Power scheduling method, device and equipment of distributed photovoltaic power station, medium and program product

Abstract

The invention relates to a power scheduling method and device for a distributed photovoltaic power station, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring module data obtained by performing data processing on a target photovoltaic module in a photovoltaic power station, wherein the module data comprises a current environment coefficient related to an environment where the target photovoltaic module is located at a current moment, a related current load coefficient and received current radiation power; based on the current environment coefficient and the current radiation power, determining the optimized output power of the target photovoltaic module by taking maximization of the conversion efficiency of the target photovoltaic module as a power optimization target; the initial output power of an inverter connected with the target photovoltaic module is determined according to the optimized output power of the target photovoltaic module, the initial output power is optimized according to the current load coefficient, the optimized output power of the inverter is obtained, the optimized output power of the inverter is used for conducting power scheduling on a power grid, and the power scheduling precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power dispatching, and in particular to a power dispatching method, apparatus, computer equipment, computer-readable storage medium and computer program product for a distributed photovoltaic power station. Background Art

[0002] With the development of photovoltaic technology, photovoltaic power generation has gradually become an important source of clean energy. Among them, distributed photovoltaic power generation technology has emerged. This distributed photovoltaic power generation technology has the characteristics of cleanliness, high efficiency, and wide distribution, and has been widely used in recent years. With the continuous increase in the number of distributed photovoltaic power stations, the demand for their effective management is also becoming increasingly strong.

[0003] In the related art, it is usually necessary to use expert knowledge to dispatch the power of the photovoltaic power station to manage the photovoltaic power station. However, the related art cannot achieve optimal dispatching, and there is a problem that the power dispatching accuracy of the photovoltaic power station is not high. Summary of the invention

[0004] Based on this, it is necessary to provide a power scheduling method, device, computer equipment, computer-readable storage medium and computer program product for a distributed photovoltaic power station that can improve the power scheduling accuracy of the photovoltaic power station in response to the above technical problems.

[0005] In a first aspect, the present application provides a power dispatching method for a distributed photovoltaic power station, comprising:

[0006] Acquire component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component;

[0007] Based on the current environmental coefficient and the current radiation power, the optimized output power of the target photovoltaic component is determined by maximizing the conversion efficiency of the target photovoltaic component as a power optimization goal;

[0008] According to the optimized output power of the target photovoltaic component, the initial output power of the inverter connected to the target photovoltaic component is determined, and the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

[0009] In a second aspect, the present application also provides a power dispatching device for a distributed photovoltaic power station, comprising:

[0010] A data acquisition module, used to acquire component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component;

[0011] A first power optimization module, configured to determine the optimized output power of the target photovoltaic component based on the current environmental coefficient and the current radiation power, with maximizing the conversion efficiency of the target photovoltaic component as a power optimization target;

[0012] The second power optimization module is used to determine the initial output power of the inverter connected to the target photovoltaic component according to the optimized output power of the target photovoltaic component, and optimize the initial output power according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

[0013] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0014] Acquire component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component;

[0015] Based on the current environmental coefficient and the current radiation power, the optimized output power of the target photovoltaic component is determined by maximizing the conversion efficiency of the target photovoltaic component as a power optimization goal;

[0016] According to the optimized output power of the target photovoltaic component, the initial output power of the inverter connected to the target photovoltaic component is determined, and the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

[0017] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0018] Acquire component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component;

[0019] Based on the current environmental coefficient and the current radiation power, the optimized output power of the target photovoltaic component is determined by maximizing the conversion efficiency of the target photovoltaic component as a power optimization goal;

[0020] According to the optimized output power of the target photovoltaic component, the initial output power of the inverter connected to the target photovoltaic component is determined, and the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

[0021] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0022] Acquire component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component;

[0023] Based on the current environmental coefficient and the current radiation power, the optimized output power of the target photovoltaic component is determined by maximizing the conversion efficiency of the target photovoltaic component as a power optimization goal;

[0024] According to the optimized output power of the target photovoltaic component, the initial output power of the inverter connected to the target photovoltaic component is determined, and the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

[0025] The power dispatching method, device, computer equipment, computer readable storage medium and computer program product of the above-mentioned distributed photovoltaic power station can obtain the component data obtained by data processing of the target photovoltaic component in the photovoltaic power station, so as to obtain the current environmental coefficient related to the environment in which the target photovoltaic component is located at the current moment, the current load coefficient related to the target photovoltaic component, and the current radiation power received by the target photovoltaic component included in the component data. Then, based on the current environmental coefficient and the current radiation power, the output power of the target photovoltaic component is optimized with the conversion efficiency of the target photovoltaic component as the power optimization target to determine the optimized output power of the target photovoltaic component, realizing the primary power optimization process in the output link of the photovoltaic component, which helps to improve the accuracy of subsequent power dispatching. According to the optimized output power of the target photovoltaic component, the initial output power of the inverter connected to the target photovoltaic component is determined, and the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power dispatching of the power grid. In this way, the secondary power optimization process in the output link of the inverter is realized, so that it can be ensured that the power used for dispatching the power grid is appropriate and accurate, and the power dispatching accuracy of the photovoltaic power station is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 An application environment diagram of a power dispatching method for a distributed photovoltaic power station in one embodiment;

[0028] Figure 2 A schematic diagram of a flow chart of a power dispatching method for a distributed photovoltaic power station in one embodiment;

[0029] Figure 3 A system architecture diagram of a distributed photovoltaic group control and group modulation system in one embodiment;

[0030] Figure 4 It is a structural block diagram of a power dispatching device of a distributed photovoltaic power station in one embodiment;

[0031] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] The power dispatching method of the distributed photovoltaic power station provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the photovoltaic power station 102 communicates with the computer device 104 through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or placed on the cloud or other network servers.

[0034] In one embodiment, the computer device 104 collects data on the photovoltaic components in the photovoltaic power station 102 to obtain component data of each photovoltaic component. The computer device 104 obtains component data of the target photovoltaic component, wherein the component data includes the current environmental coefficient related to the environment in which the target photovoltaic component is located at the current moment, the current load coefficient related to the target photovoltaic component, and the current radiation power received by the target photovoltaic component; the computer device 104 determines the optimized output power of the target photovoltaic component based on the current environmental coefficient and the current radiation power, with the conversion efficiency of the target photovoltaic component being maximized as the power optimization target; the computer device 104 determines the initial output power of the inverter connected to the target photovoltaic component based on the optimized output power of the target photovoltaic component, and optimizes the initial output power based on the current load coefficient to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

[0035] The computer device 104 may be a terminal or a server. The terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0036] In an exemplary embodiment, Figure 2 As shown, a power dispatching method for a distributed photovoltaic power station is provided. Figure 1 The computer device 104 in the example is used as an example to illustrate, including the following steps 202 to 206. Among them:

[0037] Step 202, obtaining component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component.

[0038] Among them, the photovoltaic power station is a distributed photovoltaic power station, which includes multiple photovoltaic modules, and the target photovoltaic module is a photovoltaic module to be power-scheduled. The current environmental coefficient refers to the degree of influence of the environment in which the target photovoltaic module is located on the photovoltaic resistance power at the current moment, that is, the degree of influence on the output power of the target photovoltaic module. The environment can be reflected according to data related to light intensity, temperature, and weather conditions. The current load coefficient refers to the degree of influence of the load associated with the target photovoltaic module on the inverter power at the current moment. The inverter power refers to the output power of the inverter connected to the target photovoltaic module. The load associated with the target photovoltaic module refers to the load demand in the power grid to which the inverter is connected, such as the load demand of user residences, shopping malls, etc. in the power grid. The load demand can be at least one of electricity demand, power grid demand, and load change trend. The current radiated power refers to the power of the radiation received by the target photovoltaic module at the current moment.

[0039] Optionally, the computer device collects data from the photovoltaic power station to obtain collected data, and obtains component data of the target photovoltaic component from the collected data.

[0040] Exemplarily, IoT devices are deployed on-site at a photovoltaic power station, including voltage and current sensors, power meters, communication interfaces, wireless gateways, GPRS / 4G units, etc. With the help of these IoT devices, the data acquisition unit of the computer device collects real-time data of voltage, current, and power from each photovoltaic component in the photovoltaic power station to obtain collected data, and transmits the collected data to the data processing unit of the computer device through serial interfaces, wireless data transmission, GPRS and other technologies. The data processing unit uses data cleaning technology to remove invalid or abnormal data from the collected data to improve data quality; the processed data is stored in a local database or a cloud database; data mining technology is used to discover hidden patterns, laws, and trends from the processed data, obtain the initial data of each photovoltaic component, and generate a power station operation report. The intelligent scheduling unit of the computer device obtains the initial data of the target photovoltaic component from the power station operation report, and obtains the current environmental coefficient, current load coefficient, and current radiation power at the current moment through data analysis to obtain component data.

[0041] For example, the intelligent scheduling unit obtains the historical load factor and historical environmental factor corresponding to the historical moment, as well as the current radiation power from the initial data of the target photovoltaic module. The intelligent scheduling unit determines the current load factor at the current moment through time series analysis, machine learning and other algorithms based on the historical load factor at at least one historical moment, as shown in the following formula (1); the intelligent scheduling unit determines the current environmental factor at the current moment through time series analysis, machine learning and other algorithms based on the historical environmental factor at at least one historical moment, as shown in the following formula (2):

[0042] (1)

[0043]

[0044] Among them, in formula (1), t is the time, n is a constant, f(.) is the time series analysis function for load factor prediction, or the load factor prediction model constructed by machine learning algorithm, k is a constant, L(t), L(t-1), ..., L(tn), represent the historical load factor at time t, the historical load factor at time t-1, and the historical load factor at time tn. In formula (2), t is the time, n is a constant, g(.) is the time series analysis function for environmental coefficient prediction, or the environmental coefficient prediction model constructed by machine learning algorithm, k is a constant, E(t), E(t-1), ..., E(tn) represent the historical environmental coefficient at time t, the historical environmental coefficient at time t-1, and the historical environmental coefficient at time tn, respectively.

[0045] Step 204 : Based on the current environmental factor and the current radiation power, the optimized output power of the target photovoltaic component is determined with the conversion efficiency of the target photovoltaic component being maximized as the power optimization target.

[0046] The conversion efficiency refers to the conversion of the input power of the photovoltaic module into the output power, that is, the greater the conversion efficiency, the more input power is provided to the inverter connected to the target photovoltaic module, that is, the output power of the target photovoltaic module is greater. It can be understood that when the conversion efficiency of the target photovoltaic module is maximized, the output power of the target photovoltaic module is the optimal output power, that is, the output power is the largest, which is regarded as the optimized output power of the target photovoltaic module.

[0047] Optionally, the computer device determines the optimized output power of the target photovoltaic component when the maximum conversion efficiency is achieved according to the current environmental factor and the current radiation power through a maximum power point tracking algorithm.

[0048] In one embodiment, based on the current environmental coefficient and the current radiation power, the optimized output power of the target photovoltaic component is determined with maximizing the conversion efficiency of the target photovoltaic component as the power optimization goal, including: acquiring attribute information of the target photovoltaic component, and determining multiple candidate conversion efficiencies based on the attribute information, the current environmental coefficient, and the current radiation power, each candidate conversion efficiency being determined based on the ratio of the corresponding candidate output power to the candidate input power; and taking the candidate output power corresponding to the maximum candidate conversion efficiency as the optimized output power of the target photovoltaic component.

[0049] The attribute information of the target photovoltaic module includes at least one of the model of the target photovoltaic module, the material information of the target photovoltaic module, and the electrical performance parameters.

[0050] Exemplarily, after acquiring the attribute information of the target photovoltaic component, the computer device queries multiple candidate conversion efficiencies from the historical conversion efficiency set according to the attribute information, the current environmental coefficient, and the current radiation power. Each candidate conversion efficiency in the historical conversion efficiency set is a statistical result of the corresponding photovoltaic component during historical scheduling under the corresponding historical environment and historical radiation power, that is, each candidate conversion efficiency corresponds to the attribute information, historical environmental coefficient, and historical radiation power of the photovoltaic component that is historically scheduled.

[0051] For example, the computer device can also generate prompt text related to the conversion efficiency of the photovoltaic module based on the attribute information, the current environmental factor, and the current radiation power, call the large language model, perform semantic understanding based on the prompt text, and output multiple candidate conversion efficiencies. The computer device obtains the candidate output power corresponding to each candidate conversion efficiency, and uses the candidate output power corresponding to the maximum candidate conversion efficiency as the optimized output power.

[0052] In this embodiment, multiple candidate conversion efficiencies that match the attribute information, the current environmental coefficient and the current radiation power are determined, thereby selecting the candidate output power corresponding to the maximum candidate conversion efficiency as the optimized output power. Thus, it is possible to achieve maximizing the conversion efficiency of the target photovoltaic module as the power optimization goal, optimize the output power of the target photovoltaic module, and realize a power optimization process in the photovoltaic module output link, which helps to improve the accuracy of subsequent power scheduling.

[0053] Of course, in other embodiments, multiple candidate conversion efficiencies are determined based on the attribute information, the current environmental coefficient, and the current radiation power, including: based on the attribute information, the current environmental coefficient and the current radiation power, multiple candidate combinations are determined, each candidate combination including a corresponding candidate output power and a candidate input power; for each candidate combination, the ratio of the corresponding candidate output power to the candidate input power is calculated to determine the corresponding candidate conversion efficiency.

[0054] Exemplarily, the computer device uses the attribute information, the current environment coefficient and the current radiation coefficient as search indexes to query multiple candidate combinations from the conversion efficiency search mapping table. The conversion efficiency search mapping table is obtained based on the historical power scheduling summary.

[0055] In this embodiment, based on the attribute information, the current environment coefficient and the current radiation power, multiple candidate combinations that match the attribute information are queried, and each candidate combination includes a corresponding candidate output power and a candidate input power; for each candidate combination, the ratio of the corresponding candidate output power to the candidate input power is calculated to determine the corresponding candidate conversion efficiency. In this way, the candidate combination with the maximum candidate conversion efficiency can be queried in time according to the candidate conversion efficiencies of each candidate combination, and the candidate output power in the queried candidate combination is used as the optimized output power.

[0056] In one embodiment, multiple candidate combinations are determined based on attribute information, current environmental coefficient and current radiation power, including: determining multiple candidate input powers based on the current environmental coefficient and current radiation power; determining multiple candidate output powers based on the attribute information and the current environmental coefficient; for each candidate input power, combining the candidate input power with each candidate output power of the multiple candidate output powers to obtain multiple candidate combinations.

[0057] Exemplarily, the computer device obtains a first mapping table, wherein the first mapping table reflects the corresponding relationship between the environment coefficient, the radiation power and the input power. The first mapping table contains multiple first mapping items, each of which has a corresponding environment coefficient and radiation coefficient, and the first mapping item records multiple candidate input powers that can be selected under the corresponding environment coefficient and radiation coefficient. Therefore, the computer device queries the first mapping table according to the current environment coefficient and the current radiation power to determine the corresponding multiple candidate input powers.

[0058] The computer device obtains a second mapping table, wherein the second mapping table reflects the correspondence between the attribute information, the environmental coefficient and the output power. The second mapping table includes a plurality of second mapping items, each of which has a corresponding environmental coefficient and attribute information, and the second mapping item records a plurality of selectable candidate output powers under the corresponding environmental coefficient and attribute information. Thus, the computer device queries the second mapping table according to the current environmental coefficient and attribute information to determine the corresponding plurality of candidate output powers.

[0059] For each candidate input power, the candidate input power is respectively combined with each candidate output power of the plurality of candidate output powers to obtain a plurality of candidate combinations. That is, one candidate input power is randomly selected from the plurality of candidate input powers, and one candidate output power is randomly selected from the plurality of candidate output powers to form a candidate combination, and the combined candidate combinations are not repeated, and this is repeated until all combinations are combined.

[0060] The following is a specific formula to illustrate the optimization principle of the output power of the target photovoltaic module:

[0061] (3)

[0062] (4)

[0063] Among them, in formulas (3)-(4) Reflects the optimized output power of the target PV module at time t; is the conversion efficiency function. is the environmental coefficient of the target PV module at time t; It reflects the candidate input power of the target photovoltaic module at time t, which is related to the environmental coefficient Related to attribute information. It is to find the corresponding candidate output power when the conversion efficiency function is maximized; is the candidate output power of the target PV module at time t.

[0064] In this embodiment, multiple corresponding candidate input powers are queried based on the current environmental coefficient and the current radiation power, and multiple candidate output powers are queried based on the attribute information and the current environmental coefficient. Thus, multiple candidate combinations can be freely combined to facilitate the subsequent rapid selection of the output power corresponding to the maximum conversion efficiency.

[0065] Step 206, according to the optimized output power of the target photovoltaic assembly, determine the initial output power of the inverter connected to the target photovoltaic assembly, and optimize the initial output power according to the current load factor to obtain the optimized output power of the inverter. The optimized output power of the inverter is used for power scheduling of the power grid.

[0066] In one embodiment, the initial output power of an inverter connected to the target photovoltaic assembly is determined according to the optimized output power of the target photovoltaic assembly, including: obtaining the conversion efficiency of the inverter; and determining the initial output power of the inverter connected to the target photovoltaic assembly according to the product of the conversion efficiency of the inverter and the optimized output power of the target photovoltaic assembly.

[0067] The conversion efficiency of the inverter is usually between [0, 1], reflecting the efficiency of the inverter in converting DC power into AC power.

[0068] For example, the initial output power is calculated by referring to the following formula (5):

[0069] (5)

[0070] in, is the initial output power at the current time t, is the conversion efficiency of the inverter and is a known constant. It is the optimized output power of the target PV module at the current moment.

[0071] In this embodiment, the initial output power of the inverter can be determined promptly and quickly based on the conversion efficiency of the inverter and the optimized output power of the target photovoltaic assembly.

[0072] In one embodiment, the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, including: determining the corresponding power optimization factor according to the current load factor; superimposing the power optimization factor and the initial output power to obtain the optimized output power of the inverter.

[0073] In one embodiment, according to the current load factor, the corresponding power optimization factor is determined, including: the computer device obtains multiple candidate output powers of the target photovoltaic component and determines the candidate output power set. The computer device obtains a third mapping table, and the third mapping table reflects the correspondence between the candidate output power, the load factor, the initial output power of the inverter and the power optimization factor. The third mapping table contains multiple third mapping items, each of which records the selectable power optimization factor under the corresponding candidate output power set, load factor, and initial output power of the inverter. The third mapping table is based on historical scheduling statistics.

[0074] The following is an explanation of the principle of determining the power optimization factor, first see the following formulas (6)-(7):

[0075] (6)

[0076] (7)

[0077] in, is the initial output power at time t. is the candidate output power of the target PV module at time t, is the load power allocated to the load at time t, and f(.) is the load power allocation function, which can be determined based on the neural network model. The load power obtained when the subtraction operation obtains the minimum value, that is, the optimal power required to achieve the optimal power distribution (i.e., minimize the deviation between the load demand and the actual distributed power) under the current load demand L(t) . and (power optimization factor at time t) is positively correlated, that is, if Close to ideal ,Right now , the smaller the absolute value of the power optimization factor is, the closer it will be to 0, indicating that the system is in a better state under the current conditions; if and If the gap is large, the power optimization factor will deviate significantly from 0, requiring a large adjustment range, reflecting the improvement space of the current operating efficiency of the inverter. It can be seen that the power optimization factor is related to the candidate output power, the load factor, and the initial output power of the inverter. Therefore, in historical scheduling, for each historical scheduling, the corresponding multiple historical candidate output powers, historical load factors, historical initial output powers of the inverter, and the historical difference between the output power and the initial output power during actual scheduling (that is, the actual power optimization factor during the historical scheduling) are counted to form a corresponding third mapping item to construct a third mapping table. Of course, in other embodiments, the multiple historical candidate output powers, historical load factors, and historical initial output powers of the inverter corresponding to each historical scheduling can also be used as training samples, and the historical difference between the historical actual output power and the historical initial output power during actual scheduling can be used as the label of the training sample. Based on the training sample, the predicted power optimization factor is obtained through the model to be trained, and the model training is performed based on the difference between the predicted power optimization factor and the label to obtain a trained model. Therefore, in actual use, the corresponding power optimization factor is determined according to the current load factor, including: inputting multiple candidate output powers, the current load factor and the initial output power of the inverter into the trained model to obtain the power optimization factor.

[0078] In this embodiment, according to the current load factor, a power optimization factor matching the load is determined to optimize the initial output power of the inverter and obtain the optimized output power of the inverter. In this way, the secondary power optimization process in the inverter output link is realized, so that the power used for grid dispatching can be ensured to be appropriate and accurate, and the power dispatching accuracy of the photovoltaic power station is improved.

[0079] In a specific embodiment, the distributed photovoltaic power station power dispatching method of the embodiment of the present application can be executed by a distributed photovoltaic group control and dispatching system configured in a computer device for performing distributed photovoltaic group control and dispatching (centrally controlling and dispatching distributed photovoltaic power stations of multiple distributed photovoltaic power generation through advanced information technology and communication technology). Figure 3 As shown, it is a system architecture diagram of a distributed photovoltaic group control and group dispatching system in one embodiment. The distributed photovoltaic group control and group dispatching system is a system that integrates technologies in multiple technical fields such as data acquisition, data processing, intelligent scheduling, intelligent operation and maintenance, user interface and security management, that is, a distributed photovoltaic group control and group dispatching system integrating multiple technologies. Among them, the distributed photovoltaic group control and group dispatching system includes a data acquisition unit, a data processing unit, an intelligent scheduling unit, an intelligent operation and maintenance unit, a user interface unit and a security management unit. The data acquisition unit, the data processing unit, the intelligent scheduling unit, the intelligent operation and maintenance unit, the user interface unit and the security management unit can be distributed on different servers or terminals, or distributed in the same server without specific limitation.

[0080] Among them, the data processing unit can be composed of several data processing servers and a local database; each data processing server is equipped with a high-performance computer, installed with a database, data analysis software, communication software and data acquisition software; the local database is used to store the collected real-time data and the power plant operation report generated by data mining technology; the data analysis software is used to clean, store, analyze, mine the data, and generate the power plant operation report; the data acquisition software is used to collect data from the sensors and instruments of the Internet of Things devices through wired connection methods such as serial interface, RS-485 (a connection method) or RS-232 (a connection method); the communication software is used for wireless connection between the data processing server and the Internet of Things devices.

[0081] The intelligent dispatching unit is equipped with a high-performance computer installed with artificial intelligence algorithms, databases, communication software, etc.; artificial intelligence algorithms are used for power distribution, load management, optimization control and other operations; the database is used to store analysis results and optimization strategies; the communication software is used for data exchange between the data processing unit and the intelligent dispatching unit.

[0082] The intelligent operation and maintenance unit is equipped with fault diagnosis technology, equipment health technology and reliability prediction technology; fault diagnosis technology is used to analyze the status parameters of the equipment, automatically detect and locate faults; equipment health technology is used to obtain the health status of the power station operation and predict potential faults; reliability prediction technology is used to predict the failure rate and remaining service life of photovoltaic modules. For example, the intelligent operation and maintenance unit uploads the power station operation report to the intelligent operation and maintenance service based on cloud computing technology. The intelligent operation and maintenance service automatically detects system faults in combination with the fault diagnosis algorithm, and automatically troubleshoots and performs repair operations based on historical operation data and fault types; and uses feature extraction and deep learning technology to analyze the status parameters of the power station and generate a health report to realize the health judgment function; it can also use algorithms such as time series analysis and machine learning to predict the failure probability and remaining service life of photovoltaic modules to realize the reliability prediction function. The intelligent operation and maintenance unit includes a data receiving subunit, which is used to receive data transmitted in real time by the photovoltaic power station data processing unit as input for fault diagnosis, health status diagnosis and reliability prediction; a diagnosis and prediction subunit, which is used to perform fault diagnosis, health status diagnosis and reliability prediction on the distributed photovoltaic group; and a remote control subunit, which is used to remotely control the operating status of the photovoltaic power station according to the results of the fault diagnosis of the distributed photovoltaic group, so as to achieve rapid response to the grid load and peak shaving. Therefore, based on the intelligent operation and maintenance service, distributed photovoltaic group control and group adjustment can be realized, remote monitoring and operation and maintenance can be supported, the stable operation of the power station can be guaranteed, and the intelligence level of the system can be improved.

[0083] The user interface unit involves a communication network, which is used to connect the user terminal and the intelligent operation and maintenance unit. For example, the user interface unit can set the user's authority and operation authority; display important information such as power station data, fault information, and equipment status through the user interface; provide reporting functions to meet the different needs of users, and configure push services to send early warning information; configure remote control functions, and users can adjust the power output of the photovoltaic power station, perform equipment maintenance operations, and other operations.

[0084] The security management unit sets up a multi-level security mechanism to protect the security of the distributed photovoltaic group control and group dispatching system, including identity authentication technology (used to identify and confirm the identity of the user), authority management technology (used to allocate and limit the user's access rights), and data encryption technology (used to protect key data and business information). The security management unit sets up an identity authentication mechanism to ensure that only authorized users can access the cloud or local system; uses data encryption and decryption technology to protect key data and business information; implements authority management in the system, assigns different users, roles and permissions; and monitors the system's activities and operations through a log audit system. In this way, it can prevent the leakage of sensitive information and reduce the risk of system operation.

[0085] The specific steps include:

[0086] The data acquisition unit collects real-time data of voltage, current, and power from each photovoltaic module in the photovoltaic power station, obtains the collected data, and sends it to the data processing unit. The data processing unit uses data cleaning technology to remove invalid or abnormal data from the collected data, discovers hidden patterns, laws, and trends from the processed data, obtains the initial data of each photovoltaic module, and generates a power station operation report. The intelligent scheduling unit of the computer equipment obtains the initial data of the target photovoltaic module from the power station operation report, obtains the current environmental coefficient, current load coefficient, and current radiation power at the current moment through data analysis, and obtains the module data.

[0087] The intelligent scheduling unit obtains the attribute information of the target photovoltaic component, and determines multiple candidate input powers according to the current environmental coefficient and the current radiation power; determines multiple candidate output powers based on the attribute information and the current environmental coefficient; for each candidate input power, the candidate input power is combined with each candidate output power of the multiple candidate output powers to obtain multiple candidate combinations.

[0088] For each candidate combination, the intelligent scheduling unit calculates the ratio of the corresponding candidate output power to the candidate input power, and determines the corresponding candidate conversion efficiency; the candidate output power corresponding to the maximum candidate conversion efficiency is used as the optimized output power of the target photovoltaic module.

[0089] The intelligent dispatching unit obtains the conversion efficiency of the inverter; determines the initial output power of the inverter connected to the target photovoltaic module according to the product of the conversion efficiency of the inverter and the optimized output power of the target photovoltaic module. The intelligent dispatching unit determines the corresponding power optimization factor according to the current load factor; superimposes the power optimization factor and the initial output power to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power dispatching of the power grid.

[0090] In this embodiment, by obtaining the component data obtained by data processing of the target photovoltaic component in the photovoltaic power station, the current environmental coefficient related to the environment in which the target photovoltaic component is located at the current moment, the current load coefficient related to the target photovoltaic component, and the current radiation power received by the target photovoltaic component included in the component data can be obtained. Then, based on the current environmental coefficient and the current radiation power, the output power of the target photovoltaic component is optimized with the conversion efficiency of the target photovoltaic component maximized as the power optimization target to determine the optimized output power of the target photovoltaic component, thereby realizing a primary power optimization process in the output link of the photovoltaic component, which helps to improve the accuracy of subsequent power scheduling. According to the optimized output power of the target photovoltaic component, the initial output power of the inverter connected to the target photovoltaic component is determined, and the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid. In this way, the secondary power optimization process in the output link of the inverter is realized, so that it can be ensured that the power used for scheduling the power grid is appropriate and accurate, and the power scheduling accuracy of the photovoltaic power station is improved. In this way, it is possible to reduce imbalance and loss and improve the overall efficiency of the photovoltaic power station.

[0091] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0092] Based on the same inventive concept, the embodiment of the present application also provides a distributed photovoltaic power station power dispatching device for implementing the above-mentioned distributed photovoltaic power station power dispatching method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the power dispatching device of one or more distributed photovoltaic power stations provided below can refer to the limitations of the power dispatching method of the distributed photovoltaic power station above, and will not be repeated here.

[0093] In an exemplary embodiment, Figure 4 As shown, a power dispatching device 400 for a distributed photovoltaic power station is provided, comprising: a data acquisition module 402, a first power optimization module 404 and a second power optimization module 406, wherein:

[0094] The data acquisition module 402 is used to acquire component data obtained by processing the data of the target photovoltaic component in the photovoltaic power station, wherein the component data includes the current environmental coefficient related to the environment in which the target photovoltaic component is located at the current moment, the current load coefficient related to the target photovoltaic component, and the current radiation power received by the target photovoltaic component;

[0095] A first power optimization module 404 is used to determine the optimized output power of the target photovoltaic component based on the current environmental factor and the current radiation power, with maximizing the conversion efficiency of the target photovoltaic component as the power optimization target;

[0096] The second power optimization module 406 is used to determine the initial output power of the inverter connected to the target photovoltaic component according to the optimized output power of the target photovoltaic component, and optimize the initial output power according to the current load factor to obtain the optimized output power of the inverter. The optimized output power of the inverter is used for power scheduling of the power grid.

[0097] In one embodiment, the first power optimization module 404 is used to obtain attribute information of the target photovoltaic component, and determine multiple candidate conversion efficiencies based on the attribute information, the current environmental coefficient, and the current radiation power, each candidate conversion efficiency is determined based on the ratio of the corresponding candidate output power to the candidate input power; the candidate output power corresponding to the maximum candidate conversion efficiency is used as the optimized output power of the target photovoltaic component.

[0098] In one embodiment, the first power optimization module 404 is used to determine multiple candidate combinations based on attribute information, current environmental coefficients and current radiation power, each candidate combination including a corresponding candidate output power and a candidate input power; for each candidate combination, the ratio of the corresponding candidate output power to the candidate input power is calculated to determine the corresponding candidate conversion efficiency.

[0099] In one embodiment, the first power optimization module 404 is used to determine multiple candidate input powers based on the current environmental coefficient and the current radiation power; determine multiple candidate output powers based on attribute information and the current environmental coefficient; for each candidate input power, combine the candidate input power with each candidate output power in the multiple candidate output powers to obtain multiple candidate combinations.

[0100] In one embodiment, the second power optimization module 406 is used to obtain the conversion efficiency of the inverter; and determine the initial output power of the inverter connected to the target photovoltaic component according to the product of the conversion efficiency of the inverter and the optimized output power of the target photovoltaic component.

[0101] In one embodiment, the second power optimization module 406 is used to determine a corresponding power optimization factor according to the current load factor; and superimpose the power optimization factor and the initial output power to obtain the optimized output power of the inverter.

[0102] Each module in the power dispatching device of the above-mentioned distributed photovoltaic power station can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0103] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a power scheduling method for a distributed photovoltaic power station is implemented.

[0104] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0105] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0106] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0107] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0108] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0109] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0110] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0111] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A power dispatching method for a distributed photovoltaic power station, characterized in that: The method comprises: Acquire component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component; Based on the current environmental coefficient and the current radiation power, the optimized output power of the target photovoltaic component is determined by maximizing the conversion efficiency of the target photovoltaic component as a power optimization goal; According to the optimized output power of the target photovoltaic component, the initial output power of the inverter connected to the target photovoltaic component is determined, and the initial output power is optimized according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

2. The method according to claim 1, characterized in that The step of determining the optimized output power of the target photovoltaic component based on the current environmental coefficient and the current radiation power and taking maximizing the conversion efficiency of the target photovoltaic component as the power optimization target comprises: Acquire attribute information of the target photovoltaic component, and determine a plurality of candidate conversion efficiencies according to the attribute information, the current environmental factor, and the current radiation power, wherein each candidate conversion efficiency is determined based on a ratio of a corresponding candidate output power to a candidate input power; The candidate output power corresponding to the maximum candidate conversion efficiency is used as the optimized output power of the target photovoltaic component.

3. The method according to claim 2, characterized in that The determining of a plurality of candidate conversion efficiencies according to the attribute information, the current environment coefficient, and the current radiation power includes: Based on the attribute information, the current environment coefficient and the current radiation power, determining a plurality of candidate combinations, each candidate combination comprising a corresponding candidate output power and a candidate input power; For each candidate combination, a ratio of a corresponding candidate output power to a candidate input power is calculated to determine a corresponding candidate conversion efficiency.

4. The method according to claim 3, characterized in that The determining of a plurality of candidate combinations based on the attribute information, the current environment coefficient and the current radiation power comprises: Determining a plurality of candidate input powers according to the current environment coefficient and the current radiation power; Determining a plurality of candidate output powers based on the attribute information and the current environment coefficient; For each candidate input power, the candidate input power is respectively combined with each candidate output power of the plurality of candidate output powers to obtain a plurality of candidate combinations.

5. The method according to claim 1, characterized in that The step of determining the initial output power of an inverter connected to the target photovoltaic assembly according to the optimized output power of the target photovoltaic assembly comprises: Obtain the conversion efficiency of the inverter; The initial output power of the inverter connected to the target photovoltaic assembly is determined according to the product of the conversion efficiency of the inverter and the optimized output power of the target photovoltaic assembly.

6. The method according to claim 1, characterized in that The step of optimizing the initial output power according to the current load factor to obtain the optimized output power of the inverter includes: Determining a corresponding power optimization factor according to the current load factor; The power optimization factor and the initial output power are superimposed to obtain the optimized output power of the inverter.

7. A power dispatching device for a distributed photovoltaic power station, characterized in that: The device comprises: A data acquisition module, used to acquire component data obtained by data processing of a target photovoltaic component in a photovoltaic power station, wherein the component data includes a current environmental coefficient related to the environment in which the target photovoltaic component is located at a current moment, a current load coefficient related to the target photovoltaic component, and a current radiation power received by the target photovoltaic component; A first power optimization module, configured to determine the optimized output power of the target photovoltaic component based on the current environmental coefficient and the current radiation power, with maximizing the conversion efficiency of the target photovoltaic component as a power optimization target; The second power optimization module is used to determine the initial output power of the inverter connected to the target photovoltaic component according to the optimized output power of the target photovoltaic component, and optimize the initial output power according to the current load factor to obtain the optimized output power of the inverter, and the optimized output power of the inverter is used for power scheduling of the power grid.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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