A substation carrying capacity evaluation method and device

Abstract

The application provides a power substation carrying capacity evaluation method and device. Actual data of the power substation can be acquired and cleaned, and a pre-constructed carrying capacity evaluation model is solved according to the cleaned actual data to obtain the carrying capacity of the power substation. The carrying capacity evaluation model comprises an objective function and a constraint condition. The objective function is constructed with the sum of the openable capacities of all distributed new energy stations in the power substation as the target. The constraint condition is constructed according to the utilization rate of the distributed new energy station and the maximum reverse sending ratio of the bus. The application realizes fine evaluation of the carrying capacity, that is, the accuracy of the evaluation method can be greatly improved. The application adopts an integer programming method to realize optimization calculation of the carrying capacity evaluation model, and further improves the accuracy of the evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy, in particular to a substation carrying capacity evaluation method and device. BACKGROUND

[0002] With the continuous rise of the penetration rate of distributed new energy stations, the distribution network is experiencing the development from a passive network to an active network. In order to accommodate the new energy of the distribution network and realize the observability, measurability and controllability of the distributed resource station, it is usually necessary to evaluate the carrying capacity of the substation.

[0003] In the evaluation method provided by the related art, when there is a microgrid participating in the cooperative operation at the target level, the first carrying capacity data is usually corrected according to the carrying capacity improvement value of the microgrid to the distribution network, and the carrying capacity evaluation result of the substation is obtained. It can be seen that the safety margin of the substation in the related art will have an evaluation blind area, resulting in low carrying capacity evaluation accuracy. SUMMARY

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

[0005] In a first aspect, the present application provides a substation carrying capacity evaluation method, which can include:

[0006] Obtaining actual data of the substation and cleaning the actual data.

[0007] Solving a pre-constructed carrying capacity evaluation model according to the cleaned actual data to obtain the carrying capacity of the substation.

[0008] The carrying capacity evaluation model includes a target function and a constraint condition. The target function is constructed with the maximum sum of the openable capacity of all distributed new energy stations in the substation as the target. The constraint condition is constructed according to the utilization rate of the distributed new energy station and the maximum reverse sending ratio of the bus.

[0009] In some possible implementation manners, obtaining the actual data of the substation and cleaning the actual data includes:

[0010] Obtaining the actual data of the substation. The actual data of the substation includes the static parameter actual data and the time sequence running actual data of the distributed new energy station, the power grid equipment and the energy storage equipment.

[0011] Detecting the abnormal values in the actual data by using a logistic regression algorithm, and repairing the detected abnormal values by using a distributed gradient boosting library decision method.

[0012] Filling the missing values in the actual data by using a linear interpolation method.

[0013] 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:

[0014] The carrying capacity assessment model is transformed into a mixed integer linear programming model.

[0015] 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.

[0016] 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.

[0017] In some possible implementations, the objective function satisfies: ;

[0018] 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.

[0019] 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.

[0020] For example, the utilization constraint satisfies: ;

[0021] 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.

[0022] Thermal stability constraints are satisfied: ;

[0023] 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.

[0024] Output constraints are satisfied: ;

[0025] The pressure deviation constraint satisfies: ;

[0026] 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.

[0027] The short-circuit current constraint satisfies: ;

[0028] 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.

[0029] The charge and discharge power constraints meet: ;

[0030] 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, denotes the maximum charging power of the k energy storage device.

[0031] The power constraint is satisfied: ;

[0032] wherein, denotes the power of the k energy storage device at the t time instant, denotes the power of the k energy storage device at the t -1 time instant, denotes the installed capacity of the k energy storage device.

[0033] In a second aspect, the present application provides a substation carrying capacity evaluation device, which can include:

[0034] A cleaning module is configured to obtain actual data of the substation and clean the actual data.

[0035] A solving module is configured to solve a pre-constructed carrying capacity evaluation model according to the cleaned actual data to obtain the carrying capacity of the substation.

[0036] The carrying capacity evaluation model includes an objective function and a constraint condition. The objective function is constructed to maximize the sum of the openable capacities of all distributed new energy stations in the substation. The constraint condition is constructed according to the utilization rates of the distributed new energy stations and the maximum reverse sending ratios of the busbars.

[0037] In some possible implementation manners, the cleaning module is specifically configured to:

[0038] Obtain the actual data of the substation. The actual data of the substation includes static parameter actual data and time sequence operation actual data of the distributed new energy stations, the grid equipment, and the energy storage devices.

[0039] An abnormal value in the actual data is detected by using a logistic regression algorithm, and the detected abnormal value is repaired by using a distributed gradient boosting library decision method.

[0040] A missing value in the actual data is filled by using a linear interpolation method.

[0041] In other possible implementation manners, the solving module is specifically configured to:

[0042] Convert the carrying capacity evaluation model into a mixed integer linear programming model.

[0043] Solve the mixed integer linear programming model by using an integer programming method according to the cleaned actual data to obtain the carrying capacity of the substation.

[0044] 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.

[0045] 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.

[0046] For example, the utilization constraint satisfies: ;

[0047] 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.

[0048] Thermal stability constraints are satisfied: ;

[0049] 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, represents the number of lines under the jurisdiction of a single bus, represents the number of energy storage devices.

[0050] Optionally, the output constraint is satisfied: ;

[0051] The voltage deviation constraint is satisfied: ;

[0052] wherein, represents the maximum power factor angle of the distributed new energy station, represents the internal short-circuit impedance reference value of the substation, represents the reference capacity of the substation, represents the maximum voltage deviation of the bus.

[0053] The short-circuit current constraint is satisfied: ;

[0054] wherein, represents the short-circuit impedance reference value of the bus, represents the short-circuit current limit value of the bus, represents the rated voltage of the bus, represents the short-circuit current coefficient of the bus.

[0055] The charging and discharging power constraint is satisfied: ;

[0056] wherein, represents the charging and discharging state of the k th energy storage device at the t th moment, taking 1 indicates that the k th energy storage device is in charging state, taking 0 indicates that the k th energy storage device is in discharging state. represents the maximum discharging power of the k th energy storage device, represents the maximum charging power of the k th energy storage device.

[0057] The power constraint is satisfied: ;

[0058] wherein, represents the power of the k th energy storage device at the t th moment, represents the power of the k th energy storage device at the t -1th moment, represents the installed capacity of the k th energy storage device.

[0059] In still another aspect, the present application provides a computer device, comprising: one or more processors.

[0060] The processor is configured to execute one or more programs.

[0061] When the one or more programs are executed by the one or more processors, the evaluation method as described above is implemented.

[0062] In still another aspect, the present application provides a computer readable storage medium having a computer program stored thereon. When the computer program is executed, the evaluation method as described above is implemented.

[0063] Compared with the prior art, the present application has the following beneficial effects:

[0064] In the substation carrying capacity evaluation method provided by the present application, the actual data of the substation is obtained and cleaned, and the pre-constructed carrying capacity evaluation model is solved according to the cleaned actual data to obtain the carrying capacity of the substation. The carrying capacity evaluation model includes an objective function and a constraint condition. The objective function is constructed with the maximum sum of the openable capacity of all distributed new energy stations in the substation as the target. The constraint condition is constructed according to the utilization rate of the distributed new energy station and the maximum reverse sending ratio of the bus. As can be seen, in the process of constructing the carrying capacity evaluation model, the present application considers the openable capacity of the distributed new energy station, the utilization rate of the distributed new energy station and the maximum reverse sending ratio of the bus on the basis of the actual data of the substation, realizes the fine evaluation of the carrying capacity, and significantly improves the accuracy of the carrying capacity evaluation result.

[0065] The present application considers the utilization rate constraint, the thermal stability constraint, the output constraint, the voltage deviation constraint, the short-circuit current constraint, the charging and discharging power constraint and the power constraint, can effectively and multi-dimensionally quantify the coupling influence of the safety boundary, can identify the possible missing risk points, and avoids the carrying capacity that cannot be evaluated caused by local optimization.

[0066] In the process of solving the carrying capacity evaluation model, the present application first converts the carrying capacity evaluation model into a mixed integer linear programming model, then solves the mixed integer linear programming model according to the cleaned actual data and by using the integer programming method to obtain the carrying capacity of the substation. That is, the present application realizes the optimization calculation of the carrying capacity evaluation model by using the integer programming method, ensures that the global optimal solution can be determined under complex constraint conditions, avoids the problem of possibly falling into local optimization, and further improves the accuracy of the evaluation.

[0067] The application can not only ensure the authenticity and effectiveness of the evaluation, but also reflect the real operation characteristics and load characteristics of the transformer substation, so that the bearing capacity evaluation result is more in line with the actual operation condition of the transformer substation, and a more reliable technical basis is provided for energy access and power grid planning.

[0068] The application fully considers the power generation, openable capacity and utilization of the distributed new energy field station on different busbars, calculates the maximum value of the sum of the openable capacities of all distributed new energy field stations in the transformer substation, and provides a basis for reasonable access of the distributed new energy field station, thereby effectively improving the consumption level of local new energy and enhancing the adaptability of the power grid to new energy power fluctuation at different access positions. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0070] Figure 1 A schematic flow chart of the transformer substation bearing capacity evaluation method in the embodiments of the application;

[0071] Figure 2 A schematic structural diagram of the transformer substation bearing capacity evaluation device in the embodiments of the application. DETAILED DESCRIPTION

[0072] The technical solutions in the application will be described below with reference to the drawings.

[0073] The terms "first", "second", etc. in the description, claims and drawings of the application are only used for distinguishing purposes of description, and cannot be understood as indicating or implying relative importance, nor can be understood as indicating or implying sequence. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, including a series of steps or units. The method, system, product or device is not necessarily limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0074] It should be understood that in the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B and A and B existing at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single item or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b and c can be single or multiple.

[0075] Embodiment 1

[0076] The embodiment of the present application provides a substation carrying capacity evaluation method. As shown in the figure, the evaluation method 100 comprises the following steps: Figure 1

[0077] Step S1: Obtain the actual data of the substation and clean it.

[0078] Step S2: According to the actual data after cleaning, the pre-constructed carrying capacity evaluation model is solved, and the carrying capacity of the substation is obtained.

[0079] Among them, the carrying capacity evaluation model includes objective function and constraint condition. The objective function is constructed with the sum of the openable capacity of all distributed new energy stations in the substation as the target. The constraint condition is constructed according to the utilization rate of the distributed new energy station and the maximum reverse sending ratio of the bus.

[0080] In some possible implementation ways, the actual data of the substation is obtained and cleaned in step S1, which comprises:

[0081] Obtain the actual data of the substation. The abnormal values in the actual data are detected by using the logistic regression algorithm, and the detected abnormal values are repaired by using the distributed gradient enhancement library decision method (i.e. XGBoost decision method). Linear interpolation method can also be used to fill in the missing values in the actual data.

[0082] Among them, the actual data of the substation includes the static parameter actual data and time sequence operation actual data of the distributed new energy station, power grid equipment and energy storage equipment respectively.

[0083] ​Optionally, the static parameter actual data of the distributed new energy station includes a transmission coefficient of the distributed new energy station, a utilization rate of the distributed new energy station, and the like. The static parameter actual data of the power grid device includes a number of buses, a number of lines under the jurisdiction of a single bus, a maximum reverse sending ratio of the bus, a maximum transmission capacity of the bus, and the like. The static parameter actual data of the energy storage device includes a number of energy storage devices, and the like.

[0084] The time sequence operation actual data of the distributed new energy station includes a generation coefficient of the distributed new energy station at each time point, and the like. The time sequence operation actual data of the power grid device includes a generation power of the distributed new energy station on the bus at each time point, and the like. The time sequence operation actual data of the energy storage device includes a discharge power and a charging power of the energy storage device at each time point, and the like.

[0085] In some possible implementation manners, the step S2 includes:

[0086] The carrying capacity evaluation model is converted into a mixed integer linear programming model.

[0087] The carrying capacity of the substation is obtained by solving the mixed integer linear programming model according to the cleaned actual data and by using an integer programming method.

[0088] Optionally, the integer programming method can include an enumeration method, a cutting plane method, a branch and bound method, a graph theory method, a binary development method, and the like, which are not limited in the present application.

[0089] In some possible implementation manners, the objective function satisfies: ;

[0090] wherein, f represents a sum of openable capacities of all distributed new energy stations in the substation, represents a total number of buses, represents an openable capacity of a distributed new energy station on the i th bus. n

[0091] Optionally, the constraint conditions include utilization rate constraints, thermal stability constraints, output constraints, voltage deviation constraints, short-circuit current constraints, charging and discharging power constraints, and power constraints, and the like.

[0092] Optionally, the utilization rate constraints satisfy: ;

[0093] wherein, represents a generation power of a distributed new energy station on the i th bus at the j th time point, n g t 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.

[0094] Thermal stability constraints are satisfied: ;

[0095] 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.

[0096] Output constraints are satisfied: ;

[0097] The voltage deviation constraint satisfies: ;

[0098] 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.

[0099] The short-circuit current constraint satisfies: ;

[0100] 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.

[0101] The charge and discharge power constraints meet: ;

[0102] 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.

[0103] The power constraints are satisfied: ;

[0104] 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.

[0105] Example 2:

[0106] 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:

[0107] The cleaning module 201 is used to obtain actual data of the substation and perform cleaning.

[0108] 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.

[0109] 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.

[0110] In some possible implementations, the cleaning module 201 is specifically configured to:

[0111] Actual data of the transformer substation is acquired. An abnormal value in the actual data is detected by using a logistic regression algorithm, and the detected abnormal value is repaired by using a distributed gradient boosting library decision method. A linear interpolation method can also be used to fill in missing values in the actual data.

[0112] The actual data of the transformer substation includes static parameter actual data and time sequence operation actual data of the distributed new energy station, the power grid equipment and the energy storage equipment.

[0113] Optionally, the static parameter actual data of the distributed new energy station includes a transmission coefficient of the distributed new energy station and a utilization rate of the distributed new energy station. The static parameter actual data of the power grid equipment includes a number of busbars, a number of lines under the jurisdiction of a single busbar, a maximum reverse sending ratio of the busbar, a maximum transmission capacity of the busbar, and the like. The static parameter actual data of the energy storage equipment includes a number of energy storage devices.

[0114] The time sequence operation actual data of the distributed new energy station includes a generation coefficient of the distributed new energy station at each time point. The time sequence operation actual data of the power grid equipment includes a generation power of the distributed new energy station on the busbar at each time point. The time sequence operation actual data of the energy storage equipment includes a discharge power and a charging power of the energy storage device at each time point.

[0115] In some possible implementation manners, the solving module 202 is specifically configured to:

[0116] The carrying capacity evaluation model is converted into a mixed integer linear programming model.

[0117] According to the cleaned actual data, the mixed integer linear programming model is solved by using an integer programming method, and the carrying capacity of the transformer substation is obtained.

[0118] In some possible implementation manners, the evaluation device further includes a modeling module, and the modeling module is configured to determine a target function according to the following formula: . Wherein, f represents a sum of openable capacities of all distributed new energy stations in the transformer substation, represents a total number of busbars, represents an openable capacity of a distributed new energy station on the i th busbar. n

[0119] Optionally, the modeling module is further configured to determine a constraint condition. The constraint condition includes a utilization rate constraint, a thermal stability constraint, a power output constraint, a voltage deviation constraint, a short-circuit current constraint, a charge-discharge power constraint and an electric quantity constraint.

[0120] For example, the utilization rate constraint satisfies: ;

[0121] Wherein, ​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.

[0122] Thermal stability constraints are satisfied: ;

[0123] 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.

[0124] Optionally, the output constraints satisfy: ;

[0125] The voltage deviation constraint satisfies: ;

[0126] 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.

[0127] The short-circuit current constraint satisfies: ;

[0128] wherein, denotes the short-circuit impedance limit value of the bus, denotes the short-circuit current limit value of the bus, denotes the voltage limit value of the bus, denotes the short-circuit current coefficient of the bus.

[0129] The charge-discharge power constraint satisfies: ;

[0130] wherein, denotes the charge-discharge state of the k th energy storage device at the t th time, taking 1 indicates that the k th energy storage device is in charging state, taking 0 indicates that the k th energy storage device is in discharging state. denotes the maximum discharging power of the k th energy storage device, denotes the maximum charging power of the k th energy storage device.

[0131] The power constraint satisfies: ;

[0132] wherein, denotes the power of the k th energy storage device at the t th time, denotes the power of the k th energy storage device at the t -1th time, denotes the installed capacity of the k th energy storage device.

[0133] Embodiment 3:

[0134] Based on the same inventive concept, the embodiment of the present application further provides a computer device, which comprises a processor and a memory. The memory is used to store a computer program, and the computer program comprises program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also 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 gate or transistor logic devices, discrete hardware components, etc. The processor is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the evaluation method provided in the above embodiment.

[0135] Embodiment 4:

[0136] Based on the same inventive concept, the embodiment of the present application further provides a computer readable storage medium, specifically a computer readable storage medium (Memory). The computer readable storage medium is a memory device in the computer device, and is used to store programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium in the computer device, and of course can also include an expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores an 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 the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the steps of the evaluation method provided in the above embodiment.

[0137] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the application can be embodied in a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0138] The application is described in reference to the flow diagrams and / or block diagrams of the methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0139] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0141] The above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the application should, therefore, be determined not with reference to the above description, but instead with reference to the appended claims, along with their full scope of equivalents.

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; 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 busbar; 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; 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; 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.

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 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.

5. 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; 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 busbar; 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; The modeling module is further 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; 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.

6. The evaluation device according to claim 5, 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.

7. The evaluation device according to claim 5, 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.

8. The evaluation device according to claim 5, 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.

9. 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 4 is implemented.

10. 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 4 is implemented.

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