Photovoltaic energy storage loss management method, system, equipment and medium

Abstract

The invention relates to a photovoltaic energy storage loss management method, system, device and medium, and relates to the field of photovoltaic technology, and the method comprises the steps: in each monitoring period, obtaining a temperature distribution diagram of a photovoltaic array, the temperature distribution diagram comprises the temperature of a shielded area and the surface of the photovoltaic array, and calculating and obtaining a current shielding proportion; the MPP of the photovoltaic module is adjusted through an MPPT controller, the optimal MPP in the current unit time is obtained, and the photovoltaic module works at the optimal MPP; updating a training model of the mapping relation among the shielding proportion, the temperature and the optimal MPP; and in the subsequent monitoring period, the battery charging strategy is adjusted in real time, and the charging voltage and current are preset. The optimal output state of the photovoltaic panel can be dynamically adjusted according to the real-time shielding condition, and energy storage loss caused by shielding is reduced.

Description

Technical Field

[0001] The present 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 has received widespread attention. Photovoltaic energy, as a clean and pollution-free energy form, occupies an important position in the field of new energy. Photovoltaic power generation system converts sunlight into electrical energy through solar photovoltaic arrays and has broad application prospects.

[0003] At present, 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 it when needed. In order to improve energy storage efficiency and extend battery life, a variety of battery management systems (BMS) have been developed to monitor the status of the battery, including parameters such as voltage, current, temperature, etc., and implement necessary control strategies. In addition, some systems also integrate energy management systems (EMS) to schedule and optimize the distribution and use of energy.

[0004] However, existing photovoltaic systems are prone to efficiency degradation and energy loss during energy storage, especially when faced with 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 energy loss problem is difficult to be effectively solved. Summary of the invention

[0005] The first purpose 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 shading conditions and reduce energy storage losses caused by shading.

[0006] In the first aspect, the present application provides a method for managing photovoltaic energy storage loss, which adopts the following technical solution: A method for managing photovoltaic energy storage losses, comprising: In each monitoring cycle, a temperature distribution map of the photovoltaic array is obtained by using thermal imaging technology, and the temperature distribution map includes the temperature of the shaded area and the surface of the photovoltaic array; Calculate and obtain the current shading ratio according to the ratio of the area of ​​the shaded area to the surface area of ​​the photovoltaic array; Using an 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; 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; In subsequent monitoring cycles, based on the predictive 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.

[0007] By adopting the above technical solution, the real-time shading ratio and temperature distribution are used as reference factors to adjust the MPP. This can not only enable the photovoltaic components to operate in the most efficient range as much as possible under various changes in environmental conditions due to shading, thereby improving energy conversion efficiency and reducing energy losses 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 battery charging and discharging process, and further improve the stability of the photovoltaic system and the reliability of energy storage.

[0008] In a preferred example, the present application may be further configured as follows: the step of calculating and obtaining the current shading ratio according to the ratio of the area of ​​the shaded region to the surface area of ​​the photovoltaic array includes: According to the preset temperature division rule, the current shaded area is divided into a fully shaded area, a semi-shaded area and an unshaded area; The ratios of the areas of the fully shielded region, the unshielded region and the semi-shielded region to the surface area of ​​the photovoltaic array are quantified respectively to obtain the fully shielded ratio, the unshielded ratio and the semi-shielded ratio.

[0009] By adopting the above technical solution, the shaded area is subdivided into fully shaded area, semi-shaded area and unshaded area, and the proportions of these areas are quantified. Data support is used to obtain a more accurate assessment of the shaded status, and the shaded area is divided according to temperature conditions to optimize the working point of the photovoltaic system and reduce energy storage losses caused by temperature changes.

[0010] In a preferred example, the present application may be further configured as follows: the step of training according to the detected occlusion ratio, temperature distribution map and the corresponding optimal MPP, and updating the first training model of the mapping relationship between the occlusion ratio, temperature and the optimal MPP includes: In the training model, a first training model based on the mapping relationship between the full occlusion ratio, the half occlusion ratio, the unoccluded ratio and the optimal MPP is obtained.

[0011] By adopting the above technical solution, the optimal MPP under different shading conditions can be accurately mapped, predictive adjustment of the future state of the photovoltaic system can be achieved, and the accuracy of the MPP under the same shading conditions can be improved, thereby reducing unnecessary energy losses caused by environmental changes, especially changes in shading conditions. At the same time, the working state of the photovoltaic system can be adjusted in real time, so that the photovoltaic system can be close to the optimal working output state under any shading conditions, thereby improving the immediate utilization efficiency of energy.

[0012] In a preferred example, the present application may be further configured as follows: 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 in the training model, the step 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.

[0013] By adopting the above technical solution, according to the real-time temperature of the photovoltaic array, when it is within the preset stable proportion range, that is, when the environmental fluctuations are not large, the photovoltaic system can stably operate at the known optimal points in the first training model and the second training model, thereby reducing the loss caused by frequent MPP adjustments, improving the stability of the system operation, and automatically triggering the MPP update mechanism when the shading environment changes greatly, realizing adaptive learning and self-adjustment, so that the photovoltaic system can quickly adapt to complex and changeable shading conditions and maintain efficient operation.

[0014] In a preferred example, the present application may be further configured as follows: 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 includes: 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.

[0015] By adopting the above technical solution, it is possible to accurately calculate the combination of multiple shading ratios, so that when the photovoltaic system encounters a serious shading situation where the sum of the full shading ratio and the semi-shading ratio is greater than the unshading ratio, it can immediately adjust to an MPP that is more suitable for the current conditions, thereby reducing the output power drop caused by shading.

[0016] In a preferred example, the present application can be further configured as follows: in the subsequent monitoring cycle, based on the prediction capability of the training model, according to the predicted shading conditions and temperature conditions, the battery charging strategy is adjusted in real time, and the steps of presetting the charging voltage and current include: 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.

[0017] By adopting the above technical solutions and predicting the shading conditions and temperature, the photovoltaic system can be adjusted in advance to the optimal MPP when the shading is minimal, ensuring maximum energy conversion under the best lighting conditions, thereby improving charging efficiency.

[0018] In a preferred example, the present application may be further configured as follows: the step of dividing the current shielded area into a fully shielded area, a semi-shielded area and an unshielded area according to a preset temperature division rule includes: Acquire a first temperature region and a second temperature region of the temperature distribution diagram, and temperatures of the first temperature region and the second temperature region; Acquire an area on the photovoltaic array surface that is within the temperature range of the first temperature area and the second temperature area, and mark it as a semi-shaded area; The second temperature region is marked as a fully blocked region, and the first temperature region is marked as an unblocked region.

[0019] By adopting the above technical solution, considering temperature difference as an indicator of shading, utilizing the relationship between the operating temperature and efficiency of photovoltaic panels, dynamically adjusting the shading ratio of the photovoltaic array according to real-time temperature data, the adaptability of the photovoltaic system to complex environmental changes is enhanced, especially in cloudy or partially obscured weather conditions, which can more effectively capture available solar energy and reduce energy loss.

[0020] In a second aspect, the present application provides a management system for photovoltaic energy storage loss, which adopts the following technical solution: A photovoltaic energy storage loss management system, comprising: Temperature map acquisition module: The temperature map acquisition module is used to obtain a temperature distribution map of the photovoltaic array using thermal imaging technology in each monitoring cycle, and the temperature distribution map includes the temperature of the shielded area and the surface of the photovoltaic array; Shielding ratio calculation module: The shielding ratio calculation module is used to 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; MPP adjustment module: The MPP adjustment module is used to adjust the MPP of the photovoltaic module using the MPPT controller, obtain the optimal MPP in the current unit time, and work at the optimal MPP; MPP training module: the MPP training module is used to 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; Battery control module: The battery control module is used to adjust the battery charging strategy and preset the charging voltage and current in real time according to the predicted shading and temperature conditions in the subsequent monitoring cycle based on the prediction ability of the training model.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for managing photovoltaic energy storage loss when executing the computer program.

[0022] In a fourth aspect, the present application provides a computer storage medium, the following technical solution: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned photovoltaic energy storage loss management method.

[0023] In summary, this application has the following beneficial technical effects: 1. By integrating real-time shading monitoring and temperature distribution analysis, the photovoltaic system can optimize the MPP and enable the photovoltaic components to operate efficiently under complex shading conditions, thereby enhancing the efficiency of energy conversion and helping to curb energy loss caused by environmental fluctuations.

[0024] 2. This application can utilize the first training model of the mapping relationship between shading ratio, temperature and optimal MPP. The system can pre-plan the charging strategy, perform refined management of the battery, and achieve optimization only in the charge and discharge cycle. It also ensures the robustness of the battery operation, enhances the reliability of energy storage and the overall stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for managing photovoltaic energy storage loss in one embodiment of the present application.

[0026] Figure 2 It is a sub-step flow chart of step S2 in one embodiment of the present application.

[0027] Figure 3 It is a sub-step flow chart of step S4 in one embodiment of the present application.

[0028] Figure 4 This is a flowchart of steps added before step S4 in one embodiment of the present application.

[0029] Figure 5 It is a sub-step flow chart of step S45 in one embodiment of the present application.

[0030] Figure 6 It is a sub-step flow chart of step S5 in one embodiment of the present application.

[0031] Figure 7 It is a sub-step flow chart of step S20 in one embodiment of the present application.

[0032] Figure 8 It is a structural schematic diagram of a photovoltaic energy storage loss management system according to one embodiment of the present application.

[0033] Fig. 9 It is a principle block diagram of an electronic device in one embodiment of the present application.

[0034] Figure 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

[0035] The following is combined with Figure 1-9 This application is described in further detail.

[0036] refer to Figure 1 , a method for managing photovoltaic energy storage losses, specifically comprising: S1. In each monitoring cycle, thermal imaging technology is used to obtain the temperature distribution map of the photovoltaic array. The temperature distribution map includes the temperature of the shaded area and the surface of the photovoltaic array.

[0037] Specifically, through thermal imaging technology, the temperature distribution of the photovoltaic array can be intuitively observed, thereby identifying the shaded area, and providing basic data for the photovoltaic array to analyze and adjust the shaded area and temperature.

[0038] 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.

[0039] S3. Use the MPPT controller to adjust the MPP of the photovoltaic module, obtain the optimal MPP in the current unit time, and work at the optimal MPP.

[0040] Specifically, by adjusting the MPPT controller and selecting a suitable MPP for operation, it is possible to adapt to changes caused by the photovoltaic array being blocked by clouds, tree shadows, etc., thereby optimizing the power generation efficiency of the photovoltaic components and reducing energy storage losses.

[0041] S4. Perform training 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.

[0042] Specifically, the MPP can be quickly obtained according to the first training model, and then the charging strategy of the current monitoring cycle is adjusted to ensure that the battery stores energy in the best state.

[0043] S5. In the 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 and temperature conditions, and the charging voltage and current are preset.

[0044] Specifically, by dynamically adjusting the charging strategy based on the prediction ability of the training model, the energy storage process can be optimized, unnecessary energy loss caused by shading can be reduced, and the energy utilization efficiency of the overall system can be improved.

[0045] In this embodiment, the MPP is adjusted by considering the shading ratio and temperature distribution in real time, so that the photovoltaic components can maintain the highest efficiency working range even in a variable shading 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, using the first training model of the mapping relationship between shading ratio, temperature and optimal MPP, the photovoltaic system can predict and adjust the charging strategy in advance, which not only optimizes the battery charging and discharging process, but also enhances the stability of the photovoltaic system and the reliability of energy storage.

[0046] refer to Figure 2 Further, in one embodiment, step S2 is refined into the following sub-steps: S20. Divide the current shielded area into a fully shielded area, a semi-shielded area and an unshielded area according to a preset temperature division rule.

[0047] Specifically, by dividing the photovoltaic array into different shading areas, the affected part can be more accurately identified, providing more accurate and detailed data for the subsequent calculation of the shading ratio.

[0048] S21. Quantify the ratios of the areas of the fully shielded region, the unshielded region and the semi-shielded region to the surface area of ​​the photovoltaic array respectively, and obtain the fully shielded ratio, the unshielded ratio and the semi-shielded ratio.

[0049] In this embodiment, by subdividing the shaded area into fully shaded area, semi-shaded area and unshaded area, and accurately quantifying the proportion of these areas, a data-driven method is used to achieve accurate evaluation of the shaded state. The shaded area is divided according to the temperature conditions, and then the optimal MPP of the photovoltaic system is optimized, which helps to reduce the energy storage loss caused by temperature and shade environment fluctuations.

[0050] In addition, reference Figure 3 Further, in one embodiment, step S4 is refined into the following sub-steps: S40. In the training model, a first training model is obtained based on a mapping relationship between the full occlusion ratio, the half occlusion ratio, the unoccluded ratio and the optimal MPP.

[0051] In this embodiment, by accurately mapping the optimal MPP under different shading 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 shading environment in the first training model, which not only improves the accuracy of the MPP under the same shading conditions, but also significantly reduces the unnecessary energy loss caused by environmental changes, especially changes in shading conditions. At the same time, the photovoltaic system can adjust its working state in real time to adapt to most shading environments, so that the system can be maintained in the best working output state as much as possible, thereby effectively improving the immediate utilization efficiency of energy.

[0052] In addition, reference Figure 4 Furthermore, in one embodiment, before step S4, steps S41, S42, S43, S44, and S45 are added: S41. Obtain the real-time temperature of the photovoltaic array in the current monitoring period.

[0053] Specifically, the average real-time temperature of the current photovoltaic array surface can be calculated through a temperature distribution diagram, and the real-time temperature of the photovoltaic array can also be obtained through a temperature sensor.

[0054] S42, selecting a second training model of a mapping relationship between the occlusion ratio at the real-time temperature and the optimal MPP in the training model.

[0055] Specifically, the second training model is a machine learning model that is subdivided at a fixed temperature. The real-time temperature is the standard for selecting the second training model. The second training model is first selected under the same temperature condition, and the first training model is selected within the second training model. The subdivided structure can more accurately match the optimal MPP related to temperature and occlusion ratio.

[0056] S43: Determine whether the occlusion ratio is within a preset stable ratio range according to the second training model.

[0057] S44: If it is within the preset stable ratio range, control the MPPT controller to perform disturbance at the optimal MPP corresponding to the occlusion ratio obtained in the training model.

[0058] 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.

[0059] In this embodiment, under the same temperature conditions, when the environmental fluctuations of the photovoltaic array are small, it can stably work at the known optimal MPP predicted by the first training model and the second training model, thereby reducing the loss caused by frequent adjustments and enhancing the stability of the system. In the case of significant changes in the shading environment, the photovoltaic system will automatically start the MPP update mechanism, perform adaptive learning and self-adjustment, so as to quickly respond to complex changes in shading conditions, update and obtain a new optimal MPP, which helps the photovoltaic system to continue to operate efficiently.

[0060] In addition, reference Figure 5 Further, in one embodiment, step S45 is refined into the following sub-steps: S450: Determine whether the sum of the full-blocking ratio and the half-blocking ratio is greater than the unblocking ratio.

[0061] S451: If it is greater than, the MPPT controller is controlled to track the new optimal MPP, and the first training model updates the optimal MPP at the current occlusion ratio.

[0062] Specifically, when the sum of the full occlusion ratio and the half occlusion ratio is greater than the unoccluded ratio, it is determined that the current MPPT algorithm is in a dynamic stage and needs to re-track a new optimal MPP.

[0063] S452: If it is not greater than, control the MPPT controller to perform disturbance at the optimal MPP of the previous monitoring cycle.

[0064] Specifically, when the sum of the full shading ratio and the half shading ratio is not greater than the unshading ratio, it is judged that the current MPPT algorithm is in a static stage. Perturbing the optimal MPP in the previous monitoring cycle helps to fine-tune the photovoltaic system when the shading conditions have not changed significantly, to ensure that it maintains the best working state, while avoiding unnecessary large adjustments and reducing system losses.

[0065] In this embodiment, by accurately calculating the combination of multiple shading ratios, the photovoltaic system can immediately adjust to an MPP that is more suitable for current conditions when a serious shading situation occurs, such as the sum of the full shading ratio and the semi-shading ratio is greater than the unshading ratio, so as to avoid the output power reduction caused by shading as much as possible.

[0066] In addition, reference Figure 6 Further, in one embodiment, step S5 is refined into the following sub-steps: S50: Determine 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 an optimal MPP with the minimum shading ratio.

[0067] In this embodiment, the system makes fine adjustments when the power is sufficient, predicts the shading conditions and temperature, and the photovoltaic system can be adjusted in advance to the optimal MPP when the shading is minimal, ensuring maximum energy conversion under optimal lighting conditions, thereby improving charging efficiency and optimizing energy storage and use.

[0068] In addition, reference Figure 7 Further, in one embodiment, step S20 is refined into the following sub-steps: S200: Acquire a first temperature region and a second temperature region of a temperature distribution diagram, and temperatures of the first temperature region and the second temperature region.

[0069] S201 , obtaining an area on the photovoltaic array surface that is within a temperature range between a first temperature area and a second temperature area, and marking it as a semi-shaded area.

[0070] S202: Mark the second temperature region as a fully blocked region, and mark the first temperature region as an unblocked region.

[0071] In this embodiment, the second temperature area is identified as a fully shielded area, and the area of ​​the fully shielded area exposed to sunlight is not large, so the surface temperature is relatively low; the first temperature area is identified as an unshielded area, and the unshielded area may be completely exposed to sunlight, so the surface temperature is relatively high. The temperature difference is used as the basis for judging shielding, and the connection between the operating temperature and power generation efficiency of the photovoltaic array is used to dynamically adjust the shielding ratio of the photovoltaic array, thereby improving the ability of the photovoltaic system to adapt to complex environmental changes, especially in cloudy or partially shielded climate conditions, it can more efficiently utilize the available solar energy resources and effectively reduce the loss of energy storage.

[0072] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0073] The embodiment of the present application also provides a management system for photovoltaic energy storage losses, and the management system for photovoltaic energy storage losses corresponds one-to-one to the management method for photovoltaic energy storage losses in the embodiment.

[0074] refer to Figure 8 The photovoltaic energy storage loss management system includes: a temperature map acquisition module 1, a shading ratio calculation module 2, an MPP adjustment module 3, an MPP training module 4, and a battery control module 5. The detailed description of each functional module is as follows: Temperature map acquisition module 1: The temperature map acquisition module 1 is used to obtain the temperature distribution map of the photovoltaic array using thermal imaging technology in each monitoring cycle. The temperature distribution map includes the temperature of the blocked area and the surface of the photovoltaic array; The shading ratio calculation module 2 is used to calculate and obtain the current shading ratio according to the ratio of the area of ​​the shaded area to the surface area of ​​the photovoltaic array; MPP adjustment module 3: The MPP adjustment module 3 is used to adjust the MPP of the photovoltaic module using the MPPT controller, obtain the optimal MPP in the current unit time, and work at the optimal MPP; MPP training module 4: MPP training module 4 is used to perform training 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; Battery control module 5: Battery control module 5 is used to adjust the battery charging strategy and preset the charging voltage and current in real time according to the predicted shading and temperature conditions in the subsequent monitoring cycle based on the prediction ability of the training model.

[0075] Among them, the temperature map acquisition module 1 uses thermal imaging technology to capture the overall temperature distribution of the photovoltaic array in each monitoring cycle, collect the temperature difference between the shaded area and the overall surface, and the shade ratio calculation module 2 calculates the ratio of the shaded area to the surface area of ​​the photovoltaic array to determine the degree of shade in real time. The MPP adjustment module 3 uses the MPPT controller to adjust the photovoltaic components 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 shade ratio, temperature and the optimal MPP. The battery control module 5 uses training to predict future environmental fluctuations and dynamically adjusts the charging strategy to ensure efficient and stable charging and discharging of the battery.

[0076] For specific definitions of the management system for photovoltaic energy storage losses, please refer to the definitions of the management method for photovoltaic energy storage losses in the context, which will not be repeated here. Each module in the above-mentioned management system for photovoltaic energy storage losses can be implemented in whole or in part through software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in hardware form, or can be 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, which is a user terminal. Reference Fig. 9 , the electronic device includes a processor, a memory, a network interface and a database connected through 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 a detection data table. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for managing photovoltaic energy storage losses is implemented.

[0077] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S1. In each monitoring cycle, thermal imaging technology is used to obtain the temperature distribution map of the photovoltaic array. The temperature distribution map includes the temperature of the shaded area and the surface of the photovoltaic array.

[0078] 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.

[0079] S3. Use the MPPT controller to adjust the MPP of the photovoltaic module, obtain the optimal MPP in the current unit time, and work at the optimal MPP.

[0080] S4. Perform training 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.

[0081] S5. In the 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 and temperature conditions, and the charging voltage and current are preset.

[0082] In one embodiment, the sub-steps refined in step S2 are: S20. Divide the current shielded area into a fully shielded area, a semi-shielded area and an unshielded area according to a preset temperature division rule.

[0083] S21. Quantify the ratios of the areas of the fully shielded region, the unshielded region and the semi-shielded region to the surface area of ​​the photovoltaic array respectively, and obtain the fully shielded ratio, the unshielded ratio and the semi-shielded ratio.

[0084] In one embodiment, the sub-steps of step S4 include: S40. In the training model, a first training model is obtained based on a mapping relationship between the full occlusion ratio, the half occlusion ratio, the unoccluded ratio and the optimal MPP.

[0085] In one embodiment, the steps added before step S4 include: S41. Obtain the real-time temperature of the photovoltaic array in the current monitoring period.

[0086] Specifically, the average real-time temperature of the current photovoltaic array surface can be calculated through a temperature distribution diagram, and the real-time temperature of the photovoltaic array can also be obtained through a temperature sensor.

[0087] S42, selecting a second training model of a mapping relationship between the occlusion ratio at the real-time temperature and the optimal MPP in the training model.

[0088] S43: Determine whether the occlusion ratio is within a preset stable ratio range according to the second training model.

[0089] S44: If it is within the preset stable ratio range, control the MPPT controller to perform disturbance at the optimal MPP corresponding to the occlusion ratio obtained in the training model.

[0090] 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.

[0091] In one embodiment, the sub-steps of step S45 include: S450: Determine whether the sum of the full-blocking ratio and the half-blocking ratio is greater than the unblocking ratio.

[0092] S451: If it is greater than, the MPPT controller is controlled to track the new optimal MPP, and the first training model updates the optimal MPP at the current occlusion ratio.

[0093] S452: If it is not greater than, control the MPPT controller to perform disturbance at the optimal MPP of the previous monitoring cycle.

[0094] In one embodiment, the sub-steps of step S5 include: S50: Determine 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 an optimal MPP with the minimum shading ratio.

[0095] In one embodiment, the sub-steps of step S20 include: S200: Acquire a first temperature region and a second temperature region of a temperature distribution diagram, and temperatures of the first temperature region and the second temperature region.

[0096] S201 , obtaining an area on the photovoltaic array surface that is within a temperature range between a first temperature area and a second temperature area, and marking it as a semi-shaded area.

[0097] S202: Mark the second temperature region as a fully blocked region, and mark the first temperature region as an unblocked region.

[0098] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0099] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by 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: In each monitoring cycle, a temperature distribution map of the photovoltaic array is obtained by using thermal imaging technology, and the temperature distribution map includes the temperature of the shaded area and the surface of the photovoltaic array; Calculate and obtain the current shading ratio according to the ratio of the area of ​​the shaded area to the surface area of ​​the photovoltaic array; Using an 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; 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; In subsequent monitoring cycles, based on the predictive 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 The step of calculating and obtaining the current shading ratio according to the ratio of the area of ​​the shaded region to the surface area of ​​the photovoltaic array comprises: According to the preset temperature division rule, the current shaded area is divided into a fully shaded area, a semi-shaded area and an unshaded area; The ratios of the areas of the fully shielded region, the unshielded region and the semi-shielded region to the surface area of ​​the photovoltaic array are quantified respectively to obtain the fully shielded ratio, the unshielded ratio and the semi-shielded ratio.

3. The method according to claim 2, characterized in that The step of training according to the detected shading ratio, temperature distribution map and the corresponding optimal MPP to update the first training model of the mapping relationship between the shading ratio, temperature and the optimal MPP includes: In the training model, a first training model based on the mapping relationship between the full occlusion ratio, the half occlusion ratio, the unoccluded ratio and the optimal MPP is obtained.

4. The method according to claim 3, 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.

5. The method according to claim 4, 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.

6. 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.

7. The method according to claim 2, characterized in that The step of 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 comprises: Acquire a first temperature region and a second temperature region of the temperature distribution diagram, and temperatures of the first temperature region and the second temperature region; Acquire an area on the photovoltaic array surface that is within the temperature range of the first temperature area and the second temperature area, and mark it as a semi-shaded area; The second temperature region is marked as a fully blocked region, and the first temperature region is marked as an unblocked region.

8. A photovoltaic energy storage loss management system, characterized in that: include: Temperature map acquisition module (1): the temperature map acquisition module (1) is used to acquire a temperature distribution map of the photovoltaic array using thermal imaging technology in each monitoring cycle, wherein the temperature distribution map includes the temperature of the shielded area and the surface of the photovoltaic array; A shielding ratio calculation module (2): the shielding ratio calculation module (2) is used to 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; MPP adjustment module (3): the MPP adjustment module (3) is used to adjust the MPP of the photovoltaic module using the MPPT controller, obtain the optimal MPP in the current unit time, and operate at the optimal MPP; MPP training module (4): the MPP training module (4) is used to 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; Battery control module (5): The battery control module (5) is used to adjust the battery charging strategy in real time and preset the charging voltage and current according to the predicted shading conditions and temperature conditions in the subsequent monitoring cycle based on the prediction ability of the training model.

9. 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 a method for managing photovoltaic energy storage loss according to any one of claims 1 to 7.

10. 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 7.

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