Rural digital twin photovoltaic energy management method and system based on Internet of Things

Abstract

The invention provides a rural digital twin photovoltaic energy management method and system based on the Internet of Things, and the method comprises the steps: carrying out the grid division of a rural photovoltaic energy region, and obtaining all grid points; constructing a minimum working area of each grid point based on the minimum working size and the corner of the photovoltaic panel, and screening out effective areas to form a set; establishing a corresponding relation between the minimum working area and the grid points; constructing an illumination area and a probability matrix; and taking each grid point as an energy management point, and finding out an optimal energy management point by updating an energy output probability and calculating a connection weight sum. According to the invention, the efficiency and accuracy of photovoltaic energy management can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and more specifically, to a rural digital twin photovoltaic energy management method and system based on the Internet of Things. Background Art

[0002] With the increasing popularity of renewable energy, photovoltaic energy has been widely used in rural and remote areas as a clean and renewable form of energy. However, photovoltaic energy management in rural areas faces a series of challenges, including uncertainty in energy output, complexity in energy management, and low energy efficiency. Existing photovoltaic energy management systems often lack refined control over the working area of ​​photovoltaic panels, resulting in inaccurate energy output predictions and difficulty in adapting to the changing environmental conditions in rural areas. In addition, traditional energy management systems often ignore the interactions between photovoltaic panels and between photovoltaic panels and the environment, which limits the overall performance of the system and the maximization of energy output.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: First, the existing photovoltaic energy management system fails to fully utilize the Internet of Things technology to achieve refined management of photovoltaic energy areas, resulting in inaccurate prediction and optimization of energy output. Secondly, there is a lack of an effective mechanism to dynamically adjust the working area of ​​​​the photovoltaic panel to adapt to changing environmental conditions and energy needs. Finally, the existing system often ignores the mutual influence between photovoltaic panels and the interaction between photovoltaic panels and the surrounding environment, which limits the maximization of energy output and the efficiency of energy management. Summary of the invention

[0004] The present invention provides a rural digital twin photovoltaic energy management method based on the Internet of Things, comprising:

[0005] S1: Grid the rural photovoltaic energy area model to obtain all grid points inside the rural photovoltaic energy area;

[0006] S2: Based on the minimum working size and minimum rotation angle of the photovoltaic panel, the minimum working area of ​​each grid point is constructed. Based on the positional relationship between each area in the minimum working area of ​​each grid point and the rural photovoltaic energy area, the effective minimum working area of ​​each grid point is screened out to form a minimum working area set;

[0007] S3: establishing a first correspondence between each minimum working area in the minimum working area set and the grid points it contains, and establishing a second correspondence between each grid point and the minimum working area in the minimum working area set that contains the grid point;

[0008] S4: construct the illumination area and illumination area probability matrix of the photovoltaic energy region;

[0009] S5: Take each grid point as a candidate energy management point, traverse and execute the following steps to obtain the sum of the connection weights of each grid point as the candidate energy management point, and take the grid point corresponding to the smallest sum of the connection weights as the energy management point: update the energy output probability of each grid point based on the illumination area, the illumination area probability matrix and the energy area; calculate the sum of the connection weights of the current candidate energy management point based on the updated energy output probability of each grid point and the first corresponding relationship and the second corresponding relationship.

[0010] Furthermore, the step S3 of establishing a first correspondence between each minimum working area in the minimum working area set and the grid points it contains includes:

[0011] Obtaining the grid points covered by each minimum working area in the minimum working area set, taking the grid points covered by the minimum working area as a point in the set of covered grid points of each minimum working area, and establishing a first corresponding relationship;

[0012] The second corresponding relationship between each grid point and the minimum working area in the minimum working area set including the grid point is established in step S3, including: for each grid point inside the rural photovoltaic energy area, all minimum working areas in the minimum working area set covering the grid point are obtained, thereby forming a set of covering minimum working areas corresponding to each grid point, and establishing a second corresponding relationship.

[0013] Furthermore, the step S4 specifically includes:

[0014] S41: constructing an illumination area, wherein the illumination area is composed of a plurality of sub-areas with an area of ​​A, wherein A is the square of the grid point spacing during grid division in step S1;

[0015] S42: Calculate the illumination probability of each sub-region in the illumination region based on the coordinate range of each sub-region and the one-dimensional normal distribution probability density function, and the illumination probabilities of all sub-regions constitute an illumination region probability matrix.

[0016] Furthermore, the updating of the energy output probability of each grid point based on the illumination area, the illumination area probability matrix and the energy area in step S5 further includes:

[0017] Align the center point of the illumination area with the current candidate energy management point, store the grid points in the rural photovoltaic energy area covered by the illumination area as a first grid point set, obtain the probability of the corresponding sub-area in the illumination area covering each grid point in the first grid point set from the illumination area probability matrix as the first probability of the corresponding grid point, and the first probabilities of all grid points in the first grid point set constitute a first probability value set;

[0018] The center point of the energy area is moved to each grid point of the first grid point set in turn, and the energy output probability of the grid points covered by the energy area is updated according to the first probability value set. After the traversal is completed, the updated energy output probability of all grid points in the rural photovoltaic energy area is obtained.

[0019] Furthermore, the energy output probability of the grid points covered by the energy area is updated using formula (1):

[0020] P new =P old ×P sun (1);

[0021] Among them, P new is the updated energy output probability of the grid points covered by the energy area, P old is the current energy output probability of the grid point, P sun It is the first probability corresponding to the grid point in the first probability value set that coincides with the center point of the current energy area.

[0022] Furthermore, the sum of the connection weights of the current candidate energy management points calculated based on the updated grid point energy output probability and the first corresponding relationship and the second corresponding relationship in step S5 includes:

[0023] Based on the updated grid point energy output probability and the first correspondence, the non-degradation probability of each area in the minimum working area set is calculated. Based on the non-degradation probability of each area and the second correspondence, the connection weight of each grid point is calculated. The connection weights of all grid points in the rural photovoltaic energy area are accumulated to obtain the total connection weights.

[0024] Further, the calculating of the non-degraded probability of each area in the minimum working area set based on the updated grid point energy output probability and the first corresponding relationship includes:

[0025]

[0026] Where P intact is the probability of non-degradation of the jth minimum working area in the minimum working area set, P degraded,i is the degradation probability of each grid point in the set of coverage grid points corresponding to the jth minimum working area.

[0027] Furthermore, the connection weight of each grid point is calculated based on the non-degraded probability of each area and the second corresponding relationship as follows:

[0028]

[0029] Where Wi is the connection weight of the i-th grid point, P intact,j is the non-degraded probability of each minimum working area in the set of covering minimum working areas of the i-th grid point.

[0030] Furthermore, the minimum working area of ​​each grid point is constructed based on the minimum working size and minimum rotation angle of the photovoltaic panel in step S2, including:

[0031] Taking any grid point as the reference point, select a predetermined direction, determine the boundary length of the area with the minimum working size, and rotate with the minimum rotation angle as the step length until the full range of 360 degrees is covered, forming a series of fan-shaped areas corresponding to the reference point, and extending the boundaries of these fan-shaped areas to both sides perpendicular to the selected direction by half of the minimum working size to form a series of closed fan-shaped areas, thereby obtaining the minimum working area set of the current grid point.

[0032] Furthermore, in step S2, based on the positional relationship between the minimum working area set of each grid point and the rural photovoltaic energy area, the effective minimum working area of ​​each grid point is screened out to form the minimum working area set, which is specifically:

[0033] Check whether all the minimum working areas corresponding to each grid point overlap with or are completely contained in the boundary of the rural photovoltaic energy area. If a minimum working area intersects with the rural photovoltaic energy area or is completely located inside it, the minimum working area is deemed to be a valid area, and all valid minimum working areas are aggregated to form a minimum working area set.

[0034] According to the above-mentioned embodiment of the present invention, at least the following beneficial effects are achieved: the rural digital twin photovoltaic energy management method based on the Internet of Things can accurately manage and optimize the energy output of the photovoltaic energy area through grid division and the construction of the minimum working area. By establishing the correspondence between the grid point and the minimum working area, and constructing the illumination area and the illumination area probability matrix, the method can effectively evaluate and predict the energy output probability of each grid point. In addition, by selecting the grid point with the smallest sum of connection weights as the energy management point, the energy management decision can be optimized and the energy utilization efficiency can be improved.

[0035] Furthermore, the method can more accurately reflect the actual energy output by dynamically updating the energy output probability of the grid points and calculating the sum of the connection weights of the candidate energy management points in combination with the first and second corresponding relationships. This method can not only improve the accuracy of energy management, but also reduce management costs and enhance the adaptability and flexibility of the system, especially in the changing environmental conditions such as rural areas. Through this method, efficient management and optimization of rural photovoltaic energy areas can be achieved, thereby improving overall energy output and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0037] Figure 1 A schematic flow chart of a rural digital twin photovoltaic energy management method based on the Internet of Things provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0038] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0039] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0040] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0041] Reference below Figure 1 , Figure 1 The flowchart of the rural digital twin photovoltaic energy management method based on the Internet of Things provided by one embodiment of the present invention is as follows. Figure 1 As shown, a rural digital twin photovoltaic energy management method S100 based on the Internet of Things includes:

[0042] S1: Grid the rural photovoltaic energy area model to obtain all grid points inside the rural photovoltaic energy area;

[0043] S2: Based on the minimum working size and minimum rotation angle of the photovoltaic panel, the minimum working area of ​​each grid point is constructed. Based on the positional relationship between each area in the minimum working area of ​​each grid point and the rural photovoltaic energy area, the effective minimum working area of ​​each grid point is screened out to form a minimum working area set;

[0044] S3: establishing a first correspondence between each minimum working area in the minimum working area set and the grid points it contains, and establishing a second correspondence between each grid point and the minimum working area in the minimum working area set that contains the grid point;

[0045] S4: construct the illumination area and illumination area probability matrix of the photovoltaic energy region;

[0046] S5: Take each grid point as a candidate energy management point, traverse and execute the following steps to obtain the sum of the connection weights of each grid point as the candidate energy management point, and take the grid point corresponding to the smallest sum of the connection weights as the energy management point: update the energy output probability of each grid point based on the illumination area, the illumination area probability matrix and the energy area; calculate the sum of the connection weights of the current candidate energy management point based on the updated energy output probability of each grid point and the first corresponding relationship and the second corresponding relationship.

[0047] It should be noted that the method first involves gridding the rural photovoltaic energy area, a step to accurately manage and optimize the energy output of the photovoltaic energy area. Here, gridding refers to dividing the photovoltaic energy area into multiple small areas, each of which represents a grid point, so that each grid point can be individually managed and analyzed. Rural photovoltaic energy areas refer to rural areas where photovoltaic panels are installed. These areas usually cover a wide geographical range and may be affected by different environmental factors.

[0048] Specifically, grid division can be achieved through specific software or algorithms that can determine the size and shape of the grid based on the geographic information of the photovoltaic energy area and the layout of the photovoltaic panels. For example, the size of the grid can be set based on the minimum working size of the photovoltaic panel to ensure that each grid point can cover at least one photovoltaic panel. In addition, the spacing of the grid points can be adjusted according to the complexity of the terrain and the distribution density of the photovoltaic panels to achieve the best energy management effect.

[0049] Preferably, the grid division algorithm can also take into account the slope of the terrain, vegetation coverage and other factors that may affect the efficiency of photovoltaic panels. For example, if the vegetation coverage in an area is dense, it may affect the light reception of photovoltaic panels. Therefore, in the grid division of this area, the density of grid points can be appropriately increased to more finely manage and optimize energy output.

[0050] Furthermore, the grid division can also be dynamically adjusted according to seasonal changes and weather patterns to adapt to changes in energy output in photovoltaic energy areas during different time periods.

[0051] In some embodiments, the step S3 of establishing a first correspondence between each minimum working area in the minimum working area set and the grid points it contains includes:

[0052] Obtaining the grid points covered by each minimum working area in the minimum working area set, taking the grid points covered by the minimum working area as a point in the set of covered grid points of each minimum working area, and establishing a first corresponding relationship;

[0053] The second corresponding relationship between each grid point and the minimum working area in the minimum working area set including the grid point is established in step S3, including: for each grid point inside the rural photovoltaic energy area, all minimum working areas in the minimum working area set covering the grid point are obtained, thereby forming a set of covering minimum working areas corresponding to each grid point, and establishing a second corresponding relationship.

[0054] It should be noted that this implementation describes in detail how to establish a first correspondence between the minimum working area and the grid points it covers. Here, the first correspondence refers to a mapping that links each minimum working area to all the grid points it can cover. The purpose of this is to quickly identify which grid points are affected by a specific photovoltaic panel working area during the subsequent energy management process.

[0055] Specifically, the process of establishing the first correspondence can be implemented by the following steps: First, for each minimum working area, determine its boundaries and check which grid points are covered by these boundaries. These grid points constitute the set of covered grid points of the minimum working area. Then, each minimum working area is associated with its set of covered grid points to form a correspondence. This process can be assisted by geographic information system (GIS) software, which can automatically calculate the covered grid points based on the position and direction of the photovoltaic panel and the boundaries of the minimum working area.

[0056] Preferably, in order to improve the accuracy of the corresponding relationship, some parameters can be set in the GIS software, such as the boundary length of the minimum working area and the corner step length of the photovoltaic panel. These parameters can be adjusted according to the actual size and installation angle of the photovoltaic panel.

[0057] Furthermore, some intelligent algorithms, such as machine learning algorithms, can be introduced to optimize the process of establishing the corresponding relationship. These algorithms can automatically adjust the boundaries of the minimum working area based on historical data and environmental changes to more accurately predict and adapt to changes in photovoltaic energy areas.

[0058] In some embodiments, step S4 specifically includes:

[0059] S41: constructing an illumination area, wherein the illumination area is composed of a plurality of sub-areas with an area of ​​A, wherein A is the square of the grid point spacing during grid division in step S1;

[0060] S42: Calculate the illumination probability of each sub-region in the illumination region based on the coordinate range of each sub-region and the one-dimensional normal distribution probability density function, and the illumination probabilities of all sub-regions constitute an illumination region probability matrix.

[0061] It should be noted that this implementation involves specific steps of constructing an illumination area and an illumination area probability matrix. Here, the illumination area refers to the part of the photovoltaic energy area that can receive sunlight, and the illumination area probability matrix is ​​a mathematical model used to represent the probability of each sub-area receiving sunlight. The construction of this matrix is ​​crucial for predicting the energy output of photovoltaic panels.

[0062] Specifically, the construction of the illumination area can be achieved by analyzing the geographical and meteorological data of the photovoltaic energy area. The area of ​​each sub-area can be set as the square of the grid point spacing, which means that if the grid point spacing is 1 meter, then the area of ​​each sub-area is 1 square meter.

[0063] More specifically, the calculation of the illumination probability can be performed using a one-dimensional normal distribution probability density function, which can simulate the distribution of illumination in a sub-region. The parameter settings include the size and shape of the sub-region and the orientation and tilt angle of the photovoltaic panel.

[0064] Preferably, the construction of the illumination area probability matrix can be further refined. For example, the influence of seasonal changes on illumination probability can be taken into account, and the parameters of the probability density function can be adjusted through historical meteorological data to more accurately reflect the illumination conditions in different seasons.

[0065] Furthermore, satellite images and drone aerial data can be introduced to improve the accuracy of the illumination area division. When calculating the illumination probability, factors such as cloud cover and terrain occlusion can also be taken into account to achieve a more refined energy output forecast. These data can be integrated through automated data processing processes to ensure that the illumination area probability matrix can be updated in real time to adapt to changing environmental conditions.

[0066] In some embodiments, the updating of the energy output probability of each grid point based on the illumination area, the illumination area probability matrix and the energy area in step S5 further includes:

[0067] Align the center point of the illumination area with the current candidate energy management point, store the grid points in the rural photovoltaic energy area covered by the illumination area as a first grid point set, obtain the probability of the corresponding sub-area in the illumination area covering each grid point in the first grid point set from the illumination area probability matrix as the first probability of the corresponding grid point, and the first probabilities of all grid points in the first grid point set constitute a first probability value set;

[0068] The center point of the energy area is moved to each grid point of the first grid point set in turn, and the energy output probability of the grid points covered by the energy area is updated according to the first probability value set. After the traversal is completed, the updated energy output probability of all grid points in the rural photovoltaic energy area is obtained.

[0069] It should be noted that this implementation describes in detail how to update the energy output probability of each grid point based on the illumination area, the illumination area probability matrix, and the energy area. The update here refers to adjusting the energy output probability of each grid point based on real-time or predicted environmental data to reflect actual or expected energy output changes. This process is to more accurately predict and manage photovoltaic energy output.

[0070] Specifically, the update process can be implemented through the following steps: First, align the center point of the illumination area with the current candidate energy management point, so as to ensure that the positional relationship between the grid points covered by the illumination area and the candidate energy management point is accurate. Next, extract the probability of the corresponding sub-area covering each grid point in the first grid point set from the illumination area probability matrix. These probabilities are called first probabilities, which represent the possibility of each grid point receiving illumination. Then, move the center point of the energy area to each grid point of the first grid point set in turn, and update the energy output probability of the grid points covered by the energy area according to the first probability value set. This process involves adjusting the energy output probability of each grid point to reflect the changes in the illumination it receives.

[0071] Preferably, the update process can be further refined. For example, more complex models can be introduced to consider the impact of different weather conditions on the probability of illumination, such as cloud density, atmospheric transparency, etc. In addition, machine learning algorithms can be used to predict changes in the illumination area, so as to adjust the energy output probability in advance. The aging and maintenance status of photovoltaic panels can also be considered, as these factors also affect energy output.

[0072] Furthermore, in actual operation, a threshold can be set. When the predicted energy output probability is lower than this threshold, the system will automatically trigger a maintenance reminder to ensure the efficient operation of the photovoltaic system.

[0073] In some embodiments, the energy production probability of the grid points covered by the energy area is updated using formula (1):

[0074] P new =P old ×P sun (1);

[0075] Among them, P new is the updated energy output probability of the grid points covered by the energy area, P oldis the current energy output probability of the grid point, P sun It is the first probability corresponding to the grid point in the first probability value set that coincides with the center point of the current energy area.

[0076] It should be noted that the energy output probability refers to the possibility of generating energy at each grid point, while the first probability refers to the illumination probability corresponding to the grid point that coincides with the center point of the current energy area. This update process is to more accurately predict the energy output of each grid point.

[0077] Specifically, updating the energy output probability of a grid point can be achieved by the following formula: new =P current ×P first In this formula, P new Represents the updated energy output probability, P current is the current energy output probability of the grid point, and P first It is the first probability corresponding to the grid point in the first probability value set that coincides with the center point of the current energy area. The parameter setting includes the measurement of light intensity, the efficiency of photovoltaic panels and other environmental factors.

[0078] Preferably, the updating process can be further refined. For example, real-time meteorological data can be introduced to dynamically adjust P first In addition, historical energy production data can be used to calibrate P current The initial value of PV panel can be used to ensure the accuracy of the prediction. The orientation and tilt angle of the PV panel can also be considered, as these factors will also affect its efficiency in receiving sunlight.

[0079] Furthermore, in actual operation, a feedback mechanism can be set up to automatically adjust and update the parameters in the formula according to the difference between the actual energy output and the predicted value to achieve more accurate energy management.

[0080] In some embodiments, the calculation of the connection weight sum of the current candidate energy management point based on the updated grid point energy output probability and the first corresponding relationship and the second corresponding relationship in step S5 includes:

[0081] Based on the updated grid point energy output probability and the first correspondence, the non-degradation probability of each area in the minimum working area set is calculated. Based on the non-degradation probability of each area and the second correspondence, the connection weight of each grid point is calculated. The connection weights of all grid points in the rural photovoltaic energy area are accumulated to obtain the total connection weights.

[0082] It should be noted that the total connection weight refers to a metric used to evaluate the energy output efficiency and network connection strength of each candidate energy management point. This process is to determine the best energy management point to optimize the performance of the entire photovoltaic energy network.

[0083] Specifically, the process of calculating the sum of connection weights can be achieved through the following steps: First, based on the updated grid point energy output probability and the first corresponding relationship, the non-degradation probability of each area in the minimum working area set is calculated. The non-degradation probability refers to the probability that the photovoltaic panel can still operate at close to its maximum efficiency after considering light and other environmental factors. Next, based on the non-degradation probability of each area and the second corresponding relationship, the connection weight of each grid point is calculated. Finally, the connection weights of all grid points in the rural photovoltaic energy area are accumulated to obtain the total. This process involves a comprehensive evaluation of the energy output probability of each grid point and its position in the network.

[0084] Preferably, the calculation process can be further refined. For example, more environmental parameters such as temperature, humidity and aging of photovoltaic panels can be introduced to more accurately calculate the non-degraded probability. In addition, advanced algorithms such as genetic algorithms or simulated annealing algorithms can be used to optimize the calculation process of connection weights to find the best energy management point.

[0085] Furthermore, the demand of the power grid and the supply and demand balance of photovoltaic energy can be considered to adjust the calculation of the connection weight, ensuring that the selection of energy management points is not only based on output efficiency, but also takes into account the stability of the power grid and the reasonable distribution of energy.

[0086] In some embodiments, the calculating of the non-degraded probability of each area in the minimum working area set based on the updated grid point energy production probability and the first corresponding relationship includes:

[0087]

[0088] Where P intact is the probability of non-degradation of the jth minimum working area in the minimum working area set, P degraded,i is the degradation probability of each grid point in the set of coverage grid points corresponding to the jth minimum working area.

[0089] It should be noted that this implementation describes how to calculate the non-degraded probability of each area in the minimum working area set based on the updated grid point energy output probability and the first corresponding relationship. Here, the non-degraded probability refers to the probability that the photovoltaic panel operates normally within its minimum working area without performance degradation due to shading, damage or other factors. This process is to evaluate the performance of the photovoltaic panel in each minimum working area for effective energy management and optimization.

[0090] Specifically, the process of calculating the non-degraded probability can be achieved through the following steps: First, for each minimum working area in the minimum working area set, the degradation probability of each grid point in the grid point set it covers needs to be considered. The degradation probability refers to the probability that the performance of the photovoltaic panel will degrade due to various factors. Then, the non-degraded probability of each minimum working area is calculated using the formula, which is as follows:

[0091]

[0092] Among them, P no downgrade is the undegraded probability of the minimum working area, P downgrade,i is the degradation probability of the ith grid point, and n is the number of grid points covered by the minimum working area. Parameter setting may include analysis of historical data on PV panel performance, maintenance records, and environmental monitoring data.

[0093] Preferably, the process of calculating the non-degraded probability can be further refined. For example, more detailed environmental monitoring data, such as local weather changes, vegetation growth, and cleanliness of photovoltaic panels, can be introduced to more accurately assess the degradation probability of each grid point.

[0094] Furthermore, machine learning techniques can be used to predict the performance changes of photovoltaic panels, thereby dynamically adjusting the calculation of the probability of non-degradation. The estimation of the probability of degradation can also be adjusted by considering the installation age of the photovoltaic panels and the performance guarantee provided by the manufacturer.

[0095] In some embodiments, the connection weight of each grid point is calculated based on the non-degraded probability of each area and the second corresponding relationship as follows:

[0096]

[0097] Where W i is the connection weight of the i-th grid point, P intact,j is the non-degraded probability of each minimum working area in the set of covering minimum working areas of the i-th grid point.

[0098] It should be noted that this implementation describes how to calculate the connection weight of each grid point based on the non-degraded probability of each area and the second corresponding relationship. Here, the connection weight refers to an indicator that measures the importance of the grid point in the energy network, which takes into account the energy output probability of the photovoltaic panels and their location in the network structure. In this way, the grid points that contribute the most to the entire energy network can be identified.

[0099] Specifically, the calculation of the connection weight of each grid point can be achieved by the following steps: First, determine the set of minimum working areas covered by each grid point, which involves looking at the second correspondence to find all the minimum working areas covering a specific grid point. Next, calculate the non-degraded probability of these minimum working areas, which can be done by the non-degraded probability formula mentioned earlier. Then, use these non-degraded probabilities to calculate the connection weight of each grid point, as follows:

[0100]

[0101] Among them, W i is the connection weight of the ith grid point, P no downgrade,j is the non-degraded probability of the jth minimum working area in the set of minimum working areas covering the i-th grid point, and m is the number of minimum working areas covering the grid point. Parameter setting may include analysis of historical data on PV panel performance, maintenance records, and environmental monitoring data.

[0102] Preferably, the process of calculating the connection weights can be further refined. For example, more real-time monitoring data, such as solar radiation intensity, temperature, and humidity, can be introduced to dynamically adjust the calculation of the non-degraded probability. In addition, optimization algorithms, such as linear programming or network flow algorithms, can be used to determine the optimal connection weights for each grid point, thereby maximizing the efficiency of the entire energy network.

[0103] Furthermore, the demand of the power grid and the supply and demand balance of photovoltaic energy can be considered to adjust the calculation of the connection weight, ensuring that the selection of energy management points is not only based on output efficiency, but also takes into account the stability of the power grid and the reasonable distribution of energy.

[0104] In some embodiments, the step S2 of constructing the minimum working area of ​​each grid point based on the minimum working size and minimum rotation angle of the photovoltaic panel includes:

[0105] Taking any grid point as the reference point, select a predetermined direction, determine the boundary length of the area with the minimum working size, and rotate with the minimum rotation angle as the step length until the full range of 360 degrees is covered, forming a series of fan-shaped areas corresponding to the reference point, and extending the boundaries of these fan-shaped areas to both sides perpendicular to the selected direction by half of the minimum working size to form a series of closed fan-shaped areas, thereby obtaining the minimum working area set of the current grid point.

[0106] It should be noted that this implementation describes how to construct the minimum working area of ​​each grid point based on the minimum working size and minimum rotation angle of the photovoltaic panel. Here, the minimum working area refers to the minimum area that the photovoltaic panel can cover and effectively generate energy under its normal working state. This process is to determine the exact geographical range that the photovoltaic panel can affect, thereby providing accurate data for energy management and optimization.

[0107] Specifically, the process of constructing the minimum working area can be achieved through the following steps: First, select a grid point as a reference point and determine a predetermined direction. Then, use the minimum working size of the photovoltaic panel to determine the boundary length of the area, which is usually based on the physical size and installation angle of the photovoltaic panel. Next, perform angular rotation with the minimum rotation angle as the step size, which ensures that the working areas of the photovoltaic panels in different directions are taken into account. In this way, a series of fan-shaped areas can be formed, which represent the coverage of photovoltaic panels in different directions. Finally, extend the boundaries of these fan-shaped areas to both sides perpendicular to the selected direction by half of the minimum working size to form a closed minimum working area set.

[0108] Preferably, the process of constructing the minimum working area can be further refined. For example, a more accurate measurement tool can be introduced to determine the minimum working size of the photovoltaic panel, such as using a laser rangefinder or a high-precision GPS device. In addition, computer-aided design (CAD) software can be used to simulate the coverage of the photovoltaic panel in different directions to more accurately construct the minimum working area.

[0109] Furthermore, the shape and size of the minimum working area can be adjusted to take into account changes in terrain, such as slopes and obstacles, to ensure that they truly reflect the working range of photovoltaic panels in actual environments.

[0110] In some embodiments, the positional relationship between the minimum working area set of each grid point and the rural photovoltaic energy area in step S2 is to select the effective minimum working area of ​​each grid point to form the minimum working area set:

[0111] Check whether all the minimum working areas corresponding to each grid point overlap with or are completely contained in the boundary of the rural photovoltaic energy area. If a minimum working area intersects with the rural photovoltaic energy area or is completely located inside it, the minimum working area is deemed to be a valid area, and all valid minimum working areas are aggregated to form a minimum working area set.

[0112] It should be noted that this implementation method describes how to screen out effective minimum working areas based on the positional relationship between the minimum working area set of each grid point and the rural photovoltaic energy area. Here, effective minimum working areas refer to those minimum working areas that overlap or are completely included in the boundaries of the photovoltaic energy area. These areas are considered effective because they have a direct contribution to the output of photovoltaic energy.

[0113] Specifically, the process of screening effective minimum working areas can be achieved through the following steps: First, for each grid point, check whether all the corresponding minimum working areas intersect with the boundary of the rural photovoltaic energy area or are completely located inside it. This can be done through a geographic information system (GIS) or similar spatial analysis tools that can process and analyze geographic data and determine the spatial relationship between regions. Then, if a minimum working area intersects with the photovoltaic energy area or is completely located inside it, the area is considered valid and included in the minimum working area set.

[0114] Preferably, the screening process can be further refined. For example, more advanced spatial analysis algorithms, such as spatial indexing or geometric processing libraries, can be introduced to improve screening efficiency and accuracy.

[0115] Furthermore, specific parameters can be set, such as a threshold value for the overlap ratio between the minimum working area and the photovoltaic energy area. Only when the overlap ratio exceeds this threshold value, the working area is considered valid. The expansion or change of the photovoltaic energy area can also be considered, and the minimum working area set can be dynamically updated to adapt to the growth or maintenance needs of the photovoltaic energy area.

[0116] The above-mentioned various embodiments of the present invention have the following beneficial effects: The rural digital twin photovoltaic energy management method of the present invention can significantly improve the management efficiency of photovoltaic energy areas and the accuracy of energy output. Through grid division and the construction of the minimum working area, the method can accurately identify and manage each grid point within the photovoltaic energy area, ensuring that each photovoltaic panel can operate within its most effective working area. In addition, by establishing a correspondence between grid points and minimum working areas, energy management decisions can be optimized, energy utilization efficiency can be improved, and energy output can be maximized.

[0117] Furthermore, this method can more accurately predict and evaluate the energy output probability of each grid point by constructing the illumination area and the illumination area probability matrix. This method can dynamically adjust the working area of ​​the photovoltaic panel to adapt to the changing environmental conditions and energy needs, thereby improving the adaptability and flexibility of the system. By selecting the grid point with the smallest sum of connection weights as the energy management point, this method can reduce management costs and enhance the stability and reliability of the system, especially in changing environmental conditions such as rural areas. This method can not only improve the accuracy of energy management, but also improve the overall energy output and economic benefits.

[0118] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0119] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A rural digital twin photovoltaic energy management method based on the Internet of Things, characterized in that: include: S1: Grid the rural photovoltaic energy area model to obtain all grid points inside the rural photovoltaic energy area; S2: Based on the minimum working size and minimum rotation angle of the photovoltaic panel, the minimum working area of ​​each grid point is constructed. Based on the positional relationship between each area in the minimum working area of ​​each grid point and the rural photovoltaic energy area, the effective minimum working area of ​​each grid point is screened out to form a minimum working area set; S3: establishing a first correspondence between each minimum working area in the minimum working area set and the grid points it contains, and establishing a second correspondence between each grid point and the minimum working area in the minimum working area set that contains the grid point; S4: construct the illumination area and illumination area probability matrix of the photovoltaic energy region; S5: Take each grid point as a candidate energy management point, traverse and execute the following steps to obtain the sum of the connection weights of each grid point as the candidate energy management point, and take the grid point corresponding to the minimum sum of the connection weights as the energy management point: update the energy output probability of each grid point based on the illumination area, the illumination area probability matrix and the energy area; The total connection weight of the current candidate energy management point is calculated based on the updated energy output probability of each grid point and the first corresponding relationship and the second corresponding relationship.

2. According to the rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 1, it is characterized in that: The step S3 of establishing a first correspondence between each minimum working area in the minimum working area set and the grid points it contains includes: Obtaining the grid points covered by each minimum working area in the minimum working area set, taking the grid points covered by the minimum working area as a point in the set of covered grid points of each minimum working area, and establishing a first corresponding relationship; The second corresponding relationship between each grid point and the minimum working area in the minimum working area set including the grid point is established in step S3, including: for each grid point inside the rural photovoltaic energy area, all minimum working areas in the minimum working area set covering the grid point are obtained, thereby forming a set of covering minimum working areas corresponding to each grid point, and establishing a second corresponding relationship.

3. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 2 is characterized in that: The step S4 specifically includes: S41: constructing an illumination area, wherein the illumination area is composed of a plurality of sub-areas with an area of ​​A, wherein A is the square of the grid point spacing during grid division in step S1; S42: Calculate the illumination probability of each sub-region in the illumination region based on the coordinate range of each sub-region and the one-dimensional normal distribution probability density function, and the illumination probabilities of all sub-regions constitute an illumination region probability matrix.

4. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 3 is characterized in that: The updating of the energy output probability of each grid point based on the illumination area, the illumination area probability matrix and the energy area in step S5 further includes: Align the center point of the illumination area with the current candidate energy management point, store the grid points in the rural photovoltaic energy area covered by the illumination area as a first grid point set, obtain the probability of the corresponding sub-area in the illumination area covering each grid point in the first grid point set from the illumination area probability matrix as the first probability of the corresponding grid point, and the first probabilities of all grid points in the first grid point set constitute a first probability value set; The center point of the energy area is moved to each grid point of the first grid point set in turn, and the energy output probability of the grid points covered by the energy area is updated according to the first probability value set. After the traversal is completed, the updated energy output probability of all grid points in the rural photovoltaic energy area is obtained.

5. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 4 is characterized in that: Formula (1) is used to update the energy output probability of the grid points covered by the energy area: P new =P old ×P sun (1); Among them, P new is the updated energy output probability of the grid points covered by the energy area, P old is the current energy output probability of the grid point, P sun It is the first probability corresponding to the grid point in the first probability value set that coincides with the center point of the current energy area.

6. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 5 is characterized in that: The sum of the connection weights of the current candidate energy management points calculated based on the updated grid point energy output probability and the first corresponding relationship and the second corresponding relationship in step S5 includes: Based on the updated grid point energy output probability and the first correspondence, the non-degradation probability of each area in the minimum working area set is calculated. Based on the non-degradation probability of each area and the second correspondence, the connection weight of each grid point is calculated. The connection weights of all grid points in the rural photovoltaic energy area are accumulated to obtain the total connection weights.

7. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 6 is characterized in that: The calculating of the non-degraded probability of each area in the minimum working area set based on the updated grid point energy output probability and the first corresponding relationship includes: Where P intact is the probability of non-degradation of the jth minimum working area in the minimum working area set, P degraded,i is the degradation probability of each grid point in the set of coverage grid points corresponding to the jth minimum working area.

8. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 6 is characterized in that: The connection weight of each grid point is calculated based on the non-degraded probability of each area and the second corresponding relationship as follows: Where W i is the connection weight of the i-th grid point, P intact,j is the non-degraded probability of each minimum working area in the set of covering minimum working areas of the i-th grid point.

9. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 1 is characterized in that: The step S2 of constructing the minimum working area of ​​each grid point based on the minimum working size and minimum rotation angle of the photovoltaic panel includes: Taking any grid point as the reference point, select a predetermined direction, determine the boundary length of the area with the minimum working size, and rotate with the minimum rotation angle as the step length until the full range of 360 degrees is covered, forming a series of fan-shaped areas corresponding to the reference point, and extending the boundaries of these fan-shaped areas to both sides perpendicular to the selected direction by half of the minimum working size to form a series of closed fan-shaped areas, thereby obtaining the minimum working area set of the current grid point.

10. The rural digital twin photovoltaic energy management method based on the Internet of Things according to claim 9 is characterized in that: In step S2, based on the positional relationship between the minimum working area set of each grid point and the rural photovoltaic energy area, the effective minimum working area of ​​each grid point is screened out to form the minimum working area set, which is specifically: Check whether all the minimum working areas corresponding to each grid point overlap with or are completely contained in the boundary of the rural photovoltaic energy area. If a minimum working area intersects with the rural photovoltaic energy area or is completely located inside it, the minimum working area is deemed to be a valid area, and all valid minimum working areas are aggregated to form a minimum working area set.

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