A method, system, device and medium for managing photovoltaic energy storage losses

Abstract

The present application relates to a management method, system, device and medium for photovoltaic energy storage loss, belonging to the field of photovoltaic technology, which includes: within each monitoring period, obtaining a temperature distribution map of the photovoltaic array, where the temperature distribution map includes the temperature of the shaded area and the surface of the photovoltaic array, and calculating and obtaining the current occlusion ratio; using an MPPT controller to adjust the MPP of the photovoltaic module, obtaining the optimal MPP within the current unit time, and operating at the optimal MPP; updating the training model of the mapping relationship between the occlusion ratio, temperature and optimal MPP; within subsequent monitoring periods, immediately adjusting the battery charging strategy and presetting the charging voltage and current. The present application can dynamically adjust the optimal output state of the photovoltaic panel according to real-time occlusion conditions, reducing the energy storage loss caused by shading.

Description

Technical Field

[0001] This application relates to the field of photovoltaic technology, and in particular to a method, system, device and medium for managing photovoltaic energy storage losses. Background Art

[0002] With the continuous growth of global energy demand and the increasingly severe environmental problems, the development and utilization of renewable energy have received extensive attention. As a clean and pollution-free energy form, photovoltaic energy occupies an important position in the field of new energy. Photovoltaic power generation systems convert sunlight into electrical energy through solar photovoltaic arrays and have broad application prospects.

[0003] Currently, photovoltaic energy storage mainly relies on battery energy storage systems, including but not limited to lead-acid batteries, lithium-ion batteries, etc. These energy storage systems store electrical energy in the form of chemical energy and release electrical energy when needed. To improve energy storage efficiency and extend battery life, a variety of battery management systems (BMS) have been developed to monitor the status of batteries, including parameters such as voltage, current, and temperature, and to execute necessary control strategies. In addition, some systems also integrate an energy management system (EMS) to schedule and optimize the distribution and use of energy.

[0004] However, existing photovoltaic systems are prone to efficiency decline and energy loss during the energy storage process, especially in the face of complex environmental factors such as partial shading and temperature fluctuations. The existence of these problems makes the storage and utilization efficiency of photovoltaic energy unsatisfactory, and the problem of energy loss is difficult to be effectively solved. Summary of the Invention

[0005] The first object of this application is to provide a method for managing photovoltaic energy storage losses, which can dynamically adjust the optimal output state of photovoltaic panels according to real-time occlusion conditions and reduce energy storage losses caused by shading.

[0006] In the first aspect, a method for managing photovoltaic energy storage losses provided by this application adopts the following technical solutions:

[0007] A method for managing photovoltaic energy storage losses includes:

[0008] In each monitoring period, use thermal imaging technology to obtain the temperature distribution map of the photovoltaic array, and the temperature distribution map includes the occluded area and the temperature on the surface of the photovoltaic array;

[0009] Calculate the current occlusion ratio according to the ratio of the area of the occluded area to the surface area of the photovoltaic array;

[0010] Use the MPPT controller to adjust the MPP of the photovoltaic module, obtain the optimal MPP within the current unit time, and work at the optimal MPP;

[0011] Train according to the detected occlusion ratio, temperature distribution map, and corresponding optimal MPP, and update the training model of the mapping relationship between the occlusion ratio, temperature, and optimal MPP;

[0012] During subsequent monitoring periods, based on the prediction ability of the training model, instantaneously adjust the battery charging strategy, preset the charging voltage and current, according to the predicted occlusion situation and temperature conditions.

[0013] By adopting the above technical solution, using the real-time occlusion ratio and temperature distribution as reference factors to adjust the MPP can not only enable the photovoltaic module to work in the most efficient range as much as possible under various occluded environmental conditions, improve the energy conversion efficiency, and reduce the energy loss caused by environmental changes, but also enable the photovoltaic system to adjust the charging strategy in advance by using the first training model, optimize the charging and discharging process of the battery, and further enhance the stability of the photovoltaic system and the reliability of energy storage.

[0014] In a preferred example of the present application, it can be further configured that: the step of calculating and obtaining the current occlusion ratio according to the ratio of the area of the occluded area to the surface area of the photovoltaic array includes:

[0015] According to the preset temperature division rule, divide the current occluded area into a fully occluded area, a semi-occluded area, and an unoccluded area;

[0016] Quantify the ratios of the areas of the fully occluded area, the unoccluded area, and the semi-occluded area to the surface area of the photovoltaic array respectively to obtain the full occlusion ratio, the unocclusion ratio, and the semi-occlusion ratio.

[0017] By adopting the above technical solution, the occluded area is subdivided into a fully occluded area, a semi-occluded area, and an unoccluded area, and the ratios of these areas are quantified, obtaining a more accurate evaluation of the occlusion state with data support, and dividing the occlusion area according to the temperature conditions to optimize the operating point of the photovoltaic system and reduce the energy storage loss caused by temperature changes.

[0018] In a preferred example of the present application, it can be further configured that: the step of training the first training model that updates the mapping relationship between the occlusion ratio, temperature, and optimal MPP according to the detected occlusion ratio, temperature distribution map, and corresponding optimal MPP includes:

[0019] In the training model, obtain the first training model based on the mapping relationship between the full occlusion ratio, the semi-occlusion ratio, and the unocclusion ratio and the optimal MPP.

[0020] By adopting the above technical solutions, it is possible to accurately map the optimal MPP under different occlusion conditions, realize the predictive adjustment of the future state of the photovoltaic system, and improve the accuracy of the MPP under the same occlusion condition. Furthermore, it reduces the unnecessary energy loss caused by environmental changes, especially changes in occlusion conditions. At the same time, it can instantaneously adjust the working state of the photovoltaic system, making the photovoltaic system approach the optimal working output state under any occlusion condition as much as possible, thereby improving the instant utilization efficiency of energy.

[0021] In a preferred example of the present application, it can be further configured that: before the step of obtaining the training model based on the mapping relationship between the full occlusion ratio, the half occlusion ratio, and the unoccluded ratio and the optimal MPP in the training model, it further includes:

[0022] Obtain the real-time temperature of the photovoltaic array within the current monitoring period;

[0023] In the training model, screen the second training model of the mapping relationship between the occlusion ratio and the optimal MPP at the real-time temperature;

[0024] According to the second training model, determine whether the occlusion ratio is within a preset stable ratio range;

[0025] If it is within the preset stable ratio range, control the MPPT controller to perturb at the optimal MPP corresponding to the occlusion ratio in the training model;

[0026] If it is not within the preset stable ratio range, control the MPPT controller to update and obtain a new optimal MPP into the first training model.

[0027] By adopting the above technical solutions, according to the real-time temperature of the photovoltaic array, when within the preset stable ratio range, that is, when the photovoltaic system is under little environmental fluctuation, it can stably operate at the known optimal points in the first training model and the second training model, reducing the loss caused by frequent MPP adjustments, improving the stability of the system operation, and when the occlusion environment changes greatly, automatically triggering the update mechanism of the MPP, realizing adaptive learning and self-adjustment, enabling the photovoltaic system to quickly adapt to complex and changeable occlusion conditions and maintain efficient operation.

[0028] In a preferred example of the present application, it can be further configured that: the step of, if it is not within the preset stable ratio range, controlling the MPPT controller to update and obtain a new optimal MPP includes:

[0029] Judge whether the sum of the full occlusion ratio and the half occlusion ratio is greater than the unoccluded ratio;

[0030] If it is greater than, then control the MPPT controller to track the new optimal MPP, and update the optimal MPP of the first training model at the current occlusion ratio;

[0031] If it is not greater than, then control the MPPT controller to perturb at the optimal MPP of the previous monitoring period.

[0032] By adopting the above technical solution, it is possible to accurately calculate the combinations of various occlusion ratios, so that when the sum of the full occlusion and the semi-occlusion ratio in the photovoltaic system is greater than the non-occluded ratio, that is, in the case of severe occlusion, it can be immediately adjusted to the MPP that better adapts to the current conditions, reducing the situation of output power decline caused by occlusion.

[0033] In a preferred example of the present application, it can be further configured as follows: in the subsequent monitoring period, based on the prediction ability of the training model, according to the predicted occlusion situation and temperature conditions, immediately adjust the battery charging strategy, and the steps of presetting the charging voltage and current include:

[0034] Judge whether the current battery power is lower than the preset capacity value. If it is lower than the preset capacity value, then increase the current MPP to the optimal MPP when the occlusion ratio is the smallest.

[0035] By adopting the above technical solution, predicting the occlusion situation and temperature, the photovoltaic system can be adjusted in advance to the optimal MPP when the occlusion is the smallest, ensuring maximum energy conversion under the best light conditions, thereby improving the charging efficiency.

[0036] In a preferred example of the present application, it can be further configured as follows: the steps of dividing the currently occluded area into a full occlusion area, a semi-occlusion area, and a non-occluded area according to the preset temperature division rule include:

[0037] Obtain the first temperature region and the second temperature region of the temperature distribution map, and the temperatures of the first temperature region and the second temperature region;

[0038] Obtain the region on the surface of the photovoltaic array within the temperature range of the first temperature region and the second temperature region, and mark it as the semi-occlusion area;

[0039] Mark the second temperature region as the full occlusion area and the first temperature region as the non-occluded area.

[0040] By adopting the above technical solution, considering the temperature difference as an index of occlusion, making use of the correlation between the working temperature and efficiency of the photovoltaic panel, dynamically adjusting the occlusion ratio of the photovoltaic array according to the real-time temperature data, enhancing the adaptability of the photovoltaic system to complex environmental changes, especially in cloudy or partially occluded weather conditions, it can capture available solar energy more effectively and reduce energy loss.

[0041] In a second aspect, the present application provides a management system for photovoltaic energy storage losses, adopting the following technical solutions:

[0042] A management system for photovoltaic energy storage losses, comprising:

[0043] A temperature map acquisition module: The temperature map acquisition module is used to acquire the temperature distribution map of the photovoltaic array by using thermal imaging technology in each monitoring period. The temperature distribution map includes the occluded area and the temperature on the surface of the photovoltaic array;

[0044] An occlusion ratio calculation module: The occlusion ratio calculation module is used to calculate and obtain the current occlusion ratio according to the ratio of the area of the occluded area to the surface area of the photovoltaic array;

[0045] An MPP adjustment module: The MPP adjustment module is used to adjust the MPP of the photovoltaic module by using an MPPT controller, obtain the optimal MPP in the current unit time, and operate at the optimal MPP;

[0046] An MPP training module: The MPP training module is used to train according to the detected occlusion ratio, temperature distribution map and the corresponding optimal MPP, and update the training model of the mapping relationship between the occlusion ratio, temperature and the optimal MPP;

[0047] A battery control module: The battery control module is used to instantaneously adjust the battery charging strategy, preset the charging voltage and current according to the predicted occlusion situation and temperature conditions based on the prediction ability of the training model in subsequent monitoring periods.

[0048] In a third aspect, the present application provides an electronic device, adopting the following technical solutions:

[0049] An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned management method for photovoltaic energy storage losses are implemented.

[0050] In a fourth aspect, the present application provides a computer storage medium, adopting the following technical solutions:

[0051] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned management method for photovoltaic energy storage losses are implemented.

[0052] In summary, the present application has the following beneficial technical effects:

[0053] 1. By integrating real-time occlusion monitoring and temperature distribution analysis, the photovoltaic system can optimize the MPP, enabling the photovoltaic modules to operate efficiently even under complex occlusion conditions, thereby enhancing the energy conversion efficiency and helping to curb energy losses caused by environmental fluctuations.

[0054] 2. By using the first training model of the mapping relationship between the occlusion ratio, temperature, and the optimal MPP, the system can pre-plan the charging strategy, perform refined management of the battery, achieve optimization only in the charge-discharge cycle, ensure the robustness of the battery operation, and enhance the reliability of energy storage and the overall stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of a method for managing photovoltaic energy storage losses in one embodiment of the present application.

[0056] Figure 2 is a flowchart of the sub-steps of step S2 in one embodiment of the present application.

[0057] Figure 3 is a flowchart of the sub-steps of step S4 in one embodiment of the present application.

[0058] Figure 4 is a flowchart of the steps added before step S4 in one embodiment of the present application.

[0059] Figure 5 is a flowchart of the sub-steps of step S45 in one embodiment of the present application.

[0060] Figure 6 is a flowchart of the sub-steps of step S5 in one embodiment of the present application.

[0061] Figure 7 is a flowchart of the sub-steps of step S20 in one embodiment of the present application.

[0062] Figure 8 is a schematic structural diagram of a management system for photovoltaic energy storage losses in one embodiment of the present application.

[0063] Figure 9 is a schematic block diagram of the principle of an electronic device in one embodiment of the present application.

[0064] Reference numerals: 1, temperature map acquisition module; 2, occlusion ratio calculation module; 3, MPP adjustment module; 4, MPP training module; 5, battery control module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The following is a further detailed description of the present application in conjunction with the attached Figures 1-9 drawings.

[0066] ReferenceFigure 1 , a method for managing photovoltaic energy storage losses, specifically including:

[0067] S1. In each monitoring cycle, use thermal imaging technology to obtain the temperature distribution map of the photovoltaic array. The temperature distribution map includes the occluded area and the temperature on the surface of the photovoltaic array.

[0068] Specifically, through thermal imaging technology, the temperature distribution of the photovoltaic array can be visually observed, so as to identify the occluded area and provide basic data for the analysis and adjustment of the occluded area and temperature of the photovoltaic array.

[0069] S2. Calculate and obtain the current occlusion ratio according to the ratio of the area of the occluded area to the surface area of the photovoltaic array.

[0070] S3. Use the MPPT controller to adjust the MPP of the photovoltaic module, obtain the optimal MPP within the current unit time, and operate at the optimal MPP.

[0071] Specifically, by adjusting the MPPT controller and selecting a suitable MPP for operation, it can adapt to the changes brought about by the occlusion of the photovoltaic array by clouds, tree shadows, etc., and then optimize the power generation efficiency of the photovoltaic module and reduce the loss of energy storage.

[0072] S4. Train according to the detected occlusion ratio, temperature distribution map and the corresponding optimal MPP, and update the training model of the mapping relationship between the occlusion ratio, temperature and the optimal MPP.

[0073] Specifically, the MPP can be quickly obtained according to the first training model, and then the charging strategy for the current monitoring cycle can be adjusted to ensure that the battery stores energy in the best state.

[0074] S5. In subsequent monitoring cycles, based on the prediction ability of the training model, immediately adjust the battery charging strategy, preset the charging voltage and current according to the predicted occlusion situation and temperature conditions.

[0075] Specifically, based on the prediction ability of the training model, realizing dynamic adjustment of the charging strategy can optimize the energy storage process, reduce unnecessary energy losses caused by occlusion, and improve the energy utilization efficiency of the overall system.

[0076] In this embodiment, the MPP is adjusted by considering the occlusion ratio and temperature distribution in real time, so that the photovoltaic module can maintain the highest efficiency working range even in a changing occlusion environment, which not only improves the efficiency of photovoltaic energy conversion, but also reduces the impact of environmental changes on energy output. At the same time, by using the first training model of the mapping relationship between the occlusion ratio, temperature and the optimal MPP, the photovoltaic system can predict and adjust the charging strategy in advance, which not only optimizes the charging and discharging process of the battery, but also enhances the stability of the photovoltaic system and the reliability of energy storage.

[0077] Reference Figure 2 , further, in one embodiment, step S2 is refined into the following sub-steps:

[0078] S20. According to the preset temperature division rule, divide the current occluded area into a fully occluded area, a semi-occluded area and an unoccluded area.

[0079] Specifically, by dividing different occluded areas, the affected parts in the photovoltaic array can be more accurately identified, providing more accurate and detailed data for the subsequent calculation of the occlusion ratio.

[0080] S21. Quantify the ratios of the areas of the fully occluded area, the unoccluded area and the semi-occluded area to the surface area of the photovoltaic array respectively, to obtain the full occlusion ratio, the unocclusion ratio and the semi-occlusion ratio.

[0081] In this embodiment, by subdividing the occluded area into a fully occluded area, a semi-occluded area and an unoccluded area, and accurately quantifying the ratios of these areas, a data-driven method is used to achieve an accurate assessment of the occlusion state. Dividing the occluded area according to the temperature conditions and then optimizing the optimal MPP of the photovoltaic system helps to reduce the energy storage loss caused by the fluctuations of temperature and occlusion environment.

[0082] In addition, reference Figure 3 , further, in one embodiment, step S4 is refined into the following sub-steps:

[0083] S40. In the training model, obtain the first training model based on the mapping relationship between the full occlusion ratio, the semi-occlusion ratio, the unocclusion ratio and the optimal MPP.

[0084] In this embodiment, by accurately mapping the optimal MPP under different occlusion conditions, we can achieve predictive adjustment of the future state of the photovoltaic system. The photovoltaic system can quickly obtain the MPP under the same occlusion environment in the first training model, which not only improves the accuracy of the MPP under the same occlusion conditions, but also significantly reduces the unnecessary energy loss caused by environmental changes, especially changes in occlusion conditions. At the same time, the photovoltaic system can immediately adjust its working state to adapt to most occlusion environments, enabling the system to maintain the best working output state as much as possible, thereby effectively improving the immediate utilization efficiency of energy.

[0085] In addition, referring to Figure 4 , further, in one embodiment, before step S4, there are also steps S41, S42, S43, S44, S45 added:

[0086] S41. Obtain the real-time temperature of the photovoltaic array within the current monitoring period.

[0087] Specifically, the average real-time temperature of the current photovoltaic array surface can be calculated through a temperature distribution map, or the real-time temperature of the photovoltaic array can be obtained through a temperature sensor.

[0088] S42. Screen the second training model for the mapping relationship between the occlusion ratio and the optimal MPP at the real-time temperature in the training model.

[0089] Specifically, the second training model is a machine learning model further subdivided at a fixed temperature. The real-time temperature is the criterion for selecting the second training model. First, select the second training model under the same temperature condition, and then select the first training model within the second training model. The subdivided structure can more accurately match the optimal MPP related to temperature and occlusion ratio.

[0090] S43. According to the second training model, determine whether the occlusion ratio is within the preset stable ratio range.

[0091] S44. If it is within the preset stable ratio range, control the MPPT controller to perturb at the optimal MPP corresponding to the occlusion ratio in the training model.

[0092] S45. If it is not within the preset stable ratio range, control the MPPT controller to update and obtain a new optimal MPP into the first training model.

[0093] In this embodiment, under the same temperature conditions, when the environment where the photovoltaic array is located fluctuates little, it can stably operate at the known optimal MPP predicted by the first training model and the second training model, thereby reducing the losses caused by frequent adjustments and enhancing the stability of the system. In the case of significant changes in the occlusion environment, the photovoltaic system will automatically activate the MPP update mechanism for adaptive learning and self-adjustment, so as to quickly respond to complex changes in occlusion conditions, update and obtain a new optimal MPP, which helps the photovoltaic system to operate continuously and efficiently.

[0094] In addition, referring to Figure 5 , further, in one embodiment, step S45 is refined into the following sub-steps:

[0095] S450. Determine whether the sum of the full occlusion ratio and the partial occlusion ratio is greater than the non-occlusion ratio.

[0096] S451. If it is greater, control the MPPT controller to track the new optimal MPP, and the first training model updates the optimal MPP at the current occlusion ratio.

[0097] Specifically, when the sum of the full occlusion ratio and the partial occlusion ratio is greater than the non-occlusion ratio, it is determined that the current MPPT algorithm is in the dynamic stage and needs to re-track the new optimal MPP.

[0098] S452. If it is not greater, control the MPPT controller to perturb at the optimal MPP in the previous monitoring period.

[0099] Specifically, when the sum of the full occlusion ratio and the partial occlusion ratio is not greater than the non-occlusion ratio, it is determined that the current MPPT algorithm is in the static stage. Perturbing at the optimal MPP in the previous monitoring period helps to fine-tune the photovoltaic system when the occlusion conditions do not change significantly, so as to ensure that it maintains the best working state, while avoiding unnecessary large adjustments and reducing system losses.

[0100] In this embodiment, by accurately calculating the combination of various occlusion ratios, when the photovoltaic system encounters a serious occlusion situation where the sum of the full occlusion ratio and the partial occlusion ratio is greater than the non-occlusion ratio, it can be immediately adjusted to the MPP that is more suitable for the current conditions, and try to avoid the situation of output power decline caused by occlusion.

[0101] In addition, referring to Figure 6 , further, in one embodiment, step S5 is refined into the following sub-steps:

[0102] S50. Determine whether the current battery power is lower than the preset capacity value. If it is lower than the preset capacity value, increase the current MPP to the optimal MPP when the occlusion ratio is the smallest.

[0103] In this embodiment, when the power is sufficient, the system makes fine adjustments, predicts the occlusion situation and temperature, and the photovoltaic system can be adjusted in advance to the optimal MPP with the least occlusion, ensuring maximum energy conversion under the best lighting conditions, thereby improving the charging efficiency and optimizing the storage and use of energy at the same time.

[0104] In addition, referring to Figure 7 , further, in one embodiment, step S20 is refined into the following sub-steps:

[0105] S200. Obtain the first temperature region and the second temperature region of the temperature distribution map, and the temperatures of the first temperature region and the second temperature region.

[0106] S201. Obtain the regions on the surface of the photovoltaic array within the temperature ranges of the first temperature region and the second temperature region, and mark them as semi-occluded regions.

[0107] S202. Mark the second temperature region as a fully occluded region and the first temperature region as an unoccluded region.

[0108] In this embodiment, the second temperature region is identified as a fully occluded region. The area of the fully occluded region irradiated by sunlight is small, so the surface temperature is low; the first temperature region is identified as an unoccluded region. The unoccluded region may be completely exposed to sunlight, so the surface temperature is high. Using the temperature difference as the basis for judging occlusion makes use of the connection between the working temperature of the photovoltaic array and the power generation efficiency, dynamically adjusts the occlusion ratio of the photovoltaic array, thereby enhancing the ability of the photovoltaic system to adapt to complex environmental changes. Especially in cloudy or partially occluded climate conditions, it can make more efficient use of the available solar energy resources and effectively reduce the loss of energy storage.

[0109] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0110] The embodiment of the present application also provides a management system for photovoltaic energy storage loss, and this management system for photovoltaic energy storage loss corresponds one-to-one with the management method for photovoltaic energy storage loss in the embodiment.

[0111] Referring to Figure 8 , this management system for photovoltaic energy storage loss includes: a temperature map acquisition module 1, an occlusion ratio calculation module 2, an MPP adjustment module 3, an MPP training module 4, and a battery control module 5. The detailed descriptions of each functional module are as follows:

[0112] Temperature Map Acquisition Module 1: The temperature map acquisition module 1 is used to obtain the temperature distribution map of the photovoltaic array by using thermal imaging technology in each monitoring cycle. The temperature distribution map includes the occluded area and the temperature of the surface of the photovoltaic array;

[0113] Occlusion Ratio Calculation Module 2: The occlusion ratio calculation module 2 is used to calculate and obtain the current occlusion ratio according to the ratio of the area of the occluded area to the area of the surface of the photovoltaic array;

[0114] MPP Adjustment Module 3: The MPP adjustment module 3 is used to adjust the MPP of the photovoltaic module by using the MPPT controller, obtain the optimal MPP in the current unit time, and operate at the optimal MPP;

[0115] MPP Training Module 4: The MPP training module 4 is used to train according to the detected occlusion ratio, temperature distribution map and the corresponding optimal MPP, and update the training model of the mapping relationship between the occlusion ratio, temperature and the optimal MPP;

[0116] Battery Control Module 5: The battery control module 5 is used to instantaneously adjust the battery charging strategy, preset the charging voltage and current based on the prediction ability of the training model according to the predicted occlusion situation and temperature conditions in the subsequent monitoring cycle.

[0117] Among them, the temperature map acquisition module 1 uses thermal imaging technology to capture the comprehensive temperature distribution of the photovoltaic array in each monitoring cycle, collect the temperature difference between the occluded area and the overall surface. The occlusion ratio calculation module 2 calculates the ratio of the area of the occluded area to the area of the surface of the photovoltaic array to determine the occlusion degree in real time. The MPP adjustment module 3 uses the MPPT controller to adjust the photovoltaic module to the optimal MPP according to the current environmental conditions. At the same time, the MPP training module 4 continuously updates the training model by learning the relationship between the occlusion ratio, temperature and the optimal MPP. And the battery control module 5 uses the training to predict future environmental fluctuations and dynamically adjusts the charging strategy to ensure efficient and stable charging and discharging of the battery.

[0118] For the specific limitations of the management system for photovoltaic energy storage loss, reference can be made to the limitations of the management method for photovoltaic energy storage loss in the context, which will not be elaborated here. Each module in the above management system for photovoltaic energy storage loss can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in the processor of the electronic device in hardware form or independent of it, or stored in the memory of the electronic device in software form, so that the processor can call and execute the operations corresponding to the above modules. In one embodiment, an electronic device is provided, and this electronic device is a user terminal. Refer to Figure 9, the electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store detection data tables. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a method for managing photovoltaic energy storage losses.

[0119] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0120] S1. In each monitoring period, use thermal imaging technology to obtain the temperature distribution map of the photovoltaic array. The temperature distribution map includes the occluded area and the temperature on the surface of the photovoltaic array.

[0121] S2. Calculate and obtain the current occlusion ratio according to the ratio of the area of the occluded area to the area of the surface of the photovoltaic array.

[0122] S3. Use the MPPT controller to adjust the MPP of the photovoltaic module, obtain the optimal MPP within the current unit time, and operate at the optimal MPP.

[0123] S4. Train according to the detected occlusion ratio, temperature distribution map, and the corresponding optimal MPP, and update the training model of the mapping relationship between the occlusion ratio, temperature, and optimal MPP.

[0124] S5. In subsequent monitoring periods, based on the prediction ability of the training model, instantaneously adjust the battery charging strategy, preset the charging voltage and current according to the predicted occlusion situation and temperature conditions.

[0125] In one of the embodiments, the sub-steps refined in step S2 are:

[0126] S20. According to the preset temperature division rule, divide the current occluded area into a fully occluded area, a semi-occluded area, and an unoccluded area.

[0127] S21. Quantify the ratios of the areas of the fully occluded area, the unoccluded area, and the semi-occluded area to the area of the surface of the photovoltaic array respectively, and obtain the full occlusion ratio, the unocclusion ratio, and the semi-occlusion ratio.

[0128] In one of the embodiments, the sub-steps refined in step S4 include:

[0129] S40. In the training model, obtain a first training model based on the mapping relationship between the full occlusion ratio, the partial occlusion ratio, the non-occlusion ratio, and the optimal MPP.

[0130] In one embodiment, the steps added before step S4 include:

[0131] S41. Obtain the real-time temperature of the photovoltaic array within the current monitoring period.

[0132] Specifically, the average real-time temperature of the current photovoltaic array surface can be calculated through the temperature distribution map, or the real-time temperature of the photovoltaic array can be obtained through a temperature sensor.

[0133] S42. In the training model, screen a second training model for the mapping relationship between the occlusion ratio and the optimal MPP at the real-time temperature.

[0134] S43. According to the second training model, determine whether the occlusion ratio is within a preset stable ratio range.

[0135] S44. If it is within the preset stable ratio range, control the MPPT controller to perturb at the optimal MPP corresponding to the occlusion ratio in the training model.

[0136] S45. If it is not within the preset stable ratio range, control the MPPT controller to update and obtain a new optimal MPP into the first training model.

[0137] In one embodiment, the sub-steps refined in step S45 include:

[0138] S450. Judge whether the sum of the full occlusion ratio and the partial occlusion ratio is greater than the non-occlusion ratio.

[0139] S451. If it is greater, control the MPPT controller to track the new optimal MPP, and the first training model updates the optimal MPP at the current occlusion ratio.

[0140] S452. If it is not greater, control the MPPT controller to perturb at the optimal MPP of the previous monitoring period.

[0141] In one embodiment, the sub-steps refined in step S5 include:

[0142] S50. Judge whether the current battery power is lower than a preset capacity value. If it is lower than the preset capacity value, increase the current MPP to the optimal MPP when the occlusion ratio is the smallest.

[0143] In one embodiment, the sub-steps refined in step S20 include:

[0144] S200. Obtain the first temperature region and the second temperature region of the temperature distribution map, as well as the temperatures of the first temperature region and the second temperature region.

[0145] S201. Obtain the regions on the surface of the photovoltaic array that are within the temperature range of the first temperature region and the second temperature region, and mark them as semi-shaded regions.

[0146] S202. Mark the second temperature region as a fully shaded region and the first temperature region as an unshaded region.

[0147] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0148] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for managing photovoltaic energy storage losses, characterized in that: include: S1: In each monitoring cycle, a temperature distribution map of the photovoltaic array is obtained by using thermal imaging technology, wherein the temperature distribution map includes the temperature of the shaded area and the surface of the photovoltaic array; S2: Calculate and obtain the current shielding ratio according to the ratio of the area of ​​the shielded area to the surface area of ​​the photovoltaic array; S2 includes: S20, dividing the current shaded area into a fully shaded area, a semi-shaded area and an unshaded area according to a preset temperature division rule; S21, quantifying the ratio of the area of ​​the fully shaded area, the unshaded area and the semi-shaded area to the surface area of ​​the photovoltaic array, respectively, to obtain the fully shaded ratio, the unshaded ratio and the semi-shaded ratio; S20 includes: obtaining a first temperature region and a second temperature region of the temperature distribution diagram, and the temperatures of the first temperature region and the second temperature region, wherein the temperature of the first temperature region is greater than the temperature of the second temperature region; obtaining an area of ​​the photovoltaic array surface that is within the temperature range of the first temperature region and the second temperature region, and marking it as a semi-shielded area; marking the second temperature region as a fully-shielded area, and marking the first temperature region as an unshielded area; S3: Using the MPPT controller to adjust the MPP of the photovoltaic module, obtain the optimal MPP in the current unit time, and operate at the optimal MPP; S4: performing training according to the detected shading ratio, temperature distribution map and the corresponding optimal MPP, and updating the training model of the mapping relationship between the shading ratio, temperature and the optimal MPP; S4 includes: in the training model, obtaining a first training model based on a mapping relationship between a full occlusion ratio, a semi-occlusion ratio, and an unoccluded ratio and an optimal MPP; S5: In a subsequent monitoring cycle, based on the prediction capability of the training model, the battery charging strategy is adjusted in real time according to the predicted shading conditions and temperature conditions, and the charging voltage and current are preset.

2. The method according to claim 1, characterized in that In the training model, before the step of obtaining a training model based on a mapping relationship between the full occlusion ratio, the half occlusion ratio, the unoccluded ratio and the optimal MPP, the method further includes: Get the real-time temperature of the photovoltaic array within the current monitoring period; A second training model for selecting a mapping relationship between the shading ratio and the optimal MPP at the real-time temperature in the training model; According to the second training model, determining whether the occlusion ratio is within a preset stable ratio range; If it is within the preset stable ratio range, the MPPT controller is controlled to perform disturbance at the optimal MPP corresponding to the shading ratio obtained in the training model; If it is not within the preset stable ratio range, the MPPT controller is controlled to update and obtain a new optimal MPP into the first training model.

3. The method according to claim 2, characterized in that If the ratio is not within the preset stable ratio range, the step of controlling the MPPT controller to update and obtain a new optimal MPP comprises: Determine whether the sum of the full occlusion ratio and the half occlusion ratio is greater than the unoccluded ratio; If it is greater than, the MPPT controller is controlled to track the new optimal MPP, and the first training model is updated at the optimal MPP of the current occlusion ratio; If it is not greater than, the MPPT controller is controlled to perform disturbance at the optimal MPP of the previous monitoring cycle.

4. The method according to claim 1, characterized in that: The step of adjusting the battery charging strategy in real time and presetting the charging voltage and current according to the predicted shading conditions and temperature conditions in the subsequent monitoring cycle based on the prediction capability of the training model includes: It is determined whether the current battery power is lower than a preset capacity value. If it is lower than the preset capacity value, the current MPP is increased to the optimal MPP when the shading ratio is the smallest.

5. A photovoltaic energy storage loss management system, characterized in that: include: A temperature map acquisition module (1): used to execute S1: in each monitoring cycle, using thermal imaging technology to acquire a temperature distribution map of the photovoltaic array, wherein the temperature distribution map includes the temperature of the shaded area and the surface of the photovoltaic array; A shielding ratio calculation module (2): used to execute S2: calculate and obtain the current shielding ratio according to the ratio of the area of ​​the shielded region to the surface area of ​​the photovoltaic array; S2 includes: S20, dividing the current shaded area into a fully shaded area, a semi-shaded area and an unshaded area according to a preset temperature division rule; S21, quantifying the ratio of the area of ​​the fully shaded area, the unshaded area and the semi-shaded area to the surface area of ​​the photovoltaic array, respectively, to obtain the fully shaded ratio, the unshaded ratio and the semi-shaded ratio; S20 includes: obtaining a first temperature region and a second temperature region of the temperature distribution diagram, and the temperatures of the first temperature region and the second temperature region, wherein the temperature of the first temperature region is greater than the temperature of the second temperature region; obtaining an area of ​​the photovoltaic array surface that is within the temperature range of the first temperature region and the second temperature region, and marking it as a semi-shielded area; marking the second temperature region as a fully-shielded area, and marking the first temperature region as an unshielded area; MPP adjustment module (3): used to execute S3: using the MPPT controller to adjust the MPP of the photovoltaic module, obtain the optimal MPP in the current unit time, and operate at the optimal MPP; MPP training module (4): used to execute S4: perform training according to the detected shading ratio, temperature distribution map and the corresponding optimal MPP, and update the training model of the mapping relationship between the shading ratio, temperature and the optimal MPP; S4 includes: in the training model, obtaining a first training model based on a mapping relationship between a full occlusion ratio, a semi-occlusion ratio, and an unoccluded ratio and an optimal MPP; The battery control module (5) is used to execute S5: in a subsequent monitoring cycle, based on the prediction capability of the training model, the battery charging strategy is adjusted in real time according to the predicted shading conditions and temperature conditions, and the charging voltage and current are preset.

6. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the method for managing photovoltaic energy storage loss according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method for managing photovoltaic energy storage loss according to any one of claims 1 to 4.

Images