One-stop photovoltaic, energy storage and charging integrated management system

Abstract

The invention discloses a one-stop photovoltaic, energy storage and charging integrated management system, which relates to the field of optical storage and charging intelligent system management and comprises a data acquisition module, a distribution module, a module for further optimizing power distribution and analysis among charging piles based on an energy distribution scheme, a data storage and charging management module, a data storage and charging management module and a power management module, wherein the data acquisition module is used for acquiring operation data of an optical storage and charging intelligent system through various sensors and communication modules; an optimization module which is used for inputting optimized parameters of the optical storage and charging intelligent system and calibrated equipment information into the optical storage and charging intelligent system, analyzing the performance of the optical storage and charging intelligent system, improving an algorithm and a strategy, continuously optimizing the optical storage and charging intelligent system and generating a performance analysis report; by introducing an adaptive energy distribution algorithm and an intelligent data analysis module, power distribution among the charging piles can be dynamically optimized, and system parameters can be automatically adjusted based on a performance analysis report, so that the operation efficiency of the system and the user experience are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management systems for photovoltaic energy storage and charging, and particularly to an integrated management system for one-stop photovoltaic energy storage and charging. Background Art

[0002] With the acceleration of the global energy structure transformation, the integration of photovoltaic energy storage and charging technologies has become an important development direction in the new energy field. Traditional photovoltaic systems mainly focus on power generation efficiency and cost control, while energy storage technologies are dedicated to solving the problem of power supply-demand imbalance. As a key infrastructure for the popularization of electric vehicles, the construction scale of charging piles has expanded rapidly. However, these technologies were usually designed and operated independently in the early development stage, resulting in low resource utilization, high management complexity, and limited overall system performance. Currently, various integrated photovoltaic energy storage and charging solutions have emerged in the market. Through hardware integration and software collaboration, these solutions have initially achieved centralized management of photovoltaic power generation, battery energy storage, and charging pile loads.

[0003] Although significant progress has been made in the technology of integrated photovoltaic energy storage and charging management systems, there are still some deficiencies in the existing technologies. First of all, most systems lack flexibility and self-adaptability in energy distribution and cannot dynamically adjust the power distribution strategy according to real-time load changes and environmental conditions. This may lead to overload or idleness of some charging piles, thereby affecting the overall efficiency of the system. Secondly, the data analysis ability of existing systems is limited, and the operation data is not fully utilized for in-depth performance evaluation and parameter optimization, making it difficult to achieve continuous improvement and long-term stable operation of the system. Specifically, by introducing an adaptive energy distribution algorithm and an intelligent data analysis module, this system can not only dynamically optimize the power distribution among charging piles but also automatically adjust system parameters based on the performance analysis report, thus significantly improving the operation efficiency and user experience of the system. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an integrated management system for one-stop photovoltaic energy storage and charging to solve the problems of insufficient flexibility in energy distribution and limited system performance optimization ability in the existing technologies.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides an integrated management system for one-stop photovoltaic energy storage and charging, which includes,

[0008] A data acquisition module, which collects the operation data of the intelligent photovoltaic energy storage and charging system through a variety of sensors and communication modules;

[0009] Energy management module, the central controller outputs an energy distribution plan through an adaptive energy distribution algorithm, reads the load data of each charging pile, and judges the status of the charging pile;

[0010] Distribution module, based on the energy distribution plan, further optimizes the power distribution among charging piles;

[0011] Analysis module, collects and analyzes the operation data of the photovoltaic-storage-charging intelligent system, and generates a performance analysis report;

[0012] Adjustment module, based on the performance analysis report, adjusts the parameters of the photovoltaic-storage-charging intelligent system, adjusts the working parameters of the photovoltaic modules, optimizes the charge and discharge strategies of the energy storage battery, and calibrates the sensors of the photovoltaic modules and the electricity meters of the charging piles;

[0013] Optimization module, inputs the optimized parameters of the photovoltaic-storage-charging intelligent system and the calibrated device information into the photovoltaic-storage-charging intelligent system, analyzes the performance of the photovoltaic-storage-charging intelligent system, improves the algorithms and strategies, and continuously optimizes the photovoltaic-storage-charging intelligent system.

[0014] As a preferred solution of the one-stop integrated management system for photovoltaic, energy storage, and charging of the present invention, wherein: the operation data of the photovoltaic-storage-charging intelligent system is collected through a variety of sensors and communication modules, and the specific steps are as follows,

[0015] Collect photovoltaic module data, energy storage battery data, charging pile data, and external environment data through a light intensity sensor and a temperature sensor to obtain the operation data of the photovoltaic-storage-charging intelligent system;

[0016] Upload the collected data to the central controller through a wireless communication protocol.

[0017] As a preferred solution of the one-stop integrated management system for photovoltaic, energy storage, and charging of the present invention, wherein: the central controller outputs an energy distribution plan through an adaptive energy distribution algorithm, and the specific steps are as follows,

[0018] Initialize the priority weight of the photovoltaic module as α, the priority weight of the energy storage battery as β, and the priority weight of grid power purchase as γ, and dynamically adjust the weight coefficient based on the operation data;

[0019] Use the operation adaptive energy distribution algorithm to obtain the total power of the charging piles, and output an energy distribution plan according to the total power of the charging piles.

[0020] As a preferred solution of the one-stop integrated management system for photovoltaic, energy storage, and charging of the present invention, wherein: the steps of reading the load data of each charging pile and judging the status of the charging pile are as follows,

[0021] Calculate the load rate based on the current load status and rated power of the charging pile;

[0022] According to the load rate, combined with the rated power, economy, equipment protection and user experience of the charging pile, the lower limit a and upper limit b of the load rate are set;

[0023] Based on the load rate c, the state of the charging pile is determined by the load rate lower limit a and the load rate upper limit b;

[0024] When a>c, the charging pile is in idle state;

[0025] when a <c<b时,充电桩为正常状态;

[0026] When c>b, the charging pile is in overload state.

[0027] As a preferred solution of the one-stop photovoltaic, energy storage and charging integrated management system of the present invention, the energy allocation scheme is further used to optimize the power allocation between charging piles. The specific steps are as follows:

[0028] Based on the energy allocation scheme, the load power, cumulative charge amount and remaining charging time status data of the charging pile are combined, and the power allocation between charging piles is dynamically optimized through the priority allocation method.

[0029] As a preferred solution of the one-stop photovoltaic, energy storage and charging integrated management system of the present invention, the specific steps of collecting and analyzing the operation data of the photovoltaic storage and charging intelligent system and generating a performance analysis report are as follows:

[0030] Collect PV module data, energy storage battery data, charging pile data and external environment data to obtain the operation data of the PV storage and charging smart system;

[0031] Clean the operating data of the photovoltaic storage and charging smart system, remove outliers and noise, align the data, align the data from different sources by timestamp, calculate the photovoltaic power generation efficiency and charging pile load balance, and obtain key indicators;

[0032] Based on key indicators combined with historical data and real-time data, through trend analysis, comparative analysis and correlation analysis, the key factors affecting the performance of the solar-storage-charging smart system are identified and the analysis results are obtained;

[0033] The analysis results are organized into a performance analysis report in the form of charts and text descriptions.

[0034] As a preferred solution of the one-stop photovoltaic, energy storage and charging integrated management system described in the present invention, the specific steps are as follows: based on the performance analysis report, the parameters of the photovoltaic storage and charging intelligent system are adjusted, the working parameters of the photovoltaic components are adjusted, and the charging and discharging strategy of the energy storage battery is optimized.

[0035] Based on the performance analysis report, obtain the light intensity, temperature data, photovoltaic power generation efficiency, and charging pile load balance degree;

[0036] According to the changes in the light intensity and temperature data, dynamically adjust the working parameters of the photovoltaic modules. According to the photovoltaic power generation efficiency and the charging pile load balance degree, adjust the parameters of the photovoltaic-storage-charging intelligent system;

[0037] Based on the performance analysis report, determine the current capacity. According to the initial capacity, calculate the capacity attenuation rate. Through the change trend of the capacity attenuation rate, optimize the charge and discharge strategy of the energy storage battery.

[0038] As a preferred solution of the one-stop photovoltaic, energy storage, and charging integrated management system described in the present invention, wherein: calibrate the photovoltaic module sensor and the charging pile electric energy meter, and the specific steps are as follows.

[0039] Place the standard light source in the calibration tool under the same light conditions as the photovoltaic module, record the actual light intensity value of the standard light source and the light intensity value measured by the sensor, and calibrate the parameters of the photovoltaic module sensor;

[0040] Measure the ambient temperature through the precision thermometer in the calibration tool, record the temperature value measured by the sensor, and calibrate the parameters of the photovoltaic module sensor.

[0041] Generate known current and voltage through the standard electric energy meter in the calibration tool, record the current value and voltage value measured by the charging pile electric energy meter, and adjust the parameters of the charging pile electric energy meter.

[0042] As a preferred solution of the one-stop photovoltaic, energy storage, and charging integrated management system described in the present invention, wherein: input the optimized parameters of the photovoltaic-storage-charging intelligent system and the calibrated device information into the photovoltaic-storage-charging intelligent system, and the specific steps are as follows.

[0043] Detect the optimized parameters of the photovoltaic-storage-charging intelligent system through the methods of comparison verification and regular calibration;

[0044] Verify the calibrated device information through comparison with the standard light source and the precision thermometer;

[0045] Input the inspected and verified parameter information into the central controller, and input it into the photovoltaic-storage-charging intelligent system through the communication protocol to obtain the optimized photovoltaic-storage-charging intelligent system.

[0046] As a preferred solution of the one-stop photovoltaic, energy storage, and charging integrated management system described in the present invention, wherein: analyze the performance of the photovoltaic-storage-charging intelligent system, improve the algorithms and strategies, and continuously optimize the photovoltaic-storage-charging intelligent system. The specific steps are as follows.

[0047] Based on the optimized photovoltaic energy storage and charging intelligent system, collect the light intensity, temperature, and power generation of photovoltaic modules and the load data of energy storage batteries, and analyze the performance of the operation parameter data of the optimized photovoltaic energy storage and charging intelligent system to identify the key factors affecting the performance of the photovoltaic energy storage and charging intelligent system;

[0048] Based on the key factors affecting the performance of the photovoltaic energy storage and charging intelligent system, propose improvement measures, add an adaptive adjustment function to the photovoltaic energy storage and charging intelligent system through the MPPT algorithm, and make the photovoltaic system operate at the maximum power point;

[0049] Through regular review and closed-loop optimization methods, regularly analyze the performance of the photovoltaic energy storage and charging intelligent system, improve algorithms and strategies, automatically optimize the parameters of the photovoltaic energy storage and charging intelligent system, analyze the trend of the photovoltaic energy storage and charging intelligent system in combination with historical data, and iteratively optimize the photovoltaic energy storage and charging intelligent system through a closed-loop mechanism.

[0050] Regularly analyze the performance of the photovoltaic energy storage and charging intelligent system, continuously improve algorithms and strategies, automatically optimize parameters, analyze the trend of the charging intelligent system in combination with historical data, further improve the function of the charging intelligent system, and through a closed-loop mechanism, enable the long-term stable operation and sustainable development of the photovoltaic energy storage and charging intelligent system.

[0051] The beneficial effects of the present invention are as follows: Through the introduction of the energy management module, the present invention realizes the intelligent scheduling of energy sources, not only improves the overall energy efficiency of the system, but also effectively reduces carbon emissions, providing technical support for achieving the goal of green and low-carbon. At the same time, the dynamic adjustment strategy enhances the flexibility and adaptability of the system, enabling it to better cope with complex and changing operating environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0053] Figure 1 It is a schematic diagram of an integrated management system for one-stop photovoltaic, energy storage, and charging in Embodiment 1.

[0054] Figure 2 It is a schematic diagram of an optimized photovoltaic energy storage and charging intelligent system in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0056] Embodiment 1, refer to Figure 1 andFigure 2 , which is the first embodiment of the present invention. This embodiment provides an integrated management system for one-stop photovoltaic, energy storage, and charging, including the following steps:

[0057] The data acquisition module collects the operation data of the photovoltaic, energy storage, and charging intelligent system through various sensors and communication modules.

[0058] Collect the data of photovoltaic modules, energy storage batteries, charging piles, and external environment through light intensity sensors and temperature sensors to obtain the operation data of the photovoltaic, energy storage, and charging intelligent system;

[0059] Upload the collected data to the central controller through wireless communication protocols.

[0060] It should be noted that the photovoltaic module data includes light intensity, temperature, and power generation power, the energy storage battery data covers voltage, current, remaining power, and health status, the charging pile data includes current load, cumulative charging amount, and remaining charging time, and the external environment data includes weather prediction, light, wind speed, temperature, and real-time grid electricity price.

[0061] The energy management module, the central controller outputs an energy distribution plan through an adaptive energy distribution algorithm.

[0062] Initialize the priority weight of the photovoltaic module as α, the priority weight of the energy storage battery as β, and the priority weight of grid power purchase as γ, and dynamically adjust the weight coefficients based on the operation data;

[0063] Use the operation adaptive energy distribution algorithm to obtain the total power of the charging pile, and output an energy distribution plan according to the total power of the charging pile.

[0064] It should be noted that the specific expression is:

[0065] P o = αP p + βP b + γP g ;

[0066] Among them, P o is the total power output to the charging pile, P p is the actual power generation power of the photovoltaic module, P b is the discharge power of the energy storage battery, P g is the power purchased from the grid.

[0067] It should be noted that when the photovoltaic module generates sufficient power and has low carbon emissions, then increase α and reduce γ,

[0068] when the energy storage battery has a high level and the electricity price is low, then appropriately increase β,

[0069] when the external grid electricity price is low and the carbon emissions are moderate, then it can be appropriately increased.

[0070] The energy management module reads the load data of each charging pile and judges the status of the charging pile.

[0071] Based on the current load status and rated power of the charging pile, calculate the load rate;

[0072] It should be noted that the rated power is directly marked on the equipment nameplate of the charging pile and is determined by the manufacturer according to the design specifications.

[0073] It should be noted that the specific expression is:

[0074]

[0075] Among them, c represents the load rate, n represents the current load power, and x represents the rated power.

[0076] According to the load rate, considering the rated power, economy, equipment protection and user experience factors of the charging pile comprehensively, set the lower limit a and upper limit b of the load rate;

[0077] When the load rate is c, judge the status of the charging pile through the lower limit a and upper limit b of the load rate;

[0078] When a > c, the charging pile is in the idle state,

[0079] When a < c < b, the charging pile is in the normal state,

[0080] When c > b, the charging pile is in the overload state.

[0081] The distribution module further optimizes the power distribution among the charging piles based on the energy distribution scheme.

[0082] Based on the energy distribution scheme, combined with the load power, cumulative charging amount and remaining charging time status data of the charging pile, and dynamically optimize the power distribution among the charging piles through the priority distribution method.

[0083] It should be noted that the priority weight is calculated according to factors such as the remaining charging time, cumulative charging amount and emergency tasks, and the total adjustable power is redistributed on this basis. Finally, under the constraints of the power limit of a single charging pile, the total system power limit and the minimum power guarantee, implement the optimized power distribution scheme, so as to achieve load balancing and efficient resource utilization among the charging piles.

[0084] The central controller sends the optimized power distribution to each charging pile and adjusts the charging output according to the target power.

[0085] It should be noted that the specific expression is:

[0086]

[0087] Among them, P o is the total power output to the charging pile, a j is the target power of the jth charging pile, D j is the demand weight of the jth charging pile, ∑ k D k is the sum of all charging pile demand weights, k is the index of the charging pile;

[0088] The analysis module collects and analyzes the operating data of the solar-storage-charging smart system and generates a performance analysis report.

[0089] Collect PV module data, energy storage battery data, charging pile data and external environment data to obtain the operation data of the PV storage and charging smart system;

[0090] Clean the operating data of the photovoltaic storage and charging smart system, remove outliers and noise, align the data, align the data from different sources by timestamp, calculate the photovoltaic power generation efficiency and charging pile load balance, and obtain key indicators;

[0091] The specific expression should be:

[0092]

[0093] Among them, η p is the photovoltaic power generation efficiency, k is the actual power generation power of the photovoltaic storage and charging smart system, and m is the rated power of the storage and charging smart system;

[0094]

[0095] Where l is the load balance of the charging pile, e is the discrete degree of the power output of the charging pile, and f is the average level of the power output of the charging pile;

[0096] Based on key indicators combined with historical data and real-time data, through trend analysis, comparative analysis and correlation analysis, the key factors affecting the performance of the solar-storage-charging smart system are identified and the analysis results are obtained;

[0097] It should be explained that by drawing trend graphs to identify the patterns of changes in system performance, comparative analysis and evaluation of the effects of improvement measures, and correlation analysis to find out the factors that have a greater impact on system performance, the final conclusion is drawn.

[0098] The analysis results are organized into a performance analysis report in the form of charts and text descriptions.

[0099] It should be explained that the differences in performance indicators before and after optimization are compared through bar charts or pie charts to evaluate the effectiveness of improvement measures; then use scatter plots or heat maps to show the correlation between variables and reveal the internal relationship of the system; finally, combine the chart data with detailed text descriptions to explain the influence mechanism of each key factor.

[0100] Adjustment module, based on the performance analysis report, adjusts the parameters of the photovoltaic-storage-charging intelligent system, adjusts the operating parameters of the photovoltaic modules, and optimizes the charge and discharge strategy of the energy storage battery.

[0101] Based on the performance analysis report, obtain the light intensity, temperature data, photovoltaic power generation efficiency, and charging pile load balance degree;

[0102] According to the changes in the light intensity and temperature data, dynamically adjust the operating parameters of the photovoltaic modules, and according to the photovoltaic power generation efficiency and the charging pile load balance degree, adjust the parameters of the photovoltaic-storage-charging intelligent system;

[0103] It should be noted that according to the real-time collected light intensity and temperature data, the operating parameters of the photovoltaic modules are dynamically adjusted, and the operating voltage is reduced at high temperatures to reduce efficiency loss; at the same time, based on the calculated photovoltaic power generation efficiency and the charging pile load balance degree, the overall parameters of the photovoltaic-storage-charging intelligent system are further adjusted.

[0104] It should be noted that the initial capacity refers to the nominal capacity of the energy storage battery when it leaves the factory, which can usually be obtained from the battery specification sheet.

[0105] It should be noted that the specific expression is:

[0106]

[0107] Where d is the capacity attenuation rate, g is the initial capacity, and h is the current capacity;

[0108] Adjust the charge and discharge strategy according to the capacity attenuation rate to maximize the battery life.

[0109] Adjustment module, calibrate the photovoltaic module sensor and the charging pile watt-hour meter.

[0110] Place the standard light source in the calibration tool under the same lighting conditions as the photovoltaic module sensor, compare the actual light intensity value of the standard light source with the light intensity value measured by the photovoltaic module sensor, and calibrate the parameters of the photovoltaic module sensor;

[0111] It should be noted that the specific method is to ensure that the light intensity of the standard light source is stable and evenly irradiates the sensor, record the actual light intensity value of the standard light source, read the measurement value of the photovoltaic module sensor under the same conditions, and then adjust the calibration parameters of the sensor according to the difference between the two, so that the measurement value of the sensor is consistent with the actual value of the standard light source, thereby ensuring that the photovoltaic module sensor can accurately reflect the actual light intensity.

[0112] Measure the ambient temperature through the precision thermometer in the calibration tool, compare the temperature value measured by the photovoltaic module sensor, and calibrate the parameters of the photovoltaic module sensor;

[0113] Generate known current and voltage through the standard electricity meter in the calibration tool, compare the measured current and voltage values of the charging pile electricity meter, and adjust the parameters of the charging pile electricity meter.

[0114] It should be noted that the above calibration steps are repeated under different conditions to verify whether the measurement accuracy of the calibrated sensors and electricity meters is stable, and ensure that the measurement errors of all sensors and electricity meters are within the allowable range required by the system.

[0115] Optimization module, input the optimized parameters of the photovoltaic-storage-charging intelligent system and the calibrated device information into the photovoltaic-storage-charging intelligent system.

[0116] Detect the parameters of the optimized photovoltaic-storage-charging intelligent system through the methods of comparison verification and regular calibration;

[0117] Verify the calibrated device information by comparing with a standard light source and a precision thermometer;

[0118] It should be noted that first, use a standard light source and a precision thermometer to compare and calibrate the measured values of the light intensity and temperature of the photovoltaic module sensor under the same conditions, and adjust the sensor parameters to be consistent with the standard values; then collect real-time operation data and compare and analyze it with historical data to verify whether the optimized system parameters meet the expected effects; regularly use a standard light source and a precision thermometer to recalibrate the sensors to ensure long-term stability and accuracy, so as to effectively detect and optimize the performance of the photovoltaic-storage-charging intelligent system, ensure that the information of each device is accurate, and the system operates efficiently and stably.

[0119] Input the inspected and verified parameter information into the central controller, and input it into the photovoltaic-storage-charging intelligent system through the communication protocol to obtain the optimized photovoltaic-storage-charging intelligent system.

[0120] It should be noted that test the operation performance of the system under different working conditions to ensure that the optimized parameters and calibrated devices can adapt to various scenarios.

[0121] Optimization module, analyze the performance of the photovoltaic-storage-charging intelligent system, improve algorithms and strategies, and continuously optimize the photovoltaic-storage-charging intelligent system.

[0122] Based on the optimized photovoltaic-storage-charging intelligent system, collect the light intensity, temperature, and power generation power of the photovoltaic module and the load data of the energy storage battery, and analyze the performance of the operation parameter data of the optimized photovoltaic-storage-charging intelligent system to identify the key factors affecting the performance of the photovoltaic-storage-charging intelligent system;

[0123] It should be noted that the light intensity, temperature, and power generation of photovoltaic modules, as well as the load data of energy storage batteries, are collected in real time. By comparing historical data with current data, key performance indicators such as photovoltaic power generation efficiency and the health status of energy storage batteries are calculated. Then, methods such as trend analysis, comparative analysis, and correlation analysis are used to identify the impacts of factors such as light intensity, temperature, and the charging and discharging strategies of energy storage batteries on system performance.

[0124] Based on the key factors affecting the performance of the intelligent photovoltaic-energy storage-charging system, improvement measures are proposed. An adaptive regulation function is added to the intelligent photovoltaic-energy storage-charging system through the MPPT algorithm, enabling the photovoltaic system to operate at the maximum power point.

[0125] It should be noted that the perturbation and observation method is selected. By slightly changing the working voltage of the photovoltaic module and then observing the change direction of the output power. If the power increases, continue to perturb in the same direction; if the power decreases, perturb in the opposite direction.

[0126] Based on the data of photovoltaic modules, an initial perturbation step size ΔV and an initial working voltage V are set. A reference light intensity G and a reference temperature T are set, and the perturbation step size ΔV is dynamically adjusted according to the current light intensity G and temperature T.

[0127] It should be noted that the specific expression is:

[0128]

[0129] Among them, ΔV b is the reference perturbation step size, α is the adjustment coefficient, β is the adjustment coefficient, G r is the standard light intensity, T r is the reference temperature.

[0130] Based on the change direction of the output power and the initial perturbation step size, the updated working voltage is obtained. The working voltage of the photovoltaic module is continuously adjusted through the perturbation and observation method, enabling the photovoltaic system to gradually approach the maximum power point.

[0131] Combined with a PID controller for feedback control, a proportional-integral-derivative (PID) controller is used to regulate the working voltage of the photovoltaic module. According to the real-time light intensity and temperature data, the target voltage is dynamically adjusted. It should be noted that the specific expression is:

[0132]

[0133] Among them, V t is the target voltage, V b is the reference target voltage, γ is the adjustment coefficient, and δ is the adjustment coefficient.

[0134] The PID controller is used to adjust the actual working voltage according to the error signal, making it close to the target voltage to ensure that the photovoltaic system always operates at its maximum power point.

[0135] Through regular review and closed-loop optimization methods, regularly analyze the performance of the intelligent energy storage and charging system, improve algorithms and strategies, automatically optimize the parameters of the intelligent energy storage and charging system, analyze the trend of the intelligent energy storage and charging system in combination with historical data, and iteratively optimize the intelligent energy storage and charging system through a closed-loop mechanism.

[0136] In summary, the present invention can, by introducing an adaptive energy distribution algorithm and an intelligent data analysis module, not only dynamically optimize the power distribution among charging piles, but also automatically adjust system parameters based on the performance analysis report, thereby significantly improving the operating efficiency of the system and the user experience.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. One-stop integrated management system for photovoltaic, energy storage and charging, characterized by: including, a data acquisition module that collects the operation data of the intelligent optical storage and charging system through a variety of sensors and communication modules; an energy management module where the central controller outputs an energy distribution scheme through an adaptive energy distribution algorithm, reads the load data of each charging pile, and judges the status of the charging pile; a distribution module that further optimizes the power distribution among the charging piles based on the energy distribution scheme; an analysis module that collects and analyzes the operation data of the intelligent optical storage and charging system and generates a performance analysis report; an adjustment module that adjusts the parameters of the intelligent optical storage and charging system based on the performance analysis report, adjusts the working parameters of the photovoltaic modules, optimizes the charge and discharge strategies of the energy storage batteries, and calibrates the sensors of the photovoltaic modules and the electricity meters of the charging piles; an optimization module that inputs the optimized parameters of the intelligent optical storage and charging system and the calibrated device information into the intelligent optical storage and charging system, analyzes the performance of the intelligent optical storage and charging system, improves the algorithms and strategies, and continuously optimizes the intelligent optical storage and charging system.

2. The one-stop photovoltaic, energy storage, and charging integrated management system according to claim 1, characterized in that: The step of collecting the operation data of the intelligent optical storage and charging system through a variety of sensors and communication modules is as follows. Collect photovoltaic module data, energy storage battery data, charging pile data, and external environment data through light intensity sensors and temperature sensors to obtain the operation data of the intelligent optical storage and charging system; Upload the collected data to the central controller through a wireless communication protocol.

3. The one-stop photovoltaic, energy storage, and charging integrated management system as claimed in claim 2, characterized in that: The step of the central controller outputting an energy distribution scheme through an adaptive energy distribution algorithm is as follows. Initialize the priority weight of the photovoltaic module as α, the priority weight of the energy storage battery as β, and the priority weight of grid power purchase as γ, and dynamically adjust the weight coefficient based on the operation data; Use the operation adaptive energy distribution algorithm to obtain the total power of the charging piles, and output an energy distribution scheme according to the total power of the charging piles.

4. The one-stop photovoltaic, energy storage, and charging integrated management system as claimed in claim 3, characterized in that: The step of reading the load data of each charging pile and judging the status of the charging pile is as follows. Calculate the load rate based on the current load status and rated power of the charging pile; Based on the load rate, comprehensively analyze the rated power, economy, equipment protection, and user experience factors of the charging pile, and set the lower limit a and upper limit b of the load rate; Based on the load rate c, judge the status of the charging pile through the lower limit a and upper limit b of the load rate; When a > c, the charging pile is in an idle state; When a < c < b, the charging pile is in a normal state; When c > b, the charging pile is in an overloaded state.

5. The one-stop photovoltaic, energy storage, and charging integrated management system as claimed in claim 4, characterized in that: The step of further optimizing the power distribution among the charging piles based on the energy distribution scheme is as follows. Based on the energy distribution scheme, combine the load power, cumulative charge, and remaining charge time status data of the charging piles, and dynamically optimize the power distribution among the charging piles through the priority distribution method.

6. The one-stop photovoltaic, energy storage, and charging integrated management system as claimed in claim 5, characterized in that: The step of collecting and analyzing the operation data of the intelligent optical storage and charging system and generating a performance analysis report is as follows. Collect photovoltaic module data, energy storage battery data, charging pile data, and external environment data to obtain the operation data of the intelligent optical storage and charging system; Clean the operation data of the intelligent optical storage and charging system,剔除 outliers and noise, align the data, align the data from different sources according to the time stamp, calculate the photovoltaic power generation efficiency and the charging pile load balance degree, and obtain the key indicators; Based on key indicators combined with historical data and real-time data, through trend analysis, comparative analysis and correlation analysis, the key factors affecting the performance of the solar-storage-charging smart system are identified and the analysis results are obtained; The analysis results are organized into a performance analysis report in the form of charts and text descriptions.

7. The one-stop photovoltaic, energy storage, and charging integrated management system according to claim 6, characterized in that: Based on the performance analysis report, the parameters of the photovoltaic storage and charging intelligent system are adjusted, the working parameters of the photovoltaic components are adjusted, and the charging and discharging strategy of the energy storage battery is optimized. The specific steps are as follows: Based on the performance analysis report, we can obtain the light intensity, temperature data, photovoltaic power generation efficiency and charging pile load balance; Adjust the working parameters of photovoltaic modules according to the dynamic changes of light intensity and temperature data, and adjust the parameters of the photovoltaic storage and charging smart system according to the photovoltaic power generation efficiency and the load balance of charging piles; Based on the performance analysis report, determine the current capacity, calculate the capacity decay rate based on the initial capacity, and optimize the energy storage battery charging and discharging strategy through the changing trend of the capacity decay rate.

8. The one-stop photovoltaic, energy storage, and charging integrated management system as claimed in claim 7, characterized in that: The specific steps of calibrating the photovoltaic module sensor and the charging pile electric energy meter are as follows: Place the standard light source in the calibration tool under the same lighting conditions as the PV module sensor, compare the actual light intensity value of the standard light source with the light intensity value measured by the PV module sensor, and calibrate the PV module sensor parameters; Measure the ambient temperature with the precision thermometer in the calibration tool, compare it with the temperature value measured by the PV module sensor, and calibrate the PV module sensor parameters; Generate known current and voltage through the standard energy meter in the calibration tool, compare the current and voltage values ​​measured by the charging pile energy meter, and adjust the parameters of the charging pile energy meter.

9. The one-stop photovoltaic, energy storage, and charging integrated management system according to claim 8, characterized in that: The optimized solar-storage-charging intelligent system parameters and calibrated device information are input into the solar-storage-charging intelligent system. The specific steps are as follows: Through comparative verification and regular calibration, the optimized solar-storage-charging smart system parameters are tested; Verify the calibrated equipment information by comparing with standard light source and precision thermometer; The checked and verified parameter information is input into the central controller and then into the solar-storage-charging intelligent system through the communication protocol to obtain an optimized solar-storage-charging intelligent system.

10. The one-stop photovoltaic, energy storage, and charging integrated management system according to claim 9, characterized in that: The specific steps of analyzing the performance of the photovoltaic storage and charging smart system, improving algorithms and strategies, and continuously optimizing the photovoltaic storage and charging smart system are as follows: Based on the optimized photovoltaic storage and charging smart system, collect the light intensity, temperature and power generation of photovoltaic modules and the load data of energy storage batteries, analyze the performance of the optimized photovoltaic storage and charging smart system operating parameter data, and identify the key factors affecting the performance of the photovoltaic storage and charging smart system; Based on the key factors that affect the performance of the photovoltaic storage and charging smart system, improvement measures are proposed. The adaptive adjustment function is added to the photovoltaic storage and charging smart system through the MPPT algorithm to allow the photovoltaic system to operate at the maximum power point. Through regular review and closed-loop optimization methods, the performance of the solar-storage-charging smart system is analyzed regularly, and the algorithms and strategies are improved to automatically optimize the parameters of the solar-storage-charging smart system. The trend of the solar-storage-charging smart system is analyzed in combination with historical data, and the solar-storage-charging smart system is iteratively optimized through a closed-loop mechanism.

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