Photovoltaic energy storage optimization management method and system

Abstract

The invention discloses a photovoltaic energy storage optimization management method and system, and relates to the field of photovoltaic energy storage optimization, and the main scheme is that the method comprises the steps: obtaining the capacity fading rates of different energy storage units through the analysis of meteorological data and photovoltaic module operation data, and further calculating the real-time maximum capacity values of different energy storage units; selecting a preliminary energy storage strategy in combination with the power load data and the photovoltaic generating capacity; analyzing historical electricity consumption data to obtain an electricity price prediction value; and analyzing the storage electric quantity and the electricity price prediction value of different units in the preliminary energy storage strategy, evaluating the charging income and the discharging income, and further judging whether to execute the preliminary energy storage strategy. By analyzing the storage electric quantity and the electricity price predicted value of different units in the initial energy storage strategy, the charging income and the discharging income are evaluated, the economical efficiency and the effectiveness of the energy storage strategy can be ensured, and the return on investment of the energy storage system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage optimization, and in particular to a photovoltaic energy storage optimization management method and system. Background Art

[0002] With the rapid growth of photovoltaic power generation capacity, energy storage systems have become the core means to solve the intermittent nature of photovoltaic output and improve grid stability. The current mainstream solution adopts a hybrid energy storage architecture, which combines the complementary characteristics of supercapacitors (short-term high power), lithium-ion batteries (medium-short-term high energy) and flow batteries (long-term energy storage) to cope with photovoltaic power fluctuations, peak-valley electricity price differences and grid frequency regulation needs.

[0003] However, photovoltaic output is significantly affected by factors such as sudden weather changes, component attenuation, and shadowing, resulting in hybrid energy storage systems facing errors in dynamic power allocation due to a lack of detailed adjustments, as well as a lack of economic optimization factors, posing multiple challenges. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a photovoltaic energy storage optimization management method, which at least solves the problems in the prior art of lack of economic index reference and unreasonable allocation ratio of different energy storage units in the energy storage optimization process.

[0005] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A photovoltaic energy storage optimization management method, comprising: Step 1: Obtain meteorological data and photovoltaic module operation data through the monitoring system in the photovoltaic power generation area; Step 2: Preprocess the meteorological data and photovoltaic module operation data, and calculate the photovoltaic power generation by analyzing the preprocessed data; Step 3: Obtain the operating data of the energy storage unit, analyze the operating data, obtain the capacity decay rate of different energy storage units, and further calculate the real-time maximum capacity value of different energy storage units; Step 4: Obtain power load data, analyze it in combination with photovoltaic power generation, and select a preliminary energy storage strategy; Step 5: Obtain historical electricity consumption data and obtain electricity price forecasts by analyzing the historical electricity consumption data; Step 6: Analyze the storage capacity of different units in the preliminary energy storage strategy and the predicted electricity price, evaluate the charging and discharging benefits, and then determine whether to implement the preliminary energy storage strategy.

[0006] In the preferred embodiment of the photovoltaic energy storage optimization management method, obtaining meteorological data and photovoltaic module operation data through the monitoring system of the photovoltaic power generation area includes: The monitoring system detects the light intensity and ambient temperature of different monitoring points by setting up light intensity sensors and temperature sensors at different monitoring points in the photovoltaic power generation area; the average light intensity and ambient temperature of all the detected monitoring points are taken as the real-time light intensity and real-time ambient temperature of this photovoltaic power generation area; The monitoring system obtains the historical operation and maintenance data of different photovoltaic modules and calculates the attenuation rate of photovoltaic modules. Specifically, the power values ​​at multiple historical time points are obtained as reference points based on the current time point, and the pre-processed power data and the corresponding time data are substituted into the linear regression model: , where PG(t) represents the power value at time t in the historical operation and maintenance data, P0 represents the initial power value, and t represents time. The power attenuation rate σ of the photovoltaic module is calculated by the least squares method; The image data of photovoltaic modules are collected in real time through the image monitoring equipment of the monitoring system; the pre-processed data is input into the AI ​​visual recognition model to obtain the total area of ​​photovoltaic modules and the area of ​​the shaded area, and the shadow shading coefficient is calculated according to the total area of ​​photovoltaic modules and the area of ​​the shaded area, referring to the formula: , where γ represents the shadow shielding coefficient, A1 represents the area of ​​the shaded region, and A represents the total area of ​​the photovoltaic module.

[0007] In the preferred embodiment of the photovoltaic energy storage optimization management method, the photovoltaic power generation is calculated by inputting the real-time light intensity, the real-time ambient temperature, the power attenuation rate of the photovoltaic module and the shadow shielding coefficient into the photovoltaic output prediction model, which is expressed as: ; Where P(t) represents the photovoltaic power generation of the photovoltaic module at time t; μ represents the photoelectric conversion efficiency of the photovoltaic module; G(t) represents the real-time light intensity at time t; T(t) represents the real-time ambient temperature at time t; T ref It represents the nominal temperature of the photovoltaic module; β represents the temperature correction coefficient; γ represents the shadow shielding coefficient; σ represents the power attenuation rate of the photovoltaic module.

[0008] In the preferred embodiment of the photovoltaic energy storage optimization management method, calculating the real-time maximum capacity values ​​of different energy storage units includes: Regularly test the actual capacity of different energy storage units. Fully charge the energy storage unit to be tested, then discharge it to the cut-off voltage at a constant current, record the discharge time and discharge current, calculate the actual discharge amount, repeat multiple times, and take the average value as the actual capacity of the energy storage unit; after obtaining the capacity test data, calculate the capacity attenuation rate of the capacitor unit, lithium battery unit, and flow battery unit according to the actual capacity of the energy storage unit measured most recently. , and , expressed as: ; in, Indicates the capacity decay rate of the corresponding energy storage unit; C ac is the actual capacity of the energy storage unit in the most recent test, C no is the nominal capacity of the corresponding energy storage unit; According to the capacity attenuation rate of the capacitor unit, lithium battery unit and flow battery unit , and , further calculate the real-time maximum capacity value SOC1 of the capacitor unit, lithium battery unit and flow battery unit max 、SOC2 max and SOC3 max , expressed as: SOC max =(1-ψ)*C no； Among them, SOC max It is the real-time maximum capacity value of the energy storage unit.

[0009] In the preferred embodiment of the photovoltaic energy storage optimization management method, the remaining power value is obtained by analyzing the photovoltaic power generation and power load data, including: The remaining power value is calculated by the photovoltaic power generation and power load value, which is expressed as: ; Among them, P SY (t) represents the remaining power at time t, P load (t) represents the power load value at time t.

[0010] In the preferred embodiment of the photovoltaic energy storage optimization management method, the preliminary energy storage strategy includes: when When , the remaining power is stored only by the capacitor unit; is the maximum power density of the capacitor unit; when When the capacitor unit is fully charged, the remaining electricity is stored in the lithium battery unit and the liquid flow unit respectively. The distribution ratio of the lithium battery unit and the liquid flow unit is expressed as: ; Among them, P bat (t) represents the amount of electricity distributed by the lithium battery at time t; P rf (t) represents the amount of electricity distributed by the flow battery at time t, α is the distribution ratio coefficient of the lithium battery unit; β is the distribution ratio coefficient of the flow unit; and it needs to satisfy ≤SOC2 max , ≤SOC3 max .

[0011] In the preferred embodiment of the photovoltaic energy storage optimization management method, the calculation of the allocation ratio coefficient includes: According to the comprehensive index calculation model, the comprehensive index Sc1 of the lithium battery unit and the comprehensive index Sc2 of the liquid flow unit are calculated respectively. The comprehensive index calculation model is expressed as: ; Wherein, Sc represents the comprehensive index of the corresponding energy storage unit, R represents the charge and discharge rate of the corresponding energy storage unit, η ch and η di Represents the charging efficiency and discharging efficiency of the corresponding energy storage unit; τ1 represents τ2 represents the weight coefficient of charge and discharge rate, τ3 represents the weight coefficient of charge efficiency, and τ4 represents the weight coefficient of discharge efficiency; The distribution ratio is calculated based on the comprehensive index Sc1 of the lithium battery unit and the comprehensive index Sc2 of the liquid flow unit, which is expressed as: ; ; In the preferred embodiment of the photovoltaic energy storage optimization management method, obtaining the electricity price forecast value includes: The historical electricity consumption data is analyzed by time series analysis to obtain the electricity price forecast value, which is expressed as: ; Among them, D t+1 represents the predicted value of electricity price in the next time period; ε represents the average value of electricity price at all time points in the historical time period, , ,,,,,, is the autoregressive coefficient of the model, D t , D t-1 ,,,,,D t-p+1Represents the historical electricity price at each historical time point within the historical time period; t+1 represents the error term.

[0012] In the preferred embodiment of the photovoltaic energy storage optimization management method, evaluating the charging benefit and the discharging benefit and adjusting the control strategy of the energy storage unit include: By analyzing the storage capacity of different units and the predicted electricity price in the preliminary energy storage strategy, the charging benefit is calculated as follows: when hour, ; when hour, ; Among them, C SY represents the charging income; BT represents the charging subsidy amount; DR1 represents the charging and discharging cost of the capacitor unit; DR2 represents the charging and discharging cost of the lithium battery unit; DR3 represents the charging and discharging cost of the flow battery unit; The cost calculation model is used to calculate the charge and discharge cost DR1 of the capacitor unit, the charge and discharge cost DR2 of the lithium battery unit, and the charge and discharge cost DR3 of the flow battery unit. The cost calculation model is expressed as: ; Among them, DR represents the charging cost of the corresponding energy storage unit, C in Represents the investment cost of the corresponding energy storage unit; N li Indicates the cycle life of the corresponding energy storage unit; By analyzing the storage capacity of different units and the predicted electricity price in the preliminary energy storage strategy, the discharge benefit is calculated as follows: ; Among them, F SY represents the discharge benefit; FF(t) represents the additional cost of returning electricity to the grid.

[0013] By comparing the charging income with the return income, it is determined whether to use the energy storage unit to store the surplus electricity or return the surplus electricity to the grid, including: when When the power is less than 100W, the preliminary energy storage strategy of step 4 is executed to store the remaining power in different energy storage units; when When the power is off, the remaining electricity is returned to the grid without being stored in the energy storage unit.

[0014] (III) Beneficial effects The present invention provides a photovoltaic energy storage optimization management method, which has the following beneficial effects: (1) Obtaining real-time meteorological data and photovoltaic module operation data through the monitoring system provides basic data support for accurate prediction of photovoltaic power generation and energy storage management. This helps to improve the prediction accuracy of photovoltaic power generation and optimize energy distribution and scheduling; and improves data quality by cleaning and integrating meteorological data and photovoltaic module operation data through preprocessing steps. Accurate power generation calculation provides a reliable basis for subsequent energy storage strategy selection and grid scheduling, thereby improving overall energy utilization efficiency.

[0015] (2) By analyzing the operating data of the energy storage unit, the capacity attenuation rate and real-time maximum capacity value are obtained, providing a scientific basis for the maintenance and replacement of the energy storage unit. By obtaining power load data and analyzing it in combination with photovoltaic power generation, a preliminary energy storage strategy is selected to optimize energy distribution and grid stability. This method can balance supply and demand, reduce energy waste, and improve the economy and reliability of energy utilization; it avoids the problem that traditional methods lack analysis of power load data and consideration of photovoltaic power generation, resulting in unscientific selection of energy storage strategies and affecting energy utilization efficiency.

[0016] (3) By analyzing historical electricity consumption data and obtaining electricity price forecasts, we can provide a basis for the economic analysis of energy storage strategies, help evaluate the economic benefits of energy storage strategies, optimize energy storage management decisions, and improve the competitiveness of the energy market.

[0017] (4) By analyzing the storage capacity of different units in the preliminary energy storage strategy and the predicted electricity price, the charging and discharging benefits are evaluated, and then it is determined whether to implement the preliminary energy storage strategy. This method can ensure the economy and effectiveness of the energy storage strategy, improve the return on investment of the energy storage system, promote the widespread application of energy storage technology, and overcome the problem that the existing technology is difficult to accurately calculate the charging and discharging benefits when evaluating the energy storage strategy, resulting in inaccurate strategy execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the steps of a photovoltaic energy storage optimization management method of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1, please refer to Figure 1 The present invention provides a photovoltaic energy storage optimization management method, comprising: Step 1: Obtain meteorological data and PV module operation data through the monitoring system in the PV power generation area.

[0021] Step 101: The monitoring system includes light intensity sensors and temperature sensors set at different monitoring points in the photovoltaic power generation area to detect the light intensity and ambient temperature at different monitoring points; the light intensity and ambient temperature of all the detected monitoring points are averaged as the real-time light intensity and real-time ambient temperature of this photovoltaic power generation area.

[0022] In this step, meteorological data and photovoltaic module operation data are obtained through the monitoring system of the photovoltaic power generation area: by setting light intensity sensors and temperature sensors at different monitoring points in the photovoltaic power generation area, more comprehensive and real-time data can be obtained to improve the accuracy of photovoltaic power generation calculation, avoid relying on a single monitoring point or inaccurate sensors, resulting in the acquisition of incomplete or real-time data, affecting the accurate calculation of photovoltaic power generation and the efficiency of energy storage management.

[0023] Step 102: Obtain historical operation and maintenance data of different photovoltaic modules through the monitoring system, and calculate the attenuation rate of the photovoltaic modules; specifically, taking the current time point as the reference point, obtain the power values ​​of multiple historical time points in the historical operation and maintenance data, and substitute the pre-processed power data and the corresponding time data into the linear regression model: , where PG(t) represents the power value at time t in the historical operation and maintenance data, P0 represents the initial power value, and t represents time. The power attenuation rate σ of the photovoltaic module is calculated by the least squares method.

[0024] In this step, the capacity decay rate and real-time maximum capacity value are obtained through analysis of the operating data, providing data support for the optimized management of the energy storage unit.

[0025] Step 103: Using the AI ​​visual recognition model, a large number of training samples with occlusion annotations are fed to the AI ​​visual recognition model to distinguish between normal photovoltaic module areas and occluded areas. For example, a model based on the Seg-YOLO algorithm can accurately identify occluded areas on photovoltaic modules and provide their location information. The model can identify key features in the image, such as the edges and textures of photovoltaic modules and possible obstructions, such as leaves, dust, buildings, etc.

[0026] Step 104: Collect the image data of the photovoltaic module in real time through the image monitoring equipment such as the camera of the monitoring system. Preprocess the collected image, including grayscale, noise removal, edge detection and other operations to improve the image quality; input the processed data into the AI ​​visual recognition model to obtain the total area of ​​the photovoltaic module and the area of ​​the blocked area, and calculate the shadow occlusion coefficient based on the total area of ​​the photovoltaic module and the area of ​​the blocked area, referring to the formula: , where γ represents the shadow shielding coefficient, A1 represents the area of ​​the shaded region, and A represents the total area of ​​the photovoltaic module.

[0027] In this step, by learning a large number of training samples with occlusion annotations, the AI ​​visual recognition model can accurately identify the occluded areas on the photovoltaic modules and give their location information. For example, the model based on the Seg-YOLO algorithm can distinguish between normal photovoltaic module areas and obscured areas, such as leaves, dust, buildings, etc. This helps to improve the monitoring accuracy of the operating status of photovoltaic modules and provide data support for optimizing energy management. It avoids the problem that when a photovoltaic system monitors photovoltaic modules, it is difficult to accurately identify and distinguish between normal photovoltaic module areas and obscured areas, resulting in the inability to accurately calculate the shadow occlusion coefficient, affecting the optimization of power generation efficiency.

[0028] It should be noted that the model of the Seg-YOLO algorithm is an existing technology. It is an algorithm that combines semantic segmentation (Segmentation) and target detection (YOLO), which is suitable for simultaneously identifying and segmenting target areas in images. In photovoltaic module occlusion recognition, it is mainly used to identify photovoltaic modules and occluders (such as leaves, dust, buildings, etc.) in images, segment images into different areas, and distinguish between normal photovoltaic module areas and occluded areas. When using, select a suitable image size according to the actual application scenario, such as 640x640 pixels, the initial learning rate can be set to 0.001, and adjusted according to the change of the loss function during training, select a suitable sample batch size according to computing resources, such as 32 or 64 groups of samples, and then select a suitable number of iterations according to the amount of training data and model convergence, such as 100 or 200 times. Before the training samples are input into the model, the data needs to be labeled with the location and category of the photovoltaic components and obstructions. The diversity of the training data is increased through rotation, scaling, flipping and other operations, the generalization ability of the model is improved, and preprocessing is performed. After the training is completed, the new data is input into the Seg-YOLO model, which can output the detection boxes and segmentation maps of the photovoltaic components and obstructions, from which the total area A of the photovoltaic components and the area A1 of the obstructed area can be extracted.

[0029] Step 2: Preprocess the meteorological data and photovoltaic module operation data, and calculate the photovoltaic power generation by analyzing the preprocessed data; Step 201: normalize and preprocess the meteorological data and the photovoltaic module operation data to eliminate the dimension of the parameters; wherein the meteorological data includes the real-time light intensity and real-time ambient temperature in step one, and the photovoltaic module operation data includes the photovoltaic module attenuation rate and shadow shielding coefficient in step one.

[0030] In this step, normalization preprocessing eliminates the dimension differences of different parameters in meteorological data and photovoltaic module operation data, making the data comparable and improving the accuracy of photovoltaic power generation calculation. This step ensures the consistency and reliability of the input data of the subsequent photovoltaic output prediction model.

[0031] Step 202, by inputting the real-time light intensity, the real-time ambient temperature, the power attenuation rate of the photovoltaic module and the shadow shielding coefficient into the photovoltaic output prediction model, the photovoltaic power generation is calculated according to the following formula: ; Where P(t) represents the photovoltaic power generation of the photovoltaic module at time t; μ represents the photoelectric conversion efficiency of the photovoltaic module; G(t) represents the real-time light intensity at time t; T(t) represents the real-time ambient temperature at time t; T ref It represents the nominal temperature of the photovoltaic module; β represents the temperature correction coefficient; γ represents the shadow shielding coefficient; σ represents the power attenuation rate of the photovoltaic module.

[0032] It should be noted that the calculation method of the photoelectric conversion efficiency of the photoelectric component is: , obtained by dividing the output power of the PV modules at the previous moment by the product of the total area of ​​the PV modules and the light intensity at the previous moment.

[0033] In this step, by incorporating the real-time ambient temperature and the power decay rate of the photovoltaic module into the model, the performance changes of the module under different temperatures and aging degrees can be more accurately reflected. The introduction of the temperature correction coefficient β and the power decay rate σ enables the model to adapt to different environmental conditions and module states, improving the accuracy of power generation calculation.

[0034] Step 3: Obtain the operating data of the energy storage unit, analyze the operating data, obtain the capacity decay rate of different energy storage units, and further calculate the real-time maximum capacity value of different energy storage units; Step 301: Regularly test the actual capacity of different energy storage units. The method is to fully charge the energy storage unit to be tested, then discharge it to the cut-off voltage with a constant current, record the discharge time and discharge current, calculate the actual discharge amount, repeat the above steps multiple times, and take the average value as the actual capacity of the energy storage unit; after obtaining the capacity test data, calculate the capacity attenuation rate of different energy storage units according to the actual capacity of the energy storage unit measured most recently, and the formula based on it is as follows: ; in, Indicates the capacity decay rate of the corresponding energy storage unit; C ac is the actual capacity of the energy storage unit in the most recent test, C no It is the nominal capacity of the corresponding energy storage unit.

[0035] According to the calculation principle of this formula, the capacity attenuation rate of the capacitor unit, lithium battery unit and flow battery unit is calculated respectively. , and .

[0036] In this step, accurate calculation of capacity decay rate can help rationally arrange the charging and discharging strategy of the energy storage unit, so that it can better adapt to the intermittent and instability of photovoltaic energy and improve the flexibility of the energy storage system.

[0037] Step 302: Based on the capacity decay rate of the capacitor unit, the lithium battery unit and the flow battery unit , and , further calculate the real-time maximum capacity value of different energy storage units, based on the formula model: SOC max =(1-ψ)*C no , where SOC max is the real-time maximum capacity value of the energy storage unit; according to this formula model, the real-time maximum capacity value SOC1 of the capacitor unit, lithium battery unit and flow battery unit is calculated respectively max 、SOC2 max and SOC3 max .

[0038] In this step, accurately calculating the maximum capacity value of the energy storage unit can help reasonably arrange the charging and discharging strategy of the energy storage unit, so that it can better adapt to the intermittent and instability of photovoltaic energy and improve the flexibility of the energy storage system.

[0039] Step 4: Obtain power load data, analyze photovoltaic power generation and power load data, and select a preliminary energy storage strategy.

[0040] Step 401: The power load value is obtained by real-time measurement of the monitoring equipment installed in the power system. The power load value is measured in real time by the monitoring equipment to ensure that the acquired power load data is highly timely and accurate, providing a reliable basis for the subsequent energy storage strategy selection, so that the energy storage system can respond to the real-time needs of the power system more accurately and improve the operating efficiency and stability of the entire power system.

[0041] Step 402: Calculate the remaining power value by using the photovoltaic power generation and the power load value, based on the formula: , where P SY (t) represents the remaining power at time t, P load (t) represents the power load value at time t; when When , the remaining power is stored only by the capacitor unit; is the maximum power density of the capacitor unit; when When the capacitor unit is fully charged, the remaining electricity is stored in the lithium battery unit and the liquid flow unit respectively. The distribution ratio of the lithium battery unit and the liquid flow unit is as follows: ; Among them, P bat (t) represents the amount of electricity distributed by the lithium battery at time t, α is the distribution ratio coefficient of the lithium battery unit; β is the distribution ratio coefficient of the liquid flow unit; and it needs to satisfy ≤SOC2 max , ≤SOC3 max .

[0042] In this step, multiple factors such as the charge and discharge rate, charging efficiency and discharge efficiency of the energy storage unit are comprehensively considered, and a comprehensive index is obtained through weighted calculation, which can more comprehensively and accurately reflect the performance characteristics of each energy storage unit, thereby providing a scientific basis for the reasonable allocation of energy storage ratios. The weight coefficient can be adjusted according to actual needs, making this method highly flexible and adaptable. It can dynamically adjust the allocation ratio according to different scenarios and actual performance changes of the energy storage unit, optimize the operation effect of the energy storage system, extend the service life of the energy storage unit, and improve the economy and practicality of the energy storage system; when the remaining power is small, it is only stored through the capacitor unit, and the characteristics of the capacitor unit with fast charging and discharging speed and high power density can be used to quickly and efficiently store a small amount of remaining power, avoiding the problems of low efficiency that may occur in other energy storage units when storing low power. When the remaining power is large, the capacitor unit, lithium battery unit and liquid flow unit are used for collaborative storage, giving full play to the advantages of each energy storage unit, improving the overall performance and reliability of the energy storage system, realizing efficient and reasonable storage of the remaining power, and improving energy utilization.

[0043] Step 403: Calculation of the allocation ratio coefficient includes: The comprehensive index of the lithium battery unit and the liquid flow unit are calculated respectively according to the comprehensive index calculation model. The calculation model based on the comprehensive index calculation model is as follows: ; Wherein, Sc represents the comprehensive index of the corresponding energy storage unit, R represents the charge and discharge rate of the corresponding energy storage unit, η ch and η di Represents the charging efficiency and discharging efficiency of the corresponding energy storage unit; τ1 represents The weight coefficients are: τ1=0.4, τ2=0.2, τ3=0.2, and τ4=0.2.

[0044] Through this calculation model, the comprehensive index Sc1 of the lithium battery unit and the comprehensive index Sc2 of the liquid flow unit are calculated respectively; The distribution ratio is calculated based on the comprehensive index Sc1 of the lithium battery unit and the comprehensive index Sc2 of the liquid flow unit, and the formula is as follows: ; ; Previous energy storage allocation methods may be relatively simple, without fully considering key performance indicators such as the charge and discharge rate and efficiency of the energy storage unit, resulting in unreasonable energy storage allocation, affecting the overall performance and service life of the energy storage system. The performance of different energy storage units will change with factors such as the use environment and time. Fixed allocation ratios are difficult to adapt to these changes, reducing the flexibility and adaptability of the energy storage system. When the remaining power is small, if energy storage methods such as lithium battery units or liquid flow units are used, the charging and discharging characteristics of these energy storage units may lead to low charging efficiency or equipment loss. When the remaining power is large, it is difficult to efficiently store all the electricity by relying on a single energy storage unit, and it may exceed the capacity range of a single energy storage unit, resulting in energy waste or equipment damage.

[0045] Step 5: Obtain historical electricity consumption data and obtain electricity price forecasts by analyzing the historical electricity consumption data.

[0046] Specifically: The historical electricity consumption data is analyzed by time series analysis to obtain the electricity price forecast value, based on the formula: ; Among them, D t+1 represents the predicted value of electricity price in the next time period; ε represents the average value of electricity price at all time points in the historical time period, , ,,,,,, is the autoregressive coefficient of the model, D t , D t-1 ,,,,,D t-p+1 Represents the historical electricity price at each historical time point within the historical time period, such as D t represents the electricity price at time point t, D t-1 represents the electricity price at time point t-1, that is, the electricity price at the moment before time t, and so on. t-p+1represents the electricity price at time point t-p+1, that is, the electricity price at p-1 moments before time t; ζ t+1 represents the error term.

[0047] In this step, the time series analysis method can make full use of the changing trend of historical electricity price data and predict future electricity prices by establishing a mathematical model. This method can effectively capture the periodic and trend changes in electricity prices, thereby improving the accuracy of electricity price forecasts and providing a more reliable decision-making basis for photovoltaic energy storage management. By adjusting parameters such as the autoregressive coefficient of the model, it can be better adapted to the changing laws of electricity prices in different regions and time periods. This flexibility enables this method to be widely used in various power market environments and has strong universality. Based on the predicted electricity price value, the energy storage system can allocate energy storage resources more accurately. Avoid blindly storing or releasing energy when the electricity price forecast is inaccurate, resulting in waste of resources or missing the best energy storage opportunity, and improve the overall performance and resource utilization efficiency of the energy storage system.

[0048] Step 6: Calculate the charging and discharging benefits by analyzing the storage capacity of different units in the preliminary energy storage strategy and the predicted electricity price.

[0049] Step 601: By analyzing the storage power of different units and the predicted electricity price in the preliminary energy storage strategy, the charging benefit is calculated as follows: when hour, ; when hour, ; Among them, C SY Indicates charging revenue; BT indicates the amount of charging subsidy, which can be obtained through the signed contract policy or the actual national policy situation; DR1 indicates the charging and discharging cost of the capacitor unit; DR2 indicates the charging and discharging cost of the lithium battery unit; DR3 indicates the charging and discharging cost of the flow battery unit; The cost calculation model is used to calculate the charge and discharge cost DR1 of the capacitor unit, the charge and discharge cost DR2 of the lithium battery unit, and the charge and discharge cost DR3 of the flow battery unit. The cost calculation model is expressed as: ; Among them, DR represents the charging cost of the corresponding energy storage unit, C in Represents the investment cost of the corresponding energy storage unit, which is obtained through the investment account of the photovoltaic module; N li Indicates the cycle life of the corresponding energy storage unit, which can be obtained from the equipment instructions.

[0050] Through the cost calculation model, the charge and discharge cost DR1 of the capacitor unit, the charge and discharge cost DR2 of the lithium battery unit, and the charge and discharge cost DR3 of the flow battery unit can be calculated respectively.

[0051] Step 602: The method of calculating the discharge benefit is as follows: ; Among them, F SY represents the discharge benefit; FF(t) represents the additional cost of returning electricity to the grid, such as channel fees.

[0052] Step 603: By comparing the charging income with the return electricity income, when When , the preliminary energy storage strategy of step 4 is executed to store the remaining electricity in different energy storage units; when When the power is off, the remaining electricity is returned to the grid without being stored in the energy storage unit.

[0053] In this step, the cost calculation model comprehensively considers the investment cost and cycle life of the energy storage unit, and can accurately calculate the charging and discharging costs of the capacitor unit, lithium battery unit and flow battery unit, providing a basis for the accurate calculation of the charging benefits, avoiding the large differences in the charging and discharging costs of different energy storage units, and being affected by various factors, such as investment cost, cycle life, etc., the traditional method is difficult to accurately calculate the charging cost of each energy storage unit, resulting in inaccurate calculation of charging benefits. Including the charging subsidy in the calculation, combined with the charging and discharging costs of different energy storage units, it can more comprehensively and accurately evaluate the charging benefits, and provide a scientific basis for the economic operation of the energy storage system. During the operation of the energy storage system, when to charge and when to discharge is a key issue. Traditional methods often lack scientific decision-making basis, resulting in low efficiency and poor economic benefits of the energy storage system; by comparing the charging benefits and discharging benefits, the operation strategy of the energy storage system can be scientifically formulated. When the charging benefit is greater than the discharging benefit, the allocation strategy is executed to store the remaining electricity in different energy storage units; when the discharging benefit is greater than the charging benefit, the remaining electricity is returned to the power grid without storing electricity through the energy storage unit. This strategy can maximize the economic benefits of the energy storage system, improve the system's operating efficiency, and avoid the problem of inaccurate calculation of discharge benefits caused by ignoring additional costs.

[0054] Embodiment 2, a photovoltaic energy storage optimization management system, is used to implement the photovoltaic energy storage optimization management method mentioned above.

[0055] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0056] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0057] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A photovoltaic energy storage optimization management method, characterized in that: include: Step 1: Obtain meteorological data and photovoltaic module operation data through the monitoring system in the photovoltaic power generation area; Step 2: Preprocess the meteorological data and photovoltaic module operation data, and calculate the photovoltaic power generation by analyzing the preprocessed data; Step 3: Obtain the operating data of the energy storage unit, analyze the operating data, obtain the capacity decay rate of different energy storage units, and further calculate the real-time maximum capacity value of different energy storage units; Step 4: Obtain power load data, analyze it in combination with photovoltaic power generation, and select a preliminary energy storage strategy; Step 5: Obtain historical electricity consumption data and obtain electricity price forecasts by analyzing the historical electricity consumption data; Step 6: By analyzing the storage capacity of different units in the preliminary energy storage strategy and the predicted electricity price, the charging and discharging benefits are evaluated to determine whether to implement the preliminary energy storage strategy.

2. A photovoltaic energy storage optimization management method according to claim 1, characterized in that: The meteorological data and PV module operation data obtained through the monitoring system in the PV power generation area include: The monitoring system detects the light intensity and ambient temperature of different monitoring points by setting up light intensity sensors and temperature sensors at different monitoring points in the photovoltaic power generation area; the average light intensity and ambient temperature of all the detected monitoring points are taken as the real-time light intensity and real-time ambient temperature of this photovoltaic power generation area; The monitoring system obtains the historical operation and maintenance data of different photovoltaic modules and calculates the attenuation rate of photovoltaic modules. Specifically, the power values ​​at multiple historical time points are obtained as reference points based on the current time point, and the pre-processed power data and the corresponding time data are substituted into the linear regression model: , where PG(t) represents the power value at time t in the historical operation and maintenance data, P0 represents the initial power value, and t represents time. The power attenuation rate σ of the photovoltaic module is calculated by the least squares method; The image data of photovoltaic modules are collected in real time through the image monitoring equipment of the monitoring system; the pre-processed data is input into the AI ​​visual recognition model to obtain the total area of ​​photovoltaic modules and the area of ​​the shaded area, and the shadow shading coefficient is calculated according to the total area of ​​photovoltaic modules and the area of ​​the shaded area, referring to the formula: , where γ represents the shadow shielding coefficient, A1 represents the area of ​​the shaded region, and A represents the total area of ​​the photovoltaic module.

3. A photovoltaic energy storage optimization management method according to claim 2, characterized in that: By inputting the real-time light intensity, real-time ambient temperature, power attenuation rate of photovoltaic modules and shadow shielding coefficient into the photovoltaic output prediction model, the photovoltaic power generation is calculated, which is expressed as: ； Where P(t) represents the photovoltaic power generation of the photovoltaic module at time t; μ represents the photoelectric conversion efficiency of the photovoltaic module; G(t) represents the real-time light intensity at time t; T(t) represents the real-time ambient temperature at time t; T ref It represents the nominal temperature of the photovoltaic module; β represents the temperature correction coefficient; γ represents the shadow shielding coefficient; σ represents the power attenuation rate of the photovoltaic module.

4. A photovoltaic energy storage optimization management method according to claim 3, characterized in that: Calculation of the real-time maximum capacity of different energy storage units includes: Regularly test the actual capacity of different energy storage units. Fully charge the energy storage unit to be tested, then discharge it to the cut-off voltage at a constant current, record the discharge time and discharge current, calculate the actual discharge amount, repeat multiple times, and take the average value as the actual capacity of the energy storage unit; after obtaining the capacity test data, calculate the capacity attenuation rate of the capacitor unit, lithium battery unit, and flow battery unit according to the actual capacity of the energy storage unit measured most recently. , and , expressed as: ； in, Indicates the capacity decay rate of the corresponding energy storage unit; C ac is the actual capacity of the energy storage unit in the most recent test, C no is the nominal capacity of the corresponding energy storage unit; According to the capacity attenuation rate of the capacitor unit, lithium battery unit and liquid flow battery unit , and , further calculate the real-time maximum capacity value SOC1 of the capacitor unit, lithium battery unit and flow battery unit max 、SOC2 max and SOC3 max , expressed as: SOCIETY max =(1-ψ)*C no ; Among them, SOCmax is the real-time maximum capacity value of the energy storage unit.

5. A photovoltaic energy storage optimization management method according to claim 4, characterized in that: After analyzing the photovoltaic power generation and power load data, the remaining power value is obtained, including: The remaining power value is calculated by the photovoltaic power generation and power load value, which is expressed as: ； Among them, P SY (t) represents the remaining power at time t, P load (t) represents the power load value at time t.

6. A photovoltaic energy storage optimization management method according to claim 5, characterized in that: In step 4, the preliminary energy storage strategy includes: when When , the remaining power is stored only by the capacitor unit; is the maximum power density of the capacitor unit; when When the capacitor unit is fully charged, the remaining electricity is stored in the lithium battery unit and the liquid flow unit respectively. The distribution ratio of the lithium battery unit and the liquid flow unit is expressed as: ； Among them, P bat (t) represents the amount of electricity distributed by the lithium battery at time t; P rf (t) represents the amount of electricity distributed by the flow battery at time t, α is the distribution ratio coefficient of the lithium battery unit; β is the distribution ratio coefficient of the flow unit; and it needs to satisfy ≤SOC2 max , ≤SOC3 max .

7. A photovoltaic energy storage optimization management method according to claim 6, characterized in that: The calculation of the allocation ratio coefficient includes: According to the comprehensive index calculation model, the comprehensive index Sc1 of the lithium battery unit and the comprehensive index Sc2 of the liquid flow unit are calculated respectively. The comprehensive index calculation model is expressed as: ； Wherein, Sc represents the comprehensive index of the corresponding energy storage unit, R represents the charge and discharge rate of the corresponding energy storage unit, η ch and η di Represents the charging efficiency and discharging efficiency of the corresponding energy storage unit; τ1 represents τ2 represents the weight coefficient of charge and discharge rate, τ3 represents the weight coefficient of charge efficiency, and τ4 represents the weight coefficient of discharge efficiency; The distribution ratio is calculated based on the comprehensive index Sc1 of the lithium battery unit and the comprehensive index Sc2 of the liquid flow unit, which is expressed as: ； 。 8. A photovoltaic energy storage optimization management method according to claim 7, characterized in that: Obtaining electricity price forecasts includes: The historical electricity consumption data is analyzed by time series analysis to obtain the electricity price forecast value, which is expressed as: ； Among them, D t+1 represents the predicted value of electricity price in the next time period; ε represents the average value of electricity price at all time points in the historical time period, , ,,,,,, is the autoregressive coefficient of the model, D t , D t-1 ,,,,,D t-p+1 Represents the historical electricity price at each historical time point within the historical time period; t+1 represents the error term.

9. A photovoltaic energy storage optimization management method according to claim 8, characterized in that: Evaluate the charging and discharging benefits and adjust the control strategy of the energy storage unit including: By analyzing the storage capacity of different units and the predicted electricity price in the preliminary energy storage strategy, the charging benefit is calculated as follows: when hour, ; when hour, ； Among them, C SY represents the charging income; BT represents the charging subsidy amount; DR1 represents the charging and discharging cost of the capacitor unit; DR2 represents the charging and discharging cost of the lithium battery unit; DR3 represents the charging and discharging cost of the flow battery unit; The cost calculation model is used to calculate the charge and discharge cost DR1 of the capacitor unit, the charge and discharge cost DR2 of the lithium battery unit, and the charge and discharge cost DR3 of the flow battery unit. The cost calculation model is expressed as: ； Among them, DR represents the charging cost of the corresponding energy storage unit, C in Represents the investment cost of the corresponding energy storage unit; N li Indicates the cycle life of the corresponding energy storage unit; By analyzing the storage capacity of different units and the predicted electricity price in the preliminary energy storage strategy, the discharge benefit is calculated as follows: ； Among them, F SY represents the discharge benefit; FF(t) represents the additional cost of returning electricity to the grid; By comparing the charging income with the return income, it is determined whether to use the energy storage unit to store the surplus electricity or return the surplus electricity to the grid, including: when When the power is less than 100W, the initial energy storage strategy of step 4 is executed to store the remaining power in different energy storage units; when When the power is off, the remaining electricity is returned to the grid without being stored in the energy storage unit.

10. A photovoltaic energy storage optimization management system, characterized in that: A photovoltaic energy storage optimization management method for implementing any one of claims 1-9.

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