Communication base station configuration method and apparatus, electronic device, and computer program product

Abstract

The application discloses a communication base station configuration method and device, electronic equipment and computer program product. It relates to the technical field of power supply and configuration. The method comprises the following steps: receiving base station data of a target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least comprises base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters; calculating a lower limit value of the backup power capacity of the target communication base station by using the M historical base station data, taking the lower limit value of the backup power capacity as a lower limit value of initial capacity data, and constructing an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data; and optimizing and adjusting the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station. Through the application, the technical problem of low power supply efficiency and poor stability of the communication base station after the photovoltaic and energy storage configuration of the communication base station in the related art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply and configuration, in particular to a configuration method and device of a communication base station, electronic equipment and computer program product. BACKGROUND

[0002] With the popularization of mobile communication technology and the surge in energy demand of base stations, photovoltaic- energy storage collaborative optimization configuration has become a key way for communication base stations to save energy, reduce emissions and improve energy self-sufficiency. As the infrastructure supporting high-speed communication in the information society, the normal operation of 5G (Fifth Generation Network) base stations puts unprecedented requirements on the reliability of the power supply system. Photovoltaic technology can provide clean and renewable energy for base stations, while energy storage systems can smooth the intermittent output of photovoltaic arrays, ensuring the continuity and stability of power supply.

[0003] However, the existing photovoltaic- energy storage configuration method has exposed a series of limitations in practical application. First, most schemes lack flexibility in photovoltaic configuration and energy storage capacity selection, failing to fully consider the specific needs of different base stations, such as the reliability of mains access and the impact of battery type on system performance. In addition, traditional optimization algorithms focus on improving energy utilization efficiency and reducing energy storage loss, but easily lead to the inability to reduce the cost of the configuration scheme. In addition, the lack of use of time-of-use pricing strategy limits the potential of energy storage systems to discharge during peak periods to obtain higher income, thereby affecting the economic benefits of the entire system.

[0004] Further, the existing technology lacks research on the collaborative work between photovoltaic and energy storage systems, especially in terms of system optimization operation and participation in power distribution network regulation, and fails to establish an effective technical-economic coupling model, limiting the overall performance of the system.

[0005] In view of the technical problems that after the photovoltaic and energy storage configuration of the communication base station in the related technology, the communication base station has low power supply efficiency and poor stability, no effective solution has been proposed so far. SUMMARY

[0006] The main purpose of the present application is to provide a configuration method and device of a communication base station, electronic equipment and computer program product, to solve the technical problems that after the photovoltaic and energy storage configuration of the communication base station in the related technology, the communication base station has low power supply efficiency and poor stability.

[0007] In order to achieve the above object, according to one aspect of the present application, a configuration method of a communication base station is provided. The method comprises: receiving base station data of a target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least comprises base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters, M is a positive integer; calculating a backup power capacity lower limit value of the target communication base station by using the M historical base station data, taking the backup power capacity lower limit value as a lower limit value of initial capacity data, and constructing an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value comprises an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data comprises initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy of configuring the capacity of a photovoltaic system and an energy storage system of the target communication base station; and optimizing and adjusting the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station.

[0008] Further, in the case that the backup power capacity lower limit value is the energy storage capacity lower limit value, calculating the backup power capacity lower limit value of the target communication base station by using the M historical base station data comprises: obtaining daily average load power data and voltage data of the target communication base station from the energy storage battery data, and obtaining battery discharge data from the energy storage device parameters; obtaining a pre-device electrical parameter, calculating the product of the pre-device electrical parameter and the daily average load power data to obtain a cruising load, wherein the pre-device electrical parameter refers to a preset parameter of backup cruising duration; calculating the ratio of the cruising load and the voltage data, and calculating the product of the ratio of the cruising load and the voltage data and the inverse of the battery discharge data to obtain the initial energy storage capacity data; determining a redundancy parameter according to the meteorological data, and calculating the product of the initial energy storage capacity data and the redundancy parameter to obtain the energy storage capacity lower limit value.

[0009] Further, in the case that the backup power capacity lower limit value is the photovoltaic power lower limit value, calculating the backup power capacity lower limit value of the target communication base station by using the M historical base station data comprises: obtaining a power grid access type of the target communication base station, wherein the power grid access type comprises a photovoltaic power supply type and a stacked light system type; in the case that the power grid access type is the photovoltaic power supply type, calculating the photovoltaic power lower limit value according to the M historical base station data; and in the case that the power grid access type is the stacked light system type, obtaining power supply duration data of the target communication base station, and calculating the photovoltaic power lower limit value according to the M historical base station data and the power supply duration data.

[0010] Further, the photovoltaic energy storage configuration strategy of the target communication base station is obtained by optimizing the initial configuration strategy through an optimization algorithm, including: obtaining K sets of optimization variables in K time periods, constructing K candidate configuration strategies based on the K sets of optimization variables by a particle swarm optimization algorithm, wherein each set of optimization variables includes an optimization capacity data and a candidate index corresponding to the optimization capacity data, each candidate configuration strategy refers to an optimization capacity data configured for the target communication base station, K is a positive integer; obtaining a preset extreme value, and screening the K candidate configuration strategies according to the preset extreme value to obtain the photovoltaic energy storage configuration strategy, wherein the preset extreme value includes a global extreme value and an individual extreme value, the global extreme value is used to indicate a set of optimization variables with the maximum value in the K candidate configuration strategies at each update iteration, and the individual extreme value is a set of optimization variables with the maximum value of each candidate configuration strategy at each update iteration.

[0011] Further, the candidate index in each set of optimization variables is obtained by the following method: for a set of optimization variables, the same historical time period associated with the set of optimization variables is obtained, the charging and discharging power data and the time period cost data of the same historical time period are obtained from M historical base station data, and the electricity value data is obtained, wherein the time period cost data refers to the cost value data of the target communication base station in each time period; obtaining initial cost data, calculating the algebraic sum of the charging and discharging power data, the time period cost data and the electricity value data to obtain photovoltaic energy storage value data, and calculating the difference between the photovoltaic energy storage value data and the initial cost data to obtain period value data; calculating the reference cost data through the initial cost data, and calculating the ratio of the period value data and the reference cost data to obtain the candidate index.

[0012] Further, the optimization capacity data in each set of optimization variables is obtained by the following method: for a set of optimization variables, the candidate index of the time period associated with the set of optimization variables is obtained, and the elasticity coefficient function is obtained; the partial derivative of the candidate index to the photovoltaic power data is calculated through the elasticity coefficient function to obtain the photovoltaic power data condition, and the candidate photovoltaic power data is determined based on the photovoltaic power data condition; the partial derivative of the candidate index to the energy storage capacity data is calculated through the elasticity coefficient function to obtain the energy storage capacity condition, and the candidate energy storage capacity data is determined based on the energy storage capacity condition; the optimization capacity data of the optimization variable is constructed based on the candidate photovoltaic power data and the candidate energy storage capacity data.

[0013] Further, the K candidate configuration strategies are screened according to a preset extreme value to obtain the photovoltaic energy storage configuration strategy, including: obtaining an initial speed vector of each candidate configuration strategy, and determining an iteration termination condition, wherein the initial speed vector is used to adjust the movement state of each candidate configuration strategy for the first time among all candidate configuration strategies, and the iteration termination condition refers to that the number of update iterations is greater than a preset number of iterations; the K candidate configuration strategies are updated for the first time according to the K initial speed vectors, in the process of each update iteration, the index of the global extreme value and the index of the individual extreme value in each update iteration are calculated; in the process of each update iteration, the data corresponding to the K candidate indicators, the data corresponding to the global extreme value, and the data corresponding to the individual extreme value are compared, and the preset extreme value is updated according to the comparison result to obtain a preset updated extreme value, until the number of update iterations is greater than the preset number of iterations, wherein the update iteration of each candidate configuration strategy refers to updating the position and speed of each candidate configuration strategy according to different speed vectors; in the case that the number of update iterations is greater than the preset number of iterations, an updated global extreme value in the updated preset updated extreme value is obtained, and a candidate configuration strategy of the maximum group of optimization variables corresponding to the updated global extreme value is determined as the photovoltaic energy storage configuration strategy.

[0014] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a configuration device of a communication base station is provided. The device comprises: a receiving unit configured to receive base station data of a target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least includes base station data, meteorological data, energy storage battery data, photovoltaic equipment data, and energy storage equipment parameters, and M is a positive integer; a calculation unit configured to calculate a backup capacity lower limit value of the target communication base station by using the M historical base station data, take the backup capacity lower limit value as a lower limit value of initial capacity data, and construct an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup capacity lower limit value includes an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data includes initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy for configuring the capacity of a photovoltaic system and an energy storage system of the target communication base station; an adjustment unit configured to optimize and adjust the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station.

[0015] According to another aspect of the embodiment of the present application, a computer readable storage medium is also provided, which comprises a stored executable program, wherein the executable program controls the device where the computer readable storage medium is located to execute any one of the above-mentioned configuration methods of the communication base station when the executable program is running.

[0016] According to a further aspect of the embodiments of the present application, an electronic device is also provided, comprising one or more processors and a memory storing one or more programs, wherein the one or more processors are configured to implement the configuration method of the communication base station according to any one of the above embodiments when the one or more programs are executed by the one or more processors.

[0017] According to a further aspect of the embodiments of the present application, a computer program product is also provided, comprising a computer program, wherein the computer program is configured to implement the configuration method of the communication base station according to any one of the above embodiments when executed by a processor.

[0018] In the embodiments of the present application, the configuration method of the communication base station is adopted, the base station data of a target communication base station in a historical time period is received to obtain M historical base station data, wherein the M historical base station data at least comprises base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters, and M is a positive integer; the backup power capacity lower limit value of the target communication base station is calculated by using the M historical base station data, the backup power capacity lower limit value is taken as the lower limit value of the initial capacity data, and the initial configuration strategy of the target communication base station is constructed based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value comprises the energy storage capacity lower limit value and the photovoltaic power lower limit value, the initial capacity data comprises the initial photovoltaic power data and the initial energy storage capacity data, and the initial configuration strategy refers to the strategy of configuring the capacity of the photovoltaic system and the energy storage system of the target communication base station; the initial configuration strategy is adjusted by using an optimization algorithm to obtain the photovoltaic energy storage configuration strategy of the target communication base station, thereby solving the technical problem that the communication base station has low power supply efficiency and poor stability after the photovoltaic and energy storage configuration of the communication base station in the related art, the backup power capacity lower limit value of the target communication base station is calculated by using the historical base station data, the initial configuration strategy of the target communication base station is constructed based on the lower limit value, the initial configuration strategy is adjusted by using the optimization algorithm, and the photovoltaic energy storage configuration strategy of the target communication base station is obtained, thereby achieving the technical effects of improving the power supply efficiency after the photovoltaic and energy storage configuration of the communication base station, and improving the power supply stability. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and are used to interpret the illustrative embodiments of the present application and their descriptions, and do not constitute improper limitations to the present application. In the drawings:

[0020] Figure 1 is a hardware structure block diagram of a computer terminal (or mobile device) for implementing the configuration method of the communication base station;

[0021] Figure 2 is a flowchart of the configuration method of the communication base station according to the embodiments of the present application;

[0022] Figure 3 is a schematic diagram of a processing method of an optimization algorithm according to an embodiment of the application;

[0023] Figure 4 is a schematic diagram of a configuration device of a communication base station according to an embodiment of the application;

[0024] Figure 5 is a structural block diagram of an electronic device according to an embodiment of the application. DETAILED DESCRIPTION

[0025] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0027] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties. For example, an interface is provided between the system and the relevant user or institution to provide the user with corresponding operation storage for the user to choose to agree or refuse the automatic decision result. Before obtaining the relevant information, the interface needs to send a request to the aforementioned user or institution, and after receiving the consent information fed back by the aforementioned user or institution, the relevant information is obtained; if the user chooses to refuse, the expert decision process is entered. The user can decode the purpose of data use in real time through authorization and has the right to withdraw authorization or delete data at any time. After withdrawing authorization, the system will terminate the relevant data processing within 24 hours.

[0028] It should be noted that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize use or refuse use.

[0029] Example 1

[0030] According to an embodiment of this application, a method embodiment for configuring a communication base station is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) used to implement a configuration method for a communication base station, such as... Figure 1 As shown, computer terminal 10 (or mobile device) may include one or more ( Figure 1 The processor 102 (which may include, but is not limited to, a microprocessor MCU (Microcontroller Unit) or a programmable gate array (FPGA)) is shown as 102a, 102b, ..., 102n. It also includes a memory 104 for storing data and a transmission device 106 for communication functions. In addition, it may include: a display, an input / output interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a keyboard, a cursor control device, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0032] It should be noted that the one or more processors 102 and / or other data processing circuitry described above can be generally referred to herein as "data processing circuitry". The data processing circuitry can be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuitry can be a single standalone processing module, or incorporated in whole or in part within any of the other elements of the computer terminal 10 (or mobile device). As referred to in embodiments of the present application, the data processing circuitry functions as a processor to control, for example, the selection of the variable resistance terminal path connected to the interface.

[0033] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage means corresponding to the configuration method of a communication base station in embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, i.e. implements the configuration method of a communication base station described above. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 can further include a memory remotely disposed relative to the processor 102, which can be connected to the computer terminal 10 through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The transmission device 106 is used to receive or send data via a network. Specific examples of the network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) and a network interface, which can be connected to other network devices through a base station to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

[0035] The display can be, for example, a touch screen type liquid crystal display (Liquid Crystal Display, LCD), which can enable a user to interact with the user interface of the computer terminal 10 (or mobile device).

[0036] In the above operating environment, the present application provides a configuration method of a communication base station as shown in Figure 2 Figure 2 is a flowchart of the configuration method of a communication base station according to embodiments of the present application, as shown in Figure 2 ​​

[0037] In step S201, the target communication base station receives base station data of the historical time period to obtain M historical base station data, wherein the M historical base station data at least includes base station data, meteorological data, energy storage battery data, photovoltaic device data, and energy storage device parameters, and M is a positive integer.

[0038] Specifically, the target communication base station refers to a communication base station that is being analyzed for optimal configuration of photovoltaic energy storage coordination. For example, it can be a base station in a densely populated urban area. In order to achieve optimal configuration of photovoltaic and energy storage systems to meet the efficient and stable power supply needs of the communication base station, while taking into account economic benefits and technical performance, the base station data of the target communication base station can be obtained first. For example, the base station data can be collected through sensors, meteorological platforms, and power grid interfaces and standardized processing, so as to obtain a plurality of historical base station data. The standardized processing can include cleaning and sorting to remove outliers, thereby ensuring the effectiveness and reliability of the data.

[0039] It should be noted that the historical base station data can include base station data (i.e., base station basic information), meteorological data, energy storage battery data (i.e., battery parameters), photovoltaic device data, and energy storage device parameters. The base station basic information can include daily average load power (QL, unit: kW, per hour), electricity price (Pgrid, unit: yuan / kWh), standby time requirement (Treq, unit: h), system voltage (U, base station DC system voltage), and whether there is a power grid access. The meteorological data can include regional annual peak sunshine hours (TP, unit: h), regional annual minimum peak sunshine hours (Ta, unit: h), and extreme weather probability (such as consecutive rainy day probability Prain). The energy storage battery data can include the type of energy storage battery selected by the base station (lead-acid or lithium), and the depth of discharge (Cc). The photovoltaic device data can include system efficiency (PR, %), unit installation cost (Cpv, unit: yuan / Wp), and operation and maintenance cost (COpv, unit: yuan / Wp / year). The energy storage device parameters can include energy storage unit capacity cost (Cbess, unit: yuan / Wh), and operation and maintenance cost (CObess, unit: yuan / Wh / year, including equipment maintenance and labor).

[0040] By obtaining the load characteristic data of the base station, the load that the photovoltaic and energy storage system needs to bear can be accurately calculated. The meteorological data can evaluate the effective working time of the photovoltaic system and the standby power requirement of the energy storage system, thereby guiding the reasonable configuration of the photovoltaic and energy storage capacity. The photovoltaic device data is used to quantify the economic benefits of the photovoltaic system. The energy storage device parameters are used to evaluate the economic efficiency and technical efficiency of the energy storage system to ensure that it plays the maximum value in the system configuration.

[0041] In step S202, the backup power capacity lower limit value of the target communication base station is calculated using M historical base station data, the backup power capacity lower limit value is taken as the lower limit value of the initial capacity data, and the initial configuration strategy of the target communication base station is constructed based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value includes the energy storage capacity lower limit value and the photovoltaic power lower limit value, the initial capacity data includes the initial photovoltaic power data and the initial energy storage capacity data, and the initial configuration strategy refers to the strategy for configuring the capacity of the photovoltaic system and the energy storage system of the target communication base station.

[0042] Specifically, in order to guarantee the continuity and stability of power supply of the communication base station, after obtaining the historical base station data, the daily average maximum load power and the longest period of energy demand in continuous rainy days of the base station are counted, and the energy storage capacity lower limit and the photovoltaic power lower limit required in the most unfavorable condition are calculated in combination with the extreme weather probability, so as to obtain the backup power capacity lower limit value required by the target communication base station, that is, the minimum energy storage capacity and photovoltaic power required in the most unfavorable condition (such as continuous rainy days, abnormal interruption of commercial power due to long-time non-connection of commercial power, etc.).

[0043] Then, the backup power capacity lower limit value is taken as the lower limit value of the initial capacity data, that is, a hard constraint condition is provided for the capacity selection of the photovoltaic and energy storage systems when the configuration strategy is formulated, the minimum feasible value of the photovoltaic power and the energy storage capacity is obtained, and the initial configuration strategy of the target communication base station is constructed based on the lower limit value of the initial capacity data, so as to ensure that the configuration strategy not only considers the optimization of economy and technical performance, but also meets the basic requirement of power supply reliability, avoids the risk of power supply interruption caused by too low capacity configuration, and at the same time provides a reasonable starting point for subsequent economic model optimization, ensuring that the optimization process is carried out under the premise of meeting the technical feasibility and power supply safety.

[0044] It should be noted that the initial configuration strategy refers to the capacity configuration of the photovoltaic and energy storage systems preliminarily determined based on technical feasibility and economic rationality, which can ensure that the communication base station can still operate normally under the worst condition, and on this basis, the energy self-sufficiency rate and economic benefit are improved as much as possible.

[0045] In step S203, the initial configuration strategy is optimized and adjusted by an optimization algorithm to obtain the photovoltaic energy storage configuration strategy of the target communication base station.

[0046] It should be noted that the optimization algorithm refers to an algorithm for finding the optimal solution of a target function (such as the return on investment (ROI) of investment) under given constraints. For example, the optimization algorithm can be an improved particle swarm optimization (PSO) algorithm, which simulates the foraging behavior of a bird swarm to find the optimal configuration of photovoltaic and energy storage capacity that satisfies the technical performance indicators (power supply continuity and backup time) and maximizes economic benefits.

[0047] Specifically, after obtaining the initial configuration strategy, since the strategy is not the most economical or efficient solution, the optimization algorithm can be used to adjust and optimize the strategy to obtain a photovoltaic and energy storage configuration strategy. The strategy can specify the optimal configuration of the photovoltaic system and the energy storage system in the target communication base station, including the specific values of the photovoltaic peak power (Ppv) and the energy storage capacity (Bc), as well as the optimal working mode of these configurations under different scenarios (such as power access conditions and time-of-use price fluctuations), achieving the best balance between technical performance and economic benefits.

[0048] The configuration method of the communication base station provided by the embodiments of the present application receives the base station data of the target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least includes base station data, meteorological data, energy storage battery data, photovoltaic equipment data, and energy storage equipment parameters, and M is a positive integer. The backup capacity lower limit value of the target communication base station is calculated using the M historical base station data, the backup capacity lower limit value is used as the lower limit value of the initial capacity data, and the initial configuration strategy of the target communication base station is constructed based on the lower limit value of the initial capacity data. The backup capacity lower limit value includes the energy storage capacity lower limit value and the photovoltaic power lower limit value, the initial capacity data includes the initial photovoltaic power data and the initial energy storage capacity data, and the initial configuration strategy refers to the strategy of configuring the capacity of the photovoltaic system and the energy storage system of the target communication base station. The initial configuration strategy is adjusted and optimized by an optimization algorithm to obtain the photovoltaic and energy storage configuration strategy of the target communication base station. The technical problem of low power supply efficiency and poor stability of the communication base station after the photovoltaic and energy storage configuration is solved. The backup capacity lower limit value of the target communication base station is calculated using the historical base station data, and the initial configuration strategy of the target communication base station is constructed based on the lower limit value. The initial configuration strategy is adjusted and optimized by an optimization algorithm to obtain the photovoltaic and energy storage configuration strategy of the target communication base station, thereby improving the power supply efficiency after the photovoltaic and energy storage configuration of the communication base station, and improving the power supply stability.

[0049] Optionally, in the method for configuring a communication base station provided in the embodiments of the present application, when the backup power capacity lower limit value is the energy storage capacity lower limit value, calculating the backup power capacity lower limit value of the target communication base station using M historical base station data comprises: obtaining daily average load power data and voltage data of the target communication base station from the energy storage battery data, and obtaining battery discharge data from the energy storage device parameters; obtaining a pre-device power parameter, calculating the product of the pre-device power parameter and the daily average load power data to obtain a cruising load, wherein the pre-device power parameter is a preset parameter of backup power cruising duration; calculating the ratio of the cruising load to the voltage data, and calculating the product of the ratio of the cruising load to the voltage data and the inverse of the battery discharge data to obtain initial energy storage capacity data; determining a redundancy parameter according to weather data, and calculating the product of the initial energy storage capacity data and the redundancy parameter to obtain the energy storage capacity lower limit value.

[0050] Specifically, when calculating the backup power capacity lower limit value, first, the daily average load power data, the voltage data and the battery discharge data can be obtained from the energy storage battery data, wherein the daily average load power data (QL) represents the average power consumption of the target communication base station per day, which can evaluate the basic power demand of the base station, the voltage data (U) is the working voltage of the internal power system of the base station, which can determine the voltage level of the energy storage system and directly affect the calculation of the energy storage capacity and the selection of the energy storage device, and the battery discharge data (Cc) can reflect the depth of discharge of the energy storage battery, i.e., the proportion of the amount of electricity that can be released in a complete charging and discharging cycle to the total capacity.

[0051] Further, the pre-device power parameter (Treq) representing the preset backup power cruising duration of the base station can be obtained, and then the energy storage capacity lower limit value is calculated using these data That is, it is calculated by the following formula:

[0052] ;

[0053] Wherein, the pre-device power parameter refers to the minimum power supply time that the energy storage system should meet when the power supply is interrupted and the photovoltaic system cannot provide power, which can be determined based on the service level and geographical location of the base station, QL is the daily average load power data; U is the voltage data; Treq is the pre-device power parameter; Cc is the depth of discharge of the battery, for example, the value of lead-acid battery is 70%, and the value of lithium battery is 90%; Ks is the redundancy parameter, which is affected by the probability of extreme weather, and can be valued according to the weather data.

[0054] The total power demand of the base station in the standby power duration, i.e., the endurance load, can be obtained by multiplying the pre-device electrical parameter (Treq) and the daily load power data (QL), which can quantify the minimum power supply amount that the energy storage system should bear when the commercial power is abnormally disconnected, and ensure that the base station can still operate within a specified time without external power input. Then, by dividing the endurance load by the system voltage (U) and multiplying the reciprocal of the battery discharge data (Cc), the minimum capacity of the energy storage system for supplying power to the base station in an ideal state is calculated, ensuring that the calculated energy storage capacity can effectively meet the base station load. Finally, the initial energy storage capacity data is multiplied by the redundancy parameter to obtain the minimum energy storage capacity requirement considering the influence of adverse weather, ensuring that the energy storage system can maintain the operation of the base station even in the worst case, i.e., obtaining the lower limit value of the energy storage capacity.

[0055] By calculating the lower limit value of the energy storage capacity required by the target communication base station, the embodiment ensures the uninterruptedness of communication services in extreme cases without access to commercial power and limited photovoltaic power generation, allowing the optimization decision to further consider economic indicators while meeting power supply reliability, achieving the best balance between technical performance and economic benefits, ultimately improving the energy self-sufficiency rate of the communication base station, reducing operating costs, and enhancing the overall stability and economic benefits of the system.

[0056] Optionally, in the method for configuring a communication base station provided in the embodiment of the application, when the lower limit value of the standby power capacity is the lower limit value of the photovoltaic power, calculating the lower limit value of the standby power capacity of the target communication base station using M historical base station data includes: obtaining the type of commercial power access of the target communication base station, wherein the type of commercial power access includes a photovoltaic power supply type and a stacked light system type; in the case that the type of commercial power access is the photovoltaic power supply type, calculating the lower limit value of the photovoltaic power according to the M historical base station data; in the case that the type of commercial power access is the stacked light system type, obtaining the power supply duration data of the target communication base station, and calculating the lower limit value of the photovoltaic power according to the M historical base station data and the power supply duration data.

[0057] Specifically, in order to ensure that the photovoltaic system can provide sufficient power to meet the energy demand and the lower limit requirement of the standby power capacity of the base station under any access condition, it is also necessary to calculate the lower limit value of the photovoltaic power. First, the type of commercial power access of the target communication base station needs to be obtained, wherein the type of commercial power access includes a pure photovoltaic power supply type and a stacked light system type, the pure photovoltaic power supply type refers to a type that the base station completely relies on solar power and does not access the commercial power grid, and the base station of the stacked light system type uses photovoltaic and commercial power combination to supply power, preferentially using the power generated by photovoltaic, and supplementing from commercial power when insufficient. By identifying the power supply mode of the base station, scene basis is provided for subsequent calculation of the lower limit value of the photovoltaic power.

[0058] Further, the minimum photovoltaic capacity that meets the power supply demand can be calculated according to the type of power access, solving the problem of "waste of initial investment due to excessive photovoltaic capacity" or "insufficient power supply due to insufficient capacity". When the type of power access is the photovoltaic power supply type, in the pure photovoltaic power supply type, the photovoltaic system needs to bear all the power demand, including the daily operation power directly supplied to the base station and the power required for charging the energy storage system, at this time the photovoltaic system must be able to cover the power demand of the base station at the highest load time in a day, and also meet the power demand for charging the energy storage system to full capacity, under extreme weather conditions (such as continuous rainy days), the photovoltaic system needs to provide enough power in a limited sunshine time. Ensure that the photovoltaic system itself can meet the entire power demand of the base station without power access, including daily operation and energy storage charging and discharging, provide power supply guarantee under extreme weather conditions, avoid communication interruption due to insufficient power. At this time, the lower limit value of photovoltaic power can be calculated according to the following formula:

[0059] ;

[0060] Wherein, Ps The technical calculation result of photovoltaic peak power; Tp is the minimum value of the local peak sunshine hours of the base station obtained from the meteorological data; D is the number of days required for the energy storage device from empty capacity to full capacity, the lead-acid battery group can take 6 days, and the lithium battery can take 3 days; PR is the efficiency of the photovoltaic system, for example, 80%-86%.

[0061] When the type of power access is the stacked light system, the photovoltaic power lower limit value can be determined by calculating the power supply demand of the photovoltaic system to the base station at the peak period, which can ensure that the photovoltaic system can provide enough power when the price of city power is high, reduce the dependence of the base station on high-priced city power, and thus reduce the overall operating cost, wherein the lower limit value of photovoltaic power can be calculated by the following formula:

[0062] ;

[0063] Wherein, Tsolar refers to the communication base station power supply time length data, which refers to the time length of the photovoltaic system to the base station in the peak period of city power in the stacked light system type, which can be adjusted according to the peak-valley electricity price period; Ta is the local average peak sunshine hours of the base station obtained from the meteorological data; Refers to the energy storage charging and discharging time length ratio.

[0064] The embodiment calculates the lower limit value of the photovoltaic power suitable for the actual situation according to the communication base station of different power access types, thereby realizing cost saving and economic benefit improvement while ensuring power supply safety, providing solid technical parameter support for subsequent photovoltaic energy storage collaborative configuration scheme, avoiding excessive investment or insufficient configuration, and realizing optimal allocation of resources in the economic dimension and improving the applicability and economy of the scheme.

[0065] Optionally, in the configuration method of the communication base station provided in the embodiment of the application, the initial configuration strategy is optimized and adjusted by an optimization algorithm to obtain the photovoltaic energy storage configuration strategy of the target communication base station, including: obtaining K sets of optimization variables in K time periods, constructing K candidate configuration strategies based on the K sets of optimization variables by a particle swarm algorithm, wherein each set of optimization variables includes an optimization capacity data and a candidate index corresponding to the optimization capacity data, each candidate configuration strategy refers to an optimization capacity data of the target communication base station, and K is a positive integer; obtaining a preset extreme value, and screening the K candidate configuration strategies according to the preset extreme value to obtain the photovoltaic energy storage configuration strategy, wherein the preset extreme value includes a global extreme value and an individual extreme value, the global extreme value is used to indicate a set of optimization variables with the maximum value in the K candidate configuration strategies at each update iteration, and the individual extreme value is a set of optimization variables with the maximum value of each candidate configuration strategy at each update iteration.

[0066] After the initial configuration strategy is constructed, the initial configuration strategy can be iterated and optimized by a particle swarm optimization algorithm to find a photovoltaic energy storage configuration strategy that meets the technical performance index and maximizes the economic benefit. Specifically, first, a plurality of time windows (i.e., a plurality of time periods) of optimization variables to be investigated in the algorithm iteration process can be obtained, wherein the optimization variables include optimized photovoltaic power data and optimized energy storage capacity data. Through time series analysis, the optimization variables under different scenarios are obtained, reflecting the dynamic changes of photovoltaic and energy storage system configuration under different conditions, providing a basis for constructing an adaptive configuration strategy.

[0067] Further, based on each set of optimization variables, a plurality of possible candidate configuration strategies corresponding thereto are constructed, each strategy being a set of recommended configuration values of photovoltaic power and energy storage capacity for predicting and evaluating the technical performance and economic benefit thereof in the corresponding time period. Through the dynamic optimization process of the particle swarm algorithm, configuration strategies suitable for different external environmental changes are generated, providing diversified choices for the next step of screening, ensuring that the finally selected configuration strategy is both technically feasible and economically reasonable.

[0068] After preset extreme values including a global extreme value (gbest) and a personal extreme value (pbest) are obtained, the candidate configuration strategies can be screened based on the preset extreme values, so as to obtain the photovoltaic energy storage configuration strategy, wherein the global extreme value indicates the optimal strategy in all candidate configuration strategies in each iteration, that is, the configuration scheme with the highest comprehensive benefit (such as the highest ROI or the lowest whole life cycle cost); the personal extreme value represents the optimal state of each particle (candidate configuration strategy) in the iteration process, reflecting the best performance of the strategy under specific conditions. Through comparison and screening, a set of optimization variables are determined, and the configuration strategy corresponding to the optimization variables can achieve global optimization, that is, the economic benefit index is maximized while meeting the technical performance constraints, ensuring that the configuration of the communication base station photovoltaic energy storage system is the optimal solution.

[0069] The embodiment filters a set of photovoltaic energy storage configuration strategies that best meet the technical and economic coupling requirements from a plurality of candidate configuration strategies through the iteration optimization mechanism of the particle swarm optimization algorithm, which not only considers the technical performance of the photovoltaic system and the energy storage system under different scenarios, but also ensures the economic feasibility of the configuration scheme, ensures the stable power supply of the communication base station under any weather conditions and price fluctuation environment, reduces the system investment cost and operation and maintenance cost, and improves the economic benefit.

[0070] Optionally, in the configuration method of the communication base station provided in the embodiment of the application, the candidate index in each set of optimization variables is calculated in the following manner: for a set of optimization variables, the same historical time period associated with the set of optimization variables is obtained, the charge and discharge power data and the period cost data of the same historical time period are obtained from the M historical base station data, and the electricity value data is obtained, wherein the period cost data refers to the cost value data of the target communication base station in each time period; the initial cost data is obtained, the algebraic sum of the charge and discharge power data, the period cost data and the electricity value data is calculated to obtain the photovoltaic energy storage value data, and the difference between the photovoltaic energy storage value data and the initial cost data is calculated to obtain the period value data; the benchmark cost data is calculated from the initial cost data, and the ratio of the period value data to the benchmark cost data is calculated to obtain the candidate index.

[0071] Specifically, in the process of optimizing the collaborative configuration of photovoltaic energy storage systems in communication base stations, each candidate configuration strategy needs to be analyzed to evaluate the economic benefits of different configuration schemes. Before analyzing the candidate configuration strategies, optimization variables need to be obtained, and the candidate indicators in these optimization variables need to be determined based on base station data over historical time periods. First, charging and discharging power data and time period cost data associated with a set of optimization variables for the same historical time period can be obtained, along with electricity value data. The charging and discharging power data represents the power output and input of the photovoltaic system and energy storage system of the target communication base station within the same historical time period, specifically including photovoltaic power generation, the energy storage system's discharge power to the base station, and the power of the energy storage system charging from the grid or the photovoltaic system. The time period cost data is the cost value data of the target communication base station in each historical time period, which may include electricity expenses, photovoltaic system maintenance costs, and energy storage system maintenance costs. The electricity value data reflects the economic value generated by the photovoltaic energy storage system through power generation and discharge during that time period, such as the electricity price difference earned by charging during low-price periods and discharging during high-price periods, and the photovoltaic system's power generation revenue.

[0072] Furthermore, the ROI of candidate metrics can be calculated using the above data and the following formula:

[0073] ;

[0074] Where r represents the discount rate, for example, a benchmark value of 8% can be chosen, and the benchmark cost data can be calculated using this indicator and the initial cost data Ccap; TNR represents the periodic value data, which can characterize the net income of a photovoltaic energy storage system over a complete operating cycle, and this periodic value data can be calculated using the following formula: TNR = –Ccap, where T represents the total number of operating hours throughout the year, n is the number of years the system has been operating, and Ccap is the initial investment cost of the system (i.e., the initial cost data). This represents the value data of photovoltaic energy storage.

[0075] The above-mentioned photovoltaic energy storage value data can be obtained through the following formula:

[0076] ;

[0077] in, This represents the time-period loss cost (i.e., time-period cost data). This represents the time-period operation and maintenance cost, and the peak-valley price difference revenue of energy storage in the above formula can be expressed as: Where Rbess(t) is the daily return, This represents the time-of-use electricity price (i.e., the value of electricity) for the i-th hour. and respectively, represent the i-th hour of energy storage discharge power and the i-th hour of energy storage charging power (i.e., charging and discharging power data), and represent the total economic value of the photovoltaic energy storage system in the time period.

[0078] The embodiment can not only quantitatively evaluate the economic benefits of system operation, but also identify which configuration strategy can bring excess returns, thereby guiding the system to be optimized in the direction of economic optimality on the basis of meeting the technical performance requirements, providing strong support for efficient, stable and economic power supply of the communication base station, and significantly improving the efficiency and economic benefits of system configuration.

[0079] Optionally, in the method for configuring the communication base station provided in the embodiment of the application, the optimization capacity data in each group of optimization variables is calculated in the following manner: for a group of optimization variables, candidate indicators of a time period associated with the group of optimization variables are obtained, and an elasticity coefficient function is obtained; a partial derivative of the candidate indicators with respect to photovoltaic power data is calculated by using the elasticity coefficient function to obtain a photovoltaic power data condition, and candidate photovoltaic power data is determined based on the photovoltaic power data condition; a partial derivative of the candidate indicators with respect to energy storage capacity data is calculated by using the elasticity coefficient function to obtain an energy storage capacity condition, and candidate energy storage capacity data is determined based on the energy storage capacity condition; and the optimization capacity data of the optimization variables is constituted based on the candidate photovoltaic power data and the candidate energy storage capacity data.

[0080] Before constructing the candidate configuration strategy, not only the candidate indicators need to be obtained, but also the optimization capacity data in each group of optimization variables need to be determined, so as to guide the optimization algorithm to adjust the configuration strategy of photovoltaic and energy storage, and achieve the optimal balance between technical performance and economic benefits. Specifically, first, the determined candidate indicators can be obtained, and an elasticity coefficient function a for quantifying the sensitivity of the price fluctuation to the ROI is obtained, where the function can be expressed as: which can reveal the influence of the candidate configuration strategy on the candidate indicators under the price change.

[0081] Further, to find Ppv* (i.e., the candidate photovoltaic power data) and Bc* (i.e., the candidate energy storage capacity data) that maximize the ROI, the partial derivatives of the ROI with respect to the two variables need to be calculated and set to zero (extreme condition).

[0082] Since the TNR is positively correlated with Ppv (the greater the photovoltaic power, the higher the power generation benefit), and the system investment cost Ccap is also positively correlated with Ppv (the greater the photovoltaic power, the greater the initial investment), the partial derivative of the ROI with respect to Ppv is , that is, wherein represents the increase in the net cash inflow during the operation period (mainly due to the increase in photovoltaic power generation benefit) when the photovoltaic power increases by 1 kW, Ccap represents the system investment cost (i.e. initial cost data).

[0083] Similarly, the increase of energy storage capacity Bc will increase the charging and discharging benefits, but also increase the initial investment, at this time the partial derivative of ROI to Bc(economic) can be calculated wherein, represents the increase of net cash inflow during operation when the energy storage capacity increases by 1 kWh (mainly from the increase of energy storage charging and discharging peak and valley price difference benefits), =Cbess represents the unit cost of energy storage.

[0084] Then the partial derivative of the elasticity coefficient function to the photovoltaic power data is obtained, and the energy storage capacity condition is determined. The candidate energy storage capacity data is determined, so that the influence degree of photovoltaic power change on the candidate index can be understood, that is, combined with the extreme value condition of the elasticity coefficient function a, according to the definition of the elasticity coefficient function a: When ROI is maximized, And Combined with the above partial derivative formula, the optimal solution of photovoltaic peak power Ppv* (i.e. candidate photovoltaic power data) can be derived:

[0085] ;

[0086] And the optimal Ppv* needs to satisfy that the marginal benefit of the increase of photovoltaic power is equal to the marginal proportion of photovoltaic cost .

[0087] And combined with the above partial derivative formula, the partial derivative of the candidate index to the energy storage capacity data calculated by the elasticity coefficient function can obtain the optimal solution of the energy storage capacity Bc* (i.e. candidate energy storage capacity data):

[0088] ;

[0089] And Bc* needs to satisfy that the marginal benefit of the increase of energy storage capacity is equal to the marginal proportion of energy storage cost . Finally, Ppv* and Bc* form a set of optimal configuration of photovoltaic energy storage system capacity parameters under the condition of maximizing ROI, which ensures that the communication base station has the optimal economic benefit while efficiently and stably supplying power.

[0090] ​The embodiment can accurately position the photovoltaic power and energy storage capacity under the condition of maximizing ROI by introducing the elastic coefficient function and combining the partial derivative calculation, effectively avoids the neglect of economic benefits in the optimization configuration process, and can ensure that the photovoltaic energy storage system configuration of the communication base station not only considers the power supply demand and reliability, but also fully considers the economic cost and benefit, thereby realizing the dual optimization of technical performance and indicators.

[0091] Optionally, in the method for configuring the communication base station provided in the embodiment of the application, the K candidate configuration strategies are screened according to the preset extreme value to obtain the photovoltaic energy storage configuration strategy, which includes: obtaining an initial velocity vector of each candidate configuration strategy, and determining an iteration termination condition, wherein the initial velocity vector is used to adjust the movement state adjustment of each candidate configuration strategy for the first time among all candidate configuration strategies, and the iteration termination condition refers to that the number of update iterations is greater than a preset number of iterations; the K candidate configuration strategies are updated for the first time according to the K initial velocity vectors, and in the process of each update iteration, the indicators of the global extreme value and the individual extreme value in each update iteration are calculated; in the process of each update iteration, the data corresponding to the K candidate indicators, the data corresponding to the global extreme value, and the data corresponding to the individual extreme value are compared, and the preset extreme value is updated according to the comparison result to obtain a preset updated extreme value, until the number of update iterations is greater than the preset number of iterations, wherein the update iteration of each candidate configuration strategy refers to updating the position and speed of each candidate configuration strategy according to different velocity vectors; in the case where the number of update iterations is greater than the preset number of iterations, an updated global extreme value in the updated preset updated extreme value is obtained, and a candidate configuration strategy of the maximum group of optimization variables corresponding to the updated global extreme value is determined as the photovoltaic energy storage configuration strategy.

[0092] Specifically, in order to determine the optimal photovoltaic energy storage configuration strategy, the particle swarm optimization algorithm framework can be used, and the global and individual indicators can be used to guide the iteration adjustment of the candidate configuration strategy until the iteration termination condition is met. Figure 3 is a schematic diagram of the processing method of the optimization algorithm provided in the embodiment of the application, as shown in Figure 3 The particle swarm can be initialized first, then the initial velocity vector of each candidate configuration strategy is obtained and the iteration termination condition is determined, wherein the initial velocity vector refers to a random initial velocity given to each candidate configuration strategy (i.e. particle) at the beginning of the algorithm, which can control the movement state and speed of the particle (candidate configuration strategy) in the solution space; the iteration termination condition refers to that when the number of update iterations exceeds the preset value, the algorithm stops iteration and enters the result output stage.

[0093] Further, the first update iteration can be performed according to the initial velocity vector, that is, at the start of the algorithm, based on the initial velocity vector and the position of each particle (candidate configuration strategy), the fitness value (that is, the data corresponding to the candidate indicator) is calculated. At the same time, according to the interaction rule between particles, the indicators of the global extreme value (gbest) and the individual extreme value (pbest) are calculated. Through the first iteration, the optimal solution state in the current particle population is located, providing a benchmark for subsequent iteration optimization. Then in each iteration, the new position of each particle is re-evaluated, and the updated fitness value is calculated, and the indicators of the global extreme value (gbest) and the individual extreme value (pbest) are updated (that is, the individual optimal solution is updated and the global optimal solution is updated), and then the velocity and position of the particle are updated through multiple iterations, gradually approaching the global optimal solution, wherein the indicator of the global extreme value refers to the configuration strategy with the highest fitness value in the particle population, which is the global optimal solution in the current iteration process, and the indicator of the individual extreme value refers to the highest fitness value reached by each particle in its historical search path, which can reflect the best state of each configuration strategy in its exploration process. By dynamically tracking and updating the optimal solution in the particle population, it is ensured that the algorithm can continuously converge to a better configuration strategy, improving search efficiency and accuracy.

[0094] Further, in each update iteration process, the updated extreme value is compared and the preset extreme value is updated, that is, after each iteration, the latest fitness value of all particles, the indicators of the global extreme value and the individual extreme value are compared to determine whether a new better solution has appeared. If the fitness value of a particle exceeds the current individual extreme value or global extreme value, the corresponding individual extreme value or global extreme value is updated, and the better indicator is recorded to avoid missing potential better solutions. During the iteration process, it is necessary to determine whether the termination condition is met, and the iteration is stopped when the number of iterations reaches the preset number of iterations, at which time the configuration parameters corresponding to the recorded global extreme value (gbest) represent the optimal configuration strategy in the entire search process, that is, the optimal photovoltaic energy storage configuration strategy is obtained.

[0095] It should be noted that before using the algorithm for iteration, the technical performance indicators and economic indicators need to be used as constraint conditions, and then the photovoltaic and energy storage capacity is globally optimized, wherein the technical constraints are hard constraints that must be met, which can include:

[0096] Lower limit of energy storage capacity: Bc≥Bc(tech);

[0097] Lower limit of photovoltaic power: Ppv≥Ps(tech), wherein the photovoltaic power of the pure photovoltaic power supply scene needs to additionally satisfy ); the photovoltaic power of the hybrid photovoltaic system scene (that is, the maximum power corresponding to Tsolar);

[0098] SOC safety range: 20%≤SOC(t)≤90%, wherein 20%, 90% are reference values;

[0099] The economic constraints can be soft constraints that can be relaxed, and can include:

[0100] Upper limit of initial investment: Ccap=Cpv⋅Pspv+Cbess⋅Bc≤Ccap,max;

[0101] Lower limit of ROI: ROI≥ROImin.

[0102] And the above optimization algorithm can be realized by the following iterative formula:

[0103]

[0104] ;

[0105] wherein, , are the velocity and position of the particle, respectively; represents the inertia weight, which can balance the "global exploration" (finding a better solution area) and "local development" (fine-tuning the position near the current optimal solution); represents the learning factor, which respectively controls the influence of individual experience (pbest) and group experience (gbest) on particle update; is a random number, which is used to increase the randomness of the search and avoid falling into a local optimum; is the individual extreme value; is the global extreme value, and w decreases linearly with the number of iterations c1 is greater than c2 at the beginning (emphasizing global exploration), and smaller than c2 at the later stage (emphasizing local development).

[0106] The embodiment realizes efficient screening and optimization of the candidate configuration strategy by using the particle swarm optimization algorithm, so that the finally determined photovoltaic energy storage configuration strategy not only meets the stability, reliability and technical performance requirements of power supply, but also maximizes the economic benefits in the whole life cycle, significantly reduces the overall cost, and improves the economy of system operation.

[0107] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0108] Embodiment 2

[0109] The embodiment of the application further provides a configuration device of a communication base station. It should be noted that the configuration device of the communication base station can be used to execute the configuration method of the communication base station. The configuration device of the communication base station is introduced as follows.

[0110] According to the embodiment of the application, a device for implementing the configuration method of the communication base station is further provided. Figure 4 FIG. 1 is a schematic diagram of a configuration device of a communication base station according to the embodiment of the application. As shown in FIG. 1, the device comprises a receiving unit 40, a calculating unit 41 and an adjusting unit 42. Figure 4

[0111] The receiving unit 40 is configured to receive base station data of a target communication base station in a historical time period to obtain M pieces of historical base station data, wherein the M pieces of historical base station data at least comprise base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters, and M is a positive integer.

[0112] The calculating unit 41 is configured to calculate a backup power capacity lower limit value of the target communication base station by using the M pieces of historical base station data, take the backup power capacity lower limit value as a lower limit value of initial capacity data, and construct an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value comprises an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data comprises initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy of configuring the capacity of a photovoltaic system and an energy storage system of the target communication base station.

[0113] The adjusting unit 42 is configured to optimize and adjust the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station.

[0114] ​The configuration device of the communication base station provided in the embodiment of the application receives base station data of a target communication base station in a historical time period through a receiving unit 40 to obtain M pieces of historical base station data, wherein the M pieces of historical base station data at least include base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters, M is a positive integer; a calculating unit 41 calculates a backup power capacity lower limit value of the target communication base station by using the M pieces of historical base station data, takes the backup power capacity lower limit value as a lower limit value of initial capacity data, and constructs an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value includes an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data includes initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy for configuring the capacity of a photovoltaic system and an energy storage system of the target communication base station; an adjusting unit 42 optimizes and adjusts the initial configuration strategy through an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station, thereby solving the technical problem that the communication base station has low power supply efficiency and poor stability after the photovoltaic and energy storage configuration in the related art, calculating the backup power capacity lower limit value of the target communication base station by using the historical base station data, constructing the initial configuration strategy of the target communication base station based on the lower limit value, and optimizing and adjusting the initial configuration strategy through the optimization algorithm to obtain the photovoltaic energy storage configuration strategy of the target communication base station, thereby achieving the technical effects of improving the power supply efficiency after the photovoltaic and energy storage configuration of the communication base station and improving the power supply stability.

[0115] Optionally, in the configuration device of the communication base station provided in the embodiment of the application, the calculating unit 41 includes: a first obtaining module, configured to obtain daily average load power data and voltage data of the target communication base station from the energy storage battery data, and obtain battery discharge data from the energy storage device parameters; a second obtaining module, configured to obtain a pre-device electrical parameter, calculate a product of the pre-device electrical parameter and the daily average load power data to obtain a cruising load; a first calculating module, configured to calculate a ratio of the cruising load to the voltage data, and calculate a product of the ratio of the cruising load to the voltage data and an inverse of the battery discharge data to obtain initial energy storage capacity data; and a determining module, configured to determine a redundancy parameter according to the meteorological data, and calculate a product of the initial energy storage capacity data and the redundancy parameter to obtain the energy storage capacity lower limit value.

[0116] Optionally, in the configuration device for a communication base station provided in the embodiments of the present application, the calculation unit 41 comprises: a third obtaining module, configured to obtain a power grid access type of the target communication base station, wherein the power grid access type comprises a photovoltaic power supply type and a stacked light system type; a second calculation module, configured to calculate a lower limit value of photovoltaic power according to M historical base station data in a case where the power grid access type is the photovoltaic power supply type; and a fourth obtaining module, configured to obtain power compensation duration data of the target communication base station in a case where the power grid access type is the stacked light system type, and calculate the lower limit value of photovoltaic power according to the M historical base station data and the power compensation duration data.

[0117] Optionally, in the configuration device for a communication base station provided in the embodiments of the present application, the adjustment unit 42 comprises: a fifth obtaining module, configured to obtain K sets of optimization variables in K time periods, and construct K candidate configuration strategies based on the K sets of optimization variables by using a particle swarm algorithm, wherein each set of optimization variables comprises an optimization capacity data and a candidate index corresponding to the optimization capacity data, each candidate configuration strategy refers to an optimization capacity data of the target communication base station, and K is a positive integer; and a sixth obtaining module, configured to obtain a preset extreme value, and screen the K candidate configuration strategies according to the preset extreme value to obtain a photovoltaic energy storage configuration strategy, wherein the preset extreme value comprises a global extreme value and an individual extreme value, the global extreme value is used to indicate a set of optimization variables with the maximum value in the K candidate configuration strategies at each update iteration, and the individual extreme value is a set of optimization variables with the maximum value of each candidate configuration strategy at each update iteration.

[0118] Optionally, in the configuration device for a communication base station provided in the embodiments of the present application, the adjustment unit 42 comprises: a seventh obtaining module, configured to, for a set of optimization variables, obtain a same historical time period associated with the set of optimization variables, obtain charge-discharge power data and time period cost data of the same historical time period from the M historical base station data, and obtain power value data, wherein the time period cost data refers to cost value data of the target communication base station in each time period; an eighth obtaining module, configured to obtain initial cost data, calculate an algebraic sum of the charge-discharge power data, the time period cost data and the power value data to obtain photovoltaic energy storage value data, and calculate a difference between the photovoltaic energy storage value data and the initial cost data to obtain period value data; and a third calculation module, configured to calculate reference cost data from the initial cost data, and calculate a ratio of the period value data to the reference cost data to obtain a candidate index.

[0119] Optionally, in the configuration device of the communication base station provided in the embodiment of the present application, the adjusting unit 42 comprises: a ninth obtaining module, configured to obtain a candidate index of a time period associated with a set of optimization variables, and obtain an elasticity coefficient function; a fourth calculating module, configured to calculate a partial derivative of the candidate index with respect to the photovoltaic power data by using the elasticity coefficient function, to obtain a photovoltaic power data condition, and determine candidate photovoltaic power data based on the photovoltaic power data condition; a fifth calculating module, configured to calculate a partial derivative of the candidate index with respect to the energy storage capacity data by using the elasticity coefficient function, to obtain an energy storage capacity condition, and determine candidate energy storage capacity data based on the energy storage capacity condition; and a constituting module, configured to constitute the optimization capacity data of the optimization variables based on the candidate photovoltaic power data and the candidate energy storage capacity data.

[0120] Optionally, in the configuration device of the communication base station provided in the embodiment of the present application, the adjusting unit 42 comprises: a tenth obtaining module, configured to obtain an initial velocity vector of each candidate configuration strategy, and determine an iteration termination condition, wherein the initial velocity vector is used to adjust a movement state of each candidate configuration strategy for the first time among all candidate configuration strategies, and the iteration termination condition refers to that the number of update iterations is greater than a preset number of iterations; an iteration module, configured to perform a first update iteration on the K candidate configuration strategies according to the K initial velocity vectors, calculate an index of a global extreme value and an index of an individual extreme value in each update iteration in the process of each update iteration; a comparison module, configured to compare data corresponding to the K candidate indexes, data corresponding to the index of the global extreme value, and data corresponding to the index of the individual extreme value in the process of each update iteration, and update a preset extreme value to obtain a preset updated extreme value according to a comparison result, until the number of update iterations is greater than the preset number of iterations, wherein each candidate configuration strategy update iteration refers to updating the position and the speed of each candidate configuration strategy according to different velocity vectors; and an eleventh obtaining module, configured to obtain an updated global extreme value in the updated preset updated extreme value in a case where the number of update iterations is greater than the preset number of iterations, and determine a candidate configuration strategy corresponding to a maximum set of optimization variables of the updated global extreme value as the photovoltaic energy storage configuration strategy.

[0121] It should be noted that the receiving unit 40, the calculating unit 41, and the adjusting unit 42 correspond to steps S201 to S203 in Embodiment 1, and have the same instances and application scenarios as those of the corresponding steps, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware components or software components stored in a memory (for example, the memory 104) and processed by one or more processors (for example, the processors 102a, 102b, …, 102n), and the above units can also be a part of the device and can run in the computer terminal 10 provided in Embodiment 1.

[0122] Embodiment 3

[0123] The embodiment of the present application can provide a computer terminal, which can be any one of computer terminal devices in a computer terminal group. Alternatively, in the embodiment, the computer terminal can be replaced by a mobile terminal or an electronic device or other terminal device.

[0124] Alternatively, in the embodiment, the computer terminal can be located in at least one of a plurality of network devices of a computer network.

[0125] In the embodiment, the computer terminal can execute program codes of the following steps in the configuration method of the communication base station: receiving base station data of a target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least includes base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters, and M is a positive integer; calculating a backup power capacity lower limit value of the target communication base station by using the M historical base station data, taking the backup power capacity lower limit value as a lower limit value of initial capacity data, and constructing an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value includes an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data includes initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy for configuring the capacity of the photovoltaic system and the energy storage system of the target communication base station; and optimizing and adjusting the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station.

[0126] Alternatively, the computer terminal can execute program codes of the following steps in the configuration method of the communication base station: obtaining daily average load power data and voltage data of the target communication base station from the energy storage battery data, and obtaining battery discharge data from the energy storage device parameters; obtaining a pre-device electrical parameter, calculating a product of the pre-device electrical parameter and the daily average load power data to obtain a cruising load, wherein the pre-device electrical parameter refers to a preset parameter of backup power cruising duration; calculating a ratio of the cruising load and the voltage data, and calculating a product of the ratio of the cruising load and the voltage data and the inverse of the battery discharge data to obtain initial energy storage capacity data; determining a redundancy parameter according to the meteorological data, calculating a product of the initial energy storage capacity data and the redundancy parameter to obtain an energy storage capacity lower limit value.

[0127] Optionally, the computer terminal can execute program codes of the following steps in the configuration method of the communication base station: obtaining a power supply access type of a target communication base station, wherein the power supply access type comprises a photovoltaic power supply type and a stacked light system type; in a case where the power supply access type is the photovoltaic power supply type, calculating a photovoltaic power lower limit value according to M historical base station data; in a case where the power supply access type is the stacked light system type, obtaining power supply time length data of the target communication base station, and calculating the photovoltaic power lower limit value according to the M historical base station data and the power supply time length data.

[0128] Optionally, the computer terminal can execute program codes of the following steps in the configuration method of the communication base station: obtaining K sets of optimization variables in K time periods, and constructing K candidate configuration strategies based on the K sets of optimization variables by a particle swarm algorithm, wherein each set of optimization variables comprises an optimization capacity data and a candidate index corresponding to the optimization capacity data, each candidate configuration strategy refers to an optimization capacity data configured for the target communication base station, and K is a positive integer; obtaining a preset extreme value, and screening the K candidate configuration strategies according to the preset extreme value to obtain a photovoltaic energy storage configuration strategy, wherein the preset extreme value comprises a global extreme value and an individual extreme value, the global extreme value is used to indicate a set of optimization variables with the maximum value in the K candidate configuration strategies at each update iteration, and the individual extreme value is a set of optimization variables with the maximum value of each candidate configuration strategy at each update iteration.

[0129] Optionally, the computer terminal can execute program codes of the following steps in the configuration method of the communication base station: for a set of optimization variables, obtaining a same historical time period associated with the set of optimization variables, obtaining charge-discharge power data and time period cost data of the same historical time period from M historical base station data, and obtaining electricity value data, wherein the time period cost data refers to cost value data of the target communication base station in each time period; obtaining initial cost data, calculating an algebraic sum of the charge-discharge power data, the time period cost data and the electricity value data to obtain photovoltaic energy storage value data, and calculating a difference value between the photovoltaic energy storage value data and the initial cost data to obtain period value data; calculating reference cost data from the initial cost data, and calculating a ratio of the period value data and the reference cost data to obtain a candidate index.

[0130] Optionally, the computer terminal can execute program codes of the following steps in the configuration method of the communication base station: obtaining candidate indicators of a time period associated with the set of optimization variables and obtaining the elasticity coefficient function; calculating partial derivatives of the candidate indicators with respect to the photovoltaic power data through the elasticity coefficient function to obtain photovoltaic power data conditions, and determining candidate photovoltaic power data based on the photovoltaic power data conditions; calculating partial derivatives of the candidate indicators with respect to the energy storage capacity data through the elasticity coefficient function to obtain energy storage capacity conditions, and determining candidate energy storage capacity data based on the energy storage capacity conditions; and constructing the optimization capacity data of the optimization variables based on the candidate photovoltaic power data and the candidate energy storage capacity data.

[0131] Optionally, the computer terminal can execute program codes of the following steps in the configuration method of the communication base station: obtaining an initial velocity vector of each candidate configuration strategy, and determining an iteration termination condition, wherein the initial velocity vector is used to adjust the movement state of each candidate configuration strategy for the first time among all candidate configuration strategies, and the iteration termination condition refers to that the number of update iterations is greater than a preset number of iterations; performing the first update iteration on the K candidate configuration strategies according to the K initial velocity vectors, wherein in each update iteration, the indicators of global extreme values and the indicators of individual extreme values in each update iteration are calculated; in each update iteration, the data corresponding to the K candidate indicators, the data corresponding to the indicators of global extreme values, and the data corresponding to the indicators of individual extreme values are compared, and the preset extreme value is updated according to the comparison result to obtain a preset updated extreme value, until the number of update iterations is greater than the preset number of iterations, wherein each candidate configuration strategy update iteration refers to updating the position and speed of each candidate configuration strategy according to different velocity vectors; and when the number of update iterations is greater than the preset number of iterations, obtaining an updated global extreme value in the updated preset updated extreme value, and determining the candidate configuration strategy of the maximum set of optimization variables corresponding to the updated global extreme value as the photovoltaic energy storage configuration strategy.

[0132] Optionally, Figure 5 is a structural block diagram of an electronic device according to an embodiment of the present application. As shown in Figure 5 the electronic device can include one or more (only one is shown in the figure) processors 502, memories 504, storage controllers, and peripheral interfaces, wherein the peripheral interfaces are connected with radio frequency modules, audio modules, and displays. Figure 5

[0133] ​The memory can be configured to store software programs and modules, such as program instructions / modules corresponding to the configuration method and device of the communication base station in the embodiments of the present application. The processor executes various function applications and data processing by running the software programs and modules stored in the memory, that is, implements the configuration method of the communication base station described above. The memory can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory can further include a memory remotely arranged with respect to the processor, which can be connected to the terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0134] The processor can call information and application programs stored in the memory through the transmission device to execute the steps in the configuration method of the communication base station described above.

[0135] By adopting the embodiments of the present application, a scheme for configuring a communication base station is provided. M historical base station data are obtained by receiving base station data of a target communication base station in a historical time period, wherein the M historical base station data at least include base station data, meteorological data, energy storage battery data, photovoltaic device data, and energy storage device parameters, and M is a positive integer; a backup power capacity lower limit value of the target communication base station is calculated using the M historical base station data, the backup power capacity lower limit value is taken as a lower limit value of initial capacity data, and an initial configuration strategy of the target communication base station is constructed based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value includes an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data includes initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy for configuring the capacity of a photovoltaic system and an energy storage system of the target communication base station; the initial configuration strategy is adjusted and optimized by an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station, thereby solving the technical problem that the communication base station has low power supply efficiency and poor stability after the photovoltaic and energy storage configuration in the related art. By calculating the backup power capacity lower limit value of the target communication base station using the historical base station data, constructing the initial configuration strategy of the target communication base station based on the lower limit value, and adjusting and optimizing the initial configuration strategy by the optimization algorithm to obtain the photovoltaic energy storage configuration strategy of the target communication base station, the technical effects of improving the power supply efficiency after the photovoltaic and energy storage configuration of the communication base station and improving the power supply stability are achieved.

[0136] Those skilled in the art can understand that, Figure 5 The structure shown is only schematic, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Figure 5It does not cause limitation to the structure of the electronic device. For example, the electronic device can further include more or less components (such as a network interface, a display device, etc.) or have a different configuration from that shown in FIG. 1. Figure 5 Figure 5

[0137] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the terminal device related hardware through programs, and the programs can be stored in a computer readable storage medium, which can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0138] Embodiment 4

[0139] The embodiments of the present application also provide a storage medium. Optionally, in the embodiment, the storage medium can be used to save the program code executed by the configuration method of the communication base station provided in the embodiment 1.

[0140] Optionally, in the embodiment, the storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0141] Optionally, in the embodiment, the storage medium is configured to store program code for performing the following steps: receiving base station data of a target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least includes base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters, and M is a positive integer; calculating a backup power capacity lower limit value of the target communication base station by using the M historical base station data, taking the backup power capacity lower limit value as a lower limit value of initial capacity data, and constructing an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value includes an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data includes initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy for configuring the capacity of the photovoltaic system and the energy storage system of the target communication base station; and optimizing and adjusting the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station.

[0142] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to perform the steps of the configuration method of the communication base station.

[0143] The above embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0144] ​​In the above-described embodiments of the present application, the description of each embodiment focuses on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0145] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be implemented by other ways. Among them, the above-described device embodiments are only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0146] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0147] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0148] If the integrated unit is realized in the form of software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part of the prior art that makes a contribution or the whole or part of the technical solutions can be embodied in the form of software product, which is stored in a storage medium and includes a plurality of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The foregoing storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic or optical disk and various program code storage media.

[0149] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, several improvements and refinements can be made, which should be regarded as the protection scope of the present application.

Claims

1. A configuration method of a communication base station, characterized by, The method comprises the following steps: receiving base station data of a target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least comprises base station data, meteorological data, energy storage battery data, photovoltaic device data and energy storage device parameters, and M is a positive integer; calculating a backup power capacity lower limit value of the target communication base station by using the M historical base station data, taking the backup power capacity lower limit value as a lower limit value of initial capacity data, and constructing an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup power capacity lower limit value comprises an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data comprises initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy for configuring the capacity of a photovoltaic system and an energy storage system of the target communication base station; optimizing and adjusting the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station; the step of optimizing and adjusting the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station comprises the following steps: obtaining K sets of optimization variables in K time periods, constructing K candidate configuration strategies based on the K sets of optimization variables by using a particle swarm algorithm, wherein each set of optimization variables comprises an optimization capacity data and a candidate index corresponding to the optimization capacity data, each candidate configuration strategy refers to an optimization capacity data configured for the target communication base station, and K is a positive integer; obtaining a preset extreme value, screening the K candidate configuration strategies according to the preset extreme value to obtain the photovoltaic energy storage configuration strategy, wherein the preset extreme value comprises a global extreme value and an individual extreme value, the global extreme value is used to indicate a set of optimization variables with the maximum value in the K candidate configuration strategies at each update iteration, and the individual extreme value refers to a set of optimization variables with the maximum value of each candidate configuration strategy at each update iteration; the optimization capacity data in each set of optimization variables is calculated by the following method: for a set of optimization variables, obtaining candidate indexes in a time period associated with the set of optimization variables, and obtaining an elasticity coefficient function; calculating a partial derivative of the candidate indexes with respect to photovoltaic power data by using the elasticity coefficient function to obtain a photovoltaic power data condition, and determining candidate photovoltaic power data based on the photovoltaic power data condition; calculating a partial derivative of the candidate indexes with respect to energy storage capacity data by using the elasticity coefficient function to obtain an energy storage capacity condition, and determining candidate energy storage capacity data based on the energy storage capacity condition; and constructing the optimization capacity data of the optimization variable based on the candidate photovoltaic power data and the candidate energy storage capacity data.

2. The method of claim 1, wherein, in the case that the backup power capacity lower limit value is the energy storage capacity lower limit value, the step of calculating a backup power capacity lower limit value of the target communication base station by using the M historical base station data comprises the following steps: obtaining daily average load power data and voltage data of the target communication base station from the energy storage battery data, and obtaining battery discharge data from the energy storage device parameters; Obtaining a pre-device electrical parameter, calculating a product of the pre-device electrical parameter and the daily average load power data to obtain a cruising load, wherein the pre-device electrical parameter is a preset parameter of a backup power cruising duration; Calculating a ratio of the cruising load and the voltage data, and calculating a product of the ratio of the cruising load and the voltage data and an inverse of the battery discharge data to obtain initial energy storage capacity data; Determining a redundancy parameter according to the weather data, and calculating a product of the initial energy storage capacity data and the redundancy parameter to obtain the lower limit value of the energy storage capacity.

3. The method of claim 1, wherein, In a case where the backup power capacity lower limit value is the lower limit value of the photovoltaic power, calculating the backup power capacity lower limit value of the target communication base station by using the M historical base station data includes: Obtaining a type of mains access of the target communication base station, wherein the type of mains access includes a photovoltaic power supply type and a stacked light system type; In a case where the type of mains access is the photovoltaic power supply type, calculating the lower limit value of the photovoltaic power according to the M historical base station data; In a case where the type of mains access is the stacked light system type, obtaining power supplement duration data of the target communication base station, and calculating the lower limit value of the photovoltaic power according to the M historical base station data and the power supplement duration data.

4. The method of claim 1, wherein, A candidate index in each group of optimization variables is calculated by the following method: For a group of optimization variables, a same historical time period associated with the group of optimization variables is obtained, charge and discharge power data and time period cost data of the same historical time period are obtained from the M historical base station data, and an electricity value data is obtained, wherein the time period cost data is a cost value data of the target communication base station in each time period; Obtaining initial cost data, calculating an algebraic sum of the charge and discharge power data, the time period cost data and the electricity value data to obtain photovoltaic energy storage value data, and calculating a difference between the photovoltaic energy storage value data and the initial cost data to obtain period value data; Calculating a benchmark cost data by the initial cost data, and calculating a ratio of the period value data and the benchmark cost data to obtain the candidate index.

5. The method of claim 1, wherein, Screening the K candidate configuration strategies according to the preset extreme value to obtain the photovoltaic energy storage configuration strategy includes: Obtaining an initial speed vector of each candidate configuration strategy, and determining an iteration termination condition, wherein the initial speed vector is used to adjust a movement state adjustment of each candidate configuration strategy for the first time among all candidate configuration strategies, and the iteration termination condition is that an update iteration number is greater than a preset iteration number; Performing a first update iteration on the K candidate configuration strategies according to K initial speed vectors, and calculating an index of a global extreme value and an index of an individual extreme value in each update iteration in the process of each update iteration; In each update iteration process, the data corresponding to the K candidate indicators, the data corresponding to the global extreme value, and the data corresponding to the individual extreme value are compared, and the preset extreme value is updated according to the comparison result to obtain a preset updated extreme value, until the number of update iterations is greater than the preset number of iterations, wherein each candidate configuration strategy update iteration refers to updating the position and speed of each candidate configuration strategy according to different speed vectors; In the case where the number of update iterations is greater than the preset number of iterations, an updated global extreme value in the preset updated extreme value is obtained, and a candidate configuration strategy of a maximum group of optimization variables corresponding to the updated global extreme value is determined as the photovoltaic energy storage configuration strategy.

6. An arrangement for configuring a communication base station, characterized by Comprise: A receiving unit is configured to receive base station data of a target communication base station in a historical time period to obtain M historical base station data, wherein the M historical base station data at least includes base station data, meteorological data, energy storage battery data, photovoltaic equipment data, and energy storage equipment parameters, and M is a positive integer; A calculation unit is configured to calculate a backup capacity lower limit value of the target communication base station by using the M historical base station data, take the backup capacity lower limit value as a lower limit value of initial capacity data, and construct an initial configuration strategy of the target communication base station based on the lower limit value of the initial capacity data, wherein the backup capacity lower limit value includes an energy storage capacity lower limit value and a photovoltaic power lower limit value, the initial capacity data includes initial photovoltaic power data and initial energy storage capacity data, and the initial configuration strategy refers to a strategy for configuring the capacity of a photovoltaic system and an energy storage system of the target communication base station; An adjustment unit is configured to optimize and adjust the initial configuration strategy by using an optimization algorithm to obtain a photovoltaic energy storage configuration strategy of the target communication base station; The adjustment unit comprises: a fifth acquisition module configured to acquire K groups of optimization variables in K time periods, and construct K candidate configuration strategies based on the K groups of optimization variables by using a particle swarm algorithm, wherein each group of optimization variables includes an optimization capacity data and a candidate indicator corresponding to the optimization capacity data, each candidate configuration strategy refers to an optimization capacity data configured for the target communication base station, and K is a positive integer; and a sixth acquisition module configured to acquire a preset extreme value, and screen the K candidate configuration strategies according to the preset extreme value to obtain the photovoltaic energy storage configuration strategy, wherein the preset extreme value includes a global extreme value and an individual extreme value, the global extreme value is used to indicate a group of optimization variables with the maximum value in the K candidate configuration strategies in each update iteration, and the individual extreme value refers to a group of optimization variables with the maximum value of each candidate configuration strategy in each update iteration. The adjusting unit comprises: a ninth obtaining module, configured to obtain a candidate index of a time period associated with a set of optimization variables, and obtain an elasticity coefficient function; a fourth calculating module, configured to calculate a partial derivative of the candidate index with respect to photovoltaic power data by using the elasticity coefficient function, to obtain a photovoltaic power data condition, and determine candidate photovoltaic power data based on the photovoltaic power data condition; a fifth calculating module, configured to calculate a partial derivative of the candidate index with respect to energy storage capacity data by using the elasticity coefficient function, to obtain an energy storage capacity condition, and determine candidate energy storage capacity data based on the energy storage capacity condition; and a constituting module, configured to constitute optimization capacity data of the optimization variables based on the candidate photovoltaic power data and the candidate energy storage capacity data.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the configuration method of the communication base station according to any one of claims 1 to 5.

8. An electronic device, comprising: comprise: a memory storing an executable program; a processor configured to execute the program, wherein the program, when executed, performs the configuration method of the communication base station according to any one of claims 1 to 5.

9. A computer program product comprising computer instructions, characterized in that, The computer instructions, when executed by the processor, implement the steps of the configuration method of the communication base station according to any one of claims 1 to 5.

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