A building photovoltaic window intelligent energy management method and system based on BIPV

Abstract

The application discloses a kind of based on BIPV's building photovoltaic window intelligent energy management method and system, it is related to photovoltaic power generation technical field, obtains the historical operation data of BIPV photovoltaic window, constructs system benchmark condition model, the present application is according to condition determination result and state deviation information, dynamically set MPPT tracking parameter and voltage conversion regulation parameter, realize photovoltaic module maximum power point real-time tracking and output side voltage fluctuation compensation, make photovoltaic energy capture strategy dynamic match real-time condition, improve the solar energy utilization rate and power generation output stability under variable illumination, weak light scene, simultaneously based on condition, state deviation and source storage load supply-demand relationship Switch system operation mode, by setting stage-by-stage charge-discharge threshold parameter and monomer consistency regulation parameter, execute energy storage stage-by-stage charge-discharge control and trigger monomer voltage equalization regulation as needed, make charge-discharge strategy adapt to real-time state of energy storage, improve the energy storage cycle life and operating safety stability.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, specifically to a smart energy management method and system for building photovoltaic windows based on BIPV. Background Technology

[0002] Driven by the "dual carbon" goal, building energy conservation and renewable energy utilization have become the core direction of green building development. BIPV technology, by deeply integrating photovoltaic power generation with the building envelope, realizes the transformation of buildings from energy consumption to energy production. As a component with a large area and weak thermal performance in the building envelope, building windows can be integrated with photovoltaic power generation technology to form photovoltaic windows. This can realize the on-site production and consumption of clean energy without occupying additional building space, and has broad application prospects.

[0003] Existing energy management methods mostly employ fixed-parameter MPPT or simple adjustment strategies for energy capture, and often perform charge and discharge operations through fixed thresholds. However, the stability and utilization rate of energy capture are difficult to dynamically match with operating conditions under wide voltage input, varying illumination, and low light conditions, affecting the efficient utilization of solar energy resources and the stability of power generation output. At the same time, charge and discharge operations based on fixed thresholds are difficult to adapt to the actual operating conditions and states of battery charging and discharging processes, which will adversely affect the cycle life and operational safety and stability of the energy storage system. This results in insufficient coordination and matching between load power supply and power generation and energy storage states, making it difficult to balance the power supply stability and adaptability of multiple types of loads. Therefore, this paper proposes a smart energy management method and system based on BIPV building photovoltaic windows to solve the problems mentioned above. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides a smart energy management method and system for building photovoltaic windows based on BIPV. This solution solves the problem that existing energy management methods, as mentioned in the background, often employ fixed-parameter MPPT or simple adjustment strategies for energy capture, and frequently perform charging and discharging operations through fixed thresholds. However, the stability and utilization rate of energy capture are difficult to dynamically match with operating conditions under wide voltage input, varying light levels, and low light conditions, affecting the efficient utilization of solar energy resources and the stability of power generation output. Furthermore, charging and discharging operations based on fixed thresholds are difficult to adapt to the actual operating conditions and states of the battery charging and discharging process, which adversely affects the cycle life and operational safety and stability of the energy storage system. This results in insufficient coordination between load power supply and power generation / energy storage states, making it difficult to simultaneously address the issues of power supply stability and adaptability for various types of loads.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart energy management method for building photovoltaic windows based on BIPV includes: Historical operating data of BIPV photovoltaic windows are acquired, a system baseline operating condition model is constructed, baseline operating data is generated, and real-time operating data of BIPV photovoltaic windows is collected. The baseline operating data and real-time operating data include environmental conditions, power generation and output voltage of photovoltaic modules, state of charge and energy storage capacity of energy storage units, load power requirements and load importance level of loads. The real-time operating data is compared with the baseline operating data to generate state deviation information, and the operating condition judgment result is generated based on the real-time operating data and the state deviation information. Based on the operating condition judgment results and state deviation information, MPPT tracking parameters and voltage conversion adjustment parameters are set to perform real-time tracking of the maximum power operating point and output voltage regulation of the photovoltaic module. Based on the operating condition judgment results and state deviation information, combined with the real-time power generation of photovoltaic modules, energy storage capacity and load power demand, the system operation mode is switched, the threshold parameters for charging and discharging stages and the individual consistency adjustment parameters are set, the energy storage unit is executed in stages for charging and discharging control and the individual consistency adjustment is triggered as needed. Based on the operating condition judgment results and state deviation information, the accounting system can allocate power supply and define the load power supply scheduling priority, set power supply priority allocation parameters, and dynamically allocate power supply based on the power supply scheduling priority and power supply priority allocation parameters. Periodically collect real-time operating data and status deviation information, analyze the changing patterns of operating conditions and system operating characteristics, and adjust MPPT tracking parameters, voltage transformation adjustment parameters, charging and discharging stage threshold parameters, individual unit consistency adjustment parameters and power supply priority allocation parameters in a hierarchical manner, while updating the system baseline operating conditions simultaneously. The corrected parameters and updated system baseline conditions are put into system operation and verified. The operation verification results are then fed back to the operation condition pattern analysis and parameter correction stage of the next operating cycle.

[0006] Furthermore, a building photovoltaic (BIPV)-based intelligent energy management system for building photovoltaic windows is proposed to implement the aforementioned BIPV-based intelligent energy management method for building photovoltaic windows, including: The data acquisition and benchmark modeling module is used to acquire historical operating data of BIPV photovoltaic windows, build a system benchmark operating condition model and generate benchmark operating data, while also acquiring real-time operating data; The operating condition decision module is used to generate state deviation information, complete the operating condition judgment, and output the operating condition judgment result; The energy regulation and execution module is used to perform photovoltaic MPPT tracking and output-side voltage regulation, system operation mode switching, energy storage phased charging and discharging control, single-unit consistency adjustment, and load priority scheduling and power supply allocation. The self-optimizing closed-loop module is used to periodically correct control parameters, update baseline operating conditions, and complete operational verification and data feedback. The central control module is used to coordinate the collaborative operation of the above modules and the interaction of data commands.

[0007] The beneficial effects of this invention compared to the prior art are: Based on the operating condition judgment results and state deviation information, this solution dynamically sets the MPPT tracking parameters and voltage transformation adjustment parameters to achieve real-time tracking of the maximum power point of photovoltaic modules and compensation for voltage fluctuations on the output side. This enables the photovoltaic energy capture strategy to dynamically match the real-time operating conditions, improving the solar energy utilization rate and power generation output stability of the system under varying light and low light scenarios.

[0008] This solution switches the system operation mode based on operating conditions, state deviations, and the supply and demand relationship between the energy storage and the energy source. By setting phased charging and discharging threshold parameters and single-cell consistency adjustment parameters, it executes phased charging and discharging control of energy storage and triggers single-cell voltage equalization adjustment as needed. This makes the charging and discharging strategy adapt to the real-time state of energy storage, avoids improper charging and discharging, and improves the cycle life and operational safety and stability of energy storage.

[0009] This solution calculates the adjustable power supply based on the operating condition judgment results and state deviation information, defines the load power supply scheduling priority, dynamically allocates power supply, prioritizes the power supply needs of high-priority loads, and achieves precise coordinated matching between photovoltaic power generation, energy storage power supply and load power consumption. It can effectively take into account the power supply stability and adaptability of multiple types of loads. Attached Figure Description

[0010] Figure 1 This is a flowchart of a smart energy management method for building photovoltaic windows based on BIPV proposed in this invention; Figure 2 This is a flowchart illustrating the generation of the working condition determination result in this invention; Figure 3 This is a flowchart of the output-side voltage regulation of the photovoltaic module in this invention; Figure 4 This is a flowchart of the consistency adjustment process in this invention; Figure 5 This is a structural block diagram of a building photovoltaic window intelligent energy management system based on BIPV proposed in this invention. Detailed Implementation

[0011] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0012] Reference Figures 1-4As shown, a smart energy management method for building photovoltaic windows based on BIPV includes: S1. Obtain historical operating data of BIPV photovoltaic windows, construct a system benchmark operating condition model, generate benchmark operating data, and collect real-time operating data of BIPV photovoltaic windows. The benchmark operating data and real-time operating data both include environmental conditions, power generation and output voltage of photovoltaic modules, state of charge and energy storage capacity of energy storage units, load power requirements and load importance level of loads. Specifically, historical operating data comes from the operating records of the BIPV photovoltaic window system throughout the complete seasonal cycle. The historical operating data covers the operating status of different seasons such as spring, summer, autumn, and winter, as well as different weather conditions such as sunny, cloudy, rainy, and snowy days, in order to improve the adaptability of the system's baseline operating condition model to different environmental conditions. The types of historical operating data include environmental parameters, electrical parameters, energy storage status parameters, and load operating parameters. Among them, environmental parameters include light intensity, ambient temperature, and outside wind speed; electrical parameters include photovoltaic module output voltage, output current, output power, and power generation; energy storage status parameters include state of charge (SOC), charge and discharge power, single cell voltage, and battery temperature; and load operating parameters include total load power, power of each load, load start and stop status, load power consumption period, and load importance level. The system baseline operating condition model is constructed using a multi-dimensional statistical modeling method. First, historical operating data is preprocessed, including outlier removal, missing data completion, and normalization. Outlier removal can employ the 3σ criterion in statistics or be combined with logical verification based on power system physical constraints. For example, outliers such as negative photovoltaic power generation, energy storage charging and discharging power exceeding rated limits, and output voltage significantly exceeding the equipment's allowable range are removed. For short-term missing data, linear interpolation, moving average, or adjacent effective values ​​can be used for completion. By normalizing data of different dimensions, the impact of dimensional differences on the model construction results is reduced.

[0013] After data preprocessing, environmental features, photovoltaic output features, energy storage features, and load features are extracted from historical operating data. Environmental features include light intensity, ambient temperature, and wind speed. Photovoltaic output features include power generation, power output, output voltage, and output current. Energy storage features include SOC, charge and discharge power, energy storage capacity, and cell voltage. Load features include total load power, power of each load level, and electricity consumption period.

[0014] As a preferred embodiment of this solution, a clustering algorithm is used to perform cluster analysis on the above features, and the operating status of the BIPV photovoltaic window system is divided into multiple typical operating condition categories. The clustering algorithm can be K-means clustering, or hierarchical clustering, density clustering, or other algorithms suitable for multidimensional operating data classification. The number of cluster categories can be determined according to the elbow rule, silhouette coefficient, or historical operating experience. For each typical operating condition category, multiple linear regression, multinomial regression, support vector regression, or neural network model can be used to establish the mapping relationship between environmental operating conditions and photovoltaic output characteristics, energy storage charging and discharging characteristics, and load power consumption characteristics, forming a system operating benchmark parameter set under the corresponding typical operating conditions. Historical operating data is categorized and statistically analyzed by season, month, date type, and time period. The statistical mean, variance, and confidence interval of each operating parameter under each category are calculated. The statistical mean within the normal confidence range is used as the baseline operating parameter value under the corresponding operating condition. The confidence interval can be determined according to the system's operational stability requirements and outlier exclusion requirements.

[0015] As a preferred embodiment of this solution, a 95% confidence interval can be selected to balance the coverage of normal operation data and the effect of excluding abnormal data; Real-time operational data is collected through sensors, smart meters, energy storage monitoring units, and load monitoring units deployed at key nodes of the BIPV photovoltaic window system. The collection frequency of real-time operational data can be set according to the dynamic change characteristics of different parameters. Environmental operating condition data changes slowly and can be sampled at the minute level. The electrical parameters of photovoltaic modules and energy storage units change rapidly and can be sampled at the second level or higher. Load power demand data can be sampled at the second level or minute level according to the load scheduling accuracy. The specific sampling frequency can be calibrated according to the system capacity, controller computing power, and communication bandwidth.

[0016] It should be noted that the system's baseline operating condition model adopts a dynamic update mechanism. After the subsequent self-optimization cycle ends, the system incorporates the new cycle operation data into the update process of the baseline operating condition model. The model parameters are updated through incremental learning, rolling statistics, or weighted updates, so that the baseline operating condition can adapt to changes in photovoltaic module performance degradation, energy storage unit capacity, building usage requirements, and local weather conditions.

[0017] S2. Compare the real-time operating data with the baseline operating data to generate state deviation information, and generate the working condition judgment result based on the real-time operating data and the state deviation information. Furthermore, the step of comparing real-time operating data with baseline operating data to generate state deviation information, and generating a working condition judgment result based on the real-time operating data and the state deviation information, specifically includes: S21. Calculate the deviation between the real-time running data and the baseline running data one by one; Specifically, the current real-time operating data is compared with the baseline operating data under the corresponding time period, season, environmental conditions, or typical operating condition category to obtain the deviation value of each parameter. The deviation value is calculated using relative deviation, absolute deviation, or a combination of both. For continuously changing parameters such as light intensity, ambient temperature, wind speed, and SOC, the relative deviation can be calculated. For parameters directly related to system control, such as photovoltaic output power, output voltage, load power, and energy storage charging and discharging power, both relative and absolute deviations can be calculated simultaneously. The formula for calculating relative deviation is: Relative deviation = (Real-time value - Reference value) / Reference value × 100%; The formula for calculating absolute deviation is: Absolute deviation = Real-time value - Reference value; It should be noted that when the reference value is close to zero or is not suitable as a direct denominator, the absolute deviation, normalized deviation, or deviation compared to the rated value can be used for calculation to avoid distortion of the relative deviation.

[0018] S22. Based on the deviation value, generate state deviation information, which includes environmental operating condition deviation, photovoltaic module power generation deviation and output voltage deviation, energy storage unit state of charge deviation and energy storage margin deviation, load power demand deviation, overall comprehensive deviation and corresponding comprehensive deviation level. Specifically, the overall comprehensive deviation is obtained by weighted summation after normalizing each individual deviation; For parameters that contain both relative and absolute deviations, the corresponding deviations are first converted into dimensionless deviation values. The calculation formula is: ; in, This is a real-time value. As the baseline value, This refers to the allowable fluctuation range or rated reference value corresponding to this parameter, when using relative deviation. Pick Or the rated reference value, when using absolute deviation, Take the allowable absolute fluctuation range of this parameter; When using relative deviation calculations The baseline value of this parameter under the corresponding working condition should be taken first. If the reference value is close to 0, then the rated reference value of the parameter shall be taken. When using absolute deviation calculation Take the allowable absolute fluctuation range of this parameter. The allowable absolute fluctuation range is determined by statistical analysis of historical operating data. Take the difference between the upper and lower limits of the parameter under the corresponding 95% confidence interval. Different types of parameters The statistical dimensions are: Environmental operating parameters, with allowable fluctuation ranges calculated by season and time period; Photovoltaic module parameters, calculated according to the allowable fluctuation range of light intensity range; Energy storage unit parameters are calculated based on the allowable fluctuation range of battery health status intervals. Load parameters are calculated by date type (weekday / holiday) + time period, showing the allowable fluctuation range.

[0019] The formula for calculating the overall comprehensive deviation D is: ; in, Let be the weight of the i-th individual deviation, and satisfy . =1, where n is the total number of individual deviations; Each weight is determined through historical operating data. At least one of the following is used as the evaluation index: system operation safety, photovoltaic power generation efficiency, energy storage unit charging and discharging efficiency, energy storage unit cycle life, load power supply stability and load satisfaction rate. The absolute value of the correlation coefficient, sensitivity coefficient or contribution degree between each individual deviation and the evaluation index is calculated, and the calculation results are normalized and used as the corresponding weight. When the absolute value of the correlation coefficient is used to determine the weight, the weight is: ; in, Let be the absolute value of the correlation coefficient between the i-th individual deviation and the evaluation index; The overall deviation level can be divided into multiple levels based on the magnitude of the overall deviation.

[0020] As a preferred embodiment of this solution, the comprehensive deviation level is divided into Level 1 deviation, Level 2 deviation, and Level 3 deviation. Level 1 deviation indicates that the system operating state is close to the baseline condition; Level 2 deviation indicates that the system operating state deviates significantly; and Level 3 deviation indicates that the system operating state deviates considerably and requires enhanced regulation. The specific thresholds for each level can be calibrated based on system capacity, battery type, load power supply stability requirements, and historical operating data statistics. For example, after the overall comprehensive deviation D is dimensionless and weighted, the state of D≤0.05 is classified as Level 1 deviation, the state of 0.05<D≤0.15 is classified as Level 2 deviation, and the state of D>0.15 is classified as Level 3 deviation. The threshold of 0.05 is determined based on the normal fluctuation range of each operating parameter under stable system operation, and the threshold of 0.15 is determined based on the deviation boundary before the system needs to perform enhanced regulation or safety protection. In practical applications, adjustments can be made based on the rated power, energy storage capacity, battery safe operating range, load level distribution, and the statistical distribution of normal and abnormal operating samples in historical operating data of the BIPV photovoltaic window system.

[0021] S23. Based on historical operating data statistics, seasonal patterns, light characteristics, load characteristics, and energy storage safety range, predetermine and store multiple operating condition types and corresponding judgment thresholds for each operating condition. At the same time, set the MPPT tracking parameters, voltage transformation adjustment parameters, charging and discharging stage threshold parameters, and single-cell consistency adjustment parameters corresponding to the operating conditions.

[0022] Furthermore, the operating conditions include sufficient light conditions, insufficient light conditions, peak load conditions, low load conditions, and abnormal energy storage conditions. The judgment thresholds include the standard range of real-time operating data under each operating condition and the deviation threshold of state deviation information. The charging and discharging stage threshold parameters include the upper limit of charging state threshold, the lower limit of discharging state threshold, and the charging and discharging rate thresholds under different operating conditions. Specifically, the determination methods for each working condition are as follows: The light intensity threshold, the photovoltaic output power reference range, the load power demand reference value, and the duration are determined by statistical analysis of historical operating data. As a preferred embodiment of this solution, the light intensity threshold is taken as 60% of the median light intensity during the historical effective power generation period, the photovoltaic output power corresponding benchmark range is taken as the average of the historical photovoltaic output power under the corresponding operating condition ± 2 times the standard deviation, the load power demand corresponding benchmark value is taken as the average load power demand in the same season and time period, and the duration threshold of the load peak operating condition and the load trough operating condition is taken as the duration corresponding to 3 consecutive sampling periods or 5 minutes, specifically choosing the larger of the two. Under sufficient sunlight conditions, the current light intensity is higher than the light intensity threshold, and the photovoltaic output power is within ±2 standard deviations of the historical photovoltaic output power under the corresponding conditions. In cases of insufficient sunlight, the current sunlight intensity is below the sunlight intensity threshold, or the photovoltaic output power is below 70% of the historical average photovoltaic output power under the corresponding conditions. During peak load conditions, the current load power demand is 120% higher than the average load power demand for the same period in the same season, and this continues for 3 consecutive sampling cycles or 5 minutes. During off-peak load conditions, the current load power demand is less than 60% of the average load power demand for the same period in the same season, and this continues for 3 consecutive sampling periods or 5 minutes. Abnormal operating conditions of energy storage: the SOC of the energy storage unit is below 20% or above 90%, or the voltage deviation of the energy storage unit exceeds 50mV.

[0023] It should be noted that the above thresholds can be updated based on local irradiance conditions, system rated power, safe operating range of energy storage units, load power consumption characteristics and historical operation statistics. After the end of each long-term operation cycle, the median irradiance, mean and standard deviation of photovoltaic output power, and mean load power demand for the corresponding season and time period are recalculated, and the corresponding thresholds are updated according to the above proportions or ranges. Among them, the SOC threshold of the energy storage unit and the voltage deviation threshold of the individual cells are updated under the condition that they do not exceed the safety range of the battery manufacturer.

[0024] The control parameters corresponding to each operating condition are pre-stored in the system parameter library. The parameter library can adopt a hierarchical index structure, with the operating condition type as the first-level index, the comprehensive deviation level as the second-level index, and the operating mode or load level as the auxiliary index, so as to facilitate the controller to quickly query and call the corresponding parameters.

[0025] S24. Compare the range and deviation of the current real-time running data with the judgment thresholds of each working condition one by one. If all the judgment conditions of a certain working condition are met, the current system is determined to be in that working condition type and a working condition judgment result is generated.

[0026] Specifically, the operating condition determination adopts a priority matching mechanism. When multiple operating condition determination conditions are met at the same time, the determination is made according to the priority of abnormal energy storage conditions > peak load conditions > insufficient sunlight conditions > sufficient sunlight conditions > low load conditions, so as to ensure that the system prioritizes the operating conditions that have a greater impact on operational safety and power supply stability.

[0027] S3. Based on the operating condition judgment results and state deviation information, set the MPPT tracking parameters and voltage conversion adjustment parameters to perform real-time tracking of the maximum power operating point and output voltage regulation of the photovoltaic module. Furthermore, the step of setting MPPT tracking parameters and voltage conversion adjustment parameters based on the operating condition judgment results and state deviation information to perform real-time maximum power operating point tracking and output-side voltage regulation of the photovoltaic module specifically includes: S31. Based on the operating condition type and the judgment threshold corresponding to each operating condition, establish the correlation mapping relationship between the operating condition type, the comprehensive deviation level and the MPPT tracking parameters and voltage transformation adjustment parameters. Specifically, the MPPT tracking algorithm employs the perturbation-observation method, the incremental conductance method, the adaptive step-size perturbation-observation method, or a combination of these algorithms, with different algorithm parameters used under different operating conditions. For example, when the illumination changes relatively smoothly and is strong, the incremental conductance method, which has higher tracking accuracy, is used; when the illumination is weak or fluctuates significantly, the perturbation-observation method or the adaptive step-size algorithm, which has a smaller perturbation amplitude, is used to reduce oscillation losses; and when the load change rate is large, the sampling frequency is increased or an adaptive step size is used to improve the dynamic response speed.

[0028] The voltage conversion regulation parameters include the target output voltage, switching frequency, duty cycle adjustment range, duty cycle adjustment rate, voltage compensation coefficient, and allowable fluctuation range of the output voltage of the DC / DC converter. The associated mapping relationship is established according to the parameter library method, with the operating condition type as the first-level index and the comprehensive deviation level as the second-level index. For each index combination, a set of MPPT tracking parameters and voltage conversion regulation parameters are stored. Specifically, when there is sufficient lighting and the overall deviation level is Level 1, the MPPT tracking step size is set to... Set the sampling period to And the incremental conductivity method was adopted; When there is insufficient lighting or the overall deviation level is level two or higher, the MPPT tracking step size should be set to ΔU2 = 0.005 × Set the sampling period to no more than / 2, and increase the voltage compensation coefficient, where This refers to the open-circuit voltage of the photovoltaic module.

[0029] S32. Based on the operating condition judgment result and state deviation information, match the MPPT tracking parameters and voltage conversion adjustment parameters under the corresponding operating condition and set them as the MPPT tracking parameters and voltage conversion adjustment parameters currently in operation of the system. Specifically, the primary index is determined based on the operating condition judgment result, and the secondary index is determined based on the comprehensive deviation level. The corresponding parameter group is queried from the system parameter library, and the parameter group is loaded into the photovoltaic controller to complete the real-time setting of parameters. When the comprehensive deviation level changes, the corresponding parameters are automatically rematched to realize the dynamic adjustment of control parameters.

[0030] S33. Based on the set MPPT tracking parameters, perform real-time maximum power operating point tracking on the photovoltaic module to obtain the current output voltage of the photovoltaic module; Specifically, the controller collects the output voltage and current of the photovoltaic module according to the set sampling period, calculates the current output power, and adjusts the duty cycle of the DC / DC converter according to the MPPT tracking algorithm to make the photovoltaic module work near the maximum power point. When the light intensity or load changes suddenly, the controller automatically speeds up the tracking speed to ensure that the maximum power point is relocked in the shortest possible time.

[0031] S34. Based on the set voltage transformation adjustment parameters and combined with the current output voltage of the photovoltaic module, the fluctuation of the output voltage of the photovoltaic module is compensated in real time by a combination of feedforward compensation and discrete PID feedback regulation.

[0032] Specifically, voltage compensation employs a discrete PID control method combining feedforward compensation and feedback regulation, assuming the target output voltage is... The real-time output voltage during the kth sampling period is The system sampling period is T, and the duty cycle adjustment amount for the k-th sampling period is... The calculation formula is: ; in, The voltage error in the kth sampling period is... This is the feedforward compensation coefficient, used for rapid response to sudden changes in photovoltaic output voltage. This is a proportional adjustment coefficient used to quickly reduce voltage errors. This is the integral adjustment coefficient, used to eliminate steady-state voltage error; Controller according to Adjust the duty cycle of the DC / DC converter and limit the adjusted duty cycle to a preset duty cycle adjustment range, typically 0.1~0.9, so that the output voltage is kept within the allowable fluctuation range of the target output voltage, typically ±2% of the rated voltage.

[0033] S4. Based on the operating condition judgment results and state deviation information, combined with the real-time power generation of photovoltaic modules, energy storage capacity and load power demand, switch the system operation mode, set the threshold parameters for charging and discharging stages and the individual consistency adjustment parameters, execute the phased charging and discharging control of the energy storage unit and trigger the individual consistency adjustment as needed. Furthermore, based on the operating condition judgment results and state deviation information, combined with the real-time power generation of the photovoltaic modules, energy storage capacity, and load power demand, the system operation mode is switched, charging and discharging stage threshold parameters and individual cell consistency adjustment parameters are set, and staged charging and discharging control of the energy storage unit is executed, and individual cell consistency adjustment is triggered as needed. Specifically, this includes: S41. Based on the system baseline operating condition model, and combined with the real-time power generation of photovoltaic modules, energy storage margin and load power demand, the system operation mode is divided into photovoltaic direct supply mode, photovoltaic-storage combined power supply mode and energy storage separate power supply mode, and the judgment conditions corresponding to each operation mode are generated. Furthermore, based on the system baseline operating condition model, and combined with the real-time power generation of photovoltaic modules, energy storage capacity, and load power demand, the system operation modes are divided into direct photovoltaic power supply mode, combined photovoltaic and energy storage power supply mode, and energy storage standalone power supply mode, and corresponding judgment conditions are generated for each operation mode, specifically including: S411. When the real-time power generation of the photovoltaic module can fully cover the current load power demand and the energy storage margin is within the normal range, determine and switch to photovoltaic direct supply mode. S412. When the real-time power generation of photovoltaic modules cannot meet the power demand of the entire load and energy storage is required to supplement the power, determine and switch to the photovoltaic-storage joint power supply mode. S413. When the real-time power generation of the photovoltaic module is lower than the minimum power supply threshold and there is no effective photovoltaic output, determine and switch to the energy storage separate power supply mode.

[0034] Specifically, when the real-time photovoltaic power generation can cover the current load power demand and the energy storage unit is in the allowable operating range, the photovoltaic direct supply mode is determined and switched to. In the photovoltaic direct supply mode, the photovoltaic modules prioritize power supply to the load. If there is remaining photovoltaic power and the energy storage unit has not reached the charging upper limit state of charge threshold, the remaining power will be used to charge the energy storage unit. When the real-time photovoltaic power generation cannot fully meet the current load power demand, and the state of charge of the energy storage unit is higher than the discharge lower limit state of charge threshold, the photovoltaic and energy storage joint power supply mode is determined and switched to. In this mode, the photovoltaic modules and energy storage units jointly supply power to the load to make up for the insufficient photovoltaic output. When the real-time power generation of the photovoltaic module is lower than the minimum power supply threshold, or there is no effective photovoltaic output, and the state of charge of the energy storage unit is higher than the discharge lower limit state of charge threshold, the system will determine and switch to the energy storage independent power supply mode. In this mode, the energy storage unit independently supplies power to the load. When the photovoltaic output is insufficient and the state of charge of the energy storage unit is lower than the discharge lower limit state of charge threshold, the system will perform power limiting, load reduction or disconnection of low-priority loads according to the load power supply scheduling priority to ensure the power supply of high-priority loads and the safety of energy storage units. The minimum power supply threshold is determined based on the rated power of the photovoltaic modules, the efficiency of the DC / DC converter, and the load startup power requirement. The specific calculation formula is as follows: ; in, The minimum power supply threshold, Rated power of photovoltaic modules The minimum startup power requirement allowed for the system to be connected to the load. For DC / DC converter efficiency, when the real-time power generation of photovoltaic modules is lower than At that time, it was determined that there was no effective photovoltaic output.

[0035] S42. Based on the upper limit of the state of charge threshold for charging and the lower limit of the state of charge threshold for discharging, the charging and discharging process of the energy storage unit is divided into three stages: low-capacity replenishment, normal stable charging and discharging, and high-capacity current limiting protection. Each stage is set with a corresponding charging rate threshold and discharging rate threshold. Specifically, the low-power replenishment stage, the normal stable charging and discharging stage, and the high-power current limiting protection stage are divided according to the SOC value of the energy storage unit.

[0036] As a preferred embodiment of this solution, when SOC≤20%, the system enters the low-power replenishment stage, prohibits further discharge, and replenishes power at a charging rate of no more than 0.3C. When 20% < SOC < 85%, the system enters the normal stable charging and discharging stage, and normal charging and discharging control is carried out according to a charging and discharging rate of no more than 0.5C. When SOC ≥ 85%, it enters the high-capacity current limiting protection stage, limiting the charging rate to no more than 0.2C and the discharging rate to no more than 0.4C. When SOC reaches 90%, charging is stopped or only equalization charging is maintained. The aforementioned SOC threshold and charge / discharge rate can be proportionally calibrated within the safe operating range given by the battery manufacturer, based on the type of energy storage battery and its rated capacity.

[0037] S43. Based on the operating condition judgment results and state deviation information, as well as the real-time power generation of photovoltaic modules, energy storage capacity and load power demand, analyze the current energy supply and consumption balance of the system. Specifically, the energy supply and consumption balance analysis is achieved by calculating the real-time power balance of the system, using the following formula: Power balance = Real-time power generation of photovoltaic modules + Discharge power of energy storage - Total load power; When the power balance is ≥0, the system has sufficient power supply; When the power balance is less than 0, the system is not powered enough, and it is necessary to adjust the operating mode or limit the power supply to some loads.

[0038] S44. Based on the analysis results, determine the system operation mode and switch to the corresponding operation mode. Based on the determined system operation mode, match the charging and discharging stage threshold parameters and the single-cell consistency adjustment parameters under the corresponding working conditions and set them as the charging and discharging stage threshold parameters and single-cell consistency adjustment parameters of the current system operation. Specifically, the operation mode switching process adopts seamless switching technology, and the output power of the energy storage unit is adjusted in advance before switching to ensure that the power supply to the load is not interrupted during the switching process.

[0039] It should be noted that the parameter matching process is the same as the photovoltaic control parameter matching process. The corresponding charge and discharge parameters and individual cell consistency adjustment parameters are queried from the parameter library according to the operating condition type and the overall deviation level.

[0040] S45. Determine the charging and discharging stage of the system based on the real-time state of charge of the energy storage unit, and combine the real-time environmental conditions with the charging and discharging stage threshold parameters to execute the phased charging and discharging control of the energy storage unit. Specifically, the energy storage controller collects the SOC value of the battery pack in real time, determines the current charging and discharging stage based on the SOC value, and adjusts the charging and discharging current and voltage according to the charging and discharging rate and control strategy of the corresponding stage.

[0041] It should be noted that when the ambient temperature exceeds the allowable temperature range of the energy storage unit or approaches the safety boundary, the system automatically reduces the charging and discharging rate or suspends charging and discharging to protect the safety of the energy storage unit. The temperature threshold is set according to the battery type, manufacturer's technical specifications, and system safety margin.

[0042] S46. Real-time detection of the voltage status of each cell in the energy storage unit, comparison of the detected voltage deviation with the voltage deviation threshold of each cell in the energy storage unit, and triggering consistency adjustment of each cell in the energy storage unit as needed until the voltage deviation of each cell in all energy storage units is within the cell voltage deviation threshold.

[0043] Specifically, the single-cell consistency adjustment adopts either active or passive balancing methods. Active balancing can transfer energy from higher-voltage cells to lower-voltage cells, while passive balancing can release excess energy from high-voltage cells through energy-consuming components. The specific balancing method can be selected based on the cost, efficiency, and safety requirements of the energy storage system.

[0044] It should be noted that the individual cell voltage deviation threshold is determined based on the type of energy storage battery, the series-parallel connection structure, the accuracy of the equalization circuit, and safety requirements.

[0045] As a preferred embodiment of this solution, the single-cell voltage deviation threshold is set to a millivolt level threshold to improve battery pack consistency and extend service life.

[0046] S5. Based on the working condition judgment results and state deviation information, calculate the adjustable power supply of the system and define the load power supply scheduling priority, set the power supply priority allocation parameters, and dynamically allocate the power supply based on the power supply scheduling priority and the power supply priority allocation parameters. Furthermore, based on the operating condition determination results and state deviation information, the system calculates the adjustable power supply and defines the load power supply scheduling priority, sets power supply priority allocation parameters, and dynamically allocates power supply based on the power supply scheduling priority and power supply priority allocation parameters, specifically including: S51. Based on the operating condition judgment results and state deviation information, combined with the real-time power generation of photovoltaic modules, the current available energy storage capacity of energy storage units, and the system operation deviation data corresponding to the state deviation information, calculate the adjustable power supply of the system. Specifically, the formula for calculating the adjustable power supply of the system is as follows: Adjustable power supply = real-time power generation of photovoltaic modules + maximum available discharge power of energy storage units - power loss of the system itself; The maximum available discharge power of the energy storage unit is determined based on the current SOC and discharge rate threshold. The system's own power loss includes photovoltaic converter loss, energy storage converter loss and line loss, which is usually taken as 3% to 5% of the sum of the real-time power generation of the photovoltaic module and the maximum available discharge power of the energy storage unit.

[0047] S52. Based on the importance level of the load, the loads are divided into first-level loads, second-level loads and third-level loads, and the power supply scheduling priority is from high to low as first-level loads, second-level loads and third-level loads. Specifically, the load classification is based on the necessity of safe operation of the electrical loads associated with building photovoltaic windows and the degree of uninterrupted power supply requirement, including: Level 1 loads, security and basic protection loads, including fire protection systems, security monitoring systems, emergency lighting systems, elevators, etc., must be guaranteed uninterrupted power supply; Level 2 loads are those that are essential for daily life, including indoor lighting, air conditioners, refrigerators, washing machines, etc. These loads can be operated at reduced power when the power supply is insufficient. Level 3 loads are non-essential recreational auxiliary loads, including televisions, stereos, and charging stations. Power to these loads can be temporarily cut off when there is a severe power shortage.

[0048] S53. Based on the load power demand of each load level, power supply scheduling priority, system adjustable power supply and state deviation information, set power supply priority allocation parameters. The power supply priority allocation parameters include the minimum guaranteed power supply ratio of each load level, power supply allocation order, low priority load limiting ratio and low priority load disconnection order. Specifically, based on the load power requirements of primary, secondary, and tertiary loads, power supply scheduling priorities, and the system's adjustable power supply, the minimum guaranteed power supply ratio for each load level under different power supply conditions is determined. The power supply allocation order is determined according to the power supply scheduling priority, with primary loads taking precedence over secondary loads, and secondary loads taking precedence over tertiary loads. Based on the load power demand deviation, energy storage margin deviation, and overall comprehensive deviation in the status deviation information, the power supply limitation ratio and the order of cutting off low-priority loads are determined, so that the system prioritizes reducing or cutting off low-priority loads when power supply is insufficient or the comprehensive deviation level is high.

[0049] S54. Based on the defined power supply scheduling priority, the system's adjustable power supply power, and the power supply priority allocation parameters, the power supply power is dynamically allocated to prioritize the power supply needs of high-priority loads, while the remaining adjustable power is allocated to low-priority loads.

[0050] Specifically, the power supply is first allocated according to the rated power and minimum guaranteed power supply ratio of the primary load to ensure that the primary load operates with priority. The remaining power is allocated to the secondary load according to the power supply allocation order. The power of non-critical secondary loads is appropriately reduced according to the power limiting ratio of low priority loads. If there is still remaining power, it is allocated to the tertiary load. When the system's available power supply is insufficient, the power supply to the loads is cut off in the order of low priority loads until the power is balanced.

[0051] S6. Periodically collect real-time operating data and status deviation information, analyze the operating condition change pattern and system operating characteristics, and correct MPPT tracking parameters, voltage transformation adjustment parameters, charging and discharging stage threshold parameters, individual unit consistency adjustment parameters and power supply priority allocation parameters in layers, and update the system baseline operating condition simultaneously. Furthermore, the periodic collection of real-time operating data and state deviation information, analysis of operating condition change patterns and system operating characteristics, hierarchical correction of MPPT tracking parameters, voltage transformation adjustment parameters, charging and discharging stage threshold parameters, individual unit consistency adjustment parameters, and power supply priority allocation parameters, and synchronous updating of system baseline operating conditions, specifically includes: S61. Set the operating cycle, which is set according to seasonal changes and environmental fluctuations. Specifically, the operating cycle is divided into a short-term cycle and a long-term cycle. The short-term cycle is one week and is used to correct parameters that are greatly affected by short-term weather changes. The long-term cycle is one month and is used to correct parameters that are greatly affected by seasonal changes and system performance degradation. During seasonal transitions, the operating cycle can be appropriately shortened to increase the frequency of parameter correction.

[0052] S62. According to the set operating cycle, classify and collect the parameter data in the real-time operating data and the deviation data in the status deviation information to form a periodic operating dataset, and remove abnormal data from the dataset. Specifically, the abnormal data removal adopts the 3σ criterion, which identifies and removes data that exceeds the mean ± 3 times the standard deviation. At the same time, through power physical logic verification, data that does not conform to the system's operating rules are removed, such as negative power generation or energy storage charging and discharging power exceeding the rated value, to ensure the accuracy of the periodic operation data set.

[0053] S63. Perform statistical analysis on the periodic operation dataset to obtain the correlation between various control parameters and system operating status; Specifically, the statistical analysis employs correlation and regression analysis methods to calculate the correlation coefficients between various control parameters and system performance indicators such as power generation efficiency, energy storage efficiency, and power supply stability, thereby identifying key parameters that have a significant impact on system performance and providing a basis for parameter correction.

[0054] S64. Based on the statistical analysis results of the periodic operation dataset, calculate the correction amount of various control parameters, and perform hierarchical correction of MPPT tracking parameters, voltage transformation regulation parameters, charging and discharging stage threshold parameters, individual unit consistency regulation parameters, and power supply priority allocation parameters. Specifically, the parameter correction adopts a hierarchical correction strategy: The first layer corrects the MPPT tracking parameters and voltage conversion adjustment parameters, and optimizes the perturbation step size and switching frequency based on the power generation efficiency data under different light intensities to improve photovoltaic energy capture efficiency. The second layer modifies the threshold parameters of the charging and discharging stages and the single-cell consistency adjustment parameters. Based on the charging and discharging efficiency and cycle life data of the energy storage unit, the charging and discharging rate and equalization threshold are adjusted to extend the service life of the energy storage. The third layer modifies the power supply priority allocation parameters. Based on the load power consumption patterns, load power supply satisfaction rate, and power supply stability data, it optimizes the minimum guaranteed power supply ratio, power supply allocation order, low-priority load limiting ratio, and low-priority load disconnection order for each load level, thereby improving the power supply adaptability of various types of loads.

[0055] It should be noted that the calculation of parameter correction is aimed at optimizing system performance. A comprehensive objective function is constructed based on power generation efficiency, energy storage charging and discharging efficiency, load power supply satisfaction rate, and output voltage fluctuation rate. The influence gradient of each control parameter on the comprehensive objective function is calculated based on periodic operation data, and the corresponding control parameters are updated according to the preset learning rate. When the updated performance indicators do not improve or exceed the preset safety boundary, the system reverts to the previous parameter group to ensure that the corrected parameters can improve the overall system performance.

[0056] S65. Based on the statistical mean of the periodic operation dataset, synchronously update the baseline values ​​of each parameter of the preset system baseline operating condition, so that the system baseline operating condition is adapted to the current environment and system operating status.

[0057] Specifically, the system baseline update process is as follows: the statistical mean of each parameter in the periodic running data is weighted and averaged with the original baseline value, with the weight being the proportion of the running time of the new data to the total running time, to obtain the updated baseline value. The updated baseline value needs to be verified to ensure that it can accurately reflect the current operating characteristics of the system.

[0058] S7. Put the corrected parameters and the updated system baseline into the system for operation and verification, and feed the operation verification results back to the operation condition pattern analysis and parameter correction stage of the next operation cycle.

[0059] Specifically, the operation and verification cycle is 1 day. During the verification period, the system's power generation efficiency, energy storage efficiency, power supply stability and other performance indicators are continuously monitored and compared with the performance indicators before correction. If the verification results show that the system performance has been improved, the corrected parameters and benchmark operating conditions will be officially put into use. If the verification results do not meet expectations, the cycle operation data will be re-analyzed, the parameter correction amount will be adjusted, and the verification will be carried out again.

[0060] It should be noted that all runtime verification results and parameter correction records are stored in the system database, serving as the basis for analysis in the next runtime cycle, thus forming a closed-loop optimization mechanism.

[0061] Reference Figure 5 As shown, a building photovoltaic (BIPV)-based intelligent energy management system for windows is proposed to implement a BIPV-based intelligent energy management method for windows as described in any of the above claims, including: The data acquisition and benchmark modeling module is used to acquire historical operating data of BIPV photovoltaic windows, build a system benchmark operating condition model and generate benchmark operating data, while also acquiring real-time operating data; The operating condition decision module is used to generate state deviation information, complete the operating condition judgment, and output the operating condition judgment result; The energy regulation and execution module is used to perform photovoltaic MPPT tracking and output-side voltage regulation, system operation mode switching, energy storage phased charging and discharging control, single-unit consistency adjustment, and load priority scheduling and power supply allocation. The self-optimizing closed-loop module is used to periodically correct control parameters, update baseline operating conditions, and complete operational verification and data feedback. The central control module is used to coordinate the collaborative operation of the above modules and the interaction of data commands.

[0062] Furthermore, the data acquisition and benchmark modeling module includes: The integrated data acquisition unit is used to collect real-time operating data of environmental conditions, photovoltaic modules, energy storage units, and loads. The benchmark construction unit is used to build a system benchmark operating condition model based on historical operating data and generate benchmark operating data. Furthermore, the operating condition decision module includes: The deviation analysis unit is used to calculate the deviation between real-time operating data and baseline operating data, and generate status deviation information. The working condition matching unit is used to determine the working condition based on real-time operating data and state deviation information and output the working condition determination result. Furthermore, the energy regulation and execution module includes: The photovoltaic control unit is used to perform maximum power point tracking and output voltage fluctuation compensation regulation of photovoltaic modules; The energy storage management unit is used for system operation mode determination and switching, phased charging and discharging control of energy storage, and single-cell consistency adjustment. The load scheduling unit is used to calculate the system's adjustable power supply, divide the load power supply priority, set power supply priority allocation parameters, and dynamically allocate power supply. Furthermore, the self-optimizing closed-loop module includes: The parameter correction unit is used to periodically collect real-time operating data and state deviation information, correct control parameters in layers, and update the system baseline operating conditions. The closed-loop verification unit is used to put the corrected parameters and the updated benchmark conditions into operation for verification, and to feed the verification results back to the parameter correction unit. Furthermore, the central control module includes: The main control unit is used to coordinate and schedule the collaborative operation of all modules; The communication storage unit is used to realize data transmission, instruction exchange and data storage between all modules.

[0063] This solution dynamically sets MPPT tracking parameters and voltage conversion adjustment parameters based on the operating condition judgment results and state deviation information, realizing real-time tracking of the maximum power point of photovoltaic modules and compensation for output-side voltage fluctuations. This enables the photovoltaic energy capture strategy to dynamically match real-time operating conditions, improving the solar energy utilization rate and power generation output stability of the system under varying light and low light scenarios. At the same time, the system operation mode is switched based on operating conditions, state deviations, and the supply and demand relationship between source, storage, and load. By setting phased charging and discharging threshold parameters and individual cell consistency adjustment parameters, phased charging and discharging control of energy storage is executed, and individual cell voltage equalization adjustment is triggered as needed. This makes the charging and discharging strategy adapt to the real-time state of energy storage, avoids improper charging and discharging, and improves the cycle life and operational safety and stability of energy storage.

[0064] 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 principles of 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 claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for intelligent energy management of a BIPV-based building photovoltaic window, characterized in that, include: Historical operating data of BIPV photovoltaic windows are acquired, a system baseline operating condition model is constructed, baseline operating data is generated, and real-time operating data of BIPV photovoltaic windows is collected. The baseline operating data and real-time operating data include environmental conditions, power generation and output voltage of photovoltaic modules, state of charge and energy storage capacity of energy storage units, load power requirements and load importance level of loads. The real-time operating data is compared with the baseline operating data to generate state deviation information, and the operating condition judgment result is generated based on the real-time operating data and the state deviation information. Based on the operating condition judgment results and state deviation information, MPPT tracking parameters and voltage conversion adjustment parameters are set to perform real-time tracking of the maximum power operating point and output voltage regulation of the photovoltaic module. Based on the operating condition judgment results and state deviation information, combined with the real-time power generation of photovoltaic modules, energy storage capacity and load power demand, the system operation mode is switched, the threshold parameters for charging and discharging stages and the individual consistency adjustment parameters are set, the energy storage unit is executed in stages for charging and discharging control and the individual consistency adjustment is triggered as needed. Based on the operating condition judgment results and state deviation information, the accounting system can allocate power supply and define the load power supply scheduling priority, set power supply priority allocation parameters, and dynamically allocate power supply based on the power supply scheduling priority and power supply priority allocation parameters. Periodically collect real-time operating data and status deviation information, analyze the changing patterns of operating conditions and system operating characteristics, and adjust MPPT tracking parameters, voltage transformation adjustment parameters, charging and discharging stage threshold parameters, individual unit consistency adjustment parameters and power supply priority allocation parameters in a hierarchical manner, while updating the system baseline operating conditions simultaneously. The corrected parameters and updated system baseline conditions are put into system operation and verified. The operation verification results are then fed back to the operation condition pattern analysis and parameter correction stage of the next operating cycle.

2. The intelligent energy management method for building photovoltaic windows based on BIPV according to claim 1, characterized in that, The process of comparing real-time operating data with baseline operating data to generate state deviation information, and generating a working condition judgment result based on the real-time operating data and state deviation information, specifically includes: Calculate the deviation between the real-time running data and the baseline running data one by one; Based on the deviation values, state deviation information is generated, which includes environmental operating condition deviation, photovoltaic module power generation deviation and output voltage deviation, energy storage unit state of charge deviation and energy storage margin deviation, load power demand deviation, overall comprehensive deviation and corresponding comprehensive deviation level. Based on historical operational data statistics, seasonal patterns, light characteristics, load characteristics, and energy storage safety range, multiple operating condition types and corresponding judgment thresholds are pre-determined and stored. At the same time, MPPT tracking parameters, voltage transformation adjustment parameters, charge and discharge stage threshold parameters, and single-cell consistency adjustment parameters are set for each operating condition. The operating condition types include sufficient light conditions, insufficient light conditions, peak load conditions, low load conditions, and abnormal energy storage conditions. The judgment thresholds include the standard range of real-time operating data under each operating condition and the deviation threshold of state deviation information. The charge and discharge stage threshold parameters include the upper limit of charging state of charge threshold, the lower limit of discharging state of charge threshold, and the charge and discharge rate threshold under different operating conditions. The range and deviation of the current real-time operating data are compared with the judgment thresholds of each operating condition. If all the judgment conditions of a certain operating condition are met, the current system is determined to be in that operating condition type and an operating condition judgment result is generated.

3. The intelligent energy management method for building photovoltaic windows based on BIPV according to claim 1, characterized in that, The process of setting MPPT tracking parameters and voltage conversion adjustment parameters based on the operating condition judgment results and state deviation information to perform real-time maximum power operating point tracking and output-side voltage regulation of the photovoltaic module specifically includes: Based on the operating condition type and the corresponding judgment threshold for each operating condition, a correlation mapping relationship is established between the operating condition type, the comprehensive deviation level, and the MPPT tracking parameters and voltage transformation adjustment parameters. Based on the operating condition determination results and state deviation information, match the MPPT tracking parameters and voltage conversion adjustment parameters under the corresponding operating condition and set them as the MPPT tracking parameters and voltage conversion adjustment parameters currently in operation of the system. Based on the set MPPT tracking parameters, the photovoltaic module is tracked in real time for maximum power operating point to obtain the current output voltage of the photovoltaic module; Based on the set voltage transformation adjustment parameters and combined with the current output voltage of the photovoltaic module, the fluctuation of the output voltage of the photovoltaic module is compensated in real time by a combination of feedforward compensation and discrete PID feedback regulation.

4. The intelligent energy management method for building photovoltaic windows based on BIPV according to claim 1, characterized in that, Based on the operating condition judgment results and state deviation information, combined with the real-time power generation of photovoltaic modules, energy storage capacity, and load power demand, the system operation mode is switched, threshold parameters for the charging and discharging stages and individual cell consistency adjustment parameters are set, and staged charging and discharging control of the energy storage unit is executed, and individual cell consistency adjustment is triggered as needed. Specifically, this includes: Based on the system baseline operating condition model, and combined with the real-time power generation of photovoltaic modules, energy storage margin and load power demand, the system operation mode is divided into photovoltaic direct supply mode, photovoltaic-energy storage combined power supply mode and energy storage separate power supply mode, and the corresponding judgment conditions for each operation mode are generated. Based on the upper limit of the state of charge threshold for charging and the lower limit of the state of charge threshold for discharging, the charging and discharging process of the energy storage unit is divided into three stages: low-capacity replenishment, normal stable charging and discharging, and high-capacity current limiting protection. Each stage is set with a corresponding charging rate threshold and discharging rate threshold. Based on the operating condition judgment results and state deviation information, as well as the real-time power generation of photovoltaic modules, energy storage capacity and load power demand, the current energy supply and consumption balance of the system is analyzed. Based on the analysis results, the system operation mode is determined and switched to the corresponding operation mode. Based on the determined system operation mode, the charging and discharging stage threshold parameters and the single-cell consistency adjustment parameters under the corresponding working conditions are matched and set as the charging and discharging stage threshold parameters and single-cell consistency adjustment parameters of the current system operation. The charging and discharging stage of the system is determined based on the real-time state of charge of the energy storage unit. Combined with the real-time environmental conditions, the energy storage unit is controlled in stages based on the threshold parameters of the charging and discharging stage. The voltage status of each cell in the energy storage unit is monitored in real time. The detected voltage deviation is compared with the voltage deviation threshold of each cell in the energy storage unit. The consistency adjustment of each cell in the energy storage unit is triggered as needed until the voltage deviation of each cell in all energy storage units is within the cell voltage deviation threshold.

5. The intelligent energy management method for building photovoltaic windows based on BIPV according to claim 1, characterized in that, The process involves calculating the system's adjustable power supply based on the operating condition determination results and state deviation information, defining the load power supply scheduling priority, setting power supply priority allocation parameters, and dynamically allocating power supply based on the power supply scheduling priority, the system's adjustable power supply, and the power supply priority allocation parameters. Specifically, this includes: Based on the operating condition judgment results and state deviation information, combined with the real-time power generation of photovoltaic modules, the current available energy storage capacity of energy storage units, and the system operation deviation data corresponding to the state deviation information, the adjustable power supply of the system is calculated. Based on the importance level of the load, the loads are divided into first-level loads, second-level loads and third-level loads, and the power supply dispatch priority is from high to low as first-level loads, second-level loads and third-level loads. Based on the load power requirements of each load level, power supply scheduling priority, system adjustable power supply and state deviation information, power supply priority allocation parameters are set. The power supply priority allocation parameters include the minimum guaranteed power supply ratio of each load level, power supply allocation order, low priority load power limiting ratio and low priority load disconnection order. Based on the defined power supply scheduling priority, the system's adjustable power supply capacity, and the power supply priority allocation parameters, the power supply capacity is dynamically allocated to prioritize the power supply needs of high-priority loads, while the remaining adjustable power is allocated to low-priority loads.

6. The intelligent energy management method for building photovoltaic windows based on BIPV according to claim 1, characterized in that, The periodic collection of real-time operating data and status deviation information analyzes the changing patterns of operating conditions and system operating characteristics. It then hierarchically corrects MPPT tracking parameters, voltage transformation adjustment parameters, charging / discharging stage threshold parameters, individual unit consistency adjustment parameters, and power supply priority allocation parameters, while simultaneously updating the system baseline operating conditions. Specifically, this includes: The operating cycle is set according to seasonal changes and fluctuations in environmental conditions. According to the set operating cycle, classify and collect the parameter data in the real-time operating data and the deviation data in the status deviation information to form a periodic operating dataset, and remove abnormal data in the dataset. Statistical analysis was performed on the periodic operation dataset to obtain the correlation between various control parameters and the system operating status; Based on the statistical analysis results of the periodic operation dataset, the correction amount of various control parameters is calculated, and the MPPT tracking parameters, voltage transformation regulation parameters, charging and discharging stage threshold parameters, individual unit consistency regulation parameters, and power supply priority allocation parameters are corrected in layers. Based on the statistical mean of the periodic operation dataset, the baseline values ​​of each parameter of the preset system baseline operating condition are updated synchronously to adapt the system baseline operating condition to the current environment and system operating status.

7. The intelligent energy management method for building photovoltaic windows based on BIPV according to claim 4, characterized in that, Based on the system baseline operating condition model, and combining the real-time power generation of photovoltaic modules, energy storage capacity, and load power demand, the system operation modes are divided into direct photovoltaic power supply mode, combined photovoltaic and energy storage power supply mode, and energy storage-only power supply mode, and corresponding judgment conditions are generated for each operation mode, specifically including: When the real-time power generation of photovoltaic modules can fully cover the current load power demand and the energy storage margin is within the normal range, the photovoltaic direct supply mode is determined and switched to. When the real-time power generation of photovoltaic modules cannot meet the power demand of the entire load and energy storage is required to supplement the power, the photovoltaic and energy storage combined power supply mode is determined and switched to. When the real-time power generation of the photovoltaic module is lower than the minimum power supply threshold and there is no effective photovoltaic output, the system will determine and switch to the energy storage-only power supply mode.

8. The intelligent energy management method for building photovoltaic windows based on BIPV according to claim 5, characterized in that, The classification of loads into Level 1, Level 2, and Level 3 loads based on their importance includes: Based on the necessity of safe operation of the electrical load associated with building photovoltaic windows and the degree of uninterrupted power supply, the loads for security and basic protection are classified as Level 1 loads, the loads for routine daily necessities are classified as Level 2 loads, and the loads for non-essential leisure and auxiliary functions are classified as Level 3 loads.

9. A smart energy management system for building photovoltaic windows based on BIPV, used to implement the smart energy management method for building photovoltaic windows based on BIPV as described in any one of claims 1-8, characterized in that, include: The data acquisition and benchmark modeling module is used to acquire historical operating data of BIPV photovoltaic windows, build a system benchmark operating condition model and generate benchmark operating data, while also acquiring real-time operating data; The operating condition decision module is used to generate state deviation information, complete the operating condition judgment, and output the operating condition judgment result; The energy regulation and execution module is used to perform photovoltaic MPPT tracking and output-side voltage regulation, system operation mode switching, energy storage phased charging and discharging control, single-unit consistency adjustment, and load priority scheduling and power supply allocation. The self-optimizing closed-loop module is used to periodically correct control parameters, update baseline operating conditions, and complete operational verification and data feedback. The central control module is used to coordinate the collaborative operation of the above modules and the interaction of data commands.

10. A building photovoltaic window intelligent energy management system based on BIPV according to claim 9, characterized in that, The data acquisition and benchmark modeling module includes: The integrated data acquisition unit is used to collect real-time operating data of environmental conditions, photovoltaic modules, energy storage units, and loads. The benchmark construction unit is used to build a system benchmark operating condition model based on historical operating data and generate benchmark operating data. The operating condition decision module includes: The deviation analysis unit is used to calculate the deviation between real-time operating data and baseline operating data, and generate status deviation information. The working condition matching unit is used to determine the working condition based on real-time operating data and state deviation information and output the working condition determination result. The energy regulation execution module includes: The photovoltaic control unit is used to perform maximum power point tracking and output voltage fluctuation compensation regulation of photovoltaic modules; The energy storage management unit is used for system operation mode determination and switching, phased charging and discharging control of energy storage, and single-cell consistency adjustment. The load scheduling unit is used to calculate the system's adjustable power supply, divide the load power supply priority, set power supply priority allocation parameters, and dynamically allocate power supply. The self-optimizing closed-loop module includes: The parameter correction unit is used to periodically collect real-time operating data and state deviation information, correct control parameters in layers, and update the system baseline operating conditions. The closed-loop verification unit is used to put the corrected parameters and the updated benchmark conditions into operation for verification, and to feed the verification results back to the parameter correction unit. The central control module includes: The main control unit is used to coordinate and schedule the collaborative operation of all modules; The communication storage unit is used to realize data transmission, instruction exchange and data storage between all modules.

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