Unit commitment acceleration optimization method and system based on inducible function feasibility detection

Abstract

The invention discloses a unit commitment acceleration optimization method and system based on inducible function feasibility detection. The method comprises the following steps: acquiring an LP form solution of a unit commitment problem to provide an initial reference point for a subsequent solution process; and constructing a sub-problem according to the LP form solution, solving to obtain a feasible solution of the sub-problem, substituting the feasible solution of the sub-problem into an original unit commitment problem, repairing to obtain a feasible solution of the original unit commitment problem, and obtaining a target function value of the original unit commitment problem. The objective function value is compared with the LP form solution, whether the feasible solution is within the constraint condition can be judged, and finally the optimized feasible solution of the unit commitment problem is obtained. Compared with a traditional solving method, the method has the advantages that a good feasible solution can be found in a short time, and the solving process of the optimal solution is accelerated. The method can adapt to unit combination problems of different scales and complexity degrees, and has certain flexibility for continuously changing conditions in a power system.

Description

Technical Field

[0001] The invention belongs to the technical field of electric power systems, and relates to a unit combination acceleration optimization method and system based on induction function feasibility detection. Background Art

[0002] In today's power system, the unit combination problem, as a key issue in power optimization and dispatching, has attracted much attention. With the widespread application of new energy and the continuous deepening of power market reform, the pattern of the power system has undergone major changes. More and more market players are participating in power trading, which makes the complexity of the power system grow exponentially. On the one hand, the intermittent and uncertain nature of new energy poses a huge challenge to the stable operation of the power system. For example, the output power of wind power generation and photovoltaic power generation is greatly affected by weather conditions, which makes it difficult to accurately predict and control the supply and demand balance of the power system. On the other hand, the diversification of market players has led to more complex trading rules, and different interests of different players have different demands, which further increases the difficulty of power system dispatching.

[0003] In this context, solving the unit combination problem becomes increasingly difficult. Due to the large scale of the problem, involving numerous generators and complex constraints, there are a large number of decision variables. Traditional solution methods are often inefficient when faced with such large-scale problems. Factors that need to be considered in the solution process include the start and stop status of the unit, output size, fuel cost, start and stop cost, power balance constraints, upper and lower limit constraints of unit output, line flow constraints, etc. These constraints are intertwined, making the solution space of the problem extremely complex.

[0004] At present, poor solution efficiency has become a prominent problem facing the unit combination problem. This not only affects the operating efficiency of the power system, but also increases the operating cost of the system. Since the solution process takes too long, it may not be able to respond to changes in the power market and fluctuations in load demand in a timely manner, thus affecting the reliability of power supply. In addition, inefficient solution methods are also difficult to meet the requirements of real-time scheduling, and cannot make quick decisions in emergency situations, further exacerbating the risk of instability in the power system.

[0005] In order to solve this problem, heuristic algorithms have become a potential solution. Heuristic algorithms can find feasible solutions to problems in a shorter time, providing a good starting point for searching for the optimal solution. By utilizing heuristic rules and empirical knowledge, heuristic algorithms can quickly explore the solution space of the problem and avoid falling into local optimal solutions. However, existing heuristic algorithms also have some limitations when solving unit combination problems. For example, different heuristic algorithms may have very different effects on different types of problems, and need to be selected and adjusted according to the specific problem. In addition, the performance of heuristic algorithms is also affected by the scale and complexity of the problem. When dealing with large-scale complex problems, they may still be inefficient.

[0006] In summary, the unit commitment problem of the power system faces severe challenges of poor solution efficiency under the background of new energy and power market reform. The existing solution methods are difficult to meet the needs of stable operation of the power system, and more efficient solution methods and technologies need to be further explored. Summary of the invention

[0007] The purpose of the present invention is to solve the problems in the prior art and to provide a unit combination acceleration optimization method and system based on induction function feasibility detection.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] The unit commitment acceleration optimization method based on induction function feasibility detection proposed in the present invention comprises:

[0010] Obtain the LP form solution of the unit commitment problem; based on the LP form solution of the unit commitment problem, construct and solve the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems;

[0011] Substitute the feasible solution of the subproblem into the original unit combination problem, repair and obtain the feasible solution of the original unit combination problem, and obtain the objective function value of the original unit combination problem;

[0012] The objective function value is compared with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem; the optimal solution is obtained based on the optimized feasible solution to achieve the optimal dispatch of the power system.

[0013] Preferably, the obtaining of the LP form solution of the unit commitment problem is specifically:

[0014] Determine the unit combination problem, judge whether the section data will cause the unit combination problem model to exceed the limit, if the section data will not cause the unit combination problem model to exceed the limit, then add the section constraint to the constraint of the unit combination problem; if the section data causes the unit combination problem model to exceed the limit, then add the section data to the objective function in a relaxed form; solve the unit combination problem, and obtain the LP form solution of the unit combination problem;

[0015] The LP form of the unit commitment problem is as follows:

[0016]

[0017] Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0018] Preferably, the LP formal solution based on the unit commitment problem constructs and solves the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems, specifically:

[0019] The integer solution in the 0-1 variable of the LP form solution of the unit commitment problem is retained and input into the constructed subproblem. The objective function of the subproblem is to make the 0-1 variable take 0 or 1. The constraint of the subproblem is that the unit commitment problem only contains the constraint H(I)≥0 of I_(i,t). Solve the subproblem to obtain a feasible solution of the subproblem.

[0020] The expression of the sub-problem is as follows:

[0021]

[0022] Where I1 represents the rounded 0-1 variable in the LP form solution of the unit commitment problem, I2′ represents the unrounded 0-1 variable in the LP form solution of the unit commitment problem, and I i ' ,t represents the 0-1 variable without integer in the LP form solution of the unit commitment problem corresponding to the state of unit i at time t, X represents a continuous variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0023] Preferably, the substituting the feasible solution of the subproblem into the original unit combination problem, repairing and obtaining the feasible solution of the original unit combination problem, and obtaining the objective function value of the original unit combination problem is specifically: substituting the feasible solution of the subproblem into the original unit combination problem to repair and solve the feasible solution, and the repair form of the original unit combination problem is:

[0024]

[0025] Among them, X represents a continuous variable, I represents a 0-1 variable, and Dscuc represents the solution domain, H(I) represents the constraint function containing only 0-1 variables, I1 represents the 0-1 variable rounded in the LP form solution of the unit commitment problem, M i,t The penalty factor corresponding to the 0-1 variable representing the state of unit i at time t, I upper Represents 0-1 variable I i,t The upper bound of the value of I lower Represents 0-1 variable I i,t The lower bound of the value of .

[0026] Preferably, the objective function value is compared with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem. Specifically:

[0027] Substitute all feasible solutions of the subproblem into the unit combination problem. After the solution is completed, compare the objective function value of the solution excluding the penalty item with the LP form solution. If the objective function value excluding the penalty item and the LP form solution are within the gap of 1%, then retain the objective function value as the optimized feasible solution of the unit combination problem. If all solutions are not within the gap of 1%, select the solution with the smallest objective function value, and check whether there is an out-of-limit section in the solution with the smallest objective function value. If there is section data that causes the unit combination problem model to exceed the limit in the solution with the smallest objective function value, add the section data to the objective function of the unit combination problem in the form of a large M penalty, and solve the unit combination problem with the added penalty item again to obtain the optimized feasible solution of the unit combination problem.

[0028] The unit commitment acceleration optimization system based on induction function feasibility detection proposed by the present invention comprises:

[0029] A first acquisition module, the first acquisition module is used to obtain an LP form solution to the unit combination problem; based on the LP form solution to the unit combination problem, construct and solve sub-problems of the unit combination problem to obtain feasible solutions to the sub-problems;

[0030] A second acquisition module, wherein the second acquisition module is used to substitute the feasible solution of the sub-problem into the original unit combination problem, repair and obtain the feasible solution of the original unit combination problem, and obtain the objective function value of the original unit combination problem;

[0031] A parameter comparison module is used to compare the objective function value with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem; the optimal solution is obtained based on the optimized feasible solution to achieve the optimized dispatch of the power system.

[0032] Preferably, the obtaining of the LP form solution of the unit commitment problem is specifically:

[0033] Determine the unit combination problem, judge whether the section data will cause the unit combination problem model to exceed the limit, if the section data will not cause the unit combination problem model to exceed the limit, then add the section constraint to the constraint of the unit combination problem; if the section data causes the unit combination problem model to exceed the limit, then add the section data to the objective function in a relaxed form; solve the unit combination problem, and obtain the LP form solution of the unit combination problem.

[0034] Preferably, the objective function value is compared with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is retained as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem. Specifically:

[0035] Substitute all feasible solutions of the subproblem into the unit combination problem. After the solution is completed, compare the objective function value of the solution excluding the penalty item with the LP form solution. If the objective function value excluding the penalty item and the LP form solution are within the gap of 1%, then retain the objective function value as the optimized feasible solution of the unit combination problem. If all solutions are not within the gap of 1%, select the solution with the smallest objective function value, and check whether there is an out-of-limit section in the solution with the smallest objective function value. If there is section data that causes the unit combination problem model to exceed the limit in the solution with the smallest objective function value, add the section data to the objective function of the unit combination problem in the form of a large M penalty, and solve the unit combination problem with the added penalty item again to obtain the optimized feasible solution of the unit combination problem.

[0036] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a unit combination acceleration optimization method based on induced function feasibility detection are implemented.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a unit combination acceleration optimization method based on induced function feasibility detection.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention proposes a unit combination acceleration optimization method based on induced function feasibility detection. Aiming at the problem of poor efficiency of unit combination solution in power system, the method can obtain a high-quality feasible solution to the original problem, thereby accelerating the solution process of the optimal solution of the unit combination problem. Specifically, the LP form solution of the unit combination problem is obtained to provide an initial reference point for the subsequent solution process. By solving the unit combination problem by linear programming, an approximate solution can be quickly obtained to help understand the approximate scope and trend of the problem. According to the LP form solution, a subproblem is constructed and solved to obtain a feasible solution to the subproblem, and the feasible solution of the subproblem is substituted into the original unit combination problem, and the feasible solution of the original unit combination problem is repaired to obtain the objective function value of the original unit combination problem. By comparing the objective function value with the LP form solution, it can be determined whether the feasible solution is within the constraints. If the objective function value is within the constraints and is better than the LP form solution, then the feasible solution corresponding to this objective function value may be an optimized feasible solution to the unit combination problem. If all feasible solutions are not within the constraints, then the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution. Compared with traditional solution methods, this method can find a better feasible solution in a shorter time and accelerate the process of finding the optimal solution. It can adapt to unit combination problems of different scales and complexities, and has a certain flexibility for the ever-changing situation in the power system. By comparing the objective function value with the constraints, it can be ensured that the optimal solution obtained is within the practical feasible range and meets the operation requirements of the power system.

[0040] Furthermore, if the cross-section data does not cause the model to exceed the limit, the cross-section constraint is added to the constraint of the overall problem, which can ensure that the cross-section requirements are met during the solution process. If the cross-section exceeds the limit, it is added to the objective function in a relaxed form. By introducing slack variables and penalty terms, a slight cross-section can be allowed to exceed the limit to a certain extent, but the cost of this cross-limit will be reflected in the objective function.

[0041] The present invention proposes a unit combination acceleration optimization system based on induced function feasibility detection, which obtains the optimal solution of the unit combination problem by dividing the system into a first acquisition module, a second acquisition module and a parameter comparison module, thereby accelerating the process of solving the optimal solution of the unit combination problem. The modularization concept is adopted to make each module independent of each other, which is convenient for unified management of each module. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 The present invention is a flow chart of the unit combination acceleration optimization method based on induced function feasibility detection.

[0044] Figure 2 This is a flow chart of the heuristic method for detecting the feasibility of the induced function of the present invention.

[0045] Figure 3 This is a system diagram of unit combination acceleration optimization based on induced function feasibility detection of the present invention.

[0046] Figure 4 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0048] 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 claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0049] 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, further definition and explanation thereof is not required in subsequent drawings.

[0050] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0051] 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", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0052] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0054] Example 1

[0055] The present invention proposes a unit commitment acceleration optimization method based on induced function feasibility detection. This method first finds a solution of a 0-1 variable that is relatively close to a feasible solution, and then repairs the solution to the corresponding feasible solution, thereby accelerating the process of finding the optimal solution to the unit commitment problem. Figure 1 As shown, including:

[0056] S1. Obtain the LP form solution of the unit commitment problem; based on the LP form solution of the unit commitment problem, construct and solve the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems;

[0057] The LP form solution of the unit commitment problem is obtained as follows:

[0058] Determine the unit combination problem, judge whether the section data will cause the unit combination problem model to exceed the limit, if the section data will not cause the unit combination problem model to exceed the limit, then add the section constraint to the constraint of the unit combination problem; if the section data causes the unit combination problem model to exceed the limit, then add the section data to the objective function in a relaxed form; solve the unit combination problem, and obtain the LP form solution of the unit combination problem;

[0059] The LP form of the unit commitment problem is as follows:

[0060]

[0061] Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0062] The LP formal solution based on the unit commitment problem constructs and solves the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems, specifically:

[0063] The integer solution of the 0-1 variable of the LP form solution of the unit commitment problem is retained and input into the constructed subproblem. The objective function of the subproblem is to make the variable take 0 or 1. The constraint of the subproblem is that the unit commitment problem only contains the constraint H(I)≥0 of I_(i,t). Solve the subproblem to obtain the feasible solution of the subproblem.

[0064] The expression of the sub-problem is as follows:

[0065]

[0066] Where I1 represents the rounded 0-1 variable in the LP form solution of the unit commitment problem, I2′ represents the unrounded 0-1 variable in the LP form solution of the unit commitment problem, and I i ' ,t represents the 0-1 variable without integer in the LP form solution of the unit commitment problem corresponding to the state of unit i at time t, X represents a continuous variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0067] S2. Substitute the feasible solution of the subproblem into the original unit combination problem, repair and obtain the feasible solution of the original unit combination problem, and obtain the objective function value of the original unit combination problem;

[0068] Substituting the feasible solution of the subproblem into the original unit combination problem, repairing and obtaining the feasible solution of the original unit combination problem, and obtaining the objective function value of the original unit combination problem, specifically: substituting the feasible solution of the subproblem into the following original unit combination problem to repair and solve the feasible solution, the repair form of the original unit combination problem is:

[0069]

[0070] Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, H(I) represents the constraint function containing only 0-1 variables, I1 represents the 0-1 variable rounded in the LP form solution of the unit commitment problem, M i,t The penalty factor corresponding to the 0-1 variable representing the state of unit i at time t, I upper Represents 0-1 variable I i,t The upper bound of the value of I lower Represents 0-1 variable I i,t The lower bound of the value of .

[0071] S3. Compare the objective function value with the LP form solution. If the objective function value and the LP form solution are within the constraints, then the objective function value is used as the optimized feasible solution for the unit commitment problem. If all feasible solutions after traversing the sub-problems are not within the constraints, then the solution with the smallest objective function value is selected and combined with the off-limit section situation to obtain the optimized feasible solution for the unit commitment problem. The optimal solution is obtained based on the optimized feasible solution to achieve optimal dispatch of the power system.

[0072] Compare the objective function value with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimal feasible solution to the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimal feasible solution to the unit commitment problem. Specifically:

[0073] Substitute all feasible solutions of the subproblem into the unit combination problem. After the solution is completed, compare the objective function value of the solution excluding the penalty item with the LP form solution. If the objective function value excluding the penalty item and the LP form solution are within the gap of 1%, then retain the objective function value as the optimized feasible solution of the unit combination problem. If all solutions are not within the gap of 1%, select the solution with the smallest objective function value, and check whether there is an out-of-limit section in the solution with the smallest objective function value. If there is section data that causes the unit combination problem model to exceed the limit in the solution with the smallest objective function value, add the section data to the objective function of the unit combination problem in the form of a large M penalty, and solve the unit combination problem with the added penalty item again to obtain the optimized feasible solution of the unit combination problem.

[0074] Among them, the optimized feasible solution is a high-quality feasible solution. The optimal solution to the unit combination problem is found based on the high-quality feasible solution, thereby ensuring the stable operation of the power system.

[0075] like Figure 2 As shown, the method includes the following detailed steps:

[0076] Step 1: Solve the relaxed form of the overall problem including cross-section information:

[0077] Consider the unit combination problem. If the cross-section data will not cause the model to exceed the limit, then add the cross-section constraint to the overall problem. If the cross-section exceeds the limit, add it to the objective function in a relaxed form. The problem can be solved to obtain the LP solution of the overall problem. If it is just a unit combination problem, the objective function can be set to 0.

[0078] Among them, section data usually refers to the status information of the power system at a specific time or time period. It mainly includes data on the power generation side, the load side and the network side. Section constraints are restrictions set to ensure the safe, stable and economical operation of the power system at a specific section time. They are a kind of constraint when solving unit combination problems. For example, unit combination problems include unit output constraints, maximum and minimum start and stop constraints, etc. Section constraints mainly include power balance constraints, unit operation constraints and network security constraints.

[0079] The LP form of the unit commitment problem is as follows:

[0080]

[0081] Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0082] Step 2: Keep the integer solutions of the 0-1 variables (I, W, Y) of the LP relaxation solution of the problem, and input them into the constructed subproblems. The subproblem objective function satisfies the condition that the variables are as much as possible 0 or 1, and the constraint is that the original problem only contains the constraint H(I)≥0 of I_(i, t), and solves some feasible solutions of the subproblems. Collect these solutions through the integer pool method.

[0083] The expression of the sub-problem is as follows:

[0084]

[0085] Where I1 represents the rounded 0-1 variable in the LP form solution of the unit commitment problem, I2′ represents the unrounded 0-1 variable in the LP form solution of the unit commitment problem, and I i ' ,t represents the 0-1 variable without integer in the LP form solution of the unit commitment problem corresponding to the state of unit i at time t, X represents a continuous variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0086] Step 3: The solver integer pool function obtains multiple feasible solutions in step 2, and substitutes them into the original unit combination problem in step 4 in turn to repair and solve the feasible solutions.

[0087] Step 4: Substitute the solution into the original unit combination problem, and compare it with the relaxed form of step 1 to see if it is less than the gap. If the objective function value of step 4 without the penalty term and the solution of the original LP form of the problem are within 1% of the gap, then take that value. If all solutions are not within 1% of the gap, then select the solution with the smallest objective function value and then perform the update in step 5.

[0088] The repair form of the original unit combination problem is:

[0089]

[0090] Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, H(I) represents the constraint function containing only 0-1 variables, I1 represents the 0-1 variable rounded in the LP form solution of the unit commitment problem, M i,t The penalty factor corresponding to the 0-1 variable representing the state of unit i at time t, I upper Represents 0-1 variable I i,t The upper bound of the value of I lower Represents 0-1 variable I i,t The lower bound of the value of .

[0091] After the solution of step 5 is completed, check which other cross-limit sections are left and put them into the objective function in the form of a large M penalty. Specifically, if there is a cross-limit section, introduce a large M penalty term, multiply the cross-limit amount by a large constant M, and add it to the objective function to encourage the subsequent solution process to avoid cross-limit sections as much as possible.

[0092] The examples used are extended from the examples of IEEE30, which respectively represent the unit commitment problems with different numbers of units. The experiment is set to MIPGap of 0.5%, the maximum stop time is set to 7200s, and the average test results of 12 examples using COPT are shown in the following table.

[0093] Table 1 Test results of the feasibility detection method of the induction function based on the global characteristics of unit commitment

[0094]

[0095] The unit combination acceleration optimization method based on the feasibility detection of the induced function proposed in the present invention can further improve the efficiency of solving large-scale SCUC of the engine. Basically, the feasibility detection of the induced function is about 30%-40% more efficient than the COPT direct solution method, and the relative gap compared with Gurobi is basically below 0.1%, and the solution quality is good. This method can effectively enhance the efficiency of the engine and the open source solver in solving SCUC. The advantage is that approximate solutions can be gradually found from the sub-problems, thereby repairing high-quality feasible solutions and accelerating the optimization process of the optimal solution. By analyzing the problem characteristics of the unit combination problem, a heuristic method for the power system is proposed. Therefore, in the solution process, the solution of the 0-1 variable that is relatively close to the feasible solution is first found, and a high-quality feasible solution is quickly obtained in the solution process, which has an accelerated solution effect on the optimal solution of the unit combination to be obtained in the end, thereby accelerating the solution process of the optimal solution of the unit combination problem.

[0096] Example 2

[0097] The present invention proposes a unit commitment acceleration optimization system based on induction function feasibility detection, such as Figure 3 As shown, including:

[0098] A first acquisition module, the first acquisition module is used to obtain an LP form solution to the unit combination problem; based on the LP form solution to the unit combination problem, construct and solve sub-problems of the unit combination problem to obtain feasible solutions to the sub-problems;

[0099] The LP form solution of the unit commitment problem is obtained as follows:

[0100] Determine the unit combination problem, judge whether the section data will cause the unit combination problem model to exceed the limit, if the section data will not cause the unit combination problem model to exceed the limit, then add the section constraint to the constraint of the unit combination problem; if the section data causes the unit combination problem model to exceed the limit, then add the section data to the objective function in a relaxed form; solve the unit combination problem, and obtain the LP form solution of the unit combination problem;

[0101] The LP form of the unit commitment problem is as follows:

[0102]

[0103] Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0104] The LP formal solution based on the unit commitment problem constructs and solves the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems, specifically:

[0105] The integer solution in the 0-1 variable of the LP form solution of the unit commitment problem is retained and input into the constructed subproblem. The objective function of the subproblem is to make the 0-1 variable take 0 or 1. The constraint of the subproblem is that the unit commitment problem only contains the constraint H(I)≥0 of I_(i,t). Solve the subproblem to obtain a feasible solution of the subproblem.

[0106] The expression of the sub-problem is as follows:

[0107]

[0108] Where I1 represents the rounded 0-1 variable in the LP form solution of the unit commitment problem, I2′ represents the unrounded 0-1 variable in the LP form solution of the unit commitment problem, and I i ' ,t represents the 0-1 variable without integer in the LP form solution of the unit commitment problem corresponding to the state of unit i at time t, X represents a continuous variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

[0109] A second acquisition module, wherein the second acquisition module is used to substitute the feasible solution of the sub-problem into the original unit combination problem, repair and obtain the feasible solution of the original unit combination problem, and obtain the objective function value of the original unit combination problem;

[0110] Substituting the feasible solution of the subproblem into the original unit combination problem, repairing and obtaining the feasible solution of the original unit combination problem, and obtaining the objective function value of the original unit combination problem, specifically: substituting the feasible solution of the subproblem into the following original unit combination problem to repair and solve the feasible solution, the repair form of the original unit combination problem is:

[0111]

[0112] Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, H(I) represents the constraint function containing only 0-1 variables, I1 represents the 0-1 variable rounded in the LP form solution of the unit commitment problem, M i,t The penalty factor corresponding to the 0-1 variable representing the state of unit i at time t, I upper Represents 0-1 variable I i,t The upper bound of the value of I lower Represents 0-1 variable I i,t The lower bound of the value of .

[0113] A parameter comparison module is used to compare the objective function value with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is retained as the optimal feasible solution to the unit combination problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimal feasible solution to the unit combination problem; the power system is dispatched based on the unit combination of the optimal feasible solution.

[0114] The objective function value is compared with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem. Specifically:

[0115] Substitute all feasible solutions of the subproblem into the unit combination problem. After the solution is completed, compare the objective function value of the solution excluding the penalty item with the LP form solution. If the objective function value excluding the penalty item and the LP form solution are within the gap of 1%, then retain the objective function value as the optimized feasible solution of the unit combination problem. If all solutions are not within the gap of 1%, select the solution with the smallest objective function value, and check whether there is an out-of-limit section in the solution with the smallest objective function value. If there is section data that causes the unit combination problem model to exceed the limit in the solution with the smallest objective function value, add the section data to the objective function of the unit combination problem in the form of a large M penalty, and solve the unit combination problem with the added penalty item again to obtain the optimized feasible solution of the unit combination problem.

[0116] Example 3

[0117] See also Figure 4 As shown, the present invention also provides an electronic device 100 for a unit combination acceleration optimization method based on induced function feasibility detection; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0118] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the unit combination acceleration optimization method based on induced function feasibility detection described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0119] The at least one processor 102 may 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. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0120] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a unit combination acceleration optimization method based on induced function feasibility detection, and the processor 102 can execute the plurality of instructions to implement:

[0121] Obtain the LP form solution of the unit commitment problem; based on the LP form solution of the unit commitment problem, construct and solve the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems;

[0122] Substitute the feasible solution of the subproblem into the original unit combination problem, repair and obtain the feasible solution of the original unit combination problem, and obtain the objective function value of the original unit combination problem;

[0123] The objective function value is compared with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem; the optimal solution is obtained based on the optimized feasible solution to achieve the optimal dispatch of the power system.

[0124] Example 4

[0125] If the module / unit integrated in the electronic device 100 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, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code 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 that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0126] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A unit commitment acceleration optimization method based on induced function feasibility detection, characterized in that: include: Obtain the LP form solution to the unit commitment problem; Based on the LP formal solution of the unit commitment problem, construct and solve the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems; Substitute the feasible solution of the subproblem into the original unit combination problem, repair and obtain the feasible solution of the original unit combination problem, and obtain the objective function value of the original unit combination problem; Compare the objective function value with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimal feasible solution to the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimal feasible solution to the unit commitment problem. The optimal solution is obtained based on the optimized feasible solution to achieve optimal dispatch of the power system.

2. The unit commitment acceleration optimization method based on induced function feasibility detection according to claim 1 is characterized in that: The LP form solution of the unit commitment problem is obtained as follows: Determine the unit combination problem, judge whether the section data will cause the unit combination problem model to exceed the limit, if the section data will not cause the unit combination problem model to exceed the limit, then add the section constraint to the constraint of the unit combination problem; if the section data causes the unit combination problem model to exceed the limit, then add the section data to the objective function in a relaxed form; solve the unit combination problem, and obtain the LP form solution of the unit combination problem; The LP form of the unit commitment problem is as follows: Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

3. The unit commitment acceleration optimization method based on induced function feasibility detection according to claim 1 is characterized in that: The LP formal solution based on the unit commitment problem constructs and solves the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems, specifically: The integer solution in the 0-1 variable of the LP form solution of the unit commitment problem is retained and input into the constructed subproblem. The objective function of the subproblem is to make the 0-1 variable take 0 or 1. The constraint of the subproblem is that the unit commitment problem only contains the constraint H(I)≥0 of I_(i,t). Solve the subproblem to obtain a feasible solution of the subproblem. The expression of the sub-problem is as follows: Where I1 represents the rounded 0-1 variable in the LP form solution of the unit commitment problem, I2′ represents the unrounded 0-1 variable in the LP form solution of the unit commitment problem, and I i ' ,t represents the 0-1 variable without integer in the LP form solution of the unit commitment problem corresponding to the state of unit i at time t, X represents a continuous variable, and D scuc represents the solution domain, and H(I) represents the constraint function containing only 0-1 variables.

4. The unit commitment acceleration optimization method based on induced function feasibility detection according to claim 1 is characterized in that: Substituting the feasible solution of the subproblem into the original unit combination problem, repairing and obtaining the feasible solution of the original unit combination problem, and obtaining the objective function value of the original unit combination problem, specifically: substituting the feasible solution of the subproblem into the original unit combination problem to repair and solve the feasible solution, the repair form of the original unit combination problem is: Among them, X represents a continuous variable, I represents a 0-1 variable, and D scuc represents the solution domain, H(I) represents the constraint function containing only 0-1 variables, I1 represents the 0-1 variable rounded in the LP form solution of the unit commitment problem, M i,t The penalty factor corresponding to the 0-1 variable representing the state of unit i at time t, I upper Represents 0-1 variable I i,t The upper bound of the value of I lower Represents 0-1 variable I i,t The lower bound of the value of .

5. The unit commitment acceleration optimization method based on induced function feasibility detection according to claim 1 is characterized in that: The objective function value is compared with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem. Specifically: Substitute all feasible solutions of the subproblem into the unit combination problem. After the solution is completed, compare the objective function value of the solution excluding the penalty item with the LP form solution. If the objective function value excluding the penalty item and the LP form solution are within the gap of 1%, then retain the objective function value as the optimized feasible solution of the unit combination problem. If all solutions are not within the gap of 1%, select the solution with the smallest objective function value, and check whether there is an out-of-limit section in the solution with the smallest objective function value. If there is section data that causes the unit combination problem model to exceed the limit in the solution with the smallest objective function value, add the section data to the objective function of the unit combination problem in the form of a large M penalty, and solve the unit combination problem with the added penalty item again to obtain the optimized feasible solution of the unit combination problem.

6. The unit commitment acceleration optimization system based on the feasibility detection of the induction function is characterized by: include: A first acquisition module, the first acquisition module is used to obtain an LP form solution of the unit commitment problem; Based on the LP formal solution of the unit commitment problem, construct and solve the sub-problems of the unit commitment problem to obtain feasible solutions to the sub-problems; A second acquisition module, wherein the second acquisition module is used to substitute the feasible solution of the sub-problem into the original unit combination problem, repair and obtain the feasible solution of the original unit combination problem, and obtain the objective function value of the original unit combination problem; A parameter comparison module, wherein the parameter comparison module is used to compare the objective function value with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is used as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem. The optimal solution is obtained based on the optimized feasible solution to achieve optimal dispatch of the power system.

7. The unit commitment acceleration optimization system based on induction function feasibility detection according to claim 6 is characterized in that: The LP form solution of the unit commitment problem is obtained as follows: Determine the unit combination problem, judge whether the section data will cause the unit combination problem model to exceed the limit, if the section data will not cause the unit combination problem model to exceed the limit, then add the section constraint to the constraint of the unit combination problem; if the section data causes the unit combination problem model to exceed the limit, then add the section data to the objective function in a relaxed form; solve the unit combination problem, and obtain the LP form solution of the unit combination problem.

8. The unit commitment acceleration optimization system based on induction function feasibility detection according to claim 6 is characterized in that: The objective function value is compared with the LP form solution. If the objective function value and the LP form solution are within the constraints, the objective function value is retained as the optimized feasible solution of the unit commitment problem. If all feasible solutions of the traversed sub-problems are not within the constraints, the solution with the smallest objective function value is selected and combined with the cross-limit section situation to obtain the optimized feasible solution of the unit commitment problem. Specifically: Substitute all feasible solutions of the subproblem into the unit combination problem. After the solution is completed, compare the objective function value of the solution excluding the penalty item with the LP form solution. If the objective function value excluding the penalty item and the LP form solution are within the gap of 1%, then retain the objective function value as the optimized feasible solution of the unit combination problem. If all solutions are not within the gap of 1%, select the solution with the smallest objective function value, and check whether there is an out-of-limit section in the solution with the smallest objective function value. If there is section data that causes the unit combination problem model to exceed the limit in the solution with the smallest objective function value, add the section data to the objective function of the unit combination problem in the form of a large M penalty, and solve the unit combination problem with the added penalty item again to obtain the optimized feasible solution of the unit combination problem.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the unit combination acceleration optimization method based on induced function feasibility detection as described in any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the unit combination acceleration optimization method based on induced function feasibility detection as claimed in any one of claims 1 to 5 are implemented.

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