A master-slave coordinated voltage control method and system

Abstract

The application provides a kind of main distribution coordination voltage control method and system, the method includes: first obtaining power distribution network operation data and pre-processing, then constructing load prediction model and photovoltaic prediction model, obtain the load prediction value and photovoltaic output prediction value in future control period, then construct time series voltage sensitivity model, calculate the node voltage variation and node voltage of each time in future;Further, with the minimum voltage deviation of low-voltage user as the goal, considering the node voltage constraint, bus voltage constraint, tap position constraint, reactive power regulation constraint and adjacent period regulation increment constraint, construct objective function and solve, obtain optimal control instruction.The application realizes the coordinated control of main network and distribution network through time series voltage sensitivity modeling and rolling optimization, and can effectively cope with the voltage fluctuation problem caused by high penetration rate distributed photovoltaic access.

Description

Technical Field

[0001] This invention relates to the field of voltage control technology, and in particular to a primary and secondary coordinated voltage control method and system. Background Technology

[0002] As the scale of distributed photovoltaic (PV) installations in 10kV distribution networks and low-voltage substations continues to expand, the operation mode of the distribution network is gradually shifting from traditional unidirectional power flow to bidirectional power flow, resulting in stronger fluctuations and uncertainties in system voltage distribution. Especially under conditions of high PV power generation at midday, light load, or rapid changes in PV power due to cloud disturbances, problems such as voltage exceeding limits, increased voltage fluctuations, and uneven voltage distribution at local nodes are prone to occur along 10kV feeders and on the low-voltage user side.

[0003] Most existing voltage control methods are based on single-level regulation, meaning they only control a specific type of control object, such as the bus voltage on the substation side, the tap changer of the on-load tap-changing transformer, or the reactive power compensation device on the distribution network side. While this method is relatively simple to implement, it has the following shortcomings: First, it does not fully consider the voltage coupling relationship between the main grid and the distribution network, which may lead to regulation conflicts between the control objects. Second, it does not fully utilize the short-term variation trends of load and photovoltaics, and the control is usually based on the current measurement value for passive adjustment, resulting in lag. Third, the voltage control objective often focuses on the single voltage limit violation management, without comprehensively considering network losses, tap changer operation frequency, capacitor switching frequency, and reactive power regulation costs, which can easily lead to frequent equipment operation, affecting equipment lifespan and operational economy. Fourth, in high-penetration distributed photovoltaic scenarios, relying solely on a single voltage regulation method often makes it difficult to meet the overall optimization needs of the 10kV bus, feeder nodes, and low-voltage user end voltage. Summary of the Invention

[0004] The purpose of this invention is to provide a main grid and distribution grid coordinated voltage control method and system, which aims to solve the problems existing in the prior art in high-penetration distributed photovoltaic access scenarios, such as the disconnect between the main grid and distribution grid regulation, control lag, unstable effect of low-voltage user voltage exceeding the limit, high network loss, and frequent operation of voltage regulating equipment.

[0005] In a first aspect, the present invention provides a main-distribution coordinated voltage control method, the method comprising:

[0006] Acquire distribution network operation data and preprocess the distribution network operation data to obtain preprocessed distribution network operation data;

[0007] Based on the preprocessed distribution network operation data, a load forecasting model and a photovoltaic forecasting model are constructed, and the load forecasting value and photovoltaic output forecasting value in the future control cycle are obtained based on the load forecasting model and the photovoltaic forecasting model, respectively.

[0008] A time-series voltage sensitivity model is constructed based on the predicted load and photovoltaic output values. The voltage change at each moment in the future control cycle is obtained based on the time-series voltage sensitivity model. The node voltage at each moment in the future is calculated based on the voltage change.

[0009] The low-voltage user voltage deviation is calculated based on the node voltage, and an objective function is constructed based on the low-voltage user voltage deviation. Then, node voltage constraints, 10kV bus voltage constraints, on-load tap changer tap position constraints, reactive power regulation constraints, and adjacent control cycle adjustment increment constraints are constructed for the objective function. The objective function is solved to obtain the control command.

[0010] In a second aspect, the present invention provides a master-distributor coordinated voltage control system, the system comprising:

[0011] The data acquisition module is used to acquire power distribution network operation data and preprocess the power distribution network operation data to obtain preprocessed power distribution network operation data.

[0012] The prediction model construction module is used to construct a load prediction model and a photovoltaic prediction model based on the preprocessed distribution network operation data, and to obtain the load prediction value and photovoltaic output prediction value in the future control cycle based on the load prediction model and the photovoltaic prediction model, respectively.

[0013] The node voltage calculation module is used to construct a time-series voltage sensitivity model based on the load forecast and photovoltaic output forecast, obtain the voltage change at each moment in the future control cycle based on the time-series voltage sensitivity model, and calculate the node voltage at each future moment based on the voltage change.

[0014] The optimization model construction module is used to calculate the low-voltage user voltage deviation based on the node voltage, construct an objective function based on the low-voltage user voltage deviation, and construct node voltage constraints, 10kV bus voltage constraints, on-load tap changer tap position constraints, reactive power regulation constraints, and adjacent control cycle adjustment increment constraints for the objective function. The objective function is then solved to obtain the control command.

[0015] Thirdly, the present invention provides a storage medium that stores one or more programs, which, when executed by a processor, implement the above-described master-distributor coordinated voltage control method.

[0016] Fourthly, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0017] The memory is used to store computer programs;

[0018] When the processor executes the computer program stored in the memory, it implements the above-described master-distributor coordinated voltage control method.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] In high-penetration distributed photovoltaic access scenarios, this invention improves voltage qualification rate, reduces network loss, and minimizes frequent equipment operation by "advance prediction, main and distribution coordination, and rolling optimization". Specifically, firstly, the LSTM-DA model is used to make short-term predictions of future load and photovoltaic output. The predicted values ​​are then transformed into "source-load disturbances" and substituted into the time-series voltage sensitivity model, thereby predicting the node voltage change trends at multiple future moments and solving the lag problem of traditional passive regulation. On this basis, an objective function is established with the goal of minimizing low-voltage user voltage deviation, network losses, and equipment operation costs. The optimal 10kV bus target voltage, on-load tap changer tap position, and control commands for reactive power regulation equipment (such as parallel capacitors, distribution transformer tap positions, and photovoltaic inverters) on the distribution network side are solved in each control cycle through rolling optimization (MPC). This achieves coordinated regulation between the main grid side and the distribution network side, avoiding control conflicts that may arise from independent regulation at a single level. At the same time, by introducing the adjustment increment and action number limit of adjacent control cycles into the constraints, combined with the rolling optimization mechanism, the frequent operation of tap changers and reactive power equipment is effectively suppressed. This significantly reduces the risk of voltage overruns for low-voltage users, optimizes network losses economically, lowers equipment operating costs, and improves the overall stability, economy, and security of the distribution network under high-penetration photovoltaic access. Attached Figure Description

[0021] Figure 1 This is a flowchart of a main-distributor coordinated voltage control method proposed in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a master-distributor coordinated voltage control system according to an embodiment of the present invention.

[0023] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0025] like Figure 1 As shown, an embodiment of the present invention proposes a main-distributor coordinated voltage control method, which includes steps S101 to S104, wherein:

[0026] Step S101: Obtain distribution network operation data and preprocess the distribution network operation data to obtain preprocessed distribution network operation data;

[0027] It should be noted that the runtime data specifically includes:

[0028] 1. Substation side data: including 10kV bus voltage, active power, reactive power, and voltage, current, and power of the high and low voltage sides of the 35kV / 10kV on-load tap changer transformer;

[0029] 2. Feeder-side data: including 10kV feeder head current, node power flow data, switch status and line operation status data;

[0030] 3. Distributed photovoltaic data: including the active power output of photovoltaics at each access node, the reactive power regulation of the inverter, and the grid connection status;

[0031] 4. Low-voltage user-side data: including transformer substation outlet voltage, low-voltage user node voltage, and voltage qualification rate related data;

[0032] 5. External environmental data: including at least one of solar irradiance, ambient temperature, humidity, cloud cover, and wind speed. Preferably, the data sampling period is set to 5 minutes, 10 minutes, or 15 minutes.

[0033] The collected data undergoes unified preprocessing, specifically including:

[0034] 1. Perform time alignment on multi-source data according to a unified time scale;

[0035] 2. Remove invalid data such as communication failures, out-of-bounds errors, and duplicate records;

[0036] 3. Use interpolation, moving average, or adjacent time period compensation methods to repair short-term missing data;

[0037] 4. Normalize data with different dimensions;

[0038] 5. When the tap position record of an on-load tap-changing transformer is missing or unavailable, the tap position is identified based on the voltage relationship between the high and low voltage sides. Tap position identification can be represented as:

[0039] ;

[0040] in, Let t be the voltage on the high-voltage side of the transformer. Let be the voltage on the low-voltage side of the transformer at time t. Let be the voltage ratio at time t. This is a mapping function from voltage ratio to tap position. To identify the tap position corresponding to the obtained time t.

[0041] Step S102: Construct a load forecasting model and a photovoltaic forecasting model based on the preprocessed distribution network operation data, and obtain the load forecasting value and photovoltaic output forecasting value in the future control cycle based on the load forecasting model and the photovoltaic forecasting model, respectively.

[0042] This step, based on preprocessed historical time-series data, uses deep learning models such as LSTM-DA to construct short-term load forecasting models and photovoltaic power output forecasting models. Taking historical load data, historical photovoltaic power output data, and meteorological data as inputs, it outputs load change trends and photovoltaic power output change trends at multiple future moments (i.e., within the control cycle). This enables the control system to perceive uncertain fluctuations in source load in advance, thereby transforming passive response into active prediction and providing key predictive information for subsequent voltage sensitivity modeling and rolling optimization.

[0043] Specifically, the load forecasting model is constructed based on the following formula:

[0044] ;

[0045] A photovoltaic prediction model is constructed based on the following formula:

[0046] ;

[0047] in, For the moment Load forecast, For a moment The predicted value of photovoltaic power output, For length is Historical load data, For length is Historical photovoltaic power output data and These are the load forecasting model and the photovoltaic forecasting model, respectively, where H is the preset time domain length.

[0048] Step S103: Construct a time-series voltage sensitivity model based on the predicted load value and the predicted photovoltaic output value, obtain the voltage change at each moment in the future control cycle based on the time-series voltage sensitivity model, and calculate the node voltage at each moment in the future based on the voltage change.

[0049] It should be noted that by constructing a time-series voltage sensitivity model, the mathematical relationship between the source load disturbance and the voltage change at each node is quantified, enabling the system to predict the voltage distribution trajectory at each future moment based on the predicted source load changes, thus providing a precise quantitative basis for the early prevention and control of voltage overruns.

[0050] Specifically, the time-series voltage sensitivity model is constructed based on the following formula:

[0051] ;

[0052] in, Let be the change in node voltage at time t. This represents the voltage change at time t of the 10kV bus. Let be the change in tap position of the on-load tap-changing transformer at time t. Let be the reactive power regulation on the distribution network side at time t. Let be the source load disturbance at time t. These are the corresponding weight coefficients;

[0053] The source-load disturbance is obtained by subtracting the net injected power from the net injected power at the previous moment from the net injected power at the current moment. The net injected power is obtained by subtracting the load prediction value from the photovoltaic output prediction value.

[0054] The node voltage is calculated using the following formula:

[0055] ;

[0056] in, Let be the node voltages at times t+k+1 and t+k-1, respectively. Let be the change in node voltage at time t+k.

[0057] Step S104: Calculate the low-voltage user voltage deviation based on the node voltage, construct an objective function based on the low-voltage user voltage deviation, and construct node voltage constraints, 10kV bus voltage constraints, on-load tap changer tap position constraints, reactive power regulation constraints, and adjacent control cycle adjustment increment constraints for the objective function. Solve the objective function to obtain the control command.

[0058] This step involves constructing a multi-objective optimization model that takes into account voltage quality, network losses, and equipment operating costs, and introducing adjustment increment constraints. The optimal control command obtained can minimize line losses and reduce the frequent operation of on-load tap changer taps and reactive power equipment while ensuring voltage compliance, thereby extending equipment life and improving the economy and stability of distribution network operation.

[0059] Specifically, in some embodiments, the objective function is constructed according to the following formula:

[0060]

[0061] in, The objective function value is N, where N is the total number of nodes. For the i-th node at time... node voltage, The target reference voltage; For a moment network loss, This serves as a baseline value for network loss. For a moment The equipment operating costs, This serves as the benchmark value for equipment operating costs. These are the weighting coefficients.

[0062] Furthermore, the above objective function satisfies the following constraints:

[0063] The node voltage constraint is: ;

[0064] The 10kV bus voltage constraint is: ;

[0065] The tap position constraints for on-load tap-changing transformers are as follows: ;

[0066] The reactive power regulation constraint is: ;

[0067] The adjustment increment constraint for adjacent control cycles is:

[0068] ;

[0069] in, and The first Lower and upper limits of node voltage and These are the lower and upper limits of the 10kV bus voltage, respectively. and These are the lower and upper limits of the tap position, respectively. and These are the lower and upper limits of the adjustment for the m-th reactive power regulating device, respectively. Let be the voltage of the 10kV bus at time t+k. Let be the adjustment amounts of the m-th reactive power regulating device at times t+k and t+k-1, respectively. These are the tap positions of the on-load tap-changing transformer at times t+k and t+k-1, respectively.

[0070] Power flow balance constraint satisfies the active and reactive power balance relationship of nodes in each forecast period;

[0071] Action count constraints include daily action count constraints for on-load tap changer transformers and daily switching count constraints for parallel capacitors;

[0072] Dead zone, delay, and anti-oscillation constraints are used to suppress frequent actions caused by short-term fluctuations.

[0073] Furthermore, in some embodiments, network loss is calculated according to the following formula:

[0074] ;

[0075] in, For the first The resistance of the line, and They are respectively the time t+kth Active and reactive power on the line, For time t+k, the first... The voltage at the beginning or equivalent node of a line, where M is the total number of lines;

[0076] Calculate equipment operating costs using the following formula:

[0077] ;

[0078] in, Let t+k be the number of tap changes of the on-load tap changer. Let t+k be the number of times the parallel capacitor is switched on and off. This represents the total number of reactive power regulation devices. This represents the cost coefficient for the action.

[0079] By solving the above objective function, the obtained control commands include: , Let be the voltage of the 10kV bus at time t. The tap position of the on-load tap-changing transformer at time t. Let be the adjustment amount of the m-th reactive power regulating device at time t.

[0080] Furthermore, in each control cycle, only the optimal control command corresponding to the current moment is executed, and the next control cycle is then used to re-predict and optimize based on the latest measurement values, thus achieving cyclic control.

[0081] Compared with the prior art, the present invention has the following advantages:

[0082] 1. This invention simultaneously considers the 10kV bus voltage, the tap position of the on-load tap changer on the main grid side, and the reactive power regulation on the distribution grid side, which can avoid control conflicts caused by independent regulation at a single level and improve the overall voltage management effect.

[0083] 2. This invention improves the ability to prevent voltage overruns by constructing load forecasting functions and photovoltaic forecasting functions to incorporate future source-load change trends into the control decision-making process.

[0084] 3. This invention achieves coordinated optimization of voltage quality and operational economy by jointly considering node voltage deviation, network loss and equipment operating costs through an objective function.

[0085] 4. This invention effectively reduces the frequent operation of on-load tap changers and reactive power equipment by introducing adjustment increment constraints, action number constraints, and model predictive control rolling mechanisms.

[0086] like Figure 2 As shown, one embodiment of the present invention proposes a master-distributor coordinated voltage control system, the system comprising:

[0087] The data acquisition module 10 is used to acquire power distribution network operation data and preprocess the power distribution network operation data to obtain preprocessed power distribution network operation data.

[0088] The prediction model construction module 20 is used to construct a load prediction model and a photovoltaic prediction model based on the preprocessed distribution network operation data, and to obtain the load prediction value and photovoltaic output prediction value in the future control cycle based on the load prediction model and the photovoltaic prediction model, respectively.

[0089] The node voltage calculation module 30 is used to construct a time-series voltage sensitivity model based on the load forecast value and the photovoltaic output forecast value, obtain the voltage change at each moment in the future control cycle based on the time-series voltage sensitivity model, and calculate the node voltage at each moment in the future based on the voltage change.

[0090] The optimization model construction module 40 is used to calculate the low-voltage user voltage deviation based on the node voltage, construct an objective function based on the low-voltage user voltage deviation, and construct node voltage constraints, 10kV bus voltage constraints, on-load tap changer tap position constraints, reactive power regulation constraints, and adjacent control cycle adjustment increment constraints for the objective function. The objective function is then solved to obtain control commands.

[0091] In another aspect, the present invention also proposes a storage medium having one or more programs stored thereon, which, when executed by a processor, implement the above-described master-distributor coordinated voltage control method.

[0092] In another aspect, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the above-mentioned master-distributor coordinated voltage control method.

[0093] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0094] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0095] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0096] While embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the invention as set forth in the claims. Furthermore, the invention described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A primary-distributor coordinated voltage control method, characterized in that, The method includes: Acquire distribution network operation data and preprocess the distribution network operation data to obtain preprocessed distribution network operation data; Based on the preprocessed distribution network operation data, a load forecasting model and a photovoltaic forecasting model are constructed, and the load forecasting value and photovoltaic output forecasting value in the future control cycle are obtained based on the load forecasting model and the photovoltaic forecasting model, respectively. A time-series voltage sensitivity model is constructed based on the predicted load and photovoltaic output values. The voltage change at each moment in the future control cycle is obtained based on the time-series voltage sensitivity model. The node voltage at each moment in the future is calculated based on the voltage change. The low-voltage user voltage deviation is calculated based on the node voltage, and an objective function is constructed based on the low-voltage user voltage deviation. Then, node voltage constraints, 10kV bus voltage constraints, on-load tap changer tap position constraints, reactive power regulation constraints, and adjacent control cycle adjustment increment constraints are constructed for the objective function. The objective function is solved to obtain the control command.

2. The main-distribution coordinated voltage control method according to claim 1, characterized in that, The steps of constructing a load forecasting model and a photovoltaic forecasting model based on the preprocessed distribution network operation data, and obtaining the load forecasting value and photovoltaic output forecasting value for the future control cycle based on the load forecasting model and the photovoltaic forecasting model, respectively, include: Construct a load forecasting model based on the following formula: ； A photovoltaic prediction model is constructed based on the following formula: ； in, For the moment Load forecast, For a moment The predicted value of photovoltaic power output, For length is Historical load data, For length is Historical photovoltaic power output data and These are the load forecasting model and the photovoltaic forecasting model, respectively, where H is the preset time domain length.

3. The main-distributor coordinated voltage control method according to claim 2, characterized in that, The steps of constructing a time-series voltage sensitivity model based on the load forecast and photovoltaic output forecast, obtaining the voltage change at each moment in the future control cycle based on the time-series voltage sensitivity model, and calculating the node voltage at each future moment based on the voltage change include: Construct a time-series voltage sensitivity model based on the following formula: ； in, Let be the change in node voltage at time t. This represents the voltage change at time t of the 10kV bus. Let be the change in tap position of the on-load tap-changing transformer at time t. Let be the reactive power regulation on the distribution network side at time t. Let be the source load disturbance at time t. These are the corresponding weight coefficients; The source-load disturbance is obtained by subtracting the net injected power from the net injected power at the previous moment from the net injected power at the current moment. The net injected power is obtained by subtracting the load prediction value from the photovoltaic output prediction value. The node voltage is calculated using the following formula: ； in, Let be the node voltages at times t+k+1 and t+k-1, respectively. Let be the change in node voltage at time t+k.

4. The main-distribution coordinated voltage control method according to claim 3, characterized in that, The steps of calculating the low-voltage user voltage deviation based on the node voltage and constructing an objective function based on the low-voltage user voltage deviation include: Construct the objective function based on the following formula: ； in, The objective function value is N, where N is the total number of nodes. For the i-th node at time... node voltage, The target reference voltage; For a moment network loss, This serves as a baseline value for network loss. For a moment The equipment operating costs, This serves as the benchmark value for equipment operating costs. These are the weighting coefficients.

5. The main-distributor coordinated voltage control method according to claim 4, characterized in that, The steps for constructing the nodal voltage constraints, 10kV bus voltage constraints, on-load tap changer tap position constraints, reactive power regulation constraints, and adjacent control cycle adjustment increment constraints regarding the objective function include: The node voltage constraint is: ; The 10kV bus voltage constraint is: ; The tap position constraints for on-load tap-changing transformers are as follows: ; The reactive power regulation constraint is: ; The adjustment increment constraint for adjacent control cycles is: ； in, and The first Lower and upper limits of node voltage and These are the lower and upper limits of the 10kV bus voltage, respectively. and These are the lower and upper limits of the tap position, respectively. and These are the lower and upper limits of the adjustment for the m-th reactive power regulating device, respectively. Let be the voltage of the 10kV bus at time t+k. Let be the adjustment amounts of the m-th reactive power regulating device at times t+k and t+k-1, respectively. These are the tap positions of the on-load tap-changing transformer at times t+k and t+k-1, respectively.

6. The main-distributor coordinated voltage control method according to claim 5, characterized in that, The method further includes: Calculate network loss using the following formula: ； in, For the first The resistance of the line, and They are respectively the time t+kth Active and reactive power on the line, For time t+k, the first... The voltage at the beginning or equivalent node of a line, where M is the total number of lines; Calculate equipment operating costs using the following formula: ； in, Let t+k be the number of tap changes of the on-load tap changer. Let t+k be the number of times the parallel capacitor is switched on and off. This represents the total number of reactive power regulation devices. This represents the cost coefficient for the action.

7. The main-distribution coordinated voltage control method according to claim 6, characterized in that, When tap position data for an on-load tap-changing transformer is missing or unavailable, the corresponding tap position is identified based on the timing voltage relationship between the high and low voltage sides of the transformer. The identification relationship is as follows: ； in, Let t be the voltage on the high-voltage side of the transformer. Let be the voltage on the low-voltage side of the transformer at time t. Let be the voltage ratio at time t. This is a mapping function from voltage ratio to tap position. To identify the tap position corresponding to the obtained time t; The control commands include , Let be the voltage of the 10kV bus at time t. The tap position of the on-load tap-changing transformer at time t. Let be the adjustment amount of the m-th reactive power regulating device at time t.

8. A master-distributor coordinated voltage control system, characterized in that, The system includes: The data acquisition module is used to acquire power distribution network operation data and preprocess the power distribution network operation data to obtain preprocessed power distribution network operation data. The prediction model construction module is used to construct a load prediction model and a photovoltaic prediction model based on the preprocessed distribution network operation data, and to obtain the load prediction value and photovoltaic output prediction value in the future control cycle based on the load prediction model and the photovoltaic prediction model, respectively. The node voltage calculation module is used to construct a time-series voltage sensitivity model based on the load forecast and photovoltaic output forecast, obtain the voltage change at each moment in the future control cycle based on the time-series voltage sensitivity model, and calculate the node voltage at each future moment based on the voltage change. The optimization model construction module is used to calculate the low-voltage user voltage deviation based on the node voltage, construct an objective function based on the low-voltage user voltage deviation, and construct node voltage constraints, 10kV bus voltage constraints, on-load tap changer tap position constraints, reactive power regulation constraints, and adjacent control cycle adjustment increment constraints for the objective function. The objective function is then solved to obtain the control command.

9. A storage medium, characterized in that, The storage medium stores one or more programs that, when executed by a processor, implement the master-slave coordinated voltage control method as described in any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the master-slave coordinated voltage control method as described in any one of claims 1-7.

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