Distributed reactive power optimization method for high-voltage distribution network based on high-proportion photovoltaic scenario

Abstract

The present invention discloses a distributed reactive power optimization method for a high-voltage distribution network based on a high-proportion photovoltaic scenario, relates to the technical field of reactive power optimization, and comprises the following steps: obtaining grid data of each photovoltaic inverter in a distributed photovoltaic power generation system and performing preprocessing; constructing a reactive capacity prediction model based on GCNN to obtain reactive power demand prediction values ​​of each photovoltaic inverter; establishing a Volt-Var curve, and calculating the current high-voltage distribution network voltage deviation coefficient in real time to dynamically adjust the step value and adjust the reactive power of each photovoltaic inverter based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve using the control strategy of the Volt-Var curve, thereby solving the problem that traditional methods cannot accurately predict the reactive power demand of photovoltaic inverters, and effectively solving the voltage fluctuation problem of the power grid under high photovoltaic penetration, thereby improving the stability and reliability of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power voltage optimization, and more specifically, to a distributed reactive power optimization method for a high-voltage distribution network based on a high-proportion photovoltaic scenario. Background Art

[0002] For example, the invention patent with announcement number: CN114759602B discloses a distribution network acceptance capacity assessment method considering extreme photovoltaic scenarios, including the following steps: considering extreme scenarios of distributed photovoltaics, establishing a planning model with photovoltaic access distribution network grid-connected nodes and capacity as decision variables, wherein the objective function of the planning model is to maximize the total photovoltaic installed capacity and voltage safety margin; processing the objective function and decomposing it into sub-problems and a main problem; using the Benders decomposition method to solve the double-layer optimization model of the main problem and the sub-problems, thereby obtaining the maximum photovoltaic acceptance capacity of the distribution network considering extreme photovoltaic scenarios; the purpose of the present invention is to make up for the problem of insufficient consideration of photovoltaic extreme scenarios in traditional distribution network acceptance capacity assessment methods, and to more accurately assess the maximum photovoltaic acceptance capacity of the distribution network.

[0003] In the above disclosed technical solutions, there are at least the following technical problems: the volatility and intermittent characteristics of photovoltaic power generation have brought many challenges to the operation of the distribution network. First, photovoltaic power generation is affected by sunlight conditions and has significant randomness and instability, which leads to overvoltage and undervoltage problems in the distribution network; secondly, in the case of a high proportion of photovoltaic access, the traditional power system reactive power management strategy faces severe tests and is difficult to meet the requirements of real-time voltage regulation. At the same time, the high penetration rate of photovoltaic power generation systems may lead to frequent changes in the direction of power flow in the distribution network, causing voltage quality problems and possibly weakening the stability of the power grid. In response to the above problems, the present invention proposes a solution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a distributed reactive power optimization method for a high-voltage distribution network based on a high-proportion photovoltaic scenario. The distributed reactive power optimization method for a high-voltage distribution network based on a high-proportion photovoltaic scenario is used to solve the problem that traditional methods cannot accurately predict the reactive power demand of photovoltaic inverters and the voltage fluctuation problem of the power grid under high photovoltaic penetration.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The method for decentralized reactive power optimization of high-voltage distribution network based on high-proportion photovoltaic scenario includes the following steps:

[0007] The grid data of each photovoltaic inverter in the distributed photovoltaic power generation system is obtained and preprocessed, wherein the grid data includes a grid topology diagram, line impedance data, historical active data, reactive data and voltage data; the grid data of each photovoltaic inverter is input into a preset reactive capacity prediction model based on GCNN to obtain a reactive power demand prediction value of each photovoltaic inverter; a Volt-Var curve is established, and a voltage deviation coefficient of the current high-voltage distribution network is calculated in real time to dynamically adjust the step value, and the slope of the Volt-Var curve is adjusted based on the step value to obtain an adjusted Volt-Var curve; based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve, a control strategy of the Volt-Var curve is adopted to adjust the reactive power of each photovoltaic inverter.

[0008] In a preferred embodiment, the preset GCNN-based reactive capacity prediction model is specifically as follows: constructing several feature fusion layers and LSTM layers, wherein the feature fusion layer is composed of a GCNN layer and a CNN layer; based on the power grid topology map, constructing a graph structure with photovoltaic inverters as nodes and lines connecting photovoltaic inverters as edges and calculating the power grid topology adjacency matrix of the photovoltaic inverter; using the power grid topology adjacency matrix of the photovoltaic inverter as the adjacency matrix of the GCNN layer of the reactive capacity prediction model based on GCNN; calculating the reactive power flow value between adjacent photovoltaic inverters through a power flow calculation formula and using the reactive power flow value as an element of the adjacency matrix of the reactive capacity prediction model based on GCNN.

[0009] In a preferred embodiment, the Volt-Var curve is established, and the step value is dynamically adjusted by real-time calculation of the voltage deviation coefficient of the current high-voltage distribution network and the slope of the Volt-Var curve is adjusted based on the step value to obtain the adjusted Volt-Var curve, specifically: voltage and reactive power are used as the horizontal axis and vertical axis of the Volt-Var curve respectively, and , , , and As control points, the voltage and reactive power are used as the horizontal and vertical axes of the Volt-Var curve respectively, and set , , , and As a control point, the is the minimum voltage point, The second lowest voltage point. is the rated voltage point, is the second highest voltage point, is the highest voltage point; based on the reactive power output range of the photovoltaic inverter and the voltage range in the high-voltage distribution network, the initial slope of the Volt-Var curve is calculated; the voltage deviation coefficient of the current high-voltage distribution network is calculated using a voltage deviation coefficient calculation formula; the step value is calculated based on the current high-voltage distribution network voltage deviation coefficient; the initial slope of the Volt-Var curve is adjusted based on the step value to obtain an adjusted Volt-Var curve.

[0010] In a preferred embodiment, the initial slope of the Volt-Var curve is adjusted based on the step value to obtain an adjusted Volt-Var curve, specifically: the step value is input into a Sigmoid function for calculation to obtain a new slope value; based on the new slope value, the adjusted reactive power is calculated through a preset voltage range to obtain an adjusted Volt-Var curve.

[0011] In a preferred embodiment, the reactive power of each photovoltaic inverter is adjusted by using a control strategy of a Volt-Var curve based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve, specifically: based on the adjusted Volt-Var curve, the reactive power output value of each photovoltaic inverter is calculated; based on the real-time acquired voltage value of each photovoltaic inverter and the reactive power output value of each photovoltaic inverter, the target reactive power output value of each photovoltaic inverter is obtained; the difference between the target reactive power output value of each photovoltaic inverter and the reactive power demand prediction value is calculated to obtain a reactive adjustment demand value; and the reactive power of each photovoltaic inverter is adjusted based on a preset adjustment factor and the reactive adjustment demand value.

[0012] The device of any one of the methods for decentralized reactive power optimization of high-voltage distribution networks based on high-proportion photovoltaic scenarios is characterized in that it includes a data acquisition module, a power prediction module, a curve adjustment module, and a reactive power adjustment module, and the modules are connected:

[0013] A data acquisition module is used to acquire and pre-process the grid data of each photovoltaic inverter in the distributed photovoltaic power generation system, wherein the grid data includes a grid topology diagram, line impedance data, historical active data, reactive data and voltage data;

[0014] A power prediction module, used to input the grid data of each photovoltaic inverter into a preset GCNN-based reactive capacity prediction model to obtain a reactive power demand prediction value of each photovoltaic inverter;

[0015] A curve adjustment module, used to establish a Volt-Var curve, and dynamically adjust the step value by calculating the current high-voltage distribution network voltage deviation coefficient in real time, and adjust the slope of the Volt-Var curve based on the step value to obtain an adjusted Volt-Var curve;

[0016] The reactive power adjustment module is used to adjust the reactive power of each photovoltaic inverter by adopting the control strategy of the Volt-Var curve based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve.

[0017] An electronic device, characterized in that the electronic device comprises:

[0018] at least one processor; and,

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program executable by the at least one processor,

[0021] The computer program is executed by the at least one processor,

[0022] So that the at least one processor can execute any one of the methods for decentralized reactive power optimization of high-voltage distribution networks based on high-proportion photovoltaic scenarios.

[0023] The technical effects and advantages of the distributed reactive power optimization method of high-voltage distribution network based on high-proportion photovoltaic scenarios of the present invention are as follows:

[0024] 1. The present invention can significantly improve the regulation ability of photovoltaic systems in power networks by constructing a reactive capacity data set based on distributed photovoltaic power generation systems and applying the GCNN (graph convolutional neural network) model to predict reactive power demand. First, the GCNN model can effectively process and analyze complex power grid topologies and their dynamic characteristics, thereby generating accurate reactive power demand prediction values. This precise prediction provides a timely basis for adjustment for photovoltaic inverters, which helps to optimize power quality and enhance the stability of the power grid; secondly, a Volt-Var curve is established and the step value is dynamically adjusted through real-time voltage deviation, providing a flexible reactive power regulation strategy for photovoltaic inverters. This dynamic adjustment mechanism ensures the rapid response of the inverter under various load and voltage conditions, can effectively support grid stability, and reduce voltage fluctuations and frequency deviations; finally, combined with the reactive power demand prediction value and the adjusted Volt-Var curve, the reactive power output of each photovoltaic inverter is adjusted through the control strategy, which not only improves the intelligence level of the system, but also lays the foundation for the development of future smart grids. The comprehensive application of this method can fully tap the potential of photovoltaic power generation while ensuring the safety and reliability of the power grid, and promote the widespread application of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a structural schematic diagram of the distributed reactive power optimization method of the high-voltage distribution network based on a high-proportion photovoltaic scenario of the present invention.

[0026] Figure 2 It is a schematic diagram of the device structure of the distributed reactive power optimization method of the high-voltage distribution network based on a high-proportion photovoltaic scenario of the present invention.

[0027] Figure 3 Schematic diagram of the electronic structure of the distributed reactive power optimization of the high-voltage distribution network based on a high-proportion photovoltaic scenario of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment 1, Figure 1 The present invention provides a distributed reactive power optimization method for a high-voltage distribution network based on a high-proportion photovoltaic scenario, comprising the following steps:

[0030] S1, obtaining and preprocessing the grid data of each photovoltaic inverter in the distributed photovoltaic power generation system, wherein the grid data includes a grid topology diagram, line impedance data, historical active data, reactive data and voltage data;

[0031] In this example, the grid topology diagram, line impedance data, historical active data, reactive data and voltage data of each photovoltaic inverter in the distributed photovoltaic power generation system are obtained to construct a reactive capacity data set, specifically:

[0032] Grid topology diagram: A graphical representation of the connection relationship between various components in the power system (such as photovoltaic inverters, transformers, busbars, etc.);

[0033] Line impedance data: electrical characteristics of each line in the power grid, including resistance (R) and reactance (X);

[0034] Historical active power data: active power output value of each PV inverter in the past period of time;

[0035] Reactive power data: reactive power output value of each PV inverter in the past period of time;

[0036] Voltage data: The voltage level of each PV inverter at different time points, reflecting the voltage status of the power grid.

[0037] S2, inputting the reactive capacity data set into a preset GCNN-based reactive capacity prediction model to obtain a reactive power demand prediction value of each photovoltaic inverter;

[0038] In this example, the preset GCNN-based reactive capacity prediction model is specifically:

[0039] Constructing several feature fusion layers and LSTM layers, wherein the feature fusion layer is composed of a GCNN layer and a CNN layer;

[0040] Based on the power grid topology graph, a graph structure is constructed with photovoltaic inverters as nodes and lines connecting photovoltaic inverters as edges, and a power grid topology adjacency matrix of the photovoltaic inverters is calculated;

[0041] Using the grid topology adjacency matrix of the photovoltaic inverter as the adjacency matrix of the GCNN layer of the GCNN-based reactive capacity prediction model;

[0042] The reactive power flow value between adjacent photovoltaic inverters is calculated by a power flow calculation formula and the reactive power flow value is used as an element of the adjacency matrix of the reactive capacity prediction model based on GCNN.

[0043] In this example, based on the power grid topology graph, a graph structure is constructed with photovoltaic inverters as nodes and lines connecting photovoltaic inverters as edges, and the power grid topology adjacency matrix of the photovoltaic inverter is calculated, specifically:

[0044] Each PV inverter is regarded as a node of the graph, and the lines connecting the PV inverters are regarded as the edges of the graph;

[0045] Based on the grid topology diagram, determining the location of the photovoltaic inverter in the grid, and determining whether each photovoltaic inverter is connected by a line;

[0046] Initialize the adjacency matrix A with all initial values ​​set to 0. If there is no line connection between two PV inverters, set the corresponding matrix element to 0, that is, =0; if two PV inverters are connected by a line, the reactive power flow value between adjacent PV inverters is calculated by the power flow calculation formula as the matrix element value.

[0047] It should be noted that the GCNN layer can effectively process the grid topology information of the photovoltaic inverter. The adjacency matrix is ​​constructed based on the actual electric fusion connection relationship and reactive power flow value, which fully captures the electrical coupling characteristics between photovoltaic inverters. This allows the model to not only focus on the local characteristics of a single inverter, but also fully consider its interaction with other nodes, improving the accuracy of the prediction.

[0048] Secondly, the CNN layer can extract local features from time series data such as voltage and reactive power. Especially in scenarios with frequent voltage fluctuations, CNN can quickly detect feature changes and provide sensitive response to short-term fluctuations.

[0049] Finally, the LSTM layer is used to capture long-term dependencies in historical data, ensuring that the model can predict future trends based on past voltage and power data. By combining GCNN, CNN and LSTM through the feature fusion layer, not only the accuracy of reactive power prediction is improved, but also the time perception ability and global topology adaptability of the model are enhanced, greatly improving the reactive power optimization effect of the power grid in high-penetration photovoltaic scenarios.

[0050] In this example, the reactive power flow value between adjacent photovoltaic inverters is calculated by the power flow calculation formula, specifically:

[0051]

[0052] in, is the reactive power flow value between adjacent PV inverters, and is the voltage amplitude of PV inverter i and PV inverter j, and are the real and imaginary parts of the admittance between PV inverter i and PV inverter j, respectively. and are the voltage phase angles of PV inverter i and PV inverter j respectively, sin() is the sine function, and cos() is the cosine function.

[0053] S3, establishing a Volt-Var curve, and dynamically adjusting the step value by calculating the voltage deviation coefficient of the current high-voltage distribution network in real time and adjusting the slope of the Volt-Var curve based on the step value to obtain an adjusted Volt-Var curve;

[0054] In this example, the Volt-Var curve is established, and the step value is dynamically adjusted by calculating the voltage deviation coefficient of the current high-voltage distribution network in real time, and the slope of the Volt-Var curve is adjusted based on the step value to obtain the adjusted Volt-Var curve, specifically:

[0055] Set the voltage and reactive power as the horizontal and vertical axes of the Volt-Var curve respectively, and set , , , and As a control point, the is the minimum voltage point, The second lowest voltage point. is the rated voltage point, is the second highest voltage point, is the highest voltage point;

[0056] Calculating the initial slope of the Volt-Var curve based on the reactive power output range of the photovoltaic inverter and the voltage range in the high-voltage distribution network;

[0057] The voltage deviation coefficient of the current high-voltage distribution network is calculated using a voltage deviation coefficient calculation formula;

[0058] Calculating a step value based on the current high-voltage distribution network voltage deviation coefficient;

[0059] The initial slope of the Volt-Var curve is adjusted based on the step value to obtain an adjusted Volt-Var curve.

[0060] In this example, the settings , , , and As control points, specifically:

[0061] : Minimum voltage point. When the voltage is lower than this point, the photovoltaic inverter provides reactive power to the maximum extent;

[0062] : Second lowest voltage point, when the voltage is at a lower level, the photovoltaic inverter provides reactive power with a larger slope;

[0063] : Rated voltage point, i.e. the expected normal operating voltage value, usually set to the rated voltage (e.g. 1.0 pu, unit value). Near this point, the PV inverter neither provides nor absorbs reactive power;

[0064] : The second highest voltage point, when the voltage is higher than the target voltage, the photovoltaic inverter starts to absorb reactive power to help reduce the voltage;

[0065] , the highest voltage point. When the voltage exceeds this point, the photovoltaic inverter absorbs reactive power to the maximum extent.

[0066] In this example, the voltage deviation coefficient calculation formula is used to calculate the current high-voltage distribution network voltage deviation coefficient, specifically:

[0067]

[0068] in, is the voltage deviation value, It is the voltage value measured in real time by each photovoltaic inverter in the distribution network. is the preset target voltage value, is the voltage tolerance range value, is the voltage deviation coefficient.

[0069] It should be noted that the voltage deviation coefficient calculation formula is used to evaluate the degree of voltage deviation in the current high-voltage distribution network, and can reflect the difference between the actual voltage and the target voltage. Through this formula, the voltage fluctuation of the distribution network can be dynamically monitored and the system operating parameters can be adjusted in real time, especially in the case of a high proportion of photovoltaic grid connection, where voltage fluctuations are more frequent and unstable. The advantage of the voltage deviation coefficient is its simple and effective calculation method, which enables the system to quickly capture voltage deviations and trigger corresponding compensation measures.

[0070] Specifically, monitoring and adjustment based on the voltage deviation coefficient can improve voltage stability and prevent excessively high or low voltage from affecting grid operation. At the same time, it can also provide a basis for controlling the reactive power of photovoltaic inverters. Combined with the Volt-Var control strategy, it can effectively adjust the reactive power output, thereby alleviating voltage fluctuations. In addition, this method has high adaptability and can be flexibly adjusted according to different grid operating conditions, thereby improving the reliability and power quality of the grid.

[0071] In this example, the step value is calculated based on the current high-voltage distribution network voltage deviation coefficient, specifically:

[0072]

[0073] in, is the step value, is the base step size factor, is the voltage deviation value, is the preset target voltage value, is the voltage deviation coefficient, is the reactive power influence factor, is the active power flow from PV inverter i to PV inverter j, is the maximum active power value of the distributed photovoltaic power generation system, is the reactive power flow value between adjacent PV inverters, It is the maximum reactive power value of the distributed photovoltaic power generation system.

[0074] It should be noted that the step value refers to the specific value increased or decreased when adjusting the slope of the Volt-Var curve. In the Volt-Var control strategy, the step value is used to determine how to dynamically adjust the slope of the Volt-Var curve according to the change of the grid voltage. Its advantages are as follows:

[0075] Control of adjustment amplitude: The step value directly affects the change amplitude of the slope of the Volt-Var curve, which in turn affects the reactive power output of the PV inverter. A larger step value will result in a faster reactive power adjustment, while a smaller step value can achieve a smoother response;

[0076] Dynamic response: The step size can be adjusted dynamically according to the real-time voltage deviation to ensure that the system can quickly adapt to changes in grid voltage. This dynamic response is particularly important for power systems with a high proportion of photovoltaic access.

[0077] In this example, the initial slope of the Volt-Var curve is adjusted based on the step value to obtain an adjusted Volt-Var curve, specifically:

[0078] The step value is input into the Sigmoid function for calculation to obtain a new slope value;

[0079] Based on the new slope value, the adjusted reactive power is calculated through a preset voltage range to obtain an adjusted Volt-Var curve.

[0080] It should be noted that the adjustment of the initial slope of the Volt-Var curve based on the step value can dynamically optimize the reactive power response behavior of the photovoltaic inverter, so that it can better adapt to the real-time fluctuations of the grid voltage. The advantage of this method lies in its flexibility. The step value is automatically adjusted according to the current grid operation status, ensuring that the reactive power regulation has sufficient response speed and avoids system instability caused by frequent adjustments. At the same time, by adjusting the slope, the reactive power changes in different voltage ranges can be finely controlled to ensure that the grid can obtain effective reactive power support when the voltage is too high or too low, thereby improving the voltage stability and overall power quality of the grid.

[0081] S4, adjusting the reactive power of each photovoltaic inverter by adopting a control strategy of the Volt-Var curve based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve.

[0082] In this example, the reactive power of each photovoltaic inverter is adjusted by using the control strategy of the Volt-Var curve based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve, specifically:

[0083] Based on the adjusted Volt-Var curve, calculating the reactive power output value of each photovoltaic inverter;

[0084] Based on the voltage values ​​of each photovoltaic inverter obtained in real time and the reactive power output values ​​of each photovoltaic inverter, a target reactive power output value of each photovoltaic inverter is obtained;

[0085] Calculating the difference between the target reactive power output value of each photovoltaic inverter and the reactive power demand prediction value to obtain a reactive power adjustment demand value;

[0086] The reactive power of each photovoltaic inverter is adjusted based on the preset adjustment factor and the reactive power adjustment demand value.

[0087] In this example, based on the adjusted Volt-Var curve, the reactive power output value of each photovoltaic inverter is calculated, specifically:

[0088] Based on the adjusted Volt-Var curve, a reactive power adjustment linear function is obtained;

[0089] Based on the voltage values ​​of each photovoltaic inverter obtained in real time, the reactive power output value of each photovoltaic inverter is obtained through the reactive adjustment linear function.

[0090] In this example, the voltage values ​​of each photovoltaic inverter obtained in real time are specifically:

[0091] Inverter output voltage value: Real-time acquisition of the voltage value of each PV inverter output terminal, reflecting the actual voltage level at the PV inverter connection point. This voltage value can help determine whether the PV inverter is within the normal operating range and whether the reactive power output needs to be adjusted;

[0092] Grid connection point voltage value: PV inverters are usually connected to the grid. The grid connection point voltage value is an important parameter that determines its operation, reflecting the voltage fluctuation at the point where the PV inverter is connected to the grid. Real-time acquisition of this voltage value helps to assess the overall impact of local voltage changes on the grid;

[0093] Bus voltage value: By measuring the voltage level of the bus where each photovoltaic inverter is located, the voltage coordination of multiple devices in the power grid can be judged. The bus voltage value is a comprehensive voltage information that can affect the reactive power adjustment strategy of multiple photovoltaic inverters.

[0094] In this example, the reactive power of each photovoltaic inverter is adjusted based on the preset adjustment factor and the reactive power adjustment demand value, specifically:

[0095] Adjusting the reactive power of each photovoltaic inverter based on a preset adjustment factor and reactive adjustment demand value has significant advantages. This method uses the adjustment factor to flexibly and accurately adjust the reactive power by comprehensively considering the real-time voltage of the inverter and the reactive power demand of the power grid. The adjustment factor can be dynamically set according to different power grid operating conditions, thereby ensuring the response speed while avoiding over-adjustment that causes system instability. In addition, this method can effectively distribute the reactive power of each inverter, avoid overloading individual equipment, and improve the stability and balance of the overall system.

[0096] like Figure 2 As shown, it is a functional module diagram of a high-voltage distribution network distributed reactive power optimization method device based on a high-proportion photovoltaic scenario. According to the functions to be implemented, the high-voltage distribution network distributed reactive power optimization device based on a high-proportion photovoltaic scenario may include a data acquisition module, a power prediction module, a curve adjustment module, and a reactive power adjustment module. The module described in the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of a personnel performance data processing device and can complete fixed functions, which are stored in a memory of the personnel performance data processing device.

[0097] In this embodiment, the functions of each module are as follows:

[0098] The data acquisition module acquires the grid topology diagram, line impedance data, node historical active data, reactive data and voltage data of each photovoltaic inverter in the distributed photovoltaic power generation system to construct a reactive capacity data set;

[0099] A power prediction module, which inputs the reactive capacity data set into a preset GCNN-based reactive capacity prediction model to obtain a reactive power demand prediction value of each photovoltaic inverter;

[0100] A curve adjustment module, which establishes a Volt-Var curve, dynamically adjusts the step value by calculating the voltage deviation coefficient of the current high-voltage distribution network in real time, and adjusts the slope of the Volt-Var curve based on the step value to obtain an adjusted Volt-Var curve;

[0101] The reactive power adjustment module adjusts the reactive power of each photovoltaic inverter by adopting the control strategy of the Volt-Var curve based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve.

[0102] In detail, the modules described in the distributed reactive power optimization device for a high-voltage distribution network based on a high-proportion photovoltaic scenario described in the embodiment of the present invention adopt the same technical means as the distributed reactive power optimization method for a high-voltage distribution network based on a high-proportion photovoltaic scenario described in the accompanying drawings when in use, and can produce the same technical effects, which will not be repeated here.

[0103] like Figure 3 , which is a schematic diagram of the structure of an electronic device of the distributed reactive power optimization method of a high-voltage distribution network based on a high-proportion photovoltaic scenario of the present invention.

[0104] The electronic device may include a processor, a memory, a communication bus and a communication interface, and may also include a computer program stored in the memory and executable on the processor, such as a distributed reactive power optimization program for a high-voltage distribution network based on a high-proportion photovoltaic scenario.

[0105] Among them, the processor is the control core (Control Unit) of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory and calling data stored in the memory.

[0106] The memory includes at least one type of readable storage medium, and in some embodiments, the memory may be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may be used to store not only application software installed in the electronic device but also various data.

[0107] The communication bus is configured to realize connection and communication between the memory and at least one processor.

[0108] The communication interface is used for communication between the above electronic device and other devices, including a network interface and a user interface.

[0109] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0110] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0111] The program of the method for decentralized reactive power optimization of high-voltage distribution network based on high-proportion photovoltaic scenario stored in the memory in the electronic device is a combination of multiple instructions. When running in the processor, the steps of the method for decentralized reactive power optimization of high-voltage distribution network based on high-proportion photovoltaic scenario can be implemented.

[0112] Specifically, the specific implementation system of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0115] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0116] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0118] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed reactive power optimization method for high-voltage distribution network based on a high-proportion photovoltaic scenario, characterized in that: The following steps are involved: Acquire and pre-process the grid data of each photovoltaic inverter in the distributed photovoltaic power generation system, wherein the grid data includes a grid topology diagram, line impedance data, historical active data, reactive data, and voltage data; Inputting the grid data of each photovoltaic inverter into a preset GCNN-based reactive capacity prediction model to obtain a reactive power demand prediction value of each photovoltaic inverter; Establishing a Volt-Var curve, and calculating the voltage deviation coefficient of the current high-voltage distribution network in real time to dynamically adjust the step value, and adjusting the slope of the Volt-Var curve based on the step value to obtain an adjusted Volt-Var curve; Set the voltage and reactive power as the horizontal and vertical axes of the Volt-Var curve respectively, and set , , , and As a control point, the is the minimum voltage point, The second lowest voltage point. is the rated voltage point, is the second highest voltage point, is the highest voltage point; Calculating the initial slope of the Volt-Var curve based on the reactive power output range of the photovoltaic inverter and the voltage range in the high-voltage distribution network; The voltage deviation coefficient of the current high-voltage distribution network is calculated using a voltage deviation coefficient calculation formula; The voltage deviation coefficient calculation formula is specifically: in, is the voltage deviation value, It is the voltage value measured in real time by each photovoltaic inverter in the distribution network. is the preset target voltage value, is the voltage tolerance range value, is the voltage deviation coefficient; Calculating a step value based on the current high-voltage distribution network voltage deviation coefficient; The step value is input into the Sigmoid function for calculation to obtain a new slope value; Based on the new slope value, the adjusted reactive power is calculated through a preset voltage range to obtain an adjusted Volt-Var curve; Based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve, the reactive power of each photovoltaic inverter is adjusted by adopting the Volt-Var curve control strategy.

2. The method for decentralized reactive power optimization of a high-voltage distribution network based on a high-proportion photovoltaic scenario according to claim 1 is characterized in that: The preset GCNN-based reactive capacity prediction model is specifically: Constructing several feature fusion layers and LSTM layers, wherein the feature fusion layer is composed of a GCNN layer and a CNN layer; Based on the power grid topology graph, a graph structure is constructed with photovoltaic inverters as nodes and lines connecting photovoltaic inverters as edges, and a power grid topology adjacency matrix of the photovoltaic inverters is calculated; Using the grid topology adjacency matrix of the photovoltaic inverter as the adjacency matrix of the GCNN layer of the GCNN-based reactive capacity prediction model; The reactive power flow value between adjacent photovoltaic inverters is calculated by a power flow calculation formula and the reactive power flow value is used as an element of the adjacency matrix of the reactive capacity prediction model based on GCNN.

3. The method for decentralized reactive power optimization of a high-voltage distribution network based on a high-proportion photovoltaic scenario according to claim 1 is characterized in that: The reactive power of each photovoltaic inverter is adjusted by adopting the control strategy of the Volt-Var curve based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve, specifically: Based on the adjusted Volt-Var curve, calculating the reactive power output value of each photovoltaic inverter; Based on the voltage values ​​of each photovoltaic inverter obtained in real time and the reactive power output values ​​of each photovoltaic inverter, a target reactive power output value of each photovoltaic inverter is obtained; Calculating the difference between the target reactive power output value of each photovoltaic inverter and the reactive power demand prediction value to obtain a reactive power adjustment demand value; The reactive power of each photovoltaic inverter is adjusted based on the preset adjustment factor and the reactive power adjustment demand value.

4. The method for decentralized reactive power optimization of a high-voltage distribution network based on a high-proportion photovoltaic scenario according to claim 2 is characterized in that: The reactive power flow value between adjacent photovoltaic inverters is calculated by the power flow calculation formula, specifically: in, is the reactive power flow value between adjacent PV inverters, and is the voltage amplitude of photovoltaic inverter i and photovoltaic inverter j, and are the real and imaginary parts of the admittance between PV inverter i and PV inverter j, respectively. and are the voltage phase angles of PV inverter i and PV inverter j respectively, sin() is the sine function, and cos() is the cosine function.

5. The method for decentralized reactive power optimization of high-voltage distribution network based on high-proportion photovoltaic scenario according to claim 1 is characterized in that: The step value is calculated based on the current high-voltage distribution network voltage deviation coefficient, specifically: in, is the step value, is the base step size factor, is the voltage deviation value, is the preset target voltage value, is the voltage deviation coefficient, is the reactive power influence factor, is the active power flow from PV inverter i to PV inverter j, is the maximum active power value of the distributed photovoltaic power generation system, is the reactive power flow value between adjacent PV inverters, It is the maximum reactive power value of the distributed photovoltaic power generation system.

6. A device using the method for decentralized reactive power optimization of a high-voltage distribution network based on a high-proportion photovoltaic scenario as described in any one of claims 1 to 5, characterized in that: It includes data acquisition module, power prediction module, curve adjustment module, reactive power adjustment module, and there are connections between the modules: A data acquisition module is used to acquire and pre-process the grid data of each photovoltaic inverter in the distributed photovoltaic power generation system, wherein the grid data includes a grid topology diagram, line impedance data, historical active data, reactive data and voltage data; A power prediction module, used to input the grid data of each photovoltaic inverter into a preset GCNN-based reactive capacity prediction model to obtain a reactive power demand prediction value of each photovoltaic inverter; A curve adjustment module, used to establish a Volt-Var curve, and dynamically adjust the step value by calculating the current high-voltage distribution network voltage deviation coefficient in real time, and adjust the slope of the Volt-Var curve based on the step value to obtain an adjusted Volt-Var curve; The reactive power adjustment module is used to adjust the reactive power of each photovoltaic inverter by adopting the control strategy of the Volt-Var curve based on the reactive power demand prediction value of each photovoltaic inverter and the adjusted Volt-Var curve.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, The computer program is executed by the at least one processor, So that the at least one processor can execute the decentralized reactive power optimization method for a high-voltage distribution network based on a high-proportion photovoltaic scenario as described in any one of claims 1 to 5.

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