Multi-stage robust unit combination scheduling calculation method and system

Abstract

The invention discloses a multi-stage robust unit combinatorial scheduling calculation method and system, and the method comprises the steps: carrying out the judgment of a newly added scene in a load uncertainty set before the iteration solving of a main problem in a frame based on a structure of a solving main problem in a multi-stage robust method of an implicit decision rule, eliminating redundant transmission capacity constraints, and carrying out the calculation of the multi-stage robust unit combinatorial scheduling. And solving to obtain an uncertain unit combination scheduling scheme of the power system. According to the method, the problem scale is effectively reduced, the solving efficiency is improved, and the method is suitable for solving the large-scale uncertainty UC scheduling problem.

Description

Background Art

[0002] The power industry is the most important basic energy industry in the development of the national economy and a priority development focus in the economic development strategies of countries around the world. As an advanced productive force and basic industry, the 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 issue related to national economic security, 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 system safety and power quality is the goal of system operation management, and the means of achieving it is the decision-making and scheduling of power system operation, that is, the problem of optimal scheduling of power system: through the decision-making and scheduling of the operation of generator sets in the production process of the power system, the system is ensured to achieve the best economy under the premise of meeting load balance and system safety.

[0003] The safety constrained unit commitment problem (SCUC) is an important and fundamental problem that must be considered in power system optimization. The main goal of this problem is to find the unit on / off decision solution and the unit output decision solution to minimize the power generation cost. 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 users. Depending on 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 access to renewable energy such as wind power and photovoltaics in the power system has increased rapidly, and is becoming an important source of power supply. However, the access of various types of renewable energy has also brought difficulties to the optimization and scheduling of power systems. If the decision-making plan cannot cope with the uncertainty of renewable energy, the stability and security of power grid operation will not be guaranteed. The multi-stage robust optimization method is an important algorithm for solving the uncertainty UC scheduling problem. It ensures the unpredictability of solving the uncertainty UC scheduling problem (that is, the decision in any period does not depend on the realized value of the uncertainty in its future period, but only depends on the realized value of the uncertainty in the current and historical periods), but it faces the difficulty of excessive computation in the solution 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 UC problems, but in the current research, this method is mainly applied to deterministic UC scheduling problems and has no practical application in uncertain UC scheduling problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a multi-stage robust unit combination scheduling calculation method and system in response to the deficiencies in the above-mentioned prior art, which respectively determines which transmission capacity constraints are redundant and eliminates them for each newly added scenario in the load uncertainty set, thereby greatly reducing the scale of the problem, and is used to solve the technical problems of large computational complexity and long solution time of traditional multi-stage robust methods when the problem scale is large.

[0006] The present invention adopts the following technical solutions: A multi-stage robust unit combination dispatch calculation method is proposed. The structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules is adopted. Before each iteration of solving the main problem in the framework, the newly added scenarios in the load uncertainty set are judged, and the redundant transmission capacity constraints are eliminated to obtain the uncertain unit combination dispatch scheme of the power system.

[0007] Preferably, the net load is regarded as an uncertain quantity, and the sum of the unit start-up and shutdown costs and the fuel costs is minimized in the representative scenario. The objective function of the uncertain UC problem is as follows:

[0008] in, is the unit on / off decision variable, is the decision variable of the unit power generation, is the total start-up and shutdown cost of all units during the entire dispatch period, For the unit power generation decision under the representative scenario, It is the total fuel cost for power generation of all units during the entire dispatch period.

[0009] Preferably, the constraints of the uncertain UC problem are as follows: The minimum startup / shutdown time constraints of the unit are as follows:

[0010] The load balancing constraints, the transmission capacity constraints based on DC power flow and the generation capacity constraints of the units are as follows:

[0011] The crew's climb constraints are as follows:

[0012]

[0013] in, is the unit on / off decision variable, To be under the minimum power on / off time constraint The feasible domain of , , , is the coefficient matrix of each constraint in matrix form, For the period t Internal unit power generation decision variables, For uncertain load in time period t The realized value of is the load value, For the period t The load value, is the uncertainty set of loads, For the crew i In the period t The power generation decision , This is the limit of the unit’s climbing capability.

[0014] Preferably, the load balancing constraints are as follows:

[0015] The transmission capacity constraints based on DC power flow are as follows:

[0016]

[0017] The power generation capacity constraints of the unit are as follows:

[0018] in, is the total number of generating units, is the total number of loads, i Number the generator set. is the load number, For the m Load in the period t The load value, , are the power transmission distribution factor matrices corresponding to the unit output and load demand, is the upper limit of active power that can be transmitted by the transmission line, is the transmission line number, , For the i Generators in the period t The upper and lower limits of power generation.

[0019] Preferably, the structure of the main problem in the solution framework is specifically: S1. Initialize the collection and tolerance δ ; S2. Identify and eliminate representative scenes Redundant transmission security constraints under ; S3. Solving the set S The optimal solution to the main problem is , if no feasible solution is found, the original UC problem has no solution and ends; S4. Solution The optimal solution to the sub-problem is obtained and the optimal target value ,if , otherwise go to step S5, if , then go to step S6; S5. Identify and eliminate scenes Under the redundant transmission safety constraints, let , go to step S3; S6. Output the final solution .

[0020] Preferably, in step S3, the main question is:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] in, For the scene s The power generation decision under is the power generation decision under the representative scenario, is the total fuel cost incurred by the power generation decision under the representative scenario, , , , is the coefficient matrix of each constraint in matrix form, The time period under the representative scenario t The power generation decision The time period under the representative scenario t The load value, , is an auxiliary variable introduced to meet the power generation decision of various uncertain scenarios. , is the climbing capability limit of the unit, For the scene s Next period t The power generation decision For the scene s Next period t The load value, For the scene s The load value below.

[0029] Preferably, in step S4, the sub-questions are specifically:

[0030]

[0031]

[0032]

[0033] in, is the sum of the maximum violations of the feasibility test problem in each scenario, , , , is the coefficient matrix of each constraint in matrix form, For the period t The load value, , is the violation amount of the feasibility test problem, For the period t The power generation decision , It is an auxiliary variable introduced to meet the power generation decision of various uncertain scenarios.

[0034] In a second aspect, an embodiment of the present invention provides a multi-stage robust unit commitment scheduling calculation system, including: Solution module, the structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules; The elimination module judges the newly added scenarios in the load uncertainty set before each iteration to solve the main problem in the framework, eliminates redundant transmission capacity constraints, and solves the uncertain unit combination scheduling plan of the power system.

[0035] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned multi-stage robust unit combination scheduling calculation method when executing the computer program.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned multi-stage robust unit combination scheduling calculation method.

[0037] Compared with the prior art, the present invention has at least the following beneficial effects: A multi-stage robust unit combination scheduling calculation method is proposed. The overall solution framework adopts a multi-stage robust method based on implicit decision rules. On this basis, a redundancy removal link is added. That is, before each iteration to solve the main problem, the newly added scenarios in the load uncertainty set are judged and redundant transmission capacity constraints are eliminated, thereby significantly reducing the problem scale and improving the solution efficiency of the multi-stage robust algorithm.

[0038] Furthermore, the objective function of the uncertain UC problem is set as the sum of the unit start-up and shutdown costs and the power generation fuel costs. The setting of this objective function makes the feasible solution have the smallest possible unit start-up and shutdown costs and power generation fuel costs, minimizing the scheduling cost.

[0039] Furthermore, the constraints of the uncertain UC problem include load balance constraints, transmission safety constraints, ramp constraints, power generation capacity constraints, and minimum on / off time constraints. The load balance constraints and transmission safety constraints are system-level constraints, which ensure the power balance between the unit's power generation and the user's power consumption, as well as the safety of power transmission. The ramp constraints, power generation capacity constraints, and minimum on / off time constraints are single-unit constraints, which ensure that the operation of the unit physically meets the limitations of each generator unit, making the scheduling decision feasible in actual operation.

[0040] Furthermore, the overall solution framework is implemented through the iteration of a main problem and a sub-problem. This method obtains the first-stage decision by solving the main problem, verifies the feasibility of the first-stage decision by solving the sub-problem, and gives the correction direction of the solution when the first-stage decision is not feasible. This iterative solution structure improves the solution efficiency.

[0041] Furthermore, the setting of the main problem allows each iteration to directly obtain a first-stage decision solution. Although this solution is not necessarily a feasible solution to the original uncertain UC problem, since only a known subset of the uncertainty set needs to be considered during the solution, the solution can be obtained in a very short solution time. It is only necessary to find a satisfactory feasible solution through continuous corrections in subsequent iterations.

[0042] Furthermore, the setting of sub-problems can test the feasibility of the first-stage decision solution in each iteration, and when the solution is not feasible, give the solution correction direction and proceed to the next iteration. 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 domain and the correction direction that can be considered can be obtained.

[0043] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0044] In summary, the present invention effectively reduces the problem scale, improves the solution efficiency, and is suitable for solving large-scale uncertain UC scheduling problems.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 is a flow chart of the method of the present invention; Figure 2 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 3 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0050] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0051] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to 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 represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0052] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0053] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0054] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0055] In order to improve the efficiency of solving the problem of large-scale uncertain UC scheduling problems, the multi-stage robust optimization method has too much computation and takes a long time to solve. Based on the UC problem redundant transmission capacity constraint identification method, a multi-stage robust unit combination scheduling fast calculation technology based on redundant constraint elimination is adopted. By using the problem structure, each newly added scenario in the load uncertainty set is judged to be redundant and eliminated in the iteration of the algorithm. The actual results show that most of the transmission capacity constraints can be identified as redundant constraints, and after elimination, the problem scale is greatly reduced, and the calculation efficiency is significantly improved.

[0056] The present invention provides a multi-stage robust unit combination scheduling calculation method, which utilizes the structure of the main problem in a solution framework (MRUC-I) of a multi-stage robust method based on implicit decision rules. Before each iteration to solve the main problem, it is determined which transmission capacity constraints are redundant and eliminated for the newly added scenarios in the load uncertainty set, which greatly reduces the problem scale and has a better solution speed than the existing multi-stage robust method.

[0057] The present invention provides a multi-stage robust unit commitment scheduling calculation method, comprising the following steps: S1. Initialize the collection and tolerance δ ; S2. Identify and eliminate representative scenes Redundant transmission security constraints under ; If the inequality To a certain k (1 ≤ k ≤ I ) holds, then the constraint Redundant with the original UC question.

[0058] in, , , Representation Matrix G No. j OK, express After arranging from large to small r elements, express Arranged in the corresponding order r elements.

[0059] 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 ends; (MP) (1) (2) (3) (4) (5) (6) (7) (8) S4. Solution The optimal solution to the (SP) problem is obtained. and the optimal target value ,if , then go to step S6; otherwise go to step S5; (SP) (9) (10) (11) (12) S5. Identify and eliminate scenes Under the redundant transmission safety constraints, let , go to step S3; the method for identifying redundant transmission safety constraints is consistent with that in step S2 S6. Output the final solution , the final solution represents the unit on / off decision variables, the unit power decision variables in the representative scenario, and the maximum and minimum power generation of the unit in all possible scenarios. Through this solution, we can get how the generator will output in a set of future scheduling periods, while ensuring load balance and transmission safety, and minimizing the power generation cost as much as possible.

[0060] Considering the net load as an uncertain quantity, the multi-stage robust expression of the uncertain UC problem is: The sum of the unit start-up and shutdown costs and the fuel costs is minimized in the representative scenario (with superscript "rep"), and the objective function is as follows: (13) The minimum startup / shutdown time constraints of the unit are as follows: (14) The load balancing constraints are as follows: (15) The transmission capacity constraints based on DC power flow are as follows: (16) (17) The power generation capacity constraints of the unit are as follows: (18) Constraints (15) to (18) can be simply expressed in the form of matrix multiplication as follows:

[0061] The crew's climb constraints are as follows: (19) (20) To meet the unpredictability of UC problems, the time period t The scheduling variables Completely by time period t The actual value of the uncertain net load before is determined, independent of the time period t The uncertainty after that is expressed as .

[0062] In order to satisfy the full-scenario feasibility of the UC problem, all possible scenarios satisfy constraints (15)~(20).

[0063] A multi-stage robust method solution framework based on implicit decision rules (MRUC-I) can effectively solve the uncertain UC problem. This method solves the original problem by iteratively solving a main problem and a sub-problem; the first-stage decision is obtained by solving the main problem (MP), and the feasibility of the first-stage decision is checked by solving the sub-problem (SP), and the correction direction is obtained when the first-stage decision is not feasible. The description of these two problems is as follows: (MP) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) (SP) (29) (30) (31) (32) The steps to solve MRUC-I are as follows: Step 1: Initialize the collection and tolerance δ ; Step 2: Solve 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 ends; Step 3: Solve The optimal solution to the (SP) problem is obtained. and the optimal target value ;if , then go to step 4; otherwise, , go to step 2; Step 4: Output the final solution , the algorithm ends.

[0064] In the MRUC-I solution framework, constraints (23)(24) and constraints (27)(28) have the same form, so the (MP) problem represents the decision variables in the scenario and each decision variable in each scenario Therefore, the redundant transmission safety constraints in different scenarios can be identified and eliminated respectively through the above methods.

[0065] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0066] In yet another embodiment of the present invention, a multi-stage robust unit combination scheduling calculation system is provided, which can be used to implement the above-mentioned multi-stage robust unit combination scheduling calculation method. Specifically, the multi-stage robust unit combination scheduling calculation system includes a solution module and a elimination module.

[0067] Among them, the solution module is the structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules; The elimination module judges the newly added scenarios in the load uncertainty set before each iteration to solve the main problem in the framework, eliminates redundant transmission capacity constraints, and solves the uncertain unit combination scheduling plan of the power system.

[0068] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute 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 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the multi-stage robust unit combination scheduling calculation method, including: The structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules. Before each iterative solution of the main problem in the framework, the newly added scenarios in the load uncertainty set are judged, the redundant transmission capacity constraints are eliminated, and the uncertain unit combination scheduling plan of the power system is solved.

[0069] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0070] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0071] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0072] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the multi-stage robust unit commitment scheduling calculation method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: The structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules. Before each iterative solution of the main problem in the framework, the newly added scenarios in the load uncertainty set are judged, the redundant transmission capacity constraints are eliminated, and the uncertain unit combination scheduling plan of the power system is solved.

[0073] See also Figure 2, the terminal device is a computer device, and 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. When the computer program 63 is executed by the processor 61, the multi-stage robust unit combination scheduling calculation method in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the multi-stage robust unit combination scheduling calculation system in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0074] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 2 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0075] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0076] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may 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, etc., equipped on the computer device 60.

[0077] Furthermore, the memory 62 may 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 may also be used to temporarily store data that has been output or is to be output.

[0078] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0079] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0080] See also 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 may 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.

[0081] 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 invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0082] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0083] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0084] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0085] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0086] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0087] The following table shows the simulation results of the present invention in two specific examples. The following table shows the basic information of the two examples. δ Set to 10 -3 , and correct the net load demand to the uncertain net load, and set the maximum and minimum values ​​of the uncertain net load to the values ​​of the original net load increased and decreased in proportion, respectively. As shown in the following formula:

[0088] in, is the load value representing the scene, is an uncertain set, is the load value of a scene in the uncertain set, It is the ratio of increase or decrease based on the original net load.

[0089]

[0090] The two systems are different The redundant transmission safety constraints identified under the value of are shown in the following two tables. It can be found that most transmission safety constraints are redundant and can be eliminated. In the same system, the proportion of identified redundant constraints to the total transmission safety constraints increases with This is because different Different scenarios will have different transmission security constraints.

[0091] Redundant constraint elimination effect in a 24-node system

[0092] Redundant constraint elimination effect in 118-node system

[0093] The following two tables show the improvement of solution efficiency after removing redundant constraints in two examples. It can be found that in most cases, the solution time is reduced by more than 20%. Therefore, after adopting the solution method that removes redundant transmission safety constraints, the efficiency of solving the uncertain unit combination scheduling problem can be significantly improved, which is more suitable for application in large-scale power systems.

[0094] 24-node system solution efficiency improvement

[0095] 118-node system solution efficiency improved

[0096] In summary, the present invention provides a multi-stage robust unit combination scheduling calculation method and system, which improves the solution efficiency of the multi-stage robust method by identifying and eliminating redundant transmission safety constraints. The example shows that in the unit combination scheduling problem with uncertain net load, most transmission safety constraints may be redundant, so after eliminating these redundant constraints, the problem scale will be greatly reduced, thereby improving the solution efficiency.

[0097] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0098] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0100] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0103] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0104] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

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 each iterative solution of the main problem in the framework, the newly added scenarios in the load uncertainty set are judged, the redundant transmission capacity constraints are eliminated, and the uncertain unit combination scheduling plan of the power system is solved.

2. The multi-stage robust unit commitment scheduling calculation method according to claim 1 is characterized in that: Considering the net load as an uncertain quantity, the sum of the unit start-up and shutdown costs and the fuel costs is minimized in the representative scenario, and the objective function of the uncertain UC problem is as follows: in, is the unit on / off decision variable, is the decision variable of the unit power generation, is the total start-up and shutdown cost of all units during the entire dispatch period, For the unit power generation decision under the representative scenario, It is the total fuel cost for power generation of all units during the entire dispatch period.

3. The multi-stage robust unit commitment scheduling calculation method according to claim 2 is characterized in that: The constraints of the uncertain UC problem are as follows: The minimum startup / shutdown time constraints of the unit are as follows: The load balancing constraints, the transmission capacity constraints based on DC power flow, and the generation capacity constraints of the units are as follows: The crew's climb constraints are as follows: in, is the unit on / off decision variable, To be under the minimum on / off time constraint The feasible domain of , , , is the coefficient matrix of each constraint in matrix form, For the period t Internal unit power generation decision variables, For uncertain load in time period t The realized value of is the load value, For the period t The load value, is the uncertainty set of loads, For the crew i In the period t The power generation decision , This is the limit of the unit’s climbing capability.

4. The multi-stage robust unit commitment scheduling calculation method according to claim 3 is characterized in that: The load balancing constraints are as follows: The transmission capacity constraints based on DC power flow are as follows: The power generation capacity constraints of the unit are as follows: in, is the total number of generating units, is the total number of loads, i Number the generator set. is the load number, For the m Load in the period t The load value, , are the power transmission distribution factor matrices corresponding to the unit output and load demand, is the upper limit of active power that can be transmitted by the transmission line, is the transmission line number, , For the i Generators in the period t The upper and lower limits of power generation.

5. The multi-stage robust unit commitment scheduling calculation method according to claim 1, characterized in that: The structure of the main problem in the solution framework is as follows: S1. Initialize the collection and tolerance δ ; S2. Identify and eliminate representative scenes Redundant transmission security constraints under ; S3. Solving the set S The optimal solution to the main problem is , if no feasible solution is found, the original UC problem has no solution and ends; S4. Solution The optimal solution to the sub-problem is obtained and the optimal target value ,if , otherwise go to step S5, if , then go to step S6; S5. Identify and eliminate scenes Under the redundant transmission safety constraints, let , go to step S3; S6. Output the final solution .

6. The multi-stage robust unit commitment scheduling calculation method according to claim 5, characterized in that: In step S3, the main question is: in, For the scene s The power generation decision under is the power generation decision under the representative scenario, is the total fuel cost incurred by the power generation decision under the representative scenario, , , , is the coefficient matrix of each constraint in matrix form, The period under the representative scenario t The power generation decision The time period under the representative scenario t The load value, , is an auxiliary variable introduced to meet the power generation decision of various uncertain scenarios. , is the climbing capability limit of the unit, For the scene s Next period t The power generation decision For the scene s Next period t The load value, For the scene s The load value below.

7. The multi-stage robust unit commitment scheduling calculation method according to claim 5, characterized in that: In step S4, the sub-problems are: in, is the sum of the maximum violations of the feasibility test problem in each scenario, , , , is the coefficient matrix of each constraint in matrix form, For the period t The load value, , is the violation amount of the feasibility test problem, For the period t The power generation decision , It is an auxiliary variable introduced to meet the power generation decision of various uncertain scenarios.

8. A multi-stage robust unit commitment scheduling calculation system, characterized in that: include: Solution module, the structure of the main problem in the solution framework of the multi-stage robust method based on implicit decision rules; The elimination module judges the newly added scenarios in the load uncertainty set before each iteration to solve the main problem in the framework, eliminates redundant transmission capacity constraints, and solves the uncertain unit combination scheduling plan of the power system.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 7.

10. A computing device, characterized in that include: One or more processors, a memory and 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, and the one or more programs include steps for executing the method according to any one of claims 1 to 7.

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