A multi-stage robust unit commitment scheduling calculation method and system

Abstract

The application discloses a kind of multi-stage robust unit combination scheduling calculation method and system, the structure of main problem in the solving framework of multi-stage robust method based on implicit decision rule, before solving the main problem in each iteration solving framework, the scene newly added in load uncertainty set is judged, redundant transmission capacity constraint is removed, and the uncertainty unit combination scheduling scheme of power system is solved.The present application effectively reduces the problem size, improves the solving efficiency, and is suitable for solving large-scale uncertainty UC scheduling problem.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system optimal scheduling, and particularly relates to a multi-stage robust unit commitment scheduling calculation method and system. BACKGROUND

[0002] The electric power industry is the most important basic energy industry in the development of the national economy, and is the priority development focus in the economic development strategies of countries around the world. As an advanced productive force and basic industry, the electric power industry plays an important role in promoting the development of the national economy and social progress, and has a very close relationship with social economy and social development. It is not only a strategic problem related to the economic security of the country, but also closely related to people's daily life and social stability. Once a power system is put into operation, how to ensure the best economy of the system under the premise of meeting the system safety and power quality is the goal of system operation and management, and the means to achieve it is the decision and scheduling of power system operation, that is, the power system optimal scheduling problem: through the decision and scheduling of generator unit operation in the production process of the power system, the best economy of the system is ensured under the premise of meeting the load balance, system safety and the like.

[0003] The security constrained unit commitment problem (SCUC) is an important and basic problem that must be considered in power system optimization. The main goal of the problem is to find the unit on-off decision solution and the unit output decision solution to reduce the power generation cost as much as possible. The decision must meet various system-level constraints and single-unit constraints, such as load balance constraints, power generation capacity constraints, etc., to ensure the safe and stable operation of the power system and the power supply quality of the users. According to whether the power system contains uncertain factors, the UC problem is divided into two categories: deterministic scheduling and uncertain scheduling.

[0004] In recent years, in order to alleviate the problem of carbon emissions, the scale of renewable energy such as wind power and photovoltaic power connected to the power system has rapidly increased and is becoming an important source of power supply. However, the connection of various types of renewable energy has also brought difficulties to the optimal scheduling of the power system. If the decision scheme cannot cope with the uncertainty of renewable energy, the stability and safety of the power grid operation cannot be guaranteed. The multi-stage robust optimization method is an important algorithm for solving the uncertain UC scheduling problem, which guarantees the non-anticipation of solving the uncertain UC scheduling problem (i.e. the decision of any time period does not depend on the realization value of the uncertainty quantity in the future time period, but only on the realization value of the uncertainty quantity in the current and historical time periods), but it faces the difficulty of excessive calculation amount in the solving process. The biggest problem in the current research is that identifying and eliminating redundant transmission capacity constraints is an important method to improve the efficiency of solving the UC problem, but this method is mainly applied to the deterministic UC scheduling problem in the current research, and has no practical application in the uncertain UC scheduling problem. SUMMARY

[0005] The technical problem solved by the present application is to provide a multi-stage robust unit commitment scheduling calculation method and system for solving the technical problem of large calculation amount and long solving time of traditional multi-stage robust method when the problem size is large by judging which transmission capacity constraints are redundant and eliminating them for each newly added scenario in the load uncertainty set, thereby greatly reducing the problem size.

[0006] The present application adopts the following technical solutions:

[0007] A multi-stage robust unit commitment scheduling calculation method, based on the structure of the main problem in the solving framework of the multi-stage robust method based on implicit decision rules, judges the newly added scenarios in the load uncertainty set before solving the main problem in each iteration solving framework, eliminates the redundant transmission capacity constraints, and solves the uncertainty unit commitment scheduling scheme of the power system.

[0008] Preferably, the net load is regarded as an uncertain quantity, and the sum of the unit start-stop cost and the fuel cost is minimized under the representative scenario to obtain the objective function of the uncertainty UC problem as follows:

[0009]

[0010] wherein, is the unit on-off decision variable, is the unit power generation decision variable, is the total start-stop cost of all units in all scheduling periods, is the unit power generation decision under the representative scenario, is the total fuel cost of all units in all scheduling periods.

[0011] Preferably, the constraint condition of the uncertainty UC problem is obtained as follows:

[0012] The minimum start / stop time constraint of the unit is as follows:

[0013]

[0014] The load balancing constraint, the transmission capacity constraint based on direct current flow, and the power generation capacity constraint of the unit are as follows:

[0015]

[0016] The climbing constraint of the unit is as follows:

[0017]

[0018]

[0019] wherein, For unit start-up and shutdown decision variables, To minimize power-on and power-off time constraints feasible domain, , , , The coefficient matrix is ​​in matrix form for each constraint. For time period t Decision variables for the power generation capacity of indoor units. For uncertain loads in time periods t The realized value, This is the load value. For time period t The load value, For the uncertain set of loads, For the unit i During the period t Power generation decision, , The unit's climbing ability is limited.

[0020] Preferably, the load balancing constraints are as follows:

[0021]

[0022] The transmission capacity constraints based on DC power flow are as follows:

[0023]

[0024]

[0025] The generating capacity constraints of the unit are as follows:

[0026]

[0027] in, This represents the total number of generator sets. For the total number of loads, i Number the generator set. For load numbering, For the first m The load during the time period t The load value, , These are the power transmission distribution factor matrices corresponding to unit output and load demand, respectively. This refers to the upper limit of active power that a transmission line can transmit. For transmission line numbering, , For the first i The generator set during the period t The upper and lower limits of power generation.

[0028] Preferably, the structure of solving the master problem in the framework is as follows:

[0029] S1, initialize the set and the tolerance delta ;

[0030] S2, identify and eliminate redundant transmission security constraints under the scenario ;

[0031] S3, solve the master problem under the set S , and get the optimal solution , if no feasible solution is found, the original UC problem has no solution, and the process ends;

[0032] S4, solve the sub-problem under , and get the optimal solution and the optimal objective value , if , go to step S5, if , go to step S6;

[0033] S5, identify and eliminate redundant transmission security constraints under the scenario , and let , go to step S3;

[0034] S6, output the final solution .

[0035] Preferably, in step S3, the master problem is as follows:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] wherein is the power generation decision under the scenario s , and is the power generation decision under the scenario a total fuel cost resulting from the generation power decision under the representative scenario, , , , a coefficient matrix in a matrix form of each constraint, a generation power decision of a period t under the representative scenario, a load value of a period t under the representative scenario, , an auxiliary variable of the generation power decision introduced to satisfy each uncertain scenario, , a ramping capability limit of a unit, a generation power decision of a period s under a scenario, t a load value of a period under a scenario, s a load value of a period t under a scenario, a load value under a scenario. s

[0045] Preferably, in step S4, the sub-problem is specifically:

[0046]

[0047]

[0048]

[0049]

[0050] wherein, a sum of maximum violation quantities of the feasibility checking problem under each scenario, , , , a coefficient matrix in a matrix form of each constraint, a load value of a period t , a violation quantity of the feasibility checking problem, a generation power decision of a period t , an auxiliary variable of the generation power decision introduced to satisfy each uncertain scenario.

[0051] In a second aspect, an embodiment of the present application provides a multi-stage robust unit commitment scheduling calculation system, comprising:

[0052] ​​​​​A solving module, which is used to solve the main problem in the solving framework of the multi-stage robust method based on implicit decision rules;

[0053] A pruning module, which is used to judge the newly added scenarios in the load uncertainty set before solving the main problem in each iteration of the solving framework, to prune the redundant transmission capacity constraints, and to obtain the uncertainty unit commitment scheduling scheme of the power system.

[0054] In a third aspect, a computer device is provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the multi-stage robust unit commitment scheduling method when executing the computer program.

[0055] In a fourth aspect, a computer readable storage medium is provided, which comprises a computer program, and the computer program implements the steps of the multi-stage robust unit commitment scheduling method when executed by a processor.

[0056] Compared with the prior art, the present application has at least the following beneficial effects:

[0057] The multi-stage robust unit commitment scheduling method adopts a multi-stage robust method based on implicit decision rules in the overall solving framework, and a redundancy removal link is added on this basis, i.e., the newly added scenarios in the load uncertainty set are judged before solving the main problem in each iteration, and the redundant transmission capacity constraints are pruned, so that the problem size is significantly reduced, and the solving efficiency of the multi-stage robust algorithm is improved.

[0058] Further, the objective function of the uncertainty UC problem is set as the sum of the unit start-stop cost and the power generation fuel cost. The setting of the objective function makes the feasible solution obtained have as small as possible unit start-stop cost and power generation fuel cost, and minimizes the scheduling cost.

[0059] Further, the constraints of the uncertainty UC problem include load balancing constraints, transmission safety constraints, climbing constraints, power generation capacity constraints, and minimum switch time constraints. The load balancing constraints and the transmission safety constraints belong to system-level constraints, which ensure the power balance between unit power generation and user power consumption, and the safety of power transmission. The climbing constraints, the power generation capacity constraints, and the minimum switch time constraints belong to single-unit constraints, which ensure that the operation of the unit meets the limitations of each generator in a physical manner, so that the scheduling decision is feasible in actual operation.

[0060] Further, the overall solving framework is realized through the iteration of a main problem and a sub-problem. The method obtains the first-stage decision by solving the main problem, checks the feasibility of the first-stage decision by solving the sub-problem, and gives a correction direction of the solution when the first-stage decision is infeasible. The iteration solving structure improves the solving efficiency.

[0061] Further, the main problem is set so that a first-stage decision solution can be directly obtained in each iteration, although the solution is not necessarily a feasible solution of the original uncertainty UC problem, but since only one known subset in the uncertainty set needs to be considered in the solution, the solution can be obtained in a very short solution time, and only the satisfactory feasible solution needs to be found in the subsequent iterations by continuous modification.

[0062] Further, the sub-problem can check the feasibility of the first-stage decision solution in each iteration, and when the solution is not feasible, the modification direction of the solution is given, and the next iteration is performed. The objective function of the sub-problem is set as the violation of the first-stage decision solution on the system-level constraints, so that the distance of the current solution from the feasible region and the modification direction that can be considered can be obtained.

[0063] It can be understood that the beneficial effects of the above-mentioned second aspect to the fourth aspect can be referred to the related description in the first aspect, and will not be described here.

[0064] In summary, the present application effectively reduces the problem size and improves the solution efficiency, and is suitable for solving large-scale uncertainty UC scheduling problems.

[0065] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings used in the relative embodiment description are briefly introduced as follows. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other accompanying drawings can be obtained by those skilled in the art without creative labor.

[0067] Figure 1 The flow chart of the method of the present application is shown in Figure 1.

[0068] Figure 2 The schematic diagram of the computer device provided by an embodiment of the present application is shown in Figure 2.

[0069] Figure 3 The block diagram of an electronic device provided by an embodiment of the present application is shown in Figure 3. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be described clearly and completely below with the help of the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0071] In the description of the present application, it should be understood that the terms "include" and "contain" indicate the presence of described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0072] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0073] It should be further understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations, for example, A and / or B can mean: A exists alone, A and B exist together, B exists alone. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0074] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present application to describe certain ranges, etc., these ranges should not be limited to these terms. These terms are only used to distinguish the ranges from each other. For example, the first range can also be referred to as the second range, and similarly, the second range can also be referred to as the first range without departing from the scope of the embodiments of the present application.

[0075] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted to mean "when determined" or "in response to determining" or "when detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)", depending on the context.

[0076] Various structural diagrams according to the disclosed embodiments of the present application are shown in the accompanying drawings. These drawings are not drawn to scale, in which certain details are exaggerated for the purpose of clarity and certain details can be omitted. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are only exemplary, and in actuality, there can be deviations due to manufacturing tolerances or technical limitations, and a person skilled in the art can additionally design regions / layers with different shapes, sizes, relative positions according to actual needs.

[0077] To address the issues of excessive computational complexity and long solution time associated with multi-stage robust optimization methods for solving large-scale uncertain UC scheduling problems, this paper proposes a fast computation technique for multi-stage robust unit combination scheduling based on redundant transmission capacity constraint identification methods for UC problems. Utilizing the problem structure, the algorithm iterates through each newly added scenario in the load uncertainty set to identify and remove redundant transmission capacity constraints. Practical results demonstrate that this technique effectively identifies most transmission capacity constraints as redundant, significantly reducing the problem size and substantially improving computational efficiency after removal.

[0078] This invention provides a multi-stage robust unit combination scheduling calculation method. It utilizes the structure of the main problem in a multi-stage robust method solution framework based on implicit decision rules (MRUC-I). Before solving the main problem in each iteration, it determines which transmission capacity constraints are redundant and removes them for newly added scenarios in the load uncertainty set, which greatly reduces the problem size and improves the solution speed compared to existing multi-stage robust methods.

[0079] This invention discloses a multi-stage robust unit combination scheduling calculation method, comprising the following steps:

[0080] S1. Initialize the set and tolerance delta ;

[0081] S2. Identify and remove representative scenarios. Redundant transmission security constraints under the following conditions;

[0082] If inequalities For a certain k (1 ≤ k ≤ I If the condition is met, then the constraint is met. This is redundant for the original UC problem.

[0083] in, , , Representation matrix G The j OK, express After being arranged from largest to smallest, the [number]th r One element, express Arranged in the corresponding order, the [number]th r Each element.

[0084] S3, Solving the set S The optimal solution to the (MP) problem is obtained. If no feasible solution is found, the original UC problem has no solution, and the algorithm terminates.

[0085] (MP) (1)

[0086] (2)

[0087] (3)

[0088] (4)

[0089] (5)

[0090] (6)

[0091] (7)

[0092] (8)

[0093] S4, solve the (SP) problem to get the optimal solution and the optimal objective value If , go to step S6; otherwise go to step S5;

[0094] (SP) (9)

[0095] (10)

[0096] (11)

[0097] (12)

[0098] S5, identify and eliminate redundant transmission security constraints under the scenario , go to step S3; the method of identifying redundant transmission security constraints is the same as in step S2

[0099] S6, output the final solution , end, the final solution represents the unit on-off decision variable, the unit power generation decision variable under the representative scenario, and the maximum and minimum unit power generation under all possible scenarios. Through the solution, it can be obtained that how the generator set will generate power in a group of scheduling periods in the future, while ensuring load balancing and transmission safety, the power generation cost is minimized as much as possible.

[0100] The multi-stage robust expression form of the uncertainty UC problem is:

[0101] ​​Let the sum of the unit commitment start-up and shut-down cost and fuel cost be minimum in the representative scenario (with the superscript "rep"), the objective function is as follows:

[0102] (13)

[0103] The minimum start-up / shut-down time constraints of the units are as follows:

[0104] (14)

[0105] The load balance constraint is as follows:

[0106] (15)

[0107] The transmission capacity constraint based on direct current power flow is as follows:

[0108] (16)

[0109] (17)

[0110] The power generation capacity constraints of the units are as follows:

[0111] (18)

[0112] Constraints (15)-(18) are simply expressed in the form of matrix multiplication as follows:

[0113]

[0114] The ramping constraints of the units are as follows:

[0115] (19)

[0116] (20)

[0117] In order to meet the non-anticipativity of the UC problem, the scheduling variable t of the time period is completely determined by the realized value of the uncertain net load before the time period t , and is independent of the uncertain quantity after the time period t , which is expressed as .

[0118] In order to meet the full-scenario feasibility of the UC problem, all possible scenarios satisfy constraints (15)-(20).

[0119] A multi-stage robust solution framework based on implicit decision rules (MRUC-I) can effectively solve this uncertain UC problem. This method solves the primal problem by iteratively solving a master problem and a subproblem; it obtains the first-stage decision by solving the master problem (MP), checks the feasibility of the first-stage decision by solving the subproblem (SP), and obtains a correction direction if the first-stage decision is infeasible. The two problems are described as follows:

[0120] (MP) (twenty one)

[0121] (twenty two)

[0122] (twenty three)

[0123] (twenty four)

[0124] (25)

[0125] (26)

[0126] (27)

[0127] (28)

[0128] (SP) (29)

[0129] (30)

[0130] (31)

[0131] (32)

[0132] The solution steps for MRUC-I are as follows:

[0133] Step 1: Initialize the collection and tolerance delta ;

[0134] Step 2: Solve for the set S The optimal solution to the (MP) problem is obtained. If no feasible solution is found, the original UC problem has no solution, and the algorithm terminates.

[0135] Step 3, Solve The optimal solution to the following (SP) problem is obtained. and optimal objective value ;if If yes, proceed to step 4; otherwise, let... , go to step 2;

[0136] Step 4, output the final solution , end the algorithm.

[0137] In the MRUC-I solving framework, the constraints (23) (24) and the constraints (27) (28) have the same form, so the (MP) problem has the same constraint form for the decision variables under the representative scenario and each decision variable under each scenario. Therefore, the redundant transmission security constraints under different scenarios can be identified and removed respectively by the above method.

[0138] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be embodied in the form of a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "platform" herein.

[0139] In another embodiment of the present application, a multi-stage robust unit commitment scheduling calculation system is provided, which can be used to implement the above multi-stage robust unit commitment scheduling calculation method. Specifically, the multi-stage robust unit commitment scheduling calculation system includes a solving module and a removing module.

[0140] The solving module is based on the structure of the main problem in the solving framework of the implicit decision rule-based multi-stage robust method.

[0141] The removing module judges the newly added scenarios in the load uncertainty set before solving the main problem in each iteration of the solving framework, removes the redundant transmission capacity constraints, and solves the uncertainty unit commitment scheduling scheme of the power system.

[0142] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a multi-stage robust unit combination scheduling calculation method, including:

[0143] The structure of the main problem in the solution framework based on the multi-stage robust method of implicit decision rules is as follows: before solving the main problem in each iteration, the newly added scenarios in the load uncertainty set are judged, redundant transmission capacity constraints are eliminated, and the uncertain unit combination scheduling scheme of the power system is obtained.

[0144] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device. It can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that more specific examples (a non-exhaustive list) of the computer-readable storage medium include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disk read-only memory (CD). ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0145] Computer readable storage media further includes data signals transported through a carrier wave and a communications medium, in which the tangible storage medium holds program code. Such carrier waves, comprising or carrying computer readable program code, can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0146] Program code used by or in connection with the described embodiments, when implemented in software can be stored in one or more of the computer-readable files in a computer-readable storage medium. The computer-readable storage medium can include, but is not limited to, secondary or remote storage mediums, such as one or more of volatile or non-volatile memory devices. The memory devices can include, but are not limited to, one or more of volatile or non-volatile memory devices, such as RAM, ROM, EEPROM, flash memory or other suitable memory devices including, but not limited to, one or more types of computer-readable media suitable for storing data.

[0147] The one or more instructions stored in the computer-readable storage medium can be executed by a processor to implement the corresponding steps of the multi-stage robust unit commitment scheduling calculation method in the above embodiments; the one or more instructions stored in the computer-readable storage medium are loaded and executed by the processor to implement the following steps:

[0148] The structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules is that, before solving the main problem in each iteration of the solution framework, the newly added scenarios in the load uncertainty set are judged, and the redundant transmission capacity constraints are removed, and the uncertainty unit commitment scheduling scheme of the power system is obtained.

[0149] Please refer to Figure 2, the terminal device is a computer device, the computer device 60 of this embodiment includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, and the computer program 63, when executed by the processor 61, implements the multi-stage robust unit commitment scheduling calculation method in the embodiment. To avoid repetition, details are not repeated here. Alternatively, the computer program 63, when executed by the processor 61, implements the functions of each model / unit in the multi-stage robust unit commitment scheduling calculation system of the embodiment. To avoid repetition, details are not repeated here.

[0150] The computer device 60 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device 60 can include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that the computer device 60 can include more or fewer components, or some components can be combined, or different components can be included, for example, the computer device can also include an input / output device, a network access device, a bus, and the like. Figure 2 The computer device 60 is only an example and does not constitute a limitation on the computer device 60, and can include more or fewer components than shown, or combine some components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0151] The processor 61 can be a central processing unit (CPU), and can also be other general-purpose processors, central processors, graphics processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, quantum computing-based data processing logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0152] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like.

[0153] Further, the memory 62 can include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0154] Any reference to memory, database, or other medium in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. The non-volatile memory can include Read Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0155] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0156] Please refer to Figure 3 , the terminal device is an electronic device 600, and the electronic device is in the form of a general-purpose computing device. The components of the electronic device can include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0157] ​The storage unit stores program codes which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present application described in the above method part of the present specification. For example, the processing unit 610 can perform the steps as shown in the above method part of the present specification. Figure 1

[0158] The storage unit 620 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory 6202, and can further include a read-only memory (ROM) 6203.

[0159] The storage unit 620 can further include a program / utility 6204 having a set of programs / modules 6205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or a combination thereof, which can include implementation of a network environment.

[0160] The bus 630 can represent one or more of several types of bus structures, including a storage unit bus or bus controller, a peripheral bus, a graphics acceleration port, a processing unit bus, or a local bus using any of a variety of bus architectures.

[0161] The electronic device 600 can also communicate with one or more external devices 700 such as a keyboard or pointing device, a Bluetooth device, etc.; user interfaces and / or peripheral devices such as a printer, scanner, or the like; and / or one or more devices in a communications system. Communication with one or more devices can occur via an input / output (I / O) interface 650. Still yet, the electronic device 600 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet) via a network adapter 660. As depicted, the network adapter 660 can communicate with the other components of the electronic device 600 via the bus 630. It should be appreciated that the network adapter 660 and / or the bus 630 can be implemented using one or more types of communication media, such as IO devices, I / O device adapters, wireless links, wires, cables, and the like, including bus communication to one or more other buses.

[0162] ​In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0163] The simulation results of the present application in two specific examples are shown below. The following table is the basic information of the two examples. Tolerance delta Set to 10 -3 The net load demand is corrected to an uncertain net load, and the maximum and minimum values of the uncertain net load are set to the values of the original net load increased and decreased in proportion. As shown in the following formula:

[0164]

[0165] wherein, is the load value representing a scenario, is an uncertainty set, is the load value of a scenario in the uncertainty set, is the proportion of increase and decrease based on the original net load.

[0166]

[0167] The redundant transmission security constraints identified by the two systems under different values are shown in the following two tables. It can be found that most of the transmission security constraints are redundant and can be eliminated. In the same system, the proportion of the identified redundant constraints to the total transmission security constraints changes with values. This is because different values will produce different scenarios, and different scenarios have different transmission security constraints.

[0168] Redundant constraint elimination effect in a 24-node system

[0169]

[0170] Redundant constraint elimination effect in a 118-node system

[0171]

[0172] The following two tables show the solving efficiency improvement effect of two examples after removing redundant constraints. It can be found that in most cases, the solving time is reduced by more than 20%. Therefore, after adopting the solving method of removing redundant transmission safety constraints, the efficiency of solving the uncertain unit commitment scheduling problem can be obviously improved, and it is more suitable for application in large-scale power systems.

[0173] 24-node system solving efficiency improvement

[0174]

[0175] 118-node system solving efficiency improvement

[0176]

[0177] In summary, the multi-stage robust unit commitment calculation method and system can improve the solving efficiency of the multi-stage robust method by identifying and removing redundant transmission safety constraints. The example shows that in the unit commitment scheduling problem with net load uncertainty, most of the transmission safety constraints may be redundant, so after eliminating these redundant constraints, the problem size will be greatly reduced, thereby improving the solving efficiency.

[0178] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit or module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit or module is only for easy distinction, and does not limit the protection scope of the application. The specific working process of the unit or module in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0179] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in a certain embodiment can be referred to the related description of other embodiments.

[0180] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized in electronic hardware or in combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0181] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented in other ways. For example, the apparatus / terminal embodiments described above are merely schematic. The division of the modules or units is merely a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or in other forms.

[0182] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0183] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0184] If the integrated module / 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM). Computer-readable media may include only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content of the computer-readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0185] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0186] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0187] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer implemented process, so that the instructions executed on the computer or other programmable data processing devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 one block or multiple blocks.

[0188] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the claims of the present application.

Claims

1. A multi-stage robust unit commitment scheduling calculation method, characterized in that, The structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules, before solving the main problem in each iteration of the solution framework, judges the newly added scenarios in the load uncertainty set, removes redundant transmission capacity constraints, and solves to obtain an uncertainty unit commitment scheduling scheme of the power system. The structure of the main problem in the solution framework is specifically: S1, initialize set and tolerance δ ; S2, identify and reject redundant transmissions that represent the scene underlying safety constraints. If the inequality holds for some k , 1≤ k ≤ I , then the constraint is redundant for the original UC problem; where, , , denotes the G th row of the matrix j , denotes the th element after sorting from large to small, r denotes the th element after sorting from small to large, and denotes the r th element after sorting in the corresponding order. S3, solving the set S the main problem under the optimal solution If no feasible solution is found, the original UC problem has no solution, end, the main problem is: wherein is the generation power decision under the scenario s , is the generation power decision under the representative scenario , is the total fuel cost resulting from the generation power decision under the representative scenario , , is the coefficient matrix in matrix form of each constraint is the generation power decision under the representative scenario for the time period t , is the load value under the representative scenario for the time period t , , is the auxiliary variable of the generation power decision introduced to satisfy each uncertain scenario , is the ramping capability limit of the unit is the generation power decision under the scenario s for the time period t , is the load value under the scenario s for the time period t , is the load value under the scenario s ; The net load is regarded as an uncertain quantity, and the sum of the unit start-stop cost and the fuel cost is minimized under the representative scenario, to obtain the objective function of the uncertainty UC problem as follows: wherein, is a unit commitment switching decision variable, is a unit generation power decision variable, is a total start-up and shut-down cost of all units over all scheduling periods, is a unit generation power decision under a representative scenario, is a total generation fuel cost of all units over all scheduling periods; The constraint conditions of the uncertainty UC problem are as follows: The minimum start-up / shut-down time constraint of the unit is as follows: The load balance constraint, the transmission capacity constraint based on the DC power flow, and the generation capacity constraint of the unit are as follows: The climbing constraint of the unit is as follows: wherein, is the unit on-off decision variable, is the feasible region of under the minimum on-off time constraint, , , , is the coefficient matrix in the matrix form of each constraint, is the time period t unit generation power decision variable, is the realization value of uncertain load in time period t , is the load value, is the load value in time period t , is the uncertainty set of load, is the unit i generation power decision in time period t , , is the ramping capability limit of the unit, and the load balancing constraint is as follows: The transmission capacity constraint based on the DC power flow is as follows: The generation capacity constraint of the unit is as follows: wherein, is the total number of generators, is the total number of loads, i is the generator number, is the load number, is the load value of the m th load in the time period t , , are the power transfer distribution factor matrices corresponding to the unit output and load demand, respectively, is the upper limit of active power that the transmission line can transmit, is the transmission line number, , is the upper and lower limit of the generation power of the i th generator in the time period t ; S4, solving the subproblem under the optimal solution and the optimal target value if then go to step S5, if then go to step S6, the subproblem is specifically: wherein, is the sum of the maximum violation of the feasibility test problem under each scenario, , , , is the coefficient matrix in matrix form of each constraint, is the load value of time period t , , is the violation of the feasibility test problem, is the generation power decision of time period t , , is the auxiliary variable of the generation power decision to meet each uncertain scenario; S5, identifying and eliminating the scene under the redundant transmission safety constraint, and let , go to step S3, the method of identifying the redundant transmission safety constraint is consistent with that in step S2; S6, output final solution .

2. A multi-stage robust unit commitment dispatching computing system, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of claim 1. The structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules, before solving the main problem in each iteration of the solution framework, judges the newly added scenarios in the load uncertainty set, removes redundant transmission capacity constraints, and solves to obtain an uncertainty unit commitment scheduling scheme of the power system. The structure of the main problem in the solution framework is specifically: δ S1, initialize set and tolerance The net load is regarded as an uncertain quantity, and the sum of the unit start-stop cost and the fuel cost is minimized under the representative scenario, to obtain the objective function of the uncertainty UC problem as follows: ; S2, identify and reject redundant transmissions that represent the scene underlying safety constraints. If inequalities For a certain k (1 ≤ k ≤ I If the condition is met, then the constraint is met. This is redundant for the original UC problem; among them, , , Representation matrix G The j OK, express After being arranged from largest to smallest, the [number]th r One element, express Arranged in the corresponding order, the [number]th r One element; S3, solving the set S the main problem under the optimal solution If no feasible solution is found, the original UC problem has no solution, end, the main problem is: wherein is the generation power decision under the scenario s , is the generation power decision under the representative scenario , is the total fuel cost resulting from the generation power decision under the representative scenario , , is the coefficient matrix in matrix form of the respective constraint is the generation power decision under the representative scenario for the time period t , is the load value under the representative scenario for the time period t , , is the auxiliary variable for the generation power decision introduced to satisfy the respective uncertain scenario , is the ramping capability limit of the unit is the generation power decision under the scenario s for the time period t , is the load value under the scenario s for the time period t , is the load value under the scenario s . The constraint conditions of the uncertainty UC problem are as follows: wherein, is a unit commitment switching decision variable, is a unit generation power decision variable, is a total start-up and shut-down cost of all units over all scheduling periods, is a unit generation power decision under a representative scenario, is a total generation fuel cost of all units over all scheduling periods; The minimum start-up / shut-down time constraint of the unit is as follows: The load balance constraint, the transmission capacity constraint based on the DC power flow, and the generation capacity constraint of the unit are as follows: The climbing constraint of the unit is as follows: The transmission capacity constraint based on the DC power flow is as follows: in, For unit start-up and shutdown decision variables, To minimize power-on and power-off time constraints feasible domain, , , , The coefficient matrix is ​​in matrix form for each constraint. For time period t Decision variables for the power generation capacity of indoor units. For uncertain loads in time periods t The realized value, This is the load value. For time period t The load value, For the uncertain set of loads, For the unit i During the period t Power generation decision, , Due to the unit's ramp-up capability limitations, the load balancing constraints are as follows: The generation capacity constraint of the unit is as follows: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of claim 1. wherein, is the total number of generators, is the total number of loads, i is the generator number, is the load number, is the load value of the m th load in the time period t , , are the power transfer distribution factor matrices corresponding to the unit output and load demand, respectively, is the upper limit of active power that the transmission line can transmit, is the transmission line number, , are the upper and lower limits of the generation power of the i th generator in the time period t ; S4, solving the subproblem under the optimal solution and the optimal target value if , then go to step S5, if , then go to step S6, the subproblem is specifically: wherein, is the sum of the maximum violation of the feasibility test problem under each scenario, , , , is the coefficient matrix in matrix form of each constraint, is the load value of time period t , , is the violation of the feasibility test problem, is the generation power decision of time period t , , is the auxiliary variable of the generation power decision to meet each uncertain scenario; S5, identifying and eliminating the scene under the redundant transmission safety constraint, and let , go to step S3, the method of identifying the redundant transmission safety constraint is consistent with that in step S2; S6, output final solution .

3. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions that when executed by a computer cause the computer to perform a method comprising: The one or more processors, the memory, and the one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs include steps for performing the method of claim 1.

4. A computing device, comprising: ​ ​

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