Microgrid cluster energy storage capacity configuration method and apparatus, electronic device, and storage medium

Abstract

A microgrid cluster energy storage capacity configuration method and apparatus, an electronic device, and a storage medium. The method comprises: predicting a load curve and a power output curve of each microgrid within a microgrid cluster in a preset time period (S101); determining the source-load compatibility of the microgrid cluster on the basis of the load curve and the power output curve (S102); determining whether the source-load compatibility is greater than or equal to a preset value (S103); if yes, determining that the microgrid cluster does not require energy storage (S104); if not, acquiring the average fault repair duration of the upstream power grid for the microgrid cluster (S105); determining the total required energy storage capacity of the microgrid cluster on the basis of the average fault repair duration, the load curve, and the power output curve (S106); determining an energy storage allocation coefficient of each microgrid on the basis of the load curve and the power output curve (S107); and calculating the product of the total required energy storage capacity and the energy storage allocation coefficient to obtain a target energy storage capacity of each microgrid (S108).

Description

Microgrid cluster energy storage capacity configuration methods, devices, electronic equipment and storage media

[0001] This application claims priority to Chinese Patent Application No. 202410501709.3, filed with the Chinese Patent Office on April 25, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of energy storage technology for distribution networks, and for example to a method, apparatus, electronic device and storage medium for configuring energy storage capacity in a microgrid cluster. Background Technology

[0003] With the development of microgrids such as wind power, solar power, and small hydropower, microgrids are forming microgrid groups connected to the grid. Due to the intermittent and uncertain characteristics of microgrids, effective energy storage systems are needed to balance supply and demand and improve the stability and reliability of the grid. Therefore, the energy storage configuration and distribution strategy of microgrid groups is crucial for achieving efficient and stable grid operation.

[0004] Currently, the research on the energy storage capacity configuration of microgrids within a microgrid cluster is still incomplete, and some technologies simply sum up the energy storage capacity of individual microgrids as the total energy storage capacity of the microgrid cluster. Furthermore, the energy storage allocation principles of each microgrid within the microgrid cluster have not been studied, leading to the problem of unreasonable energy storage capacity configuration of each microgrid within the microgrid cluster. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for configuring energy storage capacity in a microgrid cluster, in order to solve the problem of unreasonable energy storage capacity configuration in the microgrid within a microgrid cluster.

[0006] Firstly, this application provides a method for configuring energy storage capacity in a microgrid cluster, including:

[0007] Predict the load and output curves of each microgrid within a microgrid group during a preset time period;

[0008] The source-load adaptability of the microgrid group is determined based on the load curve and the output curve. The source-load adaptability is used to measure the power supply capacity of the microgrid group.

[0009] Determine whether the source load adaptability is greater than or equal to a preset value;

[0010] If so, it is determined that the microgrid cluster does not require energy storage;

[0011] If not, obtain the average fault repair time of the upstream power grid of the microgrid group;

[0012] The total energy storage capacity required by the microgrid cluster is determined based on the average fault repair time, the load curve, and the output curve.

[0013] The energy storage allocation coefficient for each microgrid is determined based on the load curve and output curve.

[0014] The target energy storage capacity for each microgrid is obtained by multiplying the total energy storage demand capacity by the energy storage allocation coefficient.

[0015] Secondly, this application provides a microgrid cluster energy storage capacity configuration device, comprising:

[0016] The curve prediction module is set to predict the load curve and output curve of each microgrid in the microgrid group within a preset time period.

[0017] The source-load adaptability determination module is configured to determine the source-load adaptability of the microgrid group based on the load curve and the output curve, wherein the source-load adaptability is configured to measure the power supply capability of the microgrid group.

[0018] The judgment module is configured to determine whether the source load adaptability is greater than or equal to a preset value. If yes, the determination module is executed; otherwise, the duration acquisition module is executed.

[0019] The module is configured to determine that the microgrid cluster does not require energy storage.

[0020] The duration acquisition module is configured to acquire the average fault repair time of the upstream power grid of the microgrid group;

[0021] The total energy storage demand determination module is configured to determine the total energy storage demand of the microgrid group based on the average fault repair time, the load curve, and the output curve.

[0022] The energy storage allocation coefficient determination module is configured to determine the energy storage allocation coefficient of each microgrid based on the load curve and the output curve.

[0023] The target energy storage capacity calculation module is configured to calculate the product of the total energy storage demand and the energy storage allocation coefficient to obtain the target energy storage capacity of each microgrid.

[0024] Thirdly, this application provides an electronic device, the electronic device comprising:

[0025] At least one processor; and

[0026] A memory communicatively connected to the at least one processor; wherein,

[0027] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the microgrid cluster energy storage capacity configuration method described in the first aspect of this application.

[0028] Fourthly, this application provides a computer-readable storage medium storing computer instructions that are used to cause a processor to execute the microgrid cluster energy storage capacity configuration method described in the first aspect of this application.

[0029] This application embodiment predicts the load and output curves of each microgrid within a microgrid cluster. Based on the load and output curves of multiple microgrids, it determines the source-load fit of the microgrid cluster. The power supply capacity of the microgrid cluster is then determined based on the source-load fit. When the source-load fit is greater than or equal to a preset value, the microgrid cluster is considered to have good power supply capacity and does not require energy storage. When the source-load fit is less than the preset value, the total energy storage requirement of the microgrid cluster is determined by the average fault repair time of the upper-level power grid, the predicted load and output curves, and the load and output curves of the microgrids. The energy storage allocation coefficient for each microgrid is determined, and the target energy storage capacity for each microgrid is calculated by multiplying the total energy storage demand capacity with the energy storage allocation coefficient. On the one hand, this allows the power supply capacity of the microgrid group to be measured by the source-load fit, so that energy storage can be allocated when the source-load fit is less than the preset value, thus avoiding ineffective energy storage. On the other hand, by determining the energy storage allocation coefficient based on the output and load of the microgrid, the energy storage capacity can be allocated considering the output and load of each microgrid, making the energy storage of the microgrid more reasonable and complete, and improving the stability and reliability of the entire power grid when the microgrid group is connected to the grid. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 is a flowchart of a microgrid group energy storage capacity configuration method provided in Embodiment 1 of this application;

[0032] Figure 2 is a flowchart of a microgrid group energy storage capacity configuration method provided in Embodiment 2 of this application;

[0033] Figure 3 is a schematic diagram of a microgrid group energy storage capacity configuration device provided in Embodiment 3 of this application;

[0034] Figure 4 is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of this application. Detailed Implementation

[0035] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings 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 in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0036] Example 1

[0037] Figure 1 is a flowchart of a microgrid group energy storage capacity configuration method provided in Embodiment 1 of this application. This embodiment is applicable to the allocation of energy storage capacity of each microgrid within a microgrid group. This method can be executed by a microgrid group energy storage capacity configuration device, which can be implemented in hardware and / or software and can be configured in an electronic device. As shown in Figure 1, the microgrid group energy storage capacity configuration method includes:

[0038] S101. Predict the load curve and output curve of each microgrid in the microgrid group within a preset time period.

[0039] The microgrid group in this embodiment can include multiple microgrids, which can be wind power, photovoltaic power, or small hydropower, etc. The load curve can be a curve with time as the horizontal axis and load as the vertical axis, which represents the load change of the microgrid within a preset time period. The output curve can be a curve with time as the horizontal axis and output value as the vertical axis, which represents the change of electrical energy output by the microgrid within a preset time period.

[0040] The preset time period can be an annual period. For example, the load curve and output curve of the microgrid in the next year can be predicted before entering the new year. The minimum time unit of the horizontal axis of the load curve and output curve can be one hour. That is, the load curve and output curve represent the load and output changes of the microgrid in one hour within an annual period, i.e., the load and output changes in 8760 hours within an annual period.

[0041] In one embodiment, historical load curves and output curves can be used as predicted load curves and output curves. For example, the load curves and output curves of the microgrid in the previous year can be used as the load curves and output curves for the next year.

[0042] In another embodiment, the historical load curve of the microgrid can be input into a pre-trained load curve prediction model to obtain the load curve of the microgrid within a preset time period. For example, the load curve of the microgrid in the previous year can be input into the load curve prediction model to obtain the load curve of the microgrid in the next year. Similarly, the historical meteorological data of the microgrid can be input into a pre-trained output curve prediction model to obtain the output curve of the microgrid within a preset time period. For example, the meteorological data of the microgrid in the previous year can be input into the output curve prediction model to obtain the output curve of the microgrid in the next year.

[0043] This embodiment uses a machine learning model to predict the load and output curves of a microgrid. It is highly adaptable and can continuously learn and update the machine learning model, thereby improving the accuracy of the predicted load and output curves.

[0044] S102. Determine the source-load fit of the microgrid group based on the load curve and output curve. The source-load fit is used to measure the power supply capacity of the microgrid group.

[0045] In this embodiment, the source-load fit can be the ratio of the total output value to the total load of the microgrid group. The source-load fit is used to measure the power supply capacity of the microgrid group. The larger the source-load fit, the higher the power supply capacity of the microgrid group. The total output value and total load of the microgrid group at each time point can be calculated by the load curve and output curve of each microgrid in the microgrid group. The ratio is calculated by the total output value and total load at each time point, and the minimum value of the ratio is taken as the source-load fit of the microgrid group.

[0046] In another embodiment, the load curves and output curves of each microgrid can be superimposed to generate the load curves and output curves of the microgrid group. Then, the ratio of the output value to the load at the same time point in the load curves and output curves of the microgrid group is calculated, and the minimum value of the ratio at multiple time points is determined as the source-load fit of the microgrid group.

[0047] S103. Determine whether the source load adaptability is greater than or equal to the preset value.

[0048] The preset value can be a threshold for determining whether a microgrid group needs energy storage. In one embodiment, the preset value can be the value 1. When the source-load adaptability is greater than or equal to the preset value 1, it means that the output of the microgrid group is greater than or equal to the load, the power supply capacity of the microgrid group is sufficient, and S104 is executed, the microgrid group does not need energy storage. When the source-load adaptability is less than the preset value 1, it means that the output of the microgrid group is less than the load, the power supply capacity of the microgrid group is insufficient, and energy storage is required to release electrical energy when the output is insufficient, and S105 is executed.

[0049] Of course, the preset value can also be set to a value greater than 1, such as 1.2, 1.5, etc., to improve the reliability of the power grid after the microgrid is connected to the grid.

[0050] S104. It is determined that the microgrid group does not require energy storage.

[0051] When the source-load fit is greater than or equal to the preset value, it is determined that the output of the microgrid group is greater than the load, and no energy storage is required. Each microgrid in the microgrid group does not need to charge energy storage devices such as batteries and capacitors.

[0052] S105. Obtain the average fault repair time of the upstream power grid of the microgrid group.

[0053] The upper-level power grid can be the next higher-level power grid to which the microgrid group is directly connected. The upper-level power grid and the microgrid group jointly supply power to the directly connected power grid. The number of faults and the repair time of each fault in the upper-level power grid of the microgrid group in the historical period can be obtained from the power grid system. The sum of multiple repair times can be calculated to obtain the total repair time. The ratio of the total repair time to the number of faults can be calculated to obtain the average repair time of the fault.

[0054] S106. Determine the total energy storage capacity required by the microgrid group based on the average fault repair time, load curve, and output curve.

[0055] When the upstream power grid fails, the grid is supplied by the microgrid group. The maximum power shortage of the microgrid group can be calculated. The product of the maximum power shortage and the average fault repair time is the total energy storage capacity required by the microgrid group. Specifically, the total load and total output of the microgrid group at multiple time points can be calculated by the load curves and output curves of multiple microgrids in the microgrid group. The difference between the total load and total output of the microgrid group at multiple time points can be calculated to obtain multiple difference values. The maximum value of the multiple differences is determined as the maximum power shortage of the microgrid group.

[0056] S107. Determine the energy storage allocation coefficient for each microgrid based on the load curve and output curve.

[0057] The energy storage allocation coefficient is used to determine whether each microgrid in a microgrid group needs energy storage and its share. In one embodiment, the load at each time point can be determined from the load curve of each microgrid, and the output value at each time point can be determined from the output curve. Then, for each microgrid, the difference between the load and the output value at each time point is calculated to obtain the difference at multiple time points, and the maximum difference is determined. If the maximum difference is greater than or equal to the value 0, the microgrid's mark value is set to the value 1. If the maximum difference is less than the value 0, the microgrid's mark value is set to the value 0. The sum of the mark values ​​of all microgrids is calculated, and the ratio of the mark value of each microgrid to the sum is calculated to obtain the energy storage allocation coefficient of each microgrid.

[0058] In another embodiment, the average load and average output of each microgrid can be calculated based on the load curve and output curve of each microgrid. Target microgrids with average load greater than average output are identified as microgrids requiring energy storage. For each target microgrid, the difference between the average load and average output is calculated, and the sum of the differences is calculated. The ratio of the difference to the sum is used as the energy storage allocation coefficient for the target microgrid.

[0059] In another embodiment, the allocation weight can be determined as the energy storage allocation coefficient of the microgrid based on the output curve or load curve of each microgrid. For example, the average output value of the microgrid is calculated from the output curve of each microgrid, the sum of all the average output values ​​is calculated to obtain the total output value, and the ratio of the average output value of each microgrid to the total output value is calculated as the allocation weight, that is, the energy storage allocation coefficient.

[0060] S108. Calculate the product of the total energy storage demand capacity and the energy storage allocation coefficient to obtain the target energy storage capacity for each microgrid.

[0061] After determining the energy storage allocation coefficient for each microgrid, the product of the total energy storage demand and the energy storage allocation coefficient can be calculated to obtain the target energy storage capacity for each microgrid. The energy storage devices of the microgrid can then be controlled to store energy according to this target energy storage capacity.

[0062] This application embodiment predicts the load and output curves of each microgrid within a microgrid cluster. Based on the load and output curves of multiple microgrids, it determines the source-load fit of the microgrid cluster. The power supply capacity of the microgrid cluster is then determined based on the source-load fit. When the source-load fit is greater than or equal to a preset value, the microgrid cluster is considered to have good power supply capacity and does not require energy storage. When the source-load fit is less than the preset value, the total energy storage requirement of the microgrid cluster is determined by the average fault repair time of the upper-level power grid, the predicted load and output curves, and the load and output curves of the microgrids. The energy storage allocation coefficient for each microgrid is determined, and the target energy storage capacity for each microgrid is calculated by multiplying the total energy storage demand capacity with the energy storage allocation coefficient. On the one hand, this allows the power supply capacity of the microgrid group to be measured by the source-load fit, so that energy storage can be allocated when the source-load fit is less than the preset value, thus avoiding ineffective energy storage. On the other hand, by determining the energy storage allocation coefficient based on the output and load of the microgrid, the energy storage capacity can be allocated considering the output and load of each microgrid, making the energy storage of the microgrid more reasonable and complete, and improving the stability and reliability of the entire power grid when the microgrid group is connected to the grid.

[0063] Example 2

[0064] Figure 2 is a flowchart of a microgrid cluster energy storage capacity configuration method provided in Embodiment 2 of this application. This embodiment is an optimization based on Embodiment 1 above. As shown in Figure 2, the microgrid cluster energy storage capacity configuration method includes:

[0065] S201. Predict the load curve and output curve of each microgrid in the microgrid group within a preset time period.

[0066] In this embodiment, the historical load curve of each microgrid within a historical time period can be obtained, the meteorological data of the geographical location of each microgrid within a historical time period can be obtained, the historical load curve can be input into the load curve generation model to obtain the load curve of the microgrid within a preset time period, and the meteorological data can be input into the output curve generation model to obtain the output curve of the microgrid within a preset time period.

[0067] For example, the historical load curve of the microgrid in the previous year can be obtained. This historical load curve includes the load sampled for 8760 hours in the previous year. Inputting this historical load curve into the load curve generation model yields the load curve of the microgrid in the current year, which includes the load sampled for 8760 hours in the current year. Similarly, historical meteorological data sampled for 8760 hours in the previous year can be obtained. This historical meteorological data includes light intensity, temperature, wind speed, precipitation, etc. Inputting this historical meteorological data into the output curve generation model yields the output curve of the microgrid in the current year, which includes the output value sampled for 8760 hours in the current year.

[0068] The load curve generation model can be trained as follows: First load curves for the first year of multiple microgrids are obtained, along with second load curves for the second year (the year following and adjacent to the first year). After initializing the load curve generation model, the first load curves are input into the model to obtain the predicted load curve. The loss rate is calculated using the predicted and second load curves. The training conditions are then determined (e.g., the number of training iterations reaches a preset number, the loss rate is less than a preset value, etc.). If yes, the load curve generation model is considered to have completed training. If not, the parameters of the load curve generation model are adjusted using the loss rate combined with various gradient descent algorithms. The process then returns to the step of inputting the first load curves into the model to obtain the predicted load curve, thus continuing the training of the load curve generation model.

[0069] When calculating the loss rate, the similarity between the second load curve and the predicted load curve can be calculated first, then the difference in load at the same time point can be calculated, then the mean square error of the differences at multiple time points can be calculated, and finally the weighted sum of the similarity and mean square error of the load curves (the weights of similarity and mean square error can be preset) can be calculated as the loss rate.

[0070] The output curve generation model can be trained as follows: Obtain meteorological data and a first output curve for multiple microgrids for one year. After initializing the output curve generation model, input the meteorological data into the model to obtain the predicted second output curve. Calculate the loss rate using the second and first output curves, and determine if the training conditions are met (e.g., the number of training iterations reaches a preset number, the loss rate is less than a preset value, etc.). If yes, the output curve generation model is considered to have completed training. If not, adjust the parameters of the output curve generation model using the loss rate combined with various gradient descent algorithms, and return to the step of inputting meteorological data into the model to obtain the predicted second output curve to continue training the model. The calculation method for the loss rate can refer to the calculation method for the loss rate during load curve generation model training, and will not be detailed here.

[0071] Of course, those skilled in the art can also refer to various machine learning algorithms in related technologies to train the load curve generation model and the output curve generation model, such as models based on generative adversarial networks. This embodiment does not limit the training method of the load curve generation model and the output curve generation model.

[0072] S202. Calculate the sum of the loads at the same time point in the load curve of each microgrid in the microgrid group to obtain the total load of the microgrid group at multiple time points.

[0073] In this embodiment, the load curve includes the load of the microgrid at multiple time points within a preset time period. For example, the load curve for a year includes the load of the microgrid for each hour out of 8760 hours in a year. The sum of the loads at the same time point in the load curves of each microgrid in the microgrid group can be calculated to obtain the total load of the microgrid group at multiple time points. For example, the microgrid group includes microgrid 1, microgrid 2, and microgrid 3. The load of the first hour in the load curve of microgrid 1 is W. L,11 The load in the second hour is W. L,12 The load in the first hour of the load curve of microgrid 2 is W. L,21 The load in the second hour is W. L,22 The load in the first hour of the load curve of microgrid 3 is W. L,31 The load in the second hour is W. L,32 Then the total load W of the microgrid group in the first hour can be calculated. L,1 =W L,11 +W L,21 +W L,31 The total load W in the second hour L,2 =W L,12 +W L,22 +W L,32 By analogy, the total load of the microgrid at multiple points in time can be obtained.

[0074] S203. Calculate the sum of the output values ​​at the same time point in the output curve of each microgrid in the microgrid group to obtain the total output value of the microgrid group at multiple time points.

[0075] In this embodiment, the output curve includes the output values ​​of the microgrid at multiple time points within a preset time period. For example, the output curve for a year includes the output value of the microgrid for each hour out of 8760 hours in a year. The sum of the output values ​​at the same time point in the output curve of each microgrid in the microgrid group can be calculated to obtain the total output value of the microgrid group at multiple time points. For example, the microgrid group includes microgrid 1, microgrid 2, and microgrid 3. The output value of microgrid 1 in the first hour is W. N,11 The output value in the second hour is W. N,12 The output value of microgrid 2 in the first hour is W. N,21 The output value in the second hour is W. N,22 The output value of microgrid 3 in the first hour is W. N,31 The output value in the second hour is W. N,32 Then the total output W of the microgrid cluster in the first hour can be calculated. N,1 =W N,11 +W N,21 +W N,31 The total output value W in the second hour N,2 =W N,12 +W N,22 +W N,32 By analogy, the total output value of the microgrid at multiple time points can be obtained.

[0076] S204. Calculate the ratio of total output to total load at each time point to obtain multiple ratios.

[0077] S205. The minimum value among multiple ratios is determined as the source-load fit degree of the microgrid group.

[0078] Specifically, the source-load fit of a microgrid cluster is calculated using the following formula:

[0079] In the formula, W Nt W represents the total output of the microgrid group at time t. LtLet η be the total load of the microgrid group at time t. When the source-load fit η ≥ 1, it indicates that the electrical energy output by the microgrid in the microgrid group can meet the power supply needs of the load within the microgrid group, and there is no need to consider adding energy storage. When the source-load fit η < 1, the electrical energy output by the microgrid in the microgrid group cannot meet the power supply needs of the load. At this time, energy storage needs to be configured to meet the power balance of the microgrid group. In this embodiment, the source-load fit of the microgrid group is directly calculated through the load curve and output curve of the microgrid. The calculation method is simple and efficient.

[0080] In another embodiment, the load curve of the microgrid group can be generated using the load curve of the microgrid within the microgrid group, and the output curve of the microgrid group can be generated using the output curve of the microgrid group. The ratio of the output value to the load at the same time point in the load curve and output curve of the microgrid group is calculated to obtain multiple ratios. The minimum value among the multiple ratios is determined as the source-load fit of the microgrid group. That is, the load curve and output curve of the microgrid group are generated first, and then the source-load fit of the microgrid group is determined through the load curve and output curve of the microgrid group. In this embodiment, the load curve and output curve of the microgrid group are generated through the load curve and output curve of the microgrid, and the source-load fit of the microgrid group is calculated through the load curve and output curve of the microgrid group. On the one hand, the supply and demand relationship of the microgrid group in terms of electrical energy can be reflected through the load curve and output curve of the microgrid group. On the other hand, the accuracy of the source-load fit can be improved.

[0081] S206. Determine whether the source load adaptability is greater than or equal to the preset value.

[0082] The preset value can be a threshold for determining whether a microgrid group needs energy storage. In one embodiment, the preset value can be the value 1. When the source-load adaptability is greater than or equal to the preset value 1, it means that the output of the microgrid group is greater than or equal to the load, the power supply capacity of the microgrid group is sufficient, and S207 is executed, the microgrid group does not need energy storage. When the source-load adaptability is less than the preset value 1, it means that the output of the microgrid group is less than the load, the power supply capacity of the microgrid group is insufficient, and energy storage is required to release electrical energy when the output is insufficient, and S208 is executed.

[0083] S207. It is determined that the microgrid group does not require energy storage.

[0084] When the source-load fit is greater than or equal to the preset value, it is determined that the output of the microgrid group is greater than the load, and no energy storage is required. Each microgrid in the microgrid group does not need to charge energy storage devices such as batteries and capacitors.

[0085] S208. Obtain the average fault repair time of the upstream power grid of the microgrid group.

[0086] In one embodiment, the number of faults and the repair time of each fault in the upstream power grid of the microgrid group during a historical period can be obtained, the sum of multiple repair times can be calculated to obtain the total repair time, and the ratio of the total repair time to the number of faults can be calculated to obtain the average repair time of the fault.

[0087] In another embodiment, the average fault repair time of the upper-level power grid can also be recorded within the power grid system, and the average fault repair time of the upper-level power grid can be read from the power grid system.

[0088] S209. Calculate the difference between the total output value and the total load of the microgrid group at multiple time points to obtain multiple difference values.

[0089] S210. Calculate the product of the maximum value among multiple differences and the average fault repair time to obtain the total energy storage capacity required by the microgrid group.

[0090] Specifically, S209 and S210 can be expressed by the following formulas:

[0091] In the formula, Q N Let W be the total energy storage capacity required by the microgrid cluster, T be the mean time to repair a fault, and W be the mean time to repair a fault. Nt W represents the total output of the microgrid group at time t. Lt Let be the total load of the microgrid group at time t.

[0092] S211. Determine the load at each time point from the load curve of each microgrid, and determine the output value at each time point from the output curve;

[0093] S212. For each microgrid, calculate the difference between load and output at each time point, obtain the difference at multiple time points, and determine the maximum difference;

[0094] S213. If the maximum difference is greater than or equal to the value 0, set the microgrid's flag value to the value 1; if the maximum difference is less than the value 0, set the microgrid's flag value to the value 0.

[0095] S214. Calculate the sum of the marked values ​​of all microgrids, and calculate the ratio of the marked value to the sum of the marked values ​​of each microgrid to obtain the energy storage allocation coefficient of each microgrid.

[0096] Specifically, S211-S214 can be represented by the following formulas:

[0097] Where i represents the i-th microgrid, t represents the time point, and μ i W represents the energy storage allocation coefficient of the i-th microgrid. EL,i,t Let W be the load of the i-th microgrid at time t. N,i,tLet be the output value of the i-th microgrid at time t, n be the number of microgrids in the microgrid group, and ε() be the step function, which is used to set the flag value of the microgrid.

[0098] S215. Calculate the product of the total energy storage demand and the energy storage allocation coefficient to obtain the target energy storage capacity for each microgrid.

[0099] Specifically, the target energy storage capacity Q of the i-th microgrid can be calculated using the following formula. s,i :

[0100] Q s,i =μ i ×Q N ;

[0101] This embodiment predicts the load and output curves of microgrids within a microgrid cluster. It then calculates the total load and total output of the microgrid cluster at the same time point using these curves. Multiple ratios are obtained by calculating the ratio of total output to total load at each time point. The minimum value among these ratios is determined as the source-load fit of the microgrid cluster. When the source-load fit is less than a preset value, multiple differences are calculated between the total output and total load at multiple time points. The maximum value among these differences is multiplied by the average fault repair time to obtain the total energy storage demand capacity of the microgrid cluster. For each microgrid, the difference between load and output at each time point is calculated, resulting in multiple differences at multiple time points. The maximum difference is determined. If the maximum difference is greater than or equal to 0, a setting is established. The microgrid is marked with a value of 1. If the maximum difference is less than 0, the microgrid is marked with a value of 0. The sum of the marked values ​​of all microgrids is calculated, and the ratio of the marked value of each microgrid to the sum is calculated to obtain the energy storage allocation coefficient of each microgrid. Finally, the product of the total energy storage demand and the energy storage allocation coefficient is calculated to obtain the target energy storage capacity of each microgrid. On the one hand, it realizes the measurement of the power supply capacity of the microgrid group through the source-load adaptability, so that energy storage is stored when the source-load adaptability is less than the preset value, avoiding ineffective energy storage. On the other hand, by determining the energy storage allocation coefficient through the output and load of the microgrid, the energy storage capacity can be allocated considering the output and load of each microgrid, making the energy storage of the microgrid more reasonable and improving the stability and reliability of the entire power grid when the microgrid group is connected to the grid.

[0102] Furthermore, when calculating the energy storage allocation coefficient, the difference between the load and the output value at each time point was calculated, that is, the load deficit of the microgrid was taken into account to determine the energy storage capacity, so that the energy storage capacity allocation of the microgrid within the microgrid group is more perfect and reasonable.

[0103] Example 3

[0104] Figure 3 is a schematic diagram of a microgrid cluster energy storage capacity configuration device provided in Embodiment 3 of this application. As shown in Figure 3, the microgrid cluster energy storage capacity configuration device includes:

[0105] The curve prediction module 301 is configured to predict the load curve and output curve of each microgrid in the microgrid group within a preset time period.

[0106] The source-load adaptability determination module 302 is configured to determine the source-load adaptability of the microgrid group based on the load curve and the output curve, wherein the source-load adaptability is configured to measure the power supply capability of the microgrid group.

[0107] The judgment module 303 is set to determine whether the source load adaptability is greater than or equal to a preset value. If yes, the determination module 304 is executed; otherwise, the duration acquisition module 305 is executed.

[0108] Module 304 is configured to determine that the microgrid group does not require energy storage;

[0109] The duration acquisition module 305 is configured to acquire the average fault repair time of the upstream power grid of the microgrid group;

[0110] The total energy storage demand determination module 306 is configured to determine the total energy storage demand of the microgrid group based on the average fault repair time, the load curve, and the output curve.

[0111] The energy storage allocation coefficient determination module 307 is configured to determine the energy storage allocation coefficient of each microgrid based on the load curve and the output curve.

[0112] The target energy storage capacity calculation module 308 is configured to calculate the product of the total energy storage demand capacity and the energy storage allocation coefficient to obtain the target energy storage capacity of each microgrid.

[0113] Optionally, the curve prediction module 301 includes:

[0114] The historical load curve acquisition unit is configured to acquire the historical load curve of each microgrid within a historical time period;

[0115] The historical meteorological data acquisition unit is configured to acquire meteorological data for the geographical location of each microgrid within a historical time period.

[0116] The load curve prediction unit is configured to input the historical load curve into the load curve generation model to obtain the load curve of the microgrid within a preset time period.

[0117] The power output curve prediction unit is configured to input the meteorological data into the power output curve generation model to obtain the power output curve of the microgrid within a preset time period.

[0118] Optionally, the source-load adaptability determination module 302 includes:

[0119] The total load calculation unit is configured to calculate the sum of the loads at the same time point in the load curve of each microgrid in the microgrid group, so as to obtain the total load of the microgrid group at multiple time points;

[0120] The total output value calculation unit is configured to calculate the sum of the output values ​​at the same time point in the output curve of each microgrid in the microgrid group, so as to obtain the total output value of the microgrid group at multiple time points;

[0121] The first ratio calculation unit is set to calculate the ratio of the total output value to the total load at each time point, and obtain multiple ratios;

[0122] The first source-load fit determination unit is configured to determine the minimum value among multiple ratios as the source-load fit of the microgrid group.

[0123] Optionally, the source-load adaptability determination module 302 includes:

[0124] The load curve generation unit is configured to generate the load curve of the microgrid group using the load curves of the microgrids in the microgrid group;

[0125] The output curve generation unit is configured to generate the output curve of the microgrid group using the output curve of the microgrid in the microgrid group;

[0126] The second ratio calculation unit is configured to calculate the ratio of the output value to the load at the same time point in the load curve and output curve of the microgrid group, and obtain multiple ratios.

[0127] The second source-load adaptability determination unit is configured to determine the minimum value among multiple ratios as the source-load adaptability of the microgrid group.

[0128] Optionally, the duration acquisition module 305 includes:

[0129] The historical fault data acquisition unit is configured to acquire the number of faults and the repair time of each fault in the upstream power grid of the microgrid group within a historical time period.

[0130] The average repair time calculation unit is configured to calculate the sum of multiple repair times to obtain the total repair time, and calculate the ratio of the total repair time to the number of faults to obtain the average repair time of the fault.

[0131] Optionally, the total energy storage demand determination module 306 includes:

[0132] The difference calculation unit is configured to calculate the difference between the total output value and the total load of the microgrid group at multiple time points, and obtain multiple differences;

[0133] The total demand capacity calculation unit is configured to calculate the product of the maximum value among multiple differences and the average fault repair time to obtain the total energy storage demand capacity of the microgrid group.

[0134] Optionally, the energy storage allocation coefficient determination module 307 includes:

[0135] The load and output value determination unit is configured to determine the load at each time point from the load curve of each microgrid, and to determine the output value at each time point from the output curve.

[0136] The maximum difference determination unit is set to calculate the difference between load and output at each time point for each microgrid, obtain the difference at multiple time points, and determine the maximum difference.

[0137] The marker value setting unit is configured to set the marker value of the microgrid to 1 if the maximum difference is greater than or equal to 0, and to set the marker value of the microgrid to 0 if the maximum difference is less than 0.

[0138] The energy storage allocation coefficient calculation unit is configured to calculate the sum of the marked values ​​of all microgrids, calculate the ratio of the marked value of each microgrid to the sum, and obtain the energy storage allocation coefficient of each microgrid.

[0139] The microgrid cluster energy storage capacity configuration device provided in this application embodiment can execute the microgrid cluster energy storage capacity configuration method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.

[0140] Example 4

[0141] Figure 4 illustrates a schematic diagram of an electronic device 40 that can be used to implement embodiments of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0142] As shown in Figure 4, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0143] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0144] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as microgrid cluster energy storage capacity configuration methods.

[0145] In some embodiments, the microgrid cluster energy storage capacity configuration method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the microgrid cluster energy storage capacity configuration method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to execute the microgrid cluster energy storage capacity configuration method by any other suitable means (e.g., by means of firmware).

[0146] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0151] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0152] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved.

Claims

1. A method for configuring energy storage capacity in a microgrid cluster, comprising: Predict the load and output curves of each microgrid within a microgrid group during a preset time period; The source-load adaptability of the microgrid group is determined based on the load curve and the output curve. The source-load adaptability is used to measure the power supply capacity of the microgrid group. Determine whether the source load adaptability is greater than or equal to a preset value; If so, it is determined that the microgrid cluster does not require energy storage; If not, obtain the average fault repair time of the upstream power grid of the microgrid group; The total energy storage capacity required by the microgrid cluster is determined based on the average fault repair time, the load curve, and the output curve. The energy storage allocation coefficient for each microgrid is determined based on the load curve and output curve. The target energy storage capacity for each microgrid is obtained by multiplying the total energy storage demand capacity by the energy storage allocation coefficient.

2. The microgrid cluster energy storage capacity configuration method according to claim 1, wherein, Predict the load and output curves of each microgrid within the microgrid group during a preset time period, including: Obtain the historical load curve for each microgrid within a historical time period; Obtain meteorological data for the geographical location of each microgrid within a historical time period; The historical load curve is input into the load curve generation model to obtain the load curve of the microgrid within a preset time period; The meteorological data is input into the power output curve generation model to obtain the power output curve of the microgrid within a preset time period.

3. The microgrid cluster energy storage capacity configuration method according to claim 1, wherein, Determining the source-load fit of the microgrid group based on the load curve and output curve includes: Calculate the sum of the loads at the same time point in the load curve of each microgrid in the microgrid group to obtain the total load of the microgrid group at multiple time points; The sum of the output values ​​at the same time point in the output curve of each microgrid in the microgrid group is calculated to obtain the total output value of the microgrid group at multiple time points; Calculate the ratio of total output to total load at each time point to obtain multiple ratios; The minimum value among multiple ratios is determined as the source-load fit of the microgrid group.

4. The microgrid cluster energy storage capacity configuration method according to claim 1, wherein, Determining the source-load fit of the microgrid group based on the load curve and output curve includes: The load curve of the microgrid group is generated using the load curves of the microgrids in the microgrid group; The output curve of the microgrid group is generated using the output curve of the microgrid in the microgrid group; Calculate the ratio of the output value to the load at the same time point in the load curve and output curve of the microgrid group to obtain multiple ratios; The minimum value among multiple ratios is determined as the source-load fit of the microgrid group.

5. The microgrid cluster energy storage capacity configuration method according to claim 1, wherein, The average fault repair time of the upstream power grid of the microgrid group is obtained, including: Obtain the number of faults and the repair time of each fault in the upstream power grid of the microgrid group within a historical time period; The total repair time is obtained by summing the repair times of multiple repairs, and the average repair time of the fault is obtained by calculating the ratio of the total repair time to the number of faults.

6. The microgrid cluster energy storage capacity configuration method according to claim 3, wherein, The total energy storage capacity required by the microgrid cluster is determined based on the average fault repair time, the load curve, and the output curve, including: The difference between the total output and the total load of the microgrid group at multiple time points is calculated to obtain multiple differences; The total energy storage capacity required by the microgrid cluster is obtained by multiplying the maximum value among multiple differences with the average fault repair time.

7. The microgrid cluster energy storage capacity configuration method according to claim 3, wherein, The energy storage allocation coefficient for each microgrid is determined based on the load curve and output curve, including: The load at each time point is determined from the load curve of each microgrid, and the output value at each time point is determined from the output curve. For each microgrid, the difference between load and output at each time point is calculated, the difference at multiple time points is obtained, and the maximum difference is determined; If the maximum difference is greater than or equal to the value 0, the microgrid is set to the value 1; if the maximum difference is less than the value 0, the microgrid is set to the value 0. Calculate the sum of the marked values ​​of all microgrids, and calculate the ratio of the marked value of each microgrid to the sum, to obtain the energy storage allocation coefficient of each microgrid.

8. A microgrid cluster energy storage capacity configuration device, comprising: The curve prediction module is set to predict the load curve and output curve of each microgrid in the microgrid group within a preset time period. The source-load adaptability determination module is configured to determine the source-load adaptability of the microgrid group based on the load curve and the output curve, wherein the source-load adaptability is configured to measure the power supply capability of the microgrid group. The judgment module is configured to determine whether the source load adaptability is greater than or equal to a preset value. If yes, the determination module is executed; otherwise, the duration acquisition module is executed. The module is configured to determine that the microgrid cluster does not require energy storage. The duration acquisition module is configured to acquire the average fault repair time of the upstream power grid of the microgrid group; The total energy storage demand determination module is configured to determine the total energy storage demand of the microgrid group based on the average fault repair time, the load curve, and the output curve. The energy storage allocation coefficient determination module is configured to determine the energy storage allocation coefficient of each microgrid based on the load curve and the output curve. The target energy storage capacity calculation module is configured to calculate the product of the total energy storage demand and the energy storage allocation coefficient to obtain the target energy storage capacity of each microgrid.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the microgrid cluster energy storage capacity configuration method according to any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to execute the microgrid cluster energy storage capacity configuration method according to any one of claims 1-7.

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