A transformer area energy storage power capacity initial setting method, device and energy storage system

Abstract

The application discloses a transformer area energy storage power capacity initial setting method and device and an energy storage system, relates to the technical field of micro-grid, and is used for solving the technical problem that the calculation complexity and accuracy of the energy storage capacity are difficult to balance in the prior art. The method comprises the following steps: according to a target voltage qualified rate, querying a pre-constructed correction coefficient mapping table to obtain a target interval correction coefficient; according to a critical power, a daily average power consumption, a maximum daily power consumption and a minimum daily power consumption, determining a critical power capacity coefficient and a load power curve; wherein the critical power is obtained by interval correction coefficient calculation; according to the critical power capacity coefficient and the load power curve, determining ideal energy storage capacity and load consumption photovoltaic power; and subtracting the ideal energy storage capacity from the load consumption photovoltaic power to determine target energy storage capacity. Therefore, the application can reduce the calculation complexity of the energy storage capacity and improve the accuracy by means of "various basic data, table lookup method" and the like.

Description

Technical Field

[0001] This application relates to the field of microgrid technology, and provides a method, device and energy storage system for preliminary setting of energy storage capacity in a transformer substation. Background Technology

[0002] Due to low construction standards and insufficient investment capacity, the distribution network structure is weak and its voltage regulation capacity is inadequate. This is especially true for county and village-level power supply radii, which are large and difficult to adapt to the characteristics of rapid load demand growth, large seasonal load fluctuations, dispersed users, and widespread integration of distributed energy resources. Overvoltage and undervoltage on distribution lines still exist and occur dynamically. In addition, with the large-scale integration of distributed photovoltaic power into low-voltage distribution areas, the reverse power flow of photovoltaic power will raise the voltage in the distribution areas, causing voltage exceeding limits and a decrease in IEC voltage compliance rate.

[0003] Therefore, to optimize power supply quality, distributed transformer energy storage has been widely proposed in power supply voltage quality management. However, due to the fact that excessively large distributed transformer energy storage configurations reduce capacity utilization, while insufficient configurations lead to inadequate capacity and voltage exceeding limits, determining the appropriate distributed transformer energy storage configuration is crucial. Currently, existing distributed transformer energy storage configuration technologies mainly rely on the following two methods: The first method, which estimates the reverse power and over-limit duration, uses the peak reverse power as the energy storage configuration power. The energy storage capacity is obtained by multiplying the reverse power by the over-limit time, and thus the power of the energy storage in the distribution area can be obtained. Obviously, although this method is simple to calculate, it has a large error. The second method relies on power flow simulation, time-series output data, line parameters, transformer outlet voltage, and reverse power. Real-time simulation is performed using tools such as PSIM or MATLAB to obtain the accurate power and capacity of the energy storage in the distribution area. Obviously, this method is computationally complex, time-consuming, and cannot be applied to the initial screening of distribution areas in batches. Summary of the Invention

[0004] This application provides a method, apparatus, and energy storage system for preliminary setting of energy storage capacity in a transformer substation, which solves the technical problem that existing technologies struggle to balance the computational complexity and accuracy of energy storage capacity.

[0005] On the one hand, a method for initially setting the energy storage capacity of a transformer substation is provided, the method comprising: Based on the target voltage pass rate, a pre-constructed correction coefficient mapping table is consulted to obtain the target interval correction coefficient corresponding to the target voltage pass rate. The correction coefficient mapping table is obtained by processing simulation model sample data and real model sample data using a user random distribution algorithm to determine the simulation over-limit time and the real over-limit time corresponding to different voltage pass rates. The interval correction coefficient corresponding to different voltage pass rates is then determined based on the simulation over-limit time and the real over-limit time. The critical power capacity factor and load power curve are determined based on the critical power, average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption; wherein, the critical power is calculated using an interval correction factor. Based on the critical power capacity coefficient and the load power curve, determine the ideal energy storage capacity and the load absorption of photovoltaic power. The target energy storage capacity is determined by subtracting the ideal energy storage capacity from the photovoltaic power absorbed by the load.

[0006] Optionally, the step of querying a pre-built correction coefficient mapping table based on the target voltage pass rate to obtain the target interval correction coefficient corresponding to the target voltage pass rate includes: Obtain the target voltage compliance rate, annualized total user time, and total over-limit time from the database; The annualized voltage qualification rate is obtained based on the annualized total user time and total over-limit time. Determine whether the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold; If the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold, then the target interval correction coefficient is obtained by querying the correction coefficient mapping table based on the target voltage pass rate.

[0007] Optionally, before determining the critical power capacity factor versus load power curve based on the critical power, average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption, the method further includes: The actual over-limit duration is obtained based on the target voltage pass rate and the target range correction coefficient. Obtain the photovoltaic power generation duration and photovoltaic installed capacity from the database; Curve fitting is performed on the photovoltaic power generation duration and photovoltaic power generation to obtain the power generation time curve; The critical power is obtained based on the actual over-limit duration of the target and the power generation time curve.

[0008] Optionally, the step of determining the critical power capacity factor and load power curve based on the critical power, average daily power consumption, maximum daily power consumption, and minimum daily power consumption includes: Based on the critical power, the capacity coefficient mapping table is consulted to obtain the critical power capacity coefficient; The load power curve is obtained based on the average daily electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption.

[0009] Optionally, before obtaining the critical power capacity coefficient by querying the capacity coefficient mapping table based on the critical power, the method further includes: Based on the preset power interval formula, preset percentage and photovoltaic installed capacity, the power interval between any two adjacent interval points is determined; wherein, the preset power interval formula is power interval = preset percentage × photovoltaic installed capacity; Based on the power interval, the critical power corresponding to multiple interval points is determined; wherein, one critical power corresponds to one photovoltaic over-limit power generation. Based on the ideal power generation and the photovoltaic over-limit power generation corresponding to each critical power, the critical power capacity coefficient corresponding to each critical power is obtained; among them, the critical power capacity coefficient between adjacent interval points is obtained by linear interpolation. Based on each critical power, the photovoltaic over-limit power generation corresponding to each critical power, and the critical power capacity coefficient corresponding to each critical power, the capacity coefficient mapping table is constructed.

[0010] Optionally, the step of obtaining the load power curve based on the average daily electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption includes: Obtain the average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption from the database; The daily average electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption were weighted using a random distribution method to obtain weighted daily electricity consumption data. The load power curve is obtained based on the weighted daily electricity consumption data.

[0011] Optionally, the step of determining the ideal energy storage capacity and the load absorption of photovoltaic power based on the critical power capacity coefficient and the load power curve includes: The ideal energy storage capacity is obtained by multiplying the critical power capacity coefficient by the ideal power generation. The load absorption of photovoltaic power is determined based on the actual over-limit duration of the target and the load power curve.

[0012] Optionally, the power generation time curve is represented by the following formula: P_PV=P0*SIN(3.14 / (t0_end-t0_start)*(t0_run-t0_start)) Where P_PV is the photovoltaic power generation capacity; P0 is the photovoltaic installed capacity; t0_start is the start time of photovoltaic power generation duration t0; t0_end is the end time of photovoltaic power generation duration t0; t0_run is the real-time of photovoltaic power generation duration t0; and SIN(*) is the sine trigonometric function.

[0013] Optionally, the power interval between any two adjacent interval points in the capacity coefficient mapping table is determined by a preset power interval formula, and the critical power capacity coefficient between adjacent interval points is obtained by linear interpolation; wherein, the preset power interval formula is Px = preset percentage × photovoltaic installed power.

[0014] On the one hand, a device for preliminary design of energy storage capacity in a distribution area is provided, the device comprising: The mapping table construction unit is used to process the simulation model sample data and the real model sample data using a user random distribution algorithm, determine the simulation over-limit time and the real over-limit time corresponding to different voltage pass rates, determine the interval correction coefficient corresponding to each voltage pass rate by the ratio of the real over-limit time to the simulation over-limit time, and construct the correction coefficient mapping table. The critical power acquisition unit is used to obtain the interval correction coefficient by querying the correction coefficient mapping table according to the target voltage qualification rate, and to obtain the target actual over-limit time according to the target voltage qualification rate and the interval correction coefficient; and to obtain the photovoltaic power generation time and photovoltaic installed power, fit the photovoltaic power generation time curve and determine the critical power, and obtain the energy storage power by subtracting the critical power from the photovoltaic installed power. The capacity factor and power curve determination unit is used to determine the critical power capacity factor and load power curve based on the critical power, average daily power consumption, maximum daily power consumption and minimum daily power consumption. The energy storage capacity determination unit is used to determine the ideal energy storage capacity and the photovoltaic power absorbed by the load based on the critical power capacity coefficient and the load power curve, and to determine the target energy storage capacity by subtracting the ideal energy storage capacity from the photovoltaic power absorbed by the load.

[0015] On the one hand, a storage medium is provided that stores computer program instructions, which, when executed by a processor, implement any of the above-mentioned methods for initial setting the power capacity of the power storage area.

[0016] On the one hand, an energy storage system is provided, including: a control module, a power conversion module, and a battery module; wherein the control module is connected to the power conversion module and the battery module respectively, and is used to execute the method for initial setting of the power capacity of the power storage area of ​​any of the above-mentioned areas.

[0017] Compared with the prior art, the beneficial effects of this application are as follows: In this application, when initially setting the energy storage capacity of the transformer substation, firstly, a pre-constructed correction coefficient mapping table can be consulted based on the target voltage qualification rate to obtain the target interval correction coefficient corresponding to the target voltage qualification rate. The correction coefficient mapping table is generated by processing simulation model sample data and real model sample data using a user random distribution algorithm to determine the simulated over-limit duration and the actual over-limit duration corresponding to different voltage qualification rates. The interval correction coefficient corresponding to different voltage qualification rates is then determined based on the simulated over-limit duration and the actual over-limit duration. Next, the critical power capacity coefficient and load power curve can be determined based on the critical power, average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption. The critical power is calculated using the interval correction coefficient. Then, the ideal energy storage capacity and the load absorption of photovoltaic power can be determined based on the critical power capacity coefficient and the load absorption of photovoltaic power. Finally, the target energy storage capacity can be determined by subtracting the ideal energy storage capacity from the load absorption of photovoltaic power.

[0018] Based on this, in this application, since the target energy storage capacity is obtained by using data such as voltage qualification rate, photovoltaic installed capacity, and photovoltaic power generation duration, and by looking up tables, this application can reduce the complexity of energy storage capacity calculation and improve the accuracy of target energy storage capacity calculation compared to the prior art. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A topology connection diagram of a photovoltaic access area provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the impact of photovoltaic power generation on the voltage at the photovoltaic access point, provided in an embodiment of this application. Figure 3 A flowchart illustrating a method for initially setting the power capacity of a transformer substation energy storage area according to an embodiment of this application; Figure 4 A preliminary device for calculating the power capacity of a power storage area is provided in the embodiments of this application; Figure 5 This is a schematic diagram of an energy storage system provided in an embodiment of this application.

[0021] The diagram is labeled as follows: 40-Initial design device for energy storage capacity of the distribution area, 401-Mapping table construction unit, 402-Critical power acquisition unit, 403-Capacity coefficient and power curve determination unit, 404-Energy storage capacity determination unit, 110-Control module, 120-Power conversion module, 130-Battery module. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0023] Due to low construction standards and insufficient investment capacity, the distribution network structure is weak and its voltage regulation capacity is inadequate. This is especially true for county and village-level power supply radii, which are large and difficult to adapt to the characteristics of rapid load demand growth, large seasonal load fluctuations, dispersed users, and widespread integration of distributed energy resources. Overvoltage and undervoltage on distribution lines still exist and occur dynamically. In addition, with the large-scale integration of distributed photovoltaic power into low-voltage distribution areas, the reverse power flow of photovoltaic power will raise the voltage in the distribution areas, causing voltage exceeding limits and a decrease in IEC voltage compliance rate.

[0024] Therefore, to optimize power supply quality, distributed transformer energy storage has been widely proposed in power supply voltage quality management. However, due to the fact that excessively large distributed transformer energy storage configurations reduce capacity utilization, while insufficient configurations lead to inadequate capacity and voltage exceeding limits, determining the appropriate distributed transformer energy storage configuration is crucial. Currently, existing distributed transformer energy storage configuration technologies mainly rely on the following two methods: The first method, which estimates the reverse power and over-limit duration, uses the peak reverse power as the energy storage configuration power. The energy storage capacity is obtained by multiplying the reverse power by the over-limit time, and thus the power of the energy storage in the distribution area can be obtained. Obviously, although this method is simple to calculate, it has a large error. The second method relies on power flow simulation, time-series output data, line parameters, transformer outlet voltage, and reverse power. Real-time simulation is performed using tools such as PSIM or MATLAB to obtain the accurate power and capacity of the energy storage in the distribution area. Obviously, this method is computationally complex, time-consuming, and cannot be applied to the initial screening of distribution areas in batches.

[0025] Based on this, this application provides a method for initially setting the energy storage capacity of a transformer substation. In this method, firstly, a pre-constructed correction coefficient mapping table can be consulted based on the target voltage qualification rate to obtain the target interval correction coefficient corresponding to the target voltage qualification rate. The correction coefficient mapping table is obtained by processing simulation model sample data and real model sample data using a user random distribution algorithm to determine the simulated over-limit duration and the actual over-limit duration corresponding to different voltage qualification rates. The interval correction coefficient corresponding to different voltage qualification rates is determined from the simulated over-limit duration and the actual over-limit duration. Next, a critical power capacity coefficient and a load power curve can be determined based on the critical power, average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption. The critical power is calculated using the interval correction coefficient. Then, the ideal energy storage capacity and the load-absorbed photovoltaic power can be determined based on the critical power capacity coefficient and the load power curve. Finally, the target energy storage capacity can be determined by subtracting the ideal energy storage capacity from the load-absorbed photovoltaic power. Based on this, in this application, since the target energy storage capacity is obtained by using data such as voltage qualification rate, photovoltaic installed capacity, and photovoltaic power generation duration, and by looking up tables, this application can reduce the complexity of energy storage capacity calculation and improve the accuracy of target energy storage capacity calculation compared to the prior art.

[0026] After introducing the design concept of the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions of the embodiments of this application can be applied. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0027] like Figure 1 The diagram shown is a topology connection diagram of a photovoltaic access area provided in an embodiment of this application. The output voltage of the power supply transformer in the area is Ugrid, the current flowing through the power grid is Ig, and the impedance of the power grid line between the power supply transformer in the area and the photovoltaic access point is Lg+Rg. Based on this, the photovoltaic access point voltage corresponding to the photovoltaic access point can be Upv=Ugrid+Ig*(sLg+Rg).

[0028] like Figure 2 The diagram shown illustrates the influence of photovoltaic power generation on the photovoltaic access point voltage according to an embodiment of this application. In this diagram, t0 is the total duration of photovoltaic power generation, t1 is the over-limit duration of the photovoltaic access point, P0 is the photovoltaic installed power, P2 is the critical power that does not affect the over-limit, and the over-limit power P1 = P0 - P2.

[0029] from Figure 2It can be observed that as the photovoltaic power generation gradually changes from 0 to P0, the voltage at the photovoltaic connection point also increases. For example, during the time period t1, i.e., between 9:00 and 17:00, the photovoltaic power generation is greater than P2, and the voltage will exceed the limit; at other times, the voltage will not exceed the limit.

[0030] Based on this, to ensure that the over-limit power P1 (corresponding to) is greater than the critical power P2, Figure 3 If the red part in the diagram does not exceed the limit, then energy storage needs to be configured to absorb the excess power and capacity. The excess power P1 is P0-P2, and the capacity is the integral of P0-P2 over the time period t1.

[0031] The method of the embodiments of this application will now be described in conjunction with the accompanying drawings.

[0032] like Figure 3 The diagram shown is a flowchart of a method for preliminary setting of energy storage capacity in a distribution substation provided in this application. This method is used in the planning and preliminary setting stage where there is no actual measured data, and uses the target voltage qualification rate as input to perform low-complexity batch screening of energy storage configuration for multiple distribution substations. The process of this method is described below.

[0033] Step 301: Based on the target voltage pass rate, query the pre-built correction coefficient mapping table to obtain the target interval correction coefficient corresponding to the target voltage pass rate.

[0034] The correction coefficient mapping table is obtained by using a user random distribution algorithm to process the simulation model sample data and the real model sample data to determine the simulation over-limit time and the real over-limit time corresponding to different voltage pass rates; and the interval correction coefficient corresponding to different voltage pass rates is determined by the simulation over-limit time and the real over-limit time.

[0035] In this application, before querying a pre-built correction coefficient mapping table based on the target voltage pass rate to obtain the target interval correction coefficient corresponding to the target voltage pass rate, a "pre-built correction coefficient mapping table" can also be constructed.

[0036] Specifically, firstly, a user random distribution algorithm can be used to process the simulation model sample data and the real model sample data to determine the simulated over-limit time and the real over-limit time corresponding to different voltage qualification rates. For example, if the total number of users in the distribution area exported from the database is 50, and these 50 users are randomly distributed in the distribution line, then PSIM simulation can be used to obtain the simulated over-limit time t1' obtained according to the simulated measured waveform for different voltage qualification rates, and the real over-limit time t1'' calculated according to the formula.

[0037] Next, based on the simulated over-limit time and the actual over-limit time corresponding to different voltage qualification rates, the interval correction coefficient corresponding to different voltage qualification rates can be determined; that is, the interval correction coefficient t1_crt corresponding to different voltage qualification rates can be obtained by "actual over-limit time ÷ simulated over-limit time". Similarly, according to the typical number of users in the distribution transformer area of ​​50, 100, 150 and 200, the correction coefficient t1_crt for different number of users can also be determined by linear interpolation.

[0038] Finally, a correction coefficient mapping table can be constructed based on the interval correction coefficients corresponding to the different voltage pass rates. Table 1 shows one such correction coefficient mapping table provided in an embodiment of this application.

[0039] Table 1

[0040] Based on this, after constructing the correction coefficient mapping table, the target interval correction coefficient corresponding to the target voltage pass rate can be obtained directly by querying the pre-constructed correction coefficient mapping table according to the target voltage pass rate.

[0041] Furthermore, in order to improve the accuracy and reliability of the initial capacity setting, when querying the pre-built correction coefficient mapping table based on the target voltage qualification rate to obtain the target interval correction coefficient corresponding to the target voltage qualification rate, it is also possible to "first determine whether the voltage qualification rate is abnormal" and then query the correction coefficient mapping table to obtain the target interval correction coefficient.

[0042] Specifically, firstly, the target voltage pass rate, annualized total user time t2, and total over-limit time t3 can be obtained from the database.

[0043] Then, the annualized voltage pass rate can be obtained based on the annualized total user time t2 and the total over-limit time t3; that is, the annualized voltage pass rate V1_IEC=1-t3 / t2 can be calculated by using the annualized total user time t2 and the total over-limit time t3.

[0044] Next, it can be determined whether the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold; that is, the annualized voltage pass rate V1_IEC needs to be compared and evaluated with the target voltage pass rate V_IEC to determine whether the absolute value of the difference in pass rates is less than the error threshold, for example, |V1_IEC-V_IEC|<2%. Thus, it can be determined whether the target voltage pass rate is abnormal.

[0045] Finally, if the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold, it indicates that the target voltage pass rate is normal. In this case, the correction coefficient t1_crt for the target interval can be obtained by querying the correction coefficient mapping table based on the target voltage pass rate. Conversely, if the absolute value is less than the error threshold, it indicates that the target voltage pass rate is abnormal and needs to be re-obtained.

[0046] Step 302: Determine the critical power capacity factor and load power curve based on the critical power, average daily power consumption, maximum daily power consumption, and minimum daily power consumption.

[0047] The critical power is calculated using an interval correction coefficient.

[0048] Specifically, firstly, the actual over-limit time of the target voltage can be obtained based on the target voltage compliance rate V_IEC and the target interval correction coefficient t1_crt (e.g., ...). Figure 1 The over-limit time of the photovoltaic access point in the data is calculated as t1'' = (1 - V_IEC) × 24 × t1_crt.

[0049] Next, the photovoltaic power generation duration t0 and photovoltaic installed capacity P0 can be obtained from the database.

[0050] Then, curve fitting can be performed on the photovoltaic power generation duration t0 and the photovoltaic power generation to obtain the power generation time curves at different times. For example, in engineering, photovoltaic power generation is represented by a sinusoidal equivalent, with the start time of t0 as t0_start, the end time as t0_end, and the real-time time as t0_run. Based on this, the power generation time curve shown in the following formula can be fitted: P_PV=P0*SIN(3.14 / (t0_end-t0_start)*(t0_run-t0_start)) Next, the critical power P2 (i.e., the photovoltaic power corresponding to the start time of exceeding the limit) can be obtained based on the actual over-limit duration t1'' and the power generation time curve. Then, based on the start time t1_start of t1'', ​​t0_run = t1''_start, the critical power P2 can be obtained. It is important to note that when t1'' is greater than t0, the time limit is considered exceeded; when t1'' is less than or equal to t0, t0_run = t1''_start is calculated using the method described above.

[0051] Finally, the energy storage power P_des can be obtained based on the critical power P2 and the photovoltaic installed power P0. That is, the energy storage power P_des can be calculated by "P_des = P0 - P2".

[0052] Based on this, after obtaining the critical power, the capacity coefficient can be obtained by querying the capacity coefficient mapping table according to the critical power; then, the load power curve can be determined according to the average daily electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption.

[0053] In this application, before obtaining the critical power capacity coefficient by querying the capacity coefficient mapping table based on the critical power, the capacity coefficient mapping table can also be "pre-constructed".

[0054] Specifically, firstly, the power interval between any two adjacent interval points can be determined based on the preset power interval formula, the preset percentage, and the photovoltaic installed capacity; wherein, the preset power interval formula is: Power Interval = Preset Percentage × Photovoltaic Installed Capacity. For example, the preset percentage can be set to 10%, then the preset power interval formula is Px = 10%P0, where Px is the power interval and P0 is the photovoltaic installed capacity.

[0055] Next, based on the power interval Px, the critical power corresponding to multiple interval points can be determined; where each critical power corresponds to one photovoltaic over-limit power generation. Then, based on the power generation time curve, the photovoltaic over-limit power generation Wx corresponding to each critical power can be determined.

[0056] Then, based on the ideal power generation W0 and the photovoltaic over-limit power generation Wx corresponding to each critical power, the critical power capacity coefficient corresponding to each critical power can be obtained; that is, according to the formula "Wx=fx*W0", the critical power capacity coefficient fx (fx=Wx / W0) can be obtained by the ratio of photovoltaic over-limit power generation Wx to ideal power generation W0. For example, multiple critical power capacity coefficients can be obtained at 10% intervals.

[0057] The critical power capacity coefficient between adjacent intervals (e.g., within each 10% interval) can be obtained by linear interpolation. For example, the critical power capacity coefficient fx between 0% and 10%, the critical power capacity coefficient fx between 10% and 20%, and the critical power capacity coefficient fx between 20% and 30% are all based on the critical power capacity coefficients of adjacent intervals and obtained by linear interpolation.

[0058] Finally, based on the critical power, the photovoltaic over-limit power generation corresponding to each critical power, and the critical power capacity coefficient corresponding to each critical power, the capacity coefficient mapping table can be directly constructed. For example, with a total photovoltaic power generation duration t0 of 9 hours and a maximum photovoltaic power generation of 1, the power generation time curve adopts the aforementioned sinusoidal equivalent method, that is, the power generation time curve P_PV=1*SIN(3.14 / (9-0)*(t0_run-0))=SIN(3.14*t0_run / 9), and the ideal power generation... By analogy, the critical power capacity coefficients corresponding to different critical powers can be obtained, as shown in Table 2, which is a capacity coefficient mapping table provided by the embodiments of this application.

[0059] Table 2

[0060] Based on this, after constructing the capacity coefficient mapping table, the critical power capacity coefficient can be obtained directly by querying the capacity coefficient mapping table according to the critical power.

[0061] Then, when obtaining the load power curve based on the average daily electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption, the average daily electricity consumption EC_ave, the maximum daily electricity consumption EC_max, and the minimum daily electricity consumption EC_min can first be obtained from the database.

[0062] Then, a random distribution method can be used to weight the daily average electricity consumption EC_ave, the maximum daily electricity consumption EC_max, and the minimum daily electricity consumption EC_min to obtain the weighted daily electricity consumption data EC_wght. For example, if residential users in the distribution network exhibit three high-power characteristics: 7 am, 12 pm, and 6-8 pm, and the daily average coefficient is 1, the high-power weighting coefficient is 3, and the time length is 6 hours, then the other weighting coefficients for Para_load are (24-3×6) / (24-6)=0.33.

[0063] Finally, the load power curve can be obtained based on the weighted daily electricity consumption data. That is, the load power curve Pave_load can be calculated using the formula "Pave_load=EC_wght / 24".

[0064] Step 303: Determine the ideal energy storage capacity and the load absorption of photovoltaic power based on the critical power capacity factor and the load power curve.

[0065] Specifically, firstly, the ideal energy storage capacity can be obtained by multiplying the critical power capacity coefficient by the ideal power generation; that is, the ideal energy storage capacity W1_des can be calculated using the formula "W1_des=fx* W0".

[0066] Then, the photovoltaic power absorbed by the load can be determined based on the actual over-limit duration and the load power curve; that is, based on the formula "W_load=∑Pave_load×t1''", the photovoltaic power absorbed by the load W_load can be calculated by integrating the load power curve Pave_load within the actual over-limit duration t1''.

[0067] Step 304: Calculate the difference between the ideal energy storage capacity and the photovoltaic power consumed by the load to determine the target energy storage capacity.

[0068] That is, the target energy storage capacity W2_des can be calculated based on the formula "W2_des=W1_des-W_load".

[0069] In summary, this application, based on data such as voltage qualification rate, photovoltaic installed capacity, and photovoltaic power generation duration, and using methods such as table lookup to obtain the energy storage capacity of the distribution area and the target energy storage capacity, reduces the complexity of energy storage capacity calculation while maintaining the accuracy of energy storage capacity calculation compared to existing technologies.

[0070] Based on the same inventive concept, embodiments of this application provide a preliminary device 40 for calculating the power capacity of a transformer substation energy storage system, such as... Figure 4 As shown, the initial design device 40 for the energy storage capacity of this distribution area includes: The mapping table construction unit 401 is used to process the simulation model sample data and the real model sample data using a user random distribution algorithm, determine the simulation over-limit time and the real over-limit time corresponding to different voltage pass rates, determine the interval correction coefficient corresponding to each voltage pass rate by the ratio of the real over-limit time to the simulation over-limit time, and construct the correction coefficient mapping table. The critical power acquisition unit 402 is used to obtain the interval correction coefficient by querying the correction coefficient mapping table according to the target voltage qualification rate, and to obtain the target actual over-limit time according to the target voltage qualification rate and the interval correction coefficient; and to obtain the photovoltaic power generation time and photovoltaic installed power, fit the photovoltaic power generation time curve and determine the critical power, and obtain the energy storage power by subtracting the critical power from the photovoltaic installed power. The capacity factor and power curve determination unit 403 is used to determine the critical power capacity factor and load power curve based on the critical power, average daily power consumption, maximum daily power consumption and minimum daily power consumption. The energy storage capacity determination unit 404 is used to determine the ideal energy storage capacity and the load absorption of photovoltaic power based on the critical power capacity coefficient and the load power curve, and to determine the target energy storage capacity by subtracting the ideal energy storage capacity from the load absorption of photovoltaic power.

[0071] Optionally, the mapping table building unit 401 is also used for: Obtain the target voltage compliance rate, annualized total user time, and total over-limit time from the database; The annualized voltage qualification rate is obtained based on the annualized total user time and total over-limit time. Determine whether the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold; If the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold, then the correction coefficient for the target range is obtained by querying the correction coefficient mapping table based on the target voltage pass rate.

[0072] Optionally, the critical power acquisition unit 402 is also used for: The actual over-limit duration is obtained based on the target voltage pass rate and the target range correction coefficient. Obtain the photovoltaic power generation duration and photovoltaic installed capacity from the database; Curve fitting is performed on the photovoltaic power generation duration and photovoltaic power generation to obtain the power generation time curve; The critical power is obtained based on the actual over-limit duration of the target and the power generation time curve.

[0073] Optionally, the critical power acquisition unit 402 is also used for: Based on the critical power, look up the capacity coefficient mapping table to obtain the critical power capacity coefficient; The load power curve is obtained based on the average daily electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption.

[0074] Optionally, the mapping table building unit 401 is also used for: Based on the preset power interval formula, preset percentage and photovoltaic installed capacity, determine the power interval between any two adjacent interval points; wherein, the preset power interval formula is: power interval = preset percentage × photovoltaic installed capacity; Based on the power interval, the critical power corresponding to multiple interval points is determined; whereby one critical power corresponds to one photovoltaic over-limit power generation. Based on the ideal power generation and the photovoltaic over-limit power generation corresponding to each critical power, the critical power capacity coefficient corresponding to each critical power is obtained; among them, the critical power capacity coefficient between adjacent interval points is obtained by linear interpolation. A capacity coefficient mapping table is constructed based on each critical power, the photovoltaic over-limit power generation corresponding to each critical power, and the critical power capacity coefficient corresponding to each critical power.

[0075] Optionally, the capacity factor and power curve determination unit 403 is also used for: Obtain the average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption from the database; The daily average electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption were weighted using a random distribution method to obtain weighted daily electricity consumption data. The load power curve is obtained based on the weighted daily electricity consumption data.

[0076] Optionally, the energy storage capacity determination unit 404 is also used for: The ideal energy storage capacity is obtained by multiplying the critical power capacity factor by the ideal power generation. The load absorption of photovoltaic power is determined based on the actual over-limit duration and load power curve.

[0077] The initial design device 40 for the energy storage capacity of this transformer area can be used for execution. Figure 3 The method performed in the illustrated embodiment can be used as a reference for the functions that each functional unit of the preliminary energy storage capacity design device 40 for this distribution area can achieve. Figure 3 The embodiments shown are described in detail below.

[0078] In some possible implementations, this application also provides an energy storage system, such as Figure 5 The diagram shown is a structural schematic of an energy storage system provided in an embodiment of this application. The energy storage system includes a control module 110, a power conversion module 120, and a battery module 130.

[0079] Specifically, the battery module 130 is electrically connected to the power conversion module 120, and the control module 110 is communicatively connected to both the power conversion module 120 and the battery module 130. This allows the control module 110 to monitor the battery module 130 and receive information such as battery voltage, current, and temperature. Simultaneously, the control module 110 can also determine the status of the power conversion module 120 in real time and, by determining the status of the power conversion module 120, achieve charge and discharge control management of the battery module 130.

[0080] Furthermore, the battery module 130 in the energy storage system is a device for storing electrical energy or other energy sources; the power conversion module 120 is a device for controlling the charging and discharging process of the battery module 130, and the power conversion module 120 can be a power conversion system (PCS), an alternating current to direct current (ACDC) converter, etc. The control module 110 can be the control module in the power conversion module 120, or it can be an additional control module.

[0081] It should be noted that, for example, the control module 110 in the above embodiment can communicate with the battery module 130 through a CAN (Controller Area Network) interface to obtain the status information of the battery module 130, so as to realize the protective charging and discharging of the battery module 130 and ensure the safe operation of the battery module 130.

[0082] In some possible implementations, various aspects of the methods provided in this application can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps of the methods according to the various exemplary embodiments of this application described above. For example, the computer device may perform actions such as... Figure 3 The method in the illustrated embodiment.

[0083] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Alternatively, if the integrated units of this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0084] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0085] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for preliminary design of energy storage capacity in a transformer substation, characterized in that, The method includes: Based on the target voltage pass rate, a pre-constructed correction coefficient mapping table is consulted to obtain the target interval correction coefficient corresponding to the target voltage pass rate. The correction coefficient mapping table is obtained by processing simulation model sample data and real model sample data using a user random distribution algorithm to determine the simulation over-limit time and the real over-limit time corresponding to different voltage pass rates. The interval correction coefficient corresponding to different voltage pass rates is then determined based on the simulation over-limit time and the real over-limit time. The critical power capacity factor and load power curve are determined based on the critical power, average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption; wherein, the critical power is calculated using an interval correction factor. Based on the critical power capacity coefficient and the load power curve, determine the ideal energy storage capacity and the load absorption of photovoltaic power. The target energy storage capacity is determined by subtracting the ideal energy storage capacity from the photovoltaic power absorbed by the load.

2. The method as described in claim 1, characterized in that, The step of querying a pre-built correction coefficient mapping table based on the target voltage pass rate to obtain the target interval correction coefficient corresponding to the target voltage pass rate includes: Obtain the target voltage compliance rate, annualized total user time, and total over-limit time from the database; The annualized voltage qualification rate is obtained based on the annualized total user time and total over-limit time. Determine whether the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold; If the absolute value of the difference between the target voltage pass rate and the annualized voltage pass rate is less than the error threshold, then the target interval correction coefficient is obtained by querying the correction coefficient mapping table based on the target voltage pass rate.

3. The method as described in claim 1, characterized in that, Before determining the critical power capacity factor versus load power curve based on critical power, average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption, the method further includes: The actual over-limit duration is obtained based on the target voltage pass rate and the target range correction coefficient. Obtain the photovoltaic power generation duration and photovoltaic installed capacity from the database; Curve fitting is performed on the photovoltaic power generation duration and photovoltaic power generation to obtain the power generation time curve; The critical power is obtained based on the actual over-limit duration of the target and the power generation time curve.

4. The method as described in claim 1, characterized in that, The step of determining the critical power capacity factor and load power curve based on the critical power, average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption includes: Based on the critical power, the capacity coefficient mapping table is consulted to obtain the critical power capacity coefficient; The load power curve is obtained based on the average daily electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption.

5. The method as described in claim 4, characterized in that, Before obtaining the critical power capacity coefficient by querying the capacity coefficient mapping table based on the critical power, the method further includes: Based on the preset power interval formula, preset percentage and photovoltaic installed capacity, the power interval between any two adjacent interval points is determined; wherein, the preset power interval formula is power interval = preset percentage × photovoltaic installed capacity; Based on the power interval, the critical power corresponding to multiple interval points is determined; wherein, one critical power corresponds to one photovoltaic over-limit power generation. Based on the ideal power generation and the photovoltaic over-limit power generation corresponding to each critical power, the critical power capacity coefficient corresponding to each critical power is obtained; among them, the critical power capacity coefficient between adjacent interval points is obtained by linear interpolation. Based on each critical power, the photovoltaic over-limit power generation corresponding to each critical power, and the critical power capacity coefficient corresponding to each critical power, the capacity coefficient mapping table is constructed.

6. The method as described in claim 4, characterized in that, The step of obtaining the load power curve based on the average daily electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption includes: Obtain the average daily electricity consumption, maximum daily electricity consumption, and minimum daily electricity consumption from the database; The daily average electricity consumption, the maximum daily electricity consumption, and the minimum daily electricity consumption were weighted using a random distribution method to obtain weighted daily electricity consumption data. The load power curve is obtained based on the weighted daily electricity consumption data.

7. The method as described in claim 3, characterized in that, The step of determining the ideal energy storage capacity and the load absorption of photovoltaic power based on the critical power capacity coefficient and the load power curve includes: The ideal energy storage capacity is obtained by multiplying the critical power capacity coefficient by the ideal power generation. The load absorption of photovoltaic power is determined based on the actual over-limit duration of the target and the load power curve.

8. The method as described in claim 3, characterized in that, The power generation time curve is represented by the following formula: P_PV=P0*SIN(3.14 / (t0_end-t0_start)*(t0_run-t0_start)) Where P_PV is the photovoltaic power generation capacity; P0 is the photovoltaic installed capacity; t0_start is the start time of photovoltaic power generation duration t0; t0_end is the end time of photovoltaic power generation duration t0; t0_run is the real-time of photovoltaic power generation duration t0; and SIN(*) is the sine trigonometric function.

9. A device for preliminary design of energy storage capacity in a transformer substation, characterized in that, The device includes: The mapping table construction unit is used to process the simulation model sample data and the real model sample data using a user random distribution algorithm, determine the simulation over-limit time and the real over-limit time corresponding to different voltage pass rates, determine the interval correction coefficient corresponding to each voltage pass rate by the ratio of the real over-limit time to the simulation over-limit time, and construct the correction coefficient mapping table. The critical power acquisition unit is used to obtain the interval correction coefficient by querying the correction coefficient mapping table according to the target voltage qualification rate, and to obtain the target actual over-limit time according to the target voltage qualification rate and the interval correction coefficient; and to obtain the photovoltaic power generation time and photovoltaic installed power, fit the photovoltaic power generation time curve and determine the critical power, and obtain the energy storage power by subtracting the critical power from the photovoltaic installed power. The capacity factor and power curve determination unit is used to determine the critical power capacity factor and load power curve based on the critical power, average daily power consumption, maximum daily power consumption and minimum daily power consumption. The energy storage capacity determination unit is used to determine the ideal energy storage capacity and the photovoltaic power absorbed by the load based on the critical power capacity coefficient and the load power curve, and to determine the target energy storage capacity by subtracting the ideal energy storage capacity from the photovoltaic power absorbed by the load.

10. An energy storage system, characterized in that, include: The system comprises a control module, a power conversion module, and a battery module; wherein the control module is connected to the power conversion module and the battery module respectively, and is used to execute the method for initial setting of the power capacity of the power storage area as described in any one of claims 1-8.

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