5G base station energy storage configuration optimization method, system and medium based on power grid collaboration

Abstract

The present invention discloses a 5G base station energy storage configuration optimization method, system and medium based on power grid collaboration, which relates to the technical field of base station energy storage optimization, and includes the following steps: collecting energy consumption data of 5G base stations, and analyzing high-load periods of 5G base stations; collecting peak and valley data of the power grid, and analyzing peak periods and valley periods of the power grid; based on peak periods, valley periods and high-load periods, analyzing the optimal periods for energy storage and energy use of 5G base stations, and marking them as optimal energy storage periods and optimal energy consumption periods respectively; managing energy storage and energy use of 5G base stations based on the optimal energy storage periods and optimal energy consumption periods; the present invention is used to solve the problem that the existing base station energy storage optimization technology still has insufficient analysis of base station energy storage and energy use, resulting in high electricity costs for base stations.

Description

Technical Field

[0001] The present invention relates to the field of base station energy storage optimization technology, and specifically to a 5G base station energy storage configuration optimization method, system, and medium based on power grid collaboration. Background Art

[0002] Base station energy storage optimization technology refers to the application of energy storage devices (such as batteries, supercapacitors, etc.) in communication base stations to store electrical energy, and the management and scheduling of energy storage systems through intelligent control and optimization algorithms, in order to improve the energy utilization efficiency of base stations, reduce operating costs, and enhance power supply reliability and flexibility.

[0003] Existing base station energy storage optimization technologies are usually optimized based on the peak and valley of grid electricity prices. When optimizing the configuration, the load of the base station should also be taken into consideration, because the higher the load of the base station, the more electricity it needs to consume. With such a large amount of electricity consumption, the cost will increase regardless of whether it is peak electricity or parity electricity. Only by using off-peak electricity can the electricity cost be saved to the greatest extent. However, the off-peak electricity period varies depending on the region and cannot be matched with the load of the base station. Therefore, it is necessary to optimize the energy storage of the base station at the same time during the peak electricity consumption period and the high load period of the base station. In addition, the existing base station energy storage optimization The technology does not consider whether the stored electricity during off-peak periods is sufficient, resulting in an increase in the base station's use of peak electricity, which further leads to an increase in electricity costs. For example, in the patent application with publication number CN118096439A, a method for optimizing the power supply cost of a base station is disclosed. This solution only considers the electricity price, that is, peak and off-peak electricity, and does not consider the load of the base station. Even if the base station is in a period of parity electricity during the high-load period, the electricity cost will increase significantly. The existing base station energy storage optimization technology also has the problem of insufficient comprehensive analysis of base station energy storage and energy use, resulting in the base station's electricity cost not being minimized. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by collecting energy consumption data of 5G base stations, analyzing the high load period of 5G base stations, and then collecting peak and valley data of the power grid, analyzing the peak period and valley period of the power grid, and then based on the historical load records of the 5G base stations, analyzing and predicting the estimated power consumption of the 5G base stations in high load periods and peak periods, and based on the energy storage efficiency of the 5G base stations, analyzing whether the stored energy in the valley period is sufficient for use in the high load period and peak period; if the stored energy in the valley period is not enough to support the use in the high load period and peak period, then expanding the range of the optimal energy storage period, and dividing the specific optimal energy storage period and the optimal energy consumption period; finally, based on the optimal energy storage period and the optimal energy consumption period, the 5G base station is stored and managed for energy use, so as to solve the problem that the existing base station energy storage optimization technology still has an incomplete analysis of base station energy storage and energy use, resulting in high electricity costs for the base station.

[0005] To achieve the above objectives, in a first aspect, the present application provides a 5G base station energy storage configuration optimization method based on grid collaboration, comprising the following steps:

[0006] Collect energy consumption data of 5G base stations and analyze the high-load periods of 5G base stations;

[0007] Collect peak and valley data of the power grid and analyze the peak and valley periods of the power grid;

[0008] Based on peak hours, off-peak hours, and high-load hours, the optimal times for 5G base stations to store and consume energy are analyzed and marked as optimal energy storage hours and optimal energy consumption hours respectively;

[0009] Energy storage and energy consumption management of 5G base stations are carried out based on the optimal energy storage period and the optimal energy consumption period.

[0010] Furthermore, collecting energy consumption data of 5G base stations and analyzing high-load periods of 5G base stations include the following sub-steps:

[0011] Get the high load power consumption set by the 5G base station;

[0012] Real-time monitoring of 5G base station power consumption, named real-time power consumption;

[0013] Compare the real-time power consumption with the high-load power consumption. If the real-time power consumption is less than the high-load power consumption, output a non-high-load signal; if the real-time power consumption is greater than or equal to the high-load power consumption, output a high-load signal.

[0014] If a high-load signal is output, the time corresponding to the real-time power consumption is marked as the high-load start time. If a high-load signal is output again at this time, it is directly ignored until a non-high-load signal is output. The time corresponding to the real-time power consumption when the non-high-load signal is output is marked as the high-load end time.

[0015] Marking the time period between the high load start time and the high load end time as a high load period;

[0016] Record all high-load periods of the 5G base station in a day, and mark the time between any two high-load periods as the interval time. Compare the interval time with a first duration threshold. If the interval time is less than or equal to the first duration threshold, output a period-merged signal; if the interval time is greater than the first duration threshold, output a period-independent signal.

[0017] If the time period merging signal is output, the corresponding two high-load time periods are connected and the interval time between them is integrated into the same high-load time period.

[0018] Furthermore, collecting peak and valley data of the power grid and analyzing the peak and valley periods of the power grid include the following sub-steps:

[0019] Get the city where the 5G base station is located and name it as Base Station City;

[0020] Find the latest peak and valley time divisions in the base station city, and find the peak and valley time divisions based on the latest peak and valley time divisions in the region;

[0021] The latest peak-valley period classification of the region includes five levels: deep valley, low valley, flat section, peak and sharp peak. The peak period includes sharp peak and high peak, and the low valley period includes deep valley and low valley.

[0022] All times except peak hours and off-peak hours are parity hours.

[0023] Furthermore, based on peak hours, off-peak hours, and high-load hours, analyzing the optimal times for 5G base stations to store and use energy includes the following sub-steps:

[0024] Based on the historical load records of 5G base stations, analyze and predict the estimated power consumption of 5G base stations during high-load periods and peak hours;

[0025] Based on the energy storage efficiency of 5G base stations, analyze whether the stored energy during off-peak periods is sufficient for use during high-load periods and peak periods;

[0026] If the stored energy in the off-peak period is insufficient to support the use in the high-load period and the peak period, the range of the optimal energy storage period will be expanded, and the specific optimal energy storage period and the optimal energy consumption period will be divided.

[0027] Furthermore, based on the historical load records of the 5G base station, the estimated power consumption of the 5G base station during high load periods and peak periods is analyzed and predicted, including the following sub-steps:

[0028] High-load periods and peak periods are collectively referred to as energy consumption periods. Based on the chronological order, whether two adjacent energy consumption periods are continuous or intersecting is determined. If so, an integrated period signal is output; otherwise, an independent period signal is output.

[0029] If the time period integration signal is output, the corresponding two energy consumption periods are integrated into the same energy consumption period;

[0030] Record the total amount of electricity consumed by the 5G base station during the energy consumption period, named as the total electricity consumption, establish a historical total database, collect the historical total electricity consumption of the 5G base station, and mark it as the historical total;

[0031] Get the historical totals for each day of the past year, grouping them by month, weekday, and holiday. This is represented by the symbol M(n,d), where n is a positive integer and is the sequence number of M. n represents the month, and d is 1 or 2. If d is 1, it represents a weekday, and if d is 2, it represents a holiday. That is, M(n,1) represents the set of historical totals for weekdays in month n, and M(n,2) represents the set of historical totals for holidays in month n.

[0032] Calculate the average of the historical total in M(n,d), marked as P(n,d);

[0033] Get the month of the current time, named as real-time month, represented by symbol i, and at the same time get whether the current time is a working day. If so, output a working signal; if not, output a holiday signal;

[0034] The symbol j indicates whether the current time is a working day. If a working signal is output, j = 1; if a holiday signal is output, j = 2.

[0035] Obtain P(i, j), calculate P(i, j) multiplied by the first redundancy ratio, and mark the calculation result as the estimated power consumption, where P(i, j) is P(n, d) with n=i and d=j.

[0036] Furthermore, based on the energy storage efficiency of the 5G base station, analyzing whether the stored energy during off-peak periods is sufficient for use during high-load periods and peak periods includes the following sub-steps:

[0037] Get the valley period and find out whether the valley period is a continuous period. If so, output the continuous valley segment; if not, output the interval valley segment.

[0038] Obtain the energy storage efficiency of the 5G base station. If the output is a continuous valley segment, obtain the total duration of the valley period, named the total valley duration. Calculate the total valley duration and multiply it by the energy storage efficiency to obtain the estimated storage capacity.

[0039] The estimated energy storage is compared with the estimated power consumption. If the estimated energy storage is greater than or equal to the estimated power consumption, an energy storage sufficient signal is output; if the estimated energy storage is less than the estimated power consumption, an energy storage insufficient signal is output.

[0040] If the output interval is a valley segment, the continuous time periods in the valley period are marked as valley sub-periods. Several valley sub-periods are obtained by division. It is then checked whether there is an energy consumption period between two adjacent valley sub-periods. If not, the two valley sub-periods are merged into the same valley sub-period. If so, the two valley sub-periods are kept independent.

[0041] The valley periods are numbered in chronological order, using the symbol F q Indicates, where q is a positive integer and q is the serial number of F; the energy consumption periods are numbered in chronological order, and the symbol R q Since two adjacent off-peak periods without an energy consumption period in between will be merged, the number of off-peak periods and energy consumption periods is usually the same, so the serial numbers of F and R are both represented by q;

[0042] Get F q duration, marked as TF q , get R q The duration, marked as TR q ;calculate Among them, ER q Energy consumption period R q ERA is the estimated electricity consumption during the period, TR is the estimated electricity consumption k TR with q=k q ;

[0043] Calculating TF q Multiply the energy storage efficiency and mark the result as the estimated storage energy during the period, using the symbol EF q express;

[0044] EF q With ER q Compare, if EF q Smaller than ER q , then the output period energy storage insufficient signal; if EF q Greater than or equal to ER q , then the time period energy storage sufficient signal is output; the time period energy storage insufficient signal and the time period energy storage sufficient signal are collectively referred to as the time period evaluation signal.

[0045] Furthermore, if the stored energy in the off-peak period is insufficient to support the use in the high-load period and the peak period, the range of the optimal energy storage period is expanded, and the specific optimal energy storage period and the optimal energy consumption period are divided, including the following sub-steps:

[0046] Obtaining time period evaluation signals, and if all of them are signals indicating sufficient energy storage in the time period, outputting a first optimization signal; if there is a signal indicating insufficient energy storage in the time period evaluation signals, outputting a second optimization signal;

[0047] If the first optimization signal is output, the off-peak period is marked as the optimal energy storage period, and the estimated storage amount minus the estimated power consumption is calculated, and the calculation result is marked as the estimated energy storage redundancy;

[0048] Calculate EF q and ER q The sum of the calculated results is marked as the estimated hourly electricity consumption, and EF is calculated. q and minus ER q The difference between the sum of is marked as redundant power. Calculate the redundant power and divide it by the estimated hourly power consumption. The result is expressed as the estimated redundant time. Obtain the number of energy consumption periods, marked as the number of periods. Calculate the estimated redundant time and divide it by the estimated hourly power consumption to obtain the estimated total increase. Calculate the estimated total increase by dividing it by the number of periods and then by 2. The result is marked as the estimated one-way increase.

[0049] The estimated one-way increase duration is added at the beginning and end of each energy consumption period, and the energy consumption period after the increase duration is marked as the optimal energy consumption period;

[0050] If the second optimization signal is output, the EF of the energy storage shortage signal of the time period is obtained. q With ER q , the corresponding F q and R q Mark as underpower period and energy demand period, and set the corresponding EF q With ER q Labeled as X and Y;

[0051] Calculate YX and mark the result as the estimated required electricity. Calculate the estimated required electricity divided by the energy storage efficiency and mark the result as the estimated energy storage increase time. Include the estimated energy storage increase time after the power shortage period in the power shortage period. Mark the power shortage period and the off-peak period as the optimal energy storage period. Mark the energy demand period and the energy consumption period as the optimal energy consumption period.

[0052] Furthermore, energy storage and energy consumption management for 5G base stations based on the optimal energy storage period and the optimal energy consumption period includes the following sub-steps:

[0053] Monitor the current time in real time, and if the current time is in the optimal energy storage period, start storing electrical energy in the energy storage device;

[0054] If the current time is in the optimal energy consumption period, electricity will be transmitted to the 5G base station through the energy storage device.

[0055] In a second aspect, the present application provides a 5G base station energy storage configuration optimization system based on grid collaboration, including a high-load analysis module, a peak-valley analysis module, a storage and consumption analysis module, and a storage and consumption management module; the high-load analysis module, the peak-valley analysis module, and the storage and consumption management module are respectively connected to the storage and consumption analysis module data;

[0056] The high-load analysis module is used to collect energy consumption data of 5G base stations and analyze high-load periods of 5G base stations;

[0057] The peak-valley analysis module is used to collect peak-valley data of the power grid and analyze the peak and valley periods of the power grid;

[0058] The storage and consumption analysis module is used to analyze the optimal time periods for energy storage and energy consumption of the 5G base station based on peak time periods, off-peak time periods, and high-load time periods, and mark them as optimal energy storage time periods and optimal energy consumption time periods respectively;

[0059] The storage and consumption management module is used to manage energy storage and energy consumption of 5G base stations based on the optimal energy storage period and the optimal energy consumption period.

[0060] In a third aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above method are performed.

[0061] Beneficial effects of the present invention: The present invention collects energy consumption data of 5G base stations, analyzes the high-load period of 5G base stations, collects peak and valley data of the power grid, analyzes the peak period and valley period of the power grid, and then analyzes and predicts the estimated power consumption of 5G base stations in high-load period and peak period based on the historical load records of 5G base stations. Based on the energy storage efficiency of 5G base stations, it analyzes whether the stored energy in valley period is sufficient for use in high-load period and peak period. The advantage is that, on the basis of considering the peak period of electricity consumption, the high-load period of 5G base stations is added for analysis, so that the electricity cost of 5G base stations in periods with higher electricity prices and periods with larger electricity consumption is greatly reduced, thereby improving the effectiveness and rationality of 5G base station energy storage optimization;

[0062] The present invention expands the scope of the optimal energy storage period when the electric energy stored in the off-peak period is insufficient to support the use in the high-load period and the peak period, and divides the specific optimal energy storage period and the optimal energy consumption period. Finally, the 5G base station is stored and managed based on the optimal energy storage period and the optimal energy consumption period. The advantage is that when the electric energy stored in the off-peak period cannot support the full coverage of the electricity consumption in the peak period and the high-load period, some parity electricity is added for energy storage, which maximizes the guarantee that the electric energy in the peak period is not used, so as to achieve the purpose of saving electricity costs, and further improves the effectiveness and rationality of the 5G base station energy storage optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a functional block diagram of the system of the present invention;

[0064] Figure 2 This is a schematic diagram of the latest peak-valley time division table of the present invention;

[0065] Figure 3 is a flow chart of the steps of the method of the present invention;

[0066] Figure 4 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Example 1, please refer to Figure 1 As shown, the present application provides a 5G base station energy storage configuration optimization system based on grid collaboration, including a high-load analysis module, a peak-valley analysis module, a storage and consumption analysis module, and a storage and consumption management module; the high-load analysis module, the peak-valley analysis module, and the storage and consumption management module are respectively connected to the storage and consumption analysis module data;

[0069] The high-load analysis module is used to collect energy consumption data of 5G base stations and analyze the high-load periods of 5G base stations;

[0070] The high-load analysis module is configured with high-load analysis strategies, which include:

[0071] Get the high load power consumption set by the 5G base station;

[0072] Real-time monitoring of 5G base station power consumption, named real-time power consumption;

[0073] Compare the real-time power consumption with the high-load power consumption. If the real-time power consumption is less than the high-load power consumption, output a non-high-load signal; if the real-time power consumption is greater than or equal to the high-load power consumption, output a high-load signal.

[0074] If a high-load signal is output, the time corresponding to the real-time power consumption is marked as the high-load start time. If a high-load signal is output again at this time, it is directly ignored until a non-high-load signal is output. The time corresponding to the real-time power consumption when the non-high-load signal is output is marked as the high-load end time.

[0075] Marking the time period between the high load start time and the high load end time as a high load period;

[0076] In actual applications, the high-load power consumption is set by the administrator of the 5G base station, and the power consumption in the 5G base station is divided into AAU average power consumption and BBU average power consumption. Since the BBU average power consumption is not greatly affected by the business load, the AAU average power consumption is used as a reference in this embodiment, that is, when the AAU average power consumption is greater than or equal to the high-load power consumption, it is determined that the 5G base station is in a high-load period. The high-load power consumption in this embodiment is obtained to be 1000W, and the real-time power consumption at 14:35:42 is 1072W. By comparison, it is found that the real-time power consumption is greater than the high-load power consumption, and a high-load signal is output. The time 14:35:42 is taken as the high load start time; the output signal is a high load signal for a long period of time thereafter, until 16:37:28. At this time, the real-time power consumption is 968W. By comparison, the real-time power consumption is less than the high load power consumption, and a non-high load signal is output. The time 16:37:28 is taken as the high load end time, and the time period between 14:35:42 and 16:37:28 is marked as the high load period. Similarly, the high load period also includes [16:52:35, 17:01:23] and [19:36:42, 21:15:24].

[0077] Record all high-load periods of the 5G base station in a day, and mark the time between any two high-load periods as the interval time. Compare the interval time with a first duration threshold. If the interval time is less than or equal to the first duration threshold, output a period-merged signal; if the interval time is greater than the first duration threshold, output a period-independent signal.

[0078] If the output period merge signal is output, the corresponding two high-load periods are connected and the interval time between them is integrated into the same high-load period;

[0079] In practical applications, the purpose of verifying and merging through the first time threshold is to prevent the real-time power from fluctuating repeatedly at high load power, resulting in too many high-load periods. Merging high-load periods with shorter time intervals can effectively improve computing efficiency and avoid frequent switching of energy storage devices, which may damage the energy storage devices. Since the load analysis of 5G base stations and the peak and valley electricity of the power grid are usually in units of one hour, the first time threshold in this embodiment is set to half of it, that is, 30mi n; The high-load periods in this embodiment include [14:35:42, 16:37:28], [16:52:35, 17:01:23], and [19:36:42, 21:15:24]. Among them, [14:35:42, 16:37:28] is adjacent to [16:52:35, 17:01:23]. The interval between them is 16:52:35-16:37:28=15 minutes. The calculation result only retains the minutes. By comparison, the interval time is less than the first duration threshold, and the period merge signal is output. The high-load period is [14:35:42, 16:37:28], [16:52:35, 17:01:23] and the interval time are combined to obtain the high-load period [14:35:42, 17:01:23]. Then, [14:35:42, 17:01:23] and [19:36:42, 21:15:24] are analyzed. The analysis result is an independent signal of the time period. Therefore, the final high-load period in this embodiment is [14:35:42, 17:01:23] and [19:36:42, 21:15:24].

[0080] The peak-valley analysis module is used to collect peak-valley data of the power grid and analyze the peak and valley periods of the power grid;

[0081] The peak-valley analysis module is configured with peak-valley analysis strategies, which include:

[0082] Get the city where the 5G base station is located and name it as Base Station City;

[0083] See also Figure 2 As shown, the latest peak and valley time division of the base station city is found, and the peak time and valley time are found based on the latest peak and valley time division of the region;

[0084] The latest regional peak-valley period classification includes five levels: deep valley, low valley, flat section, peak and sharp peak. Peak period includes sharp peak and peak, and low valley period includes deep valley and low valley.

[0085] All periods except peak and off-peak periods are parity periods;

[0086] In actual applications, the division of peak and valley periods in each city is based on different months, and the division results are also different. Therefore, the division of peak period, valley period and flat period will be updated every month. In this embodiment, taking July as an example, the city where the 5G base station is located is City A, that is, the base station city is City A, and the latest peak and valley period division of City A is found as follows: Figure 2 As shown, Figure 2 The table shows different plans for valley, off-peak, parity, peak, and sharp peak times for different cities in different months of the year. Since real regions are involved, the city header in the latest peak-valley period division table is omitted in this embodiment. The peak period of City A is found to be [16, 24], the off-peak period is [0, 9], and the parity period is [9, 16]. That is, from 0:00 to 9:00 in a day is the off-peak period, from 9:00 to 16:00 is the parity period, and from 16:00 to 24:00 (0:00) is the peak period.

[0087] The storage and consumption analysis module is used to analyze the optimal time periods for 5G base stations to store and consume energy based on peak hours, off-peak hours, and high-load hours, marking them as optimal energy storage hours and optimal energy consumption hours respectively. The storage and consumption analysis module includes a power consumption estimation unit, a storage and consumption analysis unit, and an energy storage replenishment unit.

[0088] The power consumption estimation unit is used to analyze and predict the estimated power consumption of 5G base stations during high-load periods and peak periods based on the historical load records of 5G base stations;

[0089] The power consumption estimation unit is configured with a power consumption estimation strategy, which includes:

[0090] High-load periods and peak periods are collectively referred to as energy consumption periods. Based on the chronological order, whether two adjacent energy consumption periods are continuous or intersecting is determined. If so, an integrated period signal is output; otherwise, an independent period signal is output.

[0091] If the time period integration signal is output, the corresponding two energy consumption periods are integrated into the same energy consumption period;

[0092] In actual applications, the high-load periods are [14:35:42,17:01:23] and [19:36:42,21:15:24], and the peak period is [16,24]. That is, the energy consumption periods include [14:35:42,17:01:23], [19:36:42,21:15:24], and [16,24]. Among them, [16,24] intersects with [14:35:42,17:01:23] and [19:36:42,21:15:24]. Therefore, the output period integration signal is merged, and [16,24] is first merged with [14:35:42,17:01:23] and then merged with [19:36:42,21:15:24]. The final energy consumption period is [14:35:42,24].

[0093] Record the total amount of electricity consumed by the 5G base station during the energy consumption period, named as the total electricity consumption, establish a historical total database, collect the historical total electricity consumption of the 5G base station, and mark it as the historical total;

[0094] In actual applications, since the load of a 5G base station is related to the use of the network by residents in a large area, there will be certain fluctuations on weekdays in the same month, but due to the large base of residents, the fluctuation range of the load of the 5G base station is small, and the measured high-load periods can be approximately regarded as equal. The same is true for holidays. In addition, the data collected in this embodiment is data on weekdays. Therefore, the high-load period analyzed in this embodiment can be used as the actual high-load period of the 5G base station on weekdays in City A in July. The high-load period on holidays is different from the high-load period on weekdays, so the difference in total electricity consumption is large and needs to be analyzed separately. Some data from the historical total database are shown in Table 1 below:

[0095] Table 1 Partial data of the historical total database

[0096] date Historical total (total electricity consumption) July 13 42.75kWh July 14 43.86kWh July 15 43.25kWh July 16 42.98kWh

[0097] The total amount of electricity used is called the historical total, representing the total amount of electricity consumed by 5G base stations during the energy consumption period of a given day. Although the amount of electricity consumed by each 5G base station may not seem high, it is more than twice that of a 4G base station. Currently, there are more than 200,000 5G base stations in operation, and the average annual electricity bill for each base station is close to 60,000 yuan. The electricity bill for all 5G base stations exceeds 10 billion yuan. This embodiment can save a significant amount of electricity costs through reasonable energy storage optimization.

[0098] Get the historical totals for each day of the past year, grouping them by month, weekday, and holiday. This is represented by the symbol M(n,d), where n is a positive integer and is the sequence number of M. n represents the month, and d is 1 or 2. If d is 1, it represents a weekday, and if d is 2, it represents a holiday. That is, M(n,1) represents the set of historical totals for weekdays in month n, and M(n,2) represents the set of historical totals for holidays in month n.

[0099] Calculate the average of the historical total in M(n,d), marked as P(n,d);

[0100] In practical applications, taking January 2024 as an example, the group is M(1,d), which includes M(1,1) and M(1,2). There are 22 working days and 9 holidays in January 2024, so M(1,1) includes 22 historical totals and M(1,2) includes 9 historical totals. Due to the large amount of data, this embodiment will not specifically display them, and only the average values calculated from them are given; the calculated values of P(1,1) and P(1,2) are 52.79 kWh and 55.86 kWh respectively.

[0101] Get the month of the current time, named as real-time month, represented by symbol i, and at the same time get whether the current time is a working day. If so, output a working signal; if not, output a holiday signal;

[0102] The symbol j indicates whether the current time is a working day. If a working signal is output, j = 1; if a holiday signal is output, j = 2.

[0103] Obtain P(i, j), calculate P(i, j) multiplied by the first redundancy ratio, and mark the result as the estimated power consumption, where P(i, j) is P(n, d) with n = i and d = j;

[0104] In actual applications, the real-time month obtained is July, that is, i=7, and the current time is 2024.7.12 13:26:37, which is a working day. The working signal is output, that is, j=1, and P(7,1) is obtained as 48.62kWh. The first redundancy ratio is set to prevent emergencies from causing the power consumption of the 5G base station to exceed expectations. Redundant energy storage can also be used for electricity consumption when electricity is at parity. In this embodiment, the first redundancy ratio is set to 1.1. The estimated power consumption is calculated to be 48.62×1.1=53.48kWh, and the calculation result is rounded to two decimal places.

[0105] The storage and consumption analysis unit is used to analyze the energy storage efficiency of the 5G base station and whether the stored energy during off-peak periods is sufficient for use during high-load periods and peak periods;

[0106] The storage and consumption analysis unit is configured with a storage and consumption analysis strategy, which includes:

[0107] Get the valley period and find out whether the valley period is a continuous period. If so, output the continuous valley segment; if not, output the interval valley segment.

[0108] Obtain the energy storage efficiency of the 5G base station. If the output is a continuous valley segment, obtain the total duration of the valley period, named the total valley duration. Calculate the total valley duration and multiply it by the energy storage efficiency to obtain the estimated storage capacity.

[0109] The estimated energy storage is compared with the estimated power consumption. If the estimated energy storage is greater than or equal to the estimated power consumption, a sufficient energy storage signal is output for the time period; if the estimated energy storage is less than the estimated power consumption, an insufficient energy storage signal is output for the time period.

[0110] In actual applications, the low-energy period is obtained as [0,9]. There is only one continuous low-energy period, so the continuous low-energy period is output. The energy storage efficiency of the 5G base station is obtained as 5kWh / h. Since the continuous low-energy period is output, the total low-energy period is obtained as 9 hours. Calculating 5kWh / h × 9 hours, the estimated energy storage is 45kWh, while the estimated power consumption is 53.48kWh. Through comparison, the estimated energy storage is less than the estimated power consumption, and the energy storage shortage signal is output.

[0111] If the output interval is a valley segment, the continuous time periods in the valley period are marked as valley sub-periods. Several valley sub-periods are obtained by division. It is then checked whether there is an energy consumption period between two adjacent valley sub-periods. If not, the two valley sub-periods are merged into the same valley sub-period. If so, the two valley sub-periods are kept independent.

[0112] In practical applications, to better explain the processing of the output interval valley segment, assume that the valley periods in this embodiment are [0, 9] and [12, 14], and the energy consumption periods are [9, 11] and [16, 23]. Among them, [0, 9] and [12, 14] are both valley sub-periods, and the energy consumption period [9, 11] exists between [0, 9] and [12, 14]. Therefore, the two valley sub-periods are kept independent. If there is no energy consumption period between them, then the energy stored in these two valley periods can be retained for energy consumption in the next energy consumption period, and therefore can be regarded as the same valley sub-period. It is also worth noting that if the valley period exists from 0:00 to any time and from any time to 24:00, they can be merged. For example, if [0, 9] and [23, 0] are both valley periods, they can be merged because the energy stored the previous night can also be used today.

[0113] The valley periods are numbered in chronological order, using the symbol F qIndicates, where q is a positive integer and q is the serial number of F; the energy consumption periods are numbered in chronological order, and the symbol R q Since two adjacent off-peak periods without an energy consumption period in between will be merged, the number of off-peak periods and energy consumption periods is usually the same, so the serial numbers of F and R are both represented by q;

[0114] In actual applications, F1 and F2 are numbered as [0,9] and [12,14], respectively, and R1 and R2 are [9,11] and [16,23], respectively. Here, α represents the off-peak period, and β represents the energy consumption period. If the distribution of off-peak and energy consumption periods within a day is "ααβαββ", it means that two off-peak periods are followed by an energy consumption period, followed by another off-peak period, and finally two energy consumption periods. Since they will be merged, the final distribution is "αβαβ". In addition, 0 o'clock in a day is usually off-peak electricity, so the number of off-peak periods and energy consumption periods is usually equal.

[0115] Get F q duration, marked as TF q , get R q The duration, marked as TR q ;calculate Among them, ER q Energy consumption period R q ERA is the estimated electricity consumption during the period, TR is the estimated electricity consumption k TR with q=k q ;

[0116] Calculating TF q Multiply the energy storage efficiency and mark the result as the estimated storage energy during the period, using the symbol EF q express;

[0117] EF q With ER q Compare, if EF q Smaller than ER q , then the output period energy storage insufficient signal; if EF q Greater than or equal to ER q , then the time period energy storage sufficient signal is output; the time period energy storage insufficient signal and the time period energy storage sufficient signal are collectively referred to as the time period evaluation signal;

[0118] In actual application, TF1 and TF2 are obtained as 9h and 2h respectively, TR1 and TR2 are 2h and 7h respectively, and the estimated power consumption ERA is 53.48kWh. The calculated ER1 is 11.88kWh and ER2 is 41.60kWh. The calculated results are rounded to two decimal places. Calculate TF qMultiplying by the energy storage efficiency, we get EF1 as 45kWh and EF2 as 10kWh. By comparison, we find that EF1 is greater than ER1, and a signal indicating sufficient energy storage is output for the time period. When comparing EF2 with ER2, we need to add the remaining power of EF1 to EF2. That is, after the power stored in EF1 is used during the ER1 period, the remaining power in the energy storage device is 45kWh-11.88kWh=33.12kWh. When comparing EF2 and ER2, EF2 is actually 10kWh+33.12kWh=43.12kWh. By comparison, we find that EF2 is greater than ER2, and a signal indicating sufficient energy storage is output for the time period.

[0119] The energy storage supplement unit is used to expand the range of the optimal energy storage period if the stored energy in the off-peak period is insufficient to support the use in the high-load period and peak period, and to divide the specific optimal energy storage period and optimal energy consumption period;

[0120] The energy storage replenishment unit is configured with an energy storage replenishment strategy, which includes:

[0121] Obtaining time period evaluation signals, and if all of them are signals indicating sufficient energy storage in the time period, outputting a first optimization signal; if there is a signal indicating insufficient energy storage in the time period evaluation signals, outputting a second optimization signal;

[0122] If the first optimization signal is output, the off-peak period is marked as the optimal energy storage period, and the estimated storage amount minus the estimated power consumption is calculated, and the calculation result is marked as the estimated energy storage redundancy;

[0123] Calculate EF q and ER q The sum of the calculated results is marked as the estimated hourly electricity consumption, and EF is calculated. q and minus ER q The difference between the sum of is marked as redundant power. Calculate the redundant power and divide it by the estimated hourly power consumption. The result is expressed as the estimated redundant time. Obtain the number of energy consumption periods, marked as the number of periods. Calculate the estimated redundant time and divide it by the estimated hourly power consumption to obtain the estimated total increase. Calculate the estimated total increase by dividing it by the number of periods and then by 2. The result is marked as the estimated one-way increase.

[0124] The estimated one-way increase duration is added at the beginning and end of each energy consumption period, and the energy consumption period after the increase duration is marked as the optimal energy consumption period;

[0125] In practical applications, in this embodiment, the time period evaluation signals outputted by the processing process of the output interval valley section are all time period energy storage sufficient signals, so the first optimization signal is outputted, in which the valley section is the optimal energy storage section, and the optimal energy storage sections are obtained as [0,9] and [12,14]; TR1 and TR2 are 2h and 7h respectively, ER1 is 11.88kWh, ER2 is 41.60kWh, and the estimated hourly power consumption is calculated to be 5.94kW, and the calculation result is rounded to two decimal places. At the same time, the redundant power is calculated to be 55kWh-53.48 kWh = 1.52kWh, the number of time periods is 2, and the estimated total incremental time is 1.52kWh ÷ 5.94kW = 0.26h. The estimated one-way incremental time is 0.065h = 3.9min. The total incremental time is divided by the number of time periods and then divided by 2. The reason for dividing by 2 is to evenly increase the duration at the beginning of each energy consumption period. The optimal Hanergy time periods are [8:56:6, 11:3:54] and [15:56:6, 23:3:54], while the optimal energy storage time periods are [0, 9] and [12, 14].

[0126] If the second optimization signal is output, the EF of the energy storage shortage signal of the time period is obtained. q With ER q , the corresponding F q and R q Mark as underpower period and energy demand period, and set the corresponding EF q With ER q Labeled as X and Y;

[0127] Calculate YX and mark the result as the estimated required electricity. Calculate the estimated required electricity divided by the energy storage efficiency and mark the result as the estimated energy storage increase time. Include the estimated energy storage increase time after the power shortage period into the power shortage period. Mark the power shortage period and the off-peak period as the optimal energy storage period. Mark the energy demand period and the energy consumption period as the optimal energy consumption period.

[0128] In actual applications, in this embodiment, the processing of outputting continuous low-valley segments outputs a signal indicating insufficient energy storage during the period, and thus outputs a second optimization signal. In addition, there is no number therein, so q in the above number is simply equal to 1, EF1 is the estimated energy storage, ER1 is the estimated power consumption, F1 is [0, 9], R1 is [14:35:42, 24], i.e., X = 45 kWh, Y = 53.48 kWh, and the estimated required power is 8.48 kWh. Further calculation results in an estimated energy storage increase of 1.696 h = 102 min. The conversion result retains integers, and the underpower period is [0, 9]. The 102 minutes after [0, 9] are also included in the underpower period, resulting in an underpower period of [0, 10:42:00]. Finally, the optimal energy storage period is [0, 10:42:00], and the optimal energy consumption period is [14:35:42, 24].

[0129] The storage and consumption management module is used to manage energy storage and consumption of 5G base stations based on the optimal energy storage period and the optimal energy consumption period;

[0130] The storage and consumption management module is configured with storage and consumption management strategies, which include:

[0131] Monitor the current time in real time, and if the current time is in the optimal energy storage period, start storing electrical energy in the energy storage device;

[0132] If the current time is in the optimal energy consumption period, power is transmitted to the 5G base station through the energy storage device;

[0133] In actual applications, on weekdays in July, if the current time is between [0, 10:42:00], electricity is available through the energy storage device. If the current time is between [14:35:42, 24], power is supplied to the 5G base station through the energy storage device. This embodiment only lists the analysis process for weekdays. The analysis process for holidays is the same, differing only in the time period.

[0134] Example 2, please refer to Figure 3 As shown, this application provides a 5G base station energy storage configuration optimization method based on grid collaboration, including the following steps:

[0135] Step S1: Collect energy consumption data of 5G base stations and analyze high-load periods of 5G base stations. Step S1 includes the following sub-steps:

[0136] Step S101, obtaining the high load power consumption set by the 5G base station;

[0137] Step S102, real-time monitoring of the power consumption of the 5G base station, named real-time power consumption;

[0138] Step S103, comparing the real-time power consumption with the high-load power consumption, if the real-time power consumption is less than the high-load power consumption, outputting a non-high-load signal; if the real-time power consumption is greater than or equal to the high-load power consumption, outputting a high-load signal;

[0139] Step S104: If a high-load signal is output, the time corresponding to the real-time power consumption is marked as the high-load start time. If a high-load signal is output again at this time, it is directly ignored until a non-high-load signal is output. The time corresponding to the real-time power consumption when the non-high-load signal is output is marked as the high-load end time.

[0140] Step S105, marking the time period between the high load start time and the high load end time as a high load period;

[0141] Step S106: Record all high-load periods of the 5G base station in a day, and mark the time between any two high-load periods as an interval time. Compare the interval time with a first duration threshold. If the interval time is less than or equal to the first duration threshold, output a period-merged signal; if the interval time is greater than the first duration threshold, output a period-independent signal.

[0142] Step S107: if the time period merging signal is output, then the corresponding two high-load time periods are connected and the interval time between them is integrated into the same high-load time period;

[0143] Step S2, collecting peak and valley data of the power grid and analyzing the peak and valley periods of the power grid; Step S2 includes the following sub-steps:

[0144] Step S201: Obtain the city where the 5G base station is located and name it as the base station city;

[0145] Step S202, searching for the latest regional peak and valley time divisions of the base station city, and obtaining peak time periods and valley time periods based on the latest regional peak and valley time divisions;

[0146] Step S203: The latest regional peak-valley period classification includes five levels: deep valley, low valley, flat section, peak, and sharp peak. The peak period includes sharp peak and peak, and the valley period includes deep valley and low valley.

[0147] Step S204: All periods except peak periods and off-peak periods are designated as parity periods.

[0148] Step S3, based on the peak period, off-peak period and high-load period, analyze the optimal period for energy storage and energy consumption of the 5G base station, and mark them as the optimal energy storage period and the optimal energy consumption period respectively; Step S3 includes the following sub-steps:

[0149] Step S301: Analyze and predict the estimated power consumption of the 5G base station during high-load periods and peak periods based on the historical load records of the 5G base station;

[0150] Step S301 includes the following sub-steps:

[0151] Step S301.1: High-load periods and peak periods are collectively referred to as energy usage periods. In chronological order, whether two adjacent energy usage periods are continuous or intersecting is determined. If so, an integrated period signal is output; otherwise, independent period signals are output.

[0152] Step S301.2: If a time period integration signal is output, the corresponding two energy usage periods are integrated into the same energy usage period;

[0153] Step S301.3: Record the total amount of electricity used by the 5G base station during the energy consumption period, name it as total electricity consumption, establish a historical total amount database, collect the historical total electricity consumption of the 5G base station, and mark it as historical total amount;

[0154] Step S301.4: Obtain the historical totals for each day of the past year, grouping them by month, weekday, and holiday. These totals are represented by the symbol M(n,d), where n is a positive integer and is the sequence number of M. n represents the month, and d is 1 or 2. If d is 1, it represents a weekday, and if d is 2, it represents a holiday. That is, M(n,1) represents the set of historical totals for weekdays in month n, and M(n,2) represents the set of historical totals for holidays in month n.

[0155] Step S301.5, calculate the average of the historical totals in M(n,d), denoted as P(n,d);

[0156] Step S301.6, obtain the month of the current time, named real-time month, represented by symbol i, and simultaneously obtain whether the current time is a weekday. If so, output a work signal; if not, output a holiday signal;

[0157] Step S301.7, using symbol j to indicate whether the current time is a working day, if the working signal is output, then j = 1, if the holiday signal is output, then j = 2;

[0158] Step S301.8, obtain P(i, j), calculate P(i, j) multiplied by the first redundancy ratio, and mark the calculated result as the estimated power consumption, where P(i, j) is P(n, d) with n = i and d = j;

[0159] Step S302, based on the energy storage efficiency of the 5G base station, analyzing whether the stored energy during the off-peak period is sufficient for use during the high-load period and the peak period;

[0160] Step S302 includes the following sub-steps:

[0161] Step S302.1, obtain the valley period, and find out whether the valley period is a continuous period. If so, output the continuous valley segment; if not, output the interval valley segment;

[0162] Step S302.2: Obtain the energy storage efficiency of the 5G base station. If continuous valley segments are output, obtain the total duration of the valley segments, named the total valley duration, and calculate the total valley duration multiplied by the energy storage efficiency to obtain the estimated energy storage capacity.

[0163] Step S302.3, compare the estimated energy storage with the estimated power consumption. If the estimated energy storage is greater than or equal to the estimated power consumption, output a sufficient energy storage signal; if the estimated energy storage is less than the estimated power consumption, output an insufficient energy storage signal.

[0164] Step S302.4: If an interval off-peak period is output, consecutive time periods within the off-peak period are marked as off-peak sub-periods. Several off-peak sub-periods are obtained by division, and an energy consumption period is checked to see if there is one between two adjacent off-peak sub-periods. If not, the two off-peak sub-periods are merged into one off-peak sub-period. If so, the two off-peak sub-periods are kept separate.

[0165] Step S302.5: Number the valley sub-periods in chronological order, using the symbol F q Indicates, where q is a positive integer and q is the serial number of F; the energy consumption periods are numbered in chronological order, and the symbol R q Since two adjacent off-peak periods without an energy consumption period in between will be merged, the number of off-peak periods and energy consumption periods is usually the same, so the serial numbers of F and R are both represented by q;

[0166] Step S302.6, obtain F q duration, marked as TF q , get R q The duration, marked as TR q ;calculate Among them, ER q Energy consumption period R q ERA is the estimated electricity consumption during the period, TR is the estimated electricity consumption k TR with q=k q ;

[0167] Step S302.7, calculate TF q Multiply the energy storage efficiency and mark the result as the estimated storage energy during the period, using the symbol EF q express;

[0168] Step S302.8, EF q With ER q Compare, if EF q Smaller than ER q , then the output period energy storage insufficient signal; if EF q Greater than or equal to ER q , then the time period energy storage sufficient signal is output; the time period energy storage insufficient signal and the time period energy storage sufficient signal are collectively referred to as the time period evaluation signal;

[0169] Step S303: If the stored energy in the off-peak period is insufficient to support the use in the high-load period and the peak period, the range of the optimal energy storage period is expanded, and specific optimal energy storage periods and optimal energy consumption periods are divided;

[0170] Step S303 includes the following sub-steps:

[0171] Step S303.1, obtaining time period evaluation signals, and if all of them indicate sufficient energy storage, outputting a first optimization signal; if any of the time period evaluation signals indicate insufficient energy storage, outputting a second optimization signal;

[0172] Step S303.2: If the first optimization signal is output, the off-peak period is marked as the optimal energy storage period, and the estimated energy storage amount minus the estimated power consumption is calculated, and the calculated result is marked as the estimated energy storage redundancy;

[0173] Step S303.3, calculate EF q and ER q The sum of the calculated results is marked as the estimated hourly electricity consumption, and EF is calculated. q and minus ER q The difference between the sum of is marked as redundant power. Calculate the redundant power and divide it by the estimated hourly power consumption. The result is expressed as the estimated redundant time. Obtain the number of energy consumption periods, marked as the number of periods. Calculate the estimated redundant time and divide it by the estimated hourly power consumption to obtain the estimated total increase. Calculate the estimated total increase by dividing it by the number of periods and then by 2. The result is marked as the estimated one-way increase.

[0174] Step S303.4: Add the estimated one-way increase duration at the beginning and end of each energy consumption period, and mark the energy consumption period after the increase duration as the optimal energy consumption period;

[0175] Step S303.5: If the second optimization signal is output, then obtain the EF output signal of insufficient energy storage during the period. q With ER q , the corresponding F q and R q Mark as underpower period and energy demand period, and set the corresponding EF q With ER qLabeled as X and Y;

[0176] Step S303.6: Calculate YX and mark the result as the estimated required energy. Calculate the estimated required energy divided by the energy storage efficiency and mark the result as the estimated energy storage increase time. Include the estimated energy storage increase time after the power-off period in the power-off period. Mark the power-off period and the off-peak period as the optimal energy storage period, and mark the energy-demand period and the energy-consuming period as the optimal energy consumption period.

[0177] Step S4, performing energy storage and energy consumption management on the 5G base station based on the optimal energy storage period and the optimal energy consumption period; Step S4 includes the following sub-steps:

[0178] Step S401: monitoring the current time in real time, and if the current time is in the optimal energy storage period, starting to store electrical energy in the energy storage device;

[0179] Step S402: If the current time is in the optimal energy consumption period, power is transmitted to the 5G base station through the energy storage device.

[0180] Example 3, please refer to Figure 4 As shown, Figure 4 An example structural diagram of an electronic device is provided. The electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in the method for optimizing 5G base station energy storage configuration based on grid collaboration are executed to achieve the following functions: collecting energy consumption data of 5G base stations and analyzing high-load periods of 5G base stations; collecting peak and valley data of the grid and analyzing peak and valley periods of the grid; analyzing the optimal periods for energy storage and energy use of the 5G base stations based on peak, valley, and high-load periods, and marking them as optimal energy storage periods and optimal energy consumption periods, respectively; and managing energy storage and energy use of the 5G base stations based on the optimal energy storage and optimal energy consumption periods.

[0181] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0182] Example 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned 5G base station energy storage configuration optimization method based on power grid collaboration are executed to achieve the following functions: collect energy consumption data of 5G base stations and analyze high-load periods of 5G base stations; collect peak and valley data of the power grid and analyze peak periods and valley periods of the power grid; based on peak periods, valley periods and high-load periods, analyze the optimal periods for energy storage and energy use of 5G base stations, and mark them as optimal energy storage periods and optimal energy consumption periods respectively; manage energy storage and energy use of 5G base stations based on the optimal energy storage periods and optimal energy consumption periods.

[0183] Through the description of the above embodiments, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on this understanding, the above technical solutions, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiment.

[0184] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A 5G base station energy storage configuration optimization method based on grid collaboration is characterized by: The steps include: Collect energy consumption data of 5G base stations and analyze the high-load periods of 5G base stations; Collect peak and valley data of the power grid and analyze the peak and valley periods of the power grid; Based on peak hours, off-peak hours, and high-load hours, the optimal times for 5G base stations to store and use energy are analyzed, and marked as optimal energy storage hours and optimal energy consumption hours, respectively. Specifically, the following steps are taken: based on the historical load records of the 5G base station, the estimated power consumption of the 5G base station during high-load hours and peak hours is analyzed and predicted; based on the energy storage efficiency and estimated power consumption of the 5G base station, whether the stored energy during the off-peak hours is sufficient for use during high-load hours and peak hours is analyzed; if the stored energy during the off-peak hours is insufficient to support use during high-load hours and peak hours, the range of the optimal energy storage hours is expanded, and specific optimal energy storage hours and optimal energy consumption hours are divided; Energy storage and energy management for 5G base stations based on optimal energy storage and consumption periods; Based on the historical load records of 5G base stations, the estimated power consumption of 5G base stations during high-load periods and peak periods is analyzed and predicted, including: High-load periods and peak periods are collectively referred to as energy consumption periods. Based on the chronological order, whether two adjacent energy consumption periods are continuous or intersecting is determined. If so, an integrated period signal is output; otherwise, an independent period signal is output. If the time period integration signal is output, the corresponding two energy consumption periods are integrated into the same energy consumption period; Record the total amount of electricity consumed by the 5G base station during the energy consumption period, named as the total electricity consumption, establish a historical total database, collect the historical total electricity consumption of the 5G base station, and mark it as the historical total; Get the historical totals for each day of the past year, grouping them by month, weekday, and holiday. This is represented by the symbol M(n,d), where n is a positive integer representing the month, and d is 1 or 2. If d is 1, it represents a weekday, and if d is 2, it represents a holiday. That is, M(n,1) represents the set of historical totals for weekdays in month n, and M(n,2) represents the set of historical totals for holidays in month n. Calculate the average of the historical total in M(n,d), marked as P(n,d); Get the month of the current time, named as real-time month, represented by symbol i, and at the same time get whether the current time is a working day. If so, output a working signal; if not, output a holiday signal; The symbol j indicates whether the current time is a working day. If a working signal is output, j=1; if a holiday signal is output, j=2. Obtain P(i, j), calculate P(i, j) multiplied by the first redundancy ratio, and mark the calculation result as the estimated power consumption, where P(i, j) is P(n, d) with n=i and d=j.

2. The 5G base station energy storage configuration optimization method based on grid collaboration according to claim 1 is characterized in that: Collecting 5G base station energy consumption data and analyzing the high-load periods of 5G base stations includes the following sub-steps: Get the high load power consumption set by the 5G base station; Real-time monitoring of 5G base station power consumption, named real-time power consumption; Compare the real-time power consumption with the high-load power consumption. If the real-time power consumption is less than the high-load power consumption, output a non-high-load signal; if the real-time power consumption is greater than or equal to the high-load power consumption, output a high-load signal. If a high-load signal is output, the time corresponding to the real-time power consumption is marked as the high-load start time. If a high-load signal is output again at this time, it is directly ignored until a non-high-load signal is output. The time corresponding to the real-time power consumption when the non-high-load signal is output is marked as the high-load end time. Marking the time period between the high load start time and the high load end time as a high load period; Record all high-load periods of the 5G base station in a day, and mark the time between any two high-load periods as the interval time. Compare the interval time with a first duration threshold. If the interval time is less than or equal to the first duration threshold, output a period-merged signal; if the interval time is greater than the first duration threshold, output a period-independent signal. If the period merging signal is output, the corresponding two high-load periods and the interval time between them are all integrated into the same high-load period.

3. The 5G base station energy storage configuration optimization method based on grid collaboration according to claim 2 is characterized in that: Collecting peak and valley data of the power grid and analyzing the peak and valley periods of the power grid includes the following sub-steps: Get the city where the 5G base station is located and name it as Base Station City; Find the latest peak and valley time divisions in the base station city, and find the peak and valley time divisions based on the latest peak and valley time divisions in the region; The latest peak-valley period classification of the region includes five levels: deep valley, low valley, flat section, peak and sharp peak. The peak period includes sharp peak and peak, and the valley period includes deep valley and low valley. All times except peak hours and off-peak hours are parity hours.

4. The 5G base station energy storage configuration optimization method based on grid collaboration according to claim 3 is characterized in that: Based on the energy storage efficiency and estimated power consumption of 5G base stations, analyzing whether the stored energy during off-peak periods is sufficient for use during high-load periods and peak periods includes the following sub-steps: Get the valley period and find out whether the valley period is a continuous period. If so, output the continuous valley period; if not, output the interval valley period. Obtain the energy storage efficiency of the 5G base station. If the output is a continuous valley segment, obtain the total duration of the valley period, named the total valley duration. Calculate the total valley duration and multiply it by the energy storage efficiency to obtain the estimated storage capacity. Compare the estimated energy storage with the estimated power consumption. If the estimated energy storage is greater than or equal to the estimated power consumption, output a sufficient energy storage signal for the time period. If the estimated energy storage is less than the estimated power consumption, an energy storage shortage signal for the time period is output; If the output interval is a valley segment, the continuous time periods in the valley period are marked as valley sub-periods. Several valley sub-periods are obtained by division. It is then checked whether there is an energy consumption period between two adjacent valley sub-periods. If not, the two valley sub-periods are merged into the same valley sub-period. If so, the two valley sub-periods are kept independent. The valley periods are numbered in chronological order, using the symbol F q Indicates, where q is a positive integer; the energy consumption periods are numbered in chronological order, and the symbol R q express; Get F q duration, marked as TF q , get R q The duration, marked as TR q ;calculate , among which ER q Energy consumption period R q ERA is the estimated electricity consumption during the period; Calculating TF q Multiply the energy storage efficiency and mark the result as the estimated storage energy during the period, using the symbol EF q express; EF q With ER q Compare, if EF q Smaller than ER q , then the output period energy storage insufficient signal; if EF q Greater than or equal to ER q , then the time period energy storage sufficient signal is output; the time period energy storage insufficient signal and the time period energy storage sufficient signal are collectively referred to as the time period evaluation signal.

5. The 5G base station energy storage configuration optimization method based on grid collaboration according to claim 4 is characterized in that: If the stored energy during the off-peak period is insufficient to support the use during the high-load period and the peak period, the range of the optimal energy storage period is expanded, and the specific optimal energy storage period and optimal energy consumption period are divided into the following sub-steps: Acquire a time period evaluation signal, and if there is a time period energy storage insufficient signal in the time period evaluation signal, output a second optimization signal; If the second optimization signal is output, the EF corresponding to the energy storage shortage signal of the output period is obtained. q With ER q , the corresponding F q and R q Mark as underpower period and energy demand period, and set the corresponding EF q With ER q Labeled as X and Y respectively; Calculate YX and mark the result as the estimated required electricity. Calculate the estimated required electricity and divide it by the energy storage efficiency. Mark the result as the estimated energy storage increase time. Include the estimated energy storage increase time after the power shortage period in the power shortage period. Mark the power shortage period and the off-peak period as the optimal energy storage period. Mark the energy demand period and the energy consumption period as the optimal energy consumption period.

6. The 5G base station energy storage configuration optimization method based on grid collaboration according to claim 5 is characterized in that: Energy storage and energy management for 5G base stations based on optimal energy storage and consumption periods includes the following sub-steps: Monitor the current time in real time, and if the current time is in the optimal energy storage period, start storing electrical energy in the energy storage device; If the current time is in the optimal energy consumption period, electricity will be transmitted to the 5G base station through the energy storage device.

7. A 5G base station energy storage configuration optimization system based on grid collaboration, used to implement the 5G base station energy storage configuration optimization method based on grid collaboration according to any one of claims 1 to 6, characterized in that: The system includes a high load analysis module, a peak-valley analysis module, a storage and consumption analysis module, and a storage and consumption management module; the high load analysis module, the peak-valley analysis module, and the storage and consumption management module are respectively connected to the storage and consumption analysis module data; The high-load analysis module is used to collect energy consumption data of 5G base stations and analyze high-load periods of 5G base stations; The peak-valley analysis module is used to collect peak-valley data of the power grid and analyze the peak and valley periods of the power grid; The storage and consumption analysis module is used to analyze the optimal time periods for energy storage and energy consumption of the 5G base station based on peak time periods, off-peak time periods, and high-load time periods, and mark them as optimal energy storage time periods and optimal energy consumption time periods respectively; The storage and consumption management module is used to manage energy storage and energy consumption of 5G base stations based on the optimal energy storage period and the optimal energy consumption period.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are executed.

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