A distributed photovoltaic resource simulation method, device, equipment and medium

Abstract

The present invention belongs to the field of system simulation technology and discloses a distributed photovoltaic resource simulation method, device, equipment and medium. The method includes: obtaining power generation information data of distributed photovoltaics that are actually connected and actually predicting power generation information; using a pre-built photovoltaic power generation resource model according to configured model parameters, combined with the distributed photovoltaic power generation information data and actual predicted power generation information, and received control commands for downlink data, dynamically simulating the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaics, and obtaining the uploaded data of each photovoltaic module inverter; dynamically simulating the communication response process of downlink data and uploaded data based on 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 aggregates. The present invention provides strong support for the flexibility and scalability of distributed photovoltaic power generation systems in practical applications.

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 promising power generation method. Its core lies in installing small photovoltaic power generation systems in places near users or electricity demand, so as to achieve the goals of local power generation, local grid connection, local conversion and local use.

[0003] The widespread integration of distributed photovoltaic power generation resources into distribution networks has become a crucial component of building a new power system. The vast number of terminal devices connected to the power grid enables information perception and processing at every level of the Power Internet of Things (PoI), promoting data sharing and integration across the PoI platform and enabling comprehensive business connectivity. Currently, the use of access and control technology for aggregated terminals is widespread. Aggregated terminals within each control area can access and control distributed photovoltaic power generation systems, meeting the need for observable, measurable, and controllable distributed photovoltaic systems.

[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 the 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 solutions:

[0007] In a first aspect, the present invention provides a distributed photovoltaic resource simulation method, comprising:

[0008] Obtaining actual power generation information data of distributed photovoltaics;

[0009] 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;

[0010] Using the pre-built photovoltaic power generation resource model and the configured model parameters, combined with the distributed photovoltaic power generation information data and actual predicted power generation information, as well as the control commands of the received downlink data, the power generation status and output status of each photovoltaic module inverter of the distributed photovoltaic system are dynamically simulated to obtain the uploaded data of the power generation status and output status of each photovoltaic module inverter;

[0011] Dynamically simulate the communication response process of downloading and uploading data based on the configured communication parameters and operating parameters;

[0012] Aggregate data is calculated based on the uploaded data, and preset classified data and total power generation data of all photovoltaic power generation aggregates are generated.

[0013] 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.

[0014] 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 photovoltaics, the sunshine amount at different times of different days and the maximum sunshine output curve.

[0015] 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 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 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.

[0016] 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 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.

[0017] The present invention is further improved in that: according to the configured communication parameters and operating parameters, in the steps of dynamically simulating the communication response process of downloading and uploading data, the configured communication parameters and operating parameters are configured with response delay parameters of different photovoltaic modules at different stages, and combined with the random function rand() to form a complete control response time The arrival time of the power regulation target value Pt.aim 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():

[0018] (2)

[0019] 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():

[0020] (3).

[0021] 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 categories and total power generation data aggregated from all photovoltaic power generation, the preset categories include: by region, by inverter brand, by inverter power and by power generation capacity.

[0022] 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 distribution method, maximum adjustable weight distribution method, and maximum adjustable priority distribution method.

[0023] In a second aspect, the present invention provides a distributed photovoltaic resource simulation device, comprising:

[0024] Photovoltaic data acquisition module, used to obtain actual power generation information data of distributed photovoltaics;

[0025] Photovoltaic power generation prediction module, which is used to combine predicted sunshine data and ambient temperature information to predict the power generation of distributed photovoltaic systems and obtain actual predicted power generation information;

[0026] The digital twin modeling module is used to dynamically simulate the power generation and output status of each photovoltaic module inverter in the distributed photovoltaic system using a pre-built photovoltaic power generation resource model based on the configured model parameters, combined with the distributed photovoltaic power generation information data and actual predicted power generation information, as well as the control commands of the received downlink data, and obtain the uploaded data of the power generation and output status of each photovoltaic module inverter;

[0027] 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;

[0028] The photovoltaic aggregation module is used to calculate the aggregated data based on the uploaded data and generate the data of preset classifications and the total power generation data of all photovoltaic power generation aggregations.

[0029] 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.

[0030] 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 photovoltaics, the sunshine amount at different times of different days and the maximum sunshine output curve.

[0031] 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 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 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.

[0032] 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 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.

[0033] The present invention is further improved in that: according to the configured communication parameters and operating parameters, in the steps of dynamically simulating the communication response process of downloading and uploading data, the configured communication parameters and operating parameters are configured with response delay parameters of different photovoltaic modules at different stages, and combined with the random function rand() to form a complete control response time The arrival time of the power regulation target value Pt.aim 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():

[0034] (2)

[0035] 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():

[0036] (3).

[0037] 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 categories and total power generation data aggregated from all photovoltaic power generation, the preset categories include: by region, by inverter brand, by inverter power and by power generation capacity.

[0038] 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 distribution method, maximum adjustable weight distribution method, and maximum adjustable priority distribution method.

[0039] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the distributed photovoltaic resource simulation method.

[0040] 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.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a distributed photovoltaic resource simulation method, comprising: obtaining power generation information data of distributed photovoltaics actually connected; predicting the power generation of distributed photovoltaics in combination with predicted sunshine data and ambient temperature information to obtain actual predicted power generation information; utilizing a pre-constructed photovoltaic power generation resource model to dynamically simulate the power generation state and output state of each photovoltaic module inverter of the distributed photovoltaics according to configured model parameters, in combination with the power generation information data of the distributed photovoltaics and the actual predicted power generation information, as well as the control command of the received downlink data, to obtain uploaded data of the power generation state and output state of each photovoltaic module inverter; dynamically simulating the communication response process of downlink data and uploaded 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 data from actual collection devices and predicted data to simulate a photovoltaic power generation model; simulates the delays that may occur in different data channels, uses a digital twin module to simulate the response time of different devices to control commands, and generates a simulation of massive resource and massive data communication access to a master station that conforms to actual scenarios; and uses a configurable instruction allocation strategy to dynamically distribute distributed photovoltaic resource control instructions, realizing a control simulation of photovoltaic power generation calling negative backup under special circumstances. The present invention uses aggregator terminal devices to simulate distributed photovoltaic resource access on demand according to actual site conditions. Using similar digital twin technology, referring to the actual photovoltaic power generation data collected on-site by the aggregator terminal and the predicted data during operation, the aggregator terminal realizes dynamic simulation of distributed photovoltaic resources on-site. At the same time, a configurable method for simulating the delays of various communication channels is designed to realize the dynamic simulation execution process of photovoltaic simulation resource collection control commands at the master station, achieving closed-loop simulation control of distributed photovoltaic aggregated resources, assisting the optimization simulation operation and related algorithm verification of the upper-level scheduling and trading system. In addition, the present invention can also realize the information model verification of simulated distributed photovoltaic access when the distributed photovoltaic resources are not actually connected. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 This is a schematic structural diagram of a distributed photovoltaic resource simulation system according to an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a flow chart of a distributed photovoltaic resource simulation method according to an embodiment of the present invention;

[0046] Figure 3A schematic diagram of a flow chart of a distributed photovoltaic resource simulation method according to an embodiment of the present invention;

[0047] Figure 4 This is a schematic structural diagram of a distributed photovoltaic resource simulation system according to an embodiment of the present invention;

[0048] Figure 5 This is a schematic structural diagram of a distributed photovoltaic resource simulation system according to an embodiment of the present invention;

[0049] Figure 6 This is a structural block diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0050] 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, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0051] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, 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.

[0052] Based on the data collection and communication capabilities of distributed photovoltaic aggregation terminal devices, this invention can achieve simulated access to distributed photovoltaic resources using digital twin simulation technology, even when partially connected to real physical distributed photovoltaic power generation resources. This allows the dispatching and trading master station to simulate and control lower-level distributed photovoltaic simulation resources through actual communication channels and simulated communication channels. This simulation method can simulate the access of photovoltaic resources of different scales at aggregation terminals at different nodes in the power grid, and achieve real-time dynamic simulation control between the dispatching and trading master station and the aggregation model through the actual communication channels between the aggregation terminals and the dispatching and trading master station.

[0053] See also Figure 1 As shown, an embodiment of the present invention provides a distributed photovoltaic resource simulation system, including:

[0054] The photovoltaic data acquisition module is used to collect actual photovoltaic power generation information, including current, voltage, capacity, and power. This information can be directly connected to the actual dispatch and trading master station (not the simulated dispatch and trading module) to monitor and control the operating photovoltaic system. The photovoltaic data acquisition module also provides reference data for data twins, including actual photovoltaic power generation capacity, daily power generation curves, and inverter equipment information.

[0055] The photovoltaic power generation prediction module is used to locally predict photovoltaic sunshine data based on meteorological data and data from environmental sensors. This sunshine data includes the predicted maximum sunshine at each time, the amount of sunshine at different times of the day, and the maximum sunshine output curve. The photovoltaic power generation prediction module combines the predicted sunshine data with ambient temperature information collected by environmental sensors to predict photovoltaic power generation and obtain actual predicted power generation information.

[0056] The digital twin modeling module is used to construct a PV power generation resource model for simulation purposes (including the inverter type and number, each inverter's maximum output power, and grid-connected power generation capacity). The PV data acquisition module acquires actual PV power generation data, while the PV power generation prediction module acquires actual predicted power generation information. The module then establishes a binding relationship between the PV power generation resource model, the power generation data, and the actual predicted power generation information. Different twin strategies are configured for the PV power generation capacity corresponding to each inverter. These twin strategies include: 1. Proportional generation curve generation based on typical daily historical or predicted data; 2. Proportional generation curve generation based on a combination of different daily historical or predicted data; and 3. Proportional generation curve generation based on current day real-time data or predicted data.

[0057] The digital twin modeling module references data from 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 (in this invention, t is an hour interval, with a value of 0-23) is designed to be the minimum power Pt.Min and the maximum power Pt.max. Generally, considering the operation of allowing abandoned solar power in new power system environments, Pt.min can be set to 0, and Pt.max is given by the digital twin modeling module as the optical power curve, as shown in the following formula. In the formula, Pt.now is the current power, which operates 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 maximum power Pt.max. Under normal operation, the current power Pt.now equals the maximum power Pt.max, and the photovoltaic power is in full power state.

[0058] (1)

[0059] 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 results. This kind of delay is very important for simulation control. The present invention adopts the configuration of 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:

[0060] (2)

[0061] 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():

[0062] (3)

[0063] The complete time process is: after the photovoltaic aggregation module receives the control command from the simulation master station, the data of the simulated photovoltaic module is updated after the time Tr+Tc.

[0064] 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 scheduling and trading system instructions.

[0065] The photovoltaic aggregation module aggregates the interface of all photovoltaic resources, connects to the aggregation model of simulated photovoltaic resources at the grid connection point, and directly interacts with the dispatching and trading center.

[0066] See also Figure 2 As shown, an embodiment of the present invention provides a distributed photovoltaic resource simulation method, comprising the following steps:

[0067] After starting the simulation process, the distributed photovoltaic simulation configuration data is called. This configuration file includes the inverter type and number, the maximum output power of each inverter, and the grid-connected power generation capacity. The photovoltaic simulation configuration data is imported into the digital twin modeling module, and a binding relationship is established with the photovoltaic data acquisition module and photovoltaic power generation prediction module data. Different twin strategies are configured for the photovoltaic power generation capacity corresponding to each inverter. 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 a combination of different daily historical or predicted data; 3. Proportional generation of power generation curves using the current day's real-time data or predicted data.

[0068] Read historical data from the photovoltaic data acquisition module and update the digital twin modeling module data associated with historical power generation data of different days, different inverters, and different power generation capacities.

[0069] Read the data of the photovoltaic power generation prediction module and update the data twin modeling module data associated with the historical prediction data of different days, different inverters, and different power generation capacities.

[0070] In the digital twin modeling module, the power generation status and output status of each photovoltaic module inverter are dynamically simulated based on the configured model parameters, combined with updated data and responses to control commands.

[0071] 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.

[0072] 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 such as by region, by inverter brand, by inverter power, by power generation capacity, and the total power generation data of all photovoltaic power generation aggregations.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] See also Figure 3 As shown, the present invention provides a distributed photovoltaic resource simulation method, comprising:

[0078] S1. Obtaining actual power generation information data of distributed photovoltaics;

[0079] In a specific embodiment, the power generation information data includes: current, voltage, capacity and power.

[0080] S2. Combine the predicted sunshine data and ambient temperature information to predict the power generation of distributed photovoltaics and obtain actual predicted power generation information;

[0081] In a specific embodiment, 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.

[0082] S3. Using a pre-built photovoltaic power generation resource model, based on configured model parameters, combined with distributed photovoltaic power generation information data and actual predicted power generation information, and received control commands for downlink data, dynamically simulate the power generation status and output status of each photovoltaic module inverter of the distributed photovoltaic system, and obtain uploaded data on the power generation status and output status of each photovoltaic module inverter;

[0083] In one specific embodiment, each photovoltaic module inverter is configured with the same or different twinning strategies; the twinning 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 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, where Pt.max is determined according to the power generation curve of the configured twinning strategy;

[0084] S4. Dynamically simulate the communication response process of downloading and uploading data based on the configured communication parameters and operating parameters;

[0085] In a specific embodiment, the response delay parameters of different photovoltaic modules at different stages are configured in the configured communication parameters and operation parameters, and the random function rand() is combined to form a complete control response time. The arrival time of the power regulation target value Pt.aim 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():

[0086] (2)

[0087] 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():

[0088] (3)

[0089] The complete time process is as follows: after the PV aggregation module receives the control command from the simulation master station, the data of the simulated PV module is updated after Tr+Tc time;

[0090] S5. Calculate aggregated data based on the uploaded data and generate data of preset categories and total power generation data of all photovoltaic power generation aggregates;

[0091] In a specific embodiment, the preset classification includes: by region, by inverter brand, by inverter power, and by power generation capacity.

[0092] 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 distribution method, maximum adjustable weight distribution method, and maximum adjustable priority distribution method.

[0093] See also Figure 4 As shown, the present invention provides a distributed photovoltaic resource simulation device, comprising:

[0094] Photovoltaic data acquisition module, used to obtain actual power generation information data of distributed photovoltaics;

[0095] Photovoltaic power generation prediction module, which is used to combine predicted sunshine data and ambient temperature information to predict the power generation of distributed photovoltaic systems and obtain actual predicted power generation information;

[0096] The digital twin modeling module is used to dynamically simulate the power generation and output status of each photovoltaic module inverter in the distributed photovoltaic system using a pre-built photovoltaic power generation resource model based on the configured model parameters, combined with the distributed photovoltaic power generation information data and actual predicted power generation information, as well as the control commands of the received downlink data, and obtain the uploaded data of the power generation and output status of each photovoltaic module inverter;

[0097] 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;

[0098] The photovoltaic aggregation module is used to calculate the aggregated data based on the uploaded data and generate the data of preset classifications and the total power generation data of all photovoltaic power generation aggregations.

[0099] In a specific embodiment, in the step of obtaining power generation information data of the distributed photovoltaic power generation actually connected, the power generation information data includes: current, voltage, capacity and power.

[0100] 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 photovoltaics, the sunshine amount at different times of different days and the maximum sunshine output curve.

[0101] In a specific embodiment, 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 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.

[0102] In a specific embodiment, 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.

[0103] In a specific embodiment, 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 combined with the random function rand() to form a complete control response time The arrival time of the power regulation target value Pt.aim 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():

[0104] (2)

[0105] 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():

[0106] (3).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the 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.); the data storage area can store data (such as audio data) created according to the use of the electronic device 100. 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 device.

[0111] 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 connects various parts of the entire electronic device 100 using various interfaces and lines.

[0112] 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:

[0113] Obtaining actual power generation information data of distributed photovoltaics;

[0114] 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;

[0115] Using the pre-built photovoltaic power generation resource model and the configured model parameters, combined with the distributed photovoltaic power generation information data and actual predicted power generation information, as well as the control commands of the received downlink data, the power generation status and output status of each photovoltaic module inverter of the distributed photovoltaic system are dynamically simulated to obtain the uploaded data of the power generation status and output status of each photovoltaic module inverter;

[0116] Dynamically simulate the communication response process of downloading and uploading data based on the configured communication parameters and operating parameters;

[0117] Aggregate data is calculated based on the uploaded data, and preset classified data and total power generation data of all photovoltaic power generation aggregates are generated.

[0118] 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 process 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, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0119] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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.

[0121] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the 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 by 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 and the configured model parameters, combined with the distributed photovoltaic power generation information data and actual predicted power generation information, as well as the control commands of the received downlink data, the power generation status and output status of each photovoltaic module inverter of the distributed photovoltaic system are dynamically simulated to obtain the uploaded data of the power generation status and output status of each photovoltaic module inverter; Dynamically simulate the communication response process of downloading and uploading data based on the configured communication parameters and operating parameters; Calculate aggregated data based on uploaded data and generate data of preset categories and total power generation data of all photovoltaic power generation aggregates; According to the configured communication parameters and operating parameters, the steps of the communication response process of downloading and uploading data are dynamically simulated. 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(). , the power adjustment response coefficient K re , response time constant T rc and the response time variable T rv Combined with the random function rand(), the power regulation target value P is formed. t.aim Arrival time: Among them, P t.now is the power of each photovoltaic module inverter at time t, P t.aim is the power regulation target value at time t; is the random function at time t; Simulation communication response time , which is fixed by the response channel time constant T cc and the change constant T cv Combined with the random function rand(), it constitutes: 。 2. A distributed photovoltaic resource simulation method according to claim 1, characterized in that: In the step of obtaining the power generation information data of the distributed photovoltaic power generation that is actually connected, 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 distributed photovoltaic power generation 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 photovoltaic, the sunshine amount at different times 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 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.

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 P of each photovoltaic module inverter at time t is t.now , running at minimum power P t.Min and maximum power P t.max Between, P t.max 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: In the step of calculating aggregated data based on uploaded data and generating data of preset categories and total power generation data of all photovoltaic power generation aggregation, the preset categories include: by region, by inverter brand, by inverter power and by power generation capacity.

7. 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.

8. A distributed photovoltaic resource simulation device, characterized in that: include: Photovoltaic data acquisition module, used to obtain actual power generation information data of distributed photovoltaics; Photovoltaic power generation prediction module, which is used to combine predicted sunshine data and ambient temperature information to predict the power generation of distributed photovoltaic systems and obtain actual predicted power generation information; The digital twin modeling module is used to dynamically simulate the power generation and output status of each photovoltaic module inverter in the distributed photovoltaic system using a pre-built photovoltaic power generation resource model based on the configured model parameters, combined with the distributed photovoltaic power generation information data and actual predicted power generation information, as well as the control commands of the received downlink data, and obtain the uploaded data of the power generation 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; Photovoltaic aggregation module, used to calculate aggregate data based on uploaded data and generate data of preset categories and total power generation data of all photovoltaic power generation aggregates; According to the configured communication parameters and operating parameters, the steps of the communication response process of downloading and uploading data are dynamically simulated. 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(). , the power adjustment response coefficient K re , response time constant T rc and the response time variable T rv Combined with the random function rand(), the power regulation target value P is formed. t.aim Arrival time: Among them, P t.now is the power of each photovoltaic module inverter at time t, P t.aim is the power regulation target value at time t; is the random function at time t; Simulation communication response time , which is determined by the time constant T of the response channel cc and the change constant T cv Combined with the random function rand(), it constitutes: 。 9. A distributed photovoltaic resource simulation device according to claim 8, characterized in that: In the step of obtaining the power generation information data of the distributed photovoltaic power generation that is actually connected, the power generation information data includes: current, voltage, capacity and power.

10. A distributed photovoltaic resource simulation device according to claim 8, characterized in that: In the step of predicting the distributed photovoltaic power generation 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 photovoltaic, the sunshine amount at different times of different days and the maximum sunshine output curve.

11. A distributed photovoltaic resource simulation device according to claim 8, 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 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.

12. A distributed photovoltaic resource simulation device according to claim 11, 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 P of each photovoltaic module inverter at time t is t.now , running at minimum power P t.Min and maximum power P t.max Between, P t.max Determined according to the power generation curve of the configured twin strategy.

13. A distributed photovoltaic resource simulation device according to claim 8, characterized in that: In the step of calculating aggregated data based on uploaded data and generating data of preset categories and total power generation data of all photovoltaic power generation aggregation, the preset categories include: by region, by inverter brand, by inverter power and by power generation capacity.

14. A distributed photovoltaic resource simulation device according to claim 8, 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.

15. 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 claimed in any one of claims 1 to 7.

16. 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 a processor, a distributed photovoltaic resource simulation method according to any one of claims 1 to 7 is implemented.

Images