Distributed photovoltaic resource analog simulation method, device, equipment and medium

Abstract

The invention belongs to the technical field of system simulation, and discloses a distributed photovoltaic resource analog simulation method and device, equipment and a medium. The method comprises the following steps: acquiring power generation information data and actual prediction power generation information of an actually accessed distributed photovoltaic system; a pre-constructed photovoltaic power generation resource model is used for dynamically simulating the power generation state and the output state of each distributed photovoltaic module inverter according to configured model parameters in combination with distributed photovoltaic power generation information data and actual prediction power generation information and a received control command of downloading data, and the distributed photovoltaic power generation resource model is used for dynamically simulating the power generation state and the output state of each photovoltaic module inverter. Acquiring the uploading data of each photovoltaic module inverter; dynamically simulating a data downloading and data uploading communication response process according to the configured communication parameters and operation parameters; aggregated data is calculated based on the uploaded data, and preset classification data and total power generation data of all photovoltaic power generation aggregation are generated. According to the invention, powerful support is provided for the flexibility and expandability of the distributed photovoltaic power generation system in practical application.

Description

Technical Field

[0001] The present invention belongs to the technical field of system simulation, and in particular relates to a distributed photovoltaic resource simulation method, device, equipment and medium. Background Art

[0002] Distributed photovoltaic power generation is a new and potential power generation method. Its core is to install small photovoltaic power generation systems in places near users or electricity demand to achieve the goals of local power generation, local grid connection, local conversion and local use.

[0003] Distributed photovoltaic power generation resources are connected to the distribution network in large quantities, becoming an important part of building a new power system. There are a large number of terminal devices connected to the power grid, which realizes information perception and processing in all links of the power Internet of Things, and promotes data sharing and integration of the power Internet of Things platform and comprehensive business integration. At present, the access and control technology of aggregation terminals is widely used. Aggregation terminals in each control area can access and control distributed photovoltaic power generation systems to meet the observable, measurable and controllable needs of distributed photovoltaics.

[0004] Although the access and control technology of aggregated terminals has been widely used, simulation methods for distributed photovoltaic resources are still lacking; this limits the flexibility and scalability of distributed photovoltaic power generation systems in practical applications. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed photovoltaic resource simulation method, device, equipment and medium to solve the technical problem of lack of simulation methods for distributed photovoltaic resources and provide strong support for the flexibility and scalability of distributed photovoltaic power generation systems in practical applications.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a distributed photovoltaic resource simulation method, comprising: Obtaining actual power generation information data of distributed photovoltaics; Combine the predicted sunshine data and ambient temperature information to predict the power generation of distributed photovoltaics and obtain the actual predicted power generation information; Using the pre-built photovoltaic power generation resource model according to the configured model parameters, combined with the distributed photovoltaic power generation information data and the actual predicted power generation information, as well as the control command of the received downlink data, the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic are dynamically simulated to obtain the uploaded data of the power generation state and output state of each photovoltaic module inverter; Dynamically simulate the communication response process of downloading and uploading data according to the configured communication parameters and operating parameters; Aggregate data is calculated based on the uploaded data, and preset classified data and total power generation data of all photovoltaic power generation aggregation are generated.

[0007] A further improvement of the present invention is that in the step of obtaining power generation information data actually connected to distributed photovoltaics, the power generation information data includes: current, voltage, capacity and power.

[0008] A further improvement of the present invention is that in the step of predicting the power generation of distributed photovoltaics by combining the predicted sunshine data and ambient temperature information to obtain the actual predicted power generation information, the photovoltaic sunshine data includes the maximum sunshine conditions predicted at each moment of the photovoltaics, the sunshine amounts at different moments of different days and the maximum sunshine output curve.

[0009] A further improvement of the present invention lies in that: in the step of dynamically simulating the power generation state and output state of each photovoltaic component inverter of the distributed photovoltaic system, each photovoltaic component inverter is configured with the same or different twin strategies; the twin strategies include: using typical daily historical or predicted data to proportionally generate a power generation curve, using a combination of different daily historical or predicted data to proportionally generate a power generation curve, or using the real-time data or predicted data of the day to proportionally generate a power generation curve.

[0010] A further improvement of the present invention is that in the step of dynamically simulating the power generation state and output state of each photovoltaic component inverter of the distributed photovoltaic system, the power Pt.now of each photovoltaic component inverter at time t operates between the minimum power Pt.Min and the maximum power Pt.max, and Pt.max is determined according to the power generation curve of the configured twin strategy.

[0011] The present invention is further improved in that: according to the configured communication parameters and operating parameters, in the step of dynamically simulating the communication response process of downloading data and uploading data, the response delay parameters of different photovoltaic modules at different stages are configured in the configured communication parameters and operating parameters, and the complete control response time is formed by combining the random function rand() , which is composed of the power regulation response coefficient Kre, the response time constant Trc and the response time variable Trv combined with the random function rand(), the arrival time of the power regulation target value Pt.aim is: (2) Simulation communication response time , which is composed of the response channel time fixed constant Tcc and variable constant Tcv combined with the random function rand(): (3).

[0012] A further improvement of the present invention is that in the step of calculating aggregated data based on uploaded data and generating data of preset classifications and total power generation data of all photovoltaic power generation aggregations, the preset classifications include: by region, by inverter brand, by inverter power and by power generation capacity.

[0013] A further improvement of the present invention is that it also includes the following steps: receiving distributed photovoltaic control instructions, and according to the distributed photovoltaic control instructions and the composition of distributed photovoltaics, different distributed photovoltaics adopt the same or different allocation algorithms to form control commands for downlink data; the allocation algorithms include: power average allocation method, maximum adjustable weight allocation method, and maximum adjustable priority allocation method.

[0014] In a second aspect, the present invention provides a distributed photovoltaic resource simulation device, comprising: Photovoltaic data collection module, used to obtain the power generation information data actually connected to distributed photovoltaics; Photovoltaic power generation prediction module, used to predict the power generation of distributed photovoltaics by combining predicted sunshine data and ambient temperature information to obtain actual predicted power generation information; The digital twin modeling module is used to dynamically simulate the power generation status and output status of each photovoltaic module inverter of the distributed photovoltaic system by using the pre-built photovoltaic power generation resource model according to the configured model parameters, combining the power generation information data and the actual predicted power generation information of the distributed photovoltaic system, and the control command of the received downlink data, and obtain the uploaded data of the power generation status and output status of each photovoltaic module inverter; Data communication simulation module, used to dynamically simulate the communication response process of downloading and uploading data according to the configured communication parameters and operating parameters; The photovoltaic aggregation module is used to calculate the aggregated data based on the uploaded data and generate the preset classified data and the total power generation data of all photovoltaic power generation aggregations.

[0015] A further improvement of the present invention is that in the step of obtaining power generation information data actually connected to distributed photovoltaics, the power generation information data includes: current, voltage, capacity and power.

[0016] A further improvement of the present invention is that in the step of predicting the power generation of distributed photovoltaics by combining the predicted sunshine data and ambient temperature information to obtain the actual predicted power generation information, the photovoltaic sunshine data includes the maximum sunshine conditions predicted at each moment of the photovoltaics, the sunshine amounts at different moments of different days and the maximum sunshine output curve.

[0017] A further improvement of the present invention lies in that: in the step of dynamically simulating the power generation state and output state of each photovoltaic component inverter of the distributed photovoltaic system, each photovoltaic component inverter is configured with the same or different twin strategies; the twin strategies include: using typical daily historical or predicted data to proportionally generate a power generation curve, using a combination of different daily historical or predicted data to proportionally generate a power generation curve, or using the real-time data or predicted data of the day to proportionally generate a power generation curve.

[0018] A further improvement of the present invention is that in the step of dynamically simulating the power generation state and output state of each photovoltaic component inverter of the distributed photovoltaic system, the power Pt.now of each photovoltaic component inverter at time t operates between the minimum power Pt.Min and the maximum power Pt.max, and Pt.max is determined according to the power generation curve of the configured twin strategy.

[0019] The present invention is further improved in that: according to the configured communication parameters and operating parameters, in the step of dynamically simulating the communication response process of downloading data and uploading data, the response delay parameters of different photovoltaic modules at different stages are configured in the configured communication parameters and operating parameters, and the complete control response time is formed by combining the random function rand() , which is composed of the power regulation response coefficient Kre, the response time constant Trc and the response time variable Trv combined with the random function rand(), the arrival time of the power regulation target value Pt.aim is: (2) Simulation communication response time , which is composed of the response channel time fixed constant Tcc and variable constant Tcv combined with the random function rand(): (3).

[0020] A further improvement of the present invention is that in the step of calculating aggregated data based on uploaded data and generating data of preset classifications and total power generation data of all photovoltaic power generation aggregations, the preset classifications include: by region, by inverter brand, by inverter power and by power generation capacity.

[0021] A further improvement of the present invention is that it also includes a control algorithm simulation module; the control algorithm simulation module is used to receive distributed photovoltaic control instructions, and according to the distributed photovoltaic control instructions and the composition of distributed photovoltaics, different distributed photovoltaics adopt the same or different allocation algorithms to form control commands for downlink data; the allocation algorithms include: power average allocation method, maximum adjustable weight allocation method, and maximum adjustable priority allocation method.

[0022] In a third aspect, the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the distributed photovoltaic resource simulation method.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the distributed photovoltaic resource simulation method is implemented.

[0024] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a distributed photovoltaic resource simulation method, comprising: obtaining power generation information data of actually connected distributed photovoltaics; predicting the power generation of distributed photovoltaics in combination with predicted sunshine data and ambient temperature information to obtain actual predicted power generation information; using a pre-constructed photovoltaic power generation resource model according to configured model parameters, combining the power generation information data of distributed photovoltaics and the actual predicted power generation information, and the control command of received downlink data, dynamically simulating the power generation state and output state of each photovoltaic component inverter of the distributed photovoltaics, and obtaining the uploaded data of the power generation state and output state of each photovoltaic component inverter; dynamically simulating the communication response process of downlink data and uplink data according to configured communication parameters and operating parameters; calculating aggregated data based on the uploaded data, and generating preset classified data and total power generation data of all photovoltaic power generation aggregations. The present invention uses the data of the actual collection device and the predicted data to simulate the photovoltaic power generation model; simulates the delay that may be generated by different data channels, uses the digital twin module to simulate the response time of different devices to the control command, and generates a simulation of the communication access of massive resources and massive data to the main station that conforms to the actual scene; adopts a configurable instruction allocation strategy to achieve the dynamic distribution of distributed photovoltaic resource control instructions, and realizes the control simulation of photovoltaic power generation calling negative standby under special circumstances. The present invention simulates the access of distributed photovoltaic resources on demand according to the actual situation on site through the aggregation terminal equipment, adopts similar digital twin technology, and refers to the real photovoltaic power generation data collected on-site by the aggregation terminal and the predicted data in operation, so as to realize the dynamic simulation of the distributed photovoltaic resources in the localization of the aggregation terminal. At the same time, a delay method that can be configured to simulate various communication channels is designed to realize the dynamic simulation execution process of the photovoltaic simulation resource acquisition control command of the main station, so as to achieve the communication closed-loop simulation control of distributed photovoltaic aggregation resources, and assist the optimization simulation operation and relevant algorithm verification of the superior scheduling transaction system. In addition, the present invention can also realize the information model verification of the simulation of distributed photovoltaic access when the distributed photovoltaic resources are not actually accessed. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of the structure of a distributed photovoltaic resource simulation system according to an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a distributed photovoltaic resource simulation method according to an embodiment of the present invention; Figure 3 A schematic diagram of a flow chart of a distributed photovoltaic resource simulation method according to an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a distributed photovoltaic resource simulation system according to an embodiment of the present invention; Figure 5 A schematic diagram of the structure of a distributed photovoltaic resource simulation system according to an embodiment of the present invention; Figure 6 The present invention is a structural block diagram of an electronic device. DETAILED DESCRIPTION

[0026] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0027] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0028] Based on the data collection and communication functions of distributed photovoltaic aggregation terminal equipment, the present invention can use similar digital twin simulation technology to achieve simulated access of distributed photovoltaic resources when partially accessing real physical distributed photovoltaic power generation resources, and realize the simulation control of subordinate distributed photovoltaic simulation resources by the dispatching and trading master station through actual communication channels and simulated communication channels. Through this simulation method, the access of photovoltaic resources of different scales can be simulated at the aggregation terminals at different nodes of the power grid, and the real-time dynamic simulation control between the dispatching and trading master station and the aggregation model can be realized through the actual communication channel from the aggregation terminal to the dispatching and trading master station.

[0029] See also Figure 1 As shown, an embodiment of the present invention provides a distributed photovoltaic resource simulation system, including: The photovoltaic data acquisition module is used to collect the actual photovoltaic power generation information data, including: current, voltage, capacity and power; the actual photovoltaic current, voltage, capacity and power collected by the photovoltaic data acquisition module can be directly connected to the actual dispatching and trading master station (non-simulation dispatching and trading module) to complete the monitoring and control of the running photovoltaic system. The photovoltaic data acquisition module is also used to provide reference data for data twins, including the actual photovoltaic power generation capacity, power generation curves on different days, and inverter equipment information.

[0030] The photovoltaic power generation prediction module is used to predict the photovoltaic sunshine data on site based on meteorological data and data from environmental sensors. The photovoltaic sunshine data includes the maximum sunshine conditions predicted at each moment of the photovoltaic, the sunshine amount at different moments of different days, and the maximum sunshine output curve. The photovoltaic power generation prediction module is used to predict the photovoltaic power generation by combining the predicted sunshine data and the ambient temperature information collected by the environmental sensor to obtain the actual predicted power generation information.

[0031] The digital twin modeling module is used to build a photovoltaic power generation resource model for simulation needs (including the type and number of inverters, the maximum output power of each inverter, and the grid-connected power generation capacity). The photovoltaic data acquisition module is used to obtain the actual photovoltaic power generation information data, and the photovoltaic power generation prediction module is used to obtain the actual predicted power generation information. The photovoltaic power generation resource model is bound to the power generation information data and the actual predicted power generation information. Different twin strategies are configured for the photovoltaic power generation capacity corresponding to each inverter. The twin strategies include: 1. Generate a power generation curve in proportion using typical daily historical or predicted data, 2. Generate a power generation curve in proportion using different daily historical or predicted data combinations, and 3. Generate a power generation curve in proportion using the real-time data or predicted data of the day.

[0032] The digital twin modeling module refers to the data of the photovoltaic power generation prediction module to simulate and construct the controllable and adjustable power generation space of different photovoltaic module units, that is, the adjustable power range. For example, the power range at time t on a certain day (the t interval in the present invention is one hour, and the value is 0-23) is designed to be the minimum power Pt.Min and the maximum power Pt.max. Generally, considering the operation of abandoning light in the new power system environment, Pt.min can be set to 0, and Pt.max gives the optical power curve according to the digital twin modeling module, as shown in the following formula. In the formula, Pt.now is the current power, which runs between the minimum power Pt.Min and the maximum power Pt.max. The adjustable part is the difference between the current power Pt.now and the two. Under normal operation, the current power Pt.now is equal to the maximum power Pt.max, and the photovoltaic is in full power state.

[0033] (1) The data communication simulation module simulates the possible communication delay when uploading and downloading data according to the actual on-site communication configuration. After the control data is downloaded, the uploading data will affect the control result. This kind of delay is very important for simulation control. The present invention adopts the response delay parameters of different photovoltaic modules at different stages, combined with the random function rand() to form a complete control response time. , which is composed of the power regulation response coefficient Kre, the response time constant Trc and the response time variable Trv combined with the random function rand(), the arrival time of the power regulation target value Pt.aim is as follows: (2) Simulation communication response time , which is composed of the response channel time fixed constant Tcc (determined by the communication method) and the variable constant Tcv combined with the random function rand(): (3) The complete time process is: after the photovoltaic aggregation module receives the control command of the simulation master station, the data of the simulated photovoltaic module is updated after Tr+Tc time.

[0034] The control algorithm simulation module receives the photovoltaic resource control instructions from the photovoltaic aggregation module during the simulation process. According to the photovoltaic resource control instructions and the composition of photovoltaic resources, different allocation algorithms (power average allocation method, maximum adjustable weight allocation method, maximum adjustable priority allocation method) are used to realize the downward allocation of the scheduling and trading system instructions.

[0035] The photovoltaic aggregation module aggregates the interface of all photovoltaic resources, accesses the aggregation model of simulated photovoltaic resources at the grid-connected point, and directly interacts with the dispatching and trading center.

[0036] See also Figure 2 As shown, an embodiment of the present invention provides a distributed photovoltaic resource simulation method, comprising the following steps: After starting the simulation process, the configuration data of the distributed photovoltaic simulation is called. The configuration file includes the type and number of inverters, the maximum output power of each inverter, and the grid-connected power generation capacity. The configuration data of the photovoltaic simulation is imported into the digital twin modeling module, and a binding relationship with the photovoltaic data acquisition module and the photovoltaic power generation prediction module data is established to configure different twin strategies for the photovoltaic power generation capacity corresponding to each inverter. The twin strategies include: 1. Proportional generation of power generation curves using typical daily historical or predicted data, 2. Proportional generation of power generation curves using different daily historical or predicted data combinations, and 3. Proportional generation of power generation curves using real-time data or predicted data of the day.

[0037] Read the historical data in the photovoltaic data acquisition module, and update the digital twin modeling module data associated with the historical power generation data of different days, different inverters, and different power generation capacities.

[0038] Read the data of the photovoltaic power generation prediction module and update the data twin modeling module data associated with the prediction historical data of different days, different inverters, and different power generation capacities.

[0039] In the digital twin modeling module, the power generation status and output status of each PV module inverter are dynamically simulated based on the configured model parameters, combined with the updated data and the response to the control commands.

[0040] The data communication module configures different communication parameters based on the model in the data twin modeling module, and dynamically simulates the data communication response process of photovoltaic resources according to the configured communication parameters and operating parameters.

[0041] In the photovoltaic aggregation module, the aggregation model of the photovoltaic module inverter simulation module based on digital twin modeling calculates the aggregation data, and generates different aggregation model data by region, by inverter brand, by inverter power, by power generation capacity, etc., as well as the total power generation data of all photovoltaic power generation aggregations.

[0042] The photovoltaic aggregation module sends simulation model data and aggregated power generation simulation data according to the configuration settings to the master station, and receives simulation control commands from the master station.

[0043] The photovoltaic aggregation module receives the power control command issued by the master station, calls the control algorithm simulation module, distributes the total power control command into power control instructions for each simulated photovoltaic inverter, and transmits the instruction to the digital twin module through the communication simulation module, during which the communication and control delay time simulation is completed.

[0044] Each data instruction is transmitted to the digital twin module through delay to form a control result and refresh the power generation status of each inverter in the digital twin module.

[0045] If the simulation process is not stopped, the process returns to the step of reading the historical data in the photovoltaic data acquisition module to form a closed loop.

[0046] See also Figure 3 As shown, the present invention provides a distributed photovoltaic resource simulation method, comprising: S1. Obtaining the power generation information data of the distributed photovoltaic power generation actually connected; In a specific implementation, the power generation information data includes: current, voltage, capacity and power.

[0047] S2. Predict the power generation of distributed photovoltaics by combining the predicted sunshine data and ambient temperature information to obtain actual predicted power generation information; In a specific implementation, the photovoltaic sunshine data includes the maximum sunshine conditions predicted at each time of the photovoltaic system, the sunshine amounts at different times of different days, and the maximum sunshine output curve.

[0048] S3. Using the pre-built photovoltaic power generation resource model according to the configured model parameters, combined with the distributed photovoltaic power generation information data and the actual predicted power generation information, and the control command of the received downlink data, dynamically simulate the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic, and obtain the uploaded data of the power generation state and output state of each photovoltaic module inverter; In a specific embodiment, each photovoltaic module inverter is configured with the same or different twin strategies; the twin strategies include: using typical daily historical or predicted data to proportionally generate a power generation curve, using different daily historical or predicted data combinations to proportionally generate a power generation curve, or using the current day's real-time data or predicted data to proportionally generate a power generation curve; the power Pt.now of each photovoltaic module inverter at time t operates between the minimum power Pt.Min and the maximum power Pt.max, and Pt.max is determined according to the power generation curve of the configured twin strategy; S4. Dynamically simulate the communication response process of downloading and uploading data according to the configured communication parameters and operating parameters; In a specific implementation, the response delay parameters of different photovoltaic modules at different stages are configured in the configured communication parameters and operation parameters, and the complete control response time is formed by combining the random function rand(). , which is composed of the power regulation response coefficient Kre, the response time constant Trc and the response time variable Trv combined with the random function rand(), the arrival time of the power regulation target value Pt.aim is: (2) Simulation communication response time , which is composed of the response channel time fixed constant Tcc and variable constant Tcv combined with the random function rand(): (3) The complete time process is: after the photovoltaic aggregation module receives the control command of the simulation master station, the data of the simulated photovoltaic module is updated after Tr+Tc time; S5. Calculate aggregate data based on the uploaded data, and generate preset classified data and total power generation data of all photovoltaic power generation aggregates; In a specific implementation, the preset classification includes: by region, by inverter brand, by inverter power, and by power generation capacity.

[0049] In a specific embodiment, a distributed photovoltaic resource simulation method of the present invention also includes the following steps: receiving distributed photovoltaic control instructions, and according to the distributed photovoltaic control instructions and the composition of distributed photovoltaics, different distributed photovoltaics adopt the same or different allocation algorithms to form control commands for downlink data; the allocation algorithms include: power average allocation method, maximum adjustable weight allocation method, and maximum adjustable priority allocation method.

[0050] See also Figure 4 As shown, the present invention provides a distributed photovoltaic resource simulation device, comprising: Photovoltaic data collection module, used to obtain the power generation information data actually connected to distributed photovoltaics; Photovoltaic power generation prediction module, used to predict the power generation of distributed photovoltaics by combining predicted sunshine data and ambient temperature information to obtain actual predicted power generation information; The digital twin modeling module is used to dynamically simulate the power generation status and output status of each photovoltaic module inverter of the distributed photovoltaic system by using the pre-built photovoltaic power generation resource model according to the configured model parameters, combining the power generation information data and the actual predicted power generation information of the distributed photovoltaic system, and the control command of the received downlink data, and obtain the uploaded data of the power generation status and output status of each photovoltaic module inverter; Data communication simulation module, used to dynamically simulate the communication response process of downloading and uploading data according to the configured communication parameters and operating parameters; The photovoltaic aggregation module is used to calculate the aggregated data based on the uploaded data and generate the preset classified data and the total power generation data of all photovoltaic power generation aggregations.

[0051] In a specific implementation, in the step of obtaining power generation information data actually connected to distributed photovoltaics, the power generation information data includes: current, voltage, capacity and power.

[0052] In a specific embodiment, in the step of predicting the power generation of distributed photovoltaics by combining the predicted sunshine data and ambient temperature information to obtain the actual predicted power generation information, the photovoltaic sunshine data includes the maximum sunshine conditions predicted at each moment of the photovoltaics, the sunshine amounts at different moments of different days, and the maximum sunshine output curve.

[0053] In a specific embodiment, in the step of dynamically simulating the power generation state and output state of each photovoltaic component inverter of the distributed photovoltaic system, each photovoltaic component inverter is configured with the same or different twin strategies; the twin strategies include: using typical daily historical or predicted data to proportionally generate a power generation curve, using a combination of different daily historical or predicted data to proportionally generate a power generation curve, or using the real-time data or predicted data of the day to proportionally generate a power generation curve.

[0054] In a specific embodiment, in the step of dynamically simulating the power generation state and output state of each photovoltaic component inverter of the distributed photovoltaic system, the power Pt.now of each photovoltaic component inverter at time t operates between the minimum power Pt.Min and the maximum power Pt.max, and Pt.max is determined according to the power generation curve of the configured twin strategy.

[0055] In a specific implementation, according to the configured communication parameters and operating parameters, in the step of dynamically simulating the communication response process of downloading data and uploading data, the configured communication parameters and operating parameters are configured with the response delay parameters of different photovoltaic modules at different stages, and the random function rand() is combined to form a complete control response time. , which is composed of the power regulation response coefficient Kre, the response time constant Trc and the response time variable Trv combined with the random function rand(), the arrival time of the power regulation target value Pt.aim is: (2) Simulation communication response time , which is composed of the response channel time fixed constant Tcc and variable constant Tcv combined with the random function rand(): (3).

[0056] In a specific embodiment, in the step of calculating aggregated data based on uploaded data and generating data of preset classifications and total power generation data of all photovoltaic power generation aggregations, the preset classifications include: by region, by inverter brand, by inverter power and by power generation capacity.

[0057] See also Figure 5 As shown, in a specific embodiment, the present invention also includes a control algorithm simulation module; the control algorithm simulation module is used to receive distributed photovoltaic control instructions, and according to the distributed photovoltaic control instructions and the composition of distributed photovoltaics, different distributed photovoltaics adopt the same or different allocation algorithms to form control commands for downlink data; the allocation algorithms include: power average allocation method, maximum adjustable weight allocation method, and maximum adjustable priority allocation method.

[0058] See also Figure 6 As shown, an embodiment of the present invention provides an electronic device 100 for implementing a distributed photovoltaic resource simulation method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0059] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of a distributed photovoltaic resource simulation method described in the embodiment by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0060] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0061] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a distributed photovoltaic resource simulation method, and the processor 102 can execute the plurality of instructions to implement: Obtaining actual power generation information data of distributed photovoltaics; Combine the predicted sunshine data and ambient temperature information to predict the power generation of distributed photovoltaics and obtain the actual predicted power generation information; Using the pre-built photovoltaic power generation resource model according to the configured model parameters, combined with the distributed photovoltaic power generation information data and the actual predicted power generation information, as well as the control command of the received downlink data, the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic are dynamically simulated to obtain the uploaded data of the power generation state and output state of each photovoltaic module inverter; Dynamically simulate the communication response process of downloading and uploading data according to the configured communication parameters and operating parameters; Aggregate data is calculated based on the uploaded data, and preset classified data and total power generation data of all photovoltaic power generation aggregation are generated.

[0062] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0063] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed photovoltaic resource simulation method, characterized in that: include: Obtaining actual power generation information data of distributed photovoltaics; Combine the predicted sunshine data and ambient temperature information to predict the power generation of distributed photovoltaics and obtain the actual predicted power generation information; Using the pre-built photovoltaic power generation resource model according to the configured model parameters, combined with the distributed photovoltaic power generation information data and the actual predicted power generation information, as well as the control command of the received downlink data, the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic are dynamically simulated to obtain the uploaded data of the power generation state and output state of each photovoltaic module inverter; Dynamically simulate the communication response process of downloading and uploading data according to the configured communication parameters and operating parameters; Aggregate data is calculated based on the uploaded data, and preset classified data and total power generation data of all photovoltaic power generation aggregation are generated.

2. A distributed photovoltaic resource simulation method according to claim 1, characterized in that: In the step of obtaining power generation information data actually connected to distributed photovoltaics, the power generation information data includes: current, voltage, capacity and power.

3. A distributed photovoltaic resource simulation method according to claim 1, characterized in that: In the step of predicting the power generation of distributed photovoltaics by combining the predicted sunshine data and ambient temperature information to obtain the actual predicted power generation information, the photovoltaic sunshine data includes the maximum sunshine conditions predicted at each moment of the photovoltaics, the sunshine amounts at different moments of different days and the maximum sunshine output curve.

4. A distributed photovoltaic resource simulation method according to claim 1, characterized in that: In the step of dynamically simulating the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic system, each photovoltaic module inverter is configured with the same or different twin strategies; the twin strategies include: using typical daily historical or predicted data to proportionally generate a power generation curve, using different daily historical or predicted data combinations to proportionally generate a power generation curve, or using the real-time data or predicted data of the day to proportionally generate a power generation curve.

5. A distributed photovoltaic resource simulation method according to claim 4, characterized in that: In the step of dynamically simulating the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic system, the power Pt.now of each photovoltaic module inverter at time t operates between the minimum power Pt.Min and the maximum power Pt.max, and Pt.max is determined according to the power generation curve of the configured twin strategy.

6. A distributed photovoltaic resource simulation method according to claim 1, characterized in that: According to the configured communication parameters and operating parameters, the steps of dynamically simulating the communication response process of downloading and uploading data are configured. The response delay parameters of different photovoltaic modules at different stages are configured in the configured communication parameters and operating parameters, and the complete control response time is formed by combining the random function rand(). , which is composed of the power regulation response coefficient Kre, the response time constant Trc and the response time variable Trv combined with the random function rand(), the arrival time of the power regulation target value Pt.aim is: （2） Simulation communication response time , which is composed of the response channel time fixed constant Tcc and variable constant Tcv combined with the random function rand(): （3）。 7. A distributed photovoltaic resource simulation method according to claim 1, characterized in that: In the step of calculating aggregated data based on uploaded data and generating data of preset classifications and total power generation data of all photovoltaic power generation aggregations, the preset classifications include: by region, by inverter brand, by inverter power and by power generation capacity.

8. A distributed photovoltaic resource simulation method according to claim 1, characterized in that: The following steps are also included: Receive distributed photovoltaic control instructions, and according to the distributed photovoltaic control instructions and the composition of distributed photovoltaics, different distributed photovoltaics adopt the same or different allocation algorithms to form control commands for downlink data; the allocation algorithms include: power average allocation method, maximum adjustable weight allocation method, and maximum adjustable priority allocation method.

9. A distributed photovoltaic resource simulation device, characterized in that: include: Photovoltaic data collection module, used to obtain the power generation information data actually connected to distributed photovoltaics; Photovoltaic power generation prediction module, used to predict the power generation of distributed photovoltaics by combining predicted sunshine data and ambient temperature information to obtain actual predicted power generation information; The digital twin modeling module is used to dynamically simulate the power generation status and output status of each photovoltaic module inverter of the distributed photovoltaic system by using the pre-built photovoltaic power generation resource model according to the configured model parameters, combining the power generation information data and the actual predicted power generation information of the distributed photovoltaic system, and the control command of the received downlink data, and obtain the uploaded data of the power generation status and output status of each photovoltaic module inverter; Data communication simulation module, used to dynamically simulate the communication response process of downloading and uploading data according to the configured communication parameters and operating parameters; The photovoltaic aggregation module is used to calculate the aggregated data based on the uploaded data and generate the preset classified data and the total power generation data of all photovoltaic power generation aggregations.

10. A distributed photovoltaic resource simulation device according to claim 9, characterized in that: In the step of obtaining power generation information data actually connected to distributed photovoltaics, the power generation information data includes: current, voltage, capacity and power.

11. A distributed photovoltaic resource simulation device according to claim 9, characterized in that: In the step of predicting the power generation of distributed photovoltaics by combining the predicted sunshine data and ambient temperature information to obtain the actual predicted power generation information, the photovoltaic sunshine data includes the maximum sunshine conditions predicted at each moment of the photovoltaics, the sunshine amounts at different moments of different days and the maximum sunshine output curve.

12. A distributed photovoltaic resource simulation device according to claim 9, characterized in that: In the step of dynamically simulating the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic system, each photovoltaic module inverter is configured with the same or different twin strategies; the twin strategies include: using typical daily historical or predicted data to proportionally generate a power generation curve, using different daily historical or predicted data combinations to proportionally generate a power generation curve, or using the real-time data or predicted data of the day to proportionally generate a power generation curve.

13. A distributed photovoltaic resource simulation device according to claim 12, characterized in that: In the step of dynamically simulating the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaic system, the power Pt.now of each photovoltaic module inverter at time t operates between the minimum power Pt.Min and the maximum power Pt.max, and Pt.max is determined according to the power generation curve of the configured twin strategy.

14. A distributed photovoltaic resource simulation method according to claim 9, characterized in that: According to the configured communication parameters and operating parameters, the steps of dynamically simulating the communication response process of downloading and uploading data are configured. The response delay parameters of different photovoltaic modules at different stages are configured in the configured communication parameters and operating parameters, and the complete control response time is formed by combining the random function rand(). , which is composed of the power regulation response coefficient Kre, the response time constant Trc and the response time variable Trv combined with the random function rand(), the arrival time of the power regulation target value Pt.aim is: （2） Simulation communication response time , which is composed of the response channel time fixed constant Tcc and variable constant Tcv combined with the random function rand(): （3）。 15. A distributed photovoltaic resource simulation method according to claim 9, characterized in that: In the step of calculating aggregated data based on uploaded data and generating data of preset classifications and total power generation data of all photovoltaic power generation aggregations, the preset classifications include: by region, by inverter brand, by inverter power and by power generation capacity.

16. A distributed photovoltaic resource simulation method according to claim 9, characterized in that: It also includes a control algorithm simulation module; the control algorithm simulation module is used to receive distributed photovoltaic control instructions. According to the distributed photovoltaic control instructions and the composition of distributed photovoltaics, different distributed photovoltaics adopt the same or different allocation algorithms to form control commands for downlink data; the allocation algorithms include: power average allocation method, maximum adjustable weight allocation method, and maximum adjustable priority allocation method.

17. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement a distributed photovoltaic resource simulation method as described in any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, a distributed photovoltaic resource simulation method as described in any one of claims 1 to 8 is implemented.

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