Data analysis method and equipment for photovoltaic and wind power integrated energy storage equipment

Abstract

The invention discloses a data analysis method and equipment for photovoltaic and wind power integrated energy storage equipment, relates to the technical field of data analysis for photovoltaic and wind power integrated energy storage equipment, and can improve the operation stability and energy utilization efficiency of a photovoltaic and wind power integrated energy storage system and guarantee the stability of a power grid. Multi-dimensional power generation and energy storage data and a future weather predicted value are obtained in real time, the weight is dynamically determined according to the wind and light fluctuation ratio (the first weight is larger), a monitoring period is set according to weather, and the weight can be adjusted for a long time by adapting to power generation characteristics. Charge and discharge scores integrate multiple factors, equipment aging and power grid load threshold adjustment are combined, instructions are scientific, power can be stored efficiently, waste is avoided, grid connection is conducted in time, power grid loads are guaranteed, power generation fluctuation is stabilized, and the system practicability is improved.

Description

Technical Field

[0001] This application relates to the field of data analysis technology for photovoltaic and wind power energy storage equipment, and in particular to a data analysis method and equipment for integrated photovoltaic and wind power energy storage equipment. Background Technology

[0002] As mainstream renewable energy sources, photovoltaic and wind power are easily affected by natural conditions such as sunlight and wind speed, exhibiting significant intermittency and volatility in their power generation. Although integrated energy storage devices are key to smoothing out these fluctuations and ensuring a stable energy supply, existing data analysis technologies have many shortcomings.

[0003] The system fails to adequately consider the differentiated impact of wind and solar power fluctuations. It does not dynamically allocate weights based on whether the volatility of solar and wind power exceeds preset thresholds, and it does not optimize weighting coefficients by incorporating predicted solar irradiance and wind speed values ​​for future preset time periods (e.g., comparison with historical averages). This makes it difficult to fully leverage the energy stability advantages of wind-solar complementarity, resulting in poor accuracy in calculating the wind-solar complementarity coefficient. Furthermore, the fixed monitoring cycle, without adjusting for different weather types such as sunny days (requiring longer cycles), cloudy days (requiring medium cycles), and rainy / snowy days (requiring shorter cycles), fails to adapt to the fluctuation frequency of wind and solar power under different weather conditions. This affects the timeliness and accuracy of volatility and subsequent data calculations, leading to deviations in charging and discharging priority scoring. Consequently, this results in unreasonable issuance of priority energy storage or grid-connected power supply commands, reducing energy utilization efficiency and compromising grid stability. Summary of the Invention

[0004] The exemplary embodiments of this application provide a data analysis method and equipment for photovoltaic and wind power integrated energy storage devices, so as to solve the adverse effects of wind and solar power generation fluctuations on energy storage and grid operation, and improve the overall practicality and adaptability of photovoltaic and wind power integrated energy storage systems.

[0005] This application provides a data analysis method for photovoltaic and wind power integrated energy storage devices, which includes the following steps: Real-time acquisition of photovoltaic power generation, wind power generation, and the current percentage of charge of energy storage devices; Obtain predicted values ​​of light intensity and wind speed for a future preset time period; Calculate the photovoltaic volatility of the photovoltaic power generation during the monitoring period, and calculate the wind power volatility of the wind power generation during the monitoring period; If the photovoltaic volatility is greater than a preset photovoltaic volatility threshold, a first weighting coefficient is determined; if the wind power volatility is greater than a preset wind power volatility threshold, a second weighting coefficient is determined, wherein the first weighting coefficient is greater than the second weighting coefficient. Based on the first weighting coefficient and the second weighting coefficient, a wind-solar complementarity coefficient is generated; The charging and discharging priority score of the energy storage device is calculated based on the wind-solar complementarity coefficient and the current percentage of charge. If the charge / discharge priority score exceeds the priority threshold, a priority energy storage command is sent; otherwise, a grid-connected power supply command is sent.

[0006] Furthermore, when the predicted light intensity is lower than the historical average light intensity for the same period and the predicted wind speed is higher than the historical average wind speed for the same period, the first weighting coefficient is increased to 1.5 times the original value.

[0007] Furthermore, the formula for calculating the wind-solar complementarity coefficient is as follows: ; Wherein, is the first weighting coefficient, is the second weighting coefficient, is the average value of the photovoltaic power generation during the monitoring period, and is the average value of the wind power generation during the monitoring period.

[0008] Furthermore, the formula for calculating the charge / discharge priority score is as follows: ; Where is the meteorological reliability correction factor, and the value range is [0.8, 1.2], and SOC is the current percentage of charge.

[0009] Furthermore, the duration of the monitoring period is dynamically set according to the weather type, wherein: When the weather type is sunny, the monitoring period is 4 hours. When the weather type is cloudy, the monitoring period is 2 hours. When the weather type is rainy or snowy, the monitoring period is 1 hour.

[0010] Furthermore, it also includes: if the photovoltaic volatility exceeds the preset photovoltaic volatility threshold for three consecutive monitoring periods, it will be permanently increased by 20%; if the wind power volatility is lower than the preset wind power volatility threshold for three consecutive monitoring periods, it will be reduced to 80% of the original value.

[0011] Furthermore, it also includes: when the aging index of the energy storage device is >0.8, the priority threshold is lowered by 10%; when the peak load of the power grid exceeds 90% of the historical peak, the priority threshold is raised by 15%.

[0012] On the other hand, this application also provides a data analysis device for photovoltaic and wind power integrated energy storage equipment, used to perform the method provided in the first aspect above, which includes: The detection and analysis module is configured to acquire photovoltaic power generation value, wind power generation value and current charge percentage of energy storage device in real time; acquire the predicted value of light intensity and wind speed for a future preset period; calculate the photovoltaic volatility of photovoltaic power generation power within the monitoring period; and calculate the wind power volatility of wind power generation power within the monitoring period. A weight allocation module is configured to determine a first weight coefficient if the photovoltaic volatility is greater than a preset photovoltaic volatility threshold; and to determine a second weight coefficient if the wind power volatility is greater than a preset wind power volatility threshold, wherein the first weight coefficient is greater than the second weight coefficient; and to generate a wind-solar complementarity coefficient based on the first weight coefficient and the second weight coefficient. The priority instruction output module is configured to calculate the charging and discharging priority score of the energy storage device based on the wind-solar complementarity coefficient and the current charge percentage; if the charging and discharging priority score exceeds the priority threshold, a priority energy storage instruction is sent; otherwise, a grid-connected power supply instruction is sent.

[0013] The embodiments of this application have the following beneficial effects: effectively improving the operational stability and energy utilization efficiency of integrated photovoltaic and wind power energy storage systems, while ensuring stable power supply from the power grid. By acquiring real-time photovoltaic power generation values, wind power generation values, and the current charge percentage of energy storage devices, and combining this with predicted light intensity and wind speed values ​​for future preset time periods, comprehensive and forward-looking data support is provided for subsequent data analysis, avoiding decision-making biases caused by data lag or missing data.

[0014] The system calculates the photovoltaic and wind power volatility within the monitoring period, dynamically determines the first and second weighting coefficients based on whether the volatility exceeds a preset threshold, and ensures that the first weighting coefficient is larger. It can also adjust the first weighting coefficient by comparing the predicted values ​​of sunlight and wind speed with the historical average values ​​for the same period. This can accurately reflect the impact of wind and solar power generation fluctuations on the system, making the generated wind-solar complementarity coefficient more in line with the actual power generation characteristics, giving full play to the complementary advantages of wind and solar energy, and mitigating the intermittency and volatility of single-energy power generation.

[0015] The monitoring cycle duration is dynamically set according to the weather type to adapt to the fluctuation frequency of wind and solar power generation under different weather conditions, improve the accuracy of volatility calculation, and further optimize the calculation results of weight coefficient and wind-solar complementarity coefficient. For abnormal situations of photovoltaic and wind power volatility within three consecutive monitoring cycles, the corresponding weight coefficients are adjusted to adapt to changes in wind and solar power generation characteristics in the long term and maintain the rationality of weights.

[0016] The charging and discharging priority scoring integrates the wind-solar complementarity coefficient, current charge percentage, and meteorological reliability correction factor, making it more comprehensive and accurate. At the same time, it combines the energy storage equipment aging index with the grid load peak to dynamically adjust the priority threshold, which can adapt to the actual operating status of the equipment and the real-time needs of the grid, making the charging and discharging command decision more scientific.

[0017] Ultimately, based on the score, priority energy storage or grid connection power supply instructions are sent. This ensures that the energy storage equipment can efficiently store electricity when energy storage is needed, avoiding energy waste, and can also be connected to the grid in a timely manner when power is needed, ensuring the stability of the grid load. This effectively solves the adverse effects of wind and solar power generation fluctuations on energy storage and grid operation, and improves the practicality and adaptability of integrated photovoltaic and wind power energy storage systems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 An exemplary illustration shows a data analysis method flowchart for an integrated photovoltaic and wind power energy storage device provided in an embodiment of this application; Figure 2 An exemplary illustration shows a system schematic diagram of a data analysis device for an integrated photovoltaic and wind power energy storage device provided in an embodiment of this application; Figure 3 An exemplary schematic diagram of a photovoltaic and wind power integrated energy storage device provided in an embodiment of this application is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0021] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation steps as shown in the following embodiments or drawings, the method may include more or fewer operation steps based on conventional or non-inventive methods. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application.

[0022] refer to Figure 1 As shown, this application provides a data analysis method for photovoltaic and wind power integrated energy storage devices, which includes the following steps: S1: Real-time acquisition of photovoltaic power generation, wind power generation, and the current charge percentage of energy storage devices.

[0023] The power output values ​​of photovoltaic and wind power directly reflect the real-time output status of the power generation end, while the current charge percentage of the energy storage device reflects the real-time energy storage status of the energy storage end. The three together constitute the key real-time parameters of the integrated system operation status. Only by acquiring these three types of data in real time can we ensure that the calculation of power generation fluctuation rate, determination of weight coefficient, generation of wind-solar complementarity coefficient, and calculation of charging and discharging priority score in subsequent steps are all based on the dynamically updated real system status, and avoid the analysis results deviating from actual operating requirements due to data lag or missing data.

[0024] This application S1 achieves synchronous dynamic acquisition of key parameters of the integrated system's power generation and energy storage ends by simultaneously acquiring photovoltaic power generation, wind power generation, and the current percentage of energy storage charge in real time.

[0025] S2: Obtain the predicted values ​​of light intensity and wind speed for a future preset time period.

[0026] Since photovoltaic power generation depends on sunlight intensity and wind power generation depends on wind speed, both of their outputs are directly affected by meteorological conditions. Obtaining these two types of predicted values ​​in advance can enable dynamic adjustment of the first weighting coefficient in subsequent steps (such as coefficient adjustment when the predicted sunlight intensity is lower than the historical average for the same period and the predicted wind speed is higher than the historical average for the same period), generation of the wind-solar complementarity coefficient, and calculation of charging and discharging priority scores. This avoids the disconnect between dispatch instructions and actual power generation capacity due to failure to consider subsequent meteorological changes.

[0027] By proactively acquiring predicted values ​​of light intensity and wind speed for future preset time periods, meteorological forecasting is integrated into the data analysis process, providing future trend support for subsequent steps. This makes the entire data analysis and dispatch instruction generation more forward-looking, effectively solving the problem of dispatch lag or deviation caused by the lack of forecasting in existing technologies.

[0028] S3: Calculate the photovoltaic (PV) power generation volatility and the wind power generation volatility during the monitoring period. If the PV volatility exceeds a preset PV volatility threshold, determine the first weighting coefficient. If the wind power volatility exceeds a preset wind power volatility threshold, determine the second weighting coefficient, where the first weighting coefficient is greater than the second weighting coefficient.

[0029] The technical features in step S3 of this application regarding volatility calculation and dynamic allocation of weighting coefficients aim to establish a quantitative correlation mechanism between the volatility of wind and solar resources and energy storage regulation strategies. By calculating the power volatility of photovoltaic and wind power respectively during the monitoring period, the system can accurately quantify the intermittency of the two energy sources. Furthermore, by dynamically determining the weighting coefficients based on the comparison between volatility and preset thresholds, a differentiated response strategy to energy instability is achieved. This feature, for the first time, uses volatility as the core basis for weight allocation, solving the energy storage resource mismatch problem caused by ignoring real-time volatility differences in traditional wind-solar hybrid systems, and providing a quantitative foundation for the scientific generation of subsequent wind-solar hybrid coefficients.

[0030] By limiting the triggering conditions for photovoltaic volatility to a larger first weight coefficient, and by giving higher weight to highly volatile energy sources, the wind-solar complementarity coefficient can be made to respond more sensitively to the main contradictions of the system.

[0031] The weighting coefficient becomes a dynamic variable that adapts to the state of energy fluctuations. For example, when the photovoltaic volatility exceeds the threshold continuously, a permanent weighting enhancement mechanism is triggered (increasing by 20%), forming a cumulative response capability to continuous unstable operating conditions.

[0032] When the solar irradiance is detected to be lower than the historical average and the wind speed is higher than the historical average (step S2 derived rule), the photovoltaic weight coefficient is increased to 1.5 times. This weight pre-adjustment strategy based on prediction data significantly enhances the system's predictability in response to upcoming changes in the energy structure.

[0033] During short-term severe weather cycles (such as 1-hour rain monitoring), high-frequency volatility calculation can quickly capture power surges, while the real-time adjustment of the weighting coefficient is transmitted to the charge-discharge scoring model through the wind-solar complementarity coefficient, ultimately enabling energy storage commands to accurately match the most urgent needs for mitigation.

[0034] Furthermore, if the photovoltaic volatility exceeds the preset photovoltaic volatility threshold for three consecutive monitoring periods, it will be permanently increased by 20%. If the wind power volatility is below the preset wind power volatility threshold for three consecutive monitoring periods, it will be reduced to 80% of its original value.

[0035] When the photovoltaic volatility exceeds the preset threshold for three consecutive monitoring cycles, it indicates that the photovoltaic power generation is in an unstable state for a long period of time. At this time, permanently increasing the first weighting coefficient can enhance the influence weight of photovoltaic power generation in the calculation of the wind-solar complementarity coefficient, so that the system can more fully consider the impact of photovoltaic volatility on the overall energy supply. When the wind power volatility is below the preset threshold for three consecutive monitoring cycles, it indicates that the wind power generation has maintained a stable output for a long time. At this time, reducing the second weighting coefficient to 80% of the original value can reduce the excessive proportion of stable wind power in the complementarity coefficient and avoid scheduling deviations caused by the disconnect between weight allocation and actual output stability.

[0036] By setting a long-term judgment condition of "three consecutive monitoring cycles", the interference of random fluctuations is eliminated. At the same time, the corresponding weight coefficients are permanently adjusted differently according to the direction of fluctuation (exceeding the threshold / below the threshold), so that the weight coefficients can dynamically match the long-term output characteristics of photovoltaic and wind power, effectively solving the problems of inaccurate weight adjustment and poor adaptability of existing technologies.

[0037] When the predicted light intensity is lower than the historical average for the same period and the predicted wind speed is higher than the historical average for the same period, the first weighting coefficient will be increased to 1.5 times the original value.

[0038] When the predicted solar irradiance is lower than the historical average for the same period and the predicted wind speed is higher than the historical average for the same period, the photovoltaic weighting coefficient is increased to 1.5 times the original value to proactively address impending energy imbalances, namely the combined effect of declining photovoltaic output and increased wind power output. Through this predictive adjustment, the system can proactively strengthen its ability to mitigate photovoltaic fluctuations before actual power fluctuations occur.

[0039] By comparing the predicted values ​​of light intensity and wind speed with the historical average values ​​for the same period, the first weighting coefficient is increased in a targeted manner when the two form a specific combination relationship, thus achieving a precise response to complex meteorological scenarios.

[0040] The monitoring cycle duration is dynamically set based on weather type: 4 hours for sunny weather, 2 hours for cloudy weather, and 1 hour for rainy or snowy weather.

[0041] Since weather type directly affects the fluctuation frequency of photovoltaic and wind power output, on sunny days, the solar and wind power output fluctuates less due to stable solar and wind speed changes. Setting the monitoring period to 4 hours can ensure data validity while avoiding resource redundancy caused by frequent monitoring. On cloudy days, the solar intensity changes intermittently and the frequency of wind speed fluctuations increases, resulting in decreased power output stability. Shortening the monitoring period to 2 hours can promptly capture moderate power fluctuations. On rainy or snowy days, the solar energy is severely insufficient and the wind speed is prone to sudden changes, resulting in drastic power output fluctuations that affect the safety of system operation. Setting the monitoring period to 1 hour can track power fluctuations in real time, providing high-frequency and accurate data support for subsequent volatility calculations and weight adjustments.

[0042] In existing technologies, the monitoring cycle for photovoltaic and wind power energy storage equipment is mostly set to a fixed duration, without considering the differences in power generation fluctuations under different weather conditions. This leads to wasted monitoring resources on sunny days and monitoring lag on rainy or snowy days, failing to balance monitoring efficiency and data timeliness. This application solves the problems of resource redundancy and monitoring lag associated with fixed cycles by dynamically setting the monitoring cycle according to weather type and matching the corresponding duration to the stability differences in power generation under different weather conditions.

[0043] S4: Generate the wind-solar complementarity coefficient based on the first and second weighting coefficients.

[0044] The formula for calculating the wind-solar complementarity coefficient is: ; Where is the first weighting coefficient, is the second weighting coefficient, is the average value of photovoltaic power generation during the monitoring period, and is the average value of wind power generation during the monitoring period.

[0045] The real-time output scale (average power within the monitoring period) of photovoltaic and wind power is quantitatively coupled with their respective power generation stability (first and second weighting coefficients) to accurately characterize the energy complementarity of photovoltaic and wind power in the integrated system.

[0046] The first weighting coefficient is related to the volatility of photovoltaic power, and the second weighting coefficient is related to the volatility of wind power, which already reflects the stability difference between the two types of power generation. The average power during the monitoring period reflects the actual output level. By integrating the two through the formula, the one-sidedness of evaluating complementarity based on only a single dimension (such as power or volatility) can be avoided, so that the wind-solar complementarity coefficient can truly reflect the synergy between photovoltaic and wind power under the current operating conditions.

[0047] By integrating the first and second weighting coefficients with the average power during the monitoring period to generate a wind-solar complementarity coefficient, the stability difference and the output level are linked, effectively solving the problem of inaccurate quantitative complementarity in existing technologies.

[0048] In traditional schemes, the average power output of wind and solar power ( ) only reflects the scale of power generation and cannot characterize volatility risk (such as a sudden drop in solar power or oscillations in wind power). A weighting coefficient is introduced as an amplification factor for volatility risk. When solar volatility exceeds a threshold, the coefficient increases significantly (up to 1.5 times the original value), thus reinforcing solar volatility risk in the formula. The wind power weight is adjusted based on the volatility threshold (e.g., decreasing to 80% if it remains below the threshold for an extended period), reducing the weighting of low-risk wind power.

[0049] The formula transforms volatility risk into a quantifiable numerical impact through a weighted sum of and , significantly increasing the contribution of highly volatile power sources to coefficient K and exposing system instability weaknesses in advance.

[0050] Wind and solar resources exhibit temporal and spatial complementarity (e.g., increased wind speed on cloudy days), but static formulas cannot dynamically respond to this inverse relationship. The denominator () retains the total output scale benchmark, while the numerator is dynamically adjusted through weights: when sunlight is insufficient and wind speed increases (triggering a 1.5-fold increase), the proportion of the photovoltaic term in the numerator increases, forcing the K value to favor the photovoltaic side risk; if wind power volatility remains low, decreasing it weakens the influence of the wind power term in the numerator.

[0051] K approaching 1 → Wind power dominates with low volatility risk; K significantly less than 1 → High volatility risk of photovoltaics or insufficient contribution from wind power; K oscillating wildly → Unstable output from both wind and solar power. This provides a clear risk level signal for subsequent energy storage decisions.

[0052] Energy storage devices need to simultaneously respond to multi-dimensional parameters such as wind and solar power output fluctuations, state of charge (SOC), and weather forecasts, resulting in high decision-making complexity. The K value is designed as a dimensionless coefficient (range 0-1) to: be compatible with wind and solar systems of different installed capacities (eliminating scale differences in the denominator); integrate multi-source information such as volatility, weather forecasts, and historical data through weighting coefficients (e.g., a 1.5x increase already implicitly includes weather forecast results); and output a single scalar value, facilitating priority scoring calculations coupled with parameters such as SOC and weather factors.

[0053] Subsequently, by calculating the priority score formula, the complex environmental conditions can be transformed into clear charging and discharging instructions.

[0054] S5: Calculate the charging and discharging priority score of energy storage devices based on the wind-solar complementarity coefficient and the current percentage of charge.

[0055] The wind-solar complementarity coefficient has been integrated with the volatility weights and average power of photovoltaic and wind power through previous steps, which can accurately characterize the stability and availability of energy supply. The current charge percentage directly reflects the remaining storage capacity and carrying capacity of energy storage equipment. The combined calculation of the two can avoid scheduling deviations caused by judging charging and discharging priorities based on only a single dimension.

[0056] The formula for calculating charge / discharge priority score is: .

[0057] Where is the meteorological reliability correction factor, the value range of which is [0.8, 1.2], and SOC is the current percentage of charge.

[0058] The current percentage of charge (SOC) directly reflects the remaining storage capacity of the energy storage device and is the core basis for judging whether the device can handle more power or needs to supply power to the outside. The meteorological reliability correction factor (with a value range of [0.8, 1.2]) can quantify the difference in the reliability of meteorological forecasts. When the deviation between the meteorological forecast result and the actual situation is small, the factor value is close to 1.2, which strengthens the positive impact of meteorological trends on the score. When the forecast deviation is large, the factor value is close to 0.8, which weakens the interference of forecast uncertainty on the score and avoids the distortion of the score due to inaccurate meteorological forecasts.

[0059] In existing technologies, the calculation of charging and discharging priorities for energy storage devices often relies solely on the current percentage of charge or a single power generation parameter, without considering the reliability differences in weather forecasts. This makes the scoring susceptible to errors in weather prediction and difficult to accurately guide dispatching. This application introduces a weather reliability correction factor (with a defined reasonable value range) into the formula and combines it with the current percentage of charge, thus quantifying the reliability of weather forecasts and integrating it into the scoring calculation.

[0060] S51: Determine the relationship between the charge / discharge priority score and the priority threshold. If the charge / discharge priority score exceeds the priority threshold, send a priority energy storage command; otherwise, send a grid-connected power supply command.

[0061] Traditional solutions typically rely on a single power threshold or fixed-time strategy for charge and discharge control, which is difficult to adapt to the stochastic coupling characteristics of wind and solar power generation. This application uses the dynamically generated wind-solar complementarity coefficient K from the aforementioned steps, combined with a dual-weight coefficient based on real-time volatility adjustment, the comparison results of weather forecasts and historical data, the state of charge percentage (SOC), and a weather reliability correction factor α, to comprehensively calculate an environmentally adaptable charge and discharge priority score S.

[0062] This scoring system quantifies and integrates key parameters from three dimensions: power generation stability (K), energy storage balance (1-|SOC-50%| / 100%), and weather forecast reliability (α×predicted total power generation / historical maximum power generation), forming a global assessment of the system's real-time operating status.

[0063] By setting a dynamic comparison mechanism between priority scoring and thresholds, the fixed threshold control is replaced, enabling the system to intelligently switch between energy storage and grid-connected power supply modes based on real-time changes in multiple factors such as the degree of fluctuation in wind and solar power output, the degree of deviation in the state of charge of energy storage, and the reliability of weather forecasts.

[0064] The scoring model introduces a nonlinear decay function with 50% state of charge as the optimal equilibrium point in the term "1-|SOC-50%| / 100%". This guides the system to actively adjust its charging and discharging strategy to restore system balance when the SOC is far from the ideal operating point, which significantly improves the service life of energy storage devices and system safety.

[0065] The priority threshold itself is not a fixed value, but can be dynamically adjusted down or up according to the equipment aging index and the peak load of the power grid (e.g., the threshold is lowered by 10% when the aging index is >0.8, and the threshold is raised by 15% when the power grid load exceeds 90% of the historical peak). This threshold adaptive mechanism further enhances the system's ability to respond to the health status of the equipment and the external demand of the power grid.

[0066] Furthermore, when the predicted light intensity is lower than the historical average for the same period and the predicted wind speed is higher than the historical average for the same period, the first weighting coefficient is increased to 1.5 times the original value.

[0067] When the system detects that the predicted solar irradiance is lower than the historical average for the same period while the predicted wind speed is higher, the first weighting coefficient for photovoltaic (PV) power generation is increased to 1.5 times its original value. This design is based on a deep understanding of the spatiotemporal complementarity of wind and solar resources: under typical meteorological combinations of insufficient sunlight but increased wind, the reliability of PV output decreases significantly while the potential for wind power output increases. By specifically increasing the PV weighting coefficient, the system can proactively enhance its sensitivity to PV fluctuation risks, avoiding energy storage decision-making biases caused by sudden drops in PV power.

[0068] Based on a dynamic benchmark setting using historical data from the same period, the degree of meteorological anomalies is more accurately reflected than a fixed threshold, making the weight adjustment more adaptable in time and space. Secondly, a specific 1.5-fold coefficient adjustment rule is proposed. This value is not an empirical constant, but an optimized parameter that has been experimentally verified to balance system stability when wind and solar resources fluctuate, so that the wind-solar complementarity coefficient K can more accurately characterize the real-time risk level of the power generation structure.

[0069] This adjustment mechanism, together with the aforementioned volatility monitoring, forms a dual optimization system: when volatility monitoring determines that photovoltaic volatility is intensifying, basic weight allocation is initiated, while the meteorological comparison mechanism implements enhanced adjustments under specific scenarios of reverse wind and solar changes. The two work together to achieve multi-level dynamic optimization of the weight coefficients.

[0070] The system enables forward-looking adjustments based on meteorological coupling characteristics, significantly improving the characterization accuracy of the wind-solar complementarity coefficient under extreme weather combinations and providing a more reliable data foundation for subsequent charge-discharge priority scoring.

[0071] Furthermore, it also includes: when the aging index of energy storage equipment is >0.8, the priority threshold is lowered by 10%. When the peak grid load exceeds 90% of the historical peak, the priority threshold is raised by 15%.

[0072] The priority threshold dynamic adjustment mechanism set in this application aims to achieve full life cycle optimization of the energy storage system's decision-making logic and grid-source collaborative self-adaptation, so as to solve the problem of decreased system reliability caused by neglecting the aging process of equipment and the real-time load status of the grid in traditional control strategies.

[0073] Specifically, when the aging index of the energy storage device is detected to be greater than 0.8, the system automatically lowers the priority threshold by 10%; when the peak load of the power grid exceeds 90% of the historical peak, the threshold is raised by 15%.

[0074] When the aging index exceeds the critical value, it indicates that the battery's internal resistance has increased and the charging and discharging efficiency has deteriorated significantly. At this time, lowering the threshold can reduce deep charge and discharge cycles under high load conditions and delay equipment degradation. When the grid load approaches the historical peak, raising the threshold will force an increase in the frequency of energy storage deployment, thereby alleviating the risk of grid congestion by releasing the stored power.

[0075] By incorporating the whole life cycle health management of equipment (lowering the threshold to avoid deep charging and discharging) and the real-time stability requirements of the power grid (raising the threshold to enhance peak shaving capability) into a unified decision analysis, the limitations of traditional fixed thresholds are broken.

[0076] The threshold adjustment ratio (10% / 15%) for specific values ​​is not a simple empirical value, but an optimal solution that has been experimentally verified to balance the extension of equipment life and the protection of grid security. For example, a 10% reduction can effectively reduce the battery degradation rate and avoid insufficient energy storage utilization due to over-protection, while a 15% increase can establish a precise match between grid emergency needs and energy storage capacity.

[0077] Please refer to Figure 2 As shown, this application also provides a data analysis device for photovoltaic and wind power integrated energy storage equipment, used to perform the method provided in the first aspect above, comprising: The detection and analysis module 10 is configured to acquire real-time photovoltaic power generation values, wind power generation values, and the current percentage of charge of energy storage devices. It also acquires predicted solar irradiance and wind speed values ​​for a preset future time period. Furthermore, it calculates the photovoltaic power generation volatility and the wind power generation volatility within the monitoring period.

[0078] The weight allocation module 20 is configured to determine a first weight coefficient if the photovoltaic volatility is greater than a preset photovoltaic volatility threshold, and a second weight coefficient if the wind power volatility is greater than a preset wind power volatility threshold, wherein the first weight coefficient is greater than the second weight coefficient. A wind-solar hybridization coefficient is generated based on the first and second weight coefficients.

[0079] The priority instruction output module 30 is configured to calculate the charging and discharging priority score of the energy storage device based on the wind-solar complementarity coefficient and the current percentage of charge. If the charging and discharging priority score exceeds the priority threshold, a priority energy storage instruction is sent; otherwise, a grid connection power supply instruction is sent.

[0080] Please refer to Figure 3 As shown, the method provided in this application is applied to Figure 3 The photovoltaic and wind power integrated energy storage device shown is capable of efficiently collecting, reliably storing, and stably outputting unstable natural energy sources, ultimately providing users with a continuous and high-quality power supply.

[0081] The energy source for this integrated photovoltaic and wind power energy storage device originates from the photovoltaic matrix unit and the wind power unit. The photovoltaic matrix unit and the wind power unit capture solar and wind energy respectively and convert them into direct current. These two power generation methods naturally complement each other. Photovoltaics are most efficient during the day when there is sufficient sunshine, while wind power may be more active at night or on cloudy or rainy days, thus greatly improving the overall power generation time and energy acquisition capacity of the system.

[0082] The generated electrical energy then enters the processing stage. For example... Figure 3 As shown, electrical energy first flows through DC circuit breakers QS1 and QS2 and DC surge arresters, which together form the first safety barrier of the system, ensuring that core equipment is not damaged in the event of lightning strikes or line faults. Subsequently, the unstable DC power flows into a unidirectional DC / DC power converter, which steps up and down the fluctuating raw DC power to precisely regulate it into a stable DC power that meets the requirements of the battery pack, preparing for efficient and safe charging.

[0083] The core of photovoltaic and wind power integrated energy storage equipment is the battery pack. The BMS continuously monitors the battery voltage, current and temperature to prevent overcharging and over-discharging, thus extending the battery's lifespan.

[0084] When a user needs electricity, the direct current (DC) stored in the battery is sent to an AC-DC converter (i.e., an inverter) to convert the DC into 220V / 50Hz sinusoidal alternating current, meeting the stringent voltage and frequency requirements of household appliances. The converted AC then passes through an AC circuit breaker (QS2, AC KM2) for final protection before being delivered to the load to provide power to household appliances.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A data analysis method for an integrated photovoltaic and wind power energy storage device, characterized in that, Includes the following steps: Real-time acquisition of photovoltaic power generation, wind power generation, and the current percentage of charge of energy storage devices; Obtain predicted values ​​of light intensity and wind speed for a future preset time period; Calculate the photovoltaic volatility of the photovoltaic power generation and the wind power volatility of the wind power generation during the monitoring period; If the photovoltaic volatility is greater than a preset photovoltaic volatility threshold, a first weighting coefficient is determined; if the wind power volatility is greater than a preset wind power volatility threshold, a second weighting coefficient is determined, wherein the first weighting coefficient is greater than the second weighting coefficient. Based on the first weighting coefficient and the second weighting coefficient, a wind-solar complementarity coefficient is generated; The charging and discharging priority score of the energy storage device is calculated based on the wind-solar complementarity coefficient and the current percentage of charge. If the charge / discharge priority score exceeds the priority threshold, a priority energy storage command is sent; otherwise, a grid-connected power supply command is sent.

2. The method as described in claim 1, characterized in that, When the predicted light intensity is lower than the historical average light intensity for the same period and the predicted wind speed is higher than the historical average wind speed for the same period, the first weighting coefficient is increased to 1.5 times the original value.

3. The method according to claim 1, characterized in that, The formula for calculating the wind-solar complementarity coefficient is as follows: ； Wherein, is the first weighting coefficient, is the second weighting coefficient, is the average value of the photovoltaic power generation during the monitoring period, and is the average value of the wind power generation during the monitoring period.

4. The method according to claim 1, characterized in that, The formula for calculating the charge / discharge priority score is as follows: ； Where is the meteorological reliability correction factor, and the value range is [0.8, 1.2], and SOC is the current percentage of charge.

5. The method according to claim 1, characterized in that, The duration of the monitoring period is dynamically set according to the weather type, wherein: When the weather type is sunny, the monitoring period is 4 hours. When the weather type is cloudy, the monitoring period is 2 hours. When the weather type is rainy or snowy, the monitoring period is 1 hour.

6. The method according to claim 3, characterized in that, Also includes: If the photovoltaic volatility exceeds the preset photovoltaic volatility threshold for three consecutive monitoring periods, it will be permanently increased by 20%. If the wind power volatility is lower than the preset wind power volatility threshold for three consecutive monitoring periods, it will be reduced to 80% of the original value.

7. The method according to claim 1, characterized in that, Also includes: When the aging index of the energy storage device is greater than 0.8, the priority threshold is lowered by 10%; when the peak load of the power grid exceeds 90% of the historical peak, the priority threshold is raised by 15%.

8. A data analysis device for an integrated photovoltaic and wind power energy storage system, characterized in that, The method for performing any one of claims 1-7 comprises: The detection and analysis module is configured to acquire photovoltaic power generation value, wind power generation value and current charge percentage of energy storage device in real time; acquire the predicted value of light intensity and wind speed for a future preset period; calculate the photovoltaic volatility of photovoltaic power generation power within the monitoring period; and calculate the wind power volatility of wind power generation power within the monitoring period. A weight allocation module is configured to determine a first weight coefficient if the photovoltaic volatility is greater than a preset photovoltaic volatility threshold; and to determine a second weight coefficient if the wind power volatility is greater than a preset wind power volatility threshold, wherein the first weight coefficient is greater than the second weight coefficient; and to generate a wind-solar complementarity coefficient based on the first weight coefficient and the second weight coefficient. The priority instruction output module is configured to calculate the charging and discharging priority score of the energy storage device based on the wind-solar complementarity coefficient and the current charge percentage; if the charging and discharging priority score exceeds the priority threshold, a priority energy storage instruction is sent; otherwise, a grid-connected power supply instruction is sent.

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