Control method of energy storage power distribution cabinet

Abstract

This application relates to a control method for an energy storage distribution cabinet. By analyzing data from different types of electricity consumption and calculating the specific capacity of the energy storage distribution cabinet, the method controls the energy storage distribution cabinet to charge and discharge under different electricity consumption environments, thereby reducing peak loads on the power grid during peak electricity consumption periods.

Description

Technical Field

[0001] This application relates to the field of energy storage distribution cabinet technology, and in particular to a control method for energy storage distribution cabinets. Background Technology

[0002] Distribution cabinets are divided into power distribution cabinets, lighting distribution cabinets, and metering cabinets, and are the final-level equipment in a power distribution system. Distribution cabinets are a general term for motor control centers. Distribution cabinets are used in situations where the load is relatively dispersed and there are fewer circuits; motor control centers are used in situations where the load is concentrated and there are more circuits. They distribute the electrical energy from a circuit of the upstream power distribution equipment to the nearest load. This level of equipment should provide protection, monitoring, and control for the load.

[0003] The existing electricity consumption types are mainly divided into peak-valley pricing and capacity pricing. Both of these types of electricity consumption will increase the load on the power grid during peak electricity consumption periods. Currently, the common approach is to use energy storage distribution cabinets and power grid distribution cabinets in combination to reduce the load on the power grid during peak electricity consumption periods. However, there is a lack of precise and reasonable control methods for energy storage distribution cabinets. Summary of the Invention

[0004] Therefore, it is necessary to provide a control method for energy storage distribution cabinets, addressing the current practice of using energy storage distribution cabinets and grid distribution cabinets in combination to reduce grid load during peak electricity consumption periods, which lacks precise and reasonable control methods for energy storage distribution cabinets.

[0005] This application provides a control method for an energy storage distribution cabinet, including:

[0006] Obtain sample data for multiple samples. The sample data for each sample includes peak-valley electricity price table or capacity electricity price table, peak-valley electricity consumption time table or capacity electricity consumption time table, and the capacity of energy storage distribution cabinet.

[0007] Based on the peak-valley electricity price table and peak-valley electricity consumption time table in the sample data of each sample, the target peak-valley electricity consumption period of each sample data is calculated, and the expected discharge time period is calculated based on the target peak-valley electricity consumption period of all sample data.

[0008] Based on each sample data, each peak-valley electricity consumption timetable, and the target peak-valley electricity consumption period for each sample data, a discharge time prediction model is constructed.

[0009] Obtain a sample data to be tested, and obtain the electricity consumption type of the sample data;

[0010] When the electricity consumption type of the sample data to be tested is peak-valley pricing type, the peak-valley electricity price table to be tested, the peak-valley electricity consumption time table to be tested, and the capacity of the energy storage distribution cabinet are obtained from the sample data to be tested. The peak-valley electricity price table to be tested and the peak-valley electricity consumption time table to be tested are input into the discharge time prediction model, the discharge time prediction model is started, and the predicted discharge time output by the discharge time prediction model is obtained.

[0011] Based on the peak-valley electricity price table and peak-valley electricity consumption time table, calculate the optimal time period for charging the energy storage distribution cabinet;

[0012] When the electricity consumption type of the sample data to be tested is capacity-based pricing, the electricity price table of the capacity to be tested, the electricity consumption time table of the capacity to be tested, and the capacity of the energy storage distribution cabinet are obtained from the sample data to be tested. At least one peak electricity consumption period is obtained based on the electricity consumption time table of the capacity to be tested and the preset electricity consumption. The preset electricity consumption is then fine-tuned so that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period.

[0013] This application relates to a control method for energy storage distribution cabinets. By analyzing data from different types of electricity consumption and calculating the specific capacity of the energy storage distribution cabinet, the method controls the energy storage distribution cabinet to charge and discharge under different electricity consumption environments, thereby reducing peak loads on the power grid during peak electricity consumption periods. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a control method for an energy storage distribution cabinet provided in an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0016] like Figure 1 As shown, in one embodiment of this application, the control method of the energy storage distribution cabinet includes the following steps S100 to S700:

[0017] S100, acquire sample data for multiple samples. The sample data for each sample includes peak-valley electricity price table or capacity electricity price table, peak-valley electricity consumption time table or capacity electricity consumption time table, and the capacity of the energy storage distribution cabinet.

[0018] Specifically, the sample data for each sample will vary depending on its electricity consumption type. The specific electricity consumption type refers to the type of electricity pricing method, which includes peak-valley pricing and capacity pricing. Therefore, the sample data for each sample will include a set of peak-valley electricity price tables and peak-valley electricity consumption time tables, or a set of capacity pricing tables and capacity electricity consumption time tables. The capacity of the energy storage distribution cabinet refers to the capacity of the internal energy storage section of the energy storage distribution cabinet in use. The peak-valley electricity price tables and capacity electricity price tables can be obtained from the latest published electricity price information, while the peak-valley electricity consumption time tables can be obtained from historical operating data records, and the capacity electricity consumption time tables can be obtained from published electricity consumption data of the same type or industry.

[0019] S200 calculates the target peak-valley electricity consumption period for each sample based on the peak-valley electricity price table and peak-valley electricity consumption time table in the sample data of each sample, and calculates the expected discharge time period based on the target peak-valley electricity consumption period of all sample data.

[0020] Specifically, by statistically analyzing the data corresponding to electricity consumption and time in the peak-valley electricity consumption timetable, the peak-valley electricity consumption peak time period with the highest electricity consumption in the specific peak-valley electricity consumption timetable can be obtained.

[0021] S300 constructs a discharge time prediction model based on each sample data, each peak-valley electricity consumption timetable, and the target peak-valley electricity consumption period for each sample data.

[0022] Specifically, the peak and off-peak electricity consumption periods are obtained for each peak and off-peak electricity consumption timetable. The obtained peak and off-peak electricity consumption periods are statistically analyzed and calculated to obtain data with specific time periods. The logic of this specific time period acquisition process is used as the underlying logic for building the discharge time period prediction model. Then, each sample data, each peak and off-peak electricity price table, each peak and off-peak electricity consumption timetable, and each peak and off-peak electricity consumption period are used as training data to train the discharge time period prediction model, resulting in a trained discharge time period prediction model with the function of predicting discharge time periods.

[0023] S400: Obtain a test sample data and obtain the power consumption type of the test sample data.

[0024] Specifically, the first step is to obtain information on whether the electricity consumption type in the sample to be tested is based on peak-valley pricing or capacity pricing, in order to perform different data processing.

[0025] S500: When the electricity consumption type of the sample data to be tested is peak-valley pricing type, the peak-valley electricity price table to be tested, the peak-valley electricity consumption time table to be tested, and the capacity of the energy storage distribution cabinet are obtained from the sample data to be tested. The peak-valley electricity price table to be tested and the peak-valley electricity consumption time table to be tested are input into the discharge time prediction model, the discharge time prediction model is started, and the predicted discharge time period output by the discharge time prediction model is obtained.

[0026] Specifically, under the premise that the electricity consumption type is peak-valley pricing, the peak-valley electricity price table, peak-valley electricity consumption time table, and capacity of energy storage distribution cabinet are obtained from the sample data to be tested. The peak-valley electricity price table and peak-valley electricity consumption time table are then input into the discharge time prediction model. The discharge time prediction model is started, and the specific predicted discharge time is obtained by using the discharge time prediction model's function of predicting the discharge time.

[0027] The S600 calculates the optimal time period for charging the energy storage distribution cabinet based on peak-valley electricity price and peak-valley electricity consumption schedules.

[0028] Specifically, the off-peak hours in the peak-valley electricity consumption schedule are used as the charging time for the energy storage distribution cabinet to avoid peak electricity consumption periods. During the off-peak hours, multiple peak-valley electricity price tables are obtained, the lowest price among the obtained prices is selected, and the time period corresponding to the lowest price is found according to the peak-valley electricity price table. This time period is then used as the optimal charging time period.

[0029] S700: When the electricity consumption type of the sample data to be tested is the capacity pricing type, the electricity price table of the capacity to be tested, the electricity consumption time table of the capacity to be tested, and the capacity of the energy storage distribution cabinet are obtained from the sample data to be tested. At least one peak electricity consumption period is obtained based on the electricity consumption time table of the capacity to be tested and the preset electricity consumption. The preset electricity consumption is fine-tuned so that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period.

[0030] Specifically, under the premise of capacity-based pricing for electricity consumption, the system acquires the electricity price table, electricity consumption time table, and capacity of the energy storage distribution cabinet from the sample data to be tested. It then performs data analysis on the electricity consumption time table based on a preset electricity consumption to identify at least one peak consumption period with the highest capacity above the preset consumption. Next, by continuously adjusting the preset consumption, the system adapts the total electricity consumption above the preset consumption during this peak consumption period with the capacity of the energy storage distribution cabinet. This yields the optimal discharge strategy for the energy storage distribution cabinet, where the optimal strategy refers to… Based on the known capacity of the energy storage distribution cabinet and the actual power consumption distribution cabinet, the energy storage distribution cabinet and the actual power consumption distribution cabinet are simultaneously activated during peak electricity consumption periods. Precise adaptation calculations are performed on the capacity of the energy storage distribution cabinet and the portion exceeding the actual power consumption distribution cabinet load. This ensures that the portion exceeding the actual power consumption distribution cabinet load is supplied by the energy storage distribution cabinet, thereby reducing the load during peak electricity consumption periods. The preset power consumption is set based on the highest unit-time power consumption of the power consumption timetable for the capacity under test, and the preset power consumption is less than the highest unit-time power consumption of the power consumption timetable for the capacity under test.

[0031] In one embodiment of this application, the calculation of the optimal time period for charging the energy storage distribution cabinet based on the peak-valley electricity price meter and the peak-valley electricity consumption time meter includes the following steps S601 to S607:

[0032] S601, Select sample data for one sample.

[0033] S602, parse the sample data to obtain the peak-valley electricity price table and peak-valley electricity consumption time table contained in the sample data.

[0034] S603 extracts multiple peak and off-peak electricity consumption periods from the peak-valley electricity consumption timetable; each peak electricity consumption period corresponds to an electricity price, and each off-peak electricity consumption period corresponds to an electricity price.

[0035] S604 defines the period with the lowest electricity price during off-peak hours as the target charging period.

[0036] S605 records the electricity price for the target charging period as the target electricity price.

[0037] S606 outputs the target charging time period as the optimal time period for charging the energy storage distribution cabinet.

[0038] S607, return to the step of selecting a sample data, until each sample data has been selected once.

[0039] Specifically, the peak-valley electricity price table is the actual electricity price table published for the current time period, while the peak-valley electricity consumption time table is the historical electricity consumption data of the distribution cabinet mentioned by the electrical load.

[0040] In this embodiment, the electricity prices during all off-peak electricity consumption periods are statistically analyzed, and the lowest electricity price is taken as the target charging price. The time period in which the target charging price occurs is taken as the target charging time period, during which the energy storage distribution cabinet is charged.

[0041] In one embodiment of this application, the extraction of multiple peak-valley electricity consumption periods and multiple off-peak electricity consumption periods from the peak-valley electricity consumption schedule includes the following steps S603a to S603k:

[0042] S603a, select a peak-valley electricity timetable.

[0043] S603b, analyze the peak-valley electricity consumption timetable to obtain the unit time electricity consumption-time curve in the peak-valley electricity consumption timetable.

[0044] S603c, obtain the preset first power consumption.

[0045] S603d, if there is a peak in the portion of the electricity consumption-time curve above the first electricity consumption in the peak-valley electricity consumption timetable, then the time period corresponding to the peak is taken as the peak period of peak-valley electricity consumption.

[0046] S603e, obtain the number of peak and valley electricity consumption periods in the unit time electricity consumption-time curve of the peak and valley electricity consumption timetable.

[0047] S603f, determine whether the number of peak periods in the peak-valley electricity consumption curve is equal to 1.

[0048] S603g, if the number of peak and valley electricity consumption periods in the unit time electricity consumption-time curve in the peak and valley electricity consumption timetable is not equal to 1, then add a preset change amount to the first electricity consumption to obtain the updated first electricity consumption.

[0049] S603h, based on the updated first electricity consumption, obtains the number of peak and valley electricity consumption periods in the electricity consumption-time curve for that unit of time.

[0050] S603i, return to whether the number of peak periods in the peak-valley electricity consumption curve of the peak-valley electricity consumption timetable is equal to 1.

[0051] S603j, if the number of peak periods in the peak-valley electricity consumption curve of the unit time electricity consumption-time is equal to 1, then the peak period is recorded as the target peak-valley electricity consumption peak period.

[0052] S603k, return to the selected peak-valley electricity timetable, until each peak-valley electricity timetable has been selected once.

[0053] Specifically, the preset first electricity consumption is set based on the highest unit time electricity consumption of the peak-valley electricity consumption timetable, and the preset first electricity consumption is less than the highest unit time electricity consumption of the peak-valley electricity consumption timetable.

[0054] In this embodiment, based on a preset first electricity consumption, the peak-valley electricity consumption timetable may have multiple peaks. It is necessary to filter the multiple peaks to obtain the peak with the highest value. By adjusting the preset first electricity consumption, the underlying logic for filtering peaks is used. When the number of peaks obtained is one, the time period defined by the time axis information of the two intersection points of the final preset first electricity consumption and the electricity consumption-time curve in the peak-valley electricity consumption timetable is taken as the peak-valley electricity consumption peak time period of the target peak-valley electricity consumption peak period.

[0055] In one embodiment of this application, the step of constructing a discharge time prediction model based on each sample data, each peak-valley electricity price table, each peak-valley electricity consumption time table, and each peak-valley electricity consumption peak time period includes the following steps S301 to S306:

[0056] S301, select a target peak-valley electricity consumption period.

[0057] S302, analyze the target peak-valley electricity consumption period to obtain at least one peak-valley electricity consumption period corresponding to the target peak-valley electricity consumption period.

[0058] S303, return to the step of selecting a target peak-valley electricity consumption period, until each target peak-valley electricity consumption period has been selected once, resulting in multiple peak-valley electricity consumption period times.

[0059] S304, calculate the time union of all peak and valley electricity consumption periods, and record the time union of all peak and valley electricity consumption periods as the expected discharge period.

[0060] S305, Construct a model for predicting discharge time periods.

[0061] S306, each sample data, each peak-valley electricity consumption timetable, each target peak-valley electricity consumption peak period, the peak-valley electricity consumption peak period corresponding to each target peak-valley electricity consumption peak period, each unit time electricity consumption-time curve, the first electricity consumption, the preset change amount, and the expected discharge time period are used as training data to train the discharge time period prediction model, and the trained discharge time period prediction model is obtained.

[0062] In this embodiment, sample data refers to the electricity consumption data of a load within a set unit time. By statistically analyzing the peak and valley electricity consumption time periods corresponding to multiple peak and valley electricity consumption periods, the union of the peak and valley electricity consumption time periods corresponding to all peak and valley electricity consumption periods is calculated. The union of all peak and valley electricity consumption time periods is recorded as the expected discharge time period. Based on this calculation logic, a discharge time period prediction model is constructed. Each sample data, each peak and valley electricity consumption time table, each target peak and valley electricity consumption period, the peak and valley electricity consumption time period corresponding to each target peak and valley electricity consumption period, the electricity consumption-time curve per unit time, the first electricity consumption, the preset change amount, and the expected discharge time period of each sample data are used as training data to train the discharge time period prediction model, resulting in the trained discharge time period prediction model.

[0063] In one embodiment of this application, when the electricity consumption type of the sample data to be tested is capacity-based pricing, the electricity price table for the capacity to be tested, the electricity consumption time table for the capacity to be tested, and the capacity of the energy storage distribution cabinet in the sample data to be tested are obtained. At least one peak electricity consumption period is obtained based on each electricity consumption time table for the capacity to be tested and the preset electricity consumption. The preset electricity consumption is then fine-tuned so that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period. This includes the following steps S701 to S708:

[0064] S701, parse the test sample data to obtain the capacity electricity consumption time table in the test sample data.

[0065] S702, analyze the electricity consumption time table of the capacity to be tested, and obtain the electricity consumption-time curve per unit time in the electricity consumption time table of the capacity to be tested.

[0066] S703, obtain a preset second power consumption, where the second power consumption is less than the maximum power consumption per unit time.

[0067] S704. Plot a horizontal line in the power consumption-time curve of the power consumption time under test, where the horizontal axis changes with time and the vertical axis remains constant at the second power consumption level. Define this horizontal line as the second power consumption horizontal line.

[0068] S705, calculate the area of ​​each closed figure enclosed by the second power consumption horizontal line and the power consumption-time curve of the power consumption time table of the capacity to be measured.

[0069] S706, select closed patterns located above the second power consumption level as candidate closed patterns.

[0070] S707: Compare the area of ​​each candidate closed shape, obtain the candidate closed shape with the largest area, and take it as the target closed shape. The time period corresponding to each target closed shape is a peak period of capacity electricity consumption.

[0071] S708, return the sample data of the selected sample, until each sample data has been selected once.

[0072] Specifically, the preset second power consumption is set based on the highest power consumption per unit time in the power consumption time table of the capacity to be tested, and the second power consumption is less than the maximum power consumption per unit time in the power consumption time table of the capacity to be tested; the area of ​​the closed figure in the power consumption time table of the capacity to be tested may include two or more closed figures with the same area.

[0073] In this embodiment, based on the power consumption-time curve of the power consumption timer of the capacity to be tested, a preset second power consumption is set to obtain a closed shape enclosed by the preset second power consumption and the power consumption-time curve of the capacity to be tested power consumption timer. The closed shape with the largest area among the multiple closed shapes obtained is taken as the target closed shape.

[0074] In one embodiment of this application, when the electricity consumption type of the sample data to be tested is capacity-based pricing, the electricity price table for the capacity to be tested, the electricity consumption time table for the capacity to be tested, and the capacity of the energy storage distribution cabinet in the sample data to be tested are obtained. At least one peak electricity consumption period is obtained based on each electricity consumption time table for the capacity to be tested and the preset electricity consumption. The preset electricity consumption is then fine-tuned so that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period. The method further includes the following steps S709 to S716:

[0075] S709, select a target closed graph corresponding to a peak electricity consumption period.

[0076] S710 converts the area of ​​the target closed shape into an undetermined capacity.

[0077] S711 compares the size of the undetermined capacity with the capacity of the energy storage distribution cabinet to determine whether the undetermined capacity is greater than the capacity of the energy storage distribution cabinet.

[0078] S712 If the undetermined capacity is greater than the capacity of the energy storage distribution cabinet, calculate the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet, and determine whether the difference is less than the preset threshold.

[0079] S713, if the difference is greater than the preset threshold, the first fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned undetermined capacity and the capacity of the energy storage distribution cabinet is less than the preset threshold, and the target second power consumption is obtained and output.

[0080] S714 If the undetermined capacity is less than the capacity of the energy storage distribution cabinet, calculate the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet, and determine whether the difference is less than the preset threshold.

[0081] S715, if the difference is greater than the preset threshold, the second fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned undetermined capacity and the capacity of the energy storage distribution cabinet is less than the preset threshold, and the target second power consumption is obtained and output.

[0082] S716, return to the step of selecting a target closed shape, until each target closed shape has been selected once.

[0083] Specifically, the area of ​​the target closed shape is equivalent to the undetermined capacity, and the area of ​​the target closed shape is the electricity consumption within a certain period of time; the preset threshold refers to the limited range of the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet.

[0084] In this embodiment, the size of the required capacity and the capacity of the energy storage distribution cabinet are first determined, and then different adjustment strategies are applied to the second power consumption based on the determination result. The purpose of the adjustment strategy is to make the difference between the required capacity and the capacity of the energy storage distribution cabinet less than a preset threshold, and the adjusted second power consumption is used as the target second power consumption output.

[0085] In one embodiment of this application, if the difference is greater than a preset threshold, a first fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned undetermined capacity and the energy storage distribution cabinet capacity is less than the preset threshold, thereby obtaining and outputting the target second power consumption, including the following S713a to S713g:

[0086] S713a, based on the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet, adjust the first unit change value; the first unit change value has a first preset initial value.

[0087] S713b calculates the value of the second power consumption before fine-tuning plus the first unit change value to obtain the second power consumption after updating in the first direction.

[0088] S713c ​​calculates the updated undetermined capacity based on the updated second power consumption in the first direction.

[0089] S713d calculates the difference between the updated undetermined capacity and the capacity of the energy storage distribution cabinet.

[0090] S713e, determine whether the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold.

[0091] S713f, if the difference between the updated pending capacity and the energy storage distribution cabinet capacity is greater than the preset threshold, then return the first unit change value based on the difference between the pending capacity and the energy storage distribution cabinet capacity.

[0092] S713g, if the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold, then the updated second power consumption in the first direction is recorded as the target second power consumption output.

[0093] Specifically, the first direction can be understood as the direction of the movement of the second electricity consumption, that is, the positive or negative value of the first unit change.

[0094] In this embodiment, the second electricity consumption is updated or adjusted by adjusting the first unit change value as a variable of the second electricity consumption, thereby obtaining the difference between the updated undetermined capacity and the capacity of the energy storage distribution cabinet, and performing a logical judgment on whether the difference between the updated undetermined capacity and the capacity of the energy storage distribution cabinet is less than a preset threshold, until the difference between the updated undetermined capacity and the capacity of the energy storage distribution cabinet is less than the preset threshold, thus obtaining the final target second electricity consumption.

[0095] In one embodiment of this application, if the difference is greater than a preset threshold, a second fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned target capacity and the energy storage distribution cabinet capacity is less than the preset threshold, thereby obtaining and outputting the target second power consumption, including the following S715a to S715g:

[0096] S715a, based on the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet, adjusts the second unit change value; the second unit change value has a second preset initial value.

[0097] S715b calculates the value of the second electricity consumption before fine-tuning minus the second unit change value, and obtains the second electricity consumption after updating in the second direction.

[0098] S715c calculates the updated undetermined capacity based on the updated second power consumption in the second direction.

[0099] S715d calculates the difference between the updated undetermined capacity and the capacity of the energy storage distribution cabinet.

[0100] S715e, determine whether the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold.

[0101] S715f, if the difference between the updated pending capacity and the energy storage distribution cabinet capacity is greater than the preset threshold, then return the second unit change value based on the difference between the pending capacity and the energy storage distribution cabinet capacity.

[0102] S715g, if the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold, then the updated second power consumption in the second direction is recorded as the target second power consumption output.

[0103] Specifically, the second direction can be understood as the direction of the second electricity consumption movement, that is, the positive or negative value of the first unit change.

[0104] In this embodiment, the second unit change value is adjusted as a variable for the second electricity consumption to update or adjust the second electricity consumption, thereby obtaining the difference between the updated undetermined capacity and the energy storage distribution cabinet capacity. A logical judgment is made on whether the difference between the updated undetermined capacity and the energy storage distribution cabinet capacity is less than a preset threshold until the difference between the updated undetermined capacity and the energy storage distribution cabinet capacity is less than the preset threshold, thus obtaining the final target second electricity consumption. The first direction and the second direction are two opposite directions, and the first direction and the second direction are selected based on the size of the undetermined capacity and the energy storage distribution cabinet capacity.

[0105] In one embodiment of this application, adjusting the first unit change value based on the difference between the undetermined capacity and the energy storage distribution cabinet capacity includes the following steps S013a to S313a:

[0106] S013a, Adjust the area of ​​the target closed shape to adjust the undetermined capacity.

[0107] S113a, obtain the difference between the adjusted undetermined capacity and the capacity of the energy storage distribution cabinet; obtain the two power consumption time nodes corresponding to the second power consumption before fine-tuning.

[0108] S213a, arrange the two electricity consumption time nodes corresponding to the second electricity consumption before fine-tuning in chronological order, and take the time period between the two electricity consumption time nodes corresponding to the second electricity consumption before fine-tuning as the time period to be fine-tuned.

[0109] S313a, calculate the ratio of the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet to the time span of the period to be fine-tuned, and record the ratio as the first unit change value.

[0110] Specifically, the first unit change value is the ratio of the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet (the difference between the area of ​​the largest closed figure and the capacity of the energy storage distribution cabinet) to the time period to be fine-tuned corresponding to the second electricity consumption before fine-tuning.

[0111] In this embodiment, the difference between the pending capacity and the energy storage distribution cabinet capacity is divided by the time period corresponding to the second electricity consumption before fine-tuning, which is defined as the acquisition logic of the first unit change value or the second unit change value. Then, the second electricity consumption is continuously adjusted to obtain the difference between the pending capacity and the energy storage distribution cabinet capacity after adjustment. This continues until the difference between the pending capacity and the energy storage distribution cabinet capacity after adjustment is less than a preset threshold. At this point, the update of the second electricity consumption is stopped, and the obtained adjusted second electricity consumption is taken as the target second electricity consumption.

[0112] In one embodiment of this application, when the electricity consumption type of the sample data to be tested is capacity-based pricing, the electricity price table for the capacity to be tested, the electricity consumption time table for the capacity to be tested, and the capacity of the energy storage distribution cabinet in the sample data to be tested are obtained. At least one peak electricity consumption period is obtained based on each electricity consumption time table for the capacity to be tested and the preset electricity consumption. The preset electricity consumption is then fine-tuned so that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period. The method further includes the following steps S717 to S719:

[0113] S717, obtain the unit time electricity consumption-time curve from the electricity consumption time table of the capacity under test.

[0114] S718, based on the target second power consumption, obtain at least one discharge time period of the power consumption-time curve of the power consumption time of the power consumption time table of the capacity to be measured.

[0115] S719, after the energy storage distribution cabinet is powered on, a discharge operation is performed on the energy storage distribution cabinet during each discharge time period.

[0116] Specifically, the discharge period is the peak electricity consumption period of the electricity consumption timetable for the capacity under test; the target second electricity consumption is the highest load of the optimal actual use distribution cabinet calculated based on the electricity consumption timetable for the capacity under test and the capacity of the energy storage distribution cabinet.

[0117] In this embodiment, the optimal actual usage maximum load of the distribution cabinet is calculated based on the electricity consumption time of the capacity to be measured and the capacity of the energy storage distribution cabinet. Then, one or more discharge time periods that exceed the actual usage maximum load of the distribution cabinet are the working time periods of the energy storage distribution cabinet.

[0118] The technical features of the above embodiments can be combined arbitrarily, and the execution order of the method steps is not restricted. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A control method for an energy storage distribution cabinet, characterized in that, The control method for the energy storage distribution cabinet includes: Obtain sample data for multiple samples. The sample data for each sample includes peak-valley electricity price table or capacity electricity price table, peak-valley electricity consumption time table or capacity electricity consumption time table, and the capacity of energy storage distribution cabinet. Based on the peak-valley electricity price table and peak-valley electricity consumption time table in the sample data of each sample, the target peak-valley electricity consumption period of each sample data is calculated, and the expected discharge time period is calculated based on the target peak-valley electricity consumption period of all sample data. Based on each sample data, each peak-valley electricity consumption timetable, and the target peak-valley electricity consumption period for each sample data, a discharge time prediction model is constructed. Obtain a sample data to be tested, and obtain the electricity consumption type of the sample data; When the electricity consumption type of the sample data to be tested is peak-valley pricing type, the peak-valley electricity price table to be tested, the peak-valley electricity consumption time table to be tested, and the capacity of the energy storage distribution cabinet are obtained from the sample data to be tested. The peak-valley electricity price table to be tested and the peak-valley electricity consumption time table to be tested are input into the discharge time prediction model, the discharge time prediction model is started, and the predicted discharge time output by the discharge time prediction model is obtained. Based on the peak-valley electricity price table and peak-valley electricity consumption time table, calculate the optimal time period for charging the energy storage distribution cabinet; When the electricity consumption type of the sample data to be tested is the capacity-based pricing type, the electricity price table of the capacity to be tested, the electricity consumption time table of the capacity to be tested, and the capacity of the energy storage distribution cabinet are obtained from the sample data to be tested. At least one peak electricity consumption period is obtained based on the electricity consumption time table of the capacity to be tested and the preset electricity consumption. The preset electricity consumption is fine-tuned so that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period. The calculation of the optimal time period for charging the energy storage distribution cabinet based on peak-valley electricity price and peak-valley electricity consumption schedules includes: Select sample data from one sample; Analyze the sample data to obtain the peak-valley electricity price table and peak-valley electricity consumption time table contained in the sample data; Extract multiple peak and off-peak electricity consumption periods from the peak-valley electricity consumption timetable; each peak electricity consumption period corresponds to an electricity price, and each off-peak electricity consumption period corresponds to an electricity price. The period with the lowest electricity price during the off-peak electricity consumption period is designated as the target charging period. The electricity price for the target charging period is recorded as the target electricity price; The target charging time period is output as the optimal time period for charging the energy storage distribution cabinet; Return to the process of selecting a sample data point, until each sample data point has been selected once; When the electricity consumption type of the sample data to be tested is capacity-based pricing, the electricity price table, electricity consumption table, and capacity of the energy storage distribution cabinet in the sample data to be tested are obtained. At least one peak electricity consumption period is obtained based on each electricity consumption table and a preset electricity consumption period. The preset electricity consumption period is then fine-tuned to ensure that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period, including: Analyze the test sample data to obtain the power consumption time table of the test capacity in the test sample data; Analyze the electricity consumption time table of the capacity under test to obtain the electricity consumption-time curve per unit time in the electricity consumption time table of the capacity under test; Obtain a preset second power consumption, where the second power consumption is less than the maximum power consumption per unit time; Plot a horizontal line in the power consumption-time curve of the power consumption time table of the capacity to be measured. The horizontal axis changes with time, and the vertical axis is constant. Define this horizontal line as the second power consumption horizontal line. Calculate the area of ​​each closed figure enclosed by the second electricity consumption horizontal line and the unit time electricity consumption-time curve of the electricity consumption time table of the capacity to be measured; Select closed shapes located above the second electricity consumption level as candidate closed shapes; Compare the area of ​​each candidate closed figure, and obtain the candidate closed figure with the largest area. This closed figure is taken as the target closed figure. The time period corresponding to each target closed figure is a peak period of capacity electricity consumption. Return the sample data of the selected sample until each sample data has been selected once; Also includes: Select a target closed graph corresponding to a peak electricity consumption period; Convert the area of ​​the target closed shape into an undetermined capacity; Compare the size of the undetermined capacity with the capacity of the energy storage distribution cabinet to determine whether the undetermined capacity is greater than the capacity of the energy storage distribution cabinet. If the undetermined capacity is greater than the capacity of the energy storage distribution cabinet, calculate the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet, and determine whether the difference is less than the preset threshold. If the difference is greater than the preset threshold, the first fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned undetermined capacity and the capacity of the energy storage distribution cabinet is less than the preset threshold, and the target second power consumption is obtained and output. If the undetermined capacity is less than the capacity of the energy storage distribution cabinet, calculate the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet, and determine whether the difference is less than the preset threshold. If the difference is greater than the preset threshold, the second fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned undetermined capacity and the capacity of the energy storage distribution cabinet is less than the preset threshold, and the target second power consumption is obtained and output. Return to the previous step of selecting a target closed shape, until each target closed shape has been selected once.

2. The control method for the energy storage distribution cabinet according to claim 1, characterized in that, The extraction of multiple peak and off-peak electricity consumption periods from the peak-valley electricity consumption timetable includes: Select a peak-valley electricity schedule; Analyze the peak-valley electricity consumption timetable to obtain the electricity consumption-time curve per unit time in the peak-valley electricity consumption timetable; Get the preset first power consumption; If there is a peak in the portion of the electricity consumption-time curve above the first electricity consumption in the peak-valley electricity consumption timetable, then the time period corresponding to the peak is taken as the peak period of peak-valley electricity consumption. Obtain the number of peak and off-peak periods in the peak-valley electricity consumption curve of the electricity consumption per unit time in the peak-valley electricity consumption timetable; Determine whether the number of peak periods in the peak-valley electricity consumption curve is equal to 1. If the number of peak and valley electricity consumption periods in the peak-valley electricity consumption curve is not equal to 1, then a preset change amount is added to the first electricity consumption to obtain the updated first electricity consumption. The number of peak and valley electricity consumption periods in the electricity consumption-time curve for that unit of time is obtained based on the updated first electricity consumption. Return to the judgment that the number of peak periods in the peak-valley electricity consumption curve is equal to 1. If the number of peak periods in the peak-valley electricity consumption curve is equal to 1, then the peak period is recorded as the target peak-valley electricity consumption peak period. Return to the previous step and select a peak-valley electricity timetable until each peak-valley electricity timetable has been selected once.

3. The control method for the energy storage distribution cabinet according to claim 2, characterized in that, The discharge time prediction model is constructed based on each sample data, each peak-valley electricity price table, each peak-valley electricity consumption time table, and each peak-valley electricity consumption peak time period, including: Select a target peak-valley electricity consumption period; Analyze the peak and valley electricity consumption periods to obtain at least one peak and valley electricity consumption period corresponding to the target peak and valley electricity consumption period; Returning to the previous step of selecting a target peak-valley electricity consumption period, until each target peak-valley electricity consumption period has been selected once, multiple peak-valley electricity consumption period times are obtained; Calculate the time union of all peak and valley electricity consumption periods, and denote the time union of all peak and valley electricity consumption periods as the expected discharge period. Construct a model for predicting discharge time periods; The discharge time prediction model is trained using each sample data, each peak-valley electricity consumption timetable, each target peak-valley electricity consumption peak period, the peak-valley electricity consumption peak period corresponding to each target peak-valley electricity consumption peak period, the electricity consumption-time curve per unit time, the first electricity consumption, the preset change amount, and the expected discharge time period as training data to obtain the trained discharge time prediction model.

4. The control method for the energy storage distribution cabinet according to claim 3, characterized in that, If the difference is greater than a preset threshold, a first fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned undetermined capacity and the energy storage distribution cabinet capacity is less than the preset threshold, thereby obtaining and outputting the target second power consumption, including: The first unit change value is adjusted based on the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet; the first unit change value has a first preset initial value. The value of the second electricity consumption before fine-tuning plus the first unit change value is calculated to obtain the second electricity consumption after updating towards the first direction; The updated undetermined capacity is calculated based on the updated second power consumption in the first direction. Calculate the difference between the updated undetermined capacity and the capacity of the energy storage distribution cabinet; Determine whether the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold. If the difference between the updated pending capacity and the energy storage distribution cabinet capacity is greater than the preset threshold, then the first unit change value is adjusted based on the difference between the pending capacity and the energy storage distribution cabinet capacity. If the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold, then the updated second power consumption in the first direction will be recorded as the target second power consumption output.

5. The control method for the energy storage distribution cabinet according to claim 4, characterized in that, If the difference is greater than a preset threshold, a second fine-tuning strategy is adopted to fine-tune the preset second power consumption until the difference between the fine-tuned target capacity and the energy storage distribution cabinet capacity is less than the preset threshold, thereby obtaining and outputting the target second power consumption, including: The second unit change value is adjusted based on the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet; the second unit change value has a second preset initial value. Calculate the value of the second electricity consumption before fine-tuning minus the second unit change value to obtain the second electricity consumption after updating towards the second direction; The updated undetermined capacity is calculated based on the updated second power consumption in the second direction. Calculate the difference between the updated undetermined capacity and the capacity of the energy storage distribution cabinet; Determine whether the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold. If the difference between the updated pending capacity and the energy storage distribution cabinet capacity is greater than the preset threshold, then the second unit change value is adjusted based on the difference between the pending capacity and the energy storage distribution cabinet capacity. If the difference between the updated pending capacity and the energy storage distribution cabinet capacity is less than the preset threshold, then the updated second power consumption in the second direction will be recorded as the target second power consumption output.

6. The control method for the energy storage distribution cabinet according to claim 5, characterized in that, The adjustment of the first unit change value based on the difference between the undetermined capacity and the energy storage distribution cabinet capacity includes: Adjust the area of ​​the target closed shape to adjust the undetermined capacity; Obtain the difference between the adjusted undetermined capacity and the energy storage distribution cabinet capacity; obtain the two electricity consumption time points corresponding to the second electricity consumption before fine-tuning; Arrange the two electricity consumption time nodes corresponding to the second electricity consumption before the fine-tuning in chronological order, and take the time period between the two electricity consumption time nodes corresponding to the second electricity consumption before the fine-tuning as the time period to be fine-tuned. Calculate the ratio of the difference between the undetermined capacity and the capacity of the energy storage distribution cabinet to the time span of the period to be fine-tuned, and record the ratio as the first unit change value.

7. The control method for the energy storage distribution cabinet according to claim 6, characterized in that, When the electricity consumption type of the sample data to be tested is capacity-based pricing, the method involves acquiring the electricity price table, electricity consumption table, and capacity of the energy storage distribution cabinet from the sample data. Based on each electricity consumption table and a preset electricity consumption, at least one peak electricity consumption period is obtained. The preset electricity consumption is then fine-tuned to ensure that the energy storage distribution cabinet discharges with the optimal strategy during each peak electricity consumption period. The method also includes: Obtain the power consumption-time curve from the power consumption time table of the capacity under test; Based on the target second power consumption, obtain at least one discharge time period of the power consumption-time curve of the power consumption time of the power consumption time table of the capacity to be measured; After the energy storage distribution cabinet is powered on, a discharge operation is performed on the energy storage distribution cabinet during each discharge time period.

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