A low voltage energy storage optimization control method for power grid frequency response

Abstract

The present invention relates to a low-voltage energy storage optimization control method for power grid frequency response, and relates to the field of low-voltage energy storage device control, including: obtaining a power grid frequency reference value; based on the power grid frequency reference value, performing a power grid non-fault cross-section data analysis on the first substation to obtain a power grid frequency reference interval; receiving power grid frequency fluctuation timing information in a preset time zone; based on the power grid frequency reference interval, performing a power grid fault analysis in combination with the power grid frequency fluctuation timing information to obtain a power grid fault probability; when it is greater than or equal to a power grid fault probability threshold, performing low-voltage energy storage device scheduling optimization on the first substation, generating a low-voltage energy storage device grid connection plan to perform low-voltage energy storage control. The present invention solves the technical problem that the prior art has serious control hysteresis because the energy storage device is connected to the grid only after a power grid fault occurs.

Description

Technical Field

[0001] The present invention relates to the field of low-voltage energy storage device control, and in particular to a low-voltage energy storage optimization control method for power grid frequency response. Background Art

[0002] The grid frequency reflects the supply and demand balance of the power system, and its stability is directly related to the reliability and security of the grid. Traditional grid frequency control methods focus on monitoring and responding to the current state of the grid, and often emergency processing can only be carried out after a grid failure occurs.

[0003] The existing technology only dispatches energy storage devices and connects them to the grid for power supply after a power grid fault occurs. This passive control method lacks predictability and makes it impossible to respond quickly before a fault occurs. Summary of the invention

[0004] The present invention aims to solve the technical problem that the prior art only connects the energy storage device to the grid for power supply after a grid fault occurs, resulting in serious control lag, and provides a low-voltage energy storage optimization control method for grid frequency response.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a low-voltage energy storage optimization control method for power grid frequency response, comprising: obtaining a power grid frequency reference value; based on the power grid frequency reference value, performing a power grid non-fault cross-section data analysis on a first substation to obtain a power grid frequency reference interval; receiving power grid frequency fluctuation timing information in a preset time zone, wherein the preset time zone refers to a time interval of a preset time step forward from the current moment; based on the power grid frequency reference interval, performing a power grid fault analysis in combination with the power grid frequency fluctuation timing information to obtain a power grid fault probability; when the power grid fault probability is greater than or equal to a power grid fault probability threshold, performing low-voltage energy storage device scheduling optimization on the first substation to generate a low-voltage energy storage device grid-connected plan; and performing low-voltage energy storage control according to the low-voltage energy storage device grid-connected plan.

[0007] In a second aspect, the present invention provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing a low-voltage energy storage optimization control method for power grid frequency response as described in the first aspect.

[0008] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, the low-voltage energy storage optimization control method for power grid frequency response described in the first aspect is implemented.

[0009] The beneficial effect of the present invention is: by real-time monitoring of the grid frequency reference value and fluctuation timing information, the probability of grid failure is evaluated. When the probability of failure exceeds the preset threshold, the system will automatically optimize the scheduling of the low-voltage energy storage device, generate a grid connection plan, and execute the corresponding control strategy to ensure the stability of the grid frequency and the continuity of power supply. Compared with the traditional grid fault response method, the present invention fully utilizes the potential of the energy storage device by analyzing the grid frequency fluctuation in real time, provides more effective auxiliary services for the grid, realizes the pre-analysis of grid failures, and achieves the technical effect of improving the timeliness of the low-voltage energy storage device being connected to the grid and reducing the risk of power supply interruption due to grid failures through active rather than passive control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of a low-voltage energy storage optimization control method for power grid frequency response provided by the present invention;

[0011] Figure 2 A schematic diagram of a grid fault analysis process of a low-voltage energy storage optimization control method for grid frequency response provided by the present invention;

[0012] Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention;

[0013] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0014] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0015] Electronic device 500 , memory 510 , processor 520 , first computer program 511 , computer-readable storage medium 600 , second computer program 611 . DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0019] Embodiment 1:

[0020] like Figure 1 As shown, an embodiment of the present invention provides a low-voltage energy storage optimization control method for power grid frequency response, comprising the steps of:

[0021] S10: Obtaining a grid frequency reference value;

[0022] Preferably, the grid frequency reference value refers to the standard frequency set in the power system, usually 50 Hz, which is used to measure the stability and operation quality of the power grid. The fluctuation of the grid frequency reflects the balance between power generation and load. The deviation of the frequency from the nominal value will affect the efficiency of the power equipment and may even threaten the safe operation of the equipment. Therefore, accurately obtaining the grid frequency reference value is crucial to the safety and stability of the power system.

[0023] S20: Based on the grid frequency reference value, perform grid non-fault cross-section data analysis on the first substation to obtain a grid frequency reference interval;

[0024] Preferably, the first substation refers to a preset fixed power supply area. In a narrow sense, it refers to a power supply substation managed by a certain substation. In a broad sense, it refers to an area that can be large or small, and it can also refer to the power supply of a certain park or a certain building; the non-fault cross-section data of the power grid refers to the power grid operation data of the first substation collected when no fault occurs in the first substation; the power grid frequency reference interval refers to the normal fluctuation range of the power grid frequency under non-fault conditions.

[0025] Furthermore, taking the grid frequency reference value as the reference value, the fluctuation range of the grid frequency in the grid non-fault cross-section data under the fault-free state is analyzed to obtain the grid frequency reference range, thereby providing a data reference for subsequent grid fault state analysis.

[0026] S30: receiving power grid frequency fluctuation timing information in a preset time zone, wherein the preset time zone refers to a time interval of a preset time step forward from the current time;

[0027] Preferably, the preset time zone refers to a time interval calculated from the current moment to the past, and the length of this time interval is pre-set, usually in minutes or hours; within this time interval, the system will collect and analyze the frequency fluctuation data of the power grid; the power grid frequency fluctuation timing information refers to the change data of the power grid frequency within the preset time zone, which can be used to identify the dynamic behavior of the power grid and potential unstable factors.

[0028] Specifically, the length of the preset time zone is determined, for example, it can be the past 30 minutes or 1 hour; within this preset time zone, the frequency data of the power grid is monitored and recorded in real time and stored as the timing information of the power grid frequency fluctuation. Subsequently, this data can be used to identify potential problems in the power grid, such as supply and demand imbalance, equipment failure and other abnormal power grid conditions, thereby providing guidance for the dispatch of low-voltage energy storage devices.

[0029] S40: Performing grid fault analysis based on the grid frequency reference interval and in combination with the grid frequency fluctuation time sequence information to obtain a grid fault probability;

[0030] Further, such as Figure 2 As shown, based on the grid frequency reference interval, combined with the grid frequency fluctuation timing information, grid fault analysis is performed to obtain the grid fault probability. Step S40 includes the following steps:

[0031] S41: Analyze the proportion of time when the grid frequency fluctuation timing information does not belong to the grid frequency reference interval, and set it as the grid frequency abnormality;

[0032] S42: when the power grid frequency abnormality is greater than or equal to the power grid frequency abnormality threshold, counting frequency deviation parameters of the power grid frequency fluctuation time series information based on the power grid frequency reference interval;

[0033] S43: Collecting grid cross-section data of the first substation according to the frequency deviation parameter to obtain a proportion of grid failure samples, which is set as the grid failure probability.

[0034] Further, based on the grid frequency reference interval, the frequency deviation parameter of the grid frequency fluctuation time series information is counted, and step S42 includes the steps of:

[0035] S421: Counting the average of the grid frequencies whose grid frequency fluctuation timing information is greater than the upper limit of the grid frequency reference interval, and setting it as a first frequency deviation parameter;

[0036] S422: Counting the average of the grid frequencies whose grid frequency fluctuation timing information is less than the lower limit of the grid frequency reference interval, and setting it as a second frequency deviation parameter;

[0037] S423: Add the first frequency deviation parameter and the second frequency deviation parameter into the frequency deviation parameter.

[0038] Preferably, the power grid failure probability refers to the probability of a power grid failure determined based on the degree of deviation from the power grid frequency reference interval and the trend analysis of historical data. The specific determination process of the power grid failure probability is as follows:

[0039] Firstly, the ratio of the number of moments when the grid frequency fluctuation timing information does not belong to the grid frequency reference interval to the total number of moments of the grid frequency fluctuation timing information is counted and stored as the grid frequency abnormality.

[0040] Secondly, the grid frequency anomaly threshold refers to the first-level threshold set by the user to determine whether the grid frequency is abnormal. If the grid frequency anomaly is abnormal, then the subsequent process is carried out. Otherwise, no subsequent process is required. By default, 0.1% ≤ grid frequency anomaly threshold ≤ 5%. The specific setting can be customized by the user. If the user does not set it, the default value of 5% is taken. Therefore, there are the following two situations:

[0041] Case 1: When the grid frequency anomaly is less than the grid frequency anomaly threshold, the process is stopped to avoid occupying system computing power and performing redundant calculations.

[0042] Case 2: When the grid frequency abnormality is greater than or equal to the grid frequency abnormality threshold, the deviation parameter of the grid frequency fluctuation timing information from the grid frequency reference interval is statistically calculated. Preferably, the frequency deviation parameter includes at least a first frequency deviation parameter and a second frequency deviation parameter. Specifically, the first frequency deviation parameter refers to the average value of all frequencies whose grid frequency of the grid frequency fluctuation timing information is greater than the upper limit of the grid frequency reference interval, and the second frequency deviation parameter refers to the average value of all frequencies whose grid frequency of the grid frequency fluctuation timing information is less than the lower limit of the grid frequency reference interval.

[0043] Finally, taking the first frequency deviation parameter and the second frequency deviation parameter as the scenario constraints of the frequency deviation, the grid cross-section data of the first substation with the same first frequency deviation parameter and the second frequency deviation parameter are collected. Preferably, the number of cross-section data is at least 50,000. Furthermore, the proportion of fault samples in the grid cross-section data of the first substation is counted and set as the grid failure probability.

[0044] S50: When the power grid failure probability is greater than or equal to the power grid failure probability threshold, optimizing the scheduling of the low-voltage energy storage device in the first substation to generate a grid connection plan for the low-voltage energy storage device;

[0045] S60: Perform low-voltage energy storage control according to the low-voltage energy storage device grid-connection plan.

[0046] Preferably, the grid fault probability threshold is a fault probability threshold value preset by the user for determining whether the grid needs to be incorporated into a low-voltage energy storage device for auxiliary power supply, which is 20% by default and can be customized by the user according to needs.

[0047] When the power grid failure probability is less than the power grid failure probability threshold, the process is stopped.

[0048] When the probability of power grid failure is greater than or equal to the power grid failure probability threshold, the time interval and power supply required are determined, and then the idle time zone and storage capacity of the low-voltage energy storage device are received. According to the time interval and power supply, combined with the idle time zone and storage capacity, the low-voltage energy storage device is selected, the low-voltage energy storage device that needs to be dispatched and the corresponding power supply are determined, and a low-voltage energy storage device grid-connected plan is generated to control the low-voltage energy storage, thereby fully utilizing the potential of the energy storage device and providing the power grid with more effective auxiliary services.

[0049] Further, based on the grid frequency reference value, the grid non-fault section data is analyzed for the first substation to obtain the grid frequency reference interval. Step S20 includes the following steps:

[0050] S21: collecting non-fault cross-sectional data of the first substation based on the grid frequency reference value as a constraint, wherein the non-fault cross-sectional data of the grid includes grid frequency record data;

[0051] S22: extracting first power grid frequency record data greater than the power grid frequency reference value from the power grid frequency record data;

[0052] S23: extracting second power grid frequency record data which is less than the power grid frequency reference value from the power grid frequency record data;

[0053] S24: performing interval upper limit analysis on the first power grid frequency record data to obtain an upper limit value of a power grid frequency reference interval;

[0054] S25: performing interval lower limit analysis on the second power grid frequency record data to obtain a power grid frequency reference interval lower limit value;

[0055] S26: Constructing the grid frequency reference interval according to the grid frequency reference interval upper limit value and the grid frequency reference interval lower limit value.

[0056] Preferably, in order to ensure the accuracy of power grid fault analysis, the accuracy of the power grid frequency reference interval must be ensured. In view of the fact that there is no feasible and clear data statistics solution in the prior art, the embodiment of the present invention innovatively proposes an analysis method of the power grid frequency reference interval:

[0057] Collect the grid non-fault section data of the first substation whose grid frequency reference value is the same as the grid frequency reference value of the present application, any grid non-fault section data includes at least one grid frequency record data. Extract the first grid frequency record data which is greater than the grid frequency reference value, that is, greater than 50 Hz, from the grid frequency record data; extract the second grid frequency record data which is less than the grid frequency reference value, that is, less than 50 Hz, from the grid frequency record data.

[0058] Outlier data is deleted from the first power grid frequency record data, and the mean of the remaining non-outlier data is calculated and stored as the upper limit value of the power grid frequency reference interval; outlier data is deleted from the second power grid frequency record data, and the mean of the remaining non-outlier data is calculated and stored as the lower limit value of the power grid frequency reference interval; a power grid frequency reference interval is constructed according to the upper limit value and the lower limit value of the power grid frequency reference interval.

[0059] By analyzing the upper and lower limit modes separately, the accuracy of the grid frequency reference interval is improved. The traditional method is mainly to directly collect all data in a concentrated distribution interval. This type of analysis method is not accurate enough and cannot be applied.

[0060] Further, the first power grid frequency record data is subjected to interval upper limit analysis to obtain the upper limit value of the power grid frequency reference interval, and step S24 includes the following steps:

[0061] S241: performing de-extreme value processing on the first power grid frequency record data to obtain centralized power grid frequency record data;

[0062] S241: Take the average of the centralized power grid frequency record data and set it as the upper limit value of the power grid frequency reference interval.

[0063] Preferably, the de-extreme value processing is the process of performing outlier analysis, and the concentrated power grid frequency record data is the data set obtained after deleting outliers from the first power grid frequency record data. The mean of the concentrated power grid frequency record data is taken and set as the upper limit of the power grid frequency reference interval. Furthermore, the process of obtaining the lower limit of the power grid frequency reference interval is exactly the same as the upper limit of the power grid frequency reference interval. After the second power grid frequency record data is de-extreme valued, the average of the concentrated second power grid frequency record data is calculated and stored as the lower limit of the power grid frequency reference interval. By removing extreme points, the interference of accidental parameters can be avoided, which reduces the accuracy of the analysis results.

[0064] Further, the first power grid frequency record data is subjected to de-extreme value processing to obtain centralized power grid frequency record data, and step S241 includes the following steps:

[0065] S2411: Step 1: Obtain the power grid frequency distribution interval of the first power grid frequency record data;

[0066] S2412: Step 2: Randomly select a value for the power grid frequency distribution interval to obtain a first segmentation value;

[0067] S2413: Step 3: Construct a root node, add the first-level first-segmented power grid frequency record data whose first power grid frequency record data is greater than or equal to the first segmentation value to the first child node of the root node, and add the first-level second-segmented power grid frequency record data whose first power grid frequency record data is less than the first segmentation value to the second child node of the root node, to construct a first-level isolated tree;

[0068] S2414: Repeat steps 1 to 3 for the first-level second-sliced ​​power grid frequency record data and the first-level first-sliced ​​power grid frequency record data that meet the slicing condition, until all bottom-level leaf nodes do not meet the slicing condition, and output the number of slicing times of the bottom-level leaf nodes, wherein the bottom-level leaf nodes refer to nodes without child nodes;

[0069] S2415: deleting the power grid frequency record data whose number of segmentation times of the bottom leaf node is less than or equal to the segmentation times threshold, and obtaining the centralized power grid frequency record data;

[0070] The segmentation condition is that the node power grid frequency record data>1, and at least one of the node power grid frequency record data is not equal to other data.

[0071] Preferably, the embodiment of the present application proposes a new and simpler de-extreme processing algorithm, which has a simpler process and higher computational efficiency than the traditional de-extreme processing algorithm, as described in detail as follows:

[0072] Step 1: Obtain the power grid frequency distribution interval of the first power grid frequency record data; Step 2: Randomly take values ​​for the power grid frequency distribution interval to obtain a first split value; Step 3: Construct a root node, add the first-level first-split power grid frequency record data whose first power grid frequency record data is greater than or equal to the first split value to the first child node of the root node, and add the first-level second-split power grid frequency record data whose first power grid frequency record data is less than the first split value to the second child node of the root node to construct a first-level isolated tree.

[0073] The first-level isolated tree has a first child node of the first-level first-cut power grid frequency record data, and a second child node of the first-level second-cut power grid frequency record data. If the first-level first-cut power grid frequency record data of the first child node meets the splitting condition, that is, the node power grid frequency record data>1, and at least one of the node power grid frequency record data is not equal to other data, then repeat steps 1 to 3 for the first-level first-cut power grid frequency record data of the first child node. If the first-level first-cut power grid frequency record data of the first child node does not meet the splitting condition, that is, the data amount of the first child node is 0, or the data of the first child node is equal, then the first child node is not split. Perform the same processing on the first-level second-cut power grid frequency record data of the second child node to generate a second-level isolated tree, until the last bottom-level leaf node does not meet the splitting condition, and output the number of splits of the bottom-level leaf node, where the bottom-level leaf node refers to a node without a child node. That is to say, each value must be distributed to a node. Since the more discrete the value is, the shorter the separation time will be, the smaller the number of times the underlying leaf node is split, the more outlier the value is. The power grid frequency record data whose number of splits of the underlying leaf node is less than or equal to the split number threshold will be deleted to obtain the centralized power grid frequency record data. The split number threshold can be customized by the user and is not limited by this application.

[0074] Further, when the power grid failure probability is greater than or equal to the power grid failure probability threshold, the low-voltage energy storage device scheduling optimization is performed on the first substation to generate a low-voltage energy storage device grid connection plan. Step S50 includes the following steps:

[0075] S51: When the power grid failure probability is greater than or equal to the power grid failure probability threshold, a fault warning signal is generated and sent to the power grid management end to obtain a fault troubleshooting time zone, wherein the fault troubleshooting time zone has a time zone duration identifier, a time zone week identifier, and a time zone season identifier;

[0076] S52: taking the time zone duration identifier, the time zone week identifier, and the time zone season identifier as constraints, counting a set of historical power consumption record values ​​of the first substation;

[0077] S53: performing de-extreme value processing on the set of historical power consumption records of the first substation to obtain centralized historical power consumption;

[0078] S54: taking the maximum value of the centralized historical power consumption and setting it as the target power consumption;

[0079] S55: Select a low-voltage energy storage device according to the target connected power amount, and generate a grid-connected plan for the low-voltage energy storage device.

[0080] Preferably, the power grid management end refers to the client that manages the power distribution of the first substation. Through the power grid management end, two types of data can be fed back. One is that no troubleshooting is required, and the process will be stopped after such instructions are received; the second is to feed back a troubleshooting time zone, that is, the client will troubleshoot the time zone that the power grid may need to store faults. The troubleshooting time zone has a time zone duration identifier representing the duration, a time zone week identifier representing the day of the week, and a time zone season identifier representing the season. The historical power consumption of the first substation with the same time zone duration identifier, the time zone week identifier, and the time zone season identifier is collected and stored as a set of historical power consumption record values ​​of the first substation; then the de-extreme value processing algorithm provided in the embodiment of the present application is used to process the set of historical power consumption record values ​​of the first substation to obtain the centralized historical power consumption. In order to ensure sufficient electricity, the maximum value of the centralized historical power consumption is taken and set as the target parallel power. According to the target parallel power, a low-voltage energy storage device that is idle in the troubleshooting time zone is selected and stored as a low-voltage energy storage device grid connection scheme. The accuracy of power consumption configuration is improved by analyzing historical power consumption using the time zone duration identifier, the time zone week identifier, and the time zone season identifier.

[0081] Further, according to the target connected power, a low-voltage energy storage device is selected to generate a grid-connected plan for the low-voltage energy storage device. Step S55 includes the following steps:

[0082] S551: Obtain a list of idle low-voltage energy storage devices and a list of idle storage capacities in the fault troubleshooting time zone;

[0083] S552: configuring the target grid-connected power according to the idle energy storage list to obtain initial grid-connected plans for several low-voltage energy storage devices;

[0084] S553: ​​extracting the sum of the lengths of several grid-connected paths of the initial grid-connected schemes of the several low-voltage energy storage devices;

[0085] S554: Extracting the initial grid-connected scheme for low-voltage energy storage devices having the minimum value of the sum of the plurality of grid-connected path lengths from the plurality of initial grid-connected schemes for low-voltage energy storage devices, and setting it as the grid-connected scheme for low-voltage energy storage devices.

[0086] Preferably, the idle low-voltage energy storage device list refers to a set of low-voltage energy storage devices that can supply power to the first substation and are in an idle state during the troubleshooting time zone; the idle energy storage list refers to a set of power that can be supplied to the substation by the idle low-voltage energy storage device; with the target parallel power as the target, select several idle energy storages from the idle energy storage list, and stop when the sum is greater than or equal to the target parallel power, and obtain several initial grid-connected schemes for low-voltage energy storage devices; analyze the sum of the energy supply path lengths of the low-voltage energy storage devices of each initial grid-connected scheme for low-voltage energy storage devices, and store it as the sum of several grid-connected path lengths; extract the initial grid-connected scheme for low-voltage energy storage devices with the minimum value of the sum of the several grid-connected path lengths from the initial grid-connected schemes for low-voltage energy storage devices, and set it as the grid-connected scheme for the low-voltage energy storage device. By selecting a control scheme with a shorter energy supply path, the convenience of energy supply scheduling is guaranteed.

[0087] The embodiment of the present invention provides a low-voltage energy storage optimization control method for power grid frequency response, which has at least the following technical effects:

[0088] By real-time monitoring of the grid frequency reference value and fluctuation timing information, the probability of grid failure is evaluated. When the probability of failure exceeds the preset threshold, the system will automatically optimize the scheduling of the low-voltage energy storage device, generate a grid connection plan, and execute the corresponding control strategy to ensure the stability of the grid frequency and the continuity of power supply. Compared with the traditional grid fault response method, the present invention fully utilizes the potential of the energy storage device by analyzing the grid frequency fluctuation in real time, provides more effective auxiliary services for the grid, realizes the pre-analysis of grid failures, and achieves the technical effect of improving the timeliness of the low-voltage energy storage device grid connection and reducing the risk of power supply interruption due to grid failures through active rather than passive control strategies.

[0089] Embodiment 2:

[0090] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented:

[0091] Obtaining a grid frequency reference value;

[0092] Based on the grid frequency reference value, performing grid non-fault cross-section data analysis on the first substation to obtain a grid frequency reference interval;

[0093] Receiving power grid frequency fluctuation timing information in a preset time zone, wherein the preset time zone refers to a time interval of a preset time step forward from the current time;

[0094] Based on the grid frequency reference interval, combined with the grid frequency fluctuation time sequence information, a grid fault analysis is performed to obtain a grid fault probability;

[0095] When the power grid failure probability is greater than or equal to the power grid failure probability threshold, optimizing the scheduling of the low-voltage energy storage device in the first substation to generate a grid connection plan for the low-voltage energy storage device;

[0096] The low-voltage energy storage control is performed according to the low-voltage energy storage device grid connection scheme.

[0097] Embodiment three:

[0098] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, the following steps are implemented:

[0099] Obtaining a grid frequency reference value;

[0100] Based on the grid frequency reference value, performing grid non-fault cross-section data analysis on the first substation to obtain a grid frequency reference interval;

[0101] Receiving power grid frequency fluctuation timing information in a preset time zone, wherein the preset time zone refers to a time interval of a preset time step forward from the current time;

[0102] Based on the grid frequency reference interval, combined with the grid frequency fluctuation time sequence information, a grid fault analysis is performed to obtain a grid fault probability;

[0103] When the power grid failure probability is greater than or equal to the power grid failure probability threshold, optimizing the scheduling of the low-voltage energy storage device in the first substation to generate a grid connection plan for the low-voltage energy storage device;

[0104] The low-voltage energy storage control is performed according to the low-voltage energy storage device grid connection scheme.

[0105] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0110] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0111] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A low voltage energy storage optimization control method for power grid frequency response, characterized in that: include: Obtaining a grid frequency reference value; Based on the grid frequency reference value, performing grid non-fault cross-section data analysis on the first substation to obtain a grid frequency reference interval; Receiving power grid frequency fluctuation timing information of a preset time zone, wherein the preset time zone is a time zone covered by a preset time length before the current moment; Based on the grid frequency reference interval, combined with the grid frequency fluctuation time sequence information, a grid fault analysis is performed to obtain a grid fault probability; When the power grid failure probability is greater than or equal to the power grid failure probability threshold, optimizing the scheduling of the low-voltage energy storage device in the first substation to generate a grid connection plan for the low-voltage energy storage device; Perform low-voltage energy storage control according to the grid-connected scheme of the low-voltage energy storage device; Further, based on the grid frequency reference interval, combined with the grid frequency fluctuation time sequence information, grid fault analysis is performed to obtain a grid fault probability, including: Analyze the proportion of time when the power grid frequency fluctuation timing information does not belong to the power grid frequency reference interval, and set it as the power grid frequency abnormality; When the power grid frequency abnormality is greater than or equal to a power grid frequency abnormality threshold, counting frequency deviation parameters of the power grid frequency fluctuation time series information based on the power grid frequency reference interval; Collect the grid section data of the first substation according to the frequency deviation parameter to obtain the grid fault sample ratio, which is set as the grid fault profile; Furthermore, when the power grid failure probability is greater than or equal to the power grid failure probability threshold, the low-voltage energy storage device scheduling optimization is performed on the first substation to generate a low-voltage energy storage device grid connection plan, including: When the power grid failure probability is greater than or equal to the power grid failure probability threshold, a fault warning signal is generated and sent to the power grid management end to obtain a fault troubleshooting time zone, wherein the fault troubleshooting time zone has a time zone duration identifier, a time zone week identifier, and a time zone season identifier; Taking the time zone duration identifier, the time zone week identifier, and the time zone season identifier as constraints, counting a set of historical power consumption record values ​​of the first substation area; De-extreme value processing is performed on the set of historical power consumption record values ​​of the first substation area to obtain centralized historical power consumption; Taking the maximum value of the centralized historical power consumption as the target power consumption; According to the target connected power amount, a low-voltage energy storage device is selected and a grid-connected plan for the low-voltage energy storage device is generated.

2. The method according to claim 1, characterized in that Based on the grid frequency reference value, the grid non-fault cross-section data of the first substation is analyzed to obtain the grid frequency reference interval, including: Taking the grid frequency reference value as a constraint, collecting grid non-fault cross-sectional data of the first substation, wherein the grid non-fault cross-sectional data includes grid frequency record data; Extracting first power grid frequency record data greater than the power grid frequency reference value from the power grid frequency record data; Extracting second power grid frequency record data which is less than the power grid frequency reference value from the power grid frequency record data; Performing interval upper limit analysis on the first power grid frequency record data to obtain an upper limit value of a power grid frequency reference interval; Performing interval lower limit analysis on the second power grid frequency record data to obtain a power grid frequency reference interval lower limit value; The grid frequency reference interval is constructed according to the grid frequency reference interval upper limit value and the grid frequency reference interval lower limit value.

3. The method according to claim 2, characterized in that Performing interval upper limit analysis on the first power grid frequency record data to obtain the upper limit value of the power grid frequency reference interval includes: Performing de-extreme value processing on the first power grid frequency record data to obtain centralized power grid frequency record data; The average value of the centralized power grid frequency record data is taken and set as the upper limit value of the power grid frequency reference interval.

4. The method according to claim 3, characterized in that The first power grid frequency record data is subjected to a de-extreme value processing to obtain centralized power grid frequency record data, including: Step 1: obtaining a power grid frequency distribution interval of the first power grid frequency record data; Step 2: Randomly select a value for the power grid frequency distribution interval to obtain a first segmentation value; Step 3: construct a root node, add the first-level first-segmented power grid frequency record data whose first power grid frequency record data is greater than or equal to the first segmentation value to the first child node of the root node, and add the first-level second-segmented power grid frequency record data whose first power grid frequency record data is less than the first segmentation value to the second child node of the root node, to construct a first-level isolated tree; Repeating steps 1 to 3 for the first-level second-sliced ​​power grid frequency record data and the first-level first-sliced ​​power grid frequency record data that meet the slicing conditions, until the bottom-level leaf nodes do not meet the slicing conditions, outputting the number of slicing times of the bottom-level leaf nodes, wherein the bottom-level leaf nodes refer to nodes without child nodes; Delete the power grid frequency record data whose number of segmentation times of the bottom leaf node is less than or equal to the segmentation times threshold, and obtain the centralized power grid frequency record data; The segmentation condition is that the node power grid frequency record data>1, and at least one of the node power grid frequency record data is not equal to other data.

5. The method according to claim 1, characterized in that The frequency deviation parameter of the power grid frequency fluctuation time series information is counted based on the power grid frequency reference interval, including: Count the average of the grid frequencies that are greater than the upper limit of the grid frequency reference interval of the grid frequency fluctuation timing information, and set it as the first frequency deviation parameter; Counting the average of the grid frequencies whose grid frequency fluctuation timing information is less than the lower limit of the grid frequency reference interval, and setting it as the second frequency deviation parameter; The first frequency deviation parameter and the second frequency deviation parameter are added to the frequency deviation parameter.

6. The method according to claim 1, characterized in that According to the target connected power, a low-voltage energy storage device is selected, and a grid-connected plan for the low-voltage energy storage device is generated, including: Obtaining a list of idle low-voltage energy storage devices and a list of idle storage capacities in the fault troubleshooting time zone; According to the idle energy storage list, the target grid-connected power is configured to obtain initial grid-connected plans for several low-voltage energy storage devices; Extracting the sum of several grid-connected path lengths of the initial grid-connected schemes of the several low-voltage energy storage devices; The initial grid-connected scheme for low-voltage energy storage devices having the minimum value of the sum of the plurality of grid-connected path lengths is extracted from the plurality of initial grid-connected schemes for low-voltage energy storage devices, and is set as the grid-connected scheme for low-voltage energy storage devices.

7. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing a low-voltage energy storage optimization control method for power grid frequency response as described in any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the low-voltage energy storage optimization control method for power grid frequency response according to any one of claims 1 to 6 is implemented.

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