Power system voltage management method and system based on district mutual aid

Abstract

The application discloses a kind of based on the voltage management method of power system of district mutual aid, including obtaining the parameter information of target district in power system;Real-time data information of target district in power system is obtained;Voltage management model of target district is constructed based on model predictive control scheme;The model constructed is converted into QP problem;QP problem is solved based on gradient descent algorithm to the obtained;According to the solving result obtained, based on district mutual aid scheme, the voltage management of target district is completed.The application also discloses a kind of system for implementing the voltage management method of power system based on the district mutual aid of the application.The application not only realizes the voltage management of power system based on the district mutual aid, but also has higher reliability, better accuracy, and better timeliness.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, specifically relating to a power system voltage management method and system based on transformer substation mutual assistance. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, the penetration rate of distributed photovoltaic and wind power and other new energy sources in low-voltage distribution network areas continues to increase, and electric vehicles are widely used, significantly enhancing the randomness and volatility of distribution network loads. These factors have led to increasingly prominent problems such as low-voltage distribution network bus voltage exceeding limits and decreased power supply stability, seriously affecting the safe and reliable operation of the power system and the user's electricity experience.

[0004] Current low-voltage distribution network voltage control schemes have significant shortcomings: traditional adjacent transformer area communication methods can only achieve on / off control and cannot perform continuous power regulation, resulting in poor cross-transformer area support adaptability; existing centralized control schemes rely on the computing power and communication links of the master station, which leads to problems such as large response delays and control failure when communication is interrupted; some embedded control schemes only support single transformer area regulation or unidirectional power transmission, lack the ability of "local priority and bidirectional mutual assistance", and the control algorithms are complex, making it difficult to adapt to the deployment requirements of low-cost intelligent converged terminals. Summary of the Invention

[0005] One of the objectives of this invention is to provide a power system voltage management method based on transformer substation mutual assistance that is highly reliable, accurate, and timely.

[0006] The second objective of this invention is to provide a system for implementing the power system voltage management method based on transformer substation mutual assistance.

[0007] The power system voltage management method based on transformer substation mutual assistance provided by this invention includes the following steps:

[0008] S1. Obtain parameter information of the target transformer area in the power system;

[0009] S2. Real-time acquisition of data information for target transformer areas in the power system;

[0010] S3. Based on the data obtained in steps S1 and S2, construct a voltage management model for the target transformer area based on the model predictive control scheme;

[0011] S4. Convert the model constructed in step S3 into a QP problem;

[0012] S5. Solve the QP problem obtained in step S4 based on the gradient descent algorithm;

[0013] S6. Based on the solution obtained in step S5, complete the voltage management of the target transformer area according to the transformer area mutual assistance scheme.

[0014] Step S1, which involves obtaining parameter information of the target distribution area in the power system, specifically includes the following steps:

[0015] The target distribution areas in the power system include distribution area A and distribution area B; distribution area A is equipped with energy storage devices. Energy storage devices are configured in transformer area B. Transformer Area A and Transformer Area B achieve low-voltage side flexible interconnection through a bidirectional flexible interconnection device SOP;

[0016] Obtain parameter information of the target transformer area in the power system;

[0017] The parameter information includes model predictive control parameters, transformer area parameters, SOP parameters, and QP problem parameters;

[0018] Model predictive control parameters include prediction time domain Control Time Domain and control cycle ;

[0019] The parameters of the transformer area include the voltage sensitivity coefficient of transformer area A. Voltage sensitivity coefficient of transformer area B Energy storage capacity of area A Energy storage capacity of area B Voltage rating Voltage acceptable range SOC (State of Charge) Qualification Range for Energy Storage Devices Maximum charging and discharging power of energy storage Energy storage power increment limit and the reference value of transformer area voltage ; This is the lower limit of the acceptable voltage range; This is the upper limit of the acceptable voltage range; This is the lower limit of the acceptable SOC range for energy storage devices; This represents the upper limit of the acceptable SOC range for energy storage devices.

[0020] SOP parameters include SOP maximum transmission power. and SOP power increment limit value ;

[0021] The parameters of the QP problem include the Hessian matrix H and its corresponding offline pre-calculated value, the constraint matrix G and its corresponding offline pre-calculated value, and the state matrix. Control matrix and output matrix Furthermore, all QP problem parameters are fixed offline.

[0022] Step S2, which involves acquiring real-time data information of the target distribution area in the power system, specifically includes the following steps:

[0023] Real-time acquisition of data information for target transformer areas in the power system;

[0024] The data information includes the bus voltage of transformer area A. Bus voltage of transformer area B State of charge of energy storage device in area A State of charge of energy storage device in area B SOP transmission power and real-time load power;

[0025] The validity of the acquired data is assessed, and the data is corrected using a proportional correction algorithm to obtain the system state vector for the current period. for ,in This is the corrected bus voltage for transformer area A. This is the corrected bus voltage for transformer area B. The corrected state of charge of the energy storage device in area A. The corrected state of charge of the energy storage device in area B. This refers to the corrected SOP transmission power.

[0026] Step S3, which involves constructing a voltage management model for the target distribution area based on the data obtained in steps S1 and S2 and a model predictive control scheme, specifically includes the following steps:

[0027] A simplified state-space model of dual-area energy storage + bidirectional SOP is established, represented as:

[0028] In the formula This is the system state vector for the next period; The state matrix; For control matrix; For control vectors, , For the power increment of the energy storage device in area A, For the power increment of the energy storage device in area B, For SOP power increment; For the output vector, , The target quantity for bus voltage control in transformer area A. The target quantity for bus voltage control in transformer area B; This is the output matrix;

[0029] The MPC cost function is constructed using the following formula and serves as the objective function of the voltage management model for the target transformer area:

[0030] In the formula The objective function value; The set voltage tracking weight value; As the reference vector, ; It is a norm; The set control increment weight; The set SOP penalty weight, and ;

[0031] Constraints for constructing the voltage management model for the target transformer area:

[0032] The following formula is used as the voltage constraint:

[0033] The following formula is used as the energy storage constraint:

[0034] The following formula is used as the SOP constraint:

[0035] In the formula The output power of the energy storage device in transformer area A; This represents the output power of the energy storage device in transformer area B.

[0036] Step S4, which involves converting the model constructed in step S3 into a QP problem, specifically includes the following steps:

[0037] The model constructed in step S3 is transformed into the standard quadratic programming form of the QP problem as follows:

[0038] In the formula This is the vector of current system state variables; For the future The optimized variable vector is formed by splicing control variables step by step; This represents the control input for the i-th step predicted at the current time k. , To control the length of the time domain; This is the coefficient vector of the linear term in the quadratic programming objective function; is the upper bound vector of the inequality constraint.

[0039] Step S5, which uses the gradient descent algorithm to solve the QP problem obtained in step S4, specifically includes the following steps:

[0040] (1) Initialize vector z and set learning rate. Maximum number of iterations and convergence accuracy;

[0041] (2) Calculate the gradient vector for ;

[0042] (3) Update vector z along the negative gradient direction: ; For the updated z;

[0043] (4) Perform constraint trimming on the updated z to ensure that the constraint conditions are met;

[0044] (5) Determine whether to terminate the iteration:

[0045] If the number of iterations reaches the set maximum number of iterations or When the iteration stops, the current vector z is output; where, Let be the optimization variable for the k-th iteration. To update and complete the optimization variables after constraint pruning, The set convergence precision;

[0046] Otherwise, the iteration count is increased by 1, and the process returns to step (2) for the next iteration.

[0047] Step S6, based on the solution obtained in step S5, involves voltage management of the target transformer area according to the transformer area mutual assistance scheme. Specifically, this includes the following steps:

[0048] Based on the solution obtained in step S5, the target station area is controlled;

[0049] If the voltage of transformer area A exceeds the limit during the control process, the following actions will be taken:

[0050] like and In this case, the energy storage device configured in area A will be used first to discharge in order to provide voltage support. The set voltage dead zone; The SOC dead zone is set;

[0051] like If so, the energy storage device configured in transformer area B will be activated to discharge, and at the same time, the SOP will be controlled to send power to transformer area A.

[0052] like If so, it will operate normally, and the energy storage device will not need to discharge;

[0053] If the voltage of transformer area B exceeds the limit during the control process, the following actions will be taken:

[0054] like and In this case, the energy storage device configured in area B will be used first to discharge in order to provide voltage support.

[0055] like If so, the energy storage device configured in transformer area A will be activated to discharge, and at the same time, the SOP will be controlled to send power to transformer area B.

[0056] like If so, it will operate normally, and the energy storage device will not need to discharge;

[0057] Complete voltage management of the target transformer area.

[0058] This invention also provides a system for implementing the power system voltage management method based on transformer substation mutual assistance, comprising a parameter acquisition module, a data acquisition module, a model building module, a model conversion module, a model solving module, and a voltage management module; the parameter acquisition module, data acquisition module, model building module, model conversion module, model solving module, and voltage management module are connected in series; the parameter acquisition module is used to acquire parameter information of the target transformer substation in the power system and upload the data information to the data acquisition module; the data acquisition module is used to acquire data information of the target transformer substation in the power system in real time based on the received data information and upload the data information to the model building module; the model building module is used to construct a voltage management model of the target transformer substation based on the received data information and a model predictive control scheme, and upload the data information to the model conversion module; the model conversion module is used to convert the constructed model into a QP problem based on the received data information and upload the data information to the model solving module; the model solving module is used to solve the obtained QP problem based on the gradient descent algorithm based on the received data information and upload the data information to the voltage management module; the voltage management module is used to complete the voltage management of the target transformer substation based on the received data information and the obtained solution, and a transformer substation mutual assistance scheme.

[0059] The voltage management method and system for power systems based on transformer substation mutual assistance provided by this invention acquires data from transformer substations, constructs a voltage management model based on a model predictive control scheme, and combines a QP problem-solving scheme. This not only achieves voltage management of power systems based on transformer substation mutual assistance, but also has higher reliability, better accuracy, and better timeliness. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0061] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0062] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The power system voltage management method based on transformer substation mutual assistance disclosed in this invention includes the following steps:

[0063] S1. Obtain parameter information for the target transformer area in the power system; specifically including the following steps:

[0064] The target distribution areas in the power system include distribution area A and distribution area B; distribution area A is equipped with energy storage devices. Energy storage devices are configured in transformer area B. Transformer Area A and Transformer Area B achieve low-voltage side flexible interconnection through a bidirectional flexible interconnection device SOP;

[0065] Obtain parameter information of the target transformer area in the power system;

[0066] The parameter information includes model predictive control parameters, transformer area parameters, SOP parameters, and QP problem parameters;

[0067] Model predictive control parameters include prediction time domain (Preferred value: 5~10), control time domain (Preferably 2~3) and control cycle (Preferred time is 0.1s to 1s);

[0068] The parameters of the transformer area include the voltage sensitivity coefficient of transformer area A. Voltage sensitivity coefficient of transformer area B Energy storage capacity of area A Energy storage capacity of area B Voltage rating Voltage acceptable range SOC (State of Charge) Qualification Range for Energy Storage Devices Maximum charging and discharging power of energy storage Energy storage power increment limit and the reference value of transformer area voltage ; This is the lower limit of the acceptable voltage range; This is the upper limit of the acceptable voltage range; This is the lower limit of the acceptable SOC range for energy storage devices; This represents the upper limit of the acceptable SOC range for energy storage devices.

[0069] SOP parameters include SOP maximum transmission power. and SOP power increment limit value ;

[0070] The parameters of the QP problem include the Hessian matrix H and its corresponding offline pre-calculated value, the constraint matrix G and its corresponding offline pre-calculated value, and the state matrix. Control matrix and output matrix Furthermore, all QP problem parameters are fixed offline;

[0071] S2. Real-time acquisition of data information for the target transformer area in the power system; specifically including the following steps:

[0072] Real-time acquisition of data information for target transformer areas in the power system;

[0073] The data information includes the bus voltage of transformer area A. Bus voltage of transformer area B State of charge of energy storage device in area A State of charge of energy storage device in area B SOP transmission power and real-time load power;

[0074] The acquired data is evaluated for validity (specifically, if the data exceeds a set range, the corresponding abnormal data is directly removed), and then corrected using a proportional correction algorithm to obtain the system state vector for the current period. for ,in This is the corrected bus voltage for transformer area A. This is the corrected bus voltage for transformer area B. The corrected state of charge of the energy storage device in area A. The corrected state of charge of the energy storage device in area B. This refers to the corrected SOP transmission power;

[0075] In practice, the deviation between the predicted data of the previous period and the current measured data can be corrected using a proportional correction algorithm.

[0076] S3. Based on the data obtained in steps S1 and S2, construct a voltage management model for the target transformer area based on the model predictive control scheme; specifically including the following steps:

[0077] Based on the principle of voltage-active power sensitivity linearization, and combined with load characteristics and equipment status, a simplified state-space model of dual-area energy storage + bidirectional SOP is established, represented as:

[0078] In the formula This is the system state vector for the next period; The state matrix; For control matrix; For control vectors, , For the power increment of the energy storage device in area A, For the power increment of the energy storage device in area B, For SOP power increment; For the output vector, , The target quantity for bus voltage control in transformer area A. The target quantity for bus voltage control in transformer area B; This is the output matrix;

[0079] Considering minimizing voltage tracking error, smoothing control actions, and maximizing SOP transmission penalty, the following formula is used to construct the MPC cost function, which serves as the objective function of the voltage management model for the target transformer area:

[0080] In the formula The objective function value; The set voltage tracking weight value; As the reference vector, ; It is a norm; The set control increment weight; The set SOP penalty weight, and This is to ensure that local priority control logic is implemented.

[0081] Constraints for constructing the voltage management model for the target transformer area:

[0082] The following formula is used as the voltage constraint:

[0083] The following formula is used as the energy storage constraint:

[0084] The following formula is used as the SOP constraint:

[0085] In the formula The output power of the energy storage device in transformer area A; The output power of the energy storage device in transformer area B;

[0086] S4. Convert the model constructed in step S3 into a QP problem; specifically, this includes the following steps:

[0087] The model constructed in step S3 is transformed into the standard quadratic programming form of the QP problem as follows:

[0088] In the formula This is the vector of current system state variables; For the future The optimized variable vector is formed by splicing control variables step by step; This represents the control input for the i-th step predicted at the current time k. , To control the length of the time domain; This is the coefficient vector of the linear term in the quadratic programming objective function; is the upper bound vector of the inequality constraint;

[0089] S5. Solve the QP problem obtained in step S4 using the gradient descent algorithm; specifically, the following steps are included:

[0090] (1) Initialize vector z and set learning rate. Maximum number of iterations and convergence accuracy;

[0091] (2) Calculate the gradient vector for ;

[0092] (3) Update vector z along the negative gradient direction: ; For the updated z;

[0093] (4) Perform constraint trimming on the updated z to ensure that the constraint conditions are met;

[0094] (5) Determine whether to terminate the iteration:

[0095] If the number of iterations reaches the set maximum number of iterations or When the iteration stops, the current vector z is output; where, Let be the optimization variable for the k-th iteration. To update and complete the optimization variables after constraint pruning, The set convergence precision;

[0096] Otherwise, increment the iteration count by 1 and return to step (2) for the next iteration;

[0097] S6. Based on the solution obtained in step S5, and using the transformer substation mutual assistance scheme, complete the voltage management of the target transformer substation; specifically, this includes the following steps:

[0098] Based on the solution obtained in step S5, the target station area is controlled;

[0099] If the voltage of transformer area A exceeds the limit during the control process, the following actions will be taken:

[0100] like and In this case, the energy storage device configured in area A will be used first to discharge in order to provide voltage support. The set voltage dead zone; The SOC dead zone is set;

[0101] like If so, the energy storage device configured in transformer area B will be activated to discharge, and at the same time, the SOP will be controlled to send power to transformer area A.

[0102] like If so, it will operate normally, and the energy storage device will not need to discharge;

[0103] If the voltage of transformer area B exceeds the limit during the control process, the following actions will be taken:

[0104] like and In this case, the energy storage device configured in area B will be used first to discharge in order to provide voltage support.

[0105] like If so, the energy storage device configured in transformer area A will be activated to discharge, and at the same time, the SOP will be controlled to send power to transformer area B.

[0106] like If so, it will operate normally, and the energy storage device will not need to discharge;

[0107] If the voltage does not exceed the limit during the control process, the current control process can remain unchanged. It can also optimize the SOC balancing and SOP transmission loss of the two energy storage systems, taking into account both user power comfort and system operation economy, and minimizing the total system loss.

[0108] Complete voltage management of the target transformer area.

[0109] This invention employs a "local priority, bidirectional mutual assistance" control logic to adapt to dual-area energy storage + SOP topology, enhancing the flexibility and reliability of voltage management. The invention combines a simplified MPC model with lightweight QP solution, balancing control accuracy and real-time performance, resulting in small voltage regulation deviations and a control cycle of 0.1~1s. Furthermore, the invention integrates multi-source data to improve state awareness and scientific decision-making, effectively solving distribution network voltage stability problems and ensuring power supply reliability, accuracy, and timeliness.

[0110] The method of the present invention will be further described below with reference to a specific embodiment:

[0111] Distribution area A and distribution area B are both 0.4kV low-voltage distribution network distribution areas. , The rated capacity is 100kWh, the maximum charge and discharge power is 50kW, the SOC acceptable range is [0.2,0.9], the maximum transmission power of SOP is 100kW, and it supports bidirectional power transmission.

[0112] System initialization and parameter fixing: Initialize core parameters: , , ; , , , ; , ; , ; , ; , ; , , Offline solidified matrices A, B, and C, and the H and G matrices for the QP problem;

[0113] Data is collected at 0.1s intervals: , , , , The real-time power consumption of marketing users is 20kW; after data cleaning and integration, combined with the prediction deviation of the previous period, the corrected state quantity is obtained through a proportional correction algorithm. ;

[0114] Construct the objective function:

[0115] Transform it into a standard QP problem and update the f and h vectors online;

[0116] QP problem solution: Gradient descent method is used, with a learning rate of 0.01, a maximum number of iterations of 120, and a convergence accuracy of [missing information]. After 32 iterations, the loop converges, and the control quantity for the current cycle is extracted. ;

[0117] Local priority and regional mutual assistance control logic execution, determination , Therefore, Channel B supports Channel A: Discharge SOP positive transmission , Do nothing;

[0118] Then repeat the above steps to manage the voltage of the target transformer area in real time.

[0119] Simulation results show that after voltage regulation, the voltage in transformer area A gradually rises to the acceptable range, while the voltage in transformer area B remains stable. The SOP and energy storage operations are smooth and without overshoot, meeting the engineering requirements for low-voltage distribution network voltage regulation.

[0120] like Figure 2 The diagram shows the functional modules of the system of this invention: The system disclosed in this invention, which implements the power system voltage management method based on transformer substation mutual assistance, includes a parameter acquisition module, a data acquisition module, a model building module, a model conversion module, a model solving module, and a voltage management module; these modules are connected in series. The parameter acquisition module acquires parameter information of the target transformer substation in the power system and uploads the data information to the data acquisition module. The data acquisition module acquires data information of the target transformer substation in the power system in real time based on the received data information and uploads the data information to the model. The system comprises the following modules: a model building module, a model conversion module, and a voltage management module. The model building module constructs a voltage management model for the target transformer area based on the received data and a model predictive control scheme, and uploads the data to the model conversion module. The model conversion module converts the constructed model into a QP problem based on the received data and uploads the data to the model solving module. The model solving module solves the QP problem using the gradient descent algorithm based on the received data and uploads the data to the voltage management module. The voltage management module, based on the received data and the solution results, completes voltage management for the target transformer area based on the transformer area mutual assistance scheme.

Claims

1. A power system voltage management method based on transformer substation mutual assistance, comprising the following steps: S1. Obtain parameter information of the target transformer area in the power system; S2. Real-time acquisition of data information for target transformer areas in the power system; S3. Based on the data obtained in steps S1 and S2, construct a voltage management model for the target transformer area based on the model predictive control scheme; S4. Convert the model constructed in step S3 into a QP problem; S5. Solve the QP problem obtained in step S4 based on the gradient descent algorithm; S6. Based on the solution obtained in step S5, complete the voltage management of the target transformer area according to the transformer area mutual assistance scheme.

2. The power system voltage management method based on transformer substation mutual assistance according to claim 1, characterized in that... Step S1, which involves obtaining parameter information of the target distribution area in the power system, specifically includes the following steps: The target distribution areas in the power system include distribution area A and distribution area B; distribution area A is equipped with energy storage devices. Energy storage devices are configured in transformer area B. Transformer Area A and Transformer Area B achieve low-voltage side flexible interconnection through a bidirectional flexible interconnection device SOP; Obtain parameter information of the target transformer area in the power system; The parameter information includes model predictive control parameters, transformer area parameters, SOP parameters, and QP problem parameters; Model predictive control parameters include prediction time domain Control Time Domain and control cycle ; The parameters of the transformer area include the voltage sensitivity coefficient of transformer area A. Voltage sensitivity coefficient of transformer area B Energy storage capacity of area A Energy storage capacity of area B Voltage rating Voltage acceptable range SOC (State of Charge) Qualification Range for Energy Storage Devices Maximum charging and discharging power of energy storage Energy storage power increment limit and the reference value of the transformer area voltage ; This is the lower limit of the acceptable voltage range; This is the upper limit of the acceptable voltage range; This is the lower limit of the acceptable SOC range for energy storage devices; This represents the upper limit of the acceptable SOC range for energy storage devices. SOP parameters include SOP maximum transmission power. and SOP power increment limit value ; The parameters of the QP problem include the Hessian matrix H and its corresponding offline pre-calculated value, the constraint matrix G and its corresponding offline pre-calculated value, and the state matrix. Control matrix and output matrix Furthermore, all QP problem parameters are fixed offline.

3. The power system voltage management method based on transformer substation mutual assistance according to claim 2, characterized in that... Step S2, which involves acquiring real-time data information of the target distribution area in the power system, specifically includes the following steps: Real-time acquisition of data information for target transformer areas in the power system; The data information includes the bus voltage of transformer area A. Bus voltage of transformer area B State of charge of energy storage device in area A State of charge of energy storage device in area B SOP transmission power and real-time load power; The validity of the acquired data is assessed, and the data is corrected using a proportional correction algorithm to obtain the system state vector for the current period. for ,in This is the corrected bus voltage for transformer area A. This is the corrected bus voltage for transformer area B. The corrected state of charge of the energy storage device in area A. The corrected state of charge of the energy storage device in area B. This refers to the corrected SOP transmission power.

4. The power system voltage management method based on transformer substation mutual assistance according to claim 3, characterized in that... Step S3, which involves constructing a voltage management model for the target distribution area based on the data obtained in steps S1 and S2 and a model predictive control scheme, specifically includes the following steps: A simplified state-space model of dual-area energy storage + bidirectional SOP is established, represented as: In the formula This is the system state vector for the next period; The state matrix; For control matrix; For control vectors, , For the power increment of the energy storage device in area A, For the power increment of the energy storage device in area B, For SOP power increment; For the output vector, , The target quantity for bus voltage control in transformer area A. The target quantity for bus voltage control in transformer area B; This is the output matrix; The MPC cost function is constructed using the following formula and serves as the objective function of the voltage management model for the target transformer area: In the formula The objective function value; The set voltage tracking weight value; As the reference vector, ; It is a norm; The set control increment weight; The SOP penalty weight is set, and ; Constraints for constructing the voltage management model for the target transformer area: The following formula is used as the voltage constraint: The following formula is used as the energy storage constraint: The following formula is used as the SOP constraint: In the formula The output power of the energy storage device in transformer area A; This represents the output power of the energy storage device in transformer area B.

5. The power system voltage management method based on transformer substation mutual assistance according to claim 4, characterized in that... Step S4, which involves converting the model constructed in step S3 into a QP problem, specifically includes the following steps: The model constructed in step S3 is transformed into the standard quadratic programming form of the QP problem as follows: In the formula This is the vector of current system state variables; For the future The optimized variable vector is formed by splicing control variables step by step; This represents the control input for the i-th step predicted at the current time k. , To control the length of the time domain; This is the coefficient vector of the linear term in the quadratic programming objective function; is the upper bound vector of the inequality constraint.

6. The power system voltage management method based on transformer substation mutual assistance according to claim 5, characterized in that... Step S5, which uses the gradient descent algorithm to solve the QP problem obtained in step S4, specifically includes the following steps: (1) Initialize vector z and set learning rate. Maximum number of iterations and convergence accuracy; (2) Calculate the gradient vector for ; (3) Update vector z along the negative gradient direction: ; For the updated z; (4) Perform constraint trimming on the updated z to ensure that the constraint conditions are met; (5) Determine whether to terminate the iteration: If the number of iterations reaches the set maximum number of iterations or When the iteration stops, the current vector z is output; where, Let be the optimization variable for the k-th iteration. To update and complete the optimization variables after constraint pruning, The set convergence precision; Otherwise, the iteration count is increased by 1, and the process returns to step (2) for the next iteration.

7. The power system voltage management method based on transformer substation mutual assistance according to claim 6, characterized in that... Step S6, based on the solution obtained in step S5, involves voltage management of the target transformer area according to the transformer area mutual assistance scheme. Specifically, this includes the following steps: Based on the solution obtained in step S5, the target station area is controlled; If the voltage of transformer area A exceeds the limit during the control process, the following actions will be taken: like and In this case, the energy storage device configured in area A will be used first to discharge in order to provide voltage support. The set voltage dead zone; The SOC dead zone is set; like If so, the energy storage device configured in transformer area B will be activated to discharge, and at the same time, the SOP will be controlled to send power to transformer area A. like If so, it will operate normally, and the energy storage device will not need to discharge; If the voltage of transformer area B exceeds the limit during the control process, the following actions will be taken: like and In this case, the energy storage device configured in area B will be used first to discharge in order to provide voltage support. like If so, the energy storage device configured in transformer area A will be activated to discharge, and at the same time, the SOP will be controlled to send power to transformer area B. like If so, it will operate normally, and the energy storage device will not need to discharge; Complete voltage management of the target transformer area.

8. A system for implementing the power system voltage management method based on transformer substation mutual assistance as described in any one of claims 1 to 7, characterized in that... It includes a parameter acquisition module, a data acquisition module, a model building module, a model conversion module, a model solving module, and a voltage management module; these modules are connected in series. The parameter acquisition module acquires parameter information of the target distribution area in the power system and uploads the data to the data acquisition module. The data acquisition module acquires real-time data information of the target distribution area in the power system based on the received data and uploads the data to the model building module. The model building module is used to build a voltage management model for the target transformer area based on the received data and the model predictive control scheme, and then upload the data to the model conversion module. The model conversion module is used to convert the constructed model into a QP problem based on the received data information, and then upload the data information to the model solving module. The model solving module is used to solve the QP problem based on the gradient descent algorithm according to the received data information, and upload the data information to the voltage management module. The voltage management module is used to manage the voltage of the target transformer area based on the received data and the obtained solution, and based on the transformer area mutual assistance scheme.

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