New energy honeycomb network carbon emission optimization method based on balance equation

Abstract

The invention discloses a new energy cellular network carbon emission optimization method based on a # imgabs0 # balance equation, and belongs to the field of wireless communication. According to the method, when the total # imgabs1 # loss of a new energy cellular network is calculated, energy consumption of an energy supply end, a base station end and the electric energy transmission process of the new energy cellular network is considered; new energy and base station load distribution is predicted by observing historical data, the randomness of new energy and base station loads is measured to a certain extent, and energy loss generated by the randomness is measured by using information thermodynamics # imgabs2 # loss; a more accurate and adaptive energy scheduling strategy is obtained by minimizing the total # imgabs3 # loss of the new energy cellular network; therefore, enough energy is scheduled in advance according to the prediction information, energy sharing of new energy is realized, the absorption capability of the new energy is improved, the commercial power consumption is reduced, and the carbon emission of the system is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication, and more specifically, relates to a method for optimizing carbon emissions of a new energy cellular network based on a balance equation. Background Art

[0002] With the full deployment of the fifth-generation mobile communication system, the number of user devices and base stations has increased exponentially. At the same time, the booming development of information technology has brought various emerging applications, resulting in an exponential explosion in mobile data traffic. The huge amount of mobile data traffic and the surge in the number of base stations have led to a large amount of energy consumption, and the communication industry is gradually becoming a high-energy-consuming and high-carbon-emitting industry that has attracted much attention.

[0003] Data shows that the energy consumption of base stations accounts for more than half of the total energy consumption of the wireless communication network. Therefore, at present, a large part of the research on energy consumption optimization of wireless communication networks focuses on the energy consumption optimization of base stations. At the same time, with the continuous development of new energy technologies, new energy sources such as solar energy and wind energy have been widely applied to various energy systems. To address the severe communication energy consumption problem, in recent years, more and more people have proposed using new energy to supply power to cellular communication networks to achieve energy conservation and carbon reduction. However, new energy power supply has volatility, intermittency, and unpredictability, and the energy consumption of base stations also has spatio-temporal volatility with the change of user data traffic. Therefore, the matching between new energy power supply and the energy demand of base stations has become a crucial problem, which may lead to a low utilization rate of new energy and cause additional energy dissipation. Therefore, how to achieve the matching of energy supply and demand and describe the energy loss caused by random volatility is a problem faced in reducing the energy consumption of new energy cellular networks. Summary of the Invention

[0004] In view of the above defects or improvement requirements of the prior art, the present invention provides a method for optimizing carbon emissions of a new energy cellular network based on a balance equation, thereby solving the problem of energy supply-demand matching caused by the randomness and volatility of new energy and data traffic, and reducing the overall energy consumption of the new energy cellular network.

[0005] To achieve the above object, according to the first aspect of the present invention, there is provided a method for optimizing carbon emissions of a new energy cellular network based on a balance equation. The new energy cellular network is powered by solar energy and a traditional power grid. Each base station is equipped with a photovoltaic power generation device with the same photovoltaic panel area, and the photovoltaic power generation devices share energy through a DC microgrid. The method is characterized in that it includes:

[0006] S1, predicting the load distribution of base station i according to the historical load data of base station i and according to the formula Information thermodynamics of base station i caused by computing load uncertainty loss Predict the solar energy distribution at the location of base station i based on the historical solar energy distribution data at the location of base station i And according to the formula Calculate the information thermodynamics of the photovoltaic power generation device of base station i caused by solar energy supply uncertainty loss

[0007] Among them, E bs is the energy of the base station load uncertainty process; f I,bs is the information entropy of; E pv is the energy of the solar energy supply uncertainty process; f I,pv is the information entropy of;

[0008] S2. Under the preset constraints, by minimizing the total loss of the new energy cellular network, obtain the optimal energy sharing scheme among base stations and the optimal mains power transmission scheme for each base station;

[0009] Among them, the total loss includes the mains power transmission loss of each base station, the DC microgrid power transmission loss of each base station, the thermodynamics loss of each base station, the thermodynamics loss of each photovoltaic power generation device; the preset constraints include: the upper and lower limit constraints of the power input to the base station, the non-negativity constraint of power transmission, and the conditional constraint of base station energy sharing;

[0010] The conditional constraint of base station energy sharing is: when the difference between the new energy supplied by the photovoltaic power generation device to the base station and the lower limit value of the input of this base station is positive, the shared new energy output by this base station is positive, otherwise it is zero.

[0011] According to the second aspect of the present invention, an electronic device is provided, including: a computer-readable storage medium and a processor;

[0012] The computer-readable storage medium is used to store executable instructions;

[0013] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method described in the first aspect.

[0014] According to a third aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to execute the method described in the first aspect.

[0015] According to a fourth aspect of the present invention, there is provided a computer program product including a computer program or instructions, which, when executed by a processor, implement the method described in the first aspect.

[0016] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0017] In the method provided by the present invention, when calculating the total loss of the new energy cellular network, the energy consumption at the energy supply end, base station end and during the power transmission process of the new energy cellular network is considered; at the same time, considering the randomness and volatility of the new energy and base station load, by observing historical data and fitting out a rough distribution, the distribution of the new energy and base station load can be predicted, which can measure the randomness of the new energy and base station load to a certain extent, and the information thermodynamics is used to measure the energy loss generated by this randomness. By minimizing the total loss of the new energy cellular network, a more accurate and adaptable energy scheduling strategy can be obtained, so as to schedule sufficient energy in advance according to the prediction information, realize the energy sharing of the new energy, improve the consumption capacity of the new energy, and reduce the consumption of the commercial power. Since the carbon emission coefficient of the new energy is lower than that of the commercial power, reducing the consumption of the commercial power means using more new energy, which can reduce the carbon emissions of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the structure of the new energy cellular network provided by an embodiment of the present invention.

[0019] Figure 2 Based on It is a flowchart of the carbon emission optimization method for the new energy cellular network based on the balance equation provided by the present invention.

[0020] Figure 3 It is a flowchart of the modeling of the thermodynamic model of the new energy cellular network provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0022] An embodiment of the present invention provides a new energy cellular network carbon emission optimization method based on a balance equation. The new energy cellular network is powered by solar energy and a traditional power grid. Each base station is equipped with a photovoltaic power generation device with the same photovoltaic panel area, and the photovoltaic power generation devices share energy through a DC microgrid. As Figure 1 shown, the method includes:

[0023] S1. Predict the load distribution of base station i according to the historical load data of base station i and calculate the information thermodynamics loss of base station i caused by load uncertainty according to the formula loss Predict the solar energy distribution at the location of base station i according to the historical solar energy distribution data at the location of base station i and calculate the information thermodynamics loss of the photovoltaic power generation device of base station i caused by solar energy supply uncertainty according to the formula loss

[0024] Among them, E bs is the energy of the base station load uncertainty process; f I,bs is information entropy; E pv is the energy of the solar energy supply uncertainty process; f I,pv is information entropy;

[0025] S2. Under preset constraints, minimize the total loss of the new energy cellular network to obtain the optimal energy sharing scheme among base stations and the optimal mains power transmission scheme from the traditional power grid to each base station;

[0026] Among them, the total loss includes mains power transmission loss of each base station, DC microgrid power transmission loss of each base station, thermodynamics loss of each base station, thermodynamics loss of each photovoltaic power generation device; the preset constraints include: upper and lower limit constraints on the power input of the base station, non-negativity constraints on power transmission, and conditional constraints on base station energy sharing;

[0027] The conditional constraint on base station energy sharing is: when the new energy supplied by the photovoltaic power generation device to the base station When the difference between the lower limit values is positive, the shared new energy output by the base station is positive, otherwise it is zero.

[0028] Specifically, the new energy cellular network provided by the embodiments of the present invention is powered by solar energy and the traditional power grid, and the photovoltaic power generation devices equipped in each base station share the energy of the new energy through the erection of a DC microgrid. That is, as Figure 2 shown, the new energy cellular network structure consists of N base stations, and each base station is equipped with a corresponding photovoltaic power generation device, which can provide new energy for the base station. This means that the base station can be powered jointly by the photovoltaic power generation device and the traditional mains power grid. At the same time, the photovoltaic power generation devices equipped in each base station share the energy of the new energy through the erection of a DC microgrid, and the base stations with surplus new energy share the energy with the base stations with insufficient new energy.

[0029] First, based on the balance equation, a thermodynamic model of the new energy cellular network provided by the present invention is established, as Figure 3 shown, including the modeling of the energy supply end of the new energy cellular network, the modeling of the power grid transmission line, and the modeling of the base station communication end.

[0030] 1. Modeling of the energy supply end of the new energy cellular network

[0031] It is assumed that the area of the photovoltaic panels of the photovoltaic power generation devices equipped in each base station is the same, and the area of the photovoltaic panels is represented by A. Each base station can collect solar energy through the equipped photovoltaic panels, and H i is used to represent the solar radiation intensity received by the photovoltaic panels at base station i. Therefore, at base station i, the (that is, the in the incident solar energy of the photovoltaic power generation device at the i-th base station) can be expressed as:

[0032]

[0033] where T i represents the ambient temperature of the photovoltaic power generation device equipped at base station i, and T sun represents the solar temperature.

[0034] The photovoltaic power generation device has different power generation powers under different solar radiation intensities, and this power generation power is the available energy that the photovoltaic power generation device can provide, which is defined as the photovoltaic output , and is expressed as:

[0035]

[0036] where represents the photovoltaic output of the photovoltaic power generation device equipped at base station i , η pv represents the photovoltaic power generation efficiency, and it is assumed that the photovoltaic power generation efficiencies of the photovoltaic power generation devices equipped in each base station in the new energy honeycomb network are the same.

[0037] For the photovoltaic power generation device, according to the balance equation, the thermodynamic loss of the device is the difference between the input and output The input is the incident on the solar energy, and the output is the photovoltaic power generation power. Therefore, the thermodynamic loss of the photovoltaic power generation device equipped at base station i is:

[0038]

[0039] Considering the uncertainty of solar energy supply, there is additional energy dissipation in the photovoltaic power generation equipment due to uncertainty. For the location of base station i, considering the uncertainty of solar energy in a specific period of the whole year, according to the past observation data, the distribution of solar energy in this specific period is predicted Therefore, the information thermodynamics loss caused by the uncertainty of solar energy supply can be calculated based on this distribution:

[0040]

[0041] where p I,pv is the information potential of solar energy supply, and the calculation method is where E pv is the energy in the process of uncertainty of solar energy supply; f I,pv is the information flow of solar energy supply, and is the information entropy of the solar energy supply distribution .

[0042] Therefore, the new energy provided by the photovoltaic power generation at base station i is:

[0043]

[0044] where i is the index of the base station; represents the photovoltaic output of the photovoltaic power generation device equipped at base station i; represents the information thermodynamics loss caused by the uncertainty of solar energy supply at base station i, and the calculation method is where p I,pv is the information potential of solar energy supply, and the calculation method is E pvis the energy for the uncertain process of solar energy supply, is the predicted solar energy supply distribution at base station i; f I,pv is the information flow of solar energy supply, and is the information entropy of the solar energy supply distribution

[0045] 2. Power Grid Transmission Line Modeling

[0046] During the transmission of electric power resources through power lines, due to the resistance of the power lines, heat dissipation occurs during the transmission of electrical energy, resulting in line losses. Since electrical energy is high-quality energy, that is, all electrical energy is , therefore, the loss during the transmission of electrical energy can be regarded as the line loss caused by the resistance of the wire. For different power lines, due to different specifications and lengths, the line losses generated are also different. Therefore, the dissipation coefficient of the electrical energy transmission process is defined as η, and it is assumed that the power provided by the energy supply end during the power transmission process is P in , and the power required by the load end is P out , so P out = ηP in . The loss during the power transmission process can be expressed as or L = P in (1 - η).

[0047] In the new energy cellular network scenario, the dissipation coefficient of the mains power transmission process of base station i is denoted as the dissipation coefficient of the DC microgrid power transmission process between base station i and base station j during the energy sharing process is Therefore, the mains power transmission loss of base station i and the DC microgrid power transmission loss are obtained respectively. Then, during the process of transmitting electrical energy to base station i, the power transmission loss is:

[0048]

[0049] Among them, is the mains power transmission loss of base station i; is the DC microgrid power transmission loss of base station i; the mains power transmission loss of base station i and the DC microgrid power transmission loss are calculated as follows Among them, is the provided by the mains power to base station i; ​Shared by the j-th base station for the base station i ; is the dissipation coefficient of the mains power transmission process of the base station i, is the dissipation coefficient of the electric energy transmission between the base station j and the base station i through the DC microgrid during the energy sharing process.

[0050] 3. Modeling of the Base Station Communication End

[0051] In the new energy cellular network, the load is the base station equipment in the cellular network. The energy required by the base station i, denoted as the base station load fluctuates with the change of user demand. Considering the uncertain situation of the base station load during a specific period of the whole year, based on the past historical data, the distribution of the load at the base station i during this specific period can be predicted Therefore, according to this distribution, the information thermodynamics caused by the uncertainty of the base station load can be calculated loss:

[0052]

[0053] where p I,bs is the information potential of the base station load, and the calculation method is where E bs is the energy of the base station load uncertainty process; f I,bs is the information flow of the base station load, which is the information entropy of the base station load distribution .

[0054] It can be understood that the above-mentioned prediction of the solar energy supply distribution by observing the historical data of solar energy supply and the prediction of the base station load distribution by observing the historical data of the base station load can directly fit the approximate distribution according to the traditional parameter estimation method, or can be predicted by using the time series prediction method such as LSTM, and the distribution is characterized by the prediction deviation and the predicted value.

[0055] Part of the input to the base station is used for necessary data processing and calculation, that is, the computing power consumption of the base station, and the other part is used for signal transmission through the antenna. Considering that the electric energy required during the base station calculation process is ultimately dissipated into the environment in the form of heat, therefore, the output of the base station can be characterized as the transmission energy consumption of the base station, that is, the energy emitted by the antenna. The antenna transmission power P of the base station i t i can be calculated using the Shannon formula:

[0056]

[0057] where g is the path loss, Let \(N_0\) be the noise power and \(B\) be the bandwidth. Here, it is assumed that the path loss and noise power of the environment where each base station is located are the same, and the bandwidths used by each base station are also the same. is the maximum achievable information rate of base station \(i\). Therefore, it can be obtained that At this time, according to the mean value of the predicted base station load distribution, the required transmit power can be calculated.

[0058] The total energy consumption of the base station can be expressed as the sum of the static power consumption and the dynamic power consumption. The total power of base station \(i\) is:

[0059]

[0060] where \(P_{t,i}\) t i is the antenna transmit power of base station \(i\); \(\eta\) bs is the base station power efficiency; \(P_{s,i}\) static is the static power consumption of the base station.

[0061] The input of the base station is the available energy input to the base station system, denoted as Considering the normal operation of the base station and combining the information thermodynamics caused by the volatility of the base station load loss The input of base station \(i\) has a lower limit Since the actual load of each base station is different, the lower limit of the input is also different; and due to the limitations of physical devices, the input of the base station also has an upper limit \(P_{max}\) max , where it is assumed that the physical device limitations of each base station are the same, that is, \(P_{max}\) max is the same. Therefore, for any base station \(i\), the input of the base station needs to satisfy the constraint:

[0062] The input of the base station can be specifically expressed as where is the power supplied by the power grid to base station \(i\), is the new energy supplied by photovoltaic power generation , is the power dissipation coefficient of the power transmitted between base station \(j\) and base station \(i\) through the DC microgrid, is the power supplied by base station \(i\) to base station \(j\) through the DC microgrid.

[0063] According to the balance equation, the thermodynamic loss of the device is the input-output The difference, i.e., the thermodynamics of base station i loss can be expressed as:

[0064]

[0065] Among them, is the input of base station i ; P t i is the antenna transmission power; is the supplied by the power grid to base station i, is the new energy supplied by photovoltaic power generation ; is the supplied by base station j to base station i through the DC microgrid, is the supplied by base station i to base station j through the DC microgrid, and N is the number of base stations.

[0066] Considering a new energy cellular network composed of N base stations, the following information is obtained through measurement, prediction, etc.: (1) The predicted solar energy distribution at each base station (2) The predicted load distribution at each base station (3) The power transmission line loss vector η gird of the main power grid, and the i-th element in the vector represents the dissipation coefficient of the main power grid energy transmission process of base station i; (4) The DC microgrid transmission line loss matrix η micro , and the element in the i-th row and j-th column of the matrix represents the dissipation coefficient of the power transmission process between base station i and base station j through the DC microgrid during the energy sharing process.

[0067] It is defined that the energy sharing situation between each base station can be represented by the matrix Ex micro , and the element in the i-th row and j-th column of the matrix is the supplied by base station i to base station j through the DC microgrid; the situation of the main power grid energy consumed by each base station can be represented by the matrix Ex grid , and the i-th element in the matrix represents the main power grid energy consumed by base station i. The goal of the minimum loss optimization algorithm is how to perform reasonable energy sharing and main power grid transmission under the above known conditions, and find the optimal energy sharing matrix Ex micro and the main power grid transmission matrix Ex grid , so that the total loss L total of the entire new energy cellular network is minimized, that is:

[0068]

[0069] Among them, constraint C1 represents the power limit that the base station input must satisfy; constraint C2 represents the prerequisite for the base station to achieve energy sharing; constraint C3 represents the non-negativity of power transmission.

[0070] The objective function and constraints of this optimization problem are both linear, which is a linear optimization problem. Therefore, the linear programming scheme in convex optimization can be used to solve it, so as to obtain the minimum loss optimization algorithm.

[0071] Based on the above network model, a carbon emission optimization scheme based on the minimum loss optimization objective is proposed for the carbon emission optimization of the new energy cellular network, including:

[0072] 1. Based on the balance equation, thermodynamically model the new energy cellular network and establish the network model of the new energy cellular network.

[0073] Before establishing the network model, first obtain the solar energy prediction information at each base station, the load prediction information of each base station, the loss information of the mains power transmission line and the new energy DC microgrid transmission line, as well as the parameters of various photovoltaic devices, communication devices, and channel environments.

[0074] Establish the network model of the new energy cellular network, including:

[0075] (1) Model the new energy supply end.

[0076] First, predict the solar energy supply distribution information Subsequently, calculate the information thermodynamics loss caused by the uncertainty of solar energy supply according to the prediction information Then, calculate the new energy Finally, based on the above calculation results and the balance equation, complete the modeling of the new energy supply end.

[0077] (2) Model the transmission line.

[0078] First, obtain the loss coefficients of the traditional power grid and the new energy DC microgrid transmission lines through measurement and calculation, and then calculate the mains loss and the new energy DC microgrid transmission loss Finally, based on the above calculation results and the Balance equations to complete the modeling of the transmission line.

[0079] (3) Model the communication end.

[0080] First, predict the distribution information of the base station load Then, calculate the information thermodynamics caused by the uncertainty of the base station load according to the prediction information loss and the required transmission power P t i . Subsequently, model the thermodynamics loss of the base station. Finally, based on the above calculation results, complete the modeling of the communication end based on the balance equations.

[0081] (4) Integrate the modeling of the new energy supply end, transmission line, and communication end to complete the modeling of the new energy cellular network.

[0082] 2. According to the established network model, design a minimum loss optimization algorithm based on the minimum loss objective. By optimizing the energy scheduling process in energy sharing, reduce the consumption of mains power and lower the carbon emissions of the system.

[0083] (1) Establish a new energy cellular network network model based on the measured and predicted new energy supply, base station load, and transmission line loss information.

[0084] (2) Construct a minimum micro loss optimization problem with the DC microgrid energy sharing matrix Ex grid and the power grid transmission matrix Ex as decision variables, so that the total loss of the new energy cellular network is minimized:

[0085]

[0086] (3) Use the linear programming solutions in convex optimization (such as the interior point method, Lagrange multiplier method, etc.) to solve this minimum loss optimization problem to obtain the decision variables that minimize the total loss of the new energy cellular network.

[0087] The method provided by the present invention, by constructing the Couple the network model with the analysis information network and the energy network, describe the random volatility of the energy supply end and the load end in the new energy cellular network, and then optimize the scheduling strategy of energy sharing in the new energy cellular network according to the minimum #imgpt207# loss optimization algorithm to achieve the matching of the information network and the energy network in the new energy cellular network, thereby reducing the energy loss of the new energy cellular network, reducing the consumption of commercial power, and reducing the carbon emissions of the system.

[0088] An embodiment of the present invention provides an electronic device, including: a computer-readable storage medium and a processor;

[0089] The computer-readable storage medium is used to store executable instructions;

[0090] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method described in any of the above embodiments.

[0091] An embodiment of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the processor to execute the method described in any of the above embodiments.

[0092] An embodiment of the present invention provides a computer program product, including a computer program or instructions, and when the computer program or instructions are executed by a processor, the method described in any of the above embodiments is implemented.

[0093] It is easy for those skilled in the art to understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method based on A carbon emission optimization method for a new energy cellular network based on a balanced equation, wherein the new energy cellular network is powered by solar energy and a traditional power grid, each base station is equipped with a photovoltaic power generation device with the same photovoltaic panel area, and the photovoltaic power generation devices share energy through a DC microgrid, characterized in that: The method comprises: S1, predict the load distribution of base station i based on the historical load data of base station i And according to the formula Computational load uncertainty-induced information thermodynamics of base station i damage Predict the solar energy distribution at the location of base station i based on the historical solar energy distribution data at the location of base station i And according to the formula Calculate the information thermodynamics of photovoltaic power generation device of base station i caused by uncertainty of solar energy supply damage in, E bs is the energy of the uncertainty process of the base station load; f I,bs for Information entropy of E pv Energy for the solar energy uncertain process; f I,pv for Information entropy of S2, under the preset constraints, by minimizing the total loss, and obtain the best energy sharing solution between base stations and the best mains power transmission solution for each base station; Among them, the total Loss includes Mains power transmission to each base station loss, DC microgrid transmission of each base station loss, thermodynamics of each base station losses, thermodynamics of each photovoltaic power generation device The preset constraints include: base station input The upper and lower power constraints, the non-negativity constraints of power transmission and the conditional constraints of base station energy sharing; The conditional constraint of the base station energy sharing is: when the photovoltaic power generation device supplies new energy to the base station With the input of the base station When the difference between the lower limit value and the lower limit value is positive, the shared new energy output by the base station is positive, otherwise it is zero.

2. The method according to claim 1, characterized in that The mains power transmission of each base station The loss calculation formula is: in, Transmitting electricity to base station i damage, The mains power supply to base station i ; is the dissipation coefficient of the mains power transmission process of base station i; DC microgrid transmission at each base station The loss calculation formula is: in, Transmitting power to the DC microgrid of base station i damage, is the jth base station shared with base station i , is the dissipation coefficient of electric energy transmitted between base station j and base station i through the DC microgrid during energy sharing, and N is the number of base stations; The thermodynamics of each base station The loss calculation formula is: in, is the thermodynamics of base station i damage, New energy for base stations i supplying photovoltaic power generation , P t i is the antenna transmission power of base station i.

3. The method according to claim 1, characterized in that The base station input The upper and lower power constraints are: in, is the input of base station i The lower power limit, P max The input of each base station The power limit of The non-negativity constraint of the power transfer is: in, is the jth base station shared with base station i , New energy for base stations i supplying photovoltaic power generation ; The conditional constraints for base station energy sharing are: in, The mains power supply to base station i .

4. The method according to claim 1 or 2, characterized in that: The thermodynamics of each photovoltaic power generation device The loss calculation formula is: in, The thermodynamics of the photovoltaic power generation device of the i-th base station is Loss, T i The ambient temperature of the photovoltaic power generation device equipped for base station i, T sun is the solar temperature, η pv The photovoltaic power generation efficiency.

5. An electronic device, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 4.

7. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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