A photovoltaic storage integrated power supply system based on MPPT tracking and a control method thereof

Abstract

This invention relates to the field of photovoltaic power generation technology, specifically to a photovoltaic-storage integrated power supply system and control method for a data center based on MPPT tracking. It includes: an MPPT intelligent tracking unit that works collaboratively with an improved perturbation observation method and a fuzzy control algorithm, and adaptively corrects the photovoltaic maximum power point reference voltage by combining a degradation compensation model established based on sunshine duration and the aging characteristics of photovoltaic modules; an energy storage adaptive management unit; and a data center load dynamic allocation unit. This invention uses the MPPT intelligent tracking unit to collect data such as the cumulative operating years and cumulative actual power generation of photovoltaic modules to calculate the module aging coefficient. It then dynamically corrects the perturbation step size of the improved perturbation observation method by combining real-time power changes of the photovoltaic array. Finally, through dual-algorithm collaborative scheduling and degradation compensation quantification, it integrates multiple parameters to adaptively correct the standard maximum power point reference voltage, effectively addressing the performance degradation problem of photovoltaic modules after long-term operation.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and more specifically, to a photovoltaic-storage integrated power supply system and its control method based on MPPT tracking. Background Technology

[0002] The integrated photovoltaic and energy storage power supply system for data centers combines photovoltaic power generation, energy storage regulation, and intelligent power supply control to ensure the cleanliness, continuity, and energy efficiency of power supply to the data center. The accuracy of maximum power point tracking (MPPT), the adaptability of energy storage management, the rationality of load distribution, and the smoothness of power supply mode switching directly determine the overall operating performance of the system.

[0003] Among existing related technologies, CN202310067566.5 discloses a control method, device, and system for a photovoltaic energy storage power supply system, which includes photovoltaic modules, photovoltaic inverters, energy storage batteries, and energy storage inverters. The control logic is as follows: control the photovoltaic inverter to supply power to the user end at maximum output power, obtain the real-time grid current value input from the public grid to the user end, and determine the execution logic based on the current value to adjust the charging and discharging state and operating parameters of the energy storage inverter, ultimately avoiding the feeding of surplus photovoltaic power into the grid, preventing curtailment faults, and maximizing the local consumption of photovoltaic power generation. CN202311294915.3 discloses a photovoltaic power supply optimization method, system, and storage medium for computer rooms. It obtains solar radiation intensity, photovoltaic panel angle images, temperature images, and absorption coefficients, uses neural networks to extract angle features, combines temperature images to calculate temperature features, and then uses machine learning to calculate the optimal efficiency angle and adjust the photovoltaic panels to achieve optimal efficiency output and improved energy collection efficiency of the photovoltaic system without human intervention.

[0004] Despite the design advantages of the above technical solutions, they also have the following technical defects: First, they do not consider the long-term impact of photovoltaic module aging on the efficiency of the power supply system: CN202310067566.5 only focuses on the design and control logic of photovoltaic surplus power consumption and energy storage charging and discharging regulation, without addressing the performance degradation caused by the aging of photovoltaic modules after long-term operation, and cannot cope with the resulting decrease in power generation efficiency; CN202311294915.3 only improves energy collection efficiency by adjusting the angle of photovoltaic panels, and similarly does not pay attention to the impact of module aging on the long-term efficiency of the power supply system, which may easily lead to deviations between actual output and design expectations after long-term operation. Secondly, there is a lack of multi-functional unit collaborative control logic for data center scenarios: CN202310067566.5 does not consider the differentiated power supply needs of data center loads, nor does it design a multi-mode switching mechanism for photovoltaic, energy storage, and grid backup power supply, making it difficult to adapt to the special requirements of data centers for power supply continuity; CN202311294915.3 only focuses on photovoltaic panel angle optimization and does not integrate energy storage management and load distribution functions, failing to form system-level power supply collaborative control and unable to meet the complex power supply needs of data centers. Therefore, we propose a photovoltaic-energy storage integrated data center power supply system and its control method based on MPPT tracking. Summary of the Invention

[0005] The purpose of this invention is to provide a photovoltaic-storage integrated power supply system and its control method based on MPPT tracking, so as to solve the problems mentioned in the background art that the long-term impact of photovoltaic module aging on the power supply system efficiency is not considered and that there is a lack of multi-functional unit collaborative control logic for the data center scenario.

[0006] To address the aforementioned technical problems, one objective of this invention is to provide a power supply system for an integrated optical-storage data center based on MPPT tracing, comprising: The MPPT intelligent tracking unit has a built-in local storage unit. It collects the output electrical parameters of the photovoltaic array and environmental operating parameters, including sunshine duration. Based on the cumulative operating data of the photovoltaic modules stored in the local storage unit, it calculates the aging coefficient of the modules. It works in conjunction with an improved perturbation observation method and a fuzzy control algorithm. Combined with the attenuation compensation model established by sunshine duration and aging characteristics of photovoltaic modules, it adaptively corrects the maximum power point reference voltage of the photovoltaic. An energy storage adaptive management unit implements charge and discharge adaptation control based on the operating status of the energy storage device, and matches the supply and demand relationship between photovoltaic output and data center load by monitoring the remaining energy storage capacity and core operating parameters. The data center load dynamic allocation unit implements targeted allocation of power resources based on the differentiated load requirements of the data center, and allocates power resources by dividing the load priority. A multi-mode power supply switching unit implements power supply mode switching control based on the operating status of photovoltaic power supply, energy storage power supply and grid backup power supply, and performs power supply mode switching by monitoring the operating status of each power supply mode; The central control unit, in conjunction with the MPPT intelligent tracking unit, the energy storage adaptive management unit, the data center load dynamic allocation unit, and the multi-mode power supply switching unit, carries out the power supply control of the integrated photovoltaic and energy storage data center. It communicates bidirectionally with the MPPT intelligent tracking unit, the energy storage adaptive management unit, the data center load dynamic allocation unit, and the multi-mode power supply switching unit, and integrates data interaction and command scheduling functions.

[0007] As a further improvement to this technical solution, the MPPT intelligent tracking unit includes a component aging coefficient acquisition module; the component aging coefficient acquisition module acquires the cumulative operating years of the photovoltaic module. Cumulative actual power generation And retrieve the pre-stored factory-calibrated rated power generation of the components. Calculate the component aging coefficient ;in, The calculation depends on the age-related decay correction factor. Power generation attenuation correction coefficient ,and The calibration was determined using data from the photovoltaic module's factory aging test.

[0008] As a further improvement to this technical solution, the MPPT intelligent tracking unit also includes a dynamic step size adjustment module, which is used to combine the real-time power change characteristics of the photovoltaic array with the component aging coefficient. Dynamically adjust the perturbation step size of the improved perturbation observation method The dynamic step size adjustment module dynamically adjusts the perturbation step size of the improved perturbation observation method to adapt to the power response characteristics of the component after attenuation. The process includes the following steps: S12.1 Real-time acquisition of power data from two adjacent sampling periods of the photovoltaic array, and calculation of the power change. Simultaneously, a signal connection is established with the component aging factor acquisition module to receive the component aging factor output by the component aging factor acquisition module. ; S12.2 Retrieve the pre-stored rated power of the photovoltaic array ,Establish and Association analysis model; S12.3. Through correlation analysis model, determine the relative position of the current power state of the photovoltaic array with the maximum power point, as well as the trend and magnitude of the power approaching the maximum power point; S12.4, Based on component aging coefficient The impact of component performance degradation on power output response speed and sensitivity was analyzed, and the initial disturbance step size was determined. Adjustment direction; S12.5, combining the changing trend and magnitude obtained from S12.3, and the adjustment direction determined in S12.4, adjust the initial disturbance step size. Dynamic corrections are performed to generate a final perturbation step size that adapts to the current component degradation state and power point tracking requirements. ; S12.6, Final perturbation step size As a basic adjustment signal, it is output to the algorithm co-execution stage of the MPPT intelligent tracking unit to provide initial adjustment basis for subsequent disturbance direction calibration.

[0009] As a further improvement to this technical solution, the MPPT intelligent tracking unit also includes a dual-algorithm collaborative scheduling module, which is based on the characteristics of environmental operating condition parameter changes and component aging coefficients. and dynamic perturbation step size This triggers the collaborative operation of the improved disturbance observation method and the fuzzy control algorithm to calibrate the disturbance direction to adapt to complex operating conditions and component attenuation states; the collaborative control process of the dual-algorithm collaborative scheduling module includes the following steps: S13.1 Establish signal connections with the dynamic step size adjustment module, component aging coefficient acquisition module, and environmental condition parameter acquisition terminal, and receive the disturbance step size output by the dynamic step size adjustment module. The component aging coefficient acquisition module output , as well as the rate of change of light intensity and the rate of change of temperature in environmental operating parameters; S13.2 Retrieve pre-stored threshold values ​​for light change rate, temperature change rate, and component aging coefficient. Each threshold is calibrated through a photovoltaic system environmental adaptability test; S13.3 Establish a threshold comparison model, comparing the real-time light change rate with the light change rate threshold, and the real-time temperature change rate with the temperature change rate threshold, respectively. and Conduct comparative analysis; S13.4 Determine the working mode based on the comparison results: If the real-time illumination change rate does not exceed the illumination change rate threshold, and the real-time temperature change rate does not exceed the temperature change rate threshold, and the component aging coefficient... Not less than the component aging factor threshold The improved perturbation-observation method independently controls maximum power point tracking, while the fuzzy control algorithm remains in standby mode. If the real-time illumination change rate exceeds the illumination change rate threshold, or the real-time temperature change rate exceeds the temperature change rate threshold, or the component aging coefficient... Less than the component aging factor threshold This triggers the intervention of the fuzzy control algorithm; S13.5, The fuzzy control algorithm is based on real-time values ​​of light intensity, temperature, and... Analyze the power response characteristics of the components under the current operating conditions and output the directional correction amount. ; S13.6, will With the output of the dynamic step size adjustment module Correlation calibration is performed to generate a coordinated adjustment signal, which is then output to the attenuation compensation processing stage of the MPPT intelligent tracking unit to provide a basis for the operating condition for compensation amount adaptation.

[0010] As a further improvement to this technical solution, the MPPT intelligent tracking unit also includes an attenuation compensation quantization module, which is used to combine the component aging coefficient. Cumulative sunshine duration And the dual-algorithm collaborative working state, calculating the component performance degradation compensation amount. To offset the power output deviation caused by long-term operation and accumulated solar radiation; the compensation calculation process of the attenuation compensation quantization module includes the following steps: S14.1 Establish signal connections with the component aging factor acquisition module, environmental condition parameter acquisition terminal, and dual-algorithm collaborative scheduling module, and receive the output from the component aging factor acquisition module. The sunshine duration in the environmental operating condition parameters is collected and accumulated to obtain... To obtain the working status of the dual-algorithm collaborative scheduling module, i.e., the fuzzy control algorithm intervention status or the fuzzy control algorithm non-intervention status; S14.2 Retrieve the pre-stored solar intensity adaptation coefficient Decline trend index Solar intensity adaptability coefficient With decay trend index Determined by fitting long-term outdoor operation data of photovoltaic modules; S14.3, based on Analyze the degree of basic degradation caused by component aging, and combine Quantify the cumulative effect of solar radiation on attenuation and establish the correlation logic between the degree of attenuation and the amount of compensation; S14.4 Dynamically adjust the association logic based on the collaborative working status of the two algorithms: If the fuzzy control algorithm is in the intervention state, it will adjust according to the preset rules. The adaptation coefficient; If the fuzzy control algorithm is in an uninterrupted state, maintain the initial association logic; S14.5 Based on the adjusted correlation logic, calculate the compensation amount adapted to the current attenuation state. The output is sent to the voltage correction and fusion stage of the MPPT intelligent tracking unit.

[0011] As a further improvement to this technical solution, the MPPT intelligent tracking unit also includes a voltage correction fusion module, which is used to fuse the dynamic disturbance step size correction amount. Direction correction amount and attenuation compensation amount To achieve the maximum power point reference voltage for photovoltaics The adaptive correction; the voltage correction process of the voltage correction fusion module includes the following steps: S15.1 Establishes signal connections with the dynamic step size adjustment module, the dual-algorithm collaborative scheduling module, the attenuation compensation quantization module, and the local storage unit, and receives the output from the dynamic step size adjustment module. The output of the dual-algorithm collaborative scheduling module The output of the attenuation compensation quantization module ; S15.2 Retrieve the standard maximum power point voltage of the photovoltaic module as specified by the manufacturer from the local storage unit. ; S15.3, Establish a multi-parameter fusion model, and... As a reference voltage, it is incorporated according to preset logic. The step size adjustment function Orientation calibration function and The attenuation compensation effect; S15.4 Calculate the photovoltaic maximum power point reference voltage adapted to the current operating conditions and module degradation state using a multi-parameter fusion model. ; S15.5, will As control commands, they are transmitted to the voltage regulation unit of the photovoltaic array to achieve adaptive tracking control of the maximum power point.

[0012] As a further improvement to this technical solution, the energy storage adaptive management unit includes an energy storage state monitoring module and a charge / discharge adaptation control module, wherein: The energy storage status monitoring module collects the remaining energy storage capacity, charging and discharging power and core operating parameters in real time based on the energy storage equipment operation data, photovoltaic real-time output data and computer room load power demand data, providing status support for charging and discharging adaptation control. The charge / discharge adaptation control module, based on the data collected by the energy storage status monitoring module, matches the supply and demand relationship between photovoltaic output and data center load, generates charge / discharge control commands, and implements adaptive charge / discharge adaptation control of the energy storage device.

[0013] As a further improvement to this technical solution, the dynamic load allocation unit for the computer room includes a load technical parameter acquisition module, a power supply priority technical classification module, and a power supply resource targeted allocation module, wherein: The load technical parameter acquisition module is used to collect the electrical power, service response time requirements, equipment rated current and power supply voltage threshold of each load in the computer room; The power supply priority classification module classifies the power supply priority of the load based on the electrical power, service response time requirements, equipment rated current and power supply voltage threshold of the load technical parameter acquisition module, combined with preset technical priority rules. The power supply resource targeted allocation module, based on the priority results of the power supply priority technology division module, implements targeted allocation of power supply resources through power supply path control of hardware circuits to meet the differentiated power supply needs of the computer room load.

[0014] As a further improvement to this technical solution, the multi-mode power supply switching unit includes a power supply status monitoring module and a power supply mode switching control module, wherein: The power supply status monitoring module monitors key operating parameters of each power supply mode in real time based on the operating status of photovoltaic power supply, energy storage power supply and grid backup power supply. The power supply mode switching control module performs power supply mode switching control based on the monitoring data of the power supply status monitoring module, ensuring the continuity and reliability of power supply to the computer room.

[0015] The second objective of this invention is to provide a power supply control method for an integrated photovoltaic and energy storage data center based on MPPT tracking. The method, based on the aforementioned MPPT tracking-based power supply system for the integrated photovoltaic and energy storage data center, includes the following steps: S1. When the system starts, the central control unit first establishes bidirectional communication with the MPPT intelligent tracking unit, the energy storage adaptive management unit, the data center load dynamic allocation unit, and the multi-mode power supply switching unit. Then, it triggers the MPPT intelligent tracking unit, the energy storage adaptive management unit, the data center load dynamic allocation unit, and the multi-mode power supply switching unit to complete initialization synchronously. After collecting the corresponding initial data, the data is uploaded to the central control unit for backup. S2. The central control unit commands the MPPT intelligent tracking unit to start maximum power point tracking: first, calculate the aging coefficient of the component, then combine the real-time power change of the photovoltaic array to correct the disturbance step size, then determine the algorithm working mode based on the environmental operating parameters and aging coefficient, then calculate the attenuation compensation amount, and finally integrate multiple parameters to correct the standard maximum power point voltage and generate control commands to be transmitted to the photovoltaic array. S3. After receiving the photovoltaic output data from the MPPT intelligent tracking unit, the central control unit instructs the energy storage adaptive management unit to collect energy storage operation parameters in real time, match supply and demand with photovoltaic output and data center load requirements, generate charging and discharging commands, and control the energy storage equipment to execute them. S4. After the energy storage and photovoltaic power supply resources are determined, the central control unit instructs the data center load dynamic allocation unit to: divide the power supply priority according to the differentiated needs of the data center load, and allocate power supply resources in a targeted manner according to the priority. S5, the central control unit synchronously commands the multi-mode power supply switching unit to monitor the status of photovoltaic, energy storage and grid backup power supply in real time. If the photovoltaic or energy storage power supply is insufficient, the power supply mode will be switched immediately to ensure the continuous power supply to the computer room. S6. The central control unit continuously receives operational feedback data from the MPPT intelligent tracking unit, energy storage adaptive management unit, data center load dynamic allocation unit, and multi-mode power supply switching unit, and dynamically optimizes the control parameters of the MPPT intelligent tracking unit, energy storage adaptive management unit, data center load dynamic allocation unit, and multi-mode power supply switching unit. If a sudden change in operating conditions, abnormal component aging, or power supply fluctuation is detected, the corresponding unit is immediately triggered to re-execute the corresponding S2-S5 to achieve closed-loop control of the entire system.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention uses an MPPT intelligent tracking unit to collect data such as the cumulative operating years and cumulative actual power generation of photovoltaic modules to calculate the module aging coefficient. It combines the real-time power change of the photovoltaic array to dynamically correct the perturbation step size of the improved perturbation observation method. Then, through dual-algorithm collaborative scheduling and attenuation compensation quantization, it finally integrates multiple parameters to adaptively correct the standard maximum power point reference voltage. This effectively addresses the performance degradation problem of photovoltaic modules after long-term operation, improves the MPPT tracking accuracy in module aging scenarios, and avoids power generation efficiency loss caused by module aging. 2. This invention uses an energy storage adaptive management unit to collect core parameters such as remaining energy storage capacity and charging / discharging power in real time, and matches the real-time output of photovoltaic power with the supply and demand relationship of the data center load to implement charging and discharging control. At the same time, the data center load dynamic allocation unit divides the power supply priority according to the differentiated needs of the data center load and allocates power supply resources in a targeted manner. The multi-mode power supply switching unit monitors the status of photovoltaic, energy storage and grid backup power supply in real time and executes switching, realizing the coordinated operation of MPPT tracking, energy storage management, load allocation and power supply switching, and adapting to the scenario requirements of data center for power supply continuity and differentiated load power supply. 3. This invention establishes bidirectional communication between the central control unit and the MPPT intelligent tracking unit, the energy storage adaptive management unit, the data center load dynamic allocation unit, and the multi-mode power supply switching unit, integrating data interaction and command scheduling functions. It receives operational feedback data from each unit in real time and dynamically optimizes control parameters. If a sudden change in operating conditions, abnormal component aging coefficient, or power supply status fluctuation is detected, the corresponding unit can be triggered to re-execute the control steps, forming a closed-loop control of the entire system, further ensuring the stable operation of the photovoltaic-storage integrated data center power supply system. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram of the method steps of the present invention; The meanings of the labels in the diagram are as follows: 1. MPPT intelligent tracking unit; 11. Component aging coefficient acquisition module; 12. Dynamic step size adjustment module; 13. Dual algorithm collaborative scheduling module; 14. Attenuation compensation quantization module; 15. Voltage correction fusion module; 2. Energy storage adaptive management unit; 21. Energy storage status monitoring module; 22. Charge and discharge adaptation control module; 3. Dynamic load distribution unit for computer room; 31. Load technical parameter acquisition module; 32. Power supply priority classification module; 33. Directed allocation module for power supply resources; 4. Multi-mode power supply switching unit; 41. Power supply status monitoring module; 42. Power supply mode switching control module; 5. Central control unit. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1As shown, this embodiment provides a power supply system for an integrated optical-storage data center based on MPPT tracing, including: MPPT Intelligent Tracking Unit 1 has a built-in local storage unit. It collects the output electrical parameters of the photovoltaic array and environmental operating parameters, including sunshine duration. Based on the cumulative operating data of the photovoltaic modules stored in the local storage unit, it calculates the aging coefficient of the modules. It works in conjunction with an improved perturbation observation method and a fuzzy control algorithm. Combined with the attenuation compensation model established by sunshine duration and aging characteristics of photovoltaic modules, it adaptively corrects the maximum power point reference voltage of the photovoltaic. In this embodiment, the MPPT intelligent tracking unit 1 includes a module aging coefficient acquisition module 11; the module aging coefficient acquisition module 11 acquires the cumulative operating years of the photovoltaic modules. Cumulative actual power generation And retrieve the pre-stored factory-calibrated rated power generation of the components. Calculate the component aging coefficient ;in, The calculation depends on the age-related decay correction factor. Power generation attenuation correction coefficient ,and The calibration was determined using data from the photovoltaic module's factory aging test.

[0020] Specifically, the component aging coefficient acquisition module 11 calculates the component aging coefficient. First, the start time of photovoltaic module operation is recorded by the built-in real-time clock (RTC) of MPPT intelligent tracking unit 1, and the cumulative operating days are converted into cumulative operating years. The conversion relationship is as follows Simultaneously, the output voltage of the photovoltaic module is acquired in real time through a high-precision analog-to-digital converter chip (such as ADS1256). With output current According to the power calculation formula The real-time power of each sampling period is obtained, and then the real-time power is processed according to the sampling period. By accumulating points, the cumulative actual power generation can be obtained. The integral formula is: ; in, , The first The output voltage and current for each sampling period This represents the total number of samples. Subsequently, the pre-stored factory-calibrated rated power generation of the photovoltaic modules is retrieved from the local storage unit. (Provided by the component manufacturer, the formula is) , The rated power of the components is specified at the factory. (This refers to the annual equivalent power generation duration under standard irradiation conditions) and the annual degradation correction factor determined through factory aging tests of photovoltaic modules. With the correction factor for power generation decline (satisfy ).

[0021] Furthermore, after completing the above data preparation, calculate the component aging factor using the following formula. : ; In the formula, The lifespan of photovoltaic modules is designed according to the manufacturer's brand value, such as crystalline silicon modules, which are typically [value missing]. =25 years), The range of values ​​is The closer the value is to 1, the less severe the component degradation; the closer it is to 0, the more severe the degradation. After the calculation is completed, the component aging coefficient acquisition module 11 will... The data is transmitted to the dynamic step size adjustment module 12 and the dual-algorithm collaborative scheduling module 13 respectively, providing core data support for subsequent step size adjustment and algorithm collaboration.

[0022] In traditional MPPT control, the tracking algorithm lacks aging adaptation capabilities, and the calculation logic does not differentiate between the aging levels of components. Using the "standard power generation model" under high aging conditions leads to distorted efficiency assessments, while the lack of differentiated strategies under low aging conditions results in wasted computing power. Furthermore, the performance data of high-aging components is easily overwhelmed by a large amount of low-aging data, making it difficult for the algorithm to grasp methods for dealing with aging components. In this embodiment, the component aging coefficient acquisition module 11 collects the cumulative operating years and cumulative actual power generation of photovoltaic modules, combined with the factory-calibrated rated power generation and the aging test-calibrated age-related degradation correction coefficient and power generation degradation correction coefficient, to calculate the component aging coefficient to adjust the tracking target priority. Its core function is: in high-aging scenarios (such as modules that have been operating for over 15 years), it enables the tracking algorithm to prioritize adapting to the component degradation characteristics, improving the accuracy of power generation efficiency quantification correction; in low-aging scenarios (such as newly commissioned modules), it enables the algorithm to use standard strategies, reducing computing power consumption. Meanwhile, the component aging coefficient acquisition module 11 accurately captures the aging degree of the components, helping the algorithm to quickly master the adaptation logic of different aging stages, fully explore the power generation potential of the components throughout their entire life cycle, and ensure the long-term effectiveness and authenticity of the power generation efficiency of the photovoltaic-storage system.

[0023] In this embodiment, the MPPT intelligent tracking unit 1 further includes a dynamic step size adjustment module 12, which is used to combine the real-time power change characteristics of the photovoltaic array with the component aging coefficient. Dynamically adjust the perturbation step size of the improved perturbation observation method The dynamic step size adjustment module 12 dynamically adjusts the perturbation step size of the improved perturbation observation method to adapt to the power response characteristics of the component after attenuation, providing a basic adjustment amount for accurate maximum power point tracking; The process includes the following steps: S12.1 Real-time acquisition of power data from two adjacent sampling periods of the photovoltaic array, and calculation of the power change. Simultaneously, a signal connection is established with the component aging coefficient acquisition module 11 to receive the component aging coefficient output by the component aging coefficient acquisition module 11. ; S12.2 Retrieve the pre-stored rated power of the photovoltaic array ,Establish and Association analysis model; S12.3. Through correlation analysis model, determine the relative position of the current power state of the photovoltaic array with the maximum power point, as well as the trend and magnitude of the power approaching the maximum power point; S12.4, Based on component aging coefficient The impact of component performance degradation on power output response speed and sensitivity was analyzed, and the initial disturbance step size was determined. Adjustment direction; S12.5, combining the changing trend and magnitude obtained from S12.3, and the adjustment direction determined in S12.4, adjust the initial disturbance step size. Dynamic corrections are performed to generate a final perturbation step size that adapts to the current component degradation state and power point tracking requirements. ; S12.6, Final perturbation step size As a basic adjustment signal, it is output to the algorithm coordination execution stage of MPPT intelligent tracking unit 1 to provide initial adjustment basis for subsequent disturbance direction calibration.

[0024] Specifically, the dynamic step size adjustment module 12 calculates the perturbation step size of the improved perturbation observation method. At the same time, power data of the photovoltaic array is first collected in real time between two adjacent sampling periods. (Real-time power in the previous sampling period) and (Real-time power during the current sampling period), both are passed through The calculated value is then used to calculate the power change using the following formula. : ; Simultaneously, the dynamic step size adjustment module 12 establishes a signal connection with the component aging coefficient acquisition module 11 to obtain the component aging coefficient. And retrieve the pre-stored factory-calibrated rated power of the photovoltaic array from the local storage unit. The standard maximum power point voltage of the photovoltaic module as specified at the factory. In the initial perturbation step size calculation stage, to balance the maximum power point tracking speed and stability, based on... Set the initial perturbation step size according to the following formula. : ; In the formula, This is the step size factor, determined through environmental adaptability testing of the photovoltaic system. It is used to avoid power oscillation caused by an excessively large initial step size or low tracking efficiency caused by an excessively small initial step size.

[0025] Subsequently established and The correlation analysis model, through The ratio determines the relative position and magnitude of change between the current power and the maximum power point of the photovoltaic array. when At that time, the current power is far from the maximum power point and the change range is large; when At that time, the current power is close to the maximum power point and the change range is moderate; when At that time, the current power is close to the maximum power point and the change is small; Simultaneously based on Analyze the impact of component performance degradation on power response sensitivity and determine the power variation coefficient. With aging effect coefficient : Time to take , Time to take , Time to take ; (When attenuation is slight) take , (When the attenuation is significant) take Finally, calculate the final perturbation step size using the following formula. : ; After the calculation is completed, the dynamic step size adjustment module 12 will... As basic adjustment signals, they are output to the algorithm collaborative execution link and voltage correction fusion module 15 of MPPT intelligent tracking unit 1, respectively.

[0026] In traditional perturbation observation methods, the perturbation step size is fixed, and the adjustment logic does not take into account power changes and component aging. In high-power-change scenarios, using a small step size leads to tracking speed lag, while in low-power-change scenarios, a large step size easily causes oscillations. Furthermore, the experience in step size optimization for high-aging scenarios is easily overwhelmed by experience from numerous conventional scenarios, making it difficult for the algorithm to grasp methods for handling aging combined with power fluctuations. The dynamic step size adjustment module 12 collects the power change of the photovoltaic array in adjacent cycles, combines it with the component aging coefficient, first sets an initial perturbation step size, then determines the power change coefficient and aging impact coefficient based on the power change amplitude and aging degree, and finally calculates the dynamic perturbation step size to adjust the perturbation amplitude priority. Its core function is: in high-power-change and high-aging scenarios (such as aging components in cloudy weather), it ensures that the step size balances tracking speed and accuracy, improving dynamic adaptability; in low-power-change and low-aging scenarios (such as new components in sunny weather), it maintains the standard step size configuration, ensuring perturbation stability. Meanwhile, the dynamic step size adjustment module 12 accurately adapts to the combination of power changes and aging scenarios, helping the algorithm to quickly master the step size strategy under complex operating conditions, improve the response efficiency of MPPT in various scenarios, and ensure the agility and stability of power generation tracking in the photovoltaic-storage system.

[0027] In this embodiment, the MPPT intelligent tracking unit 1 further includes a dual-algorithm collaborative scheduling module 13, which is based on the characteristics of environmental operating condition parameter changes and component aging coefficients. and dynamic perturbation step size This triggers the collaborative operation of the improved disturbance observation method and the fuzzy control algorithm to calibrate the disturbance direction to adapt to complex operating conditions and component attenuation states; the collaborative control process of the dual-algorithm collaborative scheduling module 13 includes the following steps: S13.1 Establish signal connections with the dynamic step size adjustment module 12, the component aging coefficient acquisition module 11, and the environmental condition parameter acquisition terminal, and receive the disturbance step size output by the dynamic step size adjustment module 12. The component aging coefficient acquisition module 11 outputs , as well as the rate of change of light intensity and the rate of change of temperature in environmental operating parameters; S13.2 Retrieve pre-stored threshold values ​​for light change rate, temperature change rate, and component aging coefficient. Each threshold is calibrated through a photovoltaic system environmental adaptability test; S13.3 Establish a threshold comparison model, comparing the real-time light change rate with the light change rate threshold, and the real-time temperature change rate with the temperature change rate threshold, respectively. and Conduct comparative analysis; S13.4 Determine the working mode based on the comparison results: If the real-time illumination change rate does not exceed the illumination change rate threshold, and the real-time temperature change rate does not exceed the temperature change rate threshold, and the component aging coefficient... Not less than the component aging factor threshold The improved perturbation-observation method independently controls maximum power point tracking, while the fuzzy control algorithm remains in standby mode. If the real-time illumination change rate exceeds the illumination change rate threshold, or the real-time temperature change rate exceeds the temperature change rate threshold, or the component aging coefficient... Less than the component aging factor threshold This triggers the intervention of the fuzzy control algorithm; S13.5, The fuzzy control algorithm is based on real-time values ​​of light intensity, temperature, and... Analyze the power response characteristics of the components under the current operating conditions and output the directional correction amount. ; S13.6, will With the output of the dynamic step size adjustment module 12 The associated calibration is performed to generate a coordinated adjustment signal, which is then output to the attenuation compensation processing stage of the MPPT intelligent tracking unit 1 to provide a basis for the working condition for the compensation amount adaptation.

[0028] Specifically, when the dual-algorithm collaborative scheduling module 13 triggers the collaboration between the improved disturbance observation method and the fuzzy control algorithm, it first establishes signal connections with the dynamic step size adjustment module 12, the component aging coefficient acquisition module 11, and the environmental condition parameter acquisition terminal, respectively, and receives the disturbance step size output by the dynamic step size adjustment module 12. The component aging coefficient acquisition module 11 outputs And the light intensity in the environmental operating parameters. (Collected via a light sensor) and component surface temperature (Data collected via a temperature sensor); The rate of change of environmental parameters was then calculated to quantify the stability of environmental conditions. The real-time rate of change of illumination was calculated using the following formulas. With real-time temperature change rate : ; ; In the formula, , These represent the light intensity and temperature of the previous sampling period, respectively. , These represent the light intensity and temperature for the current sampling period, respectively. The sampling period.

[0029] Simultaneously, retrieve the pre-stored light change rate threshold from the local storage unit. Temperature change rate threshold and component aging coefficient threshold All three were determined through photovoltaic system environmental adaptability testing and calibration. Used to determine whether fuzzy control algorithms are needed to determine the degree of component degradation; Next, a threshold comparison model is established, and the thresholds are compared separately. and , and , and Comparative analysis: If and and This indicates that the environmental conditions are stable and the component degradation is slight. The improved disturbance observation method independently dominates maximum power point tracking, while the fuzzy control algorithm is in standby mode. or or This indicates that large fluctuations in environmental operating conditions or significant component degradation trigger the intervention of the fuzzy control algorithm.

[0030] Understandably, when the fuzzy control algorithm is involved, it operates in a parallel and collaborative mode with the improved disturbance observation method: the improved disturbance observation method is based on the output of the dynamic step size adjustment module 12. The system performs power disturbance and step size adjustment, while the fuzzy control algorithm outputs a direction correction based on environmental and aging parameters. The outputs of the two are weighted and fused in the voltage correction fusion module 15 (e.g., according to preset weights). Integration Together they participate in the maximum power point reference voltage. The correction calculation is performed. This parallel collaboration is not a simple algorithm switch, but rather, while maintaining the basic step size adjustment logic of the improved perturbation-observation method, it introduces the directional calibration capability of fuzzy control. It utilizes both the intuitiveness of the perturbation-observation method and the adaptability of fuzzy control to complex working conditions to achieve dual-dimensional optimization of "step size + direction", ensuring a dual improvement in tracking accuracy and response speed under high environmental fluctuations or high aging scenarios.

[0031] Furthermore, after the fuzzy control algorithm is introduced, the input quantity is first fuzzified: Light intensity Classified as "low ( ),middle( ),high( Three fuzzy sets, representing temperature Classified as "low ( ),middle( ),high( ) "3 fuzzy sets, Classified as "slight attenuation" ), moderate attenuation ( ), severe attenuation ( The three fuzzy sets are used; the direction correction of the output is also included. Classified as "significant negative correction" ), small negative correction ( Uncorrected ), minor positive correction ( ), significant positive correction ( ) 5 fuzzy sets; Then, the preset fuzzy rule base (such as "if") is invoked. high, high, Severe attenuation, then ""like middle, middle, Moderate attenuation, then (etc.), the membership degree of each rule is calculated using the triangular membership function. Then, the centroid method is used to solve the fuzzy output to obtain the accurate direction correction. The solution formula is: ; In the formula, To determine the number of fuzzy rules, For the first The output value of the fuzzy set center value corresponding to each rule; Finally With the output of the dynamic step size adjustment module 12 The associated calibration is performed to generate a coordinated adjustment signal, which is then output to the attenuation compensation processing stage of the MPPT intelligent tracking unit 1 to provide a basis for the working condition for the compensation amount adaptation.

[0032] In traditional MPPT algorithms, the control strategy selection is singular, and the switching logic does not take into account environmental changes and component aging. In high-environmental-fluctuation scenarios, rigidly adhering to the "perturbation-observation method" leads to tracking inaccuracies. In low-environmental-fluctuation scenarios, the single algorithm lacks adaptability, and experience in switching algorithms for highly complex scenarios is easily overwhelmed by experience from numerous simple scenarios, making it difficult for the algorithm to master methods to cope with multi-variable interference. The dual-algorithm collaborative scheduling module 13 collects ambient light change rate, temperature change rate, and component aging coefficient to determine environmental conditions and aging levels to adjust algorithm priorities. Simultaneously, it introduces a fuzzy control algorithm to fuzzify light, temperature, and aging coefficients, outputting directional correction amounts through a preset fuzzy rule base. Its core function is: in high-environmental-fluctuation or high-aging scenarios (such as aged components on cloudy days), the fuzzy control algorithm intervenes, significantly enhancing tracking robustness; in low-environmental-fluctuation and low-aging scenarios (such as new components on sunny days), the perturbation-observation method dominates, ensuring tracking efficiency. Meanwhile, the dual-algorithm collaborative scheduling module 13 focuses on scheduling algorithm combinations for complex scenarios, helping the algorithm quickly master the response logic for multiple operating conditions, improving the adaptability of MPPT in various scenarios, and ensuring the intelligence and reliability of power generation control of the photovoltaic and energy storage system.

[0033] In this embodiment, the MPPT intelligent tracking unit 1 further includes an attenuation compensation quantization module 14, which is used to combine the component aging coefficient. Cumulative sunshine duration And the dual-algorithm collaborative working state, calculating the component performance degradation compensation amount. To offset the power output deviation caused by long-term operation and accumulated solar radiation; the compensation calculation process of the attenuation compensation quantization module 14 includes the following steps: S14.1 Establish a signal connection with the component aging coefficient acquisition module 11, the environmental condition parameter acquisition terminal, and the dual-algorithm collaborative scheduling module 13, and receive the output of the component aging coefficient acquisition module 11. The sunshine duration in the environmental operating condition parameters is collected and accumulated to obtain... The working status of the dual-algorithm collaborative scheduling module 13 is obtained, namely, the fuzzy control algorithm intervention status or the fuzzy control algorithm non-intervention status. S14.2 Retrieve the pre-stored solar intensity adaptation coefficient Decline trend index Solar intensity adaptability coefficient With decay trend index Determined by fitting long-term outdoor operation data of photovoltaic modules; S14.3, based on Analyze the degree of basic degradation caused by component aging, and combine Quantify the cumulative effect of solar radiation on attenuation and establish the correlation logic between the degree of attenuation and the amount of compensation; S14.4 Dynamically adjust the association logic based on the collaborative working status of the two algorithms: If the fuzzy control algorithm is in the intervention state, it will adjust according to the preset rules. The adaptation coefficient; If the fuzzy control algorithm is in an uninterrupted state, maintain the initial association logic; S14.5 Based on the adjusted correlation logic, calculate the compensation amount adapted to the current attenuation state. The output is sent to the voltage correction and fusion stage of the MPPT intelligent tracking unit 1.

[0034] Specifically, the attenuation compensation quantization module 14 calculates the component performance attenuation compensation amount. At the same time, it first establishes a signal connection with the component aging coefficient acquisition module 11, the environmental condition parameter acquisition terminal, and the dual-algorithm collaborative scheduling module 13, and receives the output of the component aging coefficient acquisition module 11. The sunshine duration is obtained by collecting the sunshine duration from the environmental operating condition parameters and accumulating them according to the sampling period. At the same time, the working status of the dual-algorithm collaborative scheduling module 13 (i.e., the fuzzy control algorithm intervention status or non-intervention status) is obtained. Then, the pre-stored solar intensity adaptation coefficient is retrieved from the local storage unit. With decay trend index Both were determined by fitting long-term outdoor operation data of photovoltaic modules, among which Used to quantify the weight of the effect of accumulated sunlight on decay. Used to characterize the trend of attenuation with the duration of sunshine; Then based on Analyze the degree of basic degradation caused by component aging: The closer to 1, the less severe the basic decay. The closer to 0, the greater the degree of basic degradation; Simultaneously combined Quantify the cumulative effect of solar radiation on attenuation and establish a logical relationship between the degree of attenuation and the amount of compensation: the greater the basic attenuation, the more... The longer the length, the more compensation is required. The larger the value, the more dynamic the correlation logic is adjusted based on the collaborative working state of the two algorithms: if the fuzzy control algorithm is in the intervention state, it indicates that the current working condition is complex or the component is significantly degraded, and the logic is adjusted according to preset rules (such as...). Increase by 10% (adjustment) The adaptation coefficient is adjusted to enhance the compensation effect; if the fuzzy control algorithm is in an uninterrupted state, it indicates that the current operating condition is stable and the component attenuation is slight, so the initial correlation logic remains unchanged. Finally, based on the adjusted correlation logic, the compensation amount adapted to the current attenuation state is calculated according to the following formula. : ; In the formula, The standard sunshine duration is the benchmark value (set to 1000h according to the industry standard). The range of values ​​is limited to To avoid overcompensation leading to abnormal output voltage of the photovoltaic array, the attenuation compensation quantization module 14 will... The voltage correction and fusion stage is output to MPPT intelligent tracking unit 1.

[0035] Traditional MPPT control lacks attenuation compensation logic, and the compensation strategy does not consider component aging and cumulative sunlight. In high-aging scenarios, "uncompensated tracking" leads to wasted power generation potential; in low-aging scenarios, blind compensation affects tracking accuracy, and the high-attenuation compensation demand is easily overwhelmed by numerous low-attenuation demands, making it difficult for the algorithm to grasp methods for dealing with severe aging. The attenuation compensation quantification module 14 collects component aging coefficients and cumulative sunlight duration, combining them with a sunlight intensity adaptation coefficient and attenuation trend index fitted from long-term operating data to calculate the attenuation compensation amount to adjust voltage correction priority. Its core function is: in high-aging and long-sunlight scenarios (such as sunny days for components that have been in operation for many years), it ensures the compensation amount accurately matches component attenuation, reducing power generation efficiency loss due to aging; in low-aging and short-sunlight scenarios (such as cloudy days for new components), it appropriately reduces the compensation amount, ensuring tracking accuracy is not disturbed. Simultaneously, the attenuation compensation quantification module 14 accurately quantifies the degree of attenuation compensation, helping the algorithm quickly grasp compensation strategies for different aging + sunlight scenarios, reducing the magnitude of power generation capacity attenuation after component aging, and ensuring the sustainability and efficiency of the photovoltaic-storage system's power generation performance.

[0036] In this embodiment, the MPPT intelligent tracking unit 1 further includes a voltage correction fusion module 15, which is used to fuse the dynamic disturbance step size correction amount. Direction correction amount and attenuation compensation amount To achieve the maximum power point reference voltage for photovoltaics The adaptive correction; the voltage correction process of the voltage correction fusion module 15 includes the following steps: S15.1 Establishes a signal connection with the dynamic step size adjustment module 12, the dual-algorithm collaborative scheduling module 13, the attenuation compensation quantization module 14, and the local storage unit, and receives the output of the dynamic step size adjustment module 12. The output of the dual-algorithm collaborative scheduling module 13 The output of the attenuation compensation quantization module 14 ; S15.2 Retrieve the standard maximum power point voltage of the photovoltaic module as specified by the manufacturer from the local storage unit. ; S15.3, Establish a multi-parameter fusion model, and... As a reference voltage, it is incorporated according to preset logic. The step size adjustment function Orientation calibration function and The attenuation compensation effect; S15.4 Calculate the photovoltaic maximum power point reference voltage adapted to the current operating conditions and module degradation state using a multi-parameter fusion model. ; S15.5, will As control commands, they are transmitted to the voltage regulation unit of the photovoltaic array to achieve adaptive tracking control of the maximum power point.

[0037] Specifically, the voltage correction fusion module 15 achieves the maximum power point reference voltage for photovoltaics. During adaptive correction, signal connections are first established with the dynamic step size adjustment module 12, the dual-algorithm collaborative scheduling module 13, the attenuation compensation quantization module 14, and the local storage unit, respectively receiving the output of the dynamic step size adjustment module 12. The output of the dual-algorithm collaborative scheduling module 13 The output of the attenuation compensation quantization module 14 And retrieve the standard maximum power point voltage of the photovoltaic module as specified at the factory from the local storage unit. ; Subsequently, a multi-parameter fusion model was established to... As a reference voltage, it is incorporated according to preset weights. The step size adjustment function Orientation calibration function and The attenuation compensation effect is determined based on the mechanism of each parameter's effect on system performance and engineering practice experience in this field (where the step size correction weight is used). =0.3, Direction Correction Weight =0.4, Attenuation Compensation Weight =0.3 (to balance tracking stability and compensation accuracy), the fusion formula is: ; The maximum power point reference voltage of the photovoltaic system, adapted to the current operating conditions and the degradation state of the modules, is calculated using this formula. Then, the voltage correction fusion module 15 will As control commands, these are transmitted to the voltage regulation unit (such as a DC-DC converter) of the photovoltaic array: by adjusting the PWM duty cycle of the voltage regulation unit (adjustment range 10%-90%), the output voltage of the photovoltaic array is directed towards... Approximation. After each adjustment, the actual output voltage of the photovoltaic array is fed back through the voltage acquisition interface, and compared with... The difference is compared, and if the difference is greater than a preset threshold (e.g., 0.1V), the adjustment process is repeated until the actual output voltage matches the threshold. If the difference is less than or equal to a preset threshold, adaptive tracking control of the maximum power point will be achieved. At the same time, after each revision and the corresponding operating parameters (light intensity, temperature, The data is stored in the local storage unit according to the timestamp (storage period is 5 minutes) to support subsequent data traceability and control parameter optimization.

[0038] In traditional voltage correction, reference voltage generation only considers a single factor, and the fusion logic does not incorporate the coordination of step size, direction, and attenuation. In scenarios with multiple correction factors, rigidly adhering to "single-factor correction" can lead to inaccurate reference voltage. In single-correction scenarios, multi-factor fusion can compromise simplicity, and the fusion experience of complex corrections can be overwhelmed by a large amount of simple correction experience, making it difficult for the algorithm to master methods for handling multi-dimensional corrections. The voltage correction fusion module 15 collects dynamic step size, direction correction amount, and attenuation compensation amount, combines the factory-calibrated standard maximum power point voltage with the weights of multiple parameters determined based on their impact on system performance and engineering practice experience in this field, and calculates the fused reference voltage to adjust the voltage correction priority. Its core function is to: enhance the adaptability of the reference voltage in scenarios with multiple correction factors (such as high aging + high power variation scenarios), significantly improving the final accuracy of maximum power point tracking; and maintain the simplicity of the reference voltage in scenarios with a single correction factor (such as low aging + low power variation scenarios), ensuring that the tracking response speed is not affected. Meanwhile, the voltage correction fusion module 15 efficiently integrates multiple correction dimensions, helping the algorithm quickly master the synthesis logic of complex corrections, improving the accuracy of reference voltage under various operating conditions, and ensuring the accuracy and stability of power generation output from the photovoltaic and energy storage system.

[0039] Energy storage adaptive management unit 2 implements charge and discharge adaptation control based on the operating status of energy storage equipment. By monitoring the remaining energy storage capacity and core operating parameters, it matches the supply and demand relationship between photovoltaic output and data center load. In this embodiment, the energy storage adaptive management unit 2 includes an energy storage status monitoring module 21 and a charge / discharge adaptation control module 22, wherein: The energy storage status monitoring module 21 collects the remaining energy storage capacity, charging and discharging power and core operating parameters in real time based on the energy storage equipment operation data, photovoltaic real-time output data and computer room load power demand data, providing status support for charging and discharging adaptation control. Specifically, the energy storage status monitoring module 21 acquires multi-dimensional parameters through three types of data interfaces, providing precise status support for charge and discharge adaptation control: It acquires the remaining energy storage capacity (SOC), individual cell voltage, total battery pack voltage, charge and discharge cycle count, and battery surface temperature in real time through the RS485 communication interface of the energy storage battery management system (BMS) (where SOC is calculated using the BMS's built-in ampere-hour integration method combined with open-circuit voltage calibration); it establishes a signal connection with the voltage correction fusion module 15 of the MPPT intelligent tracking unit 1 to receive real-time output power and power change trend data of the photovoltaic array; and it acquires the total load power through current and voltage sensors in the data center power distribution circuit, while simultaneously receiving real-time power consumption data of each load output by the data center load dynamic allocation unit 3, comprehensively determining the overall and priority-based power demand of the data center, with the data update cycle consistent with the sampling cycle of the MPPT intelligent tracking unit 1 (e.g., 100ms).

[0040] Furthermore, after the data collection is completed, the energy storage status monitoring module 21 preprocesses the data (removes abnormal fluctuation data), organizes it into a three-dimensional dataset of "energy storage status - photovoltaic output - load demand" according to the timestamp, stores it in the local cache unit and transmits it to the charge and discharge adapter control module 22 in real time.

[0041] Based on the data collected by the energy storage status monitoring module 21, the charge and discharge adaptation control module 22 matches the supply and demand relationship between photovoltaic output and data center load, generates charge and discharge control commands, and implements adaptive charge and discharge adaptation control of the energy storage device.

[0042] Specifically, the charge / discharge adaptation control module 22 implements adaptive charge / discharge control based on the three-dimensional dataset transmitted by the energy storage status monitoring module 21 through the logic of "supply and demand matching - instruction generation - execution feedback": First, it calculates the power difference between the real-time output of the photovoltaic system and the load demand of the data center. The system divides supply and demand scenarios based on the remaining energy storage capacity (SOC) (energy storage charging is initiated when there is surplus photovoltaic power, energy storage discharging is initiated when there is insufficient photovoltaic power, and charging / discharging or mode switching is stopped when supply and demand are balanced or energy storage reaches the boundary). Then, charging and discharging control commands are generated according to the scenario (charging power does not exceed 90% of the surplus photovoltaic power and the voltage does not exceed the rated upper limit; discharging power does not exceed 100% of the load deficit power and the voltage is not lower than the rated lower limit), and transmitted to the energy storage charging and discharging converter through the PWM signal interface. Finally, the system receives feedback data from the converter in real time and compares it with the command parameters. If the deviation exceeds the preset range (such as ±5%), the command is dynamically corrected to ensure charging and discharging adaptation accuracy.

[0043] The dynamic load allocation unit 3 for the computer room implements targeted allocation of power resources for the differentiated load needs of the computer room by allocating power resources by dividing the load priority. In this embodiment, the data center load dynamic allocation unit 3 includes a load technical parameter acquisition module 31, a power supply priority technical classification module 32, and a power supply resource directional allocation module 33, wherein: The load technical parameter acquisition module 31 is used to collect the electrical power, service response time requirements, equipment rated current and power supply voltage threshold of each load in the computer room; Specifically, the load technical parameter acquisition module 31 collects the load parameters of the computer room through a combination of hardware interface and software protocol: by using Hall current sensors connected in series in the power supply circuit of each load and voltage sensors connected in parallel, it collects the electrical power of the load (calculated by real-time voltage × real-time current), the rated current of the equipment, and the power supply voltage threshold (sampling period 500ms) in real time. The sensor signals are converted from analog to digital and then transmitted to the data processing unit via SPI bus. The system reads the business response time requirements corresponding to each load through the TCP / IP communication interface of the computer room monitoring system, or manually presets and enters them into the local storage unit (parameter update period 24 hours, updated immediately when business configuration changes).

[0044] Furthermore, after the data collection is completed, the load technical parameter collection module 31 establishes a load parameter database in the format of "load number - electrical power - rated current - power supply voltage threshold - business response time requirement", and synchronizes it to the power supply priority technical classification module 32 in real time.

[0045] The power supply priority classification module 32 classifies the power supply priority of the load based on the electrical power, service response time requirements, equipment rated current and power supply voltage threshold of the load technical parameter acquisition module 31, combined with the preset technical priority rules. Specifically, the power supply priority technical classification module 32, based on the database of the load technical parameter acquisition module 31, and combined with preset technical priority rules (core dimension weight 70%, including business response time requirements and power supply voltage threshold severity; auxiliary dimension weight 30%, including electrical power and equipment rated current stability requirements), determines the load priority through multi-dimensional quantitative scoring: Business response time requirement dimension (core dimension): Quantitative scores are set according to the business response time range: 10 points for response time ≤ 100ms, 8 points for 100ms < response time ≤ 300ms, 6 points for 300ms < response time ≤ 500ms, and 2 points for response time > 500ms. Power supply voltage threshold severity dimension (core dimension): Quantitative scores are set according to the voltage threshold range. Voltage threshold range ≤ ±2% gets 10 points, ±2% < voltage threshold range ≤ ±5% gets 8 points, ±5% < voltage threshold range ≤ ±10% gets 6 points, and voltage threshold range > ±10% gets 2 points. Electrical power dimension (auxiliary dimension): Quantitative scores are set according to electrical power range: 10 points for power > 10kW, 8 points for 5kW < power ≤ 10kW, 6 points for 1kW < power ≤ 5kW, and 2 points for power ≤ 1kW. Equipment rated current stability requirement dimension (auxiliary dimension): A quantitative score is set based on the allowable current fluctuation range. Allowable current fluctuation range ≤ ±1A earns 10 points; ±1A < allowable current fluctuation range ≤ ±3A earns 8 points; ±3A < allowable current fluctuation range ≤ ±5A earns 6 points; allowable current fluctuation range > ±5A earns 2 points. The above scoring ranges, scores, and weighting ratios are illustrative examples; those skilled in the art can make adaptive adjustments based on the actual operating requirements and load characteristics of the specific data center.

[0046] Furthermore, for each load, the core dimensions (business response time requirements, power supply voltage threshold stringency) and auxiliary dimensions (electrical power, equipment rated current stability requirements) are scored according to the above standards, and a comprehensive score is calculated according to the weight (comprehensive score = sum of core dimension scores × 70% + sum of auxiliary dimension scores × 30%). The scores are then divided into high priority (≥8 points, such as core servers), medium priority (4-7 points, such as office terminals), and low priority (<4 points, such as air conditioner auxiliary units). The division results are transmitted to the power supply resource directional allocation module 33 in real time and stored in the local database.

[0047] The power supply resource directional allocation module 33, based on the priority results of the power supply priority technology division module 32, implements the directional allocation of power supply resources through the power supply path control of the hardware circuit to meet the differentiated power supply needs of the computer room load.

[0048] Specifically, the power supply resource directional allocation module 33, based on the power supply priority technology classification module 32, realizes directional resource allocation through hardware circuit control and software logic: the module has a built-in multi-channel relay control board (each relay corresponds to a load power supply path), establishes a signal connection with the power distributor of the photovoltaic and energy storage power supply circuits to obtain the total amount of allocable resources; resources are preferentially allocated to high-priority loads, and the remaining resources are allocated in the order of "medium priority-low priority". When resources are insufficient, the priority loads are sorted according to the business response time to ensure critical loads, or the multi-mode power supply switching unit 4 is triggered to request grid supplementation, and low-priority loads are automatically disconnected from power or have their power reduced; at the same time, the power supply status of each load (on / off status, actual voltage and current) is monitored in real time and fed back to the central control unit 5. If the load power supply is abnormal, the power supply path is immediately adjusted or the power supply mode is switched.

[0049] Multi-mode power supply switching unit 4 implements power supply mode switching control based on the operating status of photovoltaic power supply, energy storage power supply and grid backup power supply, and performs power supply mode switching by monitoring the operating status of each power supply mode. In this embodiment, the multi-mode power supply switching unit 4 includes a power supply status monitoring module 41 and a power supply mode switching control module 42, wherein: The power supply status monitoring module 41 monitors key operating parameters of each power supply mode in real time based on the operating status of photovoltaic power supply, energy storage power supply and grid backup power supply. Specifically, the power supply status monitoring module 41 collects key parameters for each power supply mode through three independent monitoring channels: it is connected to the voltage correction fusion module 15 of the MPPT intelligent tracking unit 1 to collect the real-time output voltage, power, and maximum power point reference voltage of the photovoltaic array. It collects real-time light intensity; communicates with the energy storage status monitoring module 21 of the energy storage adaptive management unit 2 to collect the output voltage, power, SOC and charge / discharge converter operating status of the energy storage power supply circuit; and collects grid voltage, frequency, power supply and circuit breaker status through the voltage transformer and frequency sensor of the grid backup power supply circuit. The data of each channel is updated every 100ms, and after being organized in the format of "power supply mode-key parameter-timestamp", it is transmitted to the power supply mode switching control module 42 in real time and stored in the local cache.

[0050] The power supply mode switching control module 42 executes power supply mode switching control based on the monitoring data of the power supply status monitoring module 41 to ensure the continuity and reliability of the power supply to the computer room.

[0051] Specifically, the power supply mode switching control module 42 executes control based on the monitoring data from the power supply status monitoring module 41 through the logic of "status judgment - mode selection - seamless switching": The system pre-sets three modes and triggering conditions: pure photovoltaic power supply (PV power ≥ load and voltage stable, energy storage SOC ≥ 80%), PV-energy storage coordinated power supply (PV power < load and energy storage SOC > 20%), and grid backup power supply (PV interruption or energy storage SOC ≤ 20% or insufficient total PV-energy storage power). These triggering conditions are based on a matching analysis of PV output, energy storage status, and load demand, combined with equipment safety operation parameters such as the energy storage SOC safety range (20%-80%) and PV voltage stability threshold (fluctuation ≤ ±5%), to ensure a smooth power supply. It balances power supply efficiency and system reliability; switching is performed through a built-in bidirectional thyristor switching switch (e.g., when switching from mode 1 to mode 2, the energy storage discharge is triggered first and then the photovoltaic ratio is adjusted; when switching from mode 2 to mode 3, the grid circuit breaker is closed first and then the photovoltaic-storage circuit is disconnected); during the switching process, the load voltage fluctuation is monitored in real time. If it exceeds ±10%, the switching is suspended. At the same time, it has built-in overcurrent (120% over the rated current) and overvoltage (110% over the rated voltage) protection logic. In case of abnormality, the power supply of the fault mode is immediately cut off and the system is switched to the available mode to ensure the safety of the data center load and the continuity of power supply.

[0052] The central control unit 5, together with the MPPT intelligent tracking unit 1, the energy storage adaptive management unit 2, the data center load dynamic allocation unit 3, and the multi-mode power supply switching unit 4, carries out the power supply control of the integrated photovoltaic and energy storage data center. It communicates bidirectionally with the MPPT intelligent tracking unit 1, the energy storage adaptive management unit 2, the data center load dynamic allocation unit 3, and the multi-mode power supply switching unit 4, and integrates data interaction and command scheduling functions.

[0053] Specifically, the central control unit 5 is the core scheduling hub of the photovoltaic-storage integrated data center power supply system. At the hardware level, it uses an industrial-grade STM32H750VBT6 microcontroller as its core processing unit. This controller has multiple peripheral interfaces and high-speed data processing capabilities, and can simultaneously support multi-channel bidirectional communication and real-time command computation. Its communication interface configuration is specifically adapted to the needs of each unit: it establishes a connection with the voltage correction fusion module 15 and dynamic step size adjustment module 12 of the MPPT intelligent tracking unit 1 via the SPI bus to ensure high-frequency parameter interaction; it connects to the energy storage status monitoring module 21 and charge / discharge adaptation control module 22 of the energy storage adaptive management unit 2, as well as the power supply status monitoring module 41 and power supply mode switching control module 42 of the multi-mode power supply switching unit 4 via the RS485 bus to adapt to long-distance stable communication in industrial scenarios; and it communicates with the load technical parameter acquisition module 31 and power resource directional allocation module 33 of the data center load dynamic allocation unit 3 via the Ethernet interface to meet the data transmission needs of high-volume loads. Meanwhile, the central control unit 5 also integrates a 256K BEEPROM and a 4GB SD card, which are used to store inherent system parameters (such as communication addresses of each unit and threshold settings) and dynamic operating data (such as historical output curves and fault logs), respectively, providing hardware support for data interaction and command scheduling.

[0054] Specifically, the central control unit 5 communicates with each unit using a "request-response-confirmation" bidirectional communication protocol, with communication cycles set according to functional characteristics. This embodiment provides the following example: every 100ms, it interacts with the MPPT intelligent tracking unit 1 and the multi-mode power supply switching unit 4 (obtaining component aging coefficients, power supply mode parameters, etc., and issuing tracking calibration and switching threshold commands); every 200ms, it interacts with the energy storage adaptive management unit 2 (obtaining energy storage SOC, charging and discharging power, and issuing charging and discharging priority commands); and every 500ms, it interacts with the data center load dynamic allocation unit 3 (synchronizing the total amount of available power resources and obtaining load parameters and priority results). Those skilled in the art can adapt these communication cycles to the specific system's real-time requirements.

[0055] Specifically, in terms of data interaction, the central control unit 5 aggregates data from each unit and organizes it into a standardized dataset according to "timestamp-unit number-parameter type-value". During preprocessing, abnormal data exceeding reasonable ranges (such as photovoltaic power exceeding the rated value by 130% or abnormal energy storage SOC) are removed, and missing data is supplemented using linear interpolation. At the same time, a data association model is established to link photovoltaic output, energy storage supply and demand, and load demand data to provide a basis for command scheduling.

[0056] Furthermore, the command scheduling is divided into two categories: routine and abnormal. In routine operation, commands are issued according to the logic of "MPPT tracking - energy storage adaptation - load allocation - power supply switching". First, the MPPT intelligent tracking unit 1 is instructed to start tracking and provide feedback on output. Then, the energy storage adaptive management unit 2 is instructed to match charging and discharging. Subsequently, the data center load dynamic allocation unit 3 is instructed to allocate resources according to priority. Finally, the multi-mode power supply switching unit 4 is instructed to maintain the optimal power supply mode. In abnormal operation, if the MPPT tracking voltage deviation exceeds ±5%, it is instructed to re-execute the correction process. If the energy storage SOC suddenly drops below 15%, the energy storage is instructed to switch to the power supply protection mode and trigger the grid backup power supply preparation. If the load is overcurrent (exceeding the rated current by 120%), the power supply to the faulty load is cut off. If the voltage fluctuation exceeds ±10% after the power supply switch, the switch is suspended and the threshold is recalibrated.

[0057] like Figure 2 As shown, this embodiment also provides a power supply control method for an integrated photovoltaic and energy storage data center based on MPPT tracking. Based on the aforementioned MPPT tracking-based power supply system for an integrated photovoltaic and energy storage data center, the method includes the following steps: S1. When the system starts, the central control unit first establishes bidirectional communication with the MPPT intelligent tracking unit, the energy storage adaptive management unit, the data center load dynamic allocation unit, and the multi-mode power supply switching unit. Then, it triggers the MPPT intelligent tracking unit, the energy storage adaptive management unit, the data center load dynamic allocation unit, and the multi-mode power supply switching unit to complete initialization synchronously. After collecting the corresponding initial data, the data is uploaded to the central control unit for backup. S2. The central control unit commands the MPPT intelligent tracking unit to start maximum power point tracking: first, calculate the aging coefficient of the component, then combine the real-time power change of the photovoltaic array to correct the disturbance step size, then determine the algorithm working mode based on the environmental operating parameters and aging coefficient, then calculate the attenuation compensation amount, and finally integrate multiple parameters to correct the standard maximum power point voltage and generate control commands to be transmitted to the photovoltaic array. S3. After receiving the photovoltaic output data from the MPPT intelligent tracking unit, the central control unit instructs the energy storage adaptive management unit to collect energy storage operation parameters in real time, match supply and demand with photovoltaic output and data center load requirements, generate charging and discharging commands, and control the energy storage equipment to execute them. S4. After the energy storage and photovoltaic power supply resources are determined, the central control unit instructs the data center load dynamic allocation unit to: divide the power supply priority according to the differentiated needs of the data center load, and allocate power supply resources in a targeted manner according to the priority. S5, the central control unit synchronously commands the multi-mode power supply switching unit to monitor the status of photovoltaic, energy storage and grid backup power supply in real time. If the photovoltaic or energy storage power supply is insufficient, the power supply mode will be switched immediately to ensure the continuous power supply to the computer room. S6. The central control unit continuously receives operational feedback data from the MPPT intelligent tracking unit, energy storage adaptive management unit, data center load dynamic allocation unit, and multi-mode power supply switching unit, and dynamically optimizes the control parameters of the MPPT intelligent tracking unit, energy storage adaptive management unit, data center load dynamic allocation unit, and multi-mode power supply switching unit. If a sudden change in operating conditions, abnormal component aging, or power supply fluctuation is detected, the corresponding unit is immediately triggered to re-execute the corresponding S2-S5 to achieve closed-loop control of the entire system.

[0058] Those skilled in the art will understand that the process of implementing all or part of the steps of the above embodiments can be carried out by hardware or by a program instructing the relevant hardware.

[0059] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power supply system for an integrated photovoltaic and energy storage data center based on MPPT tracking, characterized in that, include: MPPT intelligent tracking unit (1), the MPPT intelligent tracking unit (1) has a built-in local storage unit, which collects the output electrical parameters of the photovoltaic array and environmental operating parameters including sunshine time, and calculates the aging coefficient of the photovoltaic module based on the cumulative operating data of the photovoltaic module stored in the local storage unit. It works in conjunction with the improved disturbance observation method and fuzzy control algorithm, and combines the attenuation compensation model established by sunshine time and photovoltaic module aging characteristics to adaptively correct the photovoltaic maximum power point reference voltage. Energy storage adaptive management unit (2), the energy storage adaptive management unit (2) implements charge and discharge adaptation control based on the operating status of energy storage equipment, and matches the supply and demand relationship between photovoltaic power output and computer room load by monitoring the remaining energy storage power and core operating parameters; The computer room load dynamic allocation unit (3) implements targeted allocation of power supply resources for the differentiated needs of the computer room load, and allocates power supply resources by dividing the load priority; Multi-mode power supply switching unit (4) implements power supply mode switching control based on the operating status of photovoltaic power supply, energy storage power supply and grid backup power supply, and performs power supply mode switching by monitoring the operating status of each power supply mode; The central control unit (5) works in conjunction with the MPPT intelligent tracking unit (1), the energy storage adaptive management unit (2), the computer room load dynamic allocation unit (3), and the multi-mode power supply switching unit (4) to carry out the power supply control of the integrated photovoltaic and energy storage computer room. It communicates bidirectionally with the MPPT intelligent tracking unit (1), the energy storage adaptive management unit (2), the computer room load dynamic allocation unit (3), and the multi-mode power supply switching unit (4), and integrates data interaction and command scheduling functions.

2. The integrated power supply system for optical and energy storage data centers based on MPPT tracing as described in claim 1, characterized in that, The MPPT intelligent tracking unit (1) includes a component aging coefficient acquisition module (11); the component aging coefficient acquisition module (11) acquires the cumulative operating years of the photovoltaic modules. Cumulative actual power generation And retrieve the pre-stored factory-calibrated rated power generation of the components. Calculate the component aging coefficient ;in, The calculation depends on the age-related decay correction factor. Power generation attenuation correction coefficient .

3. The integrated power supply system for optical and energy storage data centers based on MPPT tracing according to claim 2, characterized in that, The MPPT intelligent tracking unit (1) also includes a dynamic step size adjustment module (12), which is used to combine the real-time power change characteristics of the photovoltaic array with the component aging coefficient. Dynamically adjust the perturbation step size of the improved perturbation observation method The dynamic step size adjustment module (12) dynamically adjusts the perturbation step size of the improved perturbation observation method to adapt to the power response characteristics of the component after attenuation, providing a basic adjustment amount for accurate maximum power point tracking; The process includes the following steps: S12.1 Real-time acquisition of power data from two adjacent sampling periods of the photovoltaic array, and calculation of the power change. Simultaneously, a signal connection is established with the component aging coefficient acquisition module (11) to receive the component aging coefficient output by the component aging coefficient acquisition module (11). ; S12.2 Retrieve the pre-stored rated power of the photovoltaic array ,Establish and Association analysis model; S12.

3. Through correlation analysis model, determine the relative position of the current power state of the photovoltaic array with the maximum power point, as well as the trend and magnitude of the power approaching the maximum power point; S12.4, Based on component aging coefficient The impact of component performance degradation on power output response speed and sensitivity was analyzed, and the initial disturbance step size was determined. Adjustment direction; S12.5, combining the changing trend and magnitude obtained from S12.3, and the adjustment direction determined in S12.4, adjust the initial disturbance step size. Dynamic corrections are performed to generate a final perturbation step size that adapts to the current component degradation state and power point tracking requirements. ; S12.6, Final perturbation step size As a basic adjustment signal, it is output to the algorithm collaborative execution stage of the MPPT intelligent tracking unit (1) to provide initial adjustment basis for subsequent disturbance direction calibration.

4. The integrated power supply system for optical and energy storage data centers based on MPPT tracing according to claim 3, characterized in that, The MPPT intelligent tracking unit (1) also includes a dual-algorithm collaborative scheduling module (13), which is based on the characteristics of environmental operating condition parameter changes and component aging coefficients. and dynamic perturbation step size This triggers the collaborative operation of the improved disturbance observation method and the fuzzy control algorithm to calibrate the disturbance direction to adapt to complex operating conditions and component attenuation states; the collaborative control process of the dual-algorithm collaborative scheduling module (13) includes the following steps: S13.1 Establish signal connections with the dynamic step size adjustment module (12), the component aging coefficient acquisition module (11), and the environmental condition parameter acquisition terminal, and receive the disturbance step size output by the dynamic step size adjustment module (12). The component aging coefficient acquisition module (11) outputs , as well as the rate of change of light intensity and the rate of change of temperature in environmental operating parameters; S13.2 Retrieve pre-stored threshold values ​​for light change rate, temperature change rate, and component aging coefficient. ; S13.3 Establish a threshold comparison model, comparing the real-time light change rate with the light change rate threshold, and the real-time temperature change rate with the temperature change rate threshold, respectively. and Conduct comparative analysis; S13.4 Determine the working mode based on the comparison results: If the real-time illumination change rate does not exceed the illumination change rate threshold, and the real-time temperature change rate does not exceed the temperature change rate threshold, and the component aging coefficient... Not less than the component aging factor threshold The improved perturbation-observation method independently controls maximum power point tracking, while the fuzzy control algorithm remains in standby mode. If the real-time illumination change rate exceeds the illumination change rate threshold, or the real-time temperature change rate exceeds the temperature change rate threshold, or the component aging coefficient... Less than the component aging factor threshold This triggers the intervention of the fuzzy control algorithm; S13.5, The fuzzy control algorithm is based on real-time values ​​of light intensity, temperature, and... Analyze the power response characteristics of the components under the current operating conditions and output the directional correction amount. ; S13.6, will The output of the dynamic step size adjustment module (12) Perform correlation calibration, generate a coordinated adjustment signal, and output it to the attenuation compensation processing stage of the MPPT intelligent tracking unit (1) to provide the working condition basis for compensation amount adaptation.

5. The integrated power supply system for optical and energy storage data centers based on MPPT tracing according to claim 4, characterized in that, The MPPT intelligent tracking unit (1) also includes an attenuation compensation quantization module (14), which is used to combine the component aging coefficient. Cumulative sunshine duration And the dual-algorithm collaborative working state, calculating the component performance degradation compensation amount. To offset the power output deviation caused by long-term operation and solar radiation accumulation; the compensation calculation process of the attenuation compensation quantization module (14) includes the following steps: S14.1 Establish a signal connection with the component aging coefficient acquisition module (11), the environmental condition parameter acquisition terminal, and the dual-algorithm collaborative scheduling module (13), and receive the output of the component aging coefficient acquisition module (11). The sunshine duration in the environmental operating condition parameters is collected and accumulated to obtain... , obtain the working status of the dual-algorithm collaborative scheduling module (13), that is, the fuzzy control algorithm intervention status or the fuzzy control algorithm non-intervention status; S14.2 Retrieve the pre-stored solar intensity adaptation coefficient Decline trend index Solar intensity adaptability coefficient With decay trend index Determined by fitting long-term outdoor operation data of photovoltaic modules; S14.3, based on Analyze the degree of basic degradation caused by component aging, and combine Quantify the cumulative effect of solar radiation on attenuation and establish the correlation logic between the degree of attenuation and the amount of compensation; S14.4 Dynamically adjust the association logic based on the collaborative working status of the two algorithms: If the fuzzy control algorithm is in the intervention state, it will adjust according to the preset rules. The adaptation coefficient; If the fuzzy control algorithm is in an uninterrupted state, maintain the initial association logic; S14.5 Based on the adjusted correlation logic, calculate the compensation amount adapted to the current attenuation state. The voltage correction fusion stage is output to the MPPT intelligent tracking unit (1).

6. The integrated power supply system for optical and energy storage data centers based on MPPT tracing according to claim 5, characterized in that, The MPPT intelligent tracking unit (1) also includes a voltage correction fusion module (15), which is used to fuse dynamic disturbance step size corrections. Direction correction amount and attenuation compensation amount To achieve the maximum power point reference voltage for photovoltaics The adaptive correction; the voltage correction process of the voltage correction fusion module (15) includes the following steps: S15.1 Establish signal connections with the dynamic step size adjustment module (12), the dual-algorithm collaborative scheduling module (13), the attenuation compensation quantization module (14), and the local storage unit, and receive the output of the dynamic step size adjustment module (12). The output of the dual-algorithm collaborative scheduling module (13) The output of the attenuation compensation quantization module (14) ; S15.2 Retrieve the standard maximum power point voltage of the photovoltaic module as specified by the manufacturer from the local storage unit. ; S15.3, Establish a multi-parameter fusion model, and... As a reference voltage, it is incorporated according to preset logic. The step size adjustment function Orientation calibration function and The attenuation compensation effect; S15.4 Calculate the photovoltaic maximum power point reference voltage adapted to the current operating conditions and module degradation state using a multi-parameter fusion model. ; S15.5, will As control commands, they are transmitted to the voltage regulation unit of the photovoltaic array to achieve adaptive tracking control of the maximum power point.

7. The integrated power supply system for optical and energy storage data centers based on MPPT tracing according to claim 6, characterized in that, The energy storage adaptive management unit (2) includes an energy storage status monitoring module (21) and a charge / discharge adaptation control module (22), wherein: The energy storage status monitoring module (21) collects the remaining energy storage power, charging and discharging power and core operating parameters in real time based on the energy storage equipment operation data, photovoltaic real-time output data and computer room load power demand data, providing status support for charging and discharging adaptation control. The charge / discharge adaptation control module (22) generates charge / discharge control commands based on the data collected by the energy storage status monitoring module (21), matching the supply and demand relationship between photovoltaic output and computer room load, and implementing adaptive charge / discharge adaptation control of the energy storage device.

8. The integrated power supply system for optical and energy storage data centers based on MPPT tracing according to claim 7, characterized in that, The computer room load dynamic allocation unit (3) includes a load technical parameter acquisition module (31), a power supply priority technical division module (32), and a power supply resource directional allocation module (33), wherein: The load technical parameter acquisition module (31) is used to collect the electrical power, service response time requirements, equipment rated current and power supply voltage threshold of each load in the computer room; The power supply priority technical division module (32) divides the power supply priority of the load based on the electrical power, service response time requirements, equipment rated current and power supply voltage threshold of the load technical parameter acquisition module (31) and in combination with the preset technical priority rules. The power supply resource directional allocation module (33) implements directional allocation of power supply resources based on the priority results of the power supply priority technology division module (32) and through the power supply path control of the hardware circuit, so as to meet the differentiated power supply needs of the computer room load.

9. The integrated power supply system for optical and energy storage data centers based on MPPT tracing according to claim 8, characterized in that, The multi-mode power supply switching unit (4) includes a power supply status monitoring module (41) and a power supply mode switching control module (42), wherein: The power supply status monitoring module (41) monitors the key operating parameters of each power supply mode in real time based on the operating status of photovoltaic power supply, energy storage power supply and grid backup power supply. The power supply mode switching control module (42) performs power supply mode switching control based on the monitoring data of the power supply status monitoring module (41) to ensure the continuity and reliability of the power supply in the computer room.

10. A power supply control method for an integrated photovoltaic and energy storage data center based on MPPT tracking, based on the integrated photovoltaic and energy storage data center power supply system based on MPPT tracking as described in any one of claims 1-9, characterized in that, Includes the following steps: S1. When the system starts, the central control unit (5) first establishes bidirectional communication with the MPPT intelligent tracking unit (1), the energy storage adaptive management unit (2), the computer room load dynamic allocation unit (3), and the multi-mode power supply switching unit (4), and then triggers the MPPT intelligent tracking unit (1), the energy storage adaptive management unit (2), the computer room load dynamic allocation unit (3), and the multi-mode power supply switching unit (4) to complete the initialization synchronously, and after collecting the corresponding initial data, uploads it to the central control unit (5) for backup. S2, Central Control Unit (5) Instructs MPPT Intelligent Tracking Unit (1) to start maximum power point tracking: First calculate the component aging coefficient, then combine the real-time power change of the photovoltaic array to correct the disturbance step size, then determine the algorithm working mode according to the environmental operating parameters and aging coefficient, then calculate the attenuation compensation amount, and finally integrate the multi-parameter correction standard maximum power point voltage to generate control commands and transmit them to the photovoltaic array. S3. After receiving the photovoltaic output data from the MPPT intelligent tracking unit (1), the central control unit (5) instructs the energy storage adaptive management unit (2) to collect energy storage operation parameters in real time, match the supply and demand of photovoltaic output with the load demand of the computer room, generate charging and discharging commands and control the energy storage equipment to execute. S4. After the energy storage and photovoltaic power supply resources are determined, the central control unit (5) instructs the computer room load dynamic allocation unit (3): to divide the power supply priority according to the differentiated needs of the computer room load, and to allocate power supply resources in a targeted manner according to the priority. S5, Central Control Unit (5) Synchronous Command Multi-mode Power Supply Switching Unit (4) Real-time monitoring of photovoltaic, energy storage and grid backup power supply status. If photovoltaic or energy storage power supply is insufficient, immediately execute power supply mode switching to prioritize ensuring continuous power supply to the computer room. S6. The central control unit (5) continuously receives the operation feedback data from the MPPT intelligent tracking unit (1), the energy storage adaptive management unit (2), the computer room load dynamic allocation unit (3), and the multi-mode power supply switching unit (4), and dynamically optimizes the control parameters of the MPPT intelligent tracking unit (1), the energy storage adaptive management unit (2), the computer room load dynamic allocation unit (3), and the multi-mode power supply switching unit (4). If a sudden change in operating conditions, abnormal component aging, or power supply fluctuation is detected, the corresponding unit is immediately triggered to re-execute the corresponding S2-S5 above to achieve closed-loop control of the entire system.

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