Method and system for realizing energy efficiency optimization of photovoltaic system based on big data analysis algorithm

Abstract

The invention relates to the technical field of photovoltaic data processing, and discloses a method and system for realizing energy efficiency optimization of a photovoltaic system based on a big data analysis algorithm, and the method comprises the steps: carrying out the power preprocessing of discharge power, and obtaining the preprocessing power; calculating the power loss of the photovoltaic system by using the pre-processing power, calculating the discharge efficiency of the photovoltaic system by using the power loss and the output power, and determining a line optimization target of the photovoltaic system; initializing the total line length of the photovoltaic system, obtaining a line optimization scheme corresponding to the total line length, calculating initial fitness corresponding to the total line length by using a line optimization target and the line optimization scheme, and identifying a dominating relationship corresponding to the total line length based on the initial fitness; classifying the total length of the line, calculating a congestion distance corresponding to the classified line, and updating the total length of the line by using the congestion distance; and performing line optimization on the photovoltaic system by using the target optimization scheme. According to the invention, the numerical difference of the photovoltaic system can be reduced and the data search capability is improved.

Description

Technical Field

[0001] The present invention relates to a method and system for realizing energy efficiency optimization of a photovoltaic system based on a big data analysis algorithm, and belongs to the technical field of photovoltaic data processing. Background Art

[0002] Nowadays, photovoltaic systems are composed of main components such as solar panels, solar controllers, batteries and AC inverters. These different components are connected by lines, and the connecting lines will cause certain losses. The energy efficiency of the photovoltaic system is defined as the output performance or function that the appliance can provide while consuming a certain amount of energy. It can be seen that if the loss caused by the photovoltaic system when outputting power is reduced, it can be guaranteed to provide a certain power supply while consuming less energy, thereby improving the energy efficiency of the photovoltaic system.

[0003] At present, in the process of improving the energy efficiency of photovoltaic systems from the perspective of losses, it is necessary to evaluate the size of the losses of photovoltaic systems and optimize the losses. Since photovoltaic systems are affected by sunlight and the size of the sunlight is unstable, the power of the photovoltaic system is different at different times, and the system losses at different times are also different. Therefore, the losses between different times calculated directly using the calculation formula are quite different, and it is impossible to determine the regular distribution of losses within continuous moments, and thus it is impossible to find rules that can reduce losses. Secondly, there are two ways to optimize the energy efficiency of photovoltaic systems: layout optimization and optimization of the number of components. The former provides changes in the layout of the photovoltaic system and adjusts the lines between different components to reduce losses, thereby optimizing energy efficiency. The latter provides adjustments to the number of components to optimize energy efficiency. For the former, if the energy efficiency of the layout composed of each connection line is purely calculated, and the data search capability of selecting the optimal energy efficiency from a large number of calculated energy efficiencies is insufficient.

[0004] Therefore, the numerical differences of PV systems are large and the data search capability is insufficient when optimizing energy efficiency. Summary of the invention

[0005] The present invention provides a method and system for realizing energy efficiency optimization of a photovoltaic system based on a big data analysis algorithm, the main purpose of which is to reduce the numerical differences of the photovoltaic system and improve the data search capability during energy efficiency optimization.

[0006] To achieve the above purpose, the present invention provides a method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm, comprising: Determine a photovoltaic system to be energy-efficiently optimized, collect the discharge power of the photovoltaic system in a continuous period, and perform power preprocessing on the discharge power to obtain preprocessed power, wherein the photovoltaic system includes a solar panel, a solar controller, a battery, and an AC inverter; Calculating the power loss of the photovoltaic system using the preprocessed power, collecting the output power of the solar panel, calculating the discharge efficiency of the photovoltaic system using the power loss and the output power, and determining the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency; Initializing the total line length of the photovoltaic system, obtaining a line optimization scheme corresponding to the total line length, calculating an initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identifying a dominance relationship corresponding to the total line length based on the initial fitness; Based on the dominance relationship, the total length of the route is classified to obtain a classified route, based on the initial fitness, the congestion distance corresponding to the classified route is calculated, and the total length of the route is updated by using the congestion distance to obtain an updated route; The target optimization scheme corresponding to the updated line is obtained, and the line of the photovoltaic system is optimized using the target optimization scheme to achieve energy efficiency optimization of the photovoltaic system, thereby obtaining an energy efficiency optimization result of the photovoltaic system.

[0007] Optionally, performing power preprocessing on the discharge power to obtain preprocessed power includes: The discharge power is converted to a decimal using the following formula to obtain the decimal power: ; in, Indicates The fractional power at the moment, Indicates The discharge power at the moment, Indicates the minimum discharge power in a continuous period, Indicates the difference between the maximum discharge power and the minimum discharge power in a continuous period; According to the fractional power, the discharge power is preprocessed using the following formula to obtain the preprocessed power: ; in, represents the preprocessing power, Indicates The fractional power at the moment, represents the beta function, Indicates the minimum discharge power in a continuous period, , Indicated by A constant greater than 0 determined by the Beta distribution.

[0008] Optionally, calculating the power loss of the photovoltaic system by using the preprocessed power includes: Obtaining solar panels, solar controllers, batteries and AC inverters in the photovoltaic system; Querying a first connection line between the solar panel and the battery; querying a second connection line between the battery and the AC inverter; querying a third connection line between the solar controller and the battery; The line losses of the first connection line, the second connection line and the third connection line are calculated using the following formula: ; in, represents the line loss, Indicates The resistance per unit length of the connecting line, Indicates The line length of the connecting line, Indicates The square of the current in the connected lines; Utilizing the preprocessed power, respectively calculating the panel loss, controller loss, battery loss and inverter loss of the solar panel, the solar controller, the battery and the AC inverter; The power loss of the photovoltaic system is determined by the line loss, the panel loss, the controller loss, the battery loss and the inverter loss.

[0009] Optionally, determining a line optimization target of the photovoltaic system based on the power loss and the discharge efficiency includes: Obtaining the inverse efficiency of the discharge efficiency; The minimization of the power loss and the minimization of the inverse of the efficiency are taken as line optimization objectives.

[0010] Optionally, the obtaining a line optimization solution corresponding to the total line length includes: Acquire a first connection line, a second connection line, and a third connection line of the photovoltaic system; randomly generating a first line length, a second line length and a third line length of the first connection line, the second connection line and the third connection line respectively; Determining whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length; When the total length of the first line length, the second line length and the third line length is inconsistent with the total line length, the first line length, the second line length and the third line length are removed, and the process returns to the step of determining whether the total length of the first line length, the second line length and the third line length is consistent with the total line length; When the total length of the first line length, the second line length and the third line length is consistent with the total line length, calculating the target fitness corresponding to the first line length, the second line length and the third line length; When the target fitness is the minimum fitness, the first line length, the second line length, and the third line length are used as line optimization solutions.

[0011] Optionally, the calculating the initial fitness corresponding to the total length of the route by using the route optimization target and the route optimization solution includes: Obtaining a connection line corresponding to the line optimization target; Querying the target route corresponding to the route optimization solution in the connection route; Extracting the target length of the target route from the route optimization plan; After replacing the line length of the connection line with the target length, the initial fitness corresponding to the total line length is calculated.

[0012] Optionally, the identifying, based on the initial fitness, a dominance relationship corresponding to the total length of the line includes: Obtaining a first line total length and a second line total length in the line total length; Identify a first fitness and a second fitness of the first line total length and the second line total length from the initial fitness respectively; When at least one first fitness of the first total length of the line is better than the second fitness of the second total length of the line, and the first fitness of the first total length of the line is not worse than the second fitness of the second total length of the line, the relationship in which the first total length of the line dominates the second total length of the line is taken as a dominating relationship.

[0013] Optionally, classifying the total length of the routes based on the dominance relationship to obtain classified routes includes: Based on the dominance relationship, the dominance quantity and dominance set of the total line length are determined in the following manner: ; in, , Indicates The total length of the lines is dominated by the number of dominant relations, Indicates The total length of each line is the set of total lengths of lines dominated by the dominating relationship. Indicates that The total length of the line is dominated by Total length of line.

[0014] Querying the total length of a third line whose controlled quantity meets the preset quantity from the total length of the line; Taking the total length of the third line as the first-level total length; Obtain the fourth line total length from the dominating set corresponding to the first level total length; Querying the target controlled quantity and the number of traversal times of the total length of the fourth route; Calculate the difference between the target dominance quantity and the number of traversals to obtain an updated dominance quantity; querying the total length of the fifth line whose updated controlled quantity meets the preset quantity from the total length of the fourth line; Taking the total length of the fifth line as the second-level total length; Determine the third level total length using the fifth line total length; The first-level total length, the second-level total length and the third-level total length are used as classification routes.

[0015] Optionally, the calculating, based on the initial fitness, the congestion distance corresponding to the classified route includes: Extracting the classification fitness corresponding to the classification line from the initial fitness; According to the classification fitness, the congestion distance corresponding to the classified route is calculated using the following formula: ; in, Indicates The first among the general directors The total length of the line is The crowding degree on the target, Indicates The first among the general directors The total length of the line is The classification fitness on the target, Indicates The first among the general directors The total length of the line is The classification fitness on the target, Indicates the total length of all lines in The maximum value on the target Indicates the total length of all lines in The minimum value of the target.

[0016] In order to solve the above problems, the present invention also provides a system for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm, the system comprising: A power preprocessing module, used to determine a photovoltaic system to be energy-efficiently optimized, collect the discharge power of the photovoltaic system in a continuous period, and perform power preprocessing on the discharge power to obtain preprocessed power, wherein the photovoltaic system includes a solar panel, a solar controller, a battery, and an AC inverter; a target determination module, configured to calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency; a relationship identification module, configured to initialize the total line length of the photovoltaic system, obtain a line optimization scheme corresponding to the total line length, calculate an initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identify a dominant relationship corresponding to the total line length based on the initial fitness; A route updating module, configured to classify the total route length based on the dominance relationship to obtain a classified route, calculate a congestion distance corresponding to the classified route based on the initial fitness, and update the total route length using the congestion distance to obtain an updated route; The energy efficiency optimization module is used to obtain the target optimization scheme corresponding to the updated line, and use the target optimization scheme to optimize the line of the photovoltaic system to achieve energy efficiency optimization of the photovoltaic system and obtain the energy efficiency optimization result of the photovoltaic system.

[0017] Compared with the problems described in the background technology, the embodiment of the present invention performs power preprocessing on the discharge power to reduce the difference between the discharge powers at different times. The embodiment of the present invention calculates the power loss of the photovoltaic system by using the preprocessed power to calculate the power loss with small numerical difference. Further, the embodiment of the present invention calculates the discharge efficiency of the photovoltaic system by using the power loss and the output power to characterize the discharge efficiency by power loss. Further, the embodiment of the present invention determines the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency to characterize the line optimization target by power loss, so that the size of the line optimization target can be affected by the influence of the line length change on the power loss in the future. The embodiment of the present invention classifies the total length of the line based on the dominant relationship to obtain the classified line, so as to use the genetic algorithm for optimization. The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanism, which has the characteristics of global search capability and parallel processing, and can improve data search capability. Therefore, the energy efficiency optimization method and system for photovoltaic system based on big data analysis algorithm provided by the embodiment of the present invention can reduce the numerical difference of photovoltaic system and improve the data search capability during energy efficiency optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a process for realizing energy efficiency optimization of a photovoltaic system based on a big data analysis algorithm provided by an embodiment of the present invention; Figure 2 A schematic diagram of modules for implementing the energy efficiency optimization system of a photovoltaic system based on a big data analysis algorithm provided in one embodiment of the present invention.

[0019] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0021] The embodiment of the present application provides a method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm. The execution subject of the method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0022] Embodiment 1: Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to an embodiment of the present invention. In this embodiment, the method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm includes: S1. Determine a photovoltaic system to be energy-efficiently optimized, collect the discharge power of the photovoltaic system in a continuous period, and perform power preprocessing on the discharge power to obtain preprocessed power, wherein the photovoltaic system includes a solar panel, a solar controller, a battery, and an AC inverter.

[0023] The embodiment of the present invention performs power preprocessing on the discharge power to reduce the difference between the discharge powers at different moments.

[0024] Among them, the discharge power refers to the power output by the photovoltaic system to the distribution cabinet. It should be noted that the output end of the solar panel is connected to the battery to store electrical energy in the battery, the output end of the solar controller is connected to the battery to control the charging and discharging of the battery, the output end of the battery is connected to the AC inverter to convert the DC discharged by the battery into AC through the AC inverter, and the output end of the AC inverter is connected to the distribution cabinet to store the AC in the distribution cabinet.

[0025] In one embodiment of the present invention, the performing power preprocessing on the discharge power to obtain the preprocessed power includes: converting the discharge power into a decimal using the following formula to obtain the decimal power: ; in, Indicates The fractional power at the moment, Indicates The discharge power at the moment, Indicates the minimum discharge power in a continuous period, Indicates the difference between the maximum discharge power and the minimum discharge power in a continuous period; According to the fractional power, the discharge power is preprocessed using the following formula to obtain the preprocessed power: ; in, represents the preprocessing power, Indicates The fractional power at the moment, represents the beta function, Indicates the minimum discharge power in a continuous period, , Indicated by A constant greater than 0 determined by the Beta distribution.

[0026] S2. Calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency.

[0027] The embodiment of the present invention calculates the power loss of the photovoltaic system by using the pre-processed power to calculate the power loss with a small numerical difference.

[0028] In one embodiment of the present invention, the use of the pre-processed power to calculate the power loss of the photovoltaic system includes: obtaining a solar panel, a solar controller, a battery and an AC inverter in the photovoltaic system; querying a first connection line between the solar panel and the battery; querying a second connection line between the battery and the AC inverter; querying a third connection line between the solar controller and the battery; and calculating the line losses of the first connection line, the second connection line and the third connection line using the following formula: ; in, represents the line loss, Indicates The resistance per unit length of the connecting line, Indicates The line length of the connecting line, Indicates The square of the current in the connected lines; The pre-processed power is used to respectively calculate the panel loss, controller loss, battery loss and inverter loss of the solar panel, the solar controller, the battery and the AC inverter; the power loss of the photovoltaic system is determined by the line loss, the panel loss, the controller loss, the battery loss and the inverter loss.

[0029] Optionally, the process of using the preprocessed power to respectively calculate the panel loss, controller loss, battery loss and inverter loss of the solar panel, the solar controller, the battery and the AC inverter refers to: obtaining the discharge power of the preprocessed power before preprocessing, which is the power at the output end of the AC inverter, and knowing in advance the conversion efficiency from input to output power of the solar controller, the battery and the AC inverter respectively. The power at the input end of the AC inverter can be calculated from the conversion efficiency, and the line loss experienced from the battery to the inverter is subtracted from the input power to obtain the power at the output end of the battery. The input power and output power of each component are calculated in turn, and the difference between the input power and the output power is the loss of each component. The panel loss of the solar panel is regarded as 0. Further, the process of determining the power loss of the photovoltaic system through the line loss, the panel loss, the controller loss, the battery loss and the inverter loss refers to the process of preprocessing the power loss. The preprocessing principle is similar to the aforementioned principle of preprocessing the discharge power to obtain the preprocessed power, and will not be further elaborated here.

[0030] The output power refers to the power at the output end of the solar cell panel.

[0031] Furthermore, the embodiment of the present invention calculates the discharge efficiency of the photovoltaic system by using the power loss and the output power, so as to characterize the discharge efficiency by the power loss.

[0032] In one embodiment of the present invention, the calculation of the discharge efficiency of the photovoltaic system using the power loss and the output power includes: calculating the power difference between the output power and the power loss; and taking the ratio between the power difference and a preset power as the discharge efficiency.

[0033] The preset power refers to the output power.

[0034] Furthermore, the embodiment of the present invention determines the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency, so as to characterize the line optimization target by the power loss, so that the size of the line optimization target can be affected in the subsequent process based on the impact of the change in line length on the power loss.

[0035] In one embodiment of the present invention, determining the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency includes: obtaining the inverse of the efficiency of the discharge efficiency; and taking minimization of the power loss and minimization of the inverse of the efficiency as line optimization targets.

[0036] S3. Initialize the total line length of the photovoltaic system, obtain a line optimization scheme corresponding to the total line length, calculate the initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identify the dominance relationship corresponding to the total line length based on the initial fitness.

[0037] Among them, the total line length refers to the total length of all lines within a predetermined range, and the value of the total line length is a randomly generated value. Therefore, there are many sizes of the total line length. The predetermined range refers to the range from the shortest total line length to the longest total line length that can be accepted by the person in charge of the photovoltaic system.

[0038] In one embodiment of the present invention, the obtaining of the line optimization scheme corresponding to the total line length includes: obtaining a first connection line, a second connection line, and a third connection line of the photovoltaic system; randomly generating a first line length, a second line length, and a third line length of the first connection line, the second connection line, and the third connection line, respectively; judging whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length; when the total length of the first line length, the second line length, and the third line length is inconsistent with the total line length, removing the first line length, the second line length, and the third line length, and returning to the above step of judging whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length; when the total length of the first line length, the second line length, and the third line length is consistent with the total line length, calculating the target fitness corresponding to the first line length, the second line length, and the third line length; when the target fitness is the minimum fitness, using the first line length, the second line length, and the third line length as the line optimization scheme.

[0039] Optionally, the principle of calculating the target fitness corresponding to the first line length, the second line length and the third line length is similar to the principle of determining the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency, and the target fitness includes two values, namely, power loss and the inverse of efficiency. It should be noted that in the process of randomly generating the first line length, the second line length and the third line length of the first connection line, the second connection line and the third connection line, different line lengths will be generated, and therefore, the calculated target fitness is also multiple values.

[0040] Furthermore, in the embodiment of the present invention, the initial fitness refers to the fitness of the total length of each line, and has multiple values.

[0041] In one embodiment of the present invention, the calculating of the initial fitness corresponding to the total length of the route using the route optimization target and the route optimization scheme includes: obtaining a connection route corresponding to the route optimization target; querying a target route corresponding to the route optimization scheme in the connection route; extracting a target length of the target route in the route optimization scheme; and calculating the initial fitness corresponding to the total length of the route after replacing the route length of the connection route with the target length.

[0042] It should be noted that the principle of calculating the initial fitness corresponding to the total length of the line using the line optimization target and the line optimization scheme is similar to the aforementioned principle of determining the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency, and will not be further elaborated here.

[0043] In one embodiment of the present invention, the identifying the dominating relationship corresponding to the total line length based on the initial fitness includes: obtaining a first line total length and a second line total length in the total line length; identifying a first fitness and a second fitness of the first line total length and the second line total length respectively from the initial fitness; when at least one first fitness of the first line total length is better than the second fitness of the second line total length, and the first fitness of the first line total length is not worse than the second fitness of the second line total length, taking the relationship in which the first line total length dominates the second line total length as the dominating relationship.

[0044] The first fitness includes two values: power loss and inverse efficiency, and the second fitness is the same.

[0045] S4. Based on the dominance relationship, classify the total length of the route to obtain a classified route, calculate the congestion distance corresponding to the classified route based on the initial fitness, and use the congestion distance to update the total length of the route to obtain an updated route.

[0046] The embodiment of the present invention classifies the total length of the line based on the dominating relationship to obtain the classified lines, so as to use a genetic algorithm for optimization. The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanism, has the characteristics of global search capability and parallel processing, and can improve data search capability.

[0047] In one embodiment of the present invention, the classifying the total length of the line based on the dominance relationship to obtain the classified line includes: determining the dominance quantity and dominance set of the total length of the line based on the dominance relationship in the following manner: ; in, , Indicates The total length of the lines is dominated by the number of dominant relations, Indicates The total length of each line is the set of total lengths of lines dominated by the dominating relationship. Indicates that The total length of the line is dominated by Total length of lines; The third line total length whose domination quantity meets the preset quantity is searched from the line total length; the third line total length is used as the first-level total length; the fourth line total length is obtained from the domination set corresponding to the first-level total length; the target domination quantity and the number of traversals of the fourth line total length are searched; the difference between the target domination quantity and the number of traversals is calculated to obtain the updated domination quantity; the fifth line total length whose updated domination quantity meets the preset quantity is searched from the fourth line total length; the fifth line total length is used as the second-level total length; the third-level total length is determined by using the fifth line total length; the first-level total length, the second-level total length and the third-level total length are used as classified lines.

[0048] Wherein, the preset number is 0.

[0049] For example, firstly, the total length of each line is taken as a node individual, and As power loss, As the inverse of efficiency, As the first level total length set, As the second-level total length collection, As the third-level total length set; for nodes ,node and nodes exist and Nodes D and E can dominate C, so , while The nodes controlled are ,so ; Select individuals with a count of 0 in the group, indicating that they are not dominated by other individuals. The set is divided into the first level, that is, of , in order to find , Each individual in has a list of all individuals it dominates. For each individual in the list, each time it is traversed, the corresponding number is reduced by 1. In the current case, — once, so ,at this time , traverse Twice, so ,at this time , traverse C twice, so ,at this time ,Afterwards, nodes with counts of 0 are classified as the second category, , in order to discover , just traverse All individuals in the subtraction again, see which individual value is 0, and classify them as .

[0050] In one embodiment of the present invention, the calculating the crowding distance corresponding to the classified route based on the initial fitness includes: extracting the classified fitness corresponding to the classified route from the initial fitness; and calculating the crowding distance corresponding to the classified route according to the classified fitness using the following formula: ; in, Indicates The first among the general directors The total length of the line is The crowding degree on the target, Indicates The first among the general directors The total length of the line is The classification fitness on the target, Indicates The first among the general directors The total length of the line is The classification fitness on the target, Indicates the total length of all lines in The maximum value on the target Indicates the total length of all lines in The minimum value of the target.

[0051] in, , represents the power loss, Indicates the inverse of efficiency.

[0052] Optionally, the process of updating the total length of the line by using the congestion distance to obtain an updated line comprises: selecting the first line with a large congestion distance; The total length set of each level, for example, the total length of all lines in the first level, is then classified into lines to obtain sets of three levels: F1, F2, and F3. The crowding distance is used again to filter out a unique set until there is only one line total length in the filtered set, and this unique line total length is used as the updated line.

[0053] S5. Obtain a target optimization scheme corresponding to the updated line, and use the target optimization scheme to perform line optimization on the photovoltaic system to achieve energy efficiency optimization of the photovoltaic system, thereby obtaining an energy efficiency optimization result of the photovoltaic system.

[0054] Optionally, the process of optimizing the line of the photovoltaic system using the target optimization scheme refers to: the target optimization scheme refers to updating the first line length, the second line length and the third line length corresponding to the line, and adjusting the line length of the photovoltaic system to the first line length, the second line length and the third line length respectively.

[0055] Compared with the problems described in the background technology, the embodiment of the present invention performs power preprocessing on the discharge power to reduce the difference between the discharge powers at different times. The embodiment of the present invention calculates the power loss of the photovoltaic system by using the preprocessed power to calculate the power loss with small numerical difference. Further, the embodiment of the present invention calculates the discharge efficiency of the photovoltaic system by using the power loss and the output power to characterize the discharge efficiency by power loss. Further, the embodiment of the present invention determines the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency to characterize the line optimization target by power loss, so that the size of the line optimization target can be affected by the influence of the line length change on the power loss in the future. The embodiment of the present invention classifies the total length of the line based on the dominant relationship to obtain the classified line, so as to use the genetic algorithm for optimization. The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanism, which has the characteristics of global search capability and parallel processing, and can improve data search capability. Therefore, the energy efficiency optimization method and system for photovoltaic system based on big data analysis algorithm provided by the embodiment of the present invention can reduce the numerical difference of photovoltaic system and improve the data search capability during energy efficiency optimization.

[0056] Embodiment 2: like Figure 2 As shown, it is a functional module diagram of a system for optimizing energy efficiency of a photovoltaic system based on a big data analysis algorithm according to the present invention.

[0057] The energy efficiency optimization system 200 for photovoltaic systems based on big data analysis algorithms described in the present invention can be installed in electronic devices. According to the functions implemented, the energy efficiency optimization system for photovoltaic systems based on big data analysis algorithms can include a power preprocessing module 201, a target determination module 202, a relationship identification module 203, a line update module 204 and an energy efficiency optimization module 205. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0058] In the embodiment of the present invention, the functions of each module / unit are as follows: The power preprocessing module 201 is used to determine the photovoltaic system to be energy-efficiently optimized, collect the discharge power of the photovoltaic system in a continuous period, and perform power preprocessing on the discharge power to obtain preprocessed power, wherein the photovoltaic system includes a solar panel, a solar controller, a battery and an AC inverter; The target determination module 202 is used to calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency; The relationship identification module 203 is used to initialize the total line length of the photovoltaic system, obtain a line optimization scheme corresponding to the total line length, calculate the initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identify the dominant relationship corresponding to the total line length based on the initial fitness; The route updating module 204 is configured to classify the total route length based on the dominance relationship to obtain a classified route, calculate the congestion distance corresponding to the classified route based on the initial fitness, and update the total route length using the congestion distance to obtain an updated route; The energy efficiency optimization module 205 is used to obtain the target optimization solution corresponding to the updated line, and use the target optimization solution to optimize the line of the photovoltaic system to achieve energy efficiency optimization of the photovoltaic system and obtain the energy efficiency optimization result of the photovoltaic system.

[0059] In detail, each module in the photovoltaic system energy efficiency optimization system 200 based on the big data analysis algorithm in the embodiment of the present invention is used in the same manner as above. Figure 1 The technical means described in the method for optimizing the energy efficiency of photovoltaic systems based on big data analysis algorithms are the same and can produce the same technical effects, so they will not be repeated here.

[0060] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm, characterized in that: The method comprises: Determine a photovoltaic system to be energy-efficiently optimized, collect the discharge power of the photovoltaic system in a continuous period, and perform power preprocessing on the discharge power to obtain preprocessed power, wherein the photovoltaic system includes a solar panel, a solar controller, a battery, and an AC inverter; Calculating the power loss of the photovoltaic system using the preprocessed power, collecting the output power of the solar panel, calculating the discharge efficiency of the photovoltaic system using the power loss and the output power, and determining the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency; Initializing the total line length of the photovoltaic system, obtaining a line optimization scheme corresponding to the total line length, calculating an initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identifying a dominance relationship corresponding to the total line length based on the initial fitness; Based on the dominance relationship, the total length of the route is classified to obtain a classified route, based on the initial fitness, the congestion distance corresponding to the classified route is calculated, and the total length of the route is updated by using the congestion distance to obtain an updated route; The target optimization scheme corresponding to the updated line is obtained, and the line of the photovoltaic system is optimized using the target optimization scheme to achieve energy efficiency optimization of the photovoltaic system, thereby obtaining an energy efficiency optimization result of the photovoltaic system.

2. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, characterized in that: The performing power preprocessing on the discharge power to obtain preprocessed power includes: The discharge power is converted to a decimal using the following formula to obtain the decimal power: ； in, Indicates The fractional power at the moment, Indicates The discharge power at the moment, Indicates the minimum discharge power in a continuous period, Indicates the difference between the maximum discharge power and the minimum discharge power in a continuous period; According to the fractional power, the discharge power is preprocessed using the following formula to obtain the preprocessed power: ； in, represents the preprocessing power, Indicates The fractional power at the moment, represents the beta function, Indicates the minimum discharge power in a continuous period, , Indicated by A constant greater than 0 determined by the Beta distribution.

3. The method for realizing energy efficiency optimization of photovoltaic system based on big data analysis algorithm according to claim 1, characterized in that: The calculating the power loss of the photovoltaic system by using the pre-processed power comprises: Obtaining solar panels, solar controllers, batteries and AC inverters in the photovoltaic system; Querying a first connection line between the solar panel and the battery; querying a second connection line between the battery and the AC inverter; querying a third connection line between the solar controller and the battery; The line losses of the first connection line, the second connection line and the third connection line are calculated using the following formula: ； in, represents the line loss, Indicates The resistance per unit length of the connecting line, Indicates The line length of the connecting line, Indicates The square of the current in the connected lines; Utilizing the preprocessed power, respectively calculating the panel loss, controller loss, battery loss and inverter loss of the solar panel, the solar controller, the battery and the AC inverter; The power loss of the photovoltaic system is determined by the line loss, the panel loss, the controller loss, the battery loss and the inverter loss.

4. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, characterized in that: The determining of the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency includes: Obtaining the inverse efficiency of the discharge efficiency; The minimization of the power loss and the minimization of the inverse of the efficiency are taken as line optimization objectives.

5. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, characterized in that: The obtaining of the line optimization solution corresponding to the total line length includes: Acquire a first connection line, a second connection line, and a third connection line of the photovoltaic system; randomly generating a first line length, a second line length and a third line length of the first connection line, the second connection line and the third connection line respectively; Determining whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length; When the total length of the first line length, the second line length and the third line length is inconsistent with the total line length, the first line length, the second line length and the third line length are removed, and the process returns to the step of determining whether the total length of the first line length, the second line length and the third line length is consistent with the total line length; When the total length of the first line length, the second line length and the third line length is consistent with the total line length, calculating the target fitness corresponding to the first line length, the second line length and the third line length; When the target fitness is the minimum fitness, the first line length, the second line length, and the third line length are used as line optimization solutions.

6. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, characterized in that: The calculating the initial fitness corresponding to the total length of the line by using the line optimization target and the line optimization scheme includes: Obtaining a connection line corresponding to the line optimization target; Querying the target route corresponding to the route optimization solution in the connection route; Extracting the target length of the target route from the route optimization plan; After replacing the line length of the connection line with the target length, the initial fitness corresponding to the total line length is calculated.

7. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, characterized in that: The identifying, based on the initial fitness, a dominance relationship corresponding to the total length of the line includes: Obtaining a first line total length and a second line total length in the line total length; Identify a first fitness and a second fitness of the first line total length and the second line total length from the initial fitness respectively; When at least one first fitness of the first total length of the line is better than the second fitness of the second total length of the line, and the first fitness of the first total length of the line is not worse than the second fitness of the second total length of the line, the relationship in which the first total length of the line dominates the second total length of the line is taken as a dominating relationship.

8. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, characterized in that: The step of classifying the total length of the routes based on the dominance relationship to obtain classified routes includes: Based on the dominance relationship, the dominance quantity and dominance set of the total line length are determined in the following manner: ； in, , Indicates The total length of the lines is dominated by the number of dominant relations, Indicates The total length of each line is the set of total lengths of lines dominated by the dominating relationship. Indicates that The total length of the line Total length of lines; Querying the total length of a third line whose controlled quantity meets the preset quantity from the total length of the line; Taking the total length of the third line as the first-level total length; Obtain the fourth line total length from the dominating set corresponding to the first level total length; Querying the target controlled quantity and the number of traversal times of the total length of the fourth route; Calculate the difference between the target dominance quantity and the number of traversals to obtain an updated dominance quantity; querying the total length of the fifth line whose updated controlled quantity meets the preset quantity from the total length of the fourth line; Taking the total length of the fifth line as the second-level total length; Determine the third level total length using the fifth line total length; The first-level total length, the second-level total length and the third-level total length are used as classification routes.

9. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, characterized in that: The calculating, based on the initial fitness, the congestion distance corresponding to the classified route includes: Extracting the classification fitness corresponding to the classification line from the initial fitness; According to the classification fitness, the congestion distance corresponding to the classified route is calculated using the following formula: ； in, Indicates The first among the general directors The total length of the line is The crowding degree on the target, Indicates The first among the general directors The total length of the line is The classification fitness on the target, Indicates The first among the general directors The total length of the line is The classification fitness on the target, Indicates the total length of all lines in The maximum value on the target Indicates the total length of all lines in The minimum value of the target.

10. A photovoltaic system energy efficiency optimization system based on big data analysis algorithm, characterized in that: The system comprises: A power preprocessing module, used to determine a photovoltaic system to be energy-efficiently optimized, collect the discharge power of the photovoltaic system in a continuous period, and perform power preprocessing on the discharge power to obtain preprocessed power, wherein the photovoltaic system includes a solar panel, a solar controller, a battery, and an AC inverter; a target determination module, configured to calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency; a relationship identification module, configured to initialize the total line length of the photovoltaic system, obtain a line optimization scheme corresponding to the total line length, calculate an initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identify a dominant relationship corresponding to the total line length based on the initial fitness; A route updating module, configured to classify the total route length based on the dominance relationship to obtain a classified route, calculate a congestion distance corresponding to the classified route based on the initial fitness, and update the total route length using the congestion distance to obtain an updated route; The energy efficiency optimization module is used to obtain the target optimization scheme corresponding to the updated line, and use the target optimization scheme to optimize the line of the photovoltaic system to achieve energy efficiency optimization of the photovoltaic system and obtain the energy efficiency optimization result of the photovoltaic system.

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