A unit commitment acceleration method, system and medium based on adaptive calling neighborhood search

Abstract

The present invention discloses a unit commitment acceleration method, system, and medium based on adaptively calling a neighborhood search. The method comprises: obtaining basic unit data in a power system; constructing a unit commitment optimization model based on the obtained basic unit data in the power system; calling a relaxed enhanced neighborhood search algorithm when the unit commitment optimization model to be solved has the characteristic that the relaxed solution of the problem root node and the optimal solution of the problem satisfy constraint conditions; searching for a feasible solution based on the relaxed enhanced neighborhood search algorithm, and accelerating the optimization of the unit commitment problem based on the feasible solution to achieve optimal scheduling of the power system. The present invention adaptively calls RENS, fixes calculation-related nodes, and disables particularly expensive cutting planes when using RENS, thereby better improving the solution performance of the unit commitment problem and facilitating the stable operation of the power grid system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems and relates to a unit commitment acceleration method, system and medium based on adaptive calling neighborhood search. Background Art

[0002] With the widespread adoption of renewable energy and the continued advancement of power market reform, the unit commitment issue in power systems, as a key issue in optimal power dispatch, has received increasing attention. In today's era, the vigorous development of renewable energy has brought new opportunities and challenges to the energy sector. On the one hand, the widespread integration of renewable energy has led to a significant shift in the power system's energy structure, providing strong support for sustainable development. On the other hand, its intermittent and volatile characteristics have also brought many uncertainties to the stable operation of the power system. At the same time, the continuous advancement of power market reform has led to increasingly fierce competition in the power industry, placing higher demands on optimal power dispatch.

[0003] The unit combination problem in power systems involves multiple aspects, including generator start-up and shutdown decisions and power allocation, and is directly related to the system's operational efficiency, reliability, and economic viability. Due to the high complexity of actual power systems, which not only include numerous generators, transmission lines, and load nodes, but are also influenced by various external factors, such as weather changes and energy price fluctuations, the system is highly uncertain. Factors such as the uncertainty of renewable energy output and volatile load demand make efficient solutions to power optimization problems even more challenging.

[0004] The Relaxed Reinforced Neighborhood Search (RENS) method is often used to find feasible solutions to the original problem in order to speed up the solution because it can effectively narrow the search space of mixed integer linear programming problems. This method provides new ideas and approaches for solving complex power optimization problems to a certain extent. However, the solution effect of this method depends on the parameter settings. Different problems often require different parameter combinations to achieve better solution results. This means that when faced with complex and changeable power system problems, the RENS method cannot function stably and it is difficult to meet the requirements of stable operation of the power system. Once the solution effect is poor, it may lead to problems such as instability and low efficiency in the operation of the power system. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem that the relaxed enhanced neighborhood search method in the prior art cannot stably obtain a unit commitment optimization solution, and to provide a unit commitment acceleration method, system and medium based on adaptive calling neighborhood search.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention proposes a unit commitment acceleration method based on adaptive calling neighborhood search, comprising:

[0008] Obtain basic data of units in the power system;

[0009] Based on the basic data of units in the power system, a unit commitment optimization model is constructed;

[0010] When the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem meet the constraint conditions, the relaxed enhanced neighborhood search algorithm is called;

[0011] A feasible solution is found based on the relaxed enhanced neighborhood search algorithm, and the unit commitment problem is optimized based on the feasible solution to achieve optimal dispatch of the power system.

[0012] Preferably, the basic data of the unit includes: the operating cost of the unit, the output data of the unit, the power constraint data of the unit and the day-ahead load forecast data;

[0013] The unit's operating cost, unit output data, unit power constraint data and day-ahead load forecast data are all collected from the power system.

[0014] Preferably, the constructing of the unit commitment optimization model is specifically as follows:

[0015]

[0016] Among them, C x，u and C x，d are the startup cost and shutdown cost of unit x respectively; w x，y and z x，y are the startup state variables and shutdown state variables of unit x in period y. When unit x performs the startup action at time y, the startup state variable w of unit x in period y is x，y is 1, otherwise it is 0; when unit x performs the shutdown action at time y, the shutdown state variable z of unit x in period y x，y is 1, otherwise 0; F x (P x，y ) is the cost of unit x in period y; P xy is the power generation of unit x in period y.

[0017] Preferably, if the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem do not satisfy the constraint conditions, a simulated annealing algorithm, a genetic algorithm, an ant colony algorithm or a neural network algorithm is called to find a feasible solution for the given optimization problem;

[0018] The constraint condition is that the absolute value of the difference between the optimal solution of the unit commitment optimization model and the relaxed solution of the root node of the unit commitment optimization model is less than a set threshold.

[0019] Preferably, the method for finding a feasible solution based on the relaxed reinforced neighborhood search algorithm is as follows: setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm, and solving the unit combination optimization model based on the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm; wherein, setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm includes: limiting the discreteness of the sub-integer programming problem, limiting the relaxation scale of the total problem, the number of solution nodes and stagnation nodes, and limiting the cutting plane strength parameters.

[0020] Preferably, the unit commitment optimization model is solved by calculating the linear relaxation optimal solution after setting the heuristic parameters. If the integer variables of the original unit combination problem are in the optimal solution If the result is an integer, then fix the value of the integer, otherwise set the upper and lower bounds of the integer variable to be decided to and The integer variables of the updated unit combination subproblem are converted to 0-1 variables, and the optimal solution is obtained by solving the subproblem after updating the upper and lower bounds. Output the optimal solution As the initial feasible solution to the original unit combination problem; It means rounding down, that is, the lower bound of the variable; It means rounding up, that is, the upper bound of the variable.

[0021] The present invention proposes a unit commitment acceleration system based on adaptive calling neighborhood search, comprising:

[0022] An acquisition module, wherein the acquisition module acquires basic data of units in the power system;

[0023] A construction module, wherein the construction module constructs a unit commitment optimization model based on the acquired basic data of units in the power system;

[0024] A calling module, wherein the calling module calls a relaxed enhanced neighborhood search algorithm when a relaxed solution of a root node of a problem of a unit commitment optimization model to be solved and an optimal solution of the problem satisfy a constraint condition;

[0025] The solution module searches for a feasible solution based on a relaxed enhanced neighborhood search algorithm, and accelerates the optimization of the unit combination problem based on the feasible solution to achieve optimal scheduling of the power system.

[0026] Preferably, the constructing of the unit commitment optimization model is specifically as follows:

[0027]

[0028] Among them, C x，u and C x，dare the startup cost and shutdown cost of unit x respectively; w x，y and z x，y are the startup state variables and shutdown state variables of unit x in period y. When unit x performs the startup action at time y, the startup state variable w of unit x in period y is x，y is 1, otherwise it is 0; when unit x performs the shutdown action at time y, the shutdown state variable z of unit x in period y x，y is 1, otherwise 0; F x (P x，y ) is the cost of unit x in period y; P xy is the power generation of unit x in period y.

[0029] Preferably, if the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem do not satisfy the constraint conditions, a simulated annealing algorithm, a genetic algorithm, an ant colony algorithm or a neural network algorithm is called to find a feasible solution for the given optimization problem;

[0030] The constraint condition is that the absolute value of the difference between the optimal solution of the unit commitment optimization model and the relaxed solution of the root node of the unit commitment optimization model is less than a set threshold.

[0031] Preferably, the method for finding a feasible solution based on the relaxed reinforced neighborhood search algorithm is as follows: setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm, and solving the unit combination optimization model based on the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm; wherein, setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm includes: limiting the discreteness of the sub-integer programming problem, limiting the relaxation scale of the total problem, the number of solution nodes and stagnation nodes, and limiting the cutting plane strength parameters.

[0032] Preferably, the unit commitment optimization model is solved by calculating the linear relaxation optimal solution after setting the heuristic parameters. If the integer variables of the original unit combination problem are in the optimal solution If the result is an integer, then fix the value of the integer, otherwise set the upper and lower bounds of the integer variable to be decided to and The integer variables of the updated unit combination subproblem are converted to 0-1 variables, and the optimal solution is obtained by solving the subproblem after updating the upper and lower bounds. Output the optimal solution As the initial feasible solution to the original unit combination problem; It means rounding down, that is, the lower bound of the variable; It means rounding up, that is, the upper bound of the variable.

[0033] A terminal device includes 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 method based on adaptive calling neighborhood search are implemented.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a unit combination acceleration method based on adaptive calling neighborhood search.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention proposes a unit combination acceleration method based on adaptively calling neighborhood search. The method constructs a unit combination optimization model based on basic unit data in the power system; performs feature recognition on the unit combination optimization model to determine whether to call the relaxed enhanced neighborhood search algorithm; if the relaxed enhanced neighborhood search algorithm is called, sets the parameters of the adaptively calling relaxed enhanced neighborhood search algorithm to solve the unit combination optimization model and obtain a feasible solution to the unit combination problem; and accelerates the optimization of the unit combination problem based on the feasible solution to the unit combination problem. The present invention's adaptive calling of the relaxed enhanced neighborhood search algorithm and the use of methods such as disabling particularly expensive cutting planes can better improve the performance of solving the unit combination problem, which is beneficial to the stable operation of the power grid system.

[0037] This paper proposes a unit commitment acceleration system based on adaptive neighborhood search. By dividing the system into an acquisition module, a construction module, a solution module, and a call module, it obtains power system unit commitment optimization results, thereby achieving unit commitment acceleration. The modularization concept makes each module independent of each other, facilitating unified management of all modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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.

[0039] Figure 1 This is a flow chart of the unit commitment acceleration method based on adaptive calling neighborhood search of the present invention.

[0040] Figure 2 This is a structural diagram of the unit combination acceleration system based on adaptive calling neighborhood search of the present invention.

[0041] Figure 3The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] 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 shall fall within the scope of protection of the present invention.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] The present invention is described in further detail below with reference to the accompanying drawings:

[0049] Example 1

[0050] The present invention proposes a unit commitment acceleration method based on adaptive calling neighborhood search, such as Figure 1 Shown, including:

[0051] S1: Obtain basic data of units in the power system;

[0052] The basic data of the unit includes: the operating cost of the unit, the output data of the unit, the power constraint data of the unit and the day-ahead load forecast data;

[0053] The unit's operating cost, unit output data, unit power constraint data and day-ahead load forecast data are all collected from the power system.

[0054] S2: Based on the acquired basic data of the units in the power system, a unit commitment optimization model is constructed;

[0055] The construction of the unit commitment optimization model is specifically as follows:

[0056]

[0057] Among them, C x，u and C x，d are the startup cost and shutdown cost of unit x respectively; w x，y and z x，y are the startup state variables and shutdown state variables of unit x in period y. When unit x performs the startup action at time y, the startup state variable w of unit x in period y is x，y is 1, otherwise it is 0; when unit x performs the shutdown action at time y, the shutdown state variable z of unit x in period y x，y is 1, otherwise 0; F x (P x，y ) is the cost of unit x in period y; P xy is the power generation of unit x in period y.

[0058] S3: When the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem meet the constraint conditions, the relaxed enhanced neighborhood search algorithm is called;

[0059] If the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem do not meet the constraint conditions, the simulated annealing algorithm, genetic algorithm, ant colony algorithm or neural network algorithm is called to find a feasible solution for the given optimization problem;

[0060] The constraint condition is that the absolute value of the difference between the optimal solution of the unit commitment optimization model and the relaxed solution of the root node of the unit commitment optimization model is less than a set threshold.

[0061] S4: Find feasible solutions based on the relaxed enhanced neighborhood search algorithm, accelerate the optimization of the unit commitment problem based on the feasible solutions, and achieve optimal scheduling of the power system.

[0062] The method for finding a feasible solution based on the relaxed reinforced neighborhood search algorithm specifically includes setting parameters of the adaptively calling relaxed reinforced neighborhood search algorithm, and solving the unit combination optimization model based on the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm; wherein, setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm includes: limiting the discreteness of the sub-integer programming problem, limiting the relaxation scale of the total problem, the number of solution nodes and stagnation nodes, and limiting the cutting plane strength parameters.

[0063] The unit combination optimization model is solved as follows: after setting the heuristic parameters, the linear relaxation optimal solution is calculated. If the integer variables of the original unit combination problem are in the optimal solution If the result is an integer, then fix the value of the integer, otherwise set the upper and lower bounds of the integer variable to be decided to and The integer variables of the updated unit combination subproblem are converted to 0-1 variables, and the optimal solution is obtained by solving the subproblem after updating the upper and lower bounds. Output the optimal solution As the initial feasible solution to the original unit combination problem; It means rounding down, that is, the lower bound of the variable; It means rounding up, that is, the upper bound of the variable.

[0064] The present invention discloses a unit combination acceleration method based on adaptive calling neighborhood search. In combination with the problem characteristics, it improves the solution performance by adaptively calling RENS (calling level, number of times, etc.), fixing calculation-related nodes, and disabling cutting planes that are particularly expensive to calculate when using RENS.

[0065] The idea of ​​the RENS algorithm is to define a sub-MIP that optimizes the relaxed optimal solution This is done by fixing the rounding This is done by rounding all integer variables in the sub-MIP to integer values. For the remaining integer variables, the bounds are tightened to the two nearest integer values. If a linear outer approximation is used to solve the sub-MIP, its dual bound will usually be improved because the reduced domain will produce a stronger linear relaxation and a tighter underestimation. Since the sub-MIP is completely solved by the relaxation definition, so the process is called relaxed forced neighborhood search or RENS for short.

[0066] During this process, we implemented a feature recognition function for the problem examples being solved. Specifically, when the root node is close to the actual MIP solution, the RENS method can be effectively applied. We also added the strength of the RENS algorithm, determining whether to use it for this type of example and setting parameters to adjust the strength of the algorithm. This allows for better control of the number of nodes and the strength of the cutting planes. This, combined with COPT's additional heuristics, allows for faster and higher-quality feasible solutions.

[0067] We used several examples, extending the IEEE30 example, to represent unit commitment problems with varying numbers of units. The experiment was set to a MIPGap of 0.5% and a maximum downtime of 7200 seconds. The average test results for the 12 examples using COPT are shown in the following table.

[0068] Table 1 Test results of adaptive relaxed enhanced neighborhood search for unit commitment

[0069]

[0070] This method can further improve the engine's efficiency in solving large-scale SCUCs. For the intermediate example, COPT's adaptive RENS method achieved higher efficiency than Gurobi. Adaptive RENS was 68% more efficient than the traditional RENS method, with the relative difference compared to Gurobi being less than 0.1%, resulting in better solution quality. The red areas represent results that are superior to Gurobi. This advantage stems from the ability to use the solver's advanced and effective RENS heuristics with an optimal strategy. However, the disadvantage is that the solver's built-in heuristics fail to work in some cases, requiring the use of additional heuristics even with the optimal strategy.

[0071] Example 2

[0072] The present invention proposes a unit commitment acceleration system based on adaptive calling neighborhood search, such as Figure 2 Shown, including:

[0073] An acquisition module, wherein the acquisition module acquires basic data of units in the power system;

[0074] The basic data of the unit includes: the operating cost of the unit, the output data of the unit, the power constraint data of the unit and the day-ahead load forecast data;

[0075] The unit's operating cost, unit output data, unit power constraint data and day-ahead load forecast data are all collected from the power system.

[0076] A construction module, wherein the construction module constructs a unit commitment optimization model based on the acquired basic data of units in the power system;

[0077] The construction of the unit commitment optimization model is specifically as follows:

[0078]

[0079] Among them, C x，u and C x，d are the startup cost and shutdown cost of unit x respectively; w x，y and z x，y are the startup state variables and shutdown state variables of unit x in period y. When unit x performs the startup action at time y, the startup state variable w of unit x in period y is x，y is 1, otherwise it is 0; when unit x performs the shutdown action at time y, the shutdown state variable z of unit x in period y x，y is 1, otherwise 0; F x (P x，y ) is the cost of unit x in period y; P xy is the power generation of unit x in period y.

[0080] A calling module, wherein the calling module calls a relaxed enhanced neighborhood search algorithm when a relaxed solution of a root node of a problem of a unit commitment optimization model to be solved and an optimal solution of the problem satisfy a constraint condition;

[0081] If the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem do not meet the constraint conditions, the simulated annealing algorithm, genetic algorithm, ant colony algorithm or neural network algorithm is called to find a feasible solution for the given optimization problem;

[0082] The constraint condition is that the absolute value of the difference between the optimal solution of the unit commitment optimization model and the relaxed solution of the root node of the unit commitment optimization model is less than a set threshold.

[0083] The solution module searches for a feasible solution based on a relaxed enhanced neighborhood search algorithm, and accelerates the optimization of the unit combination problem based on the feasible solution to achieve optimal scheduling of the power system.

[0084] The method for finding a feasible solution based on the relaxed reinforced neighborhood search algorithm specifically includes setting parameters of the adaptively calling relaxed reinforced neighborhood search algorithm, and solving the unit combination optimization model based on the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm; wherein, setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm includes: limiting the discreteness of the sub-integer programming problem, limiting the relaxation scale of the total problem, the number of solution nodes and stagnation nodes, and limiting the cutting plane strength parameters.

[0085] The unit combination optimization model is solved as follows: after setting the heuristic parameters, the linear relaxation optimal solution is calculated. If the integer variables of the original unit combination problem are in the optimal solution If the result is an integer, then fix the value of the integer, otherwise set the upper and lower bounds of the integer variable to be decided to and The integer variables of the updated unit combination subproblem are converted to 0-1 variables, and the optimal solution is obtained by solving the subproblem after updating the upper and lower bounds. Output the optimal solution As the initial feasible solution to the original unit combination problem; It means rounding down, that is, the lower bound of the variable; It means rounding up, that is, the upper bound of the variable.

[0086] Example 3

[0087] See also Figure 3 As shown, the present invention also provides an electronic device 100 for a unit combination acceleration method based on adaptive calling neighborhood search; 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.

[0088] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the unit commitment acceleration method based on adaptive call neighborhood search 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. The program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data (such as audio data) created based on the use of the electronic device 100. In addition, the memory 101 can include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0089] 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 connects various parts of the entire electronic device 100 using various interfaces and lines.

[0090] The memory 101 in the electronic device 100 stores a plurality of instructions for implementing a unit commitment acceleration method based on adaptive calling neighborhood search, and the processor 102 can execute the plurality of instructions to implement:

[0091] Obtain basic data of units in the power system;

[0092] Based on the basic data of units in the power system, a unit commitment optimization model is constructed;

[0093] When the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem meet the constraint conditions, the relaxed enhanced neighborhood search algorithm is called;

[0094] A feasible solution is found based on the relaxed enhanced neighborhood search algorithm, and the unit commitment problem is optimized based on the feasible solution to achieve optimal dispatch of the power system.

[0095] Example 4

[0096] 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, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0097] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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.

[0099] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the 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 by the scope of protection of the claims of the present invention.

Claims

1. A unit commitment acceleration method based on adaptive calling neighborhood search, characterized in that: include: Obtain basic data of units in the power system; Based on the basic data of units in the power system, a unit commitment optimization model is constructed; When the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem meet the constraint conditions, the relaxed enhanced neighborhood search algorithm is called; If the relaxed solution of the root node of the unit commitment optimization model to be solved and the optimal solution of the problem do not meet the constraint conditions, the simulated annealing algorithm, genetic algorithm, ant colony algorithm or neural network algorithm is called to find a feasible solution for the given optimization problem; wherein, the constraint condition is that the absolute value of the difference between the optimal solution of the unit commitment optimization model and the relaxed solution of the root node of the unit commitment optimization model is less than a set threshold; A feasible solution is found based on the relaxed enhanced neighborhood search algorithm, and the unit commitment problem is optimized based on the feasible solution to achieve optimal dispatch of the power system.

2. The unit commitment acceleration method based on adaptive calling neighborhood search according to claim 1 is characterized in that: The basic data of the unit includes: the operating cost of the unit, the output data of the unit, the power constraint data of the unit and the day-ahead load forecast data; The unit's operating cost, unit output data, unit power constraint data and day-ahead load forecast data are all collected from the power system.

3. The unit commitment acceleration method based on adaptive calling neighborhood search according to claim 1 is characterized in that: The construction of the unit commitment optimization model is specifically as follows: in, and Respectively for units startup and shutdown costs; and Respectively for units In the period The startup state variables and shutdown state variables of the unit At the moment When starting up, the unit In the period Boot status variables is 1, otherwise it is 0; when the unit At the moment When shutting down, the unit In the period Shutdown status variable is 1, otherwise 0; It's the crew In the period Cost; For the crew In the period of power generation.

4. The unit commitment acceleration method based on adaptive calling neighborhood search according to claim 1 is characterized in that: The method for finding a feasible solution based on the relaxed reinforced neighborhood search algorithm specifically includes setting parameters of the adaptively calling relaxed reinforced neighborhood search algorithm, and solving the unit combination optimization model based on the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm; wherein, setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm includes: limiting the discreteness of the sub-integer programming problem, limiting the relaxation scale of the total problem, the number of solution nodes and stagnation nodes, and limiting the cutting plane strength parameters.

5. The unit commitment acceleration method based on adaptive calling neighborhood search according to claim 4 is characterized in that: The unit combination optimization model is solved as follows: after setting the heuristic parameters, the linear relaxation optimal solution is calculated. ; If the integer variables of the original unit combination problem are in the optimal solution If the result is an integer, then fix the value of the integer, otherwise set the upper and lower bounds of the integer variable to be decided to and ; The integer variables of the updated unit combination sub-problem are converted to 0-1 variables, and the optimal solution is obtained by solving the sub-problem after updating the upper and lower bounds. , output the optimal solution As the initial feasible solution to the original unit combination problem; It means rounding down, that is, the lower bound of the variable; It means rounding up, that is, the upper bound of the variable.

6. A unit commitment acceleration system based on adaptive calling neighborhood search, characterized in that: include: An acquisition module, wherein the acquisition module acquires basic data of units in the power system; A construction module, wherein the construction module constructs a unit commitment optimization model based on the acquired basic data of units in the power system; A calling module, wherein the calling module calls a relaxed enhanced neighborhood search algorithm when a relaxed solution of a root node of a problem of a unit commitment optimization model to be solved and an optimal solution of the problem satisfy a constraint condition; if the relaxed solution of the root node of the problem of the unit commitment optimization model to be solved and the optimal solution of the problem do not satisfy the constraint condition, then calling a simulated annealing algorithm, a genetic algorithm, an ant colony algorithm or a neural network algorithm to find a feasible solution for the given optimization problem; wherein the constraint condition is that the absolute value of the difference between the optimal solution of the unit commitment optimization model and the relaxed solution of the root node of the unit commitment optimization model is less than a set threshold value; The solution module searches for a feasible solution based on a relaxed enhanced neighborhood search algorithm, and accelerates the optimization of the unit combination problem based on the feasible solution to achieve optimal scheduling of the power system.

7. The unit commitment acceleration system based on adaptive calling neighborhood search according to claim 6 is characterized in that: The construction of the unit commitment optimization model is specifically as follows: in, and Respectively for units startup and shutdown costs; and Respectively for units In the period The startup state variables and shutdown state variables of the unit At the moment When starting up, the unit In the period Boot status variables is 1, otherwise it is 0; when the unit At the moment When shutting down, the unit In the period Shutdown status variable is 1, otherwise 0; It's the crew In the period Cost; For the crew In the period of power generation.

8. The unit commitment acceleration system based on adaptive calling neighborhood search according to claim 6 is characterized in that: The method for finding a feasible solution based on the relaxed reinforced neighborhood search algorithm specifically includes setting parameters of the adaptively calling relaxed reinforced neighborhood search algorithm, and solving the unit combination optimization model based on the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm; wherein, setting the parameters of the adaptively calling relaxed reinforced neighborhood search algorithm includes: limiting the discreteness of the sub-integer programming problem, limiting the relaxation scale of the total problem, the number of solution nodes and stagnation nodes, and limiting the cutting plane strength parameters.

9. The unit commitment acceleration system based on adaptive calling neighborhood search according to claim 8, characterized in that: The unit combination optimization model is solved as follows: after setting the heuristic parameters, the linear relaxation optimal solution is calculated. ; If the integer variables of the original unit combination problem are in the optimal solution If the result is an integer, then fix the value of the integer, otherwise set the upper and lower bounds of the integer variable to be decided to and ; The integer variables of the updated unit combination sub-problem are converted to 0-1 variables, and the optimal solution is obtained by solving the sub-problem after updating the upper and lower bounds. , output the optimal solution As the initial feasible solution to the original unit combination problem; It means rounding down, that is, the lower bound of the variable; It means rounding up, that is, the upper bound of the variable.

10. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the unit commitment acceleration method based on adaptive calling neighborhood search as described in any one of claims 1 to 5 are implemented.

11. 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 commitment acceleration method based on adaptive calling neighborhood search as claimed in any one of claims 1 to 5 are implemented.

Images