Photovoltaic inverter output simulation method and device

Abstract

The invention discloses a photovoltaic inverter output simulation method and device, and the method comprises the steps: obtaining a reference power generation curve, combining the editing operation of a user to dynamically generate an effective power generation curve, and truly simulating the illumination duration, irradiance change, cloud layer shielding and other complex working conditions; and a scheduling instruction of the energy management module is acquired in real time, a target output instruction is calculated based on the minimum value of the effective power, the scheduling instruction and the rated capacity of the inverter, and the inverter is driven by the direct current simulation source to realize actual output. According to the invention, closed loop of illumination dynamic simulation and multi-factor cooperative control is realized, the problem of test distortion caused by a static curve and single control logic of a traditional simulator is solved, and the authenticity and controllability of photovoltaic output behaviors in a micro-grid test are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation and power electronic control technology, and in particular to a method and apparatus for simulating the output of a photovoltaic inverter. Background Technology

[0002] With the widespread application of photovoltaic (PV) power generation in microgrids and new power systems, the demand for dynamic performance testing of PV inverters is increasing, including their comprehensive performance in complex illumination variations, energy management system (EMS) dispatch command response, peak shaving and valley filling, and adaptability to weak grids. Therefore, laboratories and testing platforms commonly use PV simulation systems to construct controllable testing environments to replace actual PV arrays for preliminary verification.

[0003] However, existing photovoltaic simulators have significant shortcomings in terms of realism and control logic fidelity. Traditional systems typically rely on preset fixed power output curves or only support offline import of static data files, making it impossible to dynamically adjust illumination conditions during testing. This static modeling approach struggles to simulate dynamic operating conditions commonly encountered in real power plants, such as power output fluctuations caused by cloud cover, sudden changes in irradiance due to alternating sunny and cloudy weather, and seasonal differences in sunshine duration. This results in a limited range of test scenarios that fail to reflect the uncertainties of actual operation.

[0004] More importantly, existing simulation methods simplify the output control logic. Most systems directly issue scheduling commands as the target output or simply limit them to equipment capacity, ignoring the fundamental constraint of "available power" as a physical upper limit. This causes simulation results to deviate from actual operating behavior, resulting in test distortion and affecting the accurate assessment of inverter control strategies, EMS scheduling effectiveness, and system stability.

[0005] Therefore, there is an urgent need for a new method to simulate the output of photovoltaic inverters, which can solve the technical problem that existing photovoltaic simulators cannot dynamically simulate real changes in illumination and combine EMS commands with the rated capacity of the inverter for triple constraint coordinated control, resulting in test distortion. This is to improve the simulation accuracy and engineering practicality of the microgrid test platform. Summary of the Invention

[0006] The present invention provides a method and apparatus for simulating the output of a photovoltaic inverter, which aims to solve the technical problem that existing photovoltaic simulators cannot dynamically simulate real changes in illumination and combine EMS commands with the rated capacity of the inverter for triple constraint coordinated control, resulting in test distortion.

[0007] In a first aspect, embodiments of the present invention provide a photovoltaic inverter output simulation method, applied to a photovoltaic simulation system. The system includes a photovoltaic simulation controller, an editing module, a DC simulation source, an energy management module, and an inverter. The method includes: acquiring a baseline available power curve of the photovoltaic simulation system; receiving user editing operations on the baseline available power curve through the editing module and generating corresponding editing parameters; performing sequential correction processing on the baseline available power curve through the photovoltaic simulation controller according to the editing parameters to generate a corrected effective available power curve; acquiring an active power scheduling command issued by the energy management module to the inverter; calculating a target output command for the inverter based on the effective available power curve, the active power scheduling command, and the rated capacity of the inverter, using a triple constraint minimum value principle; issuing the target output command to the inverter, driving the inverter to adapt to the DC input conditions corresponding to the target output, and completing the actual power output through the DC simulation source.

[0008] Secondly, embodiments of the present invention also provide a photovoltaic inverter output simulation device for performing the photovoltaic inverter output simulation method as described above.

[0009] Thirdly, embodiments of the present invention also provide a computer device, the computer device including a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to perform the steps of the above-described photovoltaic inverter output simulation method.

[0010] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, can implement the steps of the above-described photovoltaic inverter output simulation method.

[0011] Compared with the prior art, the beneficial effects of the present invention are: In the technical solution of this invention, the photovoltaic inverter output simulation method obtains a baseline available power curve and dynamically generates an effective available power curve by combining user editing operations, realistically simulating complex operating conditions such as sunshine duration, irradiance changes, and cloud cover. It also acquires scheduling instructions from the energy management module in real time, calculates the target output instruction based on the minimum value of the effective available power, the scheduling instruction, and the inverter's rated capacity, and drives the inverter to achieve the actual output through a DC analog source. This invention achieves a closed loop of dynamic sunshine simulation and multi-factor collaborative control, solving the test distortion problem caused by static curves and simple control logic in traditional simulators, and significantly improving the realism and controllability of photovoltaic output behavior in microgrid testing. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 2 This is the first sub-flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 3 This is a sub-flowchart of the second sub-flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 4 This is the third sub-flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 5 The fourth sub-flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 6 This is the fifth sub-flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 7 The sixth sub-flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 8 The seventh sub-flowchart of the photovoltaic inverter output simulation method provided by the present invention; Figure 9 A schematic block diagram of a unit of the photovoltaic inverter output simulation device provided by the present invention; Figure 10 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0016] It should also be understood that the terminology used in this specification is for the purpose of describing embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0017] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0018] To address the technical problem of existing photovoltaic simulators' inability to dynamically simulate real-world light variations and achieve triple-constraint collaborative control using EMS commands and inverter rated capacity, leading to test distortion, this invention discloses a photovoltaic inverter output simulation method. This method is applied to a photovoltaic simulation system, which includes a photovoltaic simulation controller, an editing module, a DC simulation source, an energy management module, and an inverter.

[0019] A photovoltaic (PV) simulation system is a test platform used to simulate the output characteristics of photovoltaic power generation in a laboratory environment. Its purpose is to replace real PV arrays and provide controllable and repeatable verification of the performance of PV inverters and their operation in microgrids. The system includes core components such as a PV simulation controller, an editing module, a DC simulation source, an energy management module, and an inverter. The PV simulation controller, as the control center of the entire system, is responsible for coordinating data processing, command generation, and control logic execution.

[0020] The photovoltaic simulation controller has a standard illumination model pre-set in its internal storage unit, such as the AM1.5 solar irradiance curve, or supports the import of historical power data from actual photovoltaic power plants as a source of the benchmark power generation curve, which is used to characterize the illumination change trend under typical days or specific weather conditions.

[0021] The editing module integrates a graphical human-computer interaction interface, allowing testers to edit the benchmark radiant power curve in real time during the test. For example, they can add "light pits" by clicking with the mouse, adjust the overall irradiance level by sliding, and set the start and end times of light to simulate seasonal differences in solar radiation. These operations are converted into corresponding editing parameters, including pit center time, duration and light attenuation coefficient, irradiance adjustment coefficient, and time ratio parameter.

[0022] The photovoltaic analog controller establishes a real-time data connection with the energy management module through communication protocols such as Modbus TCP or IEC 61850, and continuously receives active power dispatch instructions issued by the module to the inverter.

[0023] The DC simulation source dynamically adjusts its output voltage and current according to instructions from the photovoltaic simulation controller, simulating the current-voltage characteristics of the photovoltaic array to match the target output. After the inverter under test is connected to this DC input, it automatically performs maximum power point tracking or power limit control, adjusting its grid-connected active power to ensure that the actual output power is consistent with the target output command, thereby achieving a high degree of simulation of real photovoltaic power generation behavior.

[0024] Based on this photovoltaic simulation system, and referring to Figures 1 to 8 The method includes the following steps: S110. Obtain the baseline power generation curve of the photovoltaic simulation system; S120. The editing module receives the user's editing operation on the benchmark power curve and generates the corresponding editing parameters. S130. Based on the editing parameters, the photovoltaic simulation controller performs sequential correction processing on the baseline power curve to generate the corrected effective power curve. S140. Obtain the active power scheduling instruction issued by the energy management module to the inverter; S150. Based on the effective power output curve, the active power dispatching command, and the rated capacity of the inverter, the target output command of the inverter is calculated using the triple constraint minimum value principle. S160. The target output command is sent to the inverter to drive the inverter to adapt to the DC input conditions corresponding to the target output, and the actual power output is completed through the DC analog source.

[0025] The photovoltaic simulation controller first obtains the baseline power generation curve, which can be generated by calling the preset standard illumination model through built-in storage, or by importing historical illumination data of the actual photovoltaic power station, to characterize the typical solar illumination change trend.

[0026] The editing module is equipped with a graphical human-computer interaction interface, which allows testers to dynamically edit the baseline power curve during the test. For example, they can add light pits, adjust the overall irradiance level, or set the light duration ratio by clicking. The editing module generates corresponding editing parameters based on these operations, including pit center time, duration and light attenuation coefficient, irradiance adjustment coefficient, and time ratio parameters.

[0027] After receiving the above editing parameters, the photovoltaic simulation controller corrects the baseline renewable power curve in a fixed order. First, it linearly stretches or compresses the time axis according to the time scale parameter to simulate the changes in sunshine duration in different seasons. Then, it scales the curve amplitude as a whole according to the irradiance adjustment coefficient to reflect the difference in light intensity under sunny and cloudy weather. Finally, it applies the light attenuation coefficient to introduce local power reduction within a specified time period to simulate the cloud shading effect, ultimately generating an effective renewable power curve that meets the current test requirements.

[0028] Meanwhile, the photovoltaic simulation controller establishes a real-time connection with the energy management module through a standard communication protocol, continuously receiving active power dispatch commands issued by the module to the inverter, and performing validity verification and caching of the command sequence. Within each control cycle, the photovoltaic simulation controller calculates the inverter's target output command based on the current available power value, the received active power dispatch command value, and the pre-configured inverter rated capacity value, using a triple-constraint minimum value principle. This ensures that the output decision conforms to the physical available power capacity, responds to dispatch requirements, and is constrained by equipment safety boundaries.

[0029] After the calculation is completed, the photovoltaic simulation controller converts the target output command into the corresponding DC power setpoint and sends it to the DC simulation source. The DC simulation source then dynamically adjusts its output voltage and current to simulate the output characteristics of the photovoltaic array under the corresponding illumination conditions. When the inverter under test is connected to this DC input condition, it automatically performs maximum power point tracking or power limit control to adjust its grid-connected active power, ensuring that the actual output power matches the target output command, thus completing the entire photovoltaic output simulation process.

[0030] To enhance system adaptability, the photovoltaic simulation controller also supports multi-time period continuous editing, allowing users to preset multiple pit events or adjust irradiance in segments to form complex dynamic operating condition sequences, further improving the realism and comprehensiveness of microgrid testing.

[0031] In one embodiment, step S110 includes: S111. Use the standard illumination model preset in the photovoltaic simulation controller as the reference power curve; S112. The photovoltaic simulation controller performs mathematical modeling based on a preset calculation model to generate a power curve representing typical solar radiation changes as a benchmark power curve.

[0032] Obtaining the baseline power output curve of the photovoltaic simulation system is a fundamental step in the entire power output simulation process, providing raw data for subsequent dynamic editing, environmental simulation, and target power output calculation. A standard illumination model pre-installed within the photovoltaic simulation controller is used as the baseline power output curve. This standard illumination model is stored as a data sequence in the non-volatile memory of the photovoltaic simulation controller, typically constructed based on internationally recognized solar irradiance standards or typical meteorological year datasets. After processing by a power conversion algorithm, it generates the corresponding daily photovoltaic power output curve.

[0033] The photovoltaic output daily curve reflects the ideal photovoltaic power generation process under standard atmospheric conditions, without cloud cover, and with clean modules. It features a clear sunrise start time, smooth rise and fall phases, and a symmetrical power peak at noon. As the system's default benchmark template, it can be automatically invoked when no external data is imported, ensuring the system is ready to use immediately and suitable for routine performance testing and factory verification scenarios.

[0034] The photovoltaic simulation controller invokes a built-in preset calculation model to generate a power curve representing typical solar radiation variations, serving as a benchmark power curve based on mathematical modeling. The standard solar radiation model is implemented by a software module within the controller, employing a functional approach to construct the power curve, such as using a modified sine function. in The set maximum power value, and These represent sunrise and sunset times, ensuring the curve changes smoothly within the effective sunshine period. To further enhance the model's adaptability, piecewise linear adjustment terms or random perturbation terms can be superimposed on the base function to simulate non-ideal conditions such as morning fog and evening diffused light. Users can set modeling parameters, such as peak power, illumination start and end times, and ramp rate, through the human-computer interaction interface of the editing module, achieving flexible customization of the baseline curve shape.

[0035] Furthermore, step S110 also includes: S113. Import the measured historical solar power data of the photovoltaic power station into the photovoltaic simulation controller as a benchmark power curve.

[0036] Considering that simulation tests also need to reproduce specific power plant operating conditions, verify extreme weather response, or conduct regional adaptability tests, the baseline power generation curve can be further constructed by importing historical solar power data from measured photovoltaic power plants into the photovoltaic simulation controller.

[0037] In the specific implementation process, testers first export a representative historical active power data segment from the energy management system or monitoring platform of the actual photovoltaic power plant. The data is typically a sequence file composed of timestamps and corresponding power values, with sampling intervals generally of 1 minute, 5 minutes, or 15 minutes, covering the power output process of a full day or multiple consecutive days. This data file is stored in a common format on an external storage device and connected to the photovoltaic simulation controller via a communication interface. The photovoltaic simulation controller is equipped with a data parsing module that can recognize the file format, automatically extract the time-power data sequence, and normalize the time axis according to the system's internal clock, mapping it to the time range of the current test day. Simultaneously, the controller performs integrity verification on the imported data, including checking for missing data, abnormal jumps, or communication interruptions, and repairing them if necessary using linear interpolation or smoothing filtering algorithms to ensure the curve's continuity and usability.

[0038] To adapt to inverters under test with different capacities, the photovoltaic simulation controller also supports normalized scaling of imported historical power data. For example, if the original data comes from a 50MW power plant, and the current test object is a 50kW small-power inverter, the controller can proportionally reduce the original power value according to the rated capacity ratio to generate a baseline power output curve that matches its power level. This fulfills the testing requirements for verifying small equipment from large power plant settings. After importation, this historical power data serves as the baseline power output curve for the current test task, stored in the controller's running memory, and can be displayed in real time on the graphical interface of the editing module for user viewing and further editing.

[0039] In one embodiment, step S120 includes: S121. The editing operation input by the user is received through the graphical human-computer interaction interface provided in the editing module. The editing operation includes: adding a pit, adjusting the irradiance, and setting the illumination duration. S122. In response to the pitting addition operation, generate pitting parameters, the pitting parameters including pitting center time, pitting duration, and light attenuation coefficient. S123. In response to the irradiance adjustment operation, generate irradiance adjustment parameters, which are used to characterize the overall light intensity change; S124. In response to the operation of setting the illumination duration, a time ratio parameter is generated, which is used to stretch or compress the effective illumination period.

[0040] The graphical human-computer interaction interface in the editing module enables testers to intuitively and conveniently modify the preset or imported benchmark power curves in real time during system operation, so as to construct complex lighting scenarios that meet specific test objectives.

[0041] The graphical user interface (GUI) presents the current baseline achievable power curve on the screen in the form of a time-power coordinate system. The horizontal axis represents time, and the vertical axis represents power value. Users can interact with the interface via mouse, touchscreen, or keyboard input devices. The interface includes multiple function controls, each corresponding to different editing operations. When a user needs to perform the "add pit" operation, they can mark the area where cloud obstruction occurs by clicking a point on the curve or selecting a time period. The system automatically recognizes this operation and generates a set of pit parameters. These parameters include the pit center time, pit duration, and light attenuation coefficient. The pit center time refers to the midpoint of the obstruction event; the pit duration represents the total duration of obstruction; and the light attenuation coefficient describes the degree of obstruction, with values ​​between 0 and 1, such as 0.3 indicating a 30% reduction in light intensity. These parameters will be used to apply localized attenuation to the power curve within a specified time period to simulate a sudden drop in power output caused by cloud movement.

[0042] When a user performs an "Adjust Irradiance" operation, they can adjust the overall light intensity level by dragging the slider or entering a value. The system then generates an irradiance adjustment parameter based on this adjustment. This parameter is a dimensionless scaling factor that applies throughout the entire effective sunshine period and is used to globally scale the baseline renewable power curve. For example, setting the irradiance adjustment parameter to 0.6 indicates a reduction in overall sunlight under simulated cloudy conditions. In this case, the curve shifts down to 60% of its original value, thus reflecting the systematic impact of weather changes on photovoltaic power generation capacity.

[0043] When a user performs the "Set Sunshine Duration" operation, they can adjust the duration of effective sunshine by dragging the start or end point of the curve, or by directly entering the sunrise and sunset times. The system then generates a time scale parameter based on this. This time scale parameter is a real number greater than zero, used to linearly transform the time axis of the baseline curve: a value greater than 1 indicates stretched sunshine duration, such as for simulating long days in summer; a value less than 1 indicates compressed sunshine duration, such as for simulating short days in winter. This process ensures that changes in the effective sunshine duration conform to actual geographical and seasonal characteristics.

[0044] All editing operations can be previewed in real time on the graphical human-computer interaction interface. Users can view the corrected curve shape after the parameters are generated, and submit it to the photovoltaic simulation controller for further processing after confirming that it is correct. The mechanism of this embodiment realizes the precise conversion from user intention to control parameters, enabling the photovoltaic simulation system to have powerful dynamic modeling capabilities. It can flexibly construct composite test scenarios covering multiple factors such as seasonal changes, weather fluctuations, and local shading, significantly improving the comprehensiveness and engineering practicality of microgrid testing.

[0045] In one embodiment, step S130 includes: S131. Perform a linear transformation on the time axis of the reference power curve according to the time ratio parameter to obtain the time-adjusted intermediate power curve. S132. The intermediate power curve is scaled up and down according to the irradiance adjustment parameters to obtain the irradiance-adjusted intermediate power curve. S133. Apply local attenuation processing to the intermediate power curve after irradiance adjustment within a specified time period according to the pit parameters to generate the effective radiable power curve.

[0046] The photovoltaic simulation controller performs a linear transformation on the time axis of the baseline renewable power curve based on the time scaling parameters generated by the "set illumination duration operation". This process starts from sunrise time and transforms the original time interval... According to time ratio parameters Remapped to a new time interval .like This lengthens the daylight hours, simulating a long-day summer scenario; if The sunshine duration is compressed to simulate the short sunshine hours of winter. The controller resamples the original data points using an interpolation algorithm to generate a time-adjusted intermediate power curve, ensuring a continuous and smooth power change trend under the new time axis.

[0047] Subsequently, the photovoltaic simulation controller adjusts the irradiance parameters generated by the "Adjust Irradiance Operation". The controller then performs global amplitude scaling on the time-adjusted intermediate power curve. Specifically, it iterates through the power value at each time point on the curve and multiplies it by... This yields the intermediate power curve after irradiance adjustment. This operation simulates changes in overall light intensity, such as... This indicates that solar irradiance decreases under cloudy conditions, resulting in an overall decline in photovoltaic power generation capacity, thus reflecting the impact of weather conditions on the upper limit of output.

[0048] Finally, the photovoltaic simulation controller uses the pit parameters generated by the "add pit operation," including the pit center time, as the basis for its operation. Duration of pitting and light attenuation coefficient Within the specified time interval Within this period, a local attenuation process is applied to the intermediate power curve after irradiance adjustment. The controller multiplies the original power value by [a certain factor] during this time period. ,in This creates a brief power dip to simulate the output dip caused by short-term cloud cover blocking the photovoltaic array. This process can be repeated over multiple time periods to simulate multiple shading events during continuous cloudy weather.

[0049] It must be emphasized that the above three-step correction process must be strictly performed in the order of time axis transformation, amplitude scaling, and local attenuation to ensure the correctness of the physical meaning. If the dimple is applied first and then the time axis is stretched, it will lead to misalignment of the shading period; if attenuation is performed first and then amplitude scaling is applied, the attenuation degree will be distorted due to the superposition effect. Only by processing in this order can we ensure that the final generated effective radiable power curve accurately reflects the comprehensive illumination characteristics of seasonal influence duration, weather influence intensity, and transient fluctuations of local cloud shadow influence, providing a real and reliable input basis for subsequent triple-constraint power output calculations.

[0050] To more clearly illustrate the solution of this embodiment, an example is given below: The photovoltaic simulation controller first acquires a baseline power output curve generated based on the AM1.5 standard illumination model. This curve characterizes the typical daily power generation process of a photovoltaic power station under ideal sunny conditions, covering the time range from 6:00 to 18:00, with peak power occurring at 12:00. Subsequently, the testers performed a series of curve editing operations through the graphical human-computer interaction interface of the editing module to simulate the changes in illumination under complex real-world conditions.

[0051] The user executes the "Set Light Duration" operation, incorporating the shortened daylight hours in winter into the simulation scenario. The system generates time scale parameters. The photovoltaic simulation controller then performs a linear transformation on the time axis of the reference available power curve. in After processing, the original 12-hour effective sunshine period was compressed to 9.6 hours, resulting in the time-adjusted intermediate power curve. It simulates the shorter daylight hours in winter.

[0052] Next, the user executes the "Adjust Irradiance" operation to simulate a decrease in overall light intensity under cloudy conditions. The system generates irradiance adjustment parameters. The controller performs amplitude scaling on the intermediate power curve: After processing, the curve shifts downward to 60% of its original value, forming an intermediate power curve after irradiance adjustment. It reflects the low light environment in all weather conditions.

[0053] Finally, the user performs the "Add Hormone" operation to simulate a brief cloud cover event around 10:15 AM. The system generates hormone parameters: center time. Duration Light attenuation coefficient The controller is in the time interval. The above curves are subjected to local attenuation processing: The final generated It comprehensively reflects seasonal changes in sunshine, all-day weather conditions, and localized instantaneous shading, and serves as an effective power curve for subsequent power output calculations.

[0054] In one embodiment, step S140 includes: S141. Establish a data connection with the energy management module through a communication protocol; S142. Continuously receive the active power scheduling instruction sequence periodically issued by the energy management module; S143. Perform validity verification and cache processing on the received active power scheduling instruction sequence.

[0055] The photovoltaic (PV) analog controller establishes a stable data connection with the energy management module through a standard communication protocol. The communication protocol used can be flexibly selected according to the actual system configuration; common protocols include Modbus TCP, IEC 61850, IEC 104, or MQTT, and it supports data exchange via Ethernet or serial communication interfaces. During the connection establishment process, the PV analog controller, acting as a client or server, completes a handshake authentication based on the preset IP address, port number, site address, and communication parameters to ensure the security and reliability of the communication link. Once the connection is successful, the system enters a continuous listening state, ready to receive scheduling commands.

[0056] Subsequently, the photovoltaic simulation controller continuously receives a sequence of active power dispatching instructions periodically issued by the energy management module. These instructions are typically sent in the form of data frames or messages, containing information such as timestamps, target power values, instruction validity periods, or execution time periods. The issuance cycle of dispatching instructions can be set according to the microgrid's operational needs, commonly updated every 1 minute, 5 minutes, or 15 minutes, and event-triggered sending is also supported. The controller extracts the currently valid active power instructions by parsing the communication messages and incorporates them into the output calculation process.

[0057] To ensure the accuracy of command data and the stability of system operation, the photovoltaic analog controller performs validity verification and caching on the received active power dispatch command sequence. Validity verification includes multiple dimensions: first, format verification to determine if the message conforms to the protocol specifications; second, numerical rationality verification to check if the power command is between 0 and the inverter's rated capacity, eliminating jumps caused by outliers or communication errors; and third, time consistency verification to ensure that the command timestamps increment in an orderly manner, preventing retransmissions or out-of-order delivery. For commands that pass verification, the controller stores them in a circular buffer or FIFO queue for caching, forming a time-ordered dispatch command sequence for subsequent control cycles.

[0058] The caching mechanism also supports instruction interpolation. When the control cycle is shorter than the scheduling instruction update cycle, the controller can estimate the target power value at the intermediate moment using a linear interpolation method based on the time difference between two consecutive valid instructions, thereby achieving a smooth transition and avoiding interference to the inverter control performance caused by sudden changes in power instructions.

[0059] The above-described embodiments ensure the real-time, continuous, and reliable nature of dispatch commands, enabling the photovoltaic simulation system to accurately reflect the EMS's control process over the inverter during dynamic testing. This provides a solid data foundation for evaluating key performance indicators such as dispatch response speed, power point tracking accuracy, and peak shaving / valley filling effects. Furthermore, this design is compatible with multiple mainstream communication standards, possesses excellent system integration capabilities, and is suitable for various application scenarios, including laboratories, testing institutions, and engineering commissioning.

[0060] In one embodiment, step S150 includes: S151. In each control cycle, based on the effective power generation curve, the active power dispatching command, and the rated capacity of the inverter, the effective power generation value, the active power dispatching command value, and the rated capacity value of the inverter at the current moment are obtained respectively. S152. Compare the effective power output value, the active power dispatch command value, and the inverter rated capacity value, and select the minimum value among the three to generate the inverter target output command for the current moment.

[0061] The photovoltaic analog controller samples data and calculates commands in a fixed control cycle. At the beginning of each control cycle, the controller first starts from the generated effective power curve. Read the current time Corresponding power value The effective power output (EPS) is the maximum output capacity that a photovoltaic power generation system can provide under current environmental conditions. This value is derived from a composite curve that has undergone time-axis transformation, amplitude scaling, and local attenuation processing, and fully reflects the influence of multiple environmental factors such as season, weather, and instantaneous shading.

[0062] At the same time, the controller obtains the active power scheduling instruction value corresponding to the current moment from the cached scheduling instruction sequence. If the latest instruction has not yet been updated, linear interpolation or maintaining the previous value is used to ensure data continuity and avoid control jitter caused by discontinuous instructions. This scheduling instruction value represents the power regulation requirements of the energy management module for the inverter and may be used in operating scenarios such as peak shaving, frequency regulation, or power limiting.

[0063] In addition, the controller calls the pre-configured inverter rated capacity value. This value serves as the upper limit of the device's hardware capabilities. It is typically set by the tester during system initialization based on the nameplate parameters of the inverter under test and stored in the controller's configuration database.

[0064] After obtaining the above three key parameters, the controller performs a comparison operation and sets the current effective power output value. Active power dispatch command value and inverter rated capacity value The numerical values ​​are compared, and the smallest value is selected as the inverter's target output command for the current moment. The calculation process is represented by the following formula: This formula reflects the operating logic of a real photovoltaic power station: when When this occurs, it indicates that the grid side requires power generation limitation, and the inverter's target output command follows the dispatch command to achieve peak shaving control; when When this occurs, it indicates that the dispatch allows full power generation but there is insufficient sunlight, so the inverter can only generate the maximum power currently available; when When the value is the minimum of the three, the target output command is clamped to the rated capacity to prevent the inverter from being damaged due to overload.

[0065] The target output command The signal is then encapsulated into a control signal and enters the next stage of the distribution and execution process. The entire calculation process is executed cyclically within each control cycle, forming a dynamic, real-time closed-loop control. This ensures that the simulated output is always within a reasonable, safe range that conforms to actual operating rules, providing a high-precision and high-reliability test foundation for the verification of control strategies in microgrid systems.

[0066] Furthermore, step S152 includes: S1521. When the active power scheduling instruction value is less than the effective available power value, the target output instruction follows the active power scheduling instruction. S1522. When the active power scheduling command value is greater than the effective available power value, the target output command is equal to the effective available power value. S1523. When the inverter's rated capacity is at its minimum, the target output command clamp is located at the inverter's rated capacity.

[0067] When the active power dispatch command value is less than the effective available power value, it indicates that the energy management module requires the inverter to limit its output power for operational needs such as grid security, load balancing, or peak shaving and valley filling. In this case, even if sunlight conditions are good and the photovoltaic system has a higher power generation capacity, the inverter should still comply with the dispatch command. Therefore, the target output command follows the active power dispatch command to achieve on-demand power output and avoid impacting the grid.

[0068] When the active power dispatch command value is greater than the effective generateable power value, it indicates that the dispatching end allows the inverter to operate at full capacity. However, due to environmental factors such as current sunlight intensity, cloud cover, or sunshine duration, the actual generateable capacity of the photovoltaic system is lower than the command requirement. In this case, the inverter cannot operate beyond its physical power generation limits. Therefore, the target output command equals the effective generateable power value, meaning the system outputs the maximum power that can be provided under the current environmental conditions, reflecting the natural constraint characteristic of outputting power according to available capacity.

[0069] When the inverter's rated capacity is the minimum of the three, it means that even if the effective power output and dispatch commands both exceed the equipment's carrying capacity, the inverter cannot operate beyond its limits. To ensure equipment safety and prevent overload damage, the target output command is forcibly clamped to the inverter's rated capacity. This mechanism, as a hardware protection boundary, takes precedence under all circumstances, ensuring that the system operates within a safe range.

[0070] The above three scenarios together constitute a complete output decision logic, which is automatically identified and executed by the photovoltaic simulation controller in each control cycle. This logic not only reproduces the collaborative control mechanism in a real power plant, where the available power generation capacity is the upper limit, the dispatch command is the main force, and the equipment capacity is the hard limit, but also improves the transparency and verifiability of the control strategy through conditional judgment, providing an accurate and reliable simulation basis for testing the inverter's dynamic response, EMS dispatch effectiveness, and system stability in a microgrid environment.

[0071] In one embodiment, step S160 includes: S161. Convert the inverter target output command into the corresponding DC power setting value; S162. Match the output control of the DC analog source with the DC power setpoint; S163. Adjust the grid-connected power of the inverter under the DC input condition so that the actual output power of the inverter is consistent with the target output command.

[0072] First, the photovoltaic simulation controller first processes the calculated inverter target output command. This is converted to the corresponding DC power setpoint. Since the inverter's input is DC-side power, the controller needs to estimate the required DC input power based on the target output command and a preset inverter efficiency model or measured efficiency curve. This DC power setpoint serves as the control target, guiding the output adjustment of the DC analog source.

[0073] Subsequently, the photovoltaic simulation controller sends control commands to the DC simulation source, adjusting its output voltage and current to dynamically match the DC power setpoint. The DC simulation source is programmable, capable of simulating the current-voltage output characteristics of the photovoltaic array. The controller generates a voltage-current combination corresponding to the target power through internal algorithms, ensuring stable output at, for example, the maximum power point or a specified power limit. This process supports rapid dynamic response, adjusting the output in real time according to changes in the target output command to reproduce transient conditions such as fluctuations in sunlight and changes in scheduling.

[0074] After the inverter under test is connected to the DC analog source, it is treated as a real photovoltaic input source, and the maximum power point tracking algorithm or power limit control logic is automatically activated. When the target output command is lower than the current maximum available power, the inverter enters a power-limiting operation mode and actively reduces the output to the command value; when the command allows full power generation, the inverter tracks the maximum available power provided by the DC source. In this way, the inverter autonomously adjusts its AC-side grid-connected active power under real DC input conditions, ultimately ensuring that the actual output power is consistent with the target output command.

[0075] Based on the photovoltaic inverter output simulation methods disclosed in the above embodiments, it can be concluded that this method has a wide range of applications. In the development of new power systems, it can support grid stability testing in scenarios with a high proportion of renewable energy integration, and support comprehensive verification of EMS dispatch strategies, frequency response, voltage support, and other functions. In inverter factory testing and type testing, it can replace real photovoltaic arrays, achieving all-weather, repeatable automated testing, significantly improving testing efficiency and consistency. In the fields of teaching and research, this system can serve as a teaching platform for renewable energy grid-connected control, helping students intuitively understand the dynamics of sunlight, the synergistic relationship between dispatch commands and equipment capabilities.

[0076] In the future, this method can be extended to scenarios such as wind power output simulation and photovoltaic-storage joint simulation. By introducing wind speed fluctuation models or energy storage charging and discharging logic, a more complete integrated energy system testing environment can be constructed. With its high realism, strong flexibility, and good compatibility, this method is expected to become a standardized technical solution in the field of microgrid and new energy grid connection testing.

[0077] Figure 9 This is a schematic block diagram of a photovoltaic inverter output simulation device 600 provided in an embodiment of the present invention. Figure 9As shown, corresponding to the above photovoltaic inverter output simulation method, the present invention also provides a photovoltaic inverter output simulation device 600. This photovoltaic inverter output simulation device 600 includes a unit for executing the above photovoltaic inverter output simulation method, and the device can be configured in a desktop computer, tablet computer, smartphone, or other terminal.

[0078] Specifically, please refer to Figure 9 The photovoltaic inverter output simulation device 600 includes: The reference curve acquisition unit 610 is used to acquire the reference power output curve of the photovoltaic simulation system. The editing parameter generation unit 620 is used to receive the user's editing operation on the benchmark power curve through the editing module and generate the corresponding editing parameters; The curve correction processing unit 630 is used to perform sequential correction processing on the reference power curve according to the editing parameters through the photovoltaic simulation controller to generate the corrected effective power curve. The scheduling instruction acquisition unit 640 is used to acquire the active power scheduling instruction issued by the energy management module to the inverter; The target output calculation unit 650 is used to calculate the inverter target output command based on the effective generateable power curve, the active power dispatch command and the rated capacity of the inverter, using the triple constraint minimum value principle. The instruction issuing and execution unit 660 is used to issue the target output instruction to the inverter, drive the inverter to adapt to the DC input conditions corresponding to the target output, and complete the actual power output through the DC analog source.

[0079] In one embodiment, the reference curve acquisition unit 610 includes: The standard model calling unit is used to use the standard illumination model preset in the photovoltaic simulation controller as the reference power curve. The mathematical modeling generation unit is used to generate a power curve representing typical solar radiation changes as a benchmark power curve by mathematical modeling based on a preset calculation model through the photovoltaic simulation controller.

[0080] Furthermore, the baseline curve acquisition unit 610 also includes: The historical data import unit is used to import measured historical solar power data of the photovoltaic power station as a benchmark power curve into the photovoltaic simulation controller.

[0081] In one embodiment, the editing parameter generation unit 620 includes: The human-computer interaction editing unit is used to receive the editing operations input by the user through the graphical human-computer interaction interface provided in the editing module. The editing operations include: adding pits, adjusting irradiance, and setting illumination duration. A pit parameter generation unit is used to generate pit parameters in response to the pit addition operation. The pit parameters include pit center time, pit duration, and light attenuation coefficient. An irradiance parameter generation unit is used to generate irradiance adjustment parameters in response to the irradiance adjustment operation. The irradiance adjustment parameters are used to characterize the overall light intensity change. The duration parameter generation unit is used to generate a time ratio parameter in response to the setting illumination duration operation. The time ratio parameter is used to stretch or compress the effective illumination period.

[0082] In one embodiment, the curve correction processing unit 630 includes: The time axis transformation unit is used to linearly transform the time axis of the reference power curve according to the time ratio parameter to obtain the time-adjusted intermediate power curve. An amplitude scaling unit is used to scale the intermediate power curve according to the irradiance adjustment parameters to obtain an irradiance-adjusted intermediate power curve. The local attenuation processing unit is used to apply local attenuation processing to the intermediate power curve after irradiance adjustment within a specified time period according to the pit parameters, so as to generate the effective radiable power curve.

[0083] In one embodiment, the scheduling instruction acquisition unit 640 includes: A communication connection establishment unit is used to establish a data connection with the energy management module through a communication protocol; The scheduling instruction receiving unit is used to continuously receive the active power scheduling instruction sequence periodically issued by the energy management module; The instruction verification and caching unit is used to perform validity verification and caching processing on the received active power scheduling instruction sequence.

[0084] In one embodiment, the target output calculation unit 650 includes: The real-time data acquisition unit is used to acquire the effective power generation value, the active power dispatch command value, and the inverter rated capacity value at the current moment, based on the effective power generation curve, the active power dispatch command, and the inverter rated capacity, respectively, within each control cycle. The minimum value decision unit is used to compare the effective generateable power value, the active power dispatch command value, and the inverter rated capacity value, and select the minimum value among the three to generate the inverter target output command at the current moment.

[0085] Furthermore, the minimum decision unit includes: A DC power conversion unit is used to ensure that the target output command follows the active power scheduling command when the active power scheduling command value is less than the effective available power value. A DC source output control unit is used to ensure that the target output command is equal to the effective power generation value when the active power dispatch command value is greater than the effective power generation value. The grid-connected power regulation unit is used to clamp the target output command at the inverter's rated capacity when the inverter's rated capacity is at its minimum.

[0086] The aforementioned photovoltaic inverter output simulation device 600 can be implemented as a computer program, which can be used in, for example... Figure 10 It runs on the computer device shown.

[0087] Please see Figure 10 , Figure 10 This is a schematic block diagram of a computer device 500 provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a desktop computer, tablet computer, or smartphone. The server can be a standalone server or a server cluster composed of multiple servers.

[0088] See Figure 10 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0089] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a photovoltaic inverter output simulation method.

[0090] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0091] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a photovoltaic inverter output simulation method.

[0092] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 10The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0093] The processor 502 is used to run a computer program 5032 stored in a memory to implement the steps of the above method.

[0094] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0095] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0096] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the steps of the above-described method.

[0097] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0098] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0099] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0100] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0101] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for simulating the output of a photovoltaic inverter, characterized in that, Applied to a photovoltaic simulation system, the system including a photovoltaic simulation controller, an editing module, a DC simulation source, an energy management module, and an inverter, the method includes: Obtain the baseline power generation curve of the photovoltaic simulation system; The editing module receives user editing operations on the baseline power curve and generates corresponding editing parameters. Based on the editing parameters, the photovoltaic simulation controller performs sequential correction processing on the baseline power generation curve to generate the corrected effective power generation curve. Obtain the active power scheduling command issued by the energy management module to the inverter; Based on the effective power output curve, the active power dispatch command, and the rated capacity of the inverter, the target output command of the inverter is calculated using the triple constraint minimum value principle. The target output command is sent to the inverter, which is then driven to adapt to the DC input conditions corresponding to the target output, and the actual power output is completed through the DC analog source.

2. The photovoltaic inverter output simulation method according to claim 1, characterized in that, The step of obtaining the baseline power generation curve of the photovoltaic simulation system includes: The standard illumination model preset in the photovoltaic simulation controller is used as the benchmark power output curve; The photovoltaic simulation controller generates a power curve representing typical solar radiation changes by performing mathematical modeling based on a preset calculation model, which serves as a benchmark power curve.

3. The photovoltaic inverter output simulation method according to claim 2, characterized in that, The step of obtaining the baseline power generation curve of the photovoltaic simulation system further includes: The historical solar power data of the measured photovoltaic power station is imported into the photovoltaic simulation controller as a benchmark power curve.

4. The photovoltaic inverter output simulation method according to claim 1, characterized in that, The step of receiving user editing operations on the baseline transmittable power curve through the editing module and generating corresponding editing parameters includes: The editing module receives user input via a graphical human-computer interaction interface, and the editing operations include: adding pits, adjusting irradiance, and setting illumination duration. In response to the pitting addition operation, pitting parameters are generated, including pitting center time, pitting duration, and light attenuation coefficient. In response to the irradiance adjustment operation, irradiance adjustment parameters are generated, which are used to characterize the overall light intensity change; In response to the setting of illumination duration operation, a time ratio parameter is generated, which is used to stretch or compress the effective illumination period.

5. The photovoltaic inverter output simulation method according to claim 4, characterized in that, The step of sequentially correcting the baseline renewable power curve according to the editing parameters through the photovoltaic simulation controller to generate the corrected effective renewable power curve includes: The time axis of the baseline power curve is linearly transformed according to the time ratio parameter to obtain the time-adjusted intermediate power curve. The intermediate power curve is scaled up and down according to the irradiance adjustment parameters to obtain the irradiance-adjusted intermediate power curve. Based on the pit parameters, a local attenuation process is applied to the intermediate power curve after irradiance adjustment within a specified time period to generate the effective radiable power curve.

6. The photovoltaic inverter output simulation method according to claim 4, characterized in that, The step of obtaining the active power scheduling command issued by the energy management module to the inverter includes: Establish a data connection with the energy management module through a communication protocol; Continuously receive the active power scheduling instruction sequence periodically issued by the energy management module; The received active power scheduling instruction sequence is validated and cached.

7. The photovoltaic inverter output simulation method according to claim 4, characterized in that, The step of calculating the inverter target output command based on the effective generateable power curve, the active power dispatch command, and the inverter's rated capacity, using the triple constraint minimum value principle, includes: Within each control cycle, based on the effective power generation curve, the active power dispatch command, and the inverter's rated capacity, the effective power generation value, the active power dispatch command value, and the inverter's rated capacity value at the current moment are obtained respectively. The effective generateable power value, the active power dispatch command value, and the inverter rated capacity value are compared, and the minimum value among the three is selected to generate the inverter target output command for the current moment.

8. The photovoltaic inverter output simulation method according to claim 7, characterized in that, The step of comparing the effective generateable power value, the active power dispatch command value, and the inverter rated capacity value, and selecting the minimum value among the three to generate the inverter target output command at the current moment includes: When the active power scheduling instruction value is less than the effective available power value, the target output instruction follows the active power scheduling instruction; When the active power scheduling command value is greater than the effective available power value, the target output command is equal to the effective available power value; When the inverter's rated capacity is at its minimum, the target output command clamp is located at the inverter's rated capacity.

9. The photovoltaic inverter output simulation method according to claim 1, characterized in that, The steps of sending the target output command to the inverter, driving the inverter to adapt to the DC input conditions corresponding to the target output, and completing the actual power output through the DC analog source include: Convert the inverter target output command into the corresponding DC power setting value; The output control of the DC analog source will be matched with the DC power setpoint. The inverter is adjusted to match the grid-connected power under the DC input condition so that the actual output power of the inverter is consistent with the target output command.

10. A photovoltaic inverter output simulation device, characterized in that, Used to perform the photovoltaic inverter output simulation method as described in any one of claims 1 to 9.

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