Base station optical storage integrated energy-saving system and method based on intelligent power management

Abstract

The invention discloses a base station light storage integrated energy-saving system and method based on intelligent power management, and relates to the technical field of intelligent power management and optimization, and the method comprises the steps: a solar cell module converts solar energy into electric energy through a photovoltaic effect, generates direct current, carries out the voltage stabilization processing through a photovoltaic adapter, and outputs the direct current to a power supply module; the DC power supply is connected to a DC power supply busbar output by a switching power supply. The photovoltaic controller adopts a busbar voltage following mechanism to intelligently adjust the output of the photovoltaic adapter and preferentially supply power to the direct-current load, so that effective utilization of green electric energy is ensured; training a time sequence model, analyzing the electricity price through the trained time sequence model, and identifying an electricity consumption mode; and according to the power utilization mode identified by the time sequence model, the energy storage controller intelligently adjusts charging and discharging of the energy storage battery. According to the invention, efficient cooperation of the photovoltaic system and the energy storage system is realized, and renewable energy sources can be utilized to the greatest extent in different time periods.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power management and optimization technology, and in particular to a base station photovoltaic storage integrated energy-saving system and method based on intelligent power management. Background Art

[0002] With the rapid deployment of 5G networks, the number of communication base stations has increased dramatically, and energy consumption and carbon emissions have become urgent issues to be addressed. The traditional base station power supply mode mainly relies on the mains, which is costly and environmentally unfriendly. In recent years, in order to achieve green and low-carbon development, more and more research and technology have been invested in base station energy management systems, especially the application of solar photovoltaic power generation and energy storage systems has received widespread attention.

[0003] At present, common energy-saving measures for base stations mainly include single photovoltaic power generation systems or independent energy storage systems. Photovoltaic power generation systems collect solar energy by installing solar panels and convert it into electrical energy for use by base station equipment. Independent energy storage systems can store electricity during low-power periods and release it during peak periods to smooth the load curve, but they also face problems such as high initial investment and complex maintenance. In addition, these individually used systems lack an effective coordination mechanism and are difficult to achieve the best energy-saving effect.

[0004] The existing single system or simple combination system has low overall efficiency due to the lack of effective synergy between photovoltaic and energy storage, especially in extreme weather conditions, which may not guarantee the normal operation of base stations. Moreover, these systems usually do not have intelligent power management functions and cannot dynamically adjust the output ratio of photovoltaic and energy storage, thus affecting energy utilization efficiency and economy. Summary of the invention

[0005] The purpose of the present invention is to provide a base station photovoltaic storage integrated energy-saving system and method based on intelligent power management to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a base station photovoltaic storage integrated energy saving method based on intelligent power management, the method comprising the steps of:

[0007] S1. The solar cell module converts sunlight into electrical energy through the photovoltaic effect, generates direct current, and after voltage stabilization by the photovoltaic adapter, it is connected to the DC power supply busbar output by the switching power supply;

[0008] S2. The photovoltaic controller adopts the busbar voltage following mechanism to intelligently adjust the photovoltaic adapter output, so that the photovoltaic adapter output voltage is always higher than the switching power supply output voltage, and gives priority to supplying power to the DC load to ensure the effective use of green electricity;

[0009] S3, training a time series model, and analyzing electricity prices through the trained time series model to identify electricity consumption patterns;

[0010] S4. Based on the power consumption pattern identified by the time series model, the energy storage controller intelligently adjusts the charging and discharging of the energy storage battery.

[0011] According to the above scheme, the busbar voltage following mechanism includes: monitoring the voltage value of the DC power supply busbar through a voltage sensor, and transmitting the monitored voltage value of the DC power supply busbar to the photovoltaic controller; the photovoltaic controller compares the monitored voltage value of the DC power supply busbar with the output voltage of the photovoltaic adapter;

[0012] According to the result of voltage comparison, the PV controller intelligently adjusts the output voltage of the PV adapter. If the output voltage of the PV adapter is lower than the voltage of the DC power supply busbar, the output voltage is increased to ensure that the output voltage of the PV adapter is always higher than the voltage of the DC power supply busbar.

[0013] By prioritizing powering DC loads, PV systems can effectively utilize their power generation capacity, reducing waste caused by excess power or the need to feed power back to the grid;

[0014] The busbar voltage following mechanism of the photovoltaic adapter ensures that photovoltaic power generation can be maximized at any time and directly supply power to the required loads; and by directly utilizing photovoltaic power generation, fossil fuel power generation is reduced and greenhouse gas emissions are reduced.

[0015] According to the above scheme, the peak power generation configuration of the photovoltaic adapter is consistent with the load power of the power-consuming equipment, ensuring that the power generated by the photovoltaic adapter can be used in a timely manner, so that the system achieves maximum benefits.

[0016] According to the above scheme, the training time series model includes:

[0017] Collecting historical electricity price data; the historical electricity price data includes timestamp, electricity consumption and electricity price information; and preprocessing the collected historical electricity price data, the preprocessing includes removing missing values ​​and smoothing;

[0018] The time series model ARIMA model is constructed through the preprocessed historical electricity price data; the parameters (p, d, q) of the ARIMA model are determined, where p represents the number of autoregressive terms, d represents the difference order, and q represents the moving average term;

[0019] The autoregressive part of the ARIMA model is as follows:

[0020]

[0021] Among them, y tis the electricity price at time t; c is the intercept, which is the basic level of the model; φ i Expressed as the autoregressive coefficient, it is the influence of the electricity price at the past i moments on the current electricity price; y t-i Represented as the electricity price at time point ti; ε t It is expressed as the error term at time t;

[0022] The moving average part of the ARIMA model is as follows:

[0023]

[0024] Among them, θ j Expressed as a sliding average coefficient, it indicates the degree of influence of the error in the past j moments on the current electricity price; ε t-j Expressed as the error value at time point tj;

[0025] Use historical electricity price data to train the ARIMA model, and optimize the ARIMA model parameters by using the mean square error. The formula is as follows:

[0026]

[0027] Where MSE represents the mean square error value; n represents the number of samples; yt represents the actual electricity price at time point t; Expressed as the predicted electricity price at time point t.

[0028] According to the above scheme, the electricity consumption mode includes valley electricity price period and peak electricity price period; the valley electricity price period represents a time period with low electricity demand and relatively low electricity price; the peak electricity price period represents a time period with peak electricity demand and relatively high electricity price.

[0029] According to the above scheme, the intelligent regulation of the energy storage battery includes: during the valley electricity price period, the energy storage controller is started, and the energy storage battery is intelligently controlled to charge to ensure that the battery is fully charged; during the peak electricity price period, the energy storage controller is started, and the energy storage battery is intelligently controlled to discharge, and the discharge is monitored in real time to ensure that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter, so that the photovoltaic adapter and the energy storage battery can supply power to the DC load in priority order.

[0030] According to the above solution, the intelligent regulation is that the energy storage controller adjusts the output voltage of the energy storage battery through the PID control algorithm, and the formula is as follows:

[0031]

[0032] Among them, u(t) represents the PID output value; e(t) represents the error between the current state and the target value; K p Expressed as a proportionality coefficient; Ki Expressed as integral coefficient; K d Expressed as differential coefficient; K p e(t) is expressed as a proportional term, which is proportional to the error; K i ∫e(t)dt is expressed as an integral term, which eliminates the steady-state error by accumulating past errors; Expressed as a differential term, it predicts the future error trend based on the rate of change of the error;

[0033] According to the output result of the PID control algorithm, the output voltage of the energy storage battery is adjusted so that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter.

[0034] According to the above scheme, the photovoltaic adapter and the energy storage battery supply power to the DC load in a priority order, including that during the peak electricity price period, the photovoltaic system and the energy storage system work together; when the power output by the photovoltaic adapter can meet the load demand of the electrical equipment, the energy storage battery does not need to be discharged; when the power output by the photovoltaic adapter cannot meet the load demand of the electrical equipment, the energy storage battery will automatically supplement the required current.

[0035] A base station photovoltaic and energy storage integrated energy-saving system based on intelligent power management, which includes a photovoltaic system, an energy storage system and a DC power supply busbar;

[0036] The photovoltaic system includes a solar cell assembly, a photovoltaic adapter and a photovoltaic controller; the energy storage system includes an energy storage controller and an energy storage battery;

[0037] The solar cell assembly converts sunlight into electrical energy through the photovoltaic effect, generates direct current, and transmits it to the photovoltaic adapter; the photovoltaic adapter is used to receive the direct current voltage generated by the solar cell assembly, and perform voltage stabilization and conversion to meet the voltage requirements of different electrical equipment loads; the photovoltaic controller intelligently adjusts the output of the photovoltaic adapter through the busbar voltage following mechanism, and increases the output voltage if the output voltage of the photovoltaic adapter is lower than the DC power supply busbar voltage; the output voltage of the photovoltaic adapter is always higher than the output voltage of the switching power supply, and the DC load is powered first, ensuring the effective use of green electricity;

[0038] The energy storage controller is used to intelligently control the energy storage battery to complete charging and discharging; during the off-peak electricity price period, the energy storage controller intelligently controls the charging of the energy storage battery to ensure that the energy storage battery is fully charged; during the peak electricity price period, the energy storage controller intelligently controls the discharging of the energy storage battery and monitors the discharging status in real time to ensure that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter; the photovoltaic adapter and the energy storage battery are used to supply power to the DC load in priority order;

[0039] The DC power supply busbar is connected to the switching power supply output of the photovoltaic system and the energy storage system.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention realizes efficient coordination between the photovoltaic system and the energy storage system, ensuring that renewable energy can be utilized to the maximum extent in different time periods;

[0042] 2. The present invention combines the time series model to identify the electricity consumption pattern, optimizes the energy storage battery charging and discharging strategy, and reduces electricity expenses;

[0043] 3. The present invention improves the energy utilization rate of the base station and reduces the dependence on external electricity through intelligent control. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a flowchart of the steps of the base station photovoltaic storage integrated energy saving method based on intelligent power management;

[0045] Figure 2 It is a structural schematic diagram of the base station photovoltaic storage integrated energy-saving system based on intelligent power management of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a base station photovoltaic storage integrated energy-saving system and method based on intelligent power management;

[0048] A set of solar cell modules with a total power of 10kW is installed on the roof of the base station, which uses the photovoltaic effect to convert sunlight energy into electrical energy and output direct current;

[0049] After the voltage is stabilized by the photovoltaic adapter, ensure that the voltage is stable and then connect it to the DC power supply busbar output by the switching power supply;

[0050] Install a photovoltaic controller and adopt a busbar voltage following mechanism; install a voltage sensor and monitor the voltage value of the DC power supply busbar through the voltage sensor, and transmit the monitored voltage value of the DC power supply busbar to the photovoltaic controller;

[0051] The PV controller compares the monitored voltage value of the DC power supply bus with the output voltage of the PV adapter. Based on the result of the voltage comparison, the PV controller intelligently adjusts the output voltage of the PV adapter. If the output voltage of the PV adapter is lower than the voltage of the DC power supply bus, the output voltage is increased to ensure that the output voltage of the PV adapter is always higher than the voltage of the DC power supply bus.

[0052] The output voltage of the photovoltaic adapter is used to power the DC load first, and the photovoltaic system can effectively utilize its power generation capacity, reducing the waste caused by excess power or the need to feed power back to the grid;

[0053] The busbar voltage following mechanism of the photovoltaic adapter ensures that photovoltaic power generation can be maximized at any time and directly supply power to the required loads; and by directly utilizing photovoltaic power generation, fossil fuel power generation is reduced and greenhouse gas emissions are reduced.

[0054] The peak power generation configuration of the photovoltaic adapter is consistent with the load power of the power-consuming equipment, ensuring that the power generated by the photovoltaic adapter can be used in a timely manner to maximize the benefits of the system.

[0055] Collecting electricity price data of base stations in the past year, the electricity price data including timestamp, electricity consumption and electricity price information; and preprocessing the collected electricity price data, the preprocessing including removing missing values ​​and smoothing;

[0056] Using the preprocessed electricity price data, the time series model ARIMA model is constructed; the parameters (p, d, q) of the ARIMA model are determined, where p represents the number of autoregressive terms, d represents the difference order, and q represents the moving average term;

[0057] The autoregressive part of the ARIMA model is as follows:

[0058]

[0059] Among them, y t is the electricity price at time t; c is the intercept, which is the basic level of the model; φ i Expressed as the autoregressive coefficient, it is the influence of the electricity price at the past i moments on the current electricity price; y t-i Represented as the electricity price at time point ti; ε t It is expressed as the error term at time t;

[0060] The moving average part of the ARIMA model is as follows:

[0061]

[0062] Among them, θ jExpressed as a sliding average coefficient, it indicates the degree of influence of the error in the past j moments on the current electricity price; ε t-j Expressed as the error value at time point tj;

[0063] The ARIMA model is trained using the electricity price data of base stations in the past year, and the ARIMA model parameters are optimized by the mean square error. The formula is as follows:

[0064]

[0065] Where MSE represents the mean square error value; n represents the number of samples; yt represents the actual electricity price at time point t; Expressed as the predicted electricity price at time point t.

[0066] Electricity consumption patterns include valley electricity price periods and peak electricity price periods;

[0067] The valley electricity price period refers to a period of time when electricity demand is low and electricity prices are relatively low; the peak electricity price period refers to a period of time when electricity demand is peak and electricity prices are relatively high.

[0068] According to the ARIMA model and the real-time data analysis results, the current power consumption mode of the base station is dynamically identified:

[0069] For example, the off-peak electricity price period is from 12:00 p.m. to 6:00 a.m.; the peak electricity price period is from 6:00 a.m. to 12:00 p.m.

[0070] When the ARIMA model identifies that the base station is currently in the off-peak electricity price period, the energy storage controller starts and intelligently controls the charging of the energy storage battery to ensure that the battery is fully charged;

[0071] When the ARIMA model identifies that the base station is currently in the peak electricity price period, the energy storage controller starts, intelligently controls the discharge of the energy storage battery, and monitors the discharge in real time to ensure that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter, so that the photovoltaic adapter and the energy storage battery can supply power to the DC load in priority order.

[0072] During the discharge process of the energy storage battery, the energy storage controller adjusts the output voltage of the energy storage battery in real time through the PID control algorithm. The formula is as follows:

[0073]

[0074] Among them, u(t) represents the PID output value; e(t) represents the error between the current state and the target value; K p Expressed as a proportionality coefficient; K i Expressed as integral coefficient; K d Expressed as differential coefficient; K pe(t) is expressed as a proportional term, which is proportional to the error; K i ∫e(t)dt is expressed as an integral term, which eliminates the steady-state error by accumulating past errors; Expressed as a differential term, it predicts the future error trend based on the rate of change of the error;

[0075] According to the output result of the PID control algorithm, the output voltage of the energy storage battery is adjusted in real time, so that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter, so that the photovoltaic adapter and the energy storage battery can supply power to the DC load in priority order;

[0076] During peak electricity price periods, the photovoltaic system and energy storage system work together;

[0077] The priority order is that when the power output by the photovoltaic adapter can meet the load demand of the power-consuming equipment, the energy storage battery does not need to be discharged; when the power output by the photovoltaic adapter cannot meet the load demand of the power-consuming equipment, the energy storage battery will automatically supplement the required current.

[0078] The present invention provides another technical solution, a base station photovoltaic energy storage integrated energy-saving system based on intelligent power management, the system comprising a photovoltaic system, an energy storage system and a DC power supply busbar;

[0079] The photovoltaic system includes a solar cell assembly, a photovoltaic adapter and a photovoltaic controller; the energy storage system includes an energy storage controller and an energy storage battery;

[0080] Under sunny conditions, the solar cell assembly is composed of high-efficiency solar cells, which can maintain good power generation capacity under different lighting conditions; through the photovoltaic effect, the solar energy is converted into electrical energy, direct current is generated, and transmitted to the photovoltaic adapter;

[0081] The photovoltaic adapter is used to receive the DC voltage generated by the solar cell assembly, and perform voltage stabilization and conversion to meet the voltage requirements of different electrical equipment loads; the photovoltaic adapter has multiple output interfaces to facilitate the connection of different types of DC loads;

[0082] The photovoltaic controller is responsible for monitoring the operating status of the photovoltaic system, and intelligently adjusts the output of the photovoltaic adapter through the busbar voltage following mechanism. If the output voltage of the photovoltaic adapter is lower than the DC power supply busbar voltage, the output voltage is increased to ensure stable power supply of the system; the output voltage of the photovoltaic adapter is always higher than the output voltage of the switching power supply, and the DC load is given priority to ensure the effective use of green electricity;

[0083] The energy storage controller monitors the power price of the power grid in real time, identifies the power consumption pattern using a time series model, wherein the power consumption pattern includes a valley power price period and a peak power price period, and intelligently controls the charging and discharging strategy of the energy storage battery;

[0084] The energy storage battery is used to store the electric energy obtained from the photovoltaic system and the power grid;

[0085] When the time series model identifies a valley electricity price period, such as 12:00 p.m. to 6:00 a.m., the energy storage controller intelligently controls the charging of the energy storage battery to ensure that the energy storage battery is fully charged; after the energy storage battery is fully charged, the energy storage controller stops charging to avoid overcharging;

[0086] When the time series model identifies a peak electricity price period, such as 6 a.m. to 12 p.m., the energy storage controller intelligently controls the discharge of the energy storage battery and monitors the discharge in real time to ensure that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter; the photovoltaic adapter and the energy storage battery supply power to the DC load in priority order to ensure safe and stable power supply.

[0087] The DC power supply busbar is connected to the switching power supply output of the photovoltaic system and the energy storage system, and the electric energy from the photovoltaic system and the energy storage system is reasonably distributed to each DC load, ensuring that the base station can obtain stable power supply under different power demands.

[0088] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A base station photovoltaic and storage integrated energy-saving method based on intelligent power management, characterized in that: The method comprises the steps of: S1. The solar cell module converts sunlight into electrical energy through the photovoltaic effect, generates direct current, and after voltage stabilization by the photovoltaic adapter, it is connected to the DC power supply busbar output by the switching power supply; S2. The photovoltaic controller adopts the busbar voltage following mechanism to intelligently adjust the photovoltaic adapter output, so that the photovoltaic adapter output voltage is always higher than the switching power supply output voltage, and gives priority to supplying power to the DC load to ensure the effective use of green electricity; S3, training a time series model, and analyzing electricity prices through the trained time series model to identify electricity consumption patterns; S4. Based on the power consumption pattern identified by the time series model, the energy storage controller intelligently adjusts the charging and discharging of the energy storage battery.

2. The base station photovoltaic and energy storage integrated energy saving method based on intelligent power management according to claim 1 is characterized in that: The busbar voltage following mechanism includes: The voltage value of the DC power supply bus is monitored by a voltage sensor, and the monitored voltage value of the DC power supply bus is transmitted to the PV controller; the PV controller compares the monitored voltage value of the DC power supply bus with the output voltage of the PV adapter; according to the result of the voltage comparison, the PV controller intelligently adjusts the output voltage of the PV adapter, and increases the output voltage if the output voltage of the PV adapter is lower than the voltage of the DC power supply bus, ensuring that the output voltage of the PV adapter is always higher than the voltage of the DC power supply bus.

3. The base station photovoltaic and storage integrated energy-saving method based on intelligent power management according to claim 2 is characterized in that: The peak power generation configuration of the photovoltaic adapter is consistent with the load power of the power-consuming equipment, ensuring that the power generated by the photovoltaic adapter can be used in a timely manner to achieve the maximum benefit of the photovoltaic system.

4. The base station photovoltaic and storage integrated energy-saving method based on intelligent power management according to claim 1 is characterized in that: The training time series model comprises: Collecting historical electricity price data; the historical electricity price data includes timestamp, electricity consumption and electricity price information; and preprocessing the collected historical electricity price data, the preprocessing includes removing missing values ​​and smoothing; The time series model ARIMA model is constructed through the preprocessed historical electricity price data; the parameters (p, d, q) of the ARIMA model are determined, where p represents the number of autoregressive terms, d represents the difference order, and q represents the moving average term; The autoregressive part of the ARIMA model is formulated as follows: Among them, y t is the electricity price at time t; c is the intercept, which is the basic level of the model; φ i Expressed as the autoregressive coefficient, it is the degree of influence of the electricity price at the past i moments on the current electricity price; y t-i Represented as the electricity price at time point ti; ε t It is expressed as the error term at time t; The moving average part of the ARIMA model is as follows: Among them, θ j Expressed as a sliding average coefficient, it indicates the degree of influence of the error in the past j moments on the current electricity price; ε t-j Expressed as the error value at time point tj; Use historical electricity price data to train the ARIMA model, and optimize the ARIMA model parameters by using the mean square error. The formula is as follows: Where MSE represents the mean square error value; n represents the number of samples; yt represents the actual electricity price at time point t; Expressed as the predicted electricity price at time point t.

5. The base station photovoltaic and storage integrated energy-saving method based on intelligent power management according to claim 1, characterized in that: The electricity usage pattern includes a valley electricity price period and a peak electricity price period; the valley electricity price period represents a time period when electricity demand is low and electricity prices are relatively low; the peak electricity price period represents a time period when electricity demand is peak and electricity prices are relatively high.

6. The base station photovoltaic and storage integrated energy-saving method based on intelligent power management according to claim 5 is characterized in that: The intelligent regulation of the energy storage battery includes: during the valley electricity price period, the energy storage controller is started, and the energy storage battery is intelligently controlled to charge to ensure that the battery is fully charged; during the peak electricity price period, the energy storage controller is started, and the energy storage battery is intelligently controlled to discharge, and the discharge is monitored in real time to ensure that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter, so that the photovoltaic adapter and the energy storage battery can supply power to the DC load in priority order.

7. The base station photovoltaic and energy storage integrated energy saving method based on intelligent power management according to claim 6 is characterized in that: The intelligent regulation is that the energy storage controller adjusts the output voltage of the energy storage battery through the PID control algorithm, and the formula is as follows: Among them, u(t) represents the PID output value; e(t) represents the error between the current state and the target value; K p Expressed as a proportionality coefficient; K i Expressed as integral coefficient; K d Expressed as differential coefficient; K p e(t) is expressed as a proportional term, which is proportional to the error; K i ∫e(t)dt is expressed as an integral term, which eliminates the steady-state error by accumulating past errors; Expressed as a differential term, it predicts the future error trend based on the rate of change of the error; According to the output result of the PID control algorithm, the output voltage of the energy storage battery is adjusted so that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter.

8. The base station photovoltaic and storage integrated energy-saving method based on intelligent power management according to claim 6, characterized in that: The photovoltaic adapter and the energy storage battery supply power to the DC load in a priority order, including that during peak electricity price periods, the photovoltaic system and the energy storage system work together; when the power output by the photovoltaic adapter can meet the load demand of the electrical equipment, the energy storage battery does not need to be discharged; when the power output by the photovoltaic adapter cannot meet the load demand of the electrical equipment, the energy storage battery will automatically supplement the required current.

9. The base station integrated photovoltaic and energy storage energy-saving system based on intelligent power management is characterized by: The system includes a photovoltaic system, an energy storage system and a DC power supply busbar; The photovoltaic system includes a solar cell assembly, a photovoltaic adapter and a photovoltaic controller; the energy storage system includes an energy storage controller and an energy storage battery; The solar cell assembly converts sunlight into electrical energy through the photovoltaic effect, generates direct current, and transmits it to the photovoltaic adapter; the photovoltaic adapter is used to receive the direct current voltage generated by the solar cell assembly, and perform voltage stabilization and conversion to meet the voltage requirements of different electrical equipment loads; the photovoltaic controller intelligently adjusts the output of the photovoltaic adapter through the busbar voltage following mechanism, and increases the output voltage if the output voltage of the photovoltaic adapter is lower than the DC power supply busbar voltage; the output voltage of the photovoltaic adapter is always higher than the output voltage of the switching power supply, and the DC load is powered first, ensuring the effective use of green electricity; The energy storage controller is used to intelligently control the energy storage battery to complete charging and discharging; During the off-peak electricity price period, the energy storage controller intelligently controls the charging of the energy storage battery to ensure that the energy storage battery is fully charged; during the peak electricity price period, the energy storage controller intelligently controls the discharge of the energy storage battery and monitors the discharge in real time to ensure that the output voltage of the energy storage battery is higher than the voltage of the DC power supply busbar and lower than the output voltage of the photovoltaic adapter; the photovoltaic adapter and the energy storage battery supply power to the DC load in priority order; The DC power supply busbar is connected to the switching power supply output of the photovoltaic system and the energy storage system.

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