Method and device for evaluating bearing capacity of transformer substation

Abstract

The invention provides a transformer substation bearing capacity assessment method and device. Actual data of the transformer substation can be acquired and cleaned, and a pre-constructed bearing capacity evaluation model is solved according to the cleaned actual data to obtain the bearing capacity of the transformer substation. The bearing capacity evaluation model comprises an objective function and constraint conditions. The objective function is constructed by taking the maximum sum of the openable capacities of all the distributed new energy stations in the transformer substation as an objective. The constraint condition is constructed according to the utilization rate of the distributed new energy station and the maximum reverse transmission proportion of the bus. According to the invention, refined evaluation of the bearing capacity is realized, that is, the accuracy of the evaluation method can be greatly improved. Optimized calculation of the bearing capacity evaluation model is realized by adopting an integer programming method, and the evaluation accuracy is further improved.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a method and device for evaluating the carrying capacity of a substation. Background Art

[0002] As the penetration rate of distributed renewable energy sites continues to climb, distribution networks are evolving from passive to active networks. To ensure the distribution network's ability to absorb renewable energy and make distributed resource sites observable, measurable, and controllable, it's often necessary to assess the substation's carrying capacity.

[0003] In assessment methods provided by related technologies, when a microgrid participates in collaborative operation at the target level, the first carrying capacity data is typically corrected based on the microgrid's increased carrying capacity on the distribution network to obtain the substation's carrying capacity assessment result. This indicates that the substation's safety margin assessment in related technologies has blind spots, resulting in low carrying capacity assessment accuracy. Summary of the Invention

[0004] In order to solve the problem of low evaluation accuracy in the prior art, the present application provides a method and device for evaluating the carrying capacity of a substation.

[0005] In a first aspect, the present application provides a method for evaluating the carrying capacity of a substation, which may include: Obtain actual data from the substation and clean it.

[0006] The pre-built carrying capacity assessment model is solved based on the cleaned actual data to obtain the carrying capacity of the substation.

[0007] The load-carrying capacity assessment model includes an objective function and constraints. The objective function is constructed to maximize the sum of the available capacity of all distributed renewable energy stations in the substation. The constraints are constructed based on the utilization rate of the distributed renewable energy stations and the maximum reverse transmission ratio of the busbar.

[0008] Some possible implementations of obtaining and cleaning actual substation data include: Obtain actual substation data, including static parameter data and sequential operation data for distributed renewable energy stations, grid equipment, and energy storage equipment.

[0009] The logistic regression algorithm is used to detect outliers in actual data, and the distributed gradient boosting library decision method is used to repair the detected outliers.

[0010] Linear interpolation is used to fill in the missing values in the actual data.

[0011] In other possible implementations, a pre-built load capacity assessment model is solved based on the cleaned actual data to obtain the load capacity of the substation, including: The carrying capacity assessment model is transformed into a mixed integer linear programming model.

[0012] According to the cleaned actual data, the mixed integer linear programming model is solved by integer programming method to obtain the carrying capacity of the substation.

[0013] Optionally, integer programming methods may include enumeration method, cutting plane method, branch and bound method, graph theory method, binary development method, etc., which are not limited in this application.

[0014] In some possible implementations, the objective function satisfies: ; in, f It represents the sum of the open capacities of all distributed new energy stations in the substation. Indicates the total number of buses, Indicates the n The open capacity of distributed new energy stations on the bus.

[0015] Optionally, the constraints include utilization constraint, thermal stability constraint, output constraint, voltage deviation constraint, short-circuit current constraint, charge and discharge power constraint, and power constraint.

[0016] For example, the utilization constraint satisfies: ; in, Indicates the n Distributed new energy stations on busbars g exist t The power generation at the moment, Indicates the n The open capacity of distributed new energy stations on the busbars, Indicates the n Distributed new energy stations on busbars t The power generation coefficient at the moment, Indicates the utilization rate of distributed new energy stations, Indicates the time interval, T Indicates the evaluation period, Indicates the number of buses.

[0017] Thermal stability constraints are satisfied: ; in, represents the transmission coefficient of the distributed new energy station, Indicates in m Distributed new energy stations on the lines g exist The power generation at the moment, Indicates that the power grid equipment is t Active power at the moment, Indicates the k Energy storage devices in t Charging power at the moment, Indicates the k The discharge power of an energy storage device at time t is: Indicates the maximum reverse transmission ratio of the bus. Indicates the maximum transmission capacity of the bus, Indicates the number of lines under the jurisdiction of a single busbar. Indicates the number of energy storage devices.

[0018] Output constraints are satisfied: ; The pressure deviation constraint satisfies: ; in, Indicates the maximum power factor angle of the distributed new energy station, Indicates the per-unit short-circuit impedance value inside the substation. represents the base capacity of the substation, Indicates the maximum voltage deviation of the bus.

[0019] The short-circuit current constraint satisfies: ; in, Indicates the per-unit short-circuit impedance of the busbar. Indicates the short-circuit current limit of the busbar, Indicates the nominal value of the bus voltage. Indicates the short-circuit current coefficient of the busbar.

[0020] The charge and discharge power constraints meet: ; in, Indicates the k Energy storage devices in t The charge and discharge status at each moment, Take 1 to indicate the k The energy storage device is in charging state. Take 0 to indicate the k The energy storage device is in a discharged state. Indicates the k The maximum discharge power of an energy storage device, Indicates the k The maximum charging power of an energy storage device.

[0021] The power constraints are satisfied: ; in, Indicates thek Energy storage devices in t The amount of electricity at any moment, Indicates the k Energy storage devices in t -1 moment of power, Indicates the k The installed capacity of energy storage equipment.

[0022] In a second aspect, the present application provides a substation carrying capacity assessment device, which may include: The cleaning module is used to obtain the actual data of the substation and clean it.

[0023] The solution module is used to solve the pre-built carrying capacity evaluation model according to the cleaned actual data to obtain the carrying capacity of the substation.

[0024] The load-carrying capacity assessment model includes an objective function and constraints. The objective function is constructed to maximize the sum of the available capacity of all distributed renewable energy stations in the substation. The constraints are constructed based on the utilization rate of the distributed renewable energy stations and the maximum reverse transmission ratio of the busbar.

[0025] In some possible implementations, the cleaning module is specifically configured to: Obtain actual substation data, including static parameter data and sequential operation data for distributed renewable energy stations, grid equipment, and energy storage equipment.

[0026] The logistic regression algorithm is used to detect outliers in actual data, and the distributed gradient boosting library decision method is used to repair the detected outliers.

[0027] Linear interpolation is used to fill in the missing values in the actual data.

[0028] In some other possible implementations, the solution module is specifically used to: The carrying capacity assessment model is transformed into a mixed integer linear programming model.

[0029] According to the cleaned actual data, the mixed integer linear programming model is solved by integer programming method to obtain the carrying capacity of the substation.

[0030] In some further possible implementations, the evaluation device further includes a modeling module, which is configured to determine the objective function according to the following formula: ;in, f It represents the sum of the open capacities of all distributed new energy stations in the substation. Indicates the total number of buses, Indicates the nThe open capacity of distributed new energy stations on the bus.

[0031] Optionally, the modeling module is also used to determine constraints, including utilization constraints, thermal stability constraints, output constraints, voltage deviation constraints, short-circuit current constraints, charge and discharge power constraints, and power constraints.

[0032] For example, the utilization constraint satisfies: ; in, Indicates the n Distributed new energy stations on busbars g exist t The power generation at the moment, Indicates the n The open capacity of distributed new energy stations on the busbars, Indicates the n Distributed new energy stations on busbars t The power generation coefficient at the moment, Indicates the utilization rate of distributed new energy stations, Indicates the time interval, T Indicates the evaluation period, Indicates the number of buses.

[0033] Thermal stability constraints are satisfied: ; in, represents the transmission coefficient of the distributed new energy station, Indicates in m Distributed new energy stations on the lines g exist The power generation at the moment, Indicates that the power grid equipment is t Active power at the moment, Indicates the k Energy storage devices in t Charging power at the moment, Indicates the k The discharge power of an energy storage device at time t is: Indicates the maximum reverse transmission ratio of the bus. Indicates the maximum transmission capacity of the bus, Indicates the number of lines under the jurisdiction of a single busbar. Indicates the number of energy storage devices.

[0034] Optionally, the output constraints satisfy: ; The voltage deviation constraint satisfies: ; in, Indicates the maximum power factor angle of the distributed new energy station, Indicates the per-unit short-circuit impedance value inside the substation. represents the base capacity of the substation, Indicates the maximum voltage deviation of the bus.

[0035] The short-circuit current constraint satisfies: ; in, Indicates the per-unit short-circuit impedance of the busbar. Indicates the short-circuit current limit of the busbar, Indicates the nominal value of the bus voltage. Indicates the short-circuit current coefficient of the busbar.

[0036] The charge and discharge power constraints meet: ; in, Indicates the k Energy storage devices in t The charge and discharge status at each moment, Take 1 to indicate the k The energy storage device is in charging state. Take 0 to indicate the k The energy storage device is in a discharged state. Indicates the k The maximum discharge power of an energy storage device, Indicates the k The maximum charging power of an energy storage device.

[0037] The power constraints are satisfied: ; in, Indicates the k Energy storage devices in t The amount of electricity at any moment, Indicates the k Energy storage devices in t -1 moment of power, Indicates the k The installed capacity of energy storage equipment.

[0038] On the other hand, the present application also provides a computer device, including: one or more processors.

[0039] A processor is used to execute one or more programs.

[0040] When one or more programs are executed by one or more processors, the above-described evaluation method is implemented.

[0041] In another aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the above-mentioned evaluation method.

[0042] Compared with the prior art, the present invention has the following advantages: In the substation carrying capacity assessment method provided by the present application, the actual data of the substation is obtained and cleaned, and the pre-constructed carrying capacity assessment model is solved according to the actual data after cleaning to obtain the carrying capacity of the substation. The carrying capacity assessment model includes an objective function and constraints. The objective function is constructed with the goal of maximizing the sum of the openable capacities of all distributed new energy sites in the substation. The constraints are constructed based on the utilization rate of the distributed new energy sites and the maximum reverse transmission ratio of the busbar. It can be seen that in the process of constructing the carrying capacity assessment model, the present application takes into account the openable capacity of the distributed new energy sites, the utilization rate of the distributed new energy sites and the maximum reverse transmission ratio of the busbar on the basis of the actual data of the substation, thereby realizing a refined assessment of the carrying capacity, that is, significantly improving the accuracy of the carrying capacity assessment results.

[0043] This application takes into account utilization constraints, thermal stability constraints, output constraints, voltage deviation constraints, short-circuit current constraints, charging and discharging power constraints, and power constraints. It can effectively and multi-dimensionally quantify the coupling impact of safety boundaries, identify risk points that may be missed, and avoid the inability to evaluate carrying capacity due to local optimization.

[0044] In solving the load-carrying capacity assessment model, this application first converts the load-carrying capacity assessment model into a mixed-integer linear programming model. Then, based on the cleaned actual data, the mixed-integer linear programming model is solved using integer programming to determine the substation's load-carrying capacity. In other words, this application uses integer programming to optimize the load-carrying capacity assessment model, ensuring that the global optimal solution can be determined under complex constraints, avoiding the potential for local optimality and further improving the accuracy of the assessment.

[0045] This application cleans the actual operating data of the substation obtained, which not only ensures the authenticity and effectiveness of the assessment, but also reflects the actual operating characteristics and load characteristics of the substation, making the carrying capacity assessment results more consistent with the actual operating conditions of the substation, and providing a more reliable technical basis for energy access and grid planning.

[0046] This application fully considers the power generation power, open capacity, utilization rate, etc. of distributed new energy stations on different busbars, and calculates the maximum value of the sum of the open capacity of all distributed new energy stations in the substation, providing a basis for the reasonable access of distributed new energy stations, which can not only effectively improve the local new energy absorption level, but also enhance the grid's adaptability to new energy power fluctuations at different access locations. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0048] Figure 1 This is a schematic flow chart of a method for evaluating substation carrying capacity in an embodiment of the present application; Figure 2 This is a schematic structural diagram of a substation carrying capacity assessment device in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The technical solution in this application will be described below with reference to the accompanying drawings.

[0050] The terms "first," "second," and the like in the description, embodiments, claims, and drawings of this application are used solely for descriptive purposes and are not to be construed as indicating or implying relative importance or order. Furthermore, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, inclusion of a series of steps or units. A method, system, product, or apparatus is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0051] It should be understood that in this application, "at least one (item)" means one or more, and "more" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0052] Example 1: The embodiment of the present application provides a method for evaluating the carrying capacity of a substation. Figure 1 As shown, the evaluation method 100 includes the following steps: Step S1: Obtain actual data of the substation and clean it.

[0053] Step S2: Solve the pre-built carrying capacity evaluation model based on the cleaned actual data to obtain the carrying capacity of the substation.

[0054] The load-carrying capacity assessment model includes an objective function and constraints. The objective function is constructed to maximize the sum of the available capacity of all distributed renewable energy stations in the substation. The constraints are constructed based on the utilization rate of the distributed renewable energy stations and the maximum reverse transmission ratio of the busbar.

[0055] In some possible implementations, obtaining actual substation data and cleaning it in step S1 includes: Obtain actual data from the substation. Use a logistic regression algorithm to detect outliers in the data and use the distributed gradient boosting library (XGBoost) decision method to correct detected outliers. Linear interpolation can also be used to fill in missing values in the data.

[0056] Among them, the actual data of the substation includes the actual static parameter data and actual time-series operation data of the distributed new energy stations, power grid equipment and energy storage equipment.

[0057] Optionally, the actual static parameter data of a distributed renewable energy station includes the transmission coefficient and utilization rate of the distributed renewable energy station. The actual static parameter data of power grid equipment includes the number of busbars, the number of lines under a single busbar, the maximum reverse transmission ratio of the busbar, and the maximum transmission capacity of the busbar. The actual static parameter data of energy storage devices includes the number of energy storage devices.

[0058] The actual sequential operation data of distributed renewable energy stations includes the generation coefficient of distributed renewable energy stations at each moment. The actual sequential operation data of power grid equipment includes the power generation of distributed renewable energy stations on the busbar at each moment. The actual sequential operation data of energy storage equipment includes the discharge power and charging power of energy storage equipment at each moment.

[0059] In some other possible implementations, in step S2, solving a pre-built carrying capacity assessment model based on the cleaned actual data to obtain the carrying capacity of the substation includes: The carrying capacity assessment model is transformed into a mixed integer linear programming model.

[0060] According to the cleaned actual data, the mixed integer linear programming model is solved by integer programming method to obtain the carrying capacity of the substation.

[0061] Optionally, integer programming methods may include enumeration method, cutting plane method, branch and bound method, graph theory method, binary development method, etc., which are not limited in this application.

[0062] In some possible implementations, the objective function satisfies: ; in, f It represents the sum of the open capacities of all distributed new energy stations in the substation. Indicates the total number of buses, Indicates the n The open capacity of distributed new energy stations on the bus.

[0063] Optionally, the constraints include utilization constraint, thermal stability constraint, output constraint, voltage deviation constraint, short-circuit current constraint, charge and discharge power constraint, and power constraint.

[0064] For example, the utilization constraint satisfies: ; in, Indicates the n Distributed new energy stations on busbars g exist t The power generation at the moment, Indicates the n The open capacity of distributed new energy stations on the busbars, Indicates then Distributed new energy stations on busbars t The power generation coefficient at the moment, Indicates the utilization rate of distributed new energy stations, Indicates the time interval, T Indicates the evaluation period, Indicates the number of buses.

[0065] Thermal stability constraints are satisfied: ; in, represents the transmission coefficient of the distributed new energy station, Indicates in m Distributed new energy stations on the lines g exist The power generation at the moment, Indicates that the power grid equipment is t Active power at the moment, Indicates the k Energy storage devices in t Charging power at the moment, Indicates the k The discharge power of an energy storage device at time t is: Indicates the maximum reverse transmission ratio of the bus. Indicates the maximum transmission capacity of the bus, Indicates the number of lines under the jurisdiction of a single busbar. Indicates the number of energy storage devices.

[0066] Output constraints are satisfied: ; The voltage deviation constraint satisfies: ; in, Indicates the maximum power factor angle of the distributed new energy station, Indicates the per-unit short-circuit impedance value inside the substation. represents the base capacity of the substation, Indicates the maximum voltage deviation of the bus.

[0067] The short-circuit current constraint satisfies: ; in, Indicates the per-unit short-circuit impedance of the busbar. Indicates the short-circuit current limit of the busbar, Indicates the nominal value of the bus voltage. Indicates the short-circuit current coefficient of the busbar.

[0068] The charge and discharge power constraints meet: ; in, Indicates thek Energy storage devices in t The charge and discharge status at each moment, Take 1 to indicate the k The energy storage device is in charging state. Take 0 to indicate the k The energy storage device is in a discharged state. Indicates the k The maximum discharge power of an energy storage device, Indicates the k The maximum charging power of an energy storage device.

[0069] The power constraints are satisfied: ; in, Indicates the k Energy storage devices in t The amount of electricity at any moment, Indicates the k Energy storage devices in t -1 moment of power, Indicates the k The installed capacity of energy storage equipment.

[0070] Example 2: Based on the same inventive concept, the embodiment of the present application also provides a substation carrying capacity assessment device. Figure 2 As shown, the evaluation device 200 may include: The cleaning module 201 is used to obtain actual data of the substation and perform cleaning.

[0071] The solving module 202 is used to solve the pre-built carrying capacity evaluation model according to the cleaned actual data to obtain the carrying capacity of the substation.

[0072] The load-carrying capacity assessment model includes an objective function and constraints. The objective function is constructed to maximize the sum of the available capacity of all distributed renewable energy stations in the substation. The constraints are constructed based on the utilization rate of the distributed renewable energy stations and the maximum reverse transmission ratio of the busbar.

[0073] In some possible implementations, the cleaning module 201 is specifically configured to: Obtain actual data from the substation. Use a logistic regression algorithm to detect outliers in the data and a distributed gradient boosting library decision method to correct detected outliers. Linear interpolation can also be used to fill in missing values in the data.

[0074] Among them, the actual data of the substation includes the actual static parameter data and actual time-series operation data of the distributed new energy stations, power grid equipment and energy storage equipment.

[0075] Optionally, the actual static parameter data of a distributed renewable energy station includes the transmission coefficient and utilization rate of the distributed renewable energy station. The actual static parameter data of power grid equipment includes the number of busbars, the number of lines under a single busbar, the maximum reverse transmission ratio of the busbar, and the maximum transmission capacity of the busbar. The actual static parameter data of energy storage devices includes the number of energy storage devices.

[0076] The actual sequential operation data of distributed renewable energy stations includes the generation coefficient of distributed renewable energy stations at each moment. The actual sequential operation data of power grid equipment includes the power generation of distributed renewable energy stations on the busbar at each moment. The actual sequential operation data of energy storage equipment includes the discharge power and charging power of energy storage equipment at each moment.

[0077] In some other possible implementations, the solution module 202 is specifically configured to: The carrying capacity assessment model is transformed into a mixed integer linear programming model.

[0078] According to the cleaned actual data, the mixed integer linear programming model is solved by integer programming method to obtain the carrying capacity of the substation.

[0079] In some further possible implementations, the evaluation device further includes a modeling module, which is configured to determine the objective function according to the following formula: .in, f It represents the sum of the open capacities of all distributed new energy stations in the substation. Indicates the total number of buses, Indicates the n The open capacity of distributed new energy stations on the bus.

[0080] Optionally, the modeling module is also used to determine constraints, including utilization constraints, thermal stability constraints, output constraints, voltage deviation constraints, short-circuit current constraints, charge and discharge power constraints, and power constraints.

[0081] For example, the utilization constraint satisfies: ; in, Indicates the n Distributed new energy stations on busbars g exist t The power generation at the moment, Indicates the n The open capacity of distributed new energy stations on the busbars, Indicates the n Distributed new energy stations on busbars t The power generation coefficient at the moment, Indicates the utilization rate of distributed new energy stations, Indicates the time interval, T Indicates the evaluation period, Indicates the number of buses.

[0082] Thermal stability constraints are satisfied: ; in, represents the transmission coefficient of the distributed new energy station, Indicates in m Distributed new energy stations on the lines g exist The power generation at the moment, Indicates that the power grid equipment is t Active power at the moment, Indicates the k Energy storage devices in t Charging power at the moment, Indicates the k The discharge power of an energy storage device at time t is: Indicates the maximum reverse transmission ratio of the bus. Indicates the maximum transmission capacity of the bus, Indicates the number of lines under the jurisdiction of a single busbar. Indicates the number of energy storage devices.

[0083] Optionally, the output constraints satisfy: ; The voltage deviation constraint satisfies: ; in, Indicates the maximum power factor angle of the distributed new energy station, Indicates the per-unit short-circuit impedance value inside the substation. represents the base capacity of the substation, Indicates the maximum voltage deviation of the bus.

[0084] The short-circuit current constraint satisfies: ; in, Indicates the per-unit short-circuit impedance of the busbar. Indicates the short-circuit current limit of the busbar, Indicates the nominal value of the bus voltage. Indicates the short-circuit current coefficient of the busbar.

[0085] The charge and discharge power constraints meet: ; in, Indicates the k Energy storage devices in t The charge and discharge status at each moment, Take 1 to indicate the kThe energy storage device is in charging state. Take 0 to indicate the k The energy storage device is in a discharged state. Indicates the k The maximum discharge power of an energy storage device, Indicates the k The maximum charging power of an energy storage device.

[0086] The power constraints are satisfied: ; in, Indicates the k Energy storage devices in t The amount of electricity at any moment, Indicates the k Energy storage devices in t -1 moment of power, Indicates the k The installed capacity of energy storage equipment.

[0087] Example 3: Based on the same inventive concept, an embodiment of the present application further provides a computer device, comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be 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., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function, so as to implement the steps of the evaluation method provided in the above embodiment.

[0088] Example 4: Based on the same inventive concept, embodiments of the present application also provide a computer-readable storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be high-speed RAM memory or non-volatile memory, such as at least one disk storage device. The processor may load and execute the one or more instructions stored in the computer-readable storage medium to implement the steps of the evaluation method provided in the above embodiments.

[0089] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0093] The above are merely embodiments of the application and are not intended to limit the application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the application are included in the scope of the claims of the pending application.

Claims

1. A method for evaluating the carrying capacity of a substation, characterized in that: include: Obtain actual data from substations and clean it; Solving a pre-built load-carrying capacity evaluation model based on the cleaned actual data to obtain the load-carrying capacity of the substation; Among them, the carrying capacity evaluation model includes an objective function and constraints; the objective function is constructed with the goal of maximizing the sum of the open capacities of all distributed new energy stations in the substation; the constraints are constructed based on the utilization rate of the distributed new energy stations and the maximum reverse transmission ratio of the bus.

2. The evaluation method according to claim 1, wherein: The actual data of the substation is obtained and cleaned, including: Acquire actual data of the substation; wherein the actual data of the substation includes actual static parameter data and actual sequential operation data of each of the distributed new energy station, power grid equipment and energy storage equipment; A logistic regression algorithm is used to detect outliers in the actual data, and a distributed gradient boosting library decision method is used to repair the detected outliers; Linear interpolation is used to fill in the missing values in the actual data.

3. The evaluation method according to claim 1, wherein: Solving the pre-built carrying capacity evaluation model based on the cleaned actual data to obtain the carrying capacity of the substation includes: Converting the carrying capacity assessment model into a mixed integer linear programming model; According to the cleaned actual data, the mixed integer linear programming model is solved by using an integer programming method to obtain the carrying capacity of the substation.

4. The evaluation method according to claim 1, wherein: The objective function satisfies: in, f represents the sum of the open capacities of all distributed new energy stations in the substation, Indicates the total number of buses, Indicates the n The open capacity of distributed new energy stations on the bus.

5. The evaluation method according to claim 4, characterized in that The constraints include utilization constraint, thermal stability constraint, output constraint, voltage deviation constraint, short-circuit current constraint, charge and discharge power constraint, and power constraint.

6. The evaluation method according to claim 5, characterized in that The utilization constraint satisfies: in, Indicates the n Distributed new energy stations on busbars g exist t The power generation at the moment, Indicates the n The open capacity of distributed new energy stations on the busbars, Indicates the n Distributed new energy stations on busbars t The power generation coefficient at the moment, represents the utilization rate of the distributed new energy station, Indicates the time interval, T Indicates the evaluation period, Indicates the number of buses; The thermal stability constraints satisfy: in, represents the transmission coefficient of the distributed new energy station, Indicates in m Distributed new energy stations on the lines g exist The power generation at the moment, Indicates that the power grid equipment is t Active power at the moment, Indicates the k Energy storage devices in t Charging power at the moment, Indicates the k The discharge power of an energy storage device at time t is: Indicates the maximum reverse transmission ratio of the bus; Indicates the maximum transmission capacity of the bus, Indicates the number of lines under the jurisdiction of a single busbar. Indicates the number of energy storage devices.

7. The evaluation method according to claim 6, characterized in that The output constraints satisfy: The voltage deviation constraint satisfies: in, Indicates the maximum power factor angle of the distributed new energy station, Indicates the per-unit short-circuit impedance of the substation, represents the base capacity of the substation, Indicates the maximum voltage deviation of the bus; The short-circuit current constraint satisfies: in, Indicates the per-unit short-circuit impedance of the busbar, represents the short-circuit current limit of the busbar, Indicates the nominal value of the bus voltage, Indicates the short-circuit current coefficient of the busbar; The charge and discharge power constraints satisfy: in, Indicates the k Energy storage devices in t The charge and discharge status at each moment, Taking 1 means the k The energy storage device is in charging state. Taking 0 means the k An energy storage device is in a discharging state; Indicates the k The maximum discharge power of an energy storage device, Indicates the k The maximum charging power of each energy storage device; The power constraints satisfy: in, Indicates the k Energy storage devices in t The amount of electricity at any moment, Indicates the k Energy storage devices in t -1 moment of power, Indicates the k The installed capacity of energy storage equipment.

8. A substation carrying capacity assessment device, characterized in that: include: Cleaning module, used to obtain actual data of substation and clean it; A solution module, configured to solve a pre-built load-carrying capacity evaluation model based on the cleaned actual data to obtain the load-carrying capacity of the substation; Among them, the carrying capacity evaluation model includes an objective function and constraints; the objective function is constructed with the goal of maximizing the sum of the open capacities of all distributed new energy stations in the substation; the constraints are constructed based on the utilization rate of the distributed new energy stations and the maximum reverse transmission ratio of the bus.

9. The evaluation device according to claim 8, characterized in that The cleaning module is specifically used for: Acquire actual data of the substation; wherein the actual data of the substation includes actual static parameter data and actual sequential operation data of each of the distributed new energy station, power grid equipment and energy storage equipment; A logistic regression algorithm is used to detect outliers in the actual data, and a distributed gradient boosting library decision method is used to repair the detected outliers; Linear interpolation is used to fill in the missing values in the actual data.

10. The evaluation device according to claim 8, characterized in that The solution module is specifically used for: Converting the carrying capacity assessment model into a mixed integer linear programming model; According to the cleaned actual data, the mixed integer linear programming model is solved by using an integer programming method to obtain the carrying capacity of the substation.

11. The evaluation device according to claim 8, characterized in that The evaluation device further includes a modeling module, which is configured to determine the objective function as follows: in, f represents the sum of the open capacities of all distributed new energy stations in the substation, Indicates the total number of buses, Indicates the n The open capacity of distributed new energy stations on the bus.

12. The evaluation device according to claim 11, characterized in that The modeling module is also used to determine the constraint conditions; the constraint conditions include utilization constraint, thermal stability constraint, output constraint, voltage deviation constraint, short-circuit current constraint, charge and discharge power constraint and power constraint.

13. The evaluation device according to claim 12, characterized in that The utilization constraint satisfies: in, Indicates the n Distributed new energy stations on busbars g exist t The power generation at the moment, Indicates the n The open capacity of distributed new energy stations on the busbars, Indicates the n Distributed new energy stations on busbars t The power generation coefficient at the moment, represents the utilization rate of the distributed new energy station, Indicates the time interval, T Indicates the evaluation period, Indicates the number of buses; The thermal stability constraints satisfy: in, represents the transmission coefficient of the distributed new energy station, Indicates in m Distributed new energy stations on the lines g exist The power generation at the moment, Indicates that the power grid equipment is t Active power at the moment, Indicates the k Energy storage devices in t Charging power at the moment, Indicates the k The discharge power of an energy storage device at time t is: Indicates the maximum reverse transmission ratio of the bus; Indicates the maximum transmission capacity of the bus, Indicates the number of lines under the jurisdiction of a single busbar. Indicates the number of energy storage devices.

14. The evaluation device according to claim 13, characterized in that The output constraints satisfy: ; The voltage deviation constraint satisfies: ; in, Indicates the maximum power factor angle of the distributed new energy station, Indicates the per-unit short-circuit impedance of the substation, represents the base capacity of the substation, Indicates the maximum voltage deviation of the bus; The short-circuit current constraint satisfies: ; in, Indicates the per-unit short-circuit impedance of the busbar, represents the short-circuit current limit of the busbar, Indicates the nominal value of the bus voltage, Indicates the short-circuit current coefficient of the busbar; The charge and discharge power constraints satisfy: ; in, Indicates the k Energy storage devices in t The charge and discharge status at each moment, Taking 1 means the k The energy storage device is in charging state. Taking 0 means the k An energy storage device is in a discharging state; Indicates the k The maximum discharge power of an energy storage device, Indicates the k The maximum charging power of each energy storage device; The power constraints satisfy: ; in, Indicates the k Energy storage devices in t The amount of electricity at any moment, Indicates the k Energy storage devices in t -1 moment of power, Indicates the k The installed capacity of energy storage equipment.

15. A computer device, characterized in that: include: one or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the evaluation method according to any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the evaluation method according to any one of claims 1 to 7 is implemented.

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