Scheduling method and device of power system and electronic equipment

Abstract

The invention discloses a scheduling method and device of a power system and electronic equipment. The method comprises the following steps: acquiring a mathematical model of a security constraint unit commitment problem of the power system; an initial solution of the mathematical model is determined, the mathematical model is solved according to the initial solution and a target solving framework, a feasible solution of the security constraint unit commitment problem is obtained, the target solving framework is composed of a local search algorithm and a cut-plane algorithm, and the feasible solution comprises an optimal solution in a period of time in the future. The power-on time information and the power-off time information of the generator sets in the plurality of generator sets, and the output power of the generator sets in the plurality of generator sets at a preset time point; and scheduling the power system according to the feasible solution. According to the method and the device, the technical problem of relatively low scheduling efficiency of a power system caused by low efficiency of solving a security constraint unit commitment problem by adopting a branch and bound algorithm in related technologies is solved.

Description

Technical Field

[0001] This application relates to the field of power system dispatching, and more specifically, to a power system dispatching method, apparatus, and electronic equipment. Background Technology

[0002] The Security-Constrained Unit Commitment (SCUC) problem is a fundamental optimization problem faced by power system operators. Its goal is to determine the optimal plan for generating units to meet forecasted demand within the planning scope, while ensuring the safety and reliability of power system operation.

[0003] Currently, the branch and bound algorithm is commonly used in related technologies to solve the SCUC problem and find feasible solutions for power system scheduling. The branch and bound algorithm is a tree search algorithm that requires traversing all leaf nodes from the root node. When encountering an infeasible solution, it is often discarded directly, and pruning is performed by updating the upper and lower bounds to reduce the search space. However, this algorithm requires traversing all leaf nodes, and as the size of the SCUC problem increases, the search space becomes very large, resulting in low solution efficiency and long solution time, thus leading to relatively low power system scheduling efficiency.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a power system dispatching method, apparatus, and electronic device to at least solve the technical problem in the related art where the low efficiency of using the branch and bound algorithm to solve the safety constraint unit combination problem leads to relatively low dispatching efficiency of the power system.

[0006] According to one aspect of the embodiments of this application, a power system scheduling method is provided, comprising: obtaining a mathematical model of a security-constrained generator unit combination problem of the power system, wherein the mathematical model is constructed based on parameter data of multiple generator units of the power system; determining an initial solution of the mathematical model, and solving the mathematical model based on the initial solution and an objective solution framework to obtain a feasible solution of the security-constrained generator unit combination problem, wherein the objective solution framework consists of a local search algorithm and a cutting plane algorithm, and the feasible solution includes start-up time information and shutdown time information of the generator units among the multiple generator units within a future period, as well as the output power of the generator units among the multiple generator units at a preset time point; and scheduling the power system based on the feasible solution.

[0007] Furthermore, determining the initial solution of the mathematical model includes: solving the linear relaxation of the mathematical model to obtain the relaxed solution; determining whether there are non-integer decision variables among the decision variables of the relaxed solution; if there are non-integer decision variables among the decision variables of the relaxed solution, then rounding the non-integer decision variables to obtain the initial solution.

[0008] Furthermore, based on the initial solution and the objective solution framework, the mathematical model is solved to obtain a feasible solution to the safety-constrained unit combination problem, including: processing the mathematical model to obtain a relaxed model of the safety-constrained unit combination problem, and using the objective function of the mathematical model as the objective function of the relaxed model, where the objective function of the mathematical model is the total power generation cost of multiple generator units; processing the objective function of the relaxed model based on the initial solution to obtain a linear programming subproblem of the mathematical model, where the objective function of the linear programming subproblem is the Manhattan distance between the initial solution and the feasible solution; solving the linear programming subproblem to obtain the value of the objective function of the linear programming subproblem; and determining the feasible solution based on the value of the objective function of the linear programming subproblem.

[0009] Furthermore, based on the initial solution, the objective function of the relaxation model is processed to obtain the linear programming subproblems of the mathematical model, including: determining the Manhattan distance between the initial solution and the feasible solution; resetting the objective function of the relaxation model from the total power generation cost of multiple generator sets to the Manhattan distance between the initial solution and the feasible solution, thus obtaining the linear programming subproblems.

[0010] Furthermore, determining a feasible solution based on the value of the objective function of the linear programming subproblem includes: if the value of the objective function of the linear programming subproblem is a preset value, then the initial solution is taken as a feasible solution; if the value of the objective function of the linear programming subproblem is not a preset value, then the Manhattan cut plane is determined based on the initial solution and the value of the objective function of the linear programming subproblem, and a feasible solution is determined based on the Manhattan cut plane.

[0011] Furthermore, determining feasible solutions based on the Manhattan cut plane includes: determining odd-numbered cut planes based on the Manhattan cut planes, and determining the pseudo-Boolean satisfiability subproblem of the mathematical model based on the Manhattan cut planes and odd-numbered cut planes; solving the pseudo-Boolean satisfiability subproblem using a local search algorithm to obtain the solution to the pseudo-Boolean satisfiability subproblem; and calculating the solution to the pseudo-Boolean satisfiability subproblem and the linear programming subproblem to obtain feasible solutions.

[0012] Furthermore, the feasible solution is obtained by calculating based on the solution of the pseudo-Boolean satisfiability subproblem and the linear programming subproblem, including: inputting the solution of the pseudo-Boolean satisfiability subproblem into the linear programming subproblem to obtain a new linear programming subproblem; alternately solving the new linear programming subproblem and the new pseudo-Boolean satisfiability subproblem until the objective function of the new linear programming subproblem is equal to a preset value, thus obtaining a feasible solution.

[0013] Furthermore, based on the feasible solution, the power system scheduling includes: when it is detected that the current time has reached the start time of a future period, obtaining the start-up time information and shutdown time information of the generator sets among the multiple generator sets, as well as the output power of the generator sets among the multiple generator sets at a preset time point from the feasible solution; and scheduling the operation of the generator sets among the multiple generator sets based on the start-up time information and shutdown time information of the generator sets among the multiple generator sets, as well as the output power of the generator sets among the multiple generator sets at the preset time point.

[0014] According to another aspect of the embodiments of this application, a power system scheduling method is also provided, comprising: acquiring a mathematical model of a power system safety-constrained generator unit combination problem uploaded by a client, wherein the mathematical model is constructed based on parameter data of multiple generator units in the power system; determining an initial solution of the mathematical model in a cloud server, and solving the mathematical model based on the initial solution and an objective solution framework to obtain a feasible solution to the safety-constrained generator unit combination problem, wherein the objective solution framework consists of a local search algorithm and a cutting plane algorithm, and the feasible solution includes start-up and shutdown time information of the generator units in the multiple generator units within a future period, as well as the output power of the generator units in the multiple generator units at a preset time point; and feeding back the feasible solution to the client so as to schedule the power system based on the feasible solution.

[0015] According to another aspect of the embodiments of this application, a power system dispatching device is also provided, comprising: an acquisition unit, configured to acquire a mathematical model of a safety-constrained generator unit combination problem of a power system, wherein the mathematical model is constructed based on parameter data of multiple generator units of the power system; a determination unit, configured to determine an initial solution of the mathematical model, and solve the mathematical model based on the initial solution and an objective solution framework to obtain a feasible solution to the safety-constrained generator unit combination problem, wherein the objective solution framework consists of a local search algorithm and a cutting plane algorithm, and the feasible solution includes start-up time information and shutdown time information of the generator units among the multiple generator units within a future period, as well as the output power of the generator units among the multiple generator units at a preset time point; and a processing unit, configured to dispatch the power system based on the feasible solution.

[0016] Furthermore, the determining unit includes: a first solving subunit, used to solve the linear relaxation of the mathematical model to obtain the relaxed solution; a judging subunit, used to judge whether there are non-integer decision variables among the decision variables of the relaxed solution; and a first processing subunit, used to round the non-integer decision variables to obtain the initial solution if there are non-integer decision variables among the decision variables of the relaxed solution.

[0017] Furthermore, the determining unit also includes: a second processing subunit, used to process the mathematical model to obtain a relaxed model of the safety-constrained unit combination problem, and using the objective function of the mathematical model as the objective function of the relaxed model, wherein the objective function of the mathematical model is the total power generation cost of multiple generator units; a third processing subunit, used to process the objective function of the relaxed model based on the initial solution to obtain a linear programming subproblem of the mathematical model, wherein the objective function of the linear programming subproblem is the Manhattan distance between the initial solution and the feasible solution; a second solving subunit, used to solve the linear programming subproblem to obtain the value of the objective function of the linear programming subproblem; and a first determining subunit, used to determine the feasible solution based on the value of the objective function of the linear programming subproblem.

[0018] Furthermore, the third processing subunit includes: a first determining module, used to determine the Manhattan distance between the initial solution and the feasible solution; and a processing module, used to reset the objective function of the relaxation model from the total power generation cost of multiple generator sets to the Manhattan distance between the initial solution and the feasible solution, thereby obtaining a linear programming subproblem.

[0019] Furthermore, the first determining subunit includes: a second determining module, used to take the initial solution as a feasible solution if the objective function of the linear programming subproblem is a preset value; and a third determining module, used to determine the Manhattan cut plane based on the initial solution and the objective function of the linear programming subproblem if the objective function of the linear programming subproblem is not a preset value, and to determine a feasible solution based on the Manhattan cut plane.

[0020] Furthermore, the third determining module includes: a determining submodule, used to determine the odd-numbered cutting plane based on the Manhattan cutting plane, and to determine the pseudo-Boolean satisfiability subproblem of the mathematical model based on the Manhattan cutting plane and the odd-numbered cutting plane; a solving submodule, used to solve the pseudo-Boolean satisfiability subproblem based on a local search algorithm to obtain the solution to the pseudo-Boolean satisfiability subproblem; and a calculation submodule, used to perform calculations based on the solution to the pseudo-Boolean satisfiability subproblem and the linear programming subproblem to obtain a feasible solution.

[0021] Furthermore, the computation submodule includes: a first processing submodule, used to input the solution of the pseudo-Boolean satisfiability subproblem into the linear programming subproblem to obtain a new linear programming subproblem; and a second processing submodule, used to alternately solve the new linear programming subproblem and the new pseudo-Boolean satisfiability subproblem until the objective function of the new linear programming subproblem is equal to a preset value, thus obtaining a feasible solution.

[0022] Furthermore, the processing unit includes: an acquisition subunit, used to acquire, from feasible solutions, the start-up time information and shutdown time information of the generator sets among the multiple generator sets, as well as the output power of the generator sets among the multiple generator sets at a preset time point, when it is detected that the current time has reached the start time of a future period; and a scheduling subunit, used to schedule the operation of the generator sets among the multiple generator sets based on the start-up time information and shutdown time information of the generator sets among the multiple generator sets, as well as the output power of the generator sets among the multiple generator sets at the preset time point.

[0023] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the power system scheduling method described above during runtime.

[0024] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein the storage medium stores a program, wherein the program controls the device where the storage medium is located to execute the power system scheduling method described above during runtime.

[0025] According to another aspect of the present invention, a computer program product is also provided, including a computer program or instructions, which, when executed by a processor, implement the power system scheduling method described above.

[0026] In this embodiment, a mathematical model of the safety-constrained generator unit combination problem of a power system is obtained. This mathematical model is constructed based on parameter data of multiple generator units in the power system. An initial solution to the mathematical model is determined, and the model is solved based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator unit combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up and shutdown times of the generator units within a future period, as well as the output power of the generator units at preset time points. Based on the feasible solution, a method for scheduling the power system is determined. This method solves the feasible solution to the safety-constrained unit combination problem based on an initial solution that may not be feasible, thus making full use of information about infeasible solutions. It solves the feasible solution to the safety-constrained unit combination problem based on a heuristic algorithm framework (i.e., the objective solution framework) composed of local search algorithm and cutting plane algorithm, which effectively reduces the solution time and achieves the goal of making full use of information about infeasible solutions. This improves the solution efficiency of the safety-constrained unit combination problem and the dispatch efficiency of the power system. In addition, it solves the technical problem that the low efficiency of the branch and bound algorithm in solving the safety-constrained unit combination problem leads to the low dispatch efficiency of the power system. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0028] Figure 1 This is a schematic diagram of a computer terminal provided according to Embodiment 1 of this application;

[0029] Figure 2 This is a flowchart of a power system dispatching method according to Embodiment 1 of this application;

[0030] Figure 3 This is a schematic flowchart of an optional solution to the SCUC problem provided according to Embodiment 1 of this application;

[0031] Figure 4 This is a flowchart illustrating an optional random local search algorithm provided in Embodiment 1 of this application;

[0032] Figure 5 This is a flowchart of a power system dispatching method according to Embodiment 2 of this application;

[0033] Figure 6 This is a schematic diagram of a power system dispatching device according to Embodiment 3 of this application;

[0034] Figure 7 This is a structural block diagram of an electronic device provided according to Embodiment 4 of this application. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0038] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0039] Security-Constrained Unit Commitment (SCUC) problem: In order to meet the electricity demand of users, a plan is formulated to specify when each generator unit will be turned on and off and the output power at each time point in the future. The plan needs to meet a series of physical requirements and minimize the total generation cost. This problem is usually modeled as a hybrid 0-1 linear programming model.

[0040] Local Search (LS): A general term for algorithms that find optimal solutions only within a local scope (rather than globally). In this context, it refers to a heuristic method used to solve the Pseudo-Boolean Satisfiability (PB-SAT) problem. This method obtains a solution to the PB-SAT problem through steps such as initialization, iterative search, variable flipping, and updating constraint weights.

[0041] Cutting plane generation is a method for solving integer programming problems. It aims to iteratively restrict the space of feasible solutions by adding linear constraints (i.e., cutting planes) until an integer solution is found. Integer programming problems are generally more difficult to solve than their corresponding linear programming problems because the solution must not only satisfy the linear constraints but also be an integer.

[0042] Example 1

[0043] According to an embodiment of this application, a power system dispatching method is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0044] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a power system dispatching method is shown. Figure 1 As shown, the computer terminal (or mobile device) 10 may include a processor set 102 (the processor set 102 may include, but is not limited to, processing devices such as microprocessors (MCUs) or field-programmable gate arrays (FPGAs), and the processor set 102 may include a processor set, Figure 1 (Illustrated using 102a, 102b, ..., 102n), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may include: a display, an input / output interface (I / O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0045] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be wholly or partially embodied in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element within the computer terminal 10 (or mobile device).

[0046] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power system scheduling method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned power system scheduling method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0047] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0048] The display may be a touchscreen LCD display that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0049] The SCUC problem is a fundamental optimization problem faced by power system operators. Its goal is to determine the optimal plan for generating units to meet forecasted demand within the planning scope, while ensuring the safety and reliability of power system operation.

[0050] Currently, the branch and bound algorithm is commonly used in related technologies to solve the SCUC problem and find feasible solutions for power system dispatching. The branch and bound algorithm ignores integer constraints in the model, treating the corresponding variables as ordinary continuous variables, resulting in a linear programming (LP) model, which is called the linear programming relaxation (LPR) of the original model. Solving the LP yields a solution, called the relaxation solution, and the corresponding objective function is the lower bound of the original problem, meaning the objective function value of the original problem is greater than or equal to the current value. If the corresponding variable in the relaxation solution exactly satisfies the integer requirement, then the relaxation solution is a better solution to the original problem, and the solution process ends; otherwise, branching occurs, i.e., a fractional variable is selected, and its value is fixed at 0 and 1 respectively, constructing two LP subproblems. Solving the two LP subproblems separately, if the solutions obtained satisfy the integer requirement, then a feasible solution to the original problem is obtained, and the objective function value corresponding to this solution is denoted as the upper bound, meaning the objective function value of the original problem cannot be greater than the current value. If there are variables that still cannot satisfy the integer constraint requirements, the subproblem is further branched out and solved, and the upper and lower bounds are continuously updated. When the upper and lower bounds are equal, it indicates that a better solution has been found.

[0051] As can be seen, the branch and bound algorithm is a tree search algorithm that requires traversing all leaf nodes from the root node. When encountering an infeasible solution, it often discards the infeasible solution directly and prunes the tree by updating the upper and lower bounds to reduce the search space. However, this algorithm requires traversing all leaf nodes. As the size of the SCUC problem increases, the search space becomes larger, resulting in low solution efficiency and long solution time, which leads to relatively low scheduling efficiency of the power system.

[0052] Against the above-mentioned technical background, this application provides as follows Figure 2 The power system dispatching method is shown. Figure 2 This is a flowchart of a power system dispatching method according to Embodiment 1 of this application. The method includes:

[0053] Step S201: Obtain the mathematical model of the power system's safety-constrained generator unit combination problem. The mathematical model is constructed based on the parameter data of multiple generator units in the power system.

[0054] The mathematical model of the SCUC problem typically aims to minimize the total generation cost (including start-up and shutdown costs, operating costs, etc.). Constraints primarily include power balance constraints, reserve constraints, rise / fall limit constraints, minimum shutdown / start-up time constraints, and network security constraints. Specifically, the power balance constraint ensures that the total generation of all generating units in each time interval meets system demand (i.e., load); the reserve constraint requires the grid to have sufficient reserve capacity to handle unexpected fluctuations in demand or generation; the rise / fall limit constraint reflects the ability of generating units to increase (rise) or decrease (fall) output power within a single time period; the minimum shutdown / start-up time constraint refers to the minimum time a generating unit must remain offline (online) before restarting (shutting down) after being shut down (started); and the network security constraint limits the current flowing through transmission lines to prevent line overheating.

[0055] A mathematical model for the SCUC problem can be constructed based on parameter data (such as upper and lower limits of generator power) from multiple generator units in a power system. For example, using the on / off state and the output power of each generator unit in each time interval as decision variables, the SCUC problem can be abstracted into a mixed 0-1 linear programming model (i.e., a mathematical model), as follows:

[0056] min c T x

[0057] ste≤Ax≤f

[0058] l≤x≤u

[0059]

[0060] Among them, c T Let A represent the power generation cost matrix, x represent the decision variables, st represent the constraints, A represent the coefficient matrix of the constraints, e represent the lower bound of Ax, f represent the upper bound of Ax, l represent the lower bound of x, u represent the upper bound of x, and x∈R n , A∈R m×n e,f∈R m , l, u∈R n ,

[0061] For example, the mathematical model of the SCUC problem can be obtained through the power system's dispatching system.

[0062] Step S202: Determine the initial solution of the mathematical model, and solve the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained unit combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up time information and shutdown time information of the generator sets in the future, as well as the output power of the generator sets in the multiple generator sets at preset time points.

[0063] For example, by solving the mathematical model of the SCUC problem through linear relaxation, a relaxed solution can be obtained. Then, by rounding down the variables that are not integers (i.e., variables with fractional values) in the relaxed solution, an initial solution can be obtained. However, the initial solution may not be feasible.

[0064] In order to make full use of the information of the current infeasible solutions, this scheme designs a heuristic algorithm framework (i.e., the objective solution framework) for local search and cutting plane generation to generate feasible solutions to the SCUC problem. This solution framework mainly includes the LP subproblem and the PB-SAT subproblem, which will be introduced below with specific embodiments, and will not be elaborated here.

[0065] Step S203: Based on the feasible solution, the power system is dispatched.

[0066] For example, based on the generator set start-up and shutdown time information included in the feasible solution, as well as the generator set output power at a preset time point, the operation of generator sets in the power system can be scheduled.

[0067] In this scheme, feasible solutions to the safety-constrained unit combination problem are obtained based on an initial solution that may not be feasible, thus making full use of information about infeasible solutions. Feasible solutions to the safety-constrained unit combination problem are obtained based on a heuristic algorithm framework (i.e., the objective solution framework) composed of local search algorithm and cutting plane algorithm, which effectively reduces the solution time and achieves the goal of making full use of information about infeasible solutions. This achieves the technical effect of improving the solution efficiency of the safety-constrained unit combination problem and improving the dispatch efficiency of the power system. In addition, it solves the technical problem that the low efficiency of using the branch and bound algorithm to solve the safety-constrained unit combination problem in related technologies leads to the low dispatch efficiency of the power system.

[0068] In an alternative embodiment, the following can be employed: Figure 3 The diagram shown illustrates how to solve the SCUC problem. Figure 3 This is a flowchart illustrating an optional solution to the SCUC problem according to Embodiment 1 of this application. The solution provides a heuristic algorithm framework based on a local search algorithm and a cutting plane algorithm, fully utilizing information about currently infeasible solutions, such as... Figure 3As shown, by generating an initial solution, solving the LP subproblem and generating the cutting plane, solving the PB-SAT subproblem to generate a new solution, and iterating continuously, a feasible solution to the SCUC problem is obtained.

[0069] In order to obtain the initial solution of the mathematical model, the power system scheduling method provided in Embodiment 1 of this application determines the initial solution of the mathematical model by: solving the linear relaxation of the mathematical model to obtain the relaxed solution; determining whether there are non-integer decision variables in the decision variables of the relaxed solution; if there are non-integer decision variables in the decision variables of the relaxed solution, then rounding the non-integer decision variables to obtain the initial solution.

[0070] Optionally, by solving the linear relaxation of the mathematical model of the SCUC problem, a relaxed solution can be obtained. Then, it is determined whether there are non-integer decision variables in the relaxed solution. If the decision variables in the relaxed solution exactly meet the integer requirement, the relaxed solution is the better solution of the original problem, and the solution ends. If there are non-integer decision variables (i.e., variables with fractional values) in the relaxed solution, the non-integer decision variables are rounded to obtain the initial solution. The initial solution may not be feasible.

[0071] It should be noted that by determining whether there are non-integer decision variables among the decision variables of the relaxed solution, the initial solution of the mathematical model can be accurately determined.

[0072] To fully utilize information about currently infeasible solutions, the power system scheduling method provided in Embodiment 1 of this application solves the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained unit combination problem. This includes: processing the mathematical model to obtain a relaxed model of the safety-constrained unit combination problem, and using the objective function of the mathematical model as the objective function of the relaxed model, where the objective function of the mathematical model is the total power generation cost of multiple generator units; processing the objective function of the relaxed model based on the initial solution to obtain a linear programming subproblem of the mathematical model, where the objective function of the linear programming subproblem is the Manhattan distance between the initial solution and the feasible solution; solving the linear programming subproblem to obtain the value of the objective function of the linear programming subproblem; and determining a feasible solution based on the value of the objective function of the linear programming subproblem.

[0073] Optionally, the mathematical model is processed (i.e., x is changed). j Given the constraints, we can obtain a relaxed model for the SCUC problem, as follows:

[0074] min c T x

[0075] ste≤Ax≤f

[0076] l≤x≤u

[0077]

[0078] The objective function (c) of the mathematical model T x) is used as the objective function of the relaxation model, and then the objective function (c) of the relaxation model is adjusted according to the initial solution. T By processing x), we can obtain the linear programming subproblem (i.e., LP subproblem) of the mathematical model. Then, we solve the LP subproblem to obtain the value of the objective function of the LP subproblem. Based on the value of the objective function of the LP subproblem, we can determine the feasible solution of the SCUC problem.

[0079] In order to obtain the LP subproblem, in the power system scheduling method provided in Embodiment 1 of this application, the objective function of the relaxation model is processed according to the initial solution to obtain the linear programming subproblem of the mathematical model, including: determining the Manhattan distance between the initial solution and the feasible solution; resetting the objective function of the relaxation model from the total power generation cost of multiple generator sets to the Manhattan distance between the initial solution and the feasible solution to obtain the linear programming subproblem.

[0080] Optionally, the Manhattan distance between the initial solution and the feasible solution is determined, and the objective function of the relaxation model is shifted from the total generation cost of multiple generator sets (c T x) is reset to the Manhattan distance between the initial solution and the feasible solution, resulting in the LP subproblem.

[0081] For a given solution The Manhattan distance from any solution x to the given solution is defined as:

[0082]

[0083] Where δ(x) represents the Manhattan distance, This indicates the number of differences between the portions of the initial solution with values ​​of 0 and the corresponding positions in the current solution. This represents the number of differences between the portion of the initial solution with a value of 1 and the corresponding current solution at the corresponding position.

[0084] This scheme uses the Manhattan distance value to infer the distance to feasible solutions that are closer to the current solution.

[0085] The LP subproblem can be represented as follows:

[0086]

[0087] ste≤Ax≤f

[0088] l≤x≤u

[0089] The objective function of the LP subproblem is the Manhattan distance to the nearest feasible solution to the current solution.

[0090] In order to determine a feasible solution to the SCUC problem, in the power system scheduling method provided in Embodiment 1 of this application, determining a feasible solution based on the value of the objective function of the linear programming subproblem includes: if the value of the objective function of the linear programming subproblem is a preset value, then the initial solution is taken as a feasible solution; if the value of the objective function of the linear programming subproblem is not a preset value, then the Manhattan cut plane is determined based on the initial solution and the value of the objective function of the linear programming subproblem, and a feasible solution is determined based on the Manhattan cut plane.

[0091] By solving the LP subproblem, the objective function of the LP subproblem is obtained (i.e., the Manhattan distance obtained from the solution). If the objective function of the LP subproblem is 0 (i.e., the preset value), then the current solution is considered feasible, and the problem is solved by taking the initial solution as feasible and returning to the current solution. If the objective function of the LP subproblem is not 0, then the Manhattan cut is determined based on the initial solution and the objective function of the LP subproblem. That is, the Manhattan cut is generated by the Manhattan distance between the current solution and the obtained Manhattan distance. The feasible solution of the SCUC problem is determined based on the Manhattan cut. This type of cut can be represented as follows:

[0092]

[0093] in, It is a pseudo-Boolean function. The objective function obtained by solving the LP subproblem is rounded up, and θ represents the right-hand side of the cutting plane.

[0094] It should be noted that solving the mathematical model based on the initial solution and the objective solution framework enables the utilization of information about infeasible solutions. The objective function of the reset relaxation model is the Manhattan distance between the current solution and the nearest feasible solution. The purpose is to use information about infeasible solutions to determine the degree of deviation between the current solution and the nearest feasible solution, thereby determining the feasible solution of the SCUC problem and improving the solution efficiency of the SCUC problem.

[0095] In order to determine a feasible solution to the SCUC problem, the power system scheduling method provided in Embodiment 1 of this application includes determining a feasible solution based on the Manhattan cut plane, which includes: determining an odd cut plane based on the Manhattan cut plane, and determining a pseudo-Boolean satisfiability subproblem of the mathematical model based on the Manhattan cut plane and the odd cut plane; solving the pseudo-Boolean satisfiability subproblem using a local search algorithm to obtain a solution to the pseudo-Boolean satisfiability subproblem; and calculating based on the solution to the pseudo-Boolean satisfiability subproblem and the linear programming subproblem to obtain a feasible solution.

[0096] By observing the Manhattan cut, we can see that when the right-hand side is much greater than 1, the cutting plane can exclude more infeasible vertices. By continuously generating cutting planes, we can continuously approach a feasible solution. Optionally, we can determine the odd-numbered cutting plane based on the Manhattan cut plane. For example, given two different Manhattan cuts, if the sum of the right-hand sides is odd, we can add the two Manhattan cuts together, divide both sides by 2, and round the right-hand side up to obtain the odd-numbered cutting plane, represented as follows:

[0097]

[0098] Where s and t represent two different Manhattan cuts,

[0099] Optionally, a pseudo-Boolean satisfiability subproblem (i.e., PB-SAT subproblem) is determined based on the Manhattan cut plane and the odd cut plane. The PB-SAT subproblem includes the generated cut planes used to generate new solutions. For example, by adding the Manhattan cut and the Oddity cut to the PB-SAT subproblem, the PB-SAT subproblem can be represented as follows:

[0100]

[0101] Solve the PB-SAT subproblem to generate a new solution that satisfies the Manhattan cut and the Oddity cut. Alternatively, solve the PB-SAT subproblem using a local search algorithm to obtain a solution; for example, use a stochastic local search algorithm to solve the subproblem. Figure 4 This is a flowchart illustrating an optional random local search algorithm provided in Embodiment 1 of this application, as shown below. Figure 4 As shown, the algorithm mainly includes steps such as initialization, iterative search, variable selection and flipping based on the scoring function, and updating constraint weights to obtain the solution to the PB-SAT subproblem. Then, based on the solution to the PB-SAT subproblem and the LP subproblem, a feasible solution is obtained.

[0102] In order to calculate a feasible solution to the SCUC problem, the power system scheduling method provided in Embodiment 1 of this application calculates a feasible solution based on the solution of the pseudo-Boolean satisfiability subproblem and the linear programming subproblem. This includes: inputting the solution of the pseudo-Boolean satisfiability subproblem into the linear programming subproblem to obtain a new linear programming subproblem; and alternately solving the new linear programming subproblem and the new pseudo-Boolean satisfiability subproblem until the objective function of the new linear programming subproblem is a preset value, thereby obtaining a feasible solution.

[0103] Optionally, after solving the PB-SAT subproblem and generating a new solution that satisfies the Manhattan cut and Oddity cut, the solution of the PB-SAT subproblem is input into the LP subproblem to obtain a new LP subproblem. By alternately solving the new LP subproblem and the new PB-SAT subproblem, the search space is continuously reduced until the objective function of the new LP subproblem is 0, and a feasible solution is obtained.

[0104] It should be noted that the generated Manhattan cut and Odditycut are added to the constraints in multiple iterations of the PB-SAT subproblem to find solutions that satisfy all cutting planes. The PB-SAT subproblem is solved using a stochastic local search algorithm. The new solution is input into the LP subproblem. If the objective function of the LP subproblem is not 0, it means that the solution generated by the PB-SAT subproblem is still far from the nearest feasible solution and needs to continue iterating. If the objective function of the LP subproblem is 0, it means that the solution generated by the PB-SAT subproblem is a feasible solution to the SCUC problem.

[0105] To achieve power system dispatching, the power system dispatching method provided in Embodiment 1 of this application, based on feasible solutions, includes: when it is detected that the current time has reached the start time of a future period, obtaining the start-up time information and shutdown time information of the generator sets among multiple generator sets, as well as the output power of the generator sets among multiple generator sets at a preset time point from the feasible solutions; and dispatching the operation of the generator sets among multiple generator sets based on the start-up time information and shutdown time information of the generator sets among multiple generator sets, as well as the output power of the generator sets among multiple generator sets at the preset time point.

[0106] For example, when the start time of a planned future period is reached, the scheduling system obtains the start-up time and shutdown time information of each generator set from the feasible solutions, as well as the output power of each generator set at the preset time point. Based on the start-up time and shutdown time information of each generator set, as well as the output power of each generator set at the preset time point, the system schedules the operation of each generator set.

[0107] In this embodiment, a mathematical model of the safety-constrained generator unit combination problem of a power system is obtained. This mathematical model is constructed based on parameter data of multiple generator units in the power system. An initial solution to the mathematical model is determined, and the model is solved based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator unit combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up and shutdown times of the generator units within a future period, as well as the output power of the generator units at preset time points. Based on the feasible solution, a method for scheduling the power system is determined. This method solves the feasible solution to the safety-constrained unit combination problem based on an initial solution that may not be feasible, thus making full use of information about infeasible solutions. It solves the feasible solution to the safety-constrained unit combination problem based on a heuristic algorithm framework (i.e., the objective solution framework) composed of local search algorithm and cutting plane algorithm, which effectively reduces the solution time and achieves the goal of making full use of information about infeasible solutions. This improves the solution efficiency of the safety-constrained unit combination problem and the dispatch efficiency of the power system. In addition, it solves the technical problem that the low efficiency of the branch and bound algorithm in solving the safety-constrained unit combination problem leads to the low dispatch efficiency of the power system.

[0108] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0110] Example 2

[0111] According to embodiments of this application, a power system dispatching method is also provided, such as... Figure 5 As shown, the method includes:

[0112] Step S501: Obtain the mathematical model of the power system's safety constraint unit combination problem uploaded by the client. The mathematical model is constructed based on the parameter data of multiple generator units in the power system.

[0113] Step S502: Determine the initial solution of the mathematical model in the cloud server, and solve the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained unit combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up time information and shutdown time information of the generator sets in the future, as well as the output power of the generator sets in the multiple generator sets at a preset time point.

[0114] Step S503: The feasible solution is fed back to the client so that the power system can be dispatched based on the feasible solution.

[0115] The above scheme solves the feasible solution to the safety-constrained unit combination problem based on an initial solution that may not be feasible, thus making full use of the information of infeasible solutions. It solves the feasible solution to the safety-constrained unit combination problem based on a heuristic algorithm framework (i.e., the objective solution framework) composed of local search algorithm and cutting plane algorithm, which effectively reduces the solution time and achieves the goal of making full use of the information of infeasible solutions. This improves the solution efficiency of the safety-constrained unit combination problem and the dispatch efficiency of the power system. In addition, it solves the technical problem that the low efficiency of the branch and bound algorithm in solving the safety-constrained unit combination problem leads to the low dispatch efficiency of the power system.

[0116] The specific method for scheduling the power system in the cloud server is the same as that in Example 1, and will not be repeated here.

[0117] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0119] Example 3

[0120] According to an embodiment of this application, a power system dispatching apparatus for implementing the above-described power system dispatching method is also provided, such as... Figure 6 As shown, the device includes: an acquisition unit 601, a determination unit 602, and a processing unit 603.

[0121] The acquisition unit 601 is used to acquire the mathematical model of the safety constraint unit combination problem of the power system, wherein the mathematical model is constructed based on the parameter data of multiple generator units in the power system;

[0122] The determining unit 602 is used to determine the initial solution of the mathematical model and solve the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained unit combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up time information and shutdown time information of the generator sets in the future, as well as the output power of the generator sets in the multiple generator sets at a preset time point.

[0123] The processing unit 603 is used to schedule the power system based on feasible solutions.

[0124] In the power system dispatching device provided in Embodiment 3 of this application, the acquisition unit 601 acquires the mathematical model of the power system's safety-constrained generator unit combination problem. The mathematical model is constructed based on the parameter data of multiple generator units in the power system. The determination unit 602 determines the initial solution of the mathematical model and solves the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator unit combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up time information and shutdown time information of the generator units in the multiple generator units within a future period, as well as the output power of the generator units in the multiple generator units at a preset time point. The processing unit 603 dispatches the power system based on the feasible solution. In this scheme, feasible solutions to the safety-constrained unit combination problem are obtained based on an initial solution that may not be feasible, thus making full use of information about infeasible solutions. Feasible solutions to the safety-constrained unit combination problem are obtained based on a heuristic algorithm framework (i.e., the objective solution framework) composed of local search algorithm and cutting plane algorithm, which effectively reduces the solution time and achieves the goal of making full use of information about infeasible solutions. This achieves the technical effect of improving the solution efficiency of the safety-constrained unit combination problem and improving the dispatch efficiency of the power system. In addition, it solves the technical problem that the low efficiency of using the branch and bound algorithm to solve the safety-constrained unit combination problem in related technologies leads to the low dispatch efficiency of the power system.

[0125] Optionally, in the power system dispatching device provided in Embodiment 3 of this application, the determining unit 602 includes: a first solving subunit, used to solve the linear relaxation of the mathematical model to obtain a relaxed solution; a judging subunit, used to judge whether there are non-integer decision variables in the decision variables of the relaxed solution; and a first processing subunit, used to round the non-integer decision variables to obtain an initial solution if there are non-integer decision variables in the decision variables of the relaxed solution.

[0126] Optionally, in the power system dispatching device provided in Embodiment 3 of this application, the determining unit 602 further includes: a second processing subunit, used to process the mathematical model to obtain a relaxed model of the safety-constrained unit combination problem, and use the objective function of the mathematical model as the objective function of the relaxed model, wherein the objective function of the mathematical model is the total power generation cost of multiple generator units; a third processing subunit, used to process the objective function of the relaxed model based on the initial solution to obtain a linear programming subproblem of the mathematical model, wherein the objective function of the linear programming subproblem is the Manhattan distance between the initial solution and the feasible solution; a second solving subunit, used to solve the linear programming subproblem to obtain the value of the objective function of the linear programming subproblem; and a first determining subunit, used to determine a feasible solution based on the value of the objective function of the linear programming subproblem.

[0127] Optionally, in the power system dispatching device provided in Embodiment 3 of this application, the third processing subunit includes: a first determining module, used to determine the Manhattan distance between the initial solution and the feasible solution; and a processing module, used to reset the objective function of the relaxation model from the total power generation cost of multiple generator sets to the Manhattan distance between the initial solution and the feasible solution, thereby obtaining a linear programming subproblem.

[0128] Optionally, in the power system dispatching device provided in Embodiment 3 of this application, the first determining subunit includes: a second determining module, used to take the initial solution as a feasible solution if the objective function of the linear programming subproblem is a preset value; and a third determining module, used to determine the Manhattan cut plane based on the initial solution and the objective function of the linear programming subproblem if the objective function of the linear programming subproblem is not a preset value, and to determine a feasible solution based on the Manhattan cut plane.

[0129] Optionally, in the power system dispatching device provided in Embodiment 3 of this application, the third determining module includes: a determining submodule, used to determine the odd-numbered cutting plane based on the Manhattan cutting plane, and to determine the pseudo-Boolean satisfiability subproblem of the mathematical model based on the Manhattan cutting plane and the odd-numbered cutting plane; a solving submodule, used to solve the pseudo-Boolean satisfiability subproblem based on a local search algorithm to obtain the solution to the pseudo-Boolean satisfiability subproblem; and a calculation submodule, used to perform calculations based on the solution to the pseudo-Boolean satisfiability subproblem and the linear programming subproblem to obtain a feasible solution.

[0130] Optionally, in the power system dispatching device provided in Embodiment 3 of this application, the calculation submodule includes: a first processing submodule, used to input the solution of the pseudo-Boolean satisfiability subproblem into the linear programming subproblem to obtain a new linear programming subproblem; and a second processing submodule, used to alternately solve the new linear programming subproblem and the new pseudo-Boolean satisfiability subproblem until the objective function of the new linear programming subproblem is a preset value, thereby obtaining a feasible solution.

[0131] Optionally, in the power system dispatching device provided in Embodiment 3 of this application, the processing unit 603 includes: an acquisition subunit, used to acquire, from feasible solutions, the start-up time information and shutdown time information of the generator sets among the multiple generator sets, as well as the output power of the generator sets among the multiple generator sets at a preset time point, when it is detected that the current time has reached the start time of a future period; and a dispatching subunit, used to dispatch the operation of the generator sets among the multiple generator sets based on the start-up time information and shutdown time information of the generator sets among the multiple generator sets, as well as the output power of the generator sets among the multiple generator sets at the preset time point.

[0132] It should be noted that the acquisition unit 601, determination unit 602, and processing unit 603 mentioned above correspond to steps S201 to S203 in Embodiment 1. The instances and application scenarios implemented by the above units and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in Embodiment 1.

[0133] It should be noted that the preferred implementation schemes involved in the above embodiments of this application are the same as the schemes, application scenarios and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0134] Example 4

[0135] Embodiments of this application may provide an electronic device, which may be any one of a group of electronic devices. Optionally, in this embodiment, the aforementioned electronic device may also be replaced by a terminal device such as a mobile terminal.

[0136] Optionally, in this embodiment, the aforementioned electronic device may be located in at least one of a plurality of network devices in a computer network.

[0137] In this embodiment, the aforementioned electronic device can execute the program code for the following steps in the power system scheduling method: obtaining a mathematical model of the power system's safety-constrained generator unit combination problem, wherein the mathematical model is constructed based on parameter data of multiple generator units in the power system; determining the initial solution of the mathematical model, and solving the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator unit combination problem, wherein the objective solution framework consists of a local search algorithm and a cutting plane algorithm, and the feasible solution includes the start-up time information and shutdown time information of the generator units in the multiple generator units within a future period, as well as the output power of the generator units in the multiple generator units at a preset time point; and scheduling the power system based on the feasible solution.

[0138] The aforementioned electronic equipment can also execute the following steps in the power system dispatching method: solving the linear relaxation of the mathematical model to obtain the relaxed solution; determining whether there are non-integer decision variables among the decision variables of the relaxed solution; if there are non-integer decision variables among the decision variables of the relaxed solution, then rounding the non-integer decision variables to obtain the initial solution.

[0139] The aforementioned electronic equipment can also execute program code for the following steps in the power system dispatching method: processing the mathematical model to obtain a relaxed model of the safety-constrained unit combination problem, and using the objective function of the mathematical model as the objective function of the relaxed model, wherein the objective function of the mathematical model is the total power generation cost of multiple generator units; processing the objective function of the relaxed model based on the initial solution to obtain a linear programming subproblem of the mathematical model, wherein the objective function of the linear programming subproblem is the Manhattan distance between the initial solution and the feasible solution; solving the linear programming subproblem to obtain the value of the objective function of the linear programming subproblem; and determining the feasible solution based on the value of the objective function of the linear programming subproblem.

[0140] The aforementioned electronic equipment can also execute program code for the following steps in the power system dispatching method: determining the Manhattan distance between the initial solution and the feasible solution; resetting the objective function of the relaxation model from the total power generation cost of multiple generator sets to the Manhattan distance between the initial solution and the feasible solution, thus obtaining a linear programming subproblem.

[0141] The aforementioned electronic device can also execute program code for the following steps in the power system dispatching method: if the objective function of the linear programming subproblem is a preset value, then the initial solution is taken as a feasible solution; if the objective function of the linear programming subproblem is not a preset value, then the Manhattan cut plane is determined based on the initial solution and the objective function of the linear programming subproblem, and a feasible solution is determined based on the Manhattan cut plane.

[0142] The aforementioned electronic equipment can also execute the following steps in the power system dispatching method: determining the odd-numbered cutting planes based on the Manhattan cutting planes, and determining the pseudo-Boolean satisfiability subproblem of the mathematical model based on the Manhattan cutting planes and the odd-numbered cutting planes; solving the pseudo-Boolean satisfiability subproblem using a local search algorithm to obtain the solution to the pseudo-Boolean satisfiability subproblem; and calculating the solution to the pseudo-Boolean satisfiability subproblem and the linear programming subproblem to obtain a feasible solution.

[0143] The aforementioned electronic equipment can also execute the following steps in the power system dispatching method: inputting the solution of the pseudo-Boolean satisfiability subproblem into the linear programming subproblem to obtain a new linear programming subproblem; alternately solving the new linear programming subproblem and the new pseudo-Boolean satisfiability subproblem until the objective function of the new linear programming subproblem is set to a preset value, thus obtaining a feasible solution.

[0144] The aforementioned electronic equipment can also execute the following steps in the power system dispatching method: when it is detected that the current time has reached the start time of a future period, obtain the start-up time information and shutdown time information of the generator sets in the multiple generator sets from the feasible solution, as well as the output power of the generator sets in the multiple generator sets at a preset time point; and dispatch the operation of the generator sets in the multiple generator sets according to the start-up time information and shutdown time information of the generator sets in the multiple generator sets, as well as the output power of the generator sets in the multiple generator sets at the preset time point.

[0145] Optionally, Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device 70 may include: one or more ( Figure 7 (Only one is shown) Processor 702 and memory 704. The electronic device 70 may also include a memory controller to control and manage the memory 704; the electronic device 70 may also include a peripheral interface to connect to a radio frequency module, an audio module, and a display screen, etc.

[0146] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the power system scheduling method and device in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned power system scheduling method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0147] The processor can access information and application programs stored in memory via a transmission device to perform the following steps: obtaining a mathematical model of the safety-constrained generator unit combination problem of the power system, wherein the mathematical model is constructed based on parameter data of multiple generator units in the power system; determining the initial solution of the mathematical model, and solving the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator unit combination problem, wherein the objective solution framework consists of a local search algorithm and a cutting plane algorithm, and the feasible solution includes the start-up and shutdown time information of the generator units in the multiple generator units within a future period, as well as the output power of the generator units in the multiple generator units at a preset time point; and scheduling the power system based on the feasible solution.

[0148] Optionally, the processor may also execute program code that performs the following steps: solves the linear relaxation of the mathematical model to obtain the relaxed solution; determines whether there are non-integer decision variables among the decision variables of the relaxed solution; if there are non-integer decision variables among the decision variables of the relaxed solution, then rounds the non-integer decision variables to obtain the initial solution.

[0149] Optionally, the processor may also execute program code for the following steps: processing the mathematical model to obtain a relaxed model of the safety-constrained unit combination problem, and using the objective function of the mathematical model as the objective function of the relaxed model, wherein the objective function of the mathematical model is the total power generation cost of multiple generator units; processing the objective function of the relaxed model based on the initial solution to obtain a linear programming subproblem of the mathematical model, wherein the objective function of the linear programming subproblem is the Manhattan distance between the initial solution and the feasible solution; solving the linear programming subproblem to obtain the value of the objective function of the linear programming subproblem; and determining the feasible solution based on the value of the objective function of the linear programming subproblem.

[0150] Optionally, the processor may also execute program code that performs the following steps: determining the Manhattan distance between the initial solution and the feasible solution; resetting the objective function of the relaxation model from the total power generation cost of multiple generator sets to the Manhattan distance between the initial solution and the feasible solution, thereby obtaining a linear programming subproblem.

[0151] Optionally, the processor may also execute program code that performs the following steps: if the objective function of the linear programming subproblem is a preset value, then the initial solution is taken as a feasible solution; if the objective function of the linear programming subproblem is not a preset value, then the Manhattan cut plane is determined based on the initial solution and the objective function of the linear programming subproblem, and a feasible solution is determined based on the Manhattan cut plane.

[0152] Optionally, the processor may also execute program code that performs the following steps: determining odd-numbered cutting planes based on the Manhattan cutting planes, and determining the pseudo-Boolean satisfiability subproblem of the mathematical model based on the Manhattan cutting planes and odd-numbered cutting planes; solving the pseudo-Boolean satisfiability subproblem using a local search algorithm to obtain the solution to the pseudo-Boolean satisfiability subproblem; and performing calculations based on the solution to the pseudo-Boolean satisfiability subproblem and the linear programming subproblem to obtain a feasible solution.

[0153] Optionally, the processor may also execute program code that performs the following steps: inputting the solution of the pseudo-Boolean satisfiability subproblem into the linear programming subproblem to obtain a new linear programming subproblem; alternately solving the new linear programming subproblem and the new pseudo-Boolean satisfiability subproblem until the objective function of the new linear programming subproblem is set to a preset value, thus obtaining a feasible solution.

[0154] Optionally, the processor may also execute program code that performs the following steps: when it detects that the current time has reached the start time of a future period, it obtains the start-up time information and shutdown time information of the generator sets in the multiple generator sets, as well as the output power of the generator sets in the multiple generator sets at a preset time point from the feasible solutions; and schedules the operation of the generator sets in the multiple generator sets based on the start-up time information and shutdown time information of the generator sets in the multiple generator sets, as well as the output power of the generator sets in the multiple generator sets at the preset time point.

[0155] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones (such as Android phones, iOS phones, etc.), tablets, PDAs, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 7 This does not limit the structure of the aforementioned electronic device. For example, electronic device 70 may also include components that are more... Figure 7 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 7 The different configurations shown.

[0156] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0157] Example 5

[0158] Embodiments of this application also provide a computer-readable storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the power system dispatching method provided in Embodiment 1.

[0159] Optionally, in this embodiment, the storage medium may be located in any one of the electronic devices in the group of electronic devices in the computer network, or in any one of the mobile terminals in the group of mobile terminals.

[0160] Example 6

[0161] Embodiments of this application also provide a computer program product. Optionally, in this embodiment, the computer program product may include a computer program that, when executed by a processor, implements the power system scheduling method provided in Embodiment 1.

[0162] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0163] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0167] If the integrated unit is implemented as 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 technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0168] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A power system dispatching method, characterized in that, include: A mathematical model is obtained for the safety-constrained generator unit combination problem of a power system, wherein the mathematical model is constructed based on the parameter data of multiple generator units in the power system; An initial solution to the mathematical model is determined, and the mathematical model is solved based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator set combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up time and shutdown time information of the generator sets in the future, as well as the output power of the generator sets in the multiple generator sets at a preset time point. Based on the feasible solution, the power system is scheduled.

2. The method according to claim 1, characterized in that, Determining the initial solution of the mathematical model includes: Solve the linear relaxation of the mathematical model to obtain the relaxed solution; Determine whether there are any non-integer decision variables among the decision variables of the relaxed solution; If the decision variables in the relaxed solution include non-integer decision variables, then the non-integer decision variables are rounded down to obtain the initial solution.

3. The method according to claim 1, characterized in that, Based on the initial solution and the objective solution framework, the mathematical model is solved to obtain feasible solutions to the safety-constrained unit combination problem, including: The mathematical model is processed to obtain a relaxed model of the safety-constrained unit combination problem, and the objective function of the mathematical model is used as the objective function of the relaxed model, wherein the objective function of the mathematical model is the total power generation cost of the multiple generator units; The objective function of the relaxation model is processed based on the initial solution to obtain the linear programming subproblem of the mathematical model, wherein the objective function of the linear programming subproblem is the Manhattan distance between the initial solution and the feasible solution; Solve the linear programming subproblem to obtain the value of the objective function of the linear programming subproblem; The feasible solution is determined based on the value of the objective function of the linear programming subproblem.

4. The method according to claim 3, characterized in that, Based on the initial solution, the objective function of the relaxation model is processed to obtain the linear programming subproblems of the mathematical model, including: Determine the Manhattan distance between the initial solution and the feasible solution; The objective function of the relaxation model is reset from the total power generation cost of the multiple generator sets to the Manhattan distance between the initial solution and the feasible solution, thus obtaining the linear programming subproblem.

5. The method according to claim 3, characterized in that, Determining the feasible solution based on the value of the objective function of the linear programming subproblem includes: If the objective function of the linear programming subproblem has a preset value, then the initial solution is taken as the feasible solution. If the objective function of the linear programming subproblem is not the preset value, then the Manhattan cut plane is determined based on the initial solution and the objective function of the linear programming subproblem, and the feasible solution is determined based on the Manhattan cut plane.

6. The method according to claim 5, characterized in that, Determining the feasible solution based on the Manhattan cut plane includes: Based on the Manhattan cut plane, determine the odd cut plane, and based on the Manhattan cut plane and the odd cut plane, determine the pseudo-Boolean satisfiability problem of the mathematical model; The solution to the pseudo-Boolean satisfiability subproblem is obtained by solving the local search algorithm described above. The feasible solution is obtained by calculating based on the solution of the pseudo-Boolean satisfiability subproblem and the linear programming subproblem.

7. The method according to claim 6, characterized in that, The feasible solutions are calculated based on the solutions to the pseudo-Boolean satisfiability subproblem and the linear programming subproblem, including: The solution to the pseudo-Boolean satisfiability subproblem is input into the linear programming subproblem to obtain a new linear programming subproblem; The new linear programming subproblem and the new pseudo-Boolean satisfiability subproblem are solved alternately until the objective function of the new linear programming subproblem is equal to the preset value, thus obtaining the feasible solution.

8. The method according to claim 1, characterized in that, Based on the feasible solution, scheduling the power system includes: If it is detected that the current time has reached the start time of the future period, the start time information and shutdown time information of the generator sets among the multiple generator sets, as well as the output power of the generator sets among the multiple generator sets at the preset time point, are obtained from the feasible solution. Based on the start-up and shutdown time information of the generator sets, as well as the output power of the generator sets at preset time points, the operation of the generator sets is scheduled.

9. A power system dispatching method, characterized in that, include: Obtain the mathematical model of the power system's safety-constrained generator unit combination problem uploaded by the client, wherein the mathematical model is constructed based on the parameter data of multiple generator units in the power system; An initial solution to the mathematical model is determined in a cloud server, and the mathematical model is solved based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator set combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up time and shutdown time information of the generator sets in the plurality of generator sets in the future, as well as the output power of the generator sets in the plurality of generator sets at a preset time point. The feasible solution is fed back to the client so that the power system can be scheduled based on the feasible solution.

10. A dispatching device for a power system, characterized in that, include: An acquisition unit is used to acquire a mathematical model of the safety-constrained generator unit combination problem of the power system, wherein the mathematical model is constructed based on the parameter data of multiple generator units of the power system; A determining unit is used to determine the initial solution of the mathematical model, and solve the mathematical model based on the initial solution and the objective solution framework to obtain a feasible solution to the safety-constrained generator set combination problem. The objective solution framework consists of a local search algorithm and a cutting plane algorithm. The feasible solution includes the start-up time information and shutdown time information of the generator sets in the plurality of generator sets in the future, as well as the output power of the generator sets in the plurality of generator sets at a preset time point. The processing unit is used to schedule the power system based on the feasible solution.

11. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the power system scheduling method of any one of claims 1 to 9.

12. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a processor, implement the power system dispatching method according to any one of claims 1 to 9.

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