Rooftop distributed photovoltaic systems in integrated photovoltaic, energy storage and charging microgrids

Abstract

This invention belongs to the field of new energy microgrid technology, specifically relating to a rooftop distributed photovoltaic system in an integrated photovoltaic-storage-charging microgrid. The system generates a real-time photovoltaic power generation prediction curve through a first acquisition module and uses a slope monitoring module to detect power drop-type and recovery-type inflection points in the prediction curve, dynamically triggering instantaneous discharge or slow-start strategies for the energy storage module. The system also includes a second acquisition module to monitor the energy storage health status in real time, and an energy distribution module to intelligently adjust power distribution based on the inflection point type and energy storage status. Furthermore, the system integrates a self-testing module that automatically initiates drone inspections when consecutive inflection points are detected, and provides visual monitoring through a human-machine interface module. This invention effectively solves problems such as large photovoltaic fluctuations, delayed energy storage response, and difficulties in matching charging demand through photovoltaic power generation prediction, intelligent inflection point identification, and dynamic power regulation, significantly improving the power supply stability, energy utilization efficiency, and intelligent operation and maintenance level of the microgrid.

Description

Technical Field

[0001] This invention belongs to the field of new energy microgrid technology, specifically relating to rooftop distributed photovoltaic systems in integrated photovoltaic, energy storage and charging microgrids. Background Technology

[0002] Photovoltaic-energy-storage-charging integration, as an important application direction in the new energy field, achieves efficient utilization and intelligent scheduling of renewable energy by integrating photovoltaic power generation, energy storage systems, and electric vehicle charging facilities. With the rapid popularization of electric vehicles, how to optimize the energy management of charging stations and improve system operating efficiency has become a key research focus in the industry.

[0003] Chinese Patent CN116646965 A discloses an integrated photovoltaic energy storage charging and discharging management system. This system predicts daily power supply demand based on historical power data and dynamically allocates power according to real-time charging order requirements, thereby improving the accuracy of power distribution and resource utilization. For example, by analyzing the power supply patterns of historical monitoring days, the system can optimize the power allocation of energy storage batteries and proactively trigger strategies such as AC / DC intermodulation or grid power supplementation when a supply-demand mismatch is detected, thus reducing the impact on the power grid.

[0004] However, existing technologies still have significant shortcomings. First, traditional systems have limited responsiveness to fluctuations in photovoltaic power generation, especially when sunlight conditions change abruptly or equipment malfunctions, making it difficult to adjust energy management strategies in a timely manner, leading to decreased power supply reliability. Second, existing solutions lack intelligent real-time decision-making mechanisms for dynamic power allocation, failing to accurately balance charging demand with system stability requirements. Furthermore, charging resource allocation relies heavily on static historical data, failing to fully consider the impact of real-time operating status and equipment health, easily resulting in unreasonable energy allocation. These limitations lead to low overall system efficiency and difficulty in adapting to complex and ever-changing real-world application scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a rooftop distributed photovoltaic system in a photovoltaic-storage-charging integrated microgrid to solve the technical problems of large fluctuations in photovoltaic power generation, lagging energy storage response, and difficulty in dynamically matching charging demand in a photovoltaic-storage-charging integrated microgrid.

[0006] The present invention achieves the above objectives through the following technical solutions:

[0007] This invention proposes a rooftop distributed photovoltaic system in an integrated photovoltaic, energy storage, and charging microgrid. The photovoltaic system is electrically connected to an energy storage module and a charging module, respectively. The photovoltaic system includes:

[0008] A photovoltaic power generation module includes several photovoltaic power generation units, which are used to store the generated electrical energy in an energy storage module or directly supply it to a charging module or connect it to the grid;

[0009] The first acquisition module is used to interact with the photovoltaic power generation module in real time to generate a photovoltaic power generation prediction curve for a future set period with the time axis as the horizontal axis and the power generation value as the vertical axis.

[0010] The slope monitoring module is used to generate a power dispatch command when at least one inflection point is detected on the photovoltaic power generation prediction curve. The power dispatch command includes: triggering instantaneous discharge of the energy storage module when a power drop inflection point is detected; and controlling the energy storage module to start slowly when a power recovery inflection point is detected.

[0011] The second acquisition module is used to interact with the energy storage module in real time to obtain energy storage health data;

[0012] The power distribution module is used to respond to the power dispatch command and dynamically adjust the power distribution of the energy storage module according to the inflection point type and energy storage health data.

[0013] Furthermore, the energy storage module includes:

[0014] Energy storage battery packs are used to store electrical energy generated by photovoltaic power generation modules;

[0015] The instantaneous discharge control unit is used to respond to and control the energy storage battery pack to perform instantaneous discharge when it receives an energy dispatch command triggered by the detection of a power drop inflection point. The response time of the instantaneous discharge does not exceed a preset first time threshold, and the discharge power can reach a first preset discharge power within a preset second time threshold.

[0016] The energy storage management unit is used to dynamically adjust the operating parameters of the instantaneous discharge control unit and the soft start control unit based on the energy storage health data obtained by the second acquisition module.

[0017] Furthermore, the first acquisition module includes:

[0018] The meteorological data acquisition unit is used to collect meteorological data of the area where the photovoltaic power generation module is located in real time. The meteorological data includes light intensity, temperature, wind speed, and cloud thickness.

[0019] The historical power generation data storage unit is used to store historical power generation data of the photovoltaic power generation module within a set time period in the past;

[0020] The prediction algorithm processing unit is used to generate a photovoltaic power generation prediction curve for a future set period based on the collected real-time meteorological data and the stored historical power generation data, using a preset prediction algorithm.

[0021] Furthermore, the slope monitoring module includes a preset inflection point detection method, including:

[0022] After smoothing the photovoltaic power generation prediction curve, the sample is discretized. By calculating the ratio of the power generation difference between two adjacent time points to the time interval, the first derivative of the power generation function within that time period is approximately obtained.

[0023] Based on the calculated first derivative, the ratio of the difference between two adjacent first derivatives to the corresponding time interval is calculated to approximately obtain the second derivative of the power generation function at each time point. The time interval is dynamically determined according to the fluctuation frequency of photovoltaic power generation.

[0024] The system monitors the value of the second derivative at each time point in real time. When the second derivative changes from positive to negative or from negative to positive, the system calculates the absolute value of the difference between the first derivative at that time point and the first derivative at the previous time point, and determines whether the absolute value of the difference exceeds the preset threshold for the change of the first derivative.

[0025] When the second derivative crosses zero and the change in the first derivative exceeds a set threshold, the time point is determined to be a valid inflection point.

[0026] Furthermore, the inflection point detection method also includes:

[0027] Establish a historical inflection point dataset, which includes a set of inflection point locations, types, and corresponding power generation change data that occurred within a set time period in the past, as well as a set of first and second derivative characteristic data when the inflection point occurred.

[0028] Before determining the current data point as an inflection point, the similarity between the current data point and historical inflection points at the moment when the second derivative crosses zero and the change in the first derivative is calculated. If the overall similarity exceeds the preset similarity threshold, the point is confirmed as a valid inflection point. Based on the changes in power generation and the changing trends of the first and second derivatives in the historical inflection point dataset, the type of the current inflection point is corrected.

[0029] Furthermore, the photovoltaic system also includes a self-test module, which is electrically connected to the slope monitoring module;

[0030] When the slope monitoring module detects at least three consecutive inflection points in the photovoltaic power generation prediction curve by executing the inflection point detection method, the self-test module triggers a drone inspection command.

[0031] The drone responds to the inspection command and inspects the rooftop distributed photovoltaic modules according to the preset inspection route to check for faults in the photovoltaic modules, including module damage, obstruction, and connection line failures.

[0032] Furthermore, the photovoltaic system also includes a human-machine interaction module, which is electrically connected to the slope monitoring module and the self-test module respectively;

[0033] The human-computer interaction module is used to receive the photovoltaic power generation prediction curve data and display it in an intuitive graphical interface;

[0034] When the slope monitoring module detects an inflection point or the self-check module triggers an unmanned aerial vehicle inspection, the human-computer interaction module receives relevant abnormal information and displays it with a prominent identifier on the graphical interface, while marking the time, location of the abnormality, and the initially judged type of abnormality.

[0035] Further, the energy storage health data obtained by the second acquisition module includes the state of health SOH and the state of charge SOC of the energy storage battery pack.

[0036] Further, when a power sudden drop type inflection point is detected, the power distribution module performs power distribution of the energy storage module, including:

[0037] Calculate the first preset discharge power P0 of the energy storage module as follows:

[0038] ;

[0039] where, P1 represents the maximum instantaneous discharge power of the energy storage module, P2 represents the current total demand power of the charging module, P3 represents the power required for the microgrid to maintain stability, P4 represents the actual power generation power of the current photovoltaic power generation module; w1 represents the preset state of health weight coefficient, w2 represents the state of charge weight coefficient, and w1 + w2 = 1;

[0040] Calculate the actual charging power P5 of the charging module as follows:

[0041] ;

[0042] If P5 < P2, the power distribution module sends a power adjustment signal to other adjustable loads in the microgrid, requesting them to reduce power consumption, and the reduced power value is:

[0043] .

[0044] Further, when a power recovery type inflection point is detected, the power distribution module performs power distribution of the energy storage module, including:

[0045] Calculate the second preset discharge power of the energy storage module, set the total soft start time as T1, and the time from the current moment to the moment when the inflection point appears is , , the influence coefficient of the preset state of health on the soft start speed is , the influence coefficient of the state of charge on the soft start speed is , and adjust the second preset discharge power of the energy storage module based on the following formula :

[0046] ;

[0047] in, This indicates the current discharge power of the energy storage module. This indicates the normal discharge power threshold set in the soft-start strategy.

[0048] The beneficial effects of this invention are as follows:

[0049] 1. This invention integrates photovoltaic power generation, energy storage modules, and charging modules in a photovoltaic-storage-charging microgrid, achieving efficient utilization and intelligent scheduling of renewable energy. Specifically, it generates real-time photovoltaic power generation prediction curves and accurately detects power drop or recovery inflection points through a slope monitoring module, dynamically triggering instantaneous discharge or slow start strategies for the energy storage modules, significantly improving the stability and response speed of the microgrid. Simultaneously, combined with energy storage health data, the power distribution module can optimize power allocation, ensuring a balance between charging demand and grid stability.

[0050] 2. The photovoltaic system proposed in this invention achieves automatic fault inspection and visual monitoring through a self-testing module and a human-machine interaction module, further improving operation and maintenance efficiency and reliability. The overall solution not only reduces dependence on the external power grid but also reduces energy loss through intelligent scheduling, providing efficient and stable technical support for the large-scale application of rooftop distributed photovoltaics. Attached Figure Description

[0051] Figure 1 This is a system block diagram of a rooftop distributed photovoltaic system in a microgrid integrating photovoltaic, energy storage, and charging proposed in this invention;

[0052] Figure 2 This is a technical roadmap for a rooftop distributed photovoltaic system in a microgrid integrating photovoltaic, energy storage, and charging, according to the present invention. Detailed Implementation

[0053] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0054] Currently, rooftop distributed photovoltaic (PV) systems are increasingly widely used in microgrids. However, PV power generation is significantly intermittent and volatile, heavily influenced by weather conditions, which can easily lead to sudden drops or recoveries in power output, affecting the stable operation of the power grid. Simultaneously, the randomness and uncertainty of electric vehicle charging demand further exacerbates the difficulty of balancing supply and demand in microgrids. To address these challenges, this application organically combines PV power generation, energy storage systems, and charging facilities to achieve efficient energy utilization and intelligent dispatch. The energy storage module plays a "peak shaving and valley filling" role in the system, smoothing out fluctuations in PV power generation and providing emergency power when power generation is insufficient; the charging module directly serves electric vehicles, and real-time matching of its power demand with PV power generation is crucial for system optimization. Against this backdrop, this application proposes a rooftop distributed PV system integrating PV power generation prediction, intelligent inflection point identification, and dynamic power allocation.

[0055] Please see Figures 1-2 In one specific embodiment, a rooftop distributed photovoltaic system in an integrated photovoltaic-storage-charging microgrid is proposed. The photovoltaic system is electrically connected to the energy storage module and the charging module (including fast and slow charging piles). The photovoltaic system includes a photovoltaic power generation module, a first acquisition module, a slope monitoring module, a second acquisition module, and an energy distribution module. The photovoltaic power generation module includes several photovoltaic power generation units, used to store the generated electricity in the energy storage module, directly supply it to the charging module, or connect it to the grid (when the power generation exceeds the local absorption capacity, the excess electricity can be fed into the public grid through the grid connection interface). The first acquisition module is used to interact with the photovoltaic power generation module in real time to generate... A photovoltaic power generation prediction curve for a future set period is plotted with time as the horizontal axis and power generation value as the vertical axis. A slope monitoring module generates a power dispatch command when at least one inflection point is detected on the photovoltaic power generation prediction curve. The power dispatch command includes: triggering instantaneous discharge of the energy storage module when a power drop inflection point is detected; and controlling the slow start of the energy storage module when a power recovery inflection point is detected. A second acquisition module interacts with the energy storage module in real time to obtain energy storage health data. A power allocation module responds to the power dispatch command and dynamically adjusts the power allocation of the energy storage module according to the inflection point type and energy storage health data.

[0056] In practical implementation, this application introduces a prediction algorithm based on meteorological data and historical power generation data. A photovoltaic power generation prediction curve is generated through the first acquisition module. The horizontal axis of the photovoltaic power generation prediction curve represents time (unit: seconds / minutes / hours), and the vertical axis represents the real-time output power of the photovoltaic power generation module (unit: kW). This time-power two-dimensional coordinate system dynamically reflects the predicted trend of power generation changes in the future period. The slope monitoring module uses discretization sampling and a second-derivative algorithm to identify curve inflection points in real time. When a power drop-type inflection point (such as cloud cover) is detected, the energy storage module responds and releases its rated power; when a power recovery-type inflection point is identified, a tiered slow-charging strategy is initiated. The second acquisition module continuously monitors the battery's SOH and SOC states. The power distribution module integrates inflection point characteristics and battery health, dynamically adjusting the discharge power to ensure stable system operation under various operating conditions.

[0057] In a preferred embodiment, the first acquisition module includes a meteorological data acquisition unit, a historical power generation data storage unit, and a prediction algorithm processing unit. The meteorological data acquisition unit is used to acquire meteorological data of the area where the photovoltaic power generation module is located in real time. The meteorological data includes light intensity, temperature, wind speed, and cloud thickness. The historical power generation data storage unit is used to store historical power generation data of the photovoltaic power generation module within a set time period in the past. The prediction algorithm processing unit is used to generate a photovoltaic power generation prediction curve for a set future time period based on the acquired real-time meteorological data and the stored historical power generation data, using a preset prediction algorithm.

[0058] More specifically, the meteorological data acquisition unit collects meteorological parameters in real time, including sunlight intensity, ambient temperature, wind speed, and cloud thickness, through sensors installed in the photovoltaic array area. The historical power generation data storage unit stores historical power generation records and related meteorological data from the past operation of the photovoltaic system.

[0059] The prediction algorithm processing unit employs machine learning algorithms to establish a photovoltaic power generation prediction model by analyzing the correlation between real-time meteorological data and historical operational data. For example, an improved LSTM neural network model is used. This model, trained on historical data, possesses multivariate time series processing capabilities, enabling it to perform feature matching between real-time collected meteorological parameters and historical data, and to consider nonlinear factors such as seasonal variations and sudden weather changes, generating a photovoltaic power generation prediction curve for a future set time period.

[0060] In a preferred embodiment, the energy storage module includes an energy storage battery pack, an instantaneous discharge control unit, a soft-start control unit, and an energy storage management unit. The energy storage battery pack is used to store electrical energy generated by the photovoltaic power generation module. The instantaneous discharge control unit is used to respond to and control the energy storage battery pack to perform instantaneous discharge when it receives an energy dispatch command triggered by the detection of a power drop inflection point. The response time of the instantaneous discharge does not exceed a preset first time threshold, and the discharge power can reach a first preset discharge power within a preset second time threshold. The energy storage management unit is used to dynamically adjust the operating parameters of the instantaneous discharge control unit and the soft-start control unit according to the energy storage health data obtained by the second acquisition module.

[0061] As an example, based on the above embodiments, the instantaneous discharge control unit in the energy storage module includes a high-speed solid-state switching circuit and a power regulator. When a power drop-type inflection point signal is received, it can complete the discharge circuit closure within a first time threshold (e.g., 100ms) and increase the discharge power to 90% of the rated value (first preset discharge power) within a second time threshold (e.g., 500ms). The energy storage management unit monitors the battery pack's SOH (State of Health) and SOC (State of Charge) in real time and dynamically corrects the discharge parameters: when SOH < 80%, the instantaneous discharge power is limited to no more than 80% of the rated value; when SOC < 30%, the duration of each stage of the slow start is extended by 20%.

[0062] In a preferred embodiment, the slope monitoring module includes a preset inflection point detection method, comprising: discretizing the photovoltaic power generation prediction curve after smoothing, specifically using a moving average filter to smooth the photovoltaic power generation prediction curve, with the window width dynamically adjusted according to the light intensity; the discretization sampling uses an adaptive time interval, such as a basic sampling period of 10 seconds, which is automatically shortened to 2 seconds when the detected power change rate exceeds 5% / minute; approximating the first derivative of the power generation function within the time period by calculating the ratio of the power generation difference between two adjacent time points to the time interval; and based on the calculation... The calculated first derivative is used to calculate the ratio of the difference between two adjacent first derivatives to the corresponding time interval, approximating the second derivative of the power generation function at each time point. The time interval is dynamically determined based on the fluctuation frequency of photovoltaic power generation. The value of the second derivative at each time point is monitored in real time. When the second derivative changes from positive to negative or vice versa, the absolute value of the difference between the first derivative at that time point and the first derivative at the previous time point is calculated. It is then determined whether this absolute value exceeds a preset threshold for the change in the first derivative. When the second derivative crosses zero and the change in the first derivative exceeds the set threshold, that time point is determined to be a valid inflection point. After determining a valid inflection point, the module outputs a feature vector containing the inflection point type (sudden drop / recovery), timestamp, and power change rate for subsequent control modules to use.

[0063] In this embodiment, the first derivative is calculated using an improved central difference method, which calculates the instantaneous slope using the power values ​​of three adjacent sampling points. The second derivative is calculated using a recursive algorithm, which performs difference operations based on the result of the previous derivative. The time interval parameter is optimized in real time according to the fluctuation characteristics of the current power generation curve, and the calculation window is automatically shortened during the rapid power change phase.

[0064] In this embodiment, the inflection point determination employs a dual verification mechanism: first, the zero-crossing point of the second derivative is detected, and a reasonable detection tolerance range is set; second, the change amplitude of the first derivative is verified, with the threshold dynamically adjusted based on historical operating data. After a valid inflection point is determined, the system generates a standardized feature vector, containing an inflection point type identifier (encoded to indicate a sudden drop or recovery), a precise timestamp, and normalized power change characteristic parameters. This feature vector is transmitted to the energy storage control module and simultaneously updated to the historical feature database for algorithm optimization.

[0065] It is understood that in the photovoltaic-storage-charging integrated microgrid system of the present invention, the effective inflection point detection identifies the power drop-type inflection point of the photovoltaic power generation curve in real time. The system can quickly trigger the instantaneous discharge of the energy storage module, compensate for the power gap in time, and ensure the power supply continuity of the charging module. Secondly, the accurate judgment of the power recovery-type inflection point enables the energy storage module to gradually exit the compensation according to the slow start strategy, avoiding the secondary impact of power change on the system.

[0066] In a preferred embodiment, the inflection point detection method further includes: establishing a historical inflection point dataset, which includes a set of inflection point locations, types, and corresponding power generation change data that occurred within a set time period in the past, as well as a set of first and second derivative feature data when the inflection point occurred; before determining that the current data point is an inflection point, the similarity between the current data point and the historical inflection points at the time when the second derivative crosses zero and the change in the first derivative is calculated. If the overall similarity exceeds a preset similarity threshold, the point is confirmed as a valid inflection point. Based on the power generation change and the changing trends of the first and second derivatives in the historical inflection point dataset, the type of the current inflection point is corrected.

[0067] Understandably, in a preferred embodiment, this involves continuously recording all detected inflection point characteristic data from the past 30 days, including: precise timestamps, inflection point type identifiers (sudden drop / recovery), power generation sampling sequences for 5 minutes before and after the inflection point, and the first and second derivative values ​​at the corresponding times. This data is stored categorized by weather type (sunny, cloudy, rainy, etc.) and labeled with seasonal markers.

[0068] During real-time inflection point detection, the system executes a dual verification process: first, candidate inflection points are identified using derivative analysis, followed by historical data comparison. The comparison process employs feature vector similarity calculation. When the comprehensive similarity score exceeds a preset threshold, the system confirms the validity of the inflection point and calibrates its type label by referencing the subsequent power change trends of similar inflection points in historical data. For example, if the current detection is "sudden drop type" but 90% of similar historical cases are actually transient fluctuations caused by "rapid cloud passage," it is reclassified as "instantaneous fluctuation," and deep energy storage discharge is not triggered. The system automatically performs cluster analysis on historical data weekly, removing outliers and updating the feature template library.

[0069] In a preferred embodiment, the photovoltaic system further includes a self-testing module, which is electrically connected to the slope monitoring module. When the slope monitoring module detects at least three consecutive inflection points in the photovoltaic power generation prediction curve using the inflection point detection method, the self-testing module triggers a drone inspection command. The drone responds to the inspection command and inspects the rooftop distributed photovoltaic modules according to a preset inspection route to check for faults in the photovoltaic modules, including module damage, shading, and connection line faults.

[0070] In a preferred embodiment, the photovoltaic system further includes a human-machine interaction module, which is electrically connected to the slope monitoring module and the self-test module. The human-machine interaction module is used to receive photovoltaic power generation prediction curve data and display it in an intuitive graphical interface. When the slope monitoring module detects an inflection point or the self-test module triggers a drone inspection, the human-machine interaction module receives relevant abnormal information and displays it on the graphical interface with a prominent mark, while also indicating the time and location of the abnormality and the preliminary judgment of the abnormality type.

[0071] In the above embodiments, the photovoltaic system is equipped with a self-testing module and a human-machine interaction module. When the slope monitoring module detects consecutive abnormal inflection points on the power generation curve, the self-testing module automatically dispatches a drone equipped with a camera to conduct targeted inspections of the corresponding roof area based on a pre-stored 3D map. Image recognition technology is used to detect component damage, obstructions, or wiring abnormalities. Simultaneously, the human-machine interaction module integrates power generation curve data, inflection point alarm information, and drone inspection results in real time, displaying the system's operating status hierarchically in a graphical interface. This includes dynamically updated power generation curves, roof component heat maps, and a list of abnormal events. Maintenance personnel can intuitively view the fault location, type, and timestamp via a touchscreen and manually control the drone for re-inspection.

[0072] In a preferred embodiment, the energy storage health data acquired by the second acquisition module includes the state of health (SOH) and remaining charge (SOC) of the energy storage battery pack.

[0073] In a preferred embodiment, when a power-drop type inflection point is detected, the power distribution module performs power distribution for the energy storage module, including: calculating the first preset discharge power P0 of the energy storage module, as shown in the following formula:

[0074] ;

[0075] where P1 represents the maximum instantaneous discharge power of the energy storage module, P2 represents the current total demand power of the charging module, P3 represents the power required to maintain the stability of the microgrid, P4 represents the actual power generation of the current photovoltaic power generation module; w1 represents the preset health state weight coefficient, w2 represents the remaining power weight coefficient, and w1 + w2 = 1; the weight coefficients w1 and w2 are preset according to the battery type (such as for lithium-ion batteries ).

[0076] Calculate the actual charging power P5 of the charging module, as shown in the following formula:

[0077] ;

[0078] If P5 < P2, the power distribution module sends a power adjustment signal to other adjustable loads in the microgrid, requesting them to reduce power consumption, and the reduced power value is:

[0079] .

[0080] It can be understood that when a power-drop type inflection point is detected, the system gives priority to ensuring the power supply of critical loads, by ensuring that the power P3 required for the stable operation of the microgrid is met. If there is still a gap in the power supply of the charging module , a hierarchical load reduction instruction is sent to non-critical loads, and the load reduction amplitude is dynamically adjusted according to the load priority.

[0081] In a preferred embodiment, when a power-recovery type inflection point is detected, the power distribution module performs power distribution for the energy storage module, including: calculating the second preset discharge power of the energy storage module, setting the total soft-start time as T1, and the time from the current moment to the moment when the inflection point appears is , , the influence coefficient of the preset health state on the soft-start speed is , the influence coefficient of the remaining power on the soft-start speed is , and the second preset discharge power of the energy storage module is adjusted based on the following formula :

[0082] ;

[0083] where P6 represents the current discharge power of the energy storage module, and P7 represents the normal discharge power threshold set in the soft-start strategy.

[0084] Understandably, when a power recovery inflection point is detected, the power distribution module initiates a slow-discharge program, achieving a smooth transition through dynamic adjustment of coefficients. The total slow-start time T1 is set to an adjustable range of 5-15 minutes based on the photovoltaic output recovery rate and is divided into multiple control cycles. Within each control cycle, the system uses the formula... The target discharge power is calculated, where k1 and k2 are calculated using a dynamic weighting algorithm: when Increase the weight of k2 by 0.7 when Increase the weight of k1 by 0.6.

[0085] During execution, the photovoltaic output change rate is monitored in real time. If a secondary fluctuation is detected, the slow start-up process is paused. The energy storage management unit synchronously adjusts the P7 threshold, using a higher threshold (70% of rated power) during the morning's enhanced sunlight phase and a lower threshold (50% of rated power) in the afternoon. It will automatically exit the soft start mode and switch to normal frequency modulation state.

[0086] The principle of the rooftop distributed photovoltaic system in the integrated photovoltaic, energy storage, and charging microgrid of the present invention is as follows:

[0087] The photovoltaic (PV) power generation module outputs electricity in real time. The first acquisition module integrates meteorological data and historical power generation records to generate a PV output prediction curve. The slope monitoring module analyzes the curve in real time, identifying power drop and recovery inflection points through derivative calculations and verifying this information using a historical inflection point database. When a power drop is detected, the instantaneous discharge control unit responds immediately, dynamically adjusting the discharge power based on the energy storage health status (SOH / SOC) to prioritize power supply to the charging module. When power recovery is detected, the slow-start control unit gradually reduces the energy storage output according to a preset slope, achieving a smooth transition. The power distribution module continuously optimizes the power distribution strategy, establishing a dynamic balance between PV power generation, the energy storage system, and the charging load.

[0088] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0089] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A roof distributed photovoltaic system in a light storage and charging integrated micro-grid, characterized in that, The photovoltaic system is electrically connected with an energy storage module and a charging module respectively, and the photovoltaic system comprises: The photovoltaic power generation module comprises a plurality of photovoltaic power generation units, and is used for storing generated electric energy to the energy storage module or directly supplying the charging module or grid-connected; The first acquisition module is used for real-time interaction with the photovoltaic power generation module to generate a photovoltaic power generation prediction curve in a future set period with a time axis as an abscissa and a power generation power value as an ordinate; The slope monitoring module is used for generating an electric energy scheduling instruction when at least one inflection point appears on the photovoltaic power generation prediction curve, and the electric energy scheduling instruction comprises: triggering the energy storage module to instantaneously discharge when a power sudden drop type inflection point is detected; and controlling the energy storage module to slowly start when a power recovery type inflection point is detected; The second acquisition module is used for real-time interaction with the energy storage module to obtain energy storage health data; The electric energy distribution module is used for dynamically adjusting power distribution of the energy storage module according to the inflection point type and the energy storage health data in response to the electric energy scheduling instruction; The energy storage module comprises: The energy storage battery pack is used for storing electric energy generated by the photovoltaic power generation module; The instant discharge control unit is used for responding to and controlling the energy storage battery pack to instantaneously discharge when receiving the electric energy scheduling instruction triggered by detecting the power sudden drop type inflection point, and a response time of the instant discharge does not exceed a preset first time threshold, and a discharge power can reach a first preset discharge power within a preset second time threshold; The slow start control unit is used for gradually reducing energy storage output according to a preset slope, so that the energy storage module can gradually exit compensation according to a slow start strategy, and avoid secondary impact on the system caused by power mutation; The energy storage management unit is used for dynamically adjusting working parameters of the instant discharge control unit and the slow start control unit according to the energy storage health data obtained by the second acquisition module; The slope monitoring module comprises a preset inflection point correction method, and specifically comprises: establishing a historical inflection point data set, the historical inflection point data set comprises a set of inflection point positions, types and corresponding power generation power change data appearing in a past set period, and a set of first derivative and second derivative characteristic data when the inflection point occurs; Before determining that the current data point is an inflection point, the similarity of the current data point and the historical inflection point at the zero-crossing point of the second derivative and the change amount of the first derivative is determined, and if the comprehensive similarity exceeds a preset similarity threshold, the point is confirmed as an effective inflection point, and the type of the current inflection point is corrected according to the corresponding power generation power change and the change trend of the first derivative and the second derivative in the historical inflection point data set. 2.The roof distributed photovoltaic system in the optical storage and charging integrated micro-grid of claim 1, wherein, The first acquisition module comprises: The weather data acquisition unit is used for real-time acquisition of weather data of a region where the photovoltaic power generation module is located, and the weather data comprises light intensity, temperature, wind speed and cloud thickness; The historical power generation data storage unit is used for storing historical power generation data of the photovoltaic power generation module in a past set period; The prediction algorithm processing unit is used for generating a photovoltaic power generation prediction curve in a future set period by using a preset prediction algorithm based on the collected real-time weather data and the stored historical power generation data. 3.The roof distributed photovoltaic system in the optical storage and charging integrated micro-grid of claim 1, wherein, The slope monitoring module comprises a preset inflection point detection method, which comprises: The photovoltaic power generation prediction curve is smoothed and discretely sampled, and the ratio of the difference between the power generated at two adjacent time points to the time interval is calculated to approximate the first derivative of the power generation function in the time interval; Based on the calculated first derivative, the ratio of the difference between two adjacent first derivatives to the corresponding time interval is calculated to approximate the second derivative of the power generation function at each time point, and the time interval is dynamically determined according to the fluctuation frequency of photovoltaic power generation; The value of the second derivative at each time point is monitored in real time. When the second derivative changes from positive to negative or from negative to positive, the absolute value of the difference between the first derivative at the current time point and the first derivative at the previous time point is calculated to determine whether the absolute value exceeds a preset first derivative change threshold; When the second derivative crosses zero and the first derivative changes by more than the set threshold, the time point is determined to be an effective inflection point. 4.The roof distributed photovoltaic system in the optical storage and charging integrated micro-grid of claim 3, wherein, The photovoltaic system also includes a self-checking module electrically connected to the slope monitoring module; When the slope monitoring module detects at least three consecutive inflection points in the photovoltaic power generation prediction curve using the inflection point detection method, the self-checking module triggers a UAV inspection instruction; The UAV responds to the inspection instruction and inspects the rooftop distributed photovoltaic components according to a preset inspection route to check whether there is a fault in the photovoltaic components, and the fault includes component damage, obstruction, and connection line failure. 5.The roof distributed photovoltaic system in the optical storage and charging integrated micro-grid of claim 4, wherein, The photovoltaic system also includes a human-computer interaction module electrically connected to the slope monitoring module and the self-checking module; The human-computer interaction module is used to receive the photovoltaic power generation prediction curve data and display it in an intuitive graphical interface; When the slope monitoring module detects an inflection point or the self-checking module triggers a UAV inspection, the human-computer interaction module receives related abnormal information and displays it in a prominent identification on the graphical interface, along with the time, location, and preliminary judgment of the abnormal type. 6.The roof distributed photovoltaic system in the optical storage and charging integrated micro-grid of claim 1, wherein, The second acquisition module obtains energy storage health data including the state of health (SOH) and the state of charge (SOC) of the energy storage battery pack. 7.The roof distributed PV system in the optical storage and charging integrated micro-grid of claim 6, wherein, When a power drop-type inflection point is detected, the power distribution module performs power distribution of the energy storage module, including: The first preset discharge power P0 of the energy storage module is calculated as follows: ； where P1 represents the maximum instantaneous discharge power of the energy storage module, P2 represents the current total demand power of the charging module, P3 represents the power required for the microgrid to maintain stability, P4 represents the actual power generation of the current photovoltaic power generation module; w1 represents a preset health state weight coefficient, w2 represents a remaining charge weight coefficient, and w1+w2=1; The actual charging power P5 of the charging module is calculated as follows: ； If P5 < P2, the power distribution module sends a power adjustment signal to other adjustable loads in the micro-grid, requesting them to reduce power consumption, and the reduced power value is: P2 - (P5 - P1) is: 。 8.The roof distributed photovoltaic system in the optical storage and charging integrated micro-grid of claim 6, wherein, When a power recovery-type inflection point is detected, the power distribution module performs power distribution of the energy storage module, including: A second preset discharging power of the energy storage module is calculated, a total slow start time T1 is set, and a time from a current time to a time when the inflection point occurs is , , a preset health state influence coefficient on the slow start speed is , a residual power influence coefficient on the slow start speed is , and the second preset discharging power of the energy storage module is adjusted based on the following formula : ； wherein, represents the current discharge power of the energy storage module, represents the normal discharge power threshold set in the soft start strategy.

Images