Photovoltaic power station grid-connected regulation method and system for large-scale power grid security

Abstract

The present invention discloses a photovoltaic power station grid-connected regulation method and system for large-scale power grid security assurance, which relates to the field of power system regulation. The method includes: establishing a grid-connected topology structure of a photovoltaic power station and a traditional power grid in a preset area; extracting a first grid-connected node, performing photovoltaic output grid-connected current monitoring and traditional power grid grid-connected current monitoring, and performing a photovoltaic inverter current fluctuation suppression adaptability analysis based on the first two-way current monitoring results to generate a first photovoltaic suppression adaptability index; if the current fluctuation suppression is less than the preset adaptability index, performing a bidirectional current fluctuation suppression balance optimization between the photovoltaic inverter and the traditional power grid to determine a first bidirectional suppression strategy; and performing grid-connected regulation of the first grid-connected node using the first bidirectional suppression strategy. This method solves the technical problem of insufficient regulation accuracy and effectiveness in existing photovoltaic power station grid-connected regulation, achieving the technical effect of improving the accuracy and effectiveness of photovoltaic power station grid-connected regulation.

Description

Technical Field

[0001] The present application relates to fields related to power system regulation, and in particular to a photovoltaic power station grid-connected regulation method and system for ensuring large-scale power grid security. Background Art

[0002] In the power system sector, ensuring the security of large-scale power grids is a key issue in ensuring a stable energy supply and maintaining stable socioeconomic operations. Grid-connected regulation of photovoltaic power plants is crucial to ensuring this security. Typically, when connecting a photovoltaic power plant to the grid, it relies primarily on inverters to coordinate with the frequency, voltage, and other parameters of the traditional power grid to achieve grid-connected operation. However, because photovoltaic power generation is affected by various factors such as light intensity and weather conditions, power generation data fluctuates significantly. Inverters have limited inherent regulation capabilities. Faced with such complex and fluctuating power generation data, it is easy for the photovoltaic power plant to become out of sync with the grid frequency. This not only affects power quality but can also pose a threat to the safe and stable operation of the power grid.

[0003] Among the current related technologies, the grid-connected regulation of photovoltaic power stations has technical problems such as insufficient regulation accuracy and effectiveness. Summary of the Invention

[0004] The present application provides a photovoltaic power station grid-connected regulation method and system for large-scale power grid security. By establishing a preset regional photovoltaic power station and a traditional power grid grid-connected topology, extracting the first grid-connected node, performing dual-path current monitoring, analyzing and generating a first photovoltaic suppression adaptation index, and if the index is less than a preset value, executing bidirectional current fluctuation suppression balance optimization, determining a first bidirectional suppression strategy, and performing grid-connected regulation of the first grid-connected node according to this strategy, and other technical means, the present application solves the technical problem of insufficient regulation accuracy and effectiveness in the existing photovoltaic power station grid-connected regulation, and achieves the technical effect of improving the accuracy and effectiveness of the photovoltaic power station grid-connected regulation.

[0005] The present application provides a photovoltaic power station grid-connected regulation method for large-scale power grid security, including: establishing a grid-connected topology structure of a photovoltaic power station and a traditional power grid in a preset area; extracting a first grid-connected node based on the grid-connected topology structure, performing photovoltaic output grid-connected current monitoring and traditional power grid grid-connected current monitoring at the first grid-connected node, performing a photovoltaic inverter current fluctuation suppression adaptability analysis based on a first dual-path current monitoring result, and generating a first photovoltaic suppression adaptability index; if the first photovoltaic suppression adaptability index is less than a preset adaptability index, performing bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid based on the first dual-path current monitoring result, and determining a first bidirectional suppression strategy; and performing grid-connected regulation of the first grid-connected node using the first bidirectional suppression strategy.

[0006] In a possible implementation, photovoltaic output grid-connected current monitoring and traditional power grid-connected current monitoring are performed at the first grid-connected node, and the following processing is performed: current characteristics of the photovoltaic output circuit are monitored by a pre-deployed current sensor at the first grid-connected node to generate first photovoltaic grid-connected current data; current characteristics of the traditional power grid grid-connected circuit are monitored by a pre-deployed current sensor at the first grid-connected node to generate first traditional power grid-connected current data; the first dual-path current monitoring result is generated using the first photovoltaic grid-connected current data and the first traditional power grid grid-connected current data.

[0007] In a possible implementation, the following processing is performed: the photovoltaic output circuit is a circuit in which power flows from the photovoltaic power generation unit in the photovoltaic power station into the first photovoltaic inverter, and the first photovoltaic inverter is connected to the first grid-connected node; the traditional power grid-connected circuit is a circuit in the traditional power grid that is located before the first grid-connected node and is not connected to the grid.

[0008] In a possible implementation, the first dual-channel current monitoring result is used to perform an adaptability analysis of the photovoltaic inverter's current fluctuation suppression, generate a first photovoltaic suppression adaptability index, and perform the following processing: construct a first regulation target sequence of the first photovoltaic inverter using the first traditional power grid-connected current data; collect the service life, maintenance records, and inverter model of the first photovoltaic inverter and use them as modeling constraints to collect modeling data for digital modeling to generate a first twin inverter; based on the first regulation target sequence, perform a fluctuation suppression simulation of the first photovoltaic grid-connected current data using the first twin inverter, perform an adaptive matching analysis of the current fluctuation suppression regulation capability and the grid current fluctuation level, and generate the first photovoltaic suppression adaptability index.

[0009] In a possible implementation, an adaptive matching analysis of the current fluctuation suppression regulation capability and the grid current fluctuation level is performed to generate the first photovoltaic suppression adaptation index, and the following processing is performed: reading the first fluctuation suppression sequence output by the first twin inverter; analyzing the consistency ratio coefficient of the fluctuation suppression results in the first fluctuation suppression sequence and the corresponding regulation targets in the first regulation target sequence to generate the first photovoltaic suppression adaptation index.

[0010] In a possible implementation, based on the first dual-channel current monitoring result, a bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid is performed to determine the first bidirectional suppression strategy, and the following processing is performed: determine the traditional generator connected to the traditional power grid, and collect a historical power grid fluctuation suppression record library based on the traditional generator; screen multiple power grid fluctuation suppression records that match the first traditional power grid grid-connected current data in the first dual-channel current monitoring result in the historical power grid fluctuation suppression record library, and obtain multiple fluctuation suppression features and multiple suppression cost indicators; based on the multiple suppression cost indicators, extract the fluctuation suppression feature with the lowest cost, and after updating the first dual-channel current monitoring result, call the first twin inverter to perform the fluctuation suppression simulation of the first photovoltaic grid-connected current data; if the updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index, use the power grid fluctuation suppression record corresponding to the fluctuation suppression feature with the lowest cost to determine the traditional power grid side fluctuation suppression decision and the fluctuation suppression simulation decision of the first twin inverter to generate the first bidirectional suppression strategy.

[0011] In a possible implementation, the following processing is performed: if the updated photovoltaic suppression adaptation index is less than the preset adaptation index, continue to extract the fluctuation suppression characteristics corresponding to the secondary minimum suppression cost index among multiple suppression cost indicators, and perform iterative update simulation until the iteratively updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index, and generate the first bidirectional suppression strategy.

[0012] In a possible implementation, the following processing is performed: the fluctuation suppression feature corresponding to any one of the plurality of grid fluctuation suppression records is the grid current feature after fluctuation suppression, and the corresponding suppression cost index is determined based on the degree of increased burden on the frequency-regulating generator.

[0013] In a possible implementation, after determining the first bidirectional suppression strategy, the following processing is also performed: reading each grid-side fluctuation suppression decision in the bidirectional suppression strategy corresponding to each grid-connected node in the grid-connected topology; based on the grid-connected topology, analyzing the current fluctuation transmission relationship between each grid-connected node; based on the current fluctuation transmission relationship, analyzing the fluctuation transmission mutual influence between each grid-side fluctuation suppression decision, performing mutual influence offset optimization, and generating each grid-side optimization decision; optimizing and adjusting the first bidirectional suppression strategy with the each grid-side optimization decision.

[0014] The present application also provides a photovoltaic power station grid-connected regulation system for large-scale power grid security assurance, including: a grid-connected topology structure construction module, used to construct a grid-connected topology structure of a photovoltaic power station and a traditional power grid in a preset area; a current fluctuation suppression adaptability analysis module, used to extract a first grid-connected node based on the grid-connected topology structure, perform photovoltaic output grid-connected current monitoring and traditional power grid grid-connected current monitoring at the first grid-connected node, perform photovoltaic inverter current fluctuation suppression adaptability analysis based on the first dual-path current monitoring result, and generate a first photovoltaic suppression adaptability index; a bidirectional current fluctuation suppression balance optimization module, used to perform bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid based on the first dual-path current monitoring result if the first photovoltaic suppression adaptability index is less than the preset adaptability index, and determine a first bidirectional suppression strategy; a grid-connected regulation module, used to perform grid-connected regulation of the first grid-connected node based on the first bidirectional suppression strategy.

[0015] The photovoltaic power station grid-connected regulation method and system proposed in this application for large-scale power grid security first establishes a grid-connected topology structure for the photovoltaic power station and the traditional power grid in a preset area. Then, based on the grid-connected topology structure, a first grid-connected node is extracted. Photovoltaic output grid-connected current monitoring and traditional power grid grid-connected current monitoring are performed at the first grid-connected node. The first dual-path current monitoring result is used to analyze the adaptability of the photovoltaic inverter current fluctuation suppression to generate a first photovoltaic suppression adaptation index. Then, if the first photovoltaic suppression adaptation index is less than the preset adaptation index, a bidirectional current fluctuation suppression balance optimization is performed between the photovoltaic inverter and the traditional power grid based on the first dual-path current monitoring result to determine a first bidirectional suppression strategy. Finally, the first bidirectional suppression strategy is used to perform grid-connected regulation of the first grid-connected node. The technical effect of improving the accuracy and effectiveness of the photovoltaic power station grid-connected regulation is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flow chart of a photovoltaic power station grid-connected adjustment method for large-scale power grid security provided in an embodiment of the present application.

[0018] Figure 2 A schematic diagram of the structure of a photovoltaic power station grid-connected regulation system for large-scale power grid security provided in an embodiment of the present application.

[0019] Description of the accompanying drawings: grid-connected topology structure building module 10, current fluctuation suppression adaptability analysis module 20, bidirectional current fluctuation suppression balance optimization module 30, grid-connected regulation module 40. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The present application provides a photovoltaic power station grid-connected adjustment method for large-scale power grid security, such as Figure 1 As shown, the method includes:

[0024] Step S100: constructing a grid-connected topology structure of a photovoltaic power station in a preset area and a traditional power grid.

[0025] Specifically, the grid-connected topology refers to the physical structure of the connection between the photovoltaic power station and the traditional power grid, including the connection relationship between the output terminal of the photovoltaic power station, inverters, transformers, grid-connected switches, transmission lines and other equipment. Among them, the photovoltaic power station is built using photovoltaic modules, inverters, transformers, switchgear, cables, etc., and the photovoltaic power station is connected to the traditional power grid through grid-connected switches, line protection devices, etc. Communication networks, such as fiber-optic communications and wireless communications, are deployed to transmit data and control instructions between the photovoltaic power station and the power grid. Grid-connected control devices are installed to achieve synchronous grid-connected operation of the photovoltaic power station and the power grid. Monitoring equipment such as current transformers and voltage transformers are installed at the grid connection point to collect real-time grid current and voltage data.

[0026] For example, this involves the following specific operations: An inverter is installed at the output of the PV power plant to convert the DC power generated by the PV panels into AC power with the same frequency and phase as the conventional power grid. A cable is used to connect the inverter output to the low-voltage side of a transformer, and the high-voltage side of the transformer is connected to the conventional power grid's transmission lines. An intelligent switch is installed at the grid connection point to control the connection between the PV power plant and the grid, transmitting switch status information to the control center via a communications network. High-precision current transformers and voltage transformers are installed at the grid connection point to collect real-time grid current and voltage data, which is then transmitted to a monitoring system via a communications network.

[0027] Step S200: extracting a first grid-connected node based on the grid-connected topology structure, performing photovoltaic output grid-connected current monitoring and traditional power grid grid-connected current monitoring at the first grid-connected node, performing photovoltaic inverter current fluctuation suppression adaptability analysis based on the first dual-path current monitoring results, and generating a first photovoltaic suppression adaptability index.

[0028] Specifically, the grid-connected node refers to the electrical node where a photovoltaic power station connects to the traditional power grid. It is a key location for current and voltage monitoring and regulation. High-precision current sensors, such as Hall effect current sensors, are used to monitor both the photovoltaic output grid-connected current and the traditional power grid-connected current. A data acquisition module, such as an analog-to-digital converter, converts the analog signals collected by the current sensors into digital signals and transmits them to the control center via a communication network. Digital signal processing algorithms, such as fast Fourier transforms and wavelet transforms, are used to analyze the collected current data, calculate parameters such as the amplitude and frequency of current fluctuations, and generate a first photovoltaic suppression adaptability index based on a preset adaptability index calculation formula. This first photovoltaic suppression adaptability index is a quantitative indicator used to measure the photovoltaic inverter's ability to suppress current fluctuations. An adaptability analysis module is installed in the control center to determine whether the photovoltaic inverter's ability to suppress current fluctuations meets grid operation requirements based on the collected current data and the preset adaptability index threshold.

[0029] For example, the following specific operations are involved: Two Hall-effect current sensors are installed at the first grid-connected node: one for monitoring the grid-connected current of the photovoltaic output and the other for monitoring the grid-connected current of the conventional power grid. The analog current signal collected by the Hall-effect current sensor is converted into a digital signal using an analog-to-digital converter and transmitted to a server in the control center via a fiber-optic communication network. An adaptability analysis algorithm is run on the server to perform a fast Fourier transform on the collected current data, calculating the amplitude and phase of the fundamental wave and each harmonic of the current. The first photovoltaic suppression adaptability index is calculated based on a preset adaptability index formula.

[0030] In one possible implementation, photovoltaic output grid-connected current and conventional power grid-connected current are monitored at the first grid-connected node. Step S200 further includes step S210, whereby current characteristics of the photovoltaic output circuit are monitored at the first grid-connected node using pre-installed current sensors to generate first photovoltaic grid-connected current data. Specifically, high-precision current sensors (such as Hall effect current sensors) are installed in the photovoltaic output circuit of the first grid-connected node. These sensors are capable of monitoring the amplitude, frequency, phase, and other characteristics of the photovoltaic output current in real time. The analog signals collected by the current sensors are converted into digital signals via an analog-to-digital converter (ADC), stored in a local storage device, and transmitted to a control center via a communication network (such as fiber optic or wireless communication).

[0031] For example, the following steps are involved: A Hall-effect current sensor with an accuracy of 0.1% full-scale (FS) is installed on the photovoltaic output circuit of the first grid-connected node, enabling real-time monitoring of current changes. The analog current signal collected by the sensor is converted to a digital signal using an analog-to-digital converter (ADC) with a sampling frequency of 10kHz to capture high-frequency current fluctuations. The collected digital signal is stored in a local industrial-grade storage device and transmitted to a server in the control center via a fiber-optic communication network.

[0032] In step S220, current characteristics of the conventional grid-connected circuit are monitored at the first grid-connected node using pre-installed current sensors to generate first conventional grid-connected current data. Specifically, high-precision current sensors (such as Rogowski coil current sensors) are installed on the conventional grid-connected circuit at the first grid-connected node. These sensors can monitor the amplitude, frequency, phase, and other characteristics of the conventional grid-connected current in real time. The analog signals collected by the current sensors are converted into digital signals using an analog-to-digital converter (ADC). The collected digital signals are stored in a local storage device and transmitted to a control center via a communication network (such as fiber optic or wireless communication).

[0033] For example, the following specific steps are involved: A Rogowski coil current sensor with an accuracy of 0.2% FS is installed on the conventional grid-connected circuit at the first grid-connected node, enabling real-time monitoring of current changes. The analog current signal collected by the sensor is converted to a digital signal using an analog-to-digital converter (ADC) with a sampling frequency of 10 kHz to ensure that high-frequency current fluctuations are captured. The collected digital signal is stored in a local industrial-grade storage device and transmitted to a server in the control center via a fiber-optic communication network.

[0034] Step S230, generating the first dual-channel current monitoring result using the first photovoltaic grid-connected current data and the first traditional grid-connected current data. Specifically, on the server of the control center, the current data collected from the photovoltaic output circuit and the traditional grid-connected circuit are fused and processed. The time alignment of the two current data is ensured by a time synchronization algorithm. Feature extraction is performed on the fused current data, and characteristic parameters such as the current amplitude, frequency, phase, and harmonic content are calculated. The extracted characteristic parameters are organized into the first dual-channel current monitoring result and displayed in the form of a table or graph.

[0035] By monitoring the current characteristics of the photovoltaic output circuit and the traditional grid-connected circuit at the first grid-connected node, the characteristic parameters such as the amplitude, frequency, and phase of the two currents can be accurately obtained, providing an accurate data basis for the adaptability analysis of current fluctuation suppression, and based on this, a more effective bidirectional current fluctuation suppression strategy can be formulated.

[0036] For example, this involves running a time synchronization algorithm on the control center's server to align the timestamps of the PV output current data and the traditional grid-connected current data, ensuring that the two data sources are compared at the same time. A fast Fourier transform (FFT) is then performed on the fused current data to calculate the fundamental amplitude, harmonic content, and total harmonic distortion (THD). The calculated characteristic parameters are organized into a table, as shown in Table 1.

[0037] Table 1: Example of the first dual-channel current monitoring results

[0038]

[0039] In one possible implementation, step S200 further includes: the photovoltaic output circuit is a circuit in which power flows from the photovoltaic power generation unit in the photovoltaic power station into the first photovoltaic inverter, and the first photovoltaic inverter is connected to the first grid-connected node; the traditional grid-connected circuit is a circuit in the traditional grid that is located before the first grid-connected node and is not grid-connected.

[0040] Specifically, the photovoltaic output circuit refers to the circuit that flows from the photovoltaic power generation units (such as solar panels) within a photovoltaic power station, is converted by the first photovoltaic inverter, and ultimately flows into the first grid-connected node. The photovoltaic power generation units, composed of solar panels, are used to convert solar energy into direct current (DC). The first photovoltaic inverter converts the DC power generated by the photovoltaic power generation units into grid-compatible alternating current (AC), ensuring that the frequency, phase, and amplitude of the current meet grid-connected requirements. The first grid-connected node is the electrical connection point between the photovoltaic power station and the conventional power grid and is a key location for current monitoring and regulation.

[0041] The traditional grid-connected circuit refers to the unconnected circuit located before the first grid-connected node in the traditional grid. This circuit represents the original state of the traditional grid and its current characteristics unaffected by the grid connection of the PV power plant. The current state before the first grid-connected node refers to the current state of the traditional grid itself before the grid connection operation occurs. This is used to compare and analyze current changes after the PV power plant is connected to the grid.

[0042] An example of a photovoltaic output circuit layout is as follows: Within a photovoltaic power station, multiple photovoltaic power generation units (solar panels) are connected in series or parallel to form a complete DC power system. The DC power source is connected to the first photovoltaic inverter, which converts the DC power to AC power, ensuring its frequency, phase, and amplitude are consistent with those of the traditional power grid. The inverter's output is connected to the first grid-connected node, completing the photovoltaic output circuit. For example, a photovoltaic power station consists of multiple 10kW solar panels connected in series and parallel to form a 100kW DC power system. This is then connected to the first photovoltaic inverter, which outputs 220V / 50Hz AC power and is connected to the first grid-connected node.

[0043] An example of a traditional grid-connected circuit layout is as follows: In a traditional grid, select a suitable access point as the first grid-connected node. Install current and voltage sensors before this node to monitor the raw current and voltage status of the traditional grid. For example, select a suitable access point on a 10kV transmission line as the first grid-connected node. Install high-precision current transformers and voltage transformers before this node to monitor the current and voltage of the traditional grid in real time.

[0044] In one possible implementation, the photovoltaic inverter's current fluctuation suppression adaptability is analyzed based on the first dual-channel current monitoring results to generate a first photovoltaic suppression adaptability index. Step S200 further includes step S240, where a first target adjustment sequence for the first photovoltaic inverter is constructed based on the first conventional grid-connected current data. Specifically, the first conventional grid-connected current data is smoothed using a data processing algorithm, such as a sliding average or wavelet transform, to extract stable current characteristic values, such as amplitude, frequency, and harmonic content. These are then used as the target adjustment sequence for the photovoltaic inverter. These target sequences are used to guide the output current regulation of the photovoltaic inverter, enabling it to better adapt to grid current fluctuations.

[0045] For example, this involves extracting the current amplitude and frequency every second from traditional grid current data, calculating their sliding averages, and generating a stable current signature sequence. This current signature sequence is then used as the target sequence for regulating the PV inverter. For example, the target sequence may include a current amplitude target of 100A and a frequency target of 50Hz. These target values are then stored as an ordered sequence for subsequent simulation and regulation.

[0046] In step S250, the service life, maintenance records, and inverter model of the first photovoltaic inverter are collected and used as modeling constraints to collect modeling data for digital modeling and generate a first twin inverter. Specifically, information such as service life, maintenance records, and inverter model is collected through the inverter's monitoring system and maintenance records. Using the collected data as constraints, combined with the inverter's physical model and performance parameters, a digital twin model (the first twin inverter) is constructed. The digital twin model can simulate the inverter's behavior under different operating conditions. Use professional modeling software such as MATLAB / Simulink and ANSYS for modeling and simulation.

[0047] For example, this involves the following: Data is obtained from the inverter's monitoring system, indicating a three-year service life and two scheduled maintenance sessions within the past year. Based on the inverter model, performance parameters such as rated power, conversion efficiency, and maximum output current are obtained. In MATLAB / Simulink, based on these parameters and constraints, a digital twin model of the inverter is constructed, including its circuit topology, control algorithm, and dynamic response characteristics. Simulation is then used to verify the accuracy of the digital twin model, ensuring that it accurately reflects the actual operating status of the inverter.

[0048] Step S260, based on the first regulation target sequence, the first twin inverter is used to perform a fluctuation suppression simulation of the first photovoltaic grid-connected current data, and an adaptive matching analysis of the current fluctuation suppression regulation capability and the grid current fluctuation level is performed to generate the first photovoltaic suppression adaptation index. Specifically, the first photovoltaic grid-connected current data is input into the first twin inverter model, and a fluctuation suppression simulation is performed according to the first regulation target sequence. Through the simulation results, it is analyzed whether the current fluctuation suppression capability of the photovoltaic inverter can adapt to the current fluctuation level of the grid. Key indicators such as current fluctuation amplitude and response time are calculated. Based on the simulation analysis results, a first photovoltaic suppression adaptation index is generated to evaluate the current fluctuation suppression adaptability of the inverter.

[0049] For example, this involves the following specific operations: PV grid-connected current data, such as a current amplitude of 105A and a frequency of 50.1Hz, is input into the digital twin model. A simulation is run according to the target adjustment sequence, such as a target current amplitude of 100A and a frequency of 50Hz, to observe changes in the inverter output current. Based on the simulation results, metrics such as the current fluctuation amplitude (e.g., a fluctuation amplitude of 5A from 105A to 100A) and response time (e.g., the time from input to reaching the target current is 0.5 seconds) are calculated. Based on these metrics, a first PV suppression adaptation index is generated. For example, the adaptation index can be a comprehensive score calculated based on a weighted combination of fluctuation amplitude and response time.

[0050] In one possible implementation, an adaptive matching analysis is performed between the current fluctuation suppression capability and the grid current fluctuation level to generate the first photovoltaic suppression adaptation index. Step S260 further includes step S261 of reading a first fluctuation suppression sequence output by the first twin inverter. Specifically, the output current fluctuation suppression sequence is read from the simulation results of the digital twin inverter. This sequence includes the inverter's regulation results for input current fluctuations during the simulation process. The read fluctuation suppression sequence is stored in a local or cloud database.

[0051] For example, this involves the following specific steps: Run the digital twin inverter model in simulation software (such as MATLAB / Simulink), input PV grid-connected current data, and perform a fluctuation suppression simulation based on the first regulation target sequence. Read the output current fluctuation suppression sequence from the simulation results, for example, recording the output current amplitude and frequency every second. The read fluctuation suppression sequence is stored as a data file, as shown in Table 2.

[0052] Table 2: Example of the first fluctuation suppression sequence

[0053]

[0054] Step S262: Analyze the consistency coefficient between the fluctuation suppression results in the first fluctuation suppression sequence and the corresponding regulation targets in the first regulation target sequence to generate the first photovoltaic suppression adaptation index. Specifically, each data point in the first fluctuation suppression sequence is compared with the corresponding target value in the first regulation target sequence to calculate the degree of consistency between the two. A statistical method is used to calculate the percentage of data points where the fluctuation suppression results are consistent with the regulation targets to generate the consistency coefficient. Based on the consistency coefficient, the first photovoltaic suppression adaptation index is generated to quantify the inverter's current fluctuation suppression capability.

[0055] For example, the following specific operations are included: comparing the first fluctuation suppression sequence with the first adjustment target sequence point by point. Assume that the adjustment target sequence is as shown in Table 3.

[0056] Table 3: Examples of first regulatory target sequences

[0057]

[0058] Calculate the consistency of the fluctuation suppression results at each time point with the regulation target. For example, set the consistency thresholds to a current amplitude error of no more than ±1A and a frequency error of no more than ±0.01Hz. Count the percentage of data points that meet the consistency criteria. Assuming that the fluctuation suppression results meet the regulation target at 85 out of 100 time points, the consistency coefficient is 85%. Generate a first PV suppression adaptation index based on the consistency coefficient. For example, the adaptation index can be defined as a percentage of the consistency coefficient, i.e., 85%.

[0059] The consistency factor quantifies how well a PV inverter's current fluctuation suppression capability matches the grid's current fluctuation level. This method provides an intuitive metric that allows system operators to quickly evaluate inverter performance.

[0060] Step S300: If the first photovoltaic suppression adaptation index is less than a preset adaptation index, bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid is performed based on the first dual-path current monitoring result to determine a first bidirectional suppression strategy.

[0061] Specifically, when the first PV suppression adaptability index is less than the preset adaptability index, it indicates that the PV inverter's current fluctuation suppression capability is insufficient to meet the grid's safe operation requirements. Possible issues include: excessive current fluctuation (the PV inverter's output current fluctuations exceed the grid's permitted range, potentially leading to grid voltage instability or equipment operation abnormalities); insufficient response speed (the PV inverter's response time to current fluctuations is too long, preventing it from adjusting its output current to adapt to grid changes); excessive harmonic content (the PV inverter's output current has high harmonic content, impacting grid power quality); and mismatch with the grid's regulation target (the PV inverter's output current significantly deviates from the grid's regulation target, failing to effectively coordinate with the grid's operating conditions). To ensure safe and stable grid operation after the PV power station is connected to the grid, bidirectional current fluctuation suppression balancing optimization is required between the PV inverter and the traditional grid. By adjusting the operating parameters of the PV inverter and the grid, the current fluctuation suppression capability is improved to meet or exceed the preset adaptability index.

[0062] Optimization algorithms, such as particle swarm optimization and genetic algorithms, are used to optimize the control parameters of the photovoltaic inverter and the regulation parameters of the power grid. By adjusting the output power, reactive power, modulation mode and other parameters of the photovoltaic inverter, as well as the voltage regulation and reactive power compensation parameters of the power grid, bidirectional current fluctuation suppression and balance can be achieved.

[0063] The optimized bidirectional suppression strategy is simulated and verified on the control center's simulation platform to ensure its effectiveness and reliability. Control instructions are generated based on the optimization results and transmitted to the photovoltaic inverter and grid regulation equipment via the communication network.

[0064] For example, this involves the following specific operations: A particle swarm optimization algorithm is run in the control center, using the PV inverter's output power and the grid's voltage regulation parameters as optimization variables and bidirectional current fluctuation suppression as the objective function. Based on the optimization results, the PV inverter's modulation mode is adjusted, for example, from fixed to dynamic modulation, to accommodate grid current fluctuations. Simultaneously, the grid's reactive power compensation equipment is adjusted, for example, by switching capacitor banks or adjusting the output of static VAR compensators (SVCs) to improve grid voltage quality and current fluctuation characteristics. The optimized bidirectional suppression strategy is then validated using a simulation platform, simulating current fluctuations under different operating conditions to ensure the effectiveness of the optimization strategy. Once verified, control instructions are generated and transmitted to the PV inverter and grid regulation equipment via a communication network.

[0065] In one possible implementation, based on the first dual-path current monitoring results, bidirectional current fluctuation suppression balance optimization is performed between the photovoltaic inverter and the traditional power grid to determine a first bidirectional suppression strategy. Step S300 further includes step S310 of identifying a traditional generator connected to the traditional power grid and collecting a database of historical power grid fluctuation suppression records based on the traditional generator. Specifically, the traditional generator connected to the traditional power grid is identified through a power grid management system (e.g., a SCADA system). Historical power grid fluctuation suppression records of the traditional generator are collected from the power grid's historical database, including characteristic parameters of each fluctuation suppression (e.g., current amplitude, frequency, harmonic content, etc.) and corresponding suppression cost indicators (e.g., energy consumption, response time, etc.).

[0066] For example, the following specific operations are included: Multiple traditional generators (e.g., generator sets A, B, and C) connected to the traditional power grid are identified through the power grid management system. The power grid fluctuation suppression records of these generators over the past year are collected from the power grid's historical database, as shown in Table 4.

[0067] Table 4: Example of historical power grid fluctuation suppression record library

[0068]

[0069] Step S320: Filter multiple grid fluctuation suppression records from the historical grid fluctuation suppression record library to identify those that match the first traditional grid-connected current data in the first dual-current monitoring result, and obtain multiple fluctuation suppression features and multiple suppression cost indicators. Specifically, the first traditional grid-connected current data in the first dual-current monitoring result is matched with the data in the historical grid fluctuation suppression record library to identify similar fluctuation suppression records. Fluctuation suppression features, such as current amplitude, frequency, and harmonic content, and corresponding suppression cost indicators, such as energy consumption and response time, are extracted from the matching records.

[0070] For example, the following specific operations are included: First, the grid-connected current data of the traditional power grid is: amplitude 100A, frequency 50.0Hz, harmonic content 2%. The historical power grid fluctuation suppression record library is filtered to select records that match the above data, for example: Record 1: amplitude 100A, frequency 50.0Hz, harmonic content 2%, energy consumption 10kWh, response time 0.5s; Record 2: amplitude 101A, frequency 50.0Hz, harmonic content 2.5%, energy consumption 11kWh, response time 0.6s; Record 3: amplitude 100A, frequency 50.1Hz, harmonic content 2%, energy consumption 12kWh, response time 0.7s.

[0071] Step S330: Based on the multiple suppression cost indicators, extract the fluctuation suppression feature with the minimum cost, update the first dual-channel current monitoring result, and then call the first twin inverter to perform fluctuation suppression simulation of the first photovoltaic grid-connected current data. Specifically, compare the multiple suppression cost indicators and select the fluctuation suppression feature with the minimum cost. Apply the fluctuation suppression feature with the minimum cost to the first dual-channel current monitoring result and update the monitoring data. Call the first twin inverter and use the updated monitoring data to perform fluctuation suppression simulation.

[0072] For example, this involves the following specific operations: Comparing the suppression cost indicators of the three records above, it is found that record 1 has the lowest energy consumption (10 kWh) and the shortest response time (0.5 s). The fluctuation suppression characteristics of record 1 (amplitude 100 A, frequency 50.0 Hz, harmonic content 2%) are extracted and applied to the first dual-channel current monitoring results to update the monitoring data. The first twin inverter is called and a fluctuation suppression simulation is performed using the updated monitoring data.

[0073] In step S340, if the updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index, the first bidirectional suppression strategy is generated by determining the traditional grid-side fluctuation suppression decision and the fluctuation suppression simulation decision of the first twin inverter based on the grid fluctuation suppression record corresponding to the fluctuation suppression feature with the lowest cost. Specifically, based on the simulation results, an evaluation is made as to whether the updated photovoltaic suppression adaptation index meets the preset adaptation index. If the adaptation index meets the requirements, the traditional grid-side fluctuation suppression decision and the fluctuation suppression simulation decision of the first twin inverter are generated based on the grid fluctuation suppression record corresponding to the fluctuation suppression feature with the lowest cost, thereby forming the first bidirectional suppression strategy.

[0074] For example, the following specific operations are included: Simulation results show that the updated PV suppression adaptation index is 88%, exceeding the preset adaptation index of 85%. Based on record 1 (the grid fluctuation suppression record corresponding to the fluctuation suppression feature with the lowest cost), a fluctuation suppression decision is generated for the traditional grid (such as adjusting generator output power and inputting reactive power compensation devices). Simultaneously, based on the simulation results of the first twin inverter, a fluctuation suppression simulation decision is generated for the inverter (such as adjusting the inverter modulation method and reactive power output). The fluctuation suppression decision for the traditional grid and the fluctuation suppression simulation decision for the inverter are combined into a first bidirectional suppression strategy to guide actual operation.

[0075] By incorporating a database of historical grid fluctuation suppression records from traditional generators, past operating experience is leveraged to provide a reference for current fluctuation suppression optimization. This approach avoids blind optimization and improves the reliability of the optimization strategy. By comparing multiple suppression cost indicators and selecting the fluctuation suppression feature with the lowest cost, the optimization strategy achieves the best economic and efficiency. For example, selecting the fluctuation suppression solution with the lowest energy consumption and shortest response time can both save operating costs and improve system response speed.

[0076] In one possible implementation, step S300 further includes step S350. If the updated photovoltaic suppression adaptation index is less than the preset adaptation index, continue to extract the fluctuation suppression characteristics corresponding to the secondary minimum suppression cost index among multiple suppression cost indicators, and perform iterative update simulation until the iteratively updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index, thereby generating the first bidirectional suppression strategy.

[0077] Specifically, if the photovoltaic suppression adaptation index after the first update still does not reach the preset adaptation index, the system will automatically enter the iterative update process. Extract the fluctuation suppression feature corresponding to the secondary minimum suppression cost index from multiple suppression cost indicators, and continue to update the first dual-channel current monitoring results. Call the first twin inverter and re-execute the fluctuation suppression simulation using the updated monitoring data. The iterative update simulation will continue until the updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index. Once the conditions are met, the system will stop iterating and determine the fluctuation suppression decision on the traditional power grid side and the fluctuation suppression simulation decision of the first twin inverter based on the fluctuation suppression feature that finally meets the adaptation index. These decisions are combined into the first bidirectional suppression strategy to guide the actual operation of the photovoltaic power station and the traditional power grid.

[0078] For example, suppose the PV suppression adaptability index after the first update is 82%, lower than the preset adaptability index of 85%. The system then enters an iterative update process. In the first iteration, the fluctuation suppression feature corresponding to the next lowest suppression cost index is extracted from multiple suppression cost indexes. For example, a fluctuation suppression feature with an energy consumption of 11 kWh and a response time of 0.6 s is selected. The first dual-channel current monitoring results are updated, and the first twin inverter is invoked to re-execute the fluctuation suppression simulation. The simulation results show that the updated PV suppression adaptability index is 84%, still lower than the preset adaptability index. The second iteration continues to extract the fluctuation suppression feature corresponding to the next lowest suppression cost index. For example, a fluctuation suppression feature with an energy consumption of 12 kWh and a response time of 0.7 s is selected. The first dual-channel current monitoring results are again updated, and the first twin inverter is invoked to re-execute the fluctuation suppression simulation. The simulation results show that the updated PV suppression adaptability index is 86%, meeting the preset adaptability index. Based on the fluctuation suppression feature that finally meets the adaptability index (energy consumption of 12 kWh and response time of 0.7 s), the first bidirectional suppression strategy is generated. Traditional grid-side fluctuation suppression decisions include adjusting generator output power and inputting reactive power compensation devices. Simulation decisions for PV inverter fluctuation suppression include adjusting inverter modulation mode and reactive power output.

[0079] By introducing an iterative update mechanism, the system can continuously optimize until the PV suppression adaptation index reaches a preset value. This approach ensures the thoroughness and effectiveness of the optimization process and avoids grid connection quality issues caused by insufficient initial optimization strategies.

[0080] In one possible implementation, step S320 further includes step S321, wherein the fluctuation suppression feature corresponding to any one of the multiple grid fluctuation suppression records is the grid current feature after fluctuation suppression, and the corresponding suppression cost index is determined based on the degree of increased burden on the frequency regulation generator.

[0081] Specifically, each grid fluctuation suppression record captures the grid current characteristics after the frequency-regulated generator completes the fluctuation suppression operation. These characteristics, including current amplitude, frequency, and harmonic content, reflect the actual effectiveness of the fluctuation suppression operation. For example, the grid current characteristics after fluctuation suppression are: current amplitude 100A, frequency 50.0Hz, and harmonic content 2%.

[0082] Suppression cost indicators reflect the additional burden on the frequency-regulated generator after completing the fluctuation suppression operation. These indicators include increased energy consumption, increased equipment wear, and increased response time. For example, the suppression cost indicators are: energy consumption increased by 10kWh, equipment wear increased by 5%, and response time increased by 0.2 seconds.

[0083] Historical grid fluctuation suppression records for conventional generators are collected from the grid's historical database, including grid current characteristics and corresponding suppression cost indicators after each fluctuation suppression operation. Multiple matching grid fluctuation suppression records are selected based on the first conventional grid-connected current data from the first dual-current monitoring results.

[0084] In one possible implementation, after determining the first bidirectional suppression strategy, step S300 further includes: reading each grid-side fluctuation suppression decision in the bidirectional suppression strategy corresponding to each grid-connected node in the grid-connected topology; based on the grid-connected topology, analyzing the current fluctuation transmission relationship between each grid-connected node; based on the current fluctuation transmission relationship, analyzing the fluctuation transmission mutual influence between each grid-side fluctuation suppression decision, performing mutual influence offset optimization, and generating each grid-side optimization decision; optimizing and adjusting the first bidirectional suppression strategy with the each grid-side optimization decision.

[0085] Specifically, the first bidirectional suppression strategy extracts the corresponding grid-side fluctuation suppression decisions for each grid-connected node in the grid-connected topology. For example, assume there are three grid-connected nodes in the grid-connected topology (nodes D, E, and F). Each node has a corresponding grid-side fluctuation suppression decision: Node D: adjusts the generator output power and activates the reactive power compensation device; Node E: adjusts the transformer tap position and increases the capacitor bank capacity; Node F: adjusts the generator frequency and activates the static VAR compensator (SVC).

[0086] Based on the grid topology, analyze the current transfer paths and impact relationships between various grid-connected nodes. For example, current flows from PV power plant D1 through node D and enters traditional grid D2. Grid-side decisions at node D (such as adjusting generator output power) directly affect current fluctuations in traditional grid D2. If traditional grid D2 is interconnected with other grids (such as traditional grids E2 and F2), decisions made at node D may affect other nodes through the interconnected lines.

[0087] Analyze the interactions between various grid-side fluctuation mitigation decisions to identify potential conflicts or synergies. For example, a grid-side decision at node D (adjusting generator output power) may increase current fluctuations in traditional grid D2, which is transmitted to traditional grid E2 via the interconnection, affecting current fluctuations at node E. A grid-side decision at node E (adjusting the transformer tap position) may cause voltage changes in traditional grid E2, which are transmitted to traditional grid D2 via the interconnection, affecting voltage and current fluctuations at node D.

[0088] Based on the analysis results, each grid-side fluctuation suppression decision is optimized and adjusted to offset mutual influence and improve the overall suppression effect. For example, if the grid-side decision at node D causes an increase in current fluctuation at node E, the decision at node D is adjusted (e.g., reducing the adjustment range of the generator output power) to reduce the impact on node E. If the grid-side decision at node E causes a voltage change at node D, the decision at node E is adjusted (e.g., changing the direction of the transformer tap position adjustment) to reduce the impact on node D. If the grid-side decision at node E causes an increase in current fluctuation at node F, the decision at node E is adjusted (e.g., reducing the adjustment range of the transformer tap position adjustment) to reduce the impact on node F. If the grid-side decision at node F causes a frequency change at node E, the decision at node F is adjusted (e.g., changing the direction of the generator frequency adjustment) to reduce the impact on node E. If the grid-side decision at node D causes an increase in current fluctuation at node F, the decision at node D is adjusted (e.g., reducing the adjustment range of the generator output power) to reduce the impact on node F. If the grid-side decision at node F causes a frequency change at node D, the decision at node F is adjusted (e.g., changing the direction of the generator frequency adjustment) to reduce the impact on node D.

[0089] Based on the results of the offset optimization, optimization decisions are generated for each grid side. For example, the optimization decision for node D is to adjust the generator output power of traditional grid D2 to reduce the impact of current fluctuations on node E. A reactive power compensation device is deployed to optimize the voltage level. The optimization decision for node E is to adjust the transformer tap position of traditional grid E2 to ensure coordination with the decision made at node F. The capacitor bank capacity is increased to compensate for reactive power and reduce current fluctuations. The optimization decision for node F is to adjust the generator frequency of traditional grid F2 to ensure coordination with the decision made at node E. A static VAR compensator (SVC) is deployed to further optimize current quality. Each grid-side optimization decision is applied to the first bidirectional suppression strategy, and the strategy content is updated.

[0090] By analyzing the current fluctuation transmission relationships between each grid-connected node in the grid-connected topology, global optimization, rather than just local optimization, can be achieved. This approach ensures optimal current fluctuation suppression across the entire grid. By analyzing the mutual impact of individual grid-side fluctuation suppression decisions and optimizing their offsets, conflicts between decisions can be reduced and the overall suppression effect improved. For example, this prevents decisions made by one node from offsetting the effects of decisions made by another. By optimizing fluctuation suppression decisions at each grid-connected node, the quality of PV power plant grid connection can be improved, impacts on the grid can be reduced, and stable grid operation can be ensured.

[0091] Step S400: Grid-connected regulation of the first grid-connected node is performed using the first bidirectional suppression strategy.

[0092] Specifically, after receiving control commands, the PV inverter and grid regulation equipment adjust their operating parameters accordingly. During the grid-connected regulation process, they continuously monitor the grid current and voltage in real time and feed this data back to the control center. Based on this real-time monitoring data, control strategies are dynamically adjusted to address dynamic changes in grid operation. Protective devices such as overcurrent and overvoltage protection are implemented to ensure safe operation of the equipment during the regulation process.

[0093] For example, the following specific operations are included: After receiving the control command, the photovoltaic inverter adjusts its output power and modulation mode, for example, adjusting the output power from 80% of the rated power to 75% and switching the modulation mode from fixed modulation to dynamic modulation. After receiving the control command, the grid's reactive power compensation equipment adjusts its input capacity, for example, adjusting the input capacity of the capacitor bank from 50% to 60%. During the grid-connected regulation process, current sensors and voltage sensors continue to monitor the grid current and voltage data in real time and transmit the data to the control center. The control center dynamically adjusts the control strategy based on the real-time monitoring data. For example, if the current fluctuation amplitude is detected to increase, the output power of the photovoltaic inverter or the reactive power compensation parameters of the grid are further adjusted. Overcurrent protection devices are installed at the grid-connected nodes. When the monitored current exceeds the set overcurrent threshold, the grid-connected switch is immediately disconnected to protect the equipment from damage.

[0094] The embodiment of the present application adopts technical means such as building a preset regional photovoltaic power station and a traditional power grid-connected topology, extracting the first grid-connected node, performing dual-path current monitoring, analyzing and generating a first photovoltaic suppression adaptation index, and if the index is less than a preset value, performing bidirectional current fluctuation suppression balance optimization, determining a first bidirectional suppression strategy, and performing grid-connected adjustment of the first grid-connected node according to this strategy. This solves the technical problem of insufficient adjustment accuracy and effectiveness of the existing photovoltaic power station grid-connected adjustment, and achieves the technical effect of improving the accuracy and effectiveness of the photovoltaic power station grid-connected adjustment.

[0095] In the above, refer to Figure 1 The photovoltaic power station grid-connected regulation method for large-scale power grid security according to an embodiment of the present invention is described in detail. Figure 2 A photovoltaic power station grid-connected regulation system for large-scale power grid security according to an embodiment of the present invention is described.

[0096] The photovoltaic power station grid-connected regulation system for large-scale power grid security according to an embodiment of the present invention is designed to address the technical issues of insufficient accuracy and effectiveness of existing photovoltaic power station grid-connected regulation, thereby achieving the technical effect of improving the accuracy and effectiveness of photovoltaic power station grid-connected regulation. The photovoltaic power station grid-connected regulation system for large-scale power grid security includes: a grid-connected topology construction module 10, a current fluctuation suppression adaptability analysis module 20, a bidirectional current fluctuation suppression balance optimization module 30, and a grid-connected regulation module 40.

[0097] A grid-connected topology structure construction module 10 is used to construct a grid-connected topology structure of a photovoltaic power station and a traditional power grid in a preset area; a current fluctuation suppression adaptability analysis module 20 is used to extract a first grid-connected node based on the grid-connected topology structure, perform photovoltaic output grid-connected current monitoring and traditional power grid grid-connected current monitoring at the first grid-connected node, perform photovoltaic inverter current fluctuation suppression adaptability analysis based on the first dual-path current monitoring result, and generate a first photovoltaic suppression adaptability index; a bidirectional current fluctuation suppression balance optimization module 30 is used to perform bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid based on the first dual-path current monitoring result if the first photovoltaic suppression adaptability index is less than the preset adaptability index, and determine a first bidirectional suppression strategy; a grid-connected regulation module 40 is used to perform grid-connected regulation of the first grid-connected node based on the first bidirectional suppression strategy.

[0098] The specific configuration of the current fluctuation suppression adaptability analysis module 20 will be described in detail below. As described above, the photovoltaic output grid-connected current and the traditional power grid-connected current are monitored at the first grid-connected node. The current fluctuation suppression adaptability analysis module 20 may further include: a first photovoltaic grid-connected current data generation unit for monitoring the current characteristics of the photovoltaic output circuit at the first grid-connected node using a pre-deployed current sensor to generate first photovoltaic grid-connected current data; a first traditional power grid-connected current data generation unit for monitoring the current characteristics of the traditional power grid-connected circuit at the first grid-connected node using a pre-deployed current sensor to generate first traditional power grid-connected current data; and a first dual-path current monitoring result generation unit for generating the first dual-path current monitoring result using the first photovoltaic grid-connected current data and the first traditional power grid-connected current data.

[0099] Among them, the current fluctuation suppression adaptability analysis module 20 may further include: the photovoltaic output circuit is a circuit in which the photovoltaic power generation unit in the photovoltaic power station flows into the first photovoltaic inverter, and the first photovoltaic inverter is connected to the first grid-connected node; the traditional power grid connected circuit is a circuit in the traditional power grid that is located before the first grid-connected node and is not connected to the grid.

[0100] Among them, the photovoltaic inverter current fluctuation suppression adaptability analysis is performed based on the first dual-channel current monitoring result to generate a first photovoltaic suppression adaptability index. The current fluctuation suppression adaptability analysis module 20 may further include: a first adjustment target sequence construction unit for constructing a first adjustment target sequence of the first photovoltaic inverter based on the first traditional power grid connected current data; a digital modeling unit for collecting the service time, maintenance records and inverter model of the first photovoltaic inverter and collecting modeling data as modeling constraints for digital modeling to generate a first twin inverter; an adaptation matching analysis unit for performing fluctuation suppression simulation of the first photovoltaic grid-connected current data with the first twin inverter based on the first adjustment target sequence, performing adaptation matching analysis of the current fluctuation suppression regulation capability and the grid current fluctuation level, and generating the first photovoltaic suppression adaptability index.

[0101] Among them, an adaptive matching analysis of the current fluctuation suppression regulation capability and the grid current fluctuation level is performed to generate the first photovoltaic suppression adaptation index. The adaptive matching analysis unit may further include: a first fluctuation suppression sequence reading subunit for reading the first fluctuation suppression sequence output by the first twin inverter; a consistent proportion coefficient acquisition subunit for analyzing the consistent proportion coefficient of the fluctuation suppression result in the first fluctuation suppression sequence and the corresponding regulation target in the first regulation target sequence to generate the first photovoltaic suppression adaptation index.

[0102] Next, the specific configuration of the bidirectional current fluctuation suppression balance optimization module 30 will be described in detail. As described above, based on the first dual-current monitoring result, bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid is performed to determine the first bidirectional suppression strategy. The bidirectional current fluctuation suppression balance optimization module 30 may further include: a historical power grid fluctuation suppression record library acquisition unit for determining the traditional generator connected to the traditional power grid, and collecting a historical power grid fluctuation suppression record library based on the traditional generator; a screening unit for screening multiple power grid fluctuation suppression records matching the first traditional power grid grid-connected current data in the first dual-current monitoring result in the historical power grid fluctuation suppression record library, and obtaining multiple fluctuation suppression features and multiple suppression cost indicators; a fluctuation suppression simulation unit for extracting the fluctuation suppression feature with the lowest cost based on the multiple suppression cost indicators, and after updating the first dual-current monitoring result, calling the first twin inverter to perform the fluctuation suppression simulation of the first photovoltaic grid-connected current data; a first bidirectional suppression strategy generation unit for determining the fluctuation suppression decision on the traditional power grid side and the fluctuation suppression simulation decision of the first twin inverter to generate the first bidirectional suppression strategy if the updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index.

[0103] Among them, the bidirectional current fluctuation suppression balancing optimization module 30 can further include: an iterative update simulation unit is used to continue to extract the fluctuation suppression characteristics corresponding to the secondary minimum suppression cost indicator among multiple suppression cost indicators if the updated photovoltaic suppression adaptation index is less than the preset adaptation index, and perform iterative update simulation until the iteratively updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index, thereby generating the first bidirectional suppression strategy.

[0104] Among them, the screening unit may further include: the fluctuation suppression feature corresponding to any one of the multiple grid fluctuation suppression records is the grid current feature after fluctuation suppression, and the corresponding suppression cost index is determined based on the degree of increased burden on the frequency regulation generator.

[0105] Among them, after determining the first bidirectional suppression strategy, the system can further include: each grid-side fluctuation suppression decision reading module is used to read each grid-side fluctuation suppression decision in the bidirectional suppression strategy corresponding to each grid-connected node in the grid-connected topology structure; the current fluctuation transmission relationship analysis module is used to analyze the current fluctuation transmission relationship between each grid-connected node based on the grid-connected topology structure; the offset optimization module is used to analyze the fluctuation transmission mutual influence between each grid-side fluctuation suppression decision based on the current fluctuation transmission relationship, perform mutual influence offset optimization, and generate each grid-side optimization decision; the optimization adjustment module is used to optimize and adjust the first bidirectional suppression strategy with the each grid-side optimization decision.

[0106] The photovoltaic power station grid-connected regulation system for large-scale power grid security provided by an embodiment of the present invention can execute the photovoltaic power station grid-connected regulation method for large-scale power grid security provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0107] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0108] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A photovoltaic power station grid-connected regulation method for large-scale power grid security, characterized in that: include: Build the grid-connected topology of the photovoltaic power station in the preset area and the traditional power grid; extracting a first grid-connected node based on the grid-connected topology, monitoring photovoltaic output grid-connected current and traditional power grid grid-connected current at the first grid-connected node, analyzing photovoltaic inverter current fluctuation suppression adaptability based on the first dual-path current monitoring results, and generating a first photovoltaic suppression adaptability index; If the first photovoltaic suppression adaptation index is less than a preset adaptation index, performing bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid based on the first dual-path current monitoring result, and determining a first bidirectional suppression strategy; Performing grid connection adjustment of the first grid-connected node using the first bidirectional suppression strategy; Based on the first two-way current monitoring result, bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid is performed to determine a first bidirectional suppression strategy, including: Determining a traditional generator connected to the traditional power grid, and collecting a historical power grid fluctuation suppression record library based on the traditional generator; Screening the historical power grid fluctuation suppression record library for a plurality of power grid fluctuation suppression records that match the first traditional power grid connected current data in the first dual-channel current monitoring result, and obtaining a plurality of fluctuation suppression features and a plurality of suppression cost indicators; Extracting the fluctuation suppression feature with the minimum cost based on the multiple suppression cost indicators, updating the first dual-channel current monitoring result, and calling the first twin inverter to perform fluctuation suppression simulation on the first photovoltaic grid-connected current data; If the updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index, the first bidirectional suppression strategy is generated by determining the traditional grid-side fluctuation suppression decision and the fluctuation suppression simulation decision of the first twin inverter based on the grid fluctuation suppression record corresponding to the fluctuation suppression feature with the minimum cost; If the updated photovoltaic suppression adaptation index is less than the preset adaptation index, continue to extract the fluctuation suppression characteristics corresponding to the secondary minimum suppression cost index among multiple suppression cost indicators, and perform iterative update simulation until the iteratively updated photovoltaic suppression adaptation index is greater than or equal to the preset adaptation index, thereby generating the first bidirectional suppression strategy.

2. The photovoltaic power station grid-connected adjustment method for large-scale power grid security according to claim 1, characterized in that: The photovoltaic output grid-connected current monitoring and the traditional power grid grid-connected current monitoring are performed at the first grid-connected node, including: At the first grid-connected node, a pre-deployed current sensor is used to monitor the current characteristics of the photovoltaic output circuit to generate first photovoltaic grid-connected current data; At the first grid-connected node, current characteristics of the conventional grid-connected circuit are monitored by a pre-deployed current sensor to generate first conventional grid-connected current data; The first dual-path current monitoring result is generated using the first photovoltaic grid-connected current data and the first conventional power grid-connected current data.

3. The photovoltaic power station grid-connected adjustment method for large-scale power grid security according to claim 2, characterized in that: The photovoltaic output circuit is a circuit in which power flows from the photovoltaic power generation unit in the photovoltaic power station into the first photovoltaic inverter, and the first photovoltaic inverter is connected to the first grid-connected node; the traditional grid-connected circuit is a circuit in the traditional grid that is located before the first grid-connected node and is not connected to the grid.

4. The photovoltaic power station grid-connected adjustment method for large-scale power grid security according to claim 2, characterized in that: The first dual-channel current monitoring result is used to perform a photovoltaic inverter current fluctuation suppression adaptability analysis to generate a first photovoltaic suppression adaptability index, including: Constructing a first regulation target sequence for a first photovoltaic inverter using the first conventional power grid-connected current data; Collecting the service life, maintenance records, and inverter model of the first photovoltaic inverter and using them as modeling constraints to collect modeling data for digital modeling to generate a first twin inverter; Based on the first regulation target sequence, the first twin inverter is used to perform fluctuation suppression simulation of the first photovoltaic grid-connected current data, and an adaptation matching analysis between the current fluctuation suppression regulation capability and the grid current fluctuation level is performed to generate the first photovoltaic suppression adaptation index.

5. The photovoltaic power station grid-connected adjustment method for large-scale power grid security according to claim 4, characterized in that: Performing an adaptive matching analysis between the current fluctuation suppression and regulation capability and the grid current fluctuation level to generate the first photovoltaic suppression adaptation index includes: reading a first fluctuation suppression sequence output by the first twin inverter; The first photovoltaic suppression adaptation index is generated by analyzing the consistency ratio coefficient between the fluctuation suppression results in the first fluctuation suppression sequence and the corresponding regulation targets in the first regulation target sequence.

6. The photovoltaic power station grid-connected adjustment method for large-scale power grid security according to claim 1, characterized in that: The fluctuation suppression feature corresponding to any one of the plurality of grid fluctuation suppression records is a grid current feature after fluctuation suppression, and the corresponding suppression cost index is determined based on the degree of increased burden on the frequency modulation generator.

7. The photovoltaic power station grid-connected adjustment method for large-scale power grid security according to claim 1, characterized in that: After determining the first bidirectional suppression strategy, the following steps are also included: Reading each grid-side fluctuation suppression decision in the bidirectional suppression strategy corresponding to each grid-connected node in the grid-connected topology; Based on the grid-connected topology, analyzing the current fluctuation transmission relationship between each grid-connected node; Based on the current fluctuation transmission relationship, analyzing the fluctuation transmission mutual influence between each grid-side fluctuation suppression decision, performing mutual influence offset optimization, and generating each grid-side optimization decision; The first bidirectional suppression strategy is optimized and adjusted based on the respective grid-side optimization decisions.

8. A photovoltaic power station grid-connected regulation system for large-scale power grid security, characterized in that: The system is used to implement the photovoltaic power station grid-connected regulation method for large-scale power grid security according to any one of claims 1 to 7, and the system includes: The grid-connected topology structure building module is used to build the grid-connected topology structure of the photovoltaic power station in the preset area and the traditional power grid; a current fluctuation suppression adaptability analysis module, configured to extract a first grid-connected node based on the grid-connected topology, monitor the photovoltaic output grid-connected current and the traditional power grid grid-connected current at the first grid-connected node, perform current fluctuation suppression adaptability analysis of the photovoltaic inverter based on the first dual-path current monitoring results, and generate a first photovoltaic suppression adaptability index; a bidirectional current fluctuation suppression balance optimization module, configured to, if the first photovoltaic suppression adaptation index is less than a preset adaptation index, perform bidirectional current fluctuation suppression balance optimization of the photovoltaic inverter and the traditional power grid based on the first dual-path current monitoring result, and determine a first bidirectional suppression strategy; A grid-connected regulation module is configured to perform grid-connected regulation of the first grid-connected node using the first bidirectional suppression strategy.

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