029] Furthermore, redundant constraints in the minimum continuous start-stop time constraint are identified and removed. Specifically, given a new unit commitment problem, the learning-based prediction model predicts the unit state variable offset of each unit in each period of the new unit commitment problem to be solved. For the minimum continuous start-stop time constraint,

[0030] or

[0031] Make a judgment, g(v i ) represents the functional expression of the offset. If formula (10) and formula (11) still hold, the original constraint is redundant and the constraint should be removed.

[0032] The unit commitment optimization system based on machine learning to eliminate redundant constraints includes:

[0033] A first acquisition module, which acquires semi-continuous constraints and line power flow constraints of upper and lower limits of the SCUC model unit output;

[0034] An introduction module, wherein if the semi-continuous constraints of the upper and lower limits of the unit output and the line power flow constraints in the SCUC model are both met, the upper and lower limits of the two constraints are very close, and then a subproblem containing only 0-1 variables is introduced and solved;

[0035] A second acquisition module, which adds the solution of the subproblem containing only 0-1 variables to the objective function of the original unit combination problem in a penalty-based manner, so that the solution of the subproblem is as close as possible to the optimal solution of the original unit combination problem;

[0036] An offset data acquisition module, which obtains the offset of the unit state variables of each unit in each time period based on the solution of the sub-problem and the optimal solution of the original unit combination problem. The module changes the characteristic demand D of the original unit combination problem, obtains multiple optimal solutions, obtains the relationship between the demand D and the offset, and learns it into a predictive regression model.

[0037] A judgment module, wherein the judgment module is given a new unit commitment problem, and predicts the unit state variable offset of each unit in each time period of the new unit commitment problem to be solved based on the learned prediction model, identifies and removes redundant constraints in the minimum continuous start-stop time constraints, so as to achieve accelerated optimization of the unit commitment.

[0038] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0039] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0040] A chip comprising:

[0041] memory for storing computer programs;

[0042] The processor is connected to the memory and is used to call and run the computer program from the memory so that the device equipped with the chip executes the steps of the above method.

[0043] A computer program product comprises computer program instructions, wherein the computer program instructions enable a computer to execute the steps of the above method.

[0044] A computer program, when running on a computer, causes the computer to execute the steps of the above method.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This method transforms the unit commitment problem into one without line flow constraints, using 0-1 variables to control the compliance of line flow constraints, thereby eliminating dense and complex safety constraints. Machine learning methods are then used to learn the range of changes in the number of times each unit operates over all time periods, forming redundant minimum consecutive start-stop time constraint identification rules. This eliminates redundant minimum consecutive start-stop time constraints, reducing the number of constraints and the problem size, and improving the efficiency of unit commitment optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0048] FIG1 is a flow chart of a method for accelerating optimization of unit commitment based on machine learning to eliminate redundant constraints according to the present invention;

[0049] FIG2 is a schematic structural diagram of a unit commitment accelerated optimization system for eliminating redundant constraints based on machine learning according to the present invention;

[0050] FIG3 is a schematic diagram of the unit commitment accelerated optimization algorithm based on machine learning to eliminate redundant constraints according to the present invention;

[0051] FIG4 is a schematic structural diagram of a chip 400 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0054] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0055] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0056] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0057] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0058] The present invention is described in further detail below with reference to the accompanying drawings:

[0059] Referring to FIG1 , the present invention discloses a method for accelerating optimization of unit commitment based on machine learning to eliminate redundant constraints, comprising:

[0060] S101, obtaining the semi-continuous constraints of the upper and lower limits of the unit output and the line power flow constraints of the SCUC model;

[0061] The semi-continuous constraints on the upper and lower limits of the SCUC model unit output are as follows:

[0062] Among them, I i,t is the start and stop state variable of the unit, and are the minimum and maximum output of the unit respectively.

[0063] Line power flow constraints, specifically:

[0064] Among them, GSF i,l is the transfer distribution factor, which represents the proportion of power transferred from unit i to line l.

[0065] S102: If the semi-continuous constraints on the upper and lower limits of unit output and the line power flow constraints in the SCUC model are both met, then the upper and lower limits of the two constraints are very close. Then, a subproblem containing only 0-1 variables is introduced and solved;

[0066] If the line power flow constraint holds, the upper and lower limits of the line power flow constraint are very close to the upper and lower limits of the semi-continuous constraint transformation form. A subproblem containing only 0-1 variables is constructed to solve the value of the corresponding 0-1 variable. The goal of this 0-1 variable subproblem is to minimize the distance between the upper and lower limits of the semi-continuous constraint and the upper and lower limits of the line power flow constraint. The closer the upper and lower limits of the semi-continuous constraint are, the better.

[0067] The semi-continuous constraint is transformed into the same form as the line power flow constraint, as shown in formula (3):

[0068] Construct a subproblem that only contains 0-1 variables:

[0069] Among them, the constraint item is the unit commitment problem containing only I i,t 、W i,t and Y i,t The constraint H(I)≥0; W i,t and Y i,t are the decision variables for whether to start or shut down the unit;

[0070] The above objective function is transformed into a function containing only I i,t The linear expression L(I i,t), solve such sub-problems and obtain the solution S of 0-1 variables;

[0071] S103, based on a penalty method, the solution of the subproblem containing only 0-1 variables is added to the objective function of the original unit combination problem, so that the solution of the subproblem is as close as possible to the optimal solution of the original unit combination problem, specifically:

[0072] Among them, the constraints are G(I,P,W)≥0, H(I)≥0, which expresses the unit combination problem as the unit start-stop decision variable I, unit output variable P and other variables w; G(I,P,W) is a constraint set without line flow constraints; H(I)≥0 is the unit combination problem containing only I i,t , W i,t , Y i,t 0-1 variable constraints.

[0073] S104: Based on the solutions to the subproblems and the optimal solution to the original unit combination problem, the unit state variable offsets for each unit in each time period are obtained. The characteristic demand D of the original unit combination problem is changed to obtain multiple optimal solutions. The relationship between the demand D and the offset is obtained and learned into a predictive regression model.

[0074] Change the demand D, solve the unit combination problem, and obtain several optimal solutions I * , calculate each I * The distance from the solution S containing only 0-1 variables; statistics on the offset degree of each unit in each time period, forming the relationship between demand D and offset vector v; the SCUC model determined by demand d∈D has a functional relationship Make

[0075] Among them, v j is each component of the deviation degree v of each unit in each time period.

[0076] S105: Given a new unit commitment problem, the learned prediction model is used to predict the unit state variable offsets of each unit in each time period of the new unit commitment problem to be solved, and redundant constraints in the minimum continuous start-stop time constraints are identified and removed to achieve accelerated optimization of the unit commitment.

[0077] The minimum continuous start-stop time constraint is as follows:

[0078] Determine whether it is the minimum continuous start-stop time constraint, specifically: for the minimum continuous start-stop time constraint

[0079] or

[0080] Make a judgment, g(v i ) represents the functional expression of the offset. If formula (10) and formula (11) still hold, the original constraint is redundant and the constraint should be removed.

[0081] Referring to FIG2 , the present invention discloses a unit commitment accelerated optimization system for eliminating redundant constraints based on machine learning, comprising:

[0082] A first acquisition module, which acquires semi-continuous constraints and line power flow constraints of upper and lower limits of the SCUC model unit output;

[0083] An introduction module, wherein if the semi-continuous constraints of the upper and lower limits of the unit output and the line power flow constraints in the SCUC model are both met, the upper and lower limits of the two constraints are very close, and then a subproblem containing only 0-1 variables is introduced and solved;

[0084] A second acquisition module, which adds the solution of the subproblem containing only 0-1 variables to the objective function of the original unit combination problem in a penalty-based manner, so that the solution of the subproblem is as close as possible to the optimal solution of the original unit combination problem;

[0085] An offset data acquisition module, which obtains the offset of the unit state variables of each unit in each time period based on the solution of the sub-problem and the optimal solution of the original unit combination problem. The module changes the characteristic demand D of the original unit combination problem, obtains multiple optimal solutions, obtains the relationship between the demand D and the offset, and learns it into a predictive regression model.

[0086] A judgment module, wherein the judgment module is given a new unit commitment problem, and predicts the unit state variable offset of each unit in each time period of the new unit commitment problem to be solved based on the learned prediction model, identifies and removes redundant constraints in the minimum continuous start-stop time constraints, so as to achieve accelerated optimization of the unit commitment.

[0087] Example:

[0088] 3 , the present invention discloses an accelerated optimization algorithm for unit commitment based on machine learning to eliminate redundant constraints, including removing line flow constraints and removing redundant minimum continuous start-stop time constraints.

[0089] The first part converts the line power flow constraints into 0-1 variable values ​​by representing them as 0-1 variables. This information is then added to the objective function of the original problem as a penalty, allowing the solution to be restored to the original optimal solution without straying too far from the 0-1 solution. The second part uses the solution S from the first part as a benchmark. Machine learning methods are used to learn the offsets of the variable values ​​of each unit in S at 96 moments from the corresponding optimal solution. This offset is used to determine which minimum continuous start-stop time constraints are redundant and do not contribute significantly to the solution domain. Once identified, such constraints can be removed, further reducing the problem size and accelerating the solution.

[0090] Based on the connection between the safety constraints in hydropower and thermal power generation and the upper and lower constraints on unit output, the first phase uses traditional optimization methods to transform the unit combination problem into a problem without line flow constraints. 0-1 variables are used to control the compliance of line flow constraints, thereby eliminating dense and complex safety constraints. Based on this, the second phase uses machine learning methods to learn the range of the number of changes for each unit across all time periods, forming a rule to identify redundant minimum consecutive start and stop time constraints, thereby eliminating redundant minimum consecutive start and stop time constraints.

[0091] (1) Reconstructing line power flow constraints using 0-1 variables

[0092] Since the SCUC model has semi-continuous constraints such as upper and lower limits of unit output, as shown in formula (1):

[0093] Among them, I i,t is the start and stop state variable of the unit, and are the minimum and maximum output of the unit respectively.

[0094] The line power flow constraint is as shown in formula (2):

[0095] Among them, GSF i,l is the transfer distribution factor, which represents the proportion of power transferred from unit i to line l.

[0096] If the semi-continuous constraint is established, the semi-continuous constraint is converted into the same form as the line power flow constraint, as shown in formula (3):

[0097] If the line flow constraint holds, then the upper and lower bounds of the line flow constraint are very close to those of the semicontinuous constraint. Therefore, the overall problem does not need to incorporate dense and complex line flow constraints. Instead, a subproblem consisting only of 0-1 variables is constructed to solve for the corresponding 0-1 variable values. The objective of this subproblem is to minimize the distance between the upper and lower bounds of the semicontinuous constraint and the line flow constraint, with the closer the upper and lower bounds of the semicontinuous constraint are, the better. In other words, the line flow constraint is satisfied by the values ​​of these 0-1 variables. Furthermore, the solutions to these 0-1 variables can be added to the objective function of the original problem through a penalty. This ensures that the solutions to the subproblems are as close as possible to the true optimal solution, thereby fixing the variables during the solution process, accelerating solution efficiency, and ensuring that the line flow constraint holds.

[0098] The steps for constructing the subproblem are as follows:

[0099] Step 1 (constructing small-scale sub-problems):

[0100] The objective function of the unit commitment problem is:

[0101] Constraint: The unit commitment problem only contains I i,t , W i,t , Y i,t The constraint H(I)≥0; where W i,t and Y i,t are the decision variables for whether the unit should be turned on or off.

[0102] The above objective function is transformed into a function containing only I i,t The linear expression L(I i,t ), solve such subproblems and obtain the solution S of 0-1 variables;

[0103] Converted to contain only I i,t The linear expression L(I i,t ), solve such subproblems and obtain the solution S of 0-1 variables.

[0104] Step 2 (Solve the original problem with the penalty term added)

[0105] The solution of the 0-1 variable subproblem is added to the objective function of the unit commitment problem in a penalty-based manner, specifically:

[0106] Constraints: G(I,P,W)≥0, H(I)≥0, other constraints

[0107] The unit combination problem is expressed as the unit start-stop decision variable I, the unit output variable P and other variables w. Among them, G(I,P,W) is the constraint set without line flow constraints. H(I)≥0 means that the original problem only contains Ii,t , W i,t , Y i,t Constraints on 0-1 variables. This penalizes the distance between the solution to the 0-1 variable and the subproblem solution, effectively restoring the subproblem solution to a feasible solution while preserving the possibility of the line power flow constraints. This is because both the subproblem and the original problem contain a large number of constraints related to 0-1 variables, and the feasible solutions to the original problem and the subproblem are not much different. Solving this modified problem can achieve an accelerated solution.

[0108] (2) Using machine learning methods to predict the offset of the minimum continuous start-stop time

[0109] The above-mentioned method of solving subproblems in stages solves the problem that dense and complex line flow constraints affect the solution efficiency. There are many ways to express the minimum continuous start-stop time constraint. According to this solution, its form is as shown in formula (7) and formula (8):

[0110] By obtaining the 0-1 variable solution S formed by some constraints in the unit combination problem. This solution S is still some distance away from the true optimal solution, and this solution S is used to measure the changes in the true optimal solution. That is, observe the offset between the true optimal solution and the solution S. Moreover, it is only necessary to observe the offset of each unit in the 96 time periods in this offset, and use the deviation between the information of the true solution and this solution S to predict the offset of the unit state variables of each unit in the 96 time periods. The solution S is unchanged and can be solved offline as a benchmark for measurement. After predicting the offset of the unit state variables of each unit in the 96 time periods, subtract or add the offset from the value of the constraint expression of the minimum continuous start-stop time constraint of the 0-1 variable subproblem to see whether it exceeds the right-hand side of the formula. If it does not exceed, it means that the formula does not affect the overall optimality and is a redundant constraint, which can be removed. The steps of the algorithm are as follows:

[0111] Step 1: Construct a subproblem formed by the constraints of the original problem containing only 0-1 variables, as shown in (1). The modified form of the line flow constraint is used as the objective function and the solution S is obtained.

[0112] Step 2: Change the requirement D and obtain multiple optimal solutions I by solving the original problem * , calculate each I * The distance from S. For example, I *=(0,1,1,0), S =(0,0,0,0), and the distance s is calculated as 2. The dimension of the distance vector here is the number of units. The offset (distance) of each unit at its own 96 moments is calculated, forming a relationship between D and the offset vector v, and learning a prediction model. The prediction model can be used for prediction using a regression model.

[0113] For the SCUC model determined by the demand parameter d∈D, there is such a functional relationship Make

[0114] Among them, v j For each component of the deviation degree v of each unit at its own 96 time instants.

[0115] Given a new unit commitment problem, based on the learned prediction model, the unit state variable offset of each unit in each period of the new unit commitment problem to be solved is predicted.

[0116] For the minimum continuous start-stop time constraint

[0117] or

[0118] Make a judgment, g(v i ) represents the functional expression of the offset. If formula (10) and formula (11) still hold, the original constraint is redundant, and the constraint is removed to achieve the effect of reducing the problem scale and improving the solution efficiency.

[0119] Performance testing:

[0120] An adjustment example based on standard examples (IEEE 30, IEEE 118, WP 2383) is adopted, which contains many complex power flow constraints.

[0121] Table 1 Comparison of problem scale

[0122] Table 2 Comparison of SCIP solution efficiency

[0123] From the above analysis, we can see that by reducing the dense, complex, and numerous line flow constraints and minimum continuous start-stop time constraints, the present invention can significantly reduce the scale of the problem, thereby accelerating the solution of the unit combination problem and significantly improving the solution efficiency, especially in large-scale models. The solution efficiency is basically improved by about 1.7 to 2 times. The number of line flow constraint violations is 0, which shows the effectiveness and reliability of the present invention and does not significantly change the solution domain. This shows the innovation and effectiveness of the present invention, and it can also have a good acceleration effect on the slower SCIP solution.

[0124] An embodiment of the present invention provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned device embodiments are implemented.

[0125] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0126] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0127] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0128] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0129] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0130] An embodiment of the present invention provides a chip. Figure 4 is a schematic structural diagram of a chip provided by an embodiment of the present invention. The chip 400 shown in Figure 4 includes a processor 410, which can call and execute a computer program from a memory to implement the method in the embodiment of the present invention.

[0131] In some embodiments, as shown in FIG4 , the chip 400 may further include a memory 420 , wherein the processor 410 may call and execute a computer program from the memory 420 to implement the method in the embodiment of the present invention.

[0132] The memory 420 may be a separate device independent of the processor 410 , or may be integrated into the processor 410 .

[0133] In some embodiments, the chip 400 may further include an input interface 430. The processor 410 may control the input interface 430 to communicate with other devices or chips, and specifically, may obtain information or data sent by other devices or chips.

[0134] In some embodiments, the chip 400 may further include an output interface 440. The processor 410 may control the output interface 440 to communicate with other devices or chips, and specifically, may output information or data to other devices or chips.

[0135] In some embodiments, the chip can be applied to the electronic device in the embodiments of the present invention, and the chip can implement the corresponding processes implemented by the electronic device in the various methods of the embodiments of the present invention. For the sake of brevity, they will not be repeated here.

[0136] It should be understood that the chip mentioned in the embodiment of the present invention can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0137] An embodiment of the present invention further provides a computer program product, including computer program instructions.

[0138] Optionally, the computer program product can be applied to the electronic device in the embodiment of the present invention, and the computer program instructions enable the computer to execute the corresponding processes implemented by the electronic device in the various methods of the embodiment of the present invention. For the sake of brevity, they are not repeated here.

[0139] An embodiment of the present invention also provides a computer program.

[0140] Optionally, the computer program can be applied to the electronic device in the embodiment of the present invention. When the computer program runs on the computer, the computer executes the corresponding processes implemented by the electronic device in the various methods of the embodiment of the present invention. For the sake of brevity, they are not repeated here.

[0141] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A unit commitment optimization method based on machine learning to eliminate redundant constraints, including: Obtain the semi-continuous constraints and line power flow constraints of the upper and lower limits of the SCUC model unit output; If the semi-continuous constraints of the upper and lower limits of unit output and the line power flow constraints in the SCUC model are both met, the upper and lower limits of the two constraints are very close, and then a subproblem containing only 0-1 variables is introduced and solved; The solution of the subproblem containing only 0-1 variables is added to the objective function of the original unit combination problem in a penalty-based manner, so that the solution of the subproblem is as close as possible to the optimal solution of the original unit combination problem; Based on the solutions to the subproblems and the optimal solution to the original unit combination problem, the unit state variable offsets for each unit in each time period are obtained. The characteristic demand D of the original unit combination problem is changed to obtain multiple optimal solutions. The relationship between demand D and offset is obtained and learned into a predictive regression model. Given a new unit commitment problem, the learning-based prediction model predicts the unit state variable offset of each unit in each time period of the new unit commitment problem to be solved, identifies and removes redundant constraints in the minimum continuous start-stop time constraint, and realizes accelerated optimization of unit commitment.

2. The unit commitment accelerated optimization method based on machine learning to eliminate redundant constraints according to claim 1, wherein: The semi-continuous constraints of the upper and lower limits of the SCUC model unit output are specifically: Among them, I i,t is the start-stop state variable of the unit, P i L and P i U are the minimum and maximum output of the unit respectively.

3. The unit commitment accelerated optimization method based on machine learning to eliminate redundant constraints according to claim 2, wherein: The line power flow constraints are specifically: Among them, GSF i,l is the transfer distribution factor, which represents the proportion of power transferred from unit i to line l.

4. The method for accelerating unit commitment optimization based on machine learning to eliminate redundant constraints according to claim 3, wherein: The subproblem containing only 0-1 variables is introduced and solved as follows: if the line power flow constraint is satisfied, the upper and lower limits of the line power flow constraint are very close to the upper and lower limits of the semi-continuous constraint transformation form; a subproblem containing only 0-1 variables is constructed to solve the value of the corresponding 0-1 variable, and the goal of the 0-1 variable subproblem is to minimize the distance between the upper and lower limits of the semi-continuous constraint and the upper and lower limits of the line power flow constraint, and the closer the upper and lower limits of the semi-continuous constraint are, the better. The semi-continuous constraint is transformed into the same form as the line power flow constraint, as shown in formula (3): Construct a subproblem that only contains 0-1 variables: Among them, the constraint item is the unit commitment problem containing only I i,t 、W i,t and Y i,t The constraint H(I)≥0; W i,t and Y i,t are the decision variables for whether to start or shut down the unit; The above objective function is transformed into a function containing only I i,t The linear expression L(I i,t ), solve such sub-problems and obtain the solution S of 0-1 variables; 5. The unit commitment accelerated optimization method based on machine learning to eliminate redundant constraints according to claim 4, wherein: The penalty-based approach adds the solution of the subproblem containing only 0-1 variables to the objective function of the original unit commitment problem, so that the solution of the subproblem is as close as possible to the optimal solution of the unit commitment problem. Specifically, Among them, the constraints are G(I,P,W)≥0, H(I)≥0, which expresses the unit combination problem as the unit start-stop decision variable I, unit output variable P and other variables w; G(I,P,W) is a constraint set without line flow constraints; H(I)≥0 is the unit combination problem containing only I i,t , W i,t , Y i,t 0-1 variable constraints.

6. The unit commitment accelerated optimization method based on machine learning to eliminate redundant constraints according to claim 5, wherein: The minimum continuous start-stop time constraint is specifically:

7. The unit commitment accelerated optimization method based on machine learning to eliminate redundant constraints according to claim 6, wherein: The solution based on the sub-problem and the optimal solution of the original unit combination problem is used to obtain the unit state variable offset of each unit in each time period; the characteristic demand D of the original unit combination problem is changed to obtain multiple optimal solutions, the relationship between the demand D and the offset is obtained, and a prediction regression model is learned, specifically: changing the demand D, solving the unit combination problem, and obtaining several optimal solutions I * , calculate each I * The distance from the solution S of the subproblem containing only 0-1 variables; statistics on the degree of deviation of each unit in each time period, forming the relationship between demand D and deviation vector v; The SCUC model determined by the demand d∈D has a functional relationship Make Among them, v j is each component of the deviation degree v of each unit in each time period.

8. The method for accelerating unit commitment optimization based on machine learning to eliminate redundant constraints according to claim 7, wherein: The identification obtains and removes redundant constraints in the minimum continuous start-stop time constraint, specifically: given a new unit commitment problem, the learning-based prediction model predicts the unit state variable offset of each unit in each time period of the new unit commitment problem to be solved, and for the minimum continuous start-stop time constraint or Make a judgment, g(v i ) represents the functional expression of the offset. If formula (10) and formula (11) still hold, then the original If the constraint is redundant, remove it.

9. A unit commitment optimization system based on machine learning to eliminate redundant constraints, including: A first acquisition module, which acquires semi-continuous constraints and line power flow constraints of upper and lower limits of the SCUC model unit output; An introduction module, wherein if the semi-continuous constraints of the upper and lower limits of the unit output and the line power flow constraints in the SCUC model are both met, the upper and lower limits of the two constraints are very close, and then a subproblem containing only 0-1 variables is introduced and solved; A second acquisition module, which adds the solution of the subproblem containing only 0-1 variables to the objective function of the original unit combination problem in a penalty-based manner, so that the solution of the subproblem is as close as possible to the optimal solution of the original unit combination problem; An offset data acquisition module, which obtains the offset of the unit state variables of each unit in each time period based on the solution of the sub-problem and the optimal solution of the original unit combination problem. The module changes the characteristic demand D of the original unit combination problem, obtains multiple optimal solutions, obtains the relationship between the demand D and the offset, and learns it into a predictive regression model. A judgment module, wherein the judgment module is given a new unit commitment problem, and predicts the unit state variable offset of each unit in each time period of the new unit commitment problem to be solved based on the learned prediction model, identifies and removes redundant constraints in the minimum continuous start-stop time constraints, so as to achieve accelerated optimization of the unit commitment.

10. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the steps of the method according to any one of claims 1 to 8 when executed by a processor.

12. A chip, comprising: memory for storing computer programs; A processor is connected to the memory and is used to call and run a computer program from the memory, so that a device equipped with the chip executes the method according to any one of claims 1 to 8.

13. A computer program product, comprising a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

14. A computer program, causing a computer to execute the method according to any one of claims 1 to 8.

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