A new energy large base voltage on-line multi-objective optimization control method, system and electronic equipment

Abstract

The application discloses a new energy large base voltage online multi-target optimization control method and system and electronic equipment, and belongs to the technical field of new energy power generation. The control method comprises the following steps: constructing a sensitivity matrix and a weight matrix; constructing a minimum deviation optimization function according to the voltage weight matrix and the sensitivity matrix; constructing a constraint matrix according to the upper and lower limits of the voltage safety of the voltage nodes in the block, the sensitivity matrix, and the upper and lower limits of the reactive power output of the reactive power source; calculating the reactive power change value of each reactive power source based on the OSQP algorithm, the minimum deviation optimization function and the constraint matrix; calculating the voltage expected value of each reactive power source after the execution of the reactive power change value according to the reactive power change value of each reactive power source; and issuing the obtained voltage expected value to each reactive power source for reactive power regulation. Therefore, the application can significantly improve the overall stability of the regional voltage, and guarantee the overall power supply quality and power generation safety in the region.

Description

Technical Field

[0001] This application relates to the field of new energy power generation technology, and in particular to a method, system and electronic equipment for online multi-objective optimization control of voltage in a large new energy power generation base. Background Technology

[0002] The large-scale new energy base includes multiple power plants at various voltage levels. Due to the turns ratios of transformers and other equipment, different voltage levels are strongly coupled with each other. Even power plants at the same voltage level, or connected on the same line, can have different output voltages. Therefore, traditional reactive power control systems cannot be used for unified management of the voltage across the entire area; a more advanced online multi-objective optimization control method is needed. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system and electronic equipment for online multi-objective optimization control of voltage in a large-scale new energy base. It aims to handle situations where there are control objectives at different voltage levels and multiple voltage nodes within a region, and can achieve the control effect of minimizing the overall voltage deviation within the region.

[0004] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0005] In a first aspect, the present invention provides an online multi-objective optimization control method for voltage in a large-scale new energy base, comprising:

[0006] The grid structure is divided into blocks according to the voltage level to obtain several blocks containing reactive power sources and voltage nodes.

[0007] Obtain the sensitivity coefficients of reactive power sources and voltage nodes within the block, and construct a sensitivity matrix based on the sensitivity coefficients;

[0008] A voltage weight matrix is ​​constructed based on the assessment costs of voltage nodes deviating from the target value within the block; a minimum deviation optimization function is constructed based on the voltage weight matrix and the sensitivity matrix.

[0009] Based on the upper and lower limits of voltage safety of voltage nodes in the block, the sensitivity matrix, and the upper and lower limits of reactive power output of reactive power sources, a constraint matrix is ​​constructed.

[0010] Based on the OSQP algorithm, the minimum deviation optimization function, and the constraint matrix, the reactive power change value of each reactive power source is calculated.

[0011] Based on the reactive power change value of each reactive power source, the expected voltage value after each reactive power source operates according to this reactive power change value is calculated.

[0012] The obtained expected voltage value is sent to each reactive power source for reactive power regulation.

[0013] Furthermore, the segmentation of the grid structure according to voltage levels includes: a block is defined as the range extending along the line from the main step-up substation or each level of step-up substation to the next or more voltage level step-up substations or power plants.

[0014] Furthermore, the process of obtaining the sensitivity coefficients of reactive power sources and voltage nodes within the block, and constructing a sensitivity matrix based on the sensitivity coefficients, includes:

[0015] Suppose there are n reactive power sources and m voltage nodes in the block, where the nth... The reactive power of each reactive power source The node voltage of the j-th voltage node The relationship is the sensitivity coefficient. , Lockout All reactive power sources other than the individual reactive power source will reduce reactive power. From maximum inductive reactive power regulation to maximum capacitive reactive power, record the reactive power sources during the process. reactive power Change The node voltage of all voltage nodes j within the block Change Then there is a sensitivity coefficient. ; various sensitivity coefficients The matrix formed This is the sensitivity matrix.

[0016] Furthermore, a voltage weight matrix is ​​constructed based on the assessment costs of voltage nodes within the block after deviating from the target value. This includes: obtaining the assessment costs of each voltage node within the block after deviating from the target value; defining the voltage weights of the voltage nodes from high to low based on the assessment costs; and generating a voltage weight matrix based on the voltage weights of each voltage node.

[0017] Furthermore, the construction of the minimum deviation optimization function includes:

[0018] The objective function to be optimized is , ;

[0019] in, Indicates the first Target values ​​for each voltage node Indicates the first The node voltage of each voltage node. Indicates the first Initial values ​​for each voltage node, Indicates the first The weight of each voltage node.

[0020] Furthermore, the step of constructing a constraint matrix based on the voltage safety upper and lower limits and sensitivity matrix of the voltage nodes within the block, and the reactive power output upper and lower limits of the reactive power sources, includes:

[0021] Based on the increase / decrease voltage of each voltage node and the increase / decrease reactive power of each reactive power source, construct the constraint matrix:

[0022] ;

[0023] The incremental reactive power of the nth reactive power source can be calculated by using the current reactive power value, the upper limit of reactive power, and the lower limit of reactive power. And can reduce power consumption The potential voltage increase for the m-th voltage node is calculated based on the current voltage value, the upper voltage limit, and the lower voltage limit. and voltage reduction .

[0024] Furthermore, the reactive power change value of each reactive power source is calculated based on the OSQP algorithm, the minimum deviation optimization function, and the constraint matrix. Based on the reactive power change value of each reactive power source, the expected voltage value after execution according to this reactive power change value is calculated, including:

[0025] Construct the QP standard form matrix based on the aforementioned optimization objective function:

[0026] ;

[0027] ;

[0028] in, It is the coefficient matrix of the quadratic terms. For the coefficient vector of the first-order term, the standard form matrix of QP and the constraint matrix are included together in the OSQP standard tool to obtain the reactive power change matrix. ;

[0029] The voltage change is calculated based on the sensitivity coefficient and the reactive power change matrix, expressed by the following formula:

[0030] ;

[0031] Adding the voltage change to the initial voltage value, we get the expected voltage value. That is, the first The expected voltage of each voltage node is .

[0032] Furthermore, the reactive power source is a power station, a step-up substation, and a power consumption station with adjustable reactive power within the block;

[0033] The voltage nodes are all line nodes within the block that require voltage range constraints.

[0034] Secondly, this invention provides an online multi-objective optimization control system for voltage in a large-scale new energy production base, comprising a large-scale base segmentation module, a sensitivity matrix construction module, an algorithm construction and constraint module, an OSQP calculation module, and an instruction execution module:

[0035] The large base segmentation module is used to segment the grid structure according to the voltage level to obtain several blocks containing reactive power sources and voltage nodes.

[0036] The sensitivity matrix construction module is used to obtain the sensitivity coefficients of reactive power sources and voltage nodes within the block, and construct a sensitivity matrix based on the sensitivity coefficients.

[0037] The algorithm construction and constraint module is used to construct a voltage weight matrix based on the assessment cost after the voltage nodes in the block deviate from the target value; construct a minimum deviation optimization function based on the voltage weight matrix and the sensitivity matrix; and construct a constraint matrix based on the voltage safety upper and lower limits of the voltage nodes in the block, the sensitivity matrix, and the reactive power output upper and lower limits of the reactive power source.

[0038] The OSQP calculation module calculates the reactive power change value of each reactive power source based on the OSQP algorithm, the minimum deviation optimization function, and the constraint matrix.

[0039] The instruction execution module is used to calculate the expected voltage value after each reactive power source executes according to the reactive power change value of each reactive power source; and to send the obtained expected voltage value to each reactive power source for reactive power regulation.

[0040] Thirdly, the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements any of the online multi-objective optimization control methods for voltage in large-scale new energy production bases as described in the first aspect.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0042] This invention significantly reduces algorithm complexity and improves the efficiency of optimization calculations by dividing the grid structure into blocks according to voltage levels in the voltage control system of a large-scale new energy base and optimizing reactive voltage control within each block.

[0043] This invention constructs a sensitivity matrix to illustrate the relationship between voltage and reactive power source reactance in different grid structures, enabling clear and quantitative digitization of the network structure. It also constructs a weight matrix to generate an overall minimum deviation function for all voltages with weighted information and upper and lower limit constraint matrices for voltage and reactive power. These constraint matrices indicate the constraints on different voltage levels within the system, while the weighted minimum deviation function implicitly reflects the performance requirements for different voltage nodes and reveals the critical voltage parameters for each node. Finally, by solving the matrices, the reactive power regulation targets and voltage targets for each reactive power source are obtained. Control commands are then issued through the network protocol to achieve reactive power and voltage regulation for each reactive power source. Therefore, this invention can handle situations where there are control targets for different voltage levels and multiple voltage nodes within a region, achieving a control effect that minimizes the overall voltage deviation within the region. It is particularly suitable for regions with complex grid structures, multiple voltage levels, and numerous power plants, such as large-scale new energy bases, significantly improving the overall voltage stability within the region and ensuring the overall power supply quality and power generation safety. Attached Figure Description

[0044] Figure 1 This is a flowchart of an online multi-objective optimization control method for voltage in a large-scale new energy base, provided by an embodiment of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0046] Example 1

[0047] Please see Figure 1 This embodiment introduces a method for online multi-objective optimization control of voltage in a large-scale new energy base, including:

[0048] S100. Divide the grid structure into blocks according to the voltage level to obtain several blocks containing reactive power sources and voltage nodes, wherein each block contains a high-voltage node, the grid structure and several low-voltage nodes.

[0049] S200. Obtain the sensitivity coefficients of reactive power sources and voltage nodes within the block, and construct a sensitivity matrix based on the sensitivity coefficients.

[0050] S300. Construct a voltage weight matrix based on the assessment costs of voltage nodes within the block after deviating from the target value; construct a minimum deviation optimization function based on the voltage weight matrix.

[0051] S400. Construct a constraint matrix based on the upper and lower limits of voltage safety for voltage nodes within the block, and the upper and lower limits of reactive power output for reactive power sources.

[0052] S500, based on the OSQP algorithm (operator split quadratic programming algorithm), minimum deviation optimization function and constraint matrix, calculates the reactive power change value of each reactive power source;

[0053] S600. Based on the reactive power change value of each reactive power source, calculate the expected voltage value after each reactive power source operates according to this reactive power change value.

[0054] S700 sends the obtained expected voltage value to each reactive power source for reactive power regulation.

[0055] In the above scheme, voltage level-based block control is used, and reactive power voltage optimization control is performed within each block, which significantly reduces algorithm complexity and improves the efficiency of optimization calculation. This embodiment can handle situations where there are control targets at different voltage levels and multiple voltage nodes within a region, and can achieve the control effect of minimizing the overall voltage deviation within the region.

[0056] Specifically, in step S100, the grid structure is divided into blocks, which includes: organizing the entire new energy base into a tree structure according to voltage levels. The root of the tree structure is the main step-up substation of the base, the branches are step-up substations at various levels, the ends are various power stations, and each branch is the actual line connecting the various power stations. Starting from the root or a branch, following all branches to the next branch or end of one or more voltage levels, this range is a block.

[0057] Currently, the voltage levels in China are generally 500 / 220 / 110 / 35 / 10kV. Therefore, a possible segmentation method is as follows: the highest voltage level segment is from the 500kV main step-up substation to the 220kV step-up substation; the second highest voltage level segment is from the 220kV step-up substation to the 110kV step-up substation; and the lowest voltage level segment is from the 110kV substation to the 35kV and 10kV power stations. There is only one highest-level segment, while there are multiple segments for the second highest and lowest levels.

[0058] In step S200, reactive power sources include various types of power plants, substations, and power consumption stations within the block that have adjustable reactive power. Voltage nodes are all line nodes within the block that require voltage range constraints, including grid connection points of power plants, high-voltage and low-voltage sides of substations, etc.

[0059] Further, in step S200, a sensitivity matrix is ​​constructed for the reactive power sources and voltage nodes contained in each block. The method for constructing the sensitivity matrix is ​​as follows: Assume there are n reactive power sources and m voltage nodes in the block. For example, for ease of description, let n=3 and m=5, then the matrix size is 3*5. Each node in the matrix represents the corresponding reactive power source. With voltage node The sensitivity coefficient, i.e., the first The reactive power of the first reactive power source and the reactive power of the second reactive power source Node voltage of each voltage node The relationship is the sensitivity coefficient. Then there is ,Right now:

[0060] .

[0061] First, it is necessary to calculate the sensitivity coefficients of each voltage and reactive power. ,by For example, locking except the first All reactive power sources other than the one reactive power source will From maximum inductive reactive power adjustment to maximum capacitive reactive power, the process was recorded. Change in reactive power The node voltage of all voltage nodes j within the block Change Then there is a sensitivity coefficient. Various sensitivity coefficients The matrix formed This is the sensitivity matrix.

[0062] Specifically, the method for constructing the voltage weight matrix in step S300 is as follows: obtain the assessment cost of each voltage node within the block after deviating from the target value, and define the weights according to the assessment cost from high to low. Among them, the voltage node with the higher the assessment cost has the higher weight.

[0063] Specifically, the method for constructing the minimum deviation optimization function in step S300 is as follows:

[0064] Optimize the objective function: ,in ;

[0065] in Indicates the first Target values ​​for each voltage node Indicates the first The node voltage of each voltage node. Indicates the first Initial values ​​for each voltage node, Indicates the first The weight of each voltage node.

[0066] Then we have the following formula:

[0067] .

[0068] in, This represents the extended constant, which does not affect the calculation result and can be omitted during the calculation process.

[0069] In step S400, the method for constructing the constraint matrix specifically includes:

[0070] Based on the increase / decrease voltage of each voltage node and the increase / decrease reactive power of each reactive power source, construct the constraint matrix:

[0071]

[0072] in, and These are the increaseable and decreaseable reactive power of the nth reactive power source, which can be calculated using the current reactive power value and the upper and lower limits of reactive power. and These represent the increaseable and decreaseable voltages of the m-th voltage node, respectively. The increaseable and decreaseable voltages of a voltage node are calculated based on the current voltage value and the upper and lower voltage limits. Specifically, the constraint matrix and the upper and lower reactive power limits are as follows:

[0073] , and .

[0074] In step S500, the method for constructing the OSQP algorithm includes the following steps:

[0075] First, construct the standard form matrix of QP based on the optimization objective function:

[0076] ;

[0077] ;

[0078] Where P is the coefficient matrix of the quadratic term. The coefficient vector of the first-order term is then used, and the QP standard form matrix and constraint matrix are included together in the OSQP standard tool to obtain the reactive power change matrix. .

[0079] In step S600, the method for calculating the expected voltage value is as follows: based on the sensitivity coefficient Through the reactive power change matrix and sensitivity coefficient The voltage change can be calculated:

[0080]

[0081] Adding the calculated voltage change to the initial voltage value yields the expected voltage value. That is, the first The expected voltage of each voltage node is .

[0082] In summary, the online multi-objective optimization control method for voltage in a large-scale new energy base, as proposed in this invention, has the following characteristics:

[0083] This invention utilizes voltage level block control in the voltage control system of a large-scale new energy base to optimize reactive voltage control within each block, significantly reducing algorithm complexity and improving the efficiency of optimization calculations.

[0084] This invention first constructs a sensitivity coefficient to indicate the relationship between voltage and reactive power source reactance in different network structures, enabling clear and quantitative digitization of the network structure. Then, it assigns weights to each voltage node, constructing an overall minimum deviation function for all voltages with weighted information and upper and lower limit constraint matrices for voltage and reactive power. These constraint matrices indicate the constraints on different voltage levels within the system, while the weighted minimum deviation function implicitly reflects the performance requirements for different voltage nodes and reveals the critical voltage parameters for each node. Finally, by solving the matrices, the reactive power regulation targets and voltage targets for each reactive power source are obtained. Control commands are then issued through the network protocol to achieve reactive power and voltage regulation for each reactive power source.

[0085] This invention can handle situations where there are control targets at different voltage levels and multiple voltage nodes within a region, and can achieve the control effect of minimizing the overall voltage deviation within the region. It is more suitable for regions with complex grid structures, multiple voltage levels, and multiple power plants, such as large-scale new energy bases. It can significantly improve the overall voltage stability within the region and plays an important role in the overall power supply quality and power generation safety within the region.

[0086] Example 2

[0087] This embodiment provides an online multi-objective optimization control system for voltage in a large-scale new energy production base, used to execute an online multi-objective optimization control method for voltage in a large-scale new energy production base as described in Embodiment 1. The system includes a large-scale production base segmentation module, a sensitivity matrix construction module, an algorithm construction and constraint module, an OSQP calculation module, and an instruction execution module.

[0088] The large base segmentation module is used to segment the grid structure according to the voltage level to obtain several blocks containing reactive power sources and voltage nodes.

[0089] The sensitivity matrix construction module is used to obtain the sensitivity coefficients of reactive power sources and voltage nodes within the block, and construct a sensitivity matrix based on the sensitivity coefficients.

[0090] The algorithm construction and constraint module is used to construct a voltage weight matrix based on the assessment cost after the voltage nodes in the block deviate from the target value; construct a minimum deviation optimization function based on the voltage weight matrix and the sensitivity matrix; and construct a constraint matrix based on the voltage safety upper and lower limits of the voltage nodes in the block, the sensitivity matrix, and the reactive power output upper and lower limits of the reactive power source.

[0091] The OSQP calculation module calculates the reactive power change value of each reactive power source based on the OSQP algorithm, the minimum deviation optimization function, and the constraint matrix.

[0092] The instruction execution module is used to calculate the expected voltage value after each reactive power source executes according to the reactive power change value of each reactive power source; and to send the obtained expected voltage value to each reactive power source for reactive power regulation.

[0093] Example 3

[0094] This embodiment provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements a new energy large-scale base voltage online multi-objective optimization control method as described in Embodiment 1.

[0095] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0096] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A method for online multi-objective optimization control of voltage in a large-scale new energy base, characterized in that, include: The grid structure is divided into blocks according to the voltage level to obtain several blocks containing reactive power sources and voltage nodes. Obtain the sensitivity coefficients of reactive power sources and voltage nodes within the block, and construct a sensitivity matrix based on the sensitivity coefficients; A voltage weight matrix is ​​constructed based on the assessment costs of voltage nodes deviating from the target value within the block; a minimum deviation optimization function is constructed based on the voltage weight matrix and the sensitivity matrix. Based on the upper and lower limits of voltage safety of voltage nodes in the block, the sensitivity matrix, and the upper and lower limits of reactive power output of reactive power sources, a constraint matrix is ​​constructed. Based on the OSQP algorithm, the minimum deviation optimization function, and the constraint matrix, the reactive power change value of each reactive power source is calculated. Based on the reactive power change value of each reactive power source, the expected voltage value after each reactive power source operates according to this reactive power change value is calculated. The obtained expected voltage value is sent to each reactive power source for reactive power regulation.

2. The online multi-objective optimization control method for voltage in a large-scale new energy base according to claim 1, characterized in that, The grid structure is divided into blocks according to voltage levels, including: a block is the area from the main step-up substation or step-up substations at each level to the next step-up substation or power station at one or more voltage levels along the line.

3. The online multi-objective optimization control method for voltage in a large-scale new energy base according to claim 1, characterized in that, Obtain the sensitivity coefficients of reactive power sources and voltage nodes within the block, and construct a sensitivity matrix based on the sensitivity coefficients, including: Suppose there are n reactive power sources and m voltage nodes in the block, where the nth... The reactive power of each reactive power source With the Node voltage of each voltage node The relationship is the sensitivity coefficient. , Lockout All reactive power sources other than the individual reactive power source will reduce reactive power. From maximum inductive reactive power regulation to maximum capacitive reactive power, record the reactive power sources during the process. reactive power Change With all voltage nodes within the block node voltage Change Then there is a sensitivity coefficient. Various sensitivity coefficients The matrix formed This is the sensitivity matrix.

4. The online multi-objective optimization control method for voltage in a large-scale new energy base according to claim 1, characterized in that, The voltage weight matrix is ​​constructed based on the assessment costs of voltage nodes within the block after deviating from the target value. This includes: obtaining the assessment costs of each voltage node within the block after deviating from the target value; defining the voltage weight of each voltage node according to the assessment costs from high to low; and generating the voltage weight matrix based on the voltage weight of each voltage node.

5. The online multi-objective optimization control method for voltage in a large-scale new energy base according to claim 3, characterized in that, Constructing the minimum deviation optimization function includes: The objective function to be optimized is , ; in, Indicates the first Target values ​​for each voltage node Indicates the first The node voltage of each voltage node. Indicates the first Initial values ​​for each voltage node, Indicates the first The weight of each voltage node.

6. The online multi-objective optimization control method for voltage in a large-scale new energy base according to claim 5, characterized in that, Based on the voltage safety upper and lower limits and sensitivity matrix of the voltage nodes within the block, as well as the reactive power output upper and lower limits of the reactive power sources, a constraint matrix is ​​constructed, including: Based on the increase / decrease voltage of each voltage node and the increase / decrease reactive power of each reactive power source, construct the constraint matrix: ； The incremental reactive power of the nth reactive power source can be calculated by using the current reactive power value, the upper limit of reactive power, and the lower limit of reactive power. And can reduce power consumption The first voltage value is calculated based on the current voltage value, the upper voltage limit, and the lower voltage limit. Voltage boosting capability of each voltage node and voltage reduction .

7. The online multi-objective optimization control method for voltage in a large-scale new energy base according to claim 6, characterized in that, Based on the OSQP algorithm, the minimum deviation optimization function, and the constraint matrix, the reactive power change value of each reactive power source is calculated. Based on the reactive power change value of each reactive power source, the expected voltage value after execution according to this reactive power change value is calculated, including: Construct the QP standard form matrix based on the aforementioned optimization objective function: ； ； in, It is the coefficient matrix of the quadratic terms. For the coefficient vector of the first-order term, the standard form matrix of QP and the constraint matrix are included together in the OSQP standard tool to obtain the reactive power change matrix. ; The voltage change is calculated based on the sensitivity coefficient and the reactive power change matrix, expressed by the following formula: ； Adding the voltage change to the initial voltage value, we get the expected voltage value. That is, the first The expected voltage of each voltage node is .

8. The online multi-objective optimization control method for voltage in a large-scale new energy base according to claim 1, characterized in that, The reactive power sources are power plants, substations, and power consumption stations with adjustable reactive power within the block; The voltage nodes are all line nodes within the block that require voltage range constraints.

9. A voltage online multi-objective optimization control system for a large-scale new energy base, characterized in that, It includes a large-base partitioning module, a sensitivity matrix construction module, an algorithm construction and constraint module, an OSQP calculation module, and an instruction execution module: The large base segmentation module is used to segment the grid structure according to the voltage level to obtain several blocks containing reactive power sources and voltage nodes. The sensitivity matrix construction module is used to obtain the sensitivity coefficients of reactive power sources and voltage nodes within the block, and construct a sensitivity matrix based on the sensitivity coefficients. The algorithm construction and constraint module is used to construct a voltage weight matrix based on the assessment costs of voltage nodes deviating from the target value within the block; and to construct a minimum deviation optimization function based on the voltage weight matrix and the sensitivity matrix. It is also used to construct a constraint matrix based on the upper and lower limits of voltage safety of voltage nodes in the block, the sensitivity matrix, and the upper and lower limits of reactive power output of reactive power sources; The OSQP calculation module calculates the reactive power change value of each reactive power source based on the OSQP algorithm, the minimum deviation optimization function, and the constraint matrix. The instruction execution module is used to calculate the expected voltage value after each reactive power source executes according to the reactive power change value of each reactive power source; and to send the obtained expected voltage value to each reactive power source for reactive power regulation.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the online multi-objective optimization control method for voltage in a large-scale new energy production base as described in any one of claims 1 to 8.

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