Power regulation methods, devices, computer equipment, and storage media for converters

Abstract

This application relates to a power adjustment method, apparatus, computer equipment, and storage medium for a converter. The method includes: acquiring the current-side power of a converter in at least one microgrid connected to a distribution network; for each iteration, based on a power adjustment model and according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration, determining the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration; if the iteration stopping condition is not met in the current iteration round, using the current current-side power as the historical current-side power for the next iteration round, and using the current adjustment parameters as the historical adjustment parameters for the next iteration round, until the iteration stopping condition is met in the current iteration round, at which point the current current-side power is taken as the target current-side power of the corresponding converter. This method can accurately determine the converter power.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a power adjustment method, apparatus, computer device, and storage medium for a converter. Background Technology

[0002] With breakthroughs in key power electronics technologies, low-voltage flexible interconnection devices (VSCs) are widely used on the user side of distribution networks, forming a new architecture for distribution systems containing multiple microgrid clusters. This transformation has greatly improved the local consumption rate of new energy sources, but it has also increased the complexity of control and coordination of multiple microgrid clusters and distribution network systems.

[0003] As the core device for flexible interconnection of multiple microgrid areas, the VSC (Variable Residual Power Controller) effectively promotes power sharing and optimized configuration of flexible interconnected microgrid groups thanks to its rapid response characteristics and strong power flow regulation capabilities. This provides key technical support for achieving coordinated and optimized operation between microgrid groups and the distribution network. Specifically, in addition to meeting the power sharing needs within the microgrid group, the VSC can also upload excess energy to the distribution network for unified scheduling and management, further improving the overall energy efficiency of the system.

[0004] However, considering the multi-level and multi-entity characteristics of the new architecture of distribution systems containing multiple microgrids: on the one hand, the significant differences in operating characteristics and energy structure among different zones increase the complexity of coordinated control; on the other hand, the privacy and security issues of user-side microgrids are becoming increasingly prominent, placing higher demands on the security of control schemes. Therefore, how to fully couple the interactive characteristics of multiple microgrids and the distribution network, and design a multi-zone distributed control scheme to achieve safe, economical, and efficient coordinated operation of the system under this architecture, has become an important direction in the current research on the optimal scheduling of new energy distribution systems. Summary of the Invention

[0005] Therefore, it is necessary to provide a power adjustment method, apparatus, computer equipment, and storage medium for a converter that can accurately determine the converter power, in order to address the aforementioned technical problems.

[0006] In a first aspect, this application provides a power regulation method for a converter, comprising:

[0007] Obtain the current-side power of the converter in at least one microgrid connected to the distribution network;

[0008] For each iteration, based on the power adjustment model, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration are determined according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration. In the first iteration, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters.

[0009] If the iteration stop condition is not met in the current iteration round, the current current-side power is used as the historical current-side power in the next iteration process, and the current adjustment parameter is used as the historical adjustment parameter in the next iteration process, until the iteration stop condition is met in the current iteration round. The current current-side power is then used as the target current-side power of the corresponding converter. The iteration stop condition is that the target residual of all converters is less than the preset residual threshold or the current iteration round reaches the preset round threshold.

[0010] In one embodiment, based on the power adjustment model, and according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration are determined, including:

[0011] Based on the historical current-side power and historical adjustment parameters from the previous iteration, determine the current-side power in the current iteration.

[0012] For each converter, the target residual is determined based on the converter's historical current-side power and current current-side power.

[0013] Based on the target residual of the converter, the historical adjustment parameters of the converter are adjusted to obtain the current adjustment parameters of the converter;

[0014] Among them, historical current-side power includes historical AC-side power and historical DC-side power; current current-side power includes current AC-side power and current DC-side power; target residual includes dual residual and original residual; historical adjustment parameters include historical dual variables and historical penalty coefficients.

[0015] In one embodiment, determining the current current-side power in the current iteration process based on the historical current-side power and historical adjustment parameters from the previous iteration process includes:

[0016] For each converter, the historical DC-side power of the converter is adjusted based on the historical AC-side power and historical adjustment parameters to obtain the current DC-side power of the converter; and,

[0017] For each converter, the historical AC power of the converter is adjusted based on the historical DC power and historical adjustment parameters to obtain the current AC power of the converter.

[0018] In one embodiment, the target residual of the converter is determined based on the historical current-side power and the current current-side power of the converter, including:

[0019] The difference between the current AC-side power and the current DC-side power is used as the original residual of the converter; and,

[0020] The dual residual of the converter is determined based on the difference between the current DC-side power and the historical DC-side power, as well as the historical penalty coefficient.

[0021] In one embodiment, the historical adjustment parameters of the converter are adjusted based on the target residual of the converter to obtain the current adjustment parameters of the converter, including:

[0022] Based on the historical penalty coefficient, the original residuals, and the historical dual variables, the historical dual variables of the converter are adjusted to obtain the current dual variables of the converter; and,

[0023] Based on the relationship between the dual residual and the original residual, the historical penalty coefficient of the converter is adjusted to obtain the current penalty coefficient of the converter.

[0024] In one embodiment, the historical penalty coefficient of the converter is adjusted according to the magnitude relationship between the dual residual and the original residual to obtain the current penalty coefficient of the converter, including:

[0025] If the dual residual is greater than the product of the original residual and the preset compensation coefficient, the historical penalty coefficient is reduced to obtain the current penalty coefficient of the converter.

[0026] If the original residual is greater than the product between the dual residual and the preset compensation coefficient, the historical penalty coefficient is increased to obtain the current penalty coefficient of the converter.

[0027] If the dual residual is not greater than the product of the original residual and the preset compensation coefficient, and the original residual is not greater than the product of the dual residual and the preset compensation coefficient, the historical penalty coefficient is used as the current penalty coefficient of the converter.

[0028] Secondly, this application also provides a power regulation device for a converter, comprising:

[0029] The acquisition module is used to acquire the current-side power of the converter in at least one microgrid connected to the distribution network;

[0030] The determination module is used to determine the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration process based on the power adjustment model and the historical current-side power and historical adjustment parameters of each converter in the previous iteration process for each iteration process; in the first iteration process, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters;

[0031] The target module is used to take the current current-side power as the historical current-side power in the next iteration process and the current adjustment parameter as the historical adjustment parameter in the next iteration process when the iteration stop condition is not met in the current iteration round, until the current current-side power is taken as the target current-side power of the corresponding converter. The iteration stop condition is that the target residual of all converters is less than the preset residual threshold or the current iteration round reaches the preset round threshold.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0033] Obtain the current-side power of the converter in at least one microgrid connected to the distribution network;

[0034] For each iteration, based on the power adjustment model, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration are determined according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration. In the first iteration, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters.

[0035] If the iteration stop condition is not met in the current iteration round, the current current-side power is used as the historical current-side power in the next iteration process, and the current adjustment parameter is used as the historical adjustment parameter in the next iteration process, until the iteration stop condition is met in the current iteration round. The current current-side power is then used as the target current-side power of the corresponding converter. The iteration stop condition is that the target residual of all converters is less than the preset residual threshold or the current iteration round reaches the preset round threshold.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0037] Obtain the current-side power of the converter in at least one microgrid connected to the distribution network;

[0038] For each iteration, based on the power adjustment model, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration are determined according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration. In the first iteration, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters.

[0039] If the iteration stop condition is not met in the current iteration round, the current current-side power is used as the historical current-side power in the next iteration process, and the current adjustment parameter is used as the historical adjustment parameter in the next iteration process, until the iteration stop condition is met in the current iteration round. The current current-side power is then used as the target current-side power of the corresponding converter. The iteration stop condition is that the target residual of all converters is less than the preset residual threshold or the current iteration round reaches the preset round threshold.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0041] Obtain the current-side power of the converter in at least one microgrid connected to the distribution network;

[0042] For each iteration, based on the power adjustment model, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration are determined according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration. In the first iteration, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters.

[0043] If the iteration stop condition is not met in the current iteration round, the current current-side power is used as the historical current-side power in the next iteration process, and the current adjustment parameter is used as the historical adjustment parameter in the next iteration process, until the iteration stop condition is met in the current iteration round. The current current-side power is then used as the target current-side power of the corresponding converter. The iteration stop condition is that the target residual of all converters is less than the preset residual threshold or the current iteration round reaches the preset round threshold.

[0044] The aforementioned converter power adjustment method, device, computer equipment, and storage medium acquire the current-side power of converters in multiple microgrids connected to the distribution network. Employing an iterative power adjustment model, in each iteration, the current power, adjustment parameters, and target residual are dynamically calculated based on the historical power and adjustment parameters from the previous iteration. The method uses residual convergence or the upper limit of the iteration count as stopping conditions, ultimately outputting the optimized target current-side power. This method not only achieves coordinated and adaptive adjustment of converter power in multi-microgrid systems, improving the overall system's economy and stability, but also effectively protects the local data privacy of each microgrid through distributed iterative computation, reducing reliance on centralized control and global communication, and enhancing the system's scalability and robustness. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is an application environment diagram of a power adjustment method for a converter provided in this embodiment;

[0047] Figure 2 This is a flowchart illustrating the first power adjustment method for a converter provided in this embodiment;

[0048] Figure 3 A flowchart illustrating the first parameter determination step provided in this embodiment;

[0049] Figure 4 A flowchart illustrating the second parameter determination step provided in this embodiment;

[0050] Figure 5 This is a structural block diagram of a power adjustment device for a converter provided in this embodiment;

[0051] Figure 6 This is an internal structural diagram of a computer device provided in this embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] The power adjustment method for converters provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. The computer equipment acquires the current-side power of at least one converter in a microgrid connected to the distribution network. For each iteration, based on the power adjustment model, according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration are determined. In the first iteration, the historical current-side power is used as the current-side power, and the historical adjustment parameters are preset adjustment parameters. If the iteration stop condition is not met in the current iteration round, the current current-side power is used as the historical current-side power in the next iteration round, and the current adjustment parameters are used as the historical adjustment parameters in the next iteration round, until the iteration stop condition is met in the current iteration round, at which point the current current-side power is used as the target current-side power of the corresponding converter. The iteration stop condition is that the target residuals of all converters are less than a preset residual threshold or the current iteration round reaches a preset round threshold. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0054] In one exemplary embodiment, such as Figure 2 As shown, a power regulation method for a converter is provided, which can be applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps S201 to S203. Wherein:

[0055] S201 obtains the current-side power of the converter in at least one microgrid connected to the distribution network.

[0056] A microgrid is a small-scale power generation and distribution system composed of distributed power sources (such as photovoltaic and wind power), energy storage devices, loads, and monitoring and protection devices, capable of self-control, protection, and management. It can operate in parallel with an external distribution network or, as needed, operate independently from the main grid (islanding operation) during planned operations or in case of faults. In the scenario of this invention, multiple such microgrids are flexibly interconnected with each other and with the main power grid (distribution network) through power electronic converters (VSCs), forming a "multi-microgrid cluster" system to improve energy utilization efficiency, reliability, and the capacity for renewable energy absorption.

[0057] Among them, the converter is a fully controlled power electronic conversion device. As the core interface device connecting AC systems (such as distribution networks) and DC systems (such as the DC bus of a microgrid or another AC / DC system), it can independently control the active and reactive power on both its AC and DC sides. Its core functions are to achieve bidirectional energy flow, precise power control, and provide the necessary voltage support for the system. In the "multi-microgrid cluster-distribution network" architecture, the VSC (Variable Voltage Controller) is the key physical link for achieving flexible interconnection, power sharing, and collaborative optimization between microgrids and between microgrids and the distribution network.

[0058] This refers to the active power (which usually also includes reactive power, but the optimization model often focuses on active power) at the instantaneous or scheduled time period through the AC or DC port of the converter.

[0059] In some embodiments, this embodiment can construct a mathematical model of the corresponding converter based on at least one converter in a microgrid connected to the distribution network, so as to determine the current-side power of the converter in at least one microgrid connected to the distribution network.

[0060] It should be noted that this embodiment can also obtain parameter information of the target flexible interconnected distribution network, new energy equipment, load, etc., specifically including: the topology and line parameters of the flexible interconnected distribution network, the access location and predicted output of wind power and photovoltaic at each time, the installation location, maximum capacity and maximum power of energy storage and voltage source converter (VSC), and the predicted output of the load at each time.

[0061] For example, to ensure the economical operation of the distribution network, the operation of the distribution network must satisfy the objective function of minimizing network losses. As shown in the following formula (1):

[0062] (1)

[0063] in, To optimize variables, This indicates an optimized scheduling period. Indicates distribution network lines, This represents the resistance of the i-th line. This represents the active power transmitted on the i-th line at time t. This represents the reactive power transmitted on the i-th line at time t.

[0064] For example, to ensure the safe operation of the distribution network, the line transmission power of the distribution network needs to meet its safety domain. The node injection power of the distribution network is shown in the following formulas (2) and (3):

[0065] (2)

[0066] (3)

[0067] in, This represents the amount of active power injected into node i at time t. This represents the active load of node i at time t. This represents the active power flowing into the VSC connected to node i at time t. This represents the set containing all nodes of the microgrid group; This represents the amount of reactive power injected into node i at time t. This represents the reactive load of node i at time t. This represents the reactive power flowing into the VSC connected to node i at time t.

[0068] For example, the compact matrix form of the active / reactive power injection at the nodes is shown in the following formulas (4) and (5):

[0069] (4)

[0070] (5)

[0071] in, , These represent the column vectors of active power and reactive power injected into the node at time t, respectively. , These are the column vectors of active and reactive loads at node time t, respectively. , These are the active power column vector and reactive power column vector flowing into the AC side of the VSC at time t, respectively. This is the node-VSC correlation matrix, where N represents the number of nodes in the distribution network. This represents the number of VSCs in the distribution network. If the j-th VSC device is connected to the i-th node, then... The element in the i-th row and j-th column is 1, and all other elements are 0.

[0072] For example, the line transmission power can be expressed as shown in formulas (6) and (7):

[0073] (6)

[0074] (7)

[0075] in, Let represent the association matrix between nodes and lines. If the j-th line flows out from node i, then... The element in the i-th row and j-th column is 1; if the j-th line flows in from node i, then The element in the i-th row and j-th column is -1, and all other elements are 0; , These represent the active power transmitted through the line and the reactive power transmitted through the line, respectively.

[0076] For example, the line transmission power must meet certain capacity constraints, as shown in formula (8):

[0077] (8)

[0078] Where | represents the magnitude of the orientation quantity, This represents the column vector representing the maximum capacity of the line.

[0079] Each node in the distribution network must meet voltage safety constraints, as shown in formulas (9) and (10):

[0080] (9)

[0081] (10)

[0082] in, Represents the column vector of voltages at each node; , The correlation matrix representing nodes and lines. The `diag()` function extracts elements from a given matrix and uses them as a diagonal matrix. These represent the resistances of lines 1 to N, respectively. Inject an active power column vector into the node at time t; , , These represent the impedances from line 1 to line N, respectively. Inject an active power column vector into the node at time t; This is the voltage reference value. It is a column vector consisting entirely of 1s; The node voltage column vector, , These are the column vectors for the minimum and maximum node voltages, respectively.

[0083] Flexible interconnect devices (VSCs) need to meet capacity constraints, as shown in formula (11):

[0084] (11)

[0085] in, , Let be the column vectors of active power and reactive power flowing into the AC side of the VSC at time t, respectively. This is the column vector of maximum capacity for VSC.

[0086] The capacity of the distribution transformers in the distribution network area must meet certain constraints, as shown in formula (12):

[0087] (12)

[0088] in, , These represent the column vectors of active power and reactive power injected into the node at time t, respectively. This is the column vector representing the maximum capacity of the distribution transformers in the area.

[0089] For example, multiple distribution substations in a distribution network are interconnected through VSC devices to form a flexible interconnected microgrid group. Assuming the distribution network contains M microgrid groups, and the operating objective of each microgrid group is optimal economic efficiency, then the objective function of the m-th microgrid group is... As shown in formula (13):

[0090] (13)

[0091] As shown in formula (13), To optimize variables, This represents the active power purchased by node i from the distribution network at time t. This represents the active power output of the i-th energy storage device at time t. This indicates an optimized scheduling period. Let represent the set of all nodes in the m-th microgrid group. Indicates the electricity purchase price. This indicates taking the positive part of x. If x is a positive number, then... If x is negative, then ; This indicates the electricity price. Let |x| represent the unit power operating cost of energy storage, where |x| represents the absolute value of x.

[0092] For example, in a flexible interconnected microgrid cluster, each node is connected to loads, new energy sources such as wind power, photovoltaics, and energy storage, and is connected to the upper distribution network via VSC equipment. Therefore, the node injection power can be expressed as shown in formula (14):

[0093] (14)

[0094] in, This represents the column vector of injected power at nodes in the m-th microgrid group. , , Let them represent: the node-PV correlation matrix, the node-Wind power correlation matrix, and the node-energy storage correlation matrix in the m-th microgrid group, respectively. For example, if the j-th photovoltaic device is connected to node i, then the matrix... The element in the i-th row and j-th column is 1, and the others are 0; , , , , These represent column vectors for photovoltaic power output, wind power output, VSC node injected power, energy storage charging power, and load power, respectively.

[0095] The line transmission power can be derived from the node injection power as shown in formulas (15) and (16):

[0096] (15)

[0097] (16)

[0098] in, This represents the column vector of DC line transmission power in the microgrid group at time t. To augment the node-line correlation matrix, , Let represent the correlation matrix between nodes and lines in the m-th microgrid group. If the j-th line flows out from node i, then... The element in the i-th row and j-th column is 1; if the j-th line flows in from node i, then The element in the i-th row and j-th column is -1, and all other elements are 0; This represents the augmented node injection power column vector. , Inject power into the first node; , These represent the column vectors for the minimum and maximum transmission capacity of the line, respectively.

[0099] The node voltages are expressed as shown in the following formulas (17) and (18):

[0100] (17)

[0101] (18)

[0102] in, Represents the column vector of voltages at each DC node; To augment the resistance matrix, , The correlation matrix representing nodes and lines. The `diag()` function extracts elements from a given matrix and uses them as a diagonal matrix. These represent the resistances of lines 1 to N, respectively. This represents the column vector of augmented node injection power at time t; To augment the resistance matrix, , , These represent the impedances from line 1 to line N, respectively. Inject a reactive power column vector into the node at time t; This is the voltage reference value. It is a column vector of all 1s, where n represents the number of DC nodes in the microgrid group; The node voltage column vector, , These are the column vectors for the minimum and maximum node voltages, respectively.

[0103] In microgrid cluster systems, the operation of energy storage devices is subject to certain limitations, as shown in the following formulas (19)-(22):

[0104] (19)

[0105] (20)

[0106] (twenty one)

[0107] (twenty two)

[0108] in, This represents the column vector of energy storage charging power at time t, with charging as the positive direction; , These represent the minimum and maximum output column vectors of the energy storage, respectively. , Let be the column vectors of energy storage capacity at time t+1 and time t, respectively. The self-discharge coefficient of energy storage. This is the charging efficiency coefficient. Let be the column vector of energy storage charging power at time t+1. This refers to the charging interval; , These are the column vectors representing the minimum and maximum energy storage capacities. This represents the column vector of energy storage capacity at the final moment of optimized scheduling. This is the column vector of energy storage capacity at the initial scheduling time.

[0109] For example, the coupling between the microgrid and the distribution network must satisfy the power balance constraint, as shown in the following formula (23):

[0110] (twenty three)

[0111] in, This represents the column vector of active power purchased from the distribution network at time t. Inject the power column vector of the DC node into the VSC. Let t be the column vector of active power load at each node.

[0112] For example, the centralized collaborative operation model of a distribution network containing multiple microgrid groups is established as shown in the following formula (24):

[0113] (twenty four)

[0114] in, This represents the set of optimization variables for all microgrids. The objective function and constraints of this model combine the optimization objectives and constraints of the upper-level distribution network and the lower-level multi-microgrid group. The weights are dynamic, automatically reassessing the contribution of the distribution network and microgrids to the overall objective based on the optimization results, and adapting to the dynamic weights. This is used to optimize and obtain the globally optimal solution.

[0115] For each iteration, S202, based on the power adjustment model, determines the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration.

[0116] In the first iteration, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters.

[0117] The power adjustment model refers to the mathematical optimization framework or computational rules used to guide the updating of converter power and adjustment parameters in each iteration. Essentially, this model is the core iterative step of a distributed optimization algorithm, typically built upon the principles of the augmented Lagrange method or the alternating direction multiplier method. It specifies how to utilize the "historical" information (historical power, historical parameters) from the previous round to calculate the "current" output (current power, current parameters, and residuals) of the current round, in order to progressively approximate the optimal power allocation that satisfies the global constraints of the system.

[0118] The historical current-side power refers to the converter power value calculated at the end of the previous iteration (round k-1) and used as the input condition for the current round (round k). Specifically, it includes the historical AC-side power and historical DC-side power for each VSC. In the first iteration (k=1), this value is taken from the initially acquired actual measurement or the initial setpoint.

[0119] Among them, historical adjustment parameters refer to the key internal variables calculated and saved in the previous iteration (k-1th iteration) and used for algorithm adjustment in the current iteration. These mainly include: Historical dual variables: Lagrange multipliers corresponding to system coupling constraints (such as AC / DC power balance), reflecting the "price" or "pressure" of the constraint in historical iterations; Historical penalty coefficient: A positive scalar parameter used to control the penalty for constraint violations, directly affecting the convergence speed and stability of the algorithm.

[0120] The current current-side power, in the context of the iteration process, refers to the converter power value calculated latest in the current iteration (round k) based on the power adjustment model, historical power, and historical parameters. It is an intermediate result of this iteration, specifically including the current AC-side power and current DC-side power of each VSC.

[0121] The current adjustment parameter, in the iterative process, refers to the latest value obtained in the current iteration (the k-th iteration) after updating the historical adjustment parameters based on the newly calculated target residual. It mainly includes: the current dual variable: the dual multiplier updated based on the original residual and the historical penalty coefficients; and the current penalty coefficient: the new penalty coefficient obtained by adjusting the dual residual according to a preset rule based on the comparison between the magnitudes of the dual residual and the original residual.

[0122] The target residual is a key error metric used to evaluate the quality of the current iterative solution and the convergence status of the algorithm. It is the core criterion for deciding whether to continue the iteration. It includes: Original residual: typically defined as the difference between the current AC power and the current DC power, it directly measures the degree to which the core equality constraint of power balance on both sides of the converter is satisfied. Dual residual: typically defined as the norm of the difference (or related quantity) between the current DC power and the historical DC power, weighted by a penalty coefficient. It measures the magnitude of change in the converter power solution between two successive iterations, reflecting the gradient of the dual variable or the stability of the algorithm.

[0123] In some embodiments, for each iteration, the historical current-side power and historical adjustment parameters of each converter in the previous iteration are input into the power adjustment model. The power adjustment model analyzes the historical current-side power and historical adjustment parameters of each converter in the previous iteration to determine the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration.

[0124] If the iteration stop condition is not met in the current iteration round, S203 will use the current current-side power as the historical current-side power in the next iteration process and the current adjustment parameter as the historical adjustment parameter in the next iteration process until the iteration stop condition is met in the current iteration round, and then use the current current-side power as the target current-side power of the corresponding converter.

[0125] In some embodiments, the iteration stopping condition can be that the target residuals of all converters are less than a preset residual threshold or the current iteration round reaches a preset round threshold. Specifically, if the convergence condition is met, the output can be... , The convergence condition can be expressed as: ; where, in the formula, This represents a very small number. If the number of iterations k exceeds the maximum number of iterations k_max, the iteration process can be forcibly terminated, and the output should be... , .

[0126] It should be noted that in this embodiment, the solver can also solve the proposed distributed alternating solution model for the coordinated operation of distribution networks containing multiple microgrids based on the distributed alternating solution algorithm for the coordinated operation of distribution networks containing multiple microgrids. This will obtain the DC-side output power column vector and AC-side output power column vector of the VSC, as well as the energy storage output of the lower-level multiple microgrids, thereby realizing the distributed coordinated optimization operation of multiple microgrids and the distribution network.

[0127] The aforementioned converter power adjustment method acquires the current-side power of converters in multiple microgrids connected to the distribution network. Employing an iterative power adjustment model, it dynamically calculates the current power, adjustment parameters, and target residual in each iteration based on the historical power and adjustment parameters from the previous iteration. The method uses residual convergence or the upper limit of the iteration count as stopping conditions, ultimately outputting the optimized target current-side power. This method not only achieves coordinated and adaptive adjustment of converter power in multi-microgrid systems, improving the overall system's economy and stability, but also effectively protects the local data privacy of each microgrid through distributed iterative computation, reducing reliance on centralized control and global communication, and enhancing the system's scalability and robustness.

[0128] Figure 3 This is a flowchart illustrating the parameter determination steps in one embodiment. This embodiment refines the steps described in the previous embodiment, which, based on the power adjustment model, determine the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration. The steps include the following:

[0129] S301 determines the current current-side power in the current iteration based on the historical current-side power and historical adjustment parameters from the previous iteration.

[0130] Among them, historical current-side power includes historical AC-side power and historical DC-side power; current current-side power includes current AC-side power and current DC-side power; target residual includes dual residual and original residual; historical adjustment parameters include historical dual variables and historical penalty coefficients.

[0131] In some embodiments, based on the power determination model, the current current-side power in the current iteration is determined according to the historical current-side power and historical adjustment parameters in the previous iteration.

[0132] In some embodiments, for each converter, the historical DC-side power of the converter is adjusted according to each historical AC-side power and historical adjustment parameters to obtain the current DC-side power of the converter; and for each converter, the historical AC-side power of the converter is adjusted according to each historical DC-side power and historical adjustment parameters to obtain the current AC-side power of the converter.

[0133] For example, some parameters of the lower-level microgrid group are transmitted to the upper-level distribution network, which will affect the iterative optimization scheduling results of the upper-level distribution network; based on the parameters transmitted from the lower level, the upper-level distribution network model can be iteratively solved, i.e.

[0134] Based on the following formula (25), the historical DC power of the converter is adjusted according to the historical AC power and historical adjustment parameters to obtain the current DC power of the converter.

[0135] (25)

[0136] in, The weight is dynamically updated for the kth time. Let be the dual multiplier in the k-th iteration of the m-th microgrid group. This represents the column vector of active power output on the AC side of all VSCs connected to the m-th microgrid in the upper-level distribution network. This is the column vector of active power output on the DC side of the VSC in the m-th microgrid obtained in the k-th iteration. Let be the penalty coefficient for the k-th iteration in the m-th microgrid group. This represents calculating the square of the second norm of a vector.

[0137] For example, the iterative optimization results of the upper-level distribution network are passed to the lower-level microgrid group as known parameters. The lower-level microgrid group realizes the optimized scheduling of its internal system based on this. The iterative solution model of the m-th lower-level microgrid group is as follows (26). Based on the following formula (26), the historical AC-side power of the converter is adjusted according to the historical DC-side power and historical adjustment parameters to obtain the current AC-side power of the converter.

[0138] (26)

[0139] in, This represents the column vector of active power output on the DC side of all VSCs connected to the m-th microgrid in the upper-level distribution network. This is the column vector of active power output on the AC side of the VSC in the m-th microgrid obtained in the k-th iteration.

[0140] It should be noted that, in order to effectively solve the proposed distributed alternating model for the coordinated operation of distribution networks containing multiple microgrids, a distributed alternating solution algorithm for the coordinated operation of distribution networks containing multiple microgrids must be constructed. In the first iteration, this embodiment initializes the AC-side active power output column vector and the DC-side active power output column vector of the VSC, i.e.: ; .in, , Let represent the initial values ​​of the active power output column vectors on the AC and DC sides of the VSC in the m-th microgrid group, respectively. Set the initial duality factor. Penalty coefficient Weighting coefficient Set the initial iteration count k=0.

[0141] S302 determines the target residual for each converter based on its historical current-side power and current current-side power.

[0142] In some embodiments, the target residual of the converter is determined based on a preset residual determination formula, according to the historical current-side power and the current-side power of the converter.

[0143] In some embodiments, the difference between the current AC-side power and the current DC-side power is used as the original residual of the converter; and the dual residual of the converter is determined based on the difference between the current DC-side power and the historical DC-side power, and the historical penalty coefficient.

[0144] For example, based on , Alternatively, using the results of the previous iteration, solve the upper-level distribution network model to obtain the optimization parameters. Solve the lower-level microgrid group model separately. , to obtain optimized parameters Calculate the original residuals. With dual residual Based on the following formula (27):

[0145] (27)

[0146] S303 adjusts the historical adjustment parameters of the converter based on the target residual of the converter to obtain the current adjustment parameters of the converter.

[0147] In some embodiments, based on the parameter determination model, the historical adjustment parameters of the converter are adjusted according to the target residual of the converter to obtain the current adjustment parameters of the converter.

[0148] In the above embodiment, by sequentially executing a closed-loop process of "power update - residual calculation - parameter adaptive adjustment" in each iteration, the current power of each converter is first updated collaboratively based on historical states. Then, the original residual and dual residual are calculated in real time based on power changes to accurately evaluate the optimization state. Finally, key parameters such as the penalty coefficient and dual variables are dynamically and adaptively adjusted based on the residuals. This design achieves simultaneous and mutually reinforcing power optimization and parameter tuning, which not only improves the convergence speed and numerical stability of the distributed collaborative algorithm, but also autonomously balances the target conflicts between each microgrid and the distribution network during the iteration process. Thus, while ensuring convergence accuracy, it significantly enhances the algorithm's adaptability to different operating conditions and boundary conditions, effectively improving the overall efficiency and robustness of multi-microgrid system collaborative optimization.

[0149] Figure 4 This is a flowchart illustrating the parameter determination steps in one embodiment. This embodiment refines the steps described in the previous embodiment, which involve adjusting the historical adjustment parameters of the converter based on the target residual to obtain the current adjustment parameters of the converter. The steps include the following:

[0150] S401 adjusts the historical dual variables of the converter based on the historical penalty coefficient, the original residual, and the historical dual variables to obtain the current dual variables of the converter.

[0151] In some embodiments, based on the following formula (28), the historical dual variable of the converter is adjusted according to the historical penalty coefficient, the original residual and the historical dual variable to obtain the current dual variable of the converter.

[0152] (28)

[0153] S402 adjusts the historical penalty coefficient of the converter based on the relationship between the dual residual and the original residual to obtain the current penalty coefficient of the converter.

[0154] In some embodiments, when the dual residual is greater than the product of the original residual and the preset compensation coefficient, the historical penalty coefficient is reduced to obtain the current penalty coefficient of the converter; when the original residual is greater than the product of the dual residual and the preset compensation coefficient, the historical penalty coefficient is increased to obtain the current penalty coefficient of the converter; when the dual residual is not greater than the product of the original residual and the preset compensation coefficient, and the original residual is not greater than the product of the dual residual and the preset compensation coefficient, the historical penalty coefficient is used as the current penalty coefficient of the converter.

[0155] In some embodiments, based on the following formula (29), the historical penalty coefficient of the converter is adjusted according to the relationship between the dual residual and the original residual to obtain the current penalty coefficient of the converter.

[0156] (29)

[0157] in, Represents the adaptive coefficient. To adjust the coefficient, it is usually taken as... , .

[0158] In some embodiments, closed-loop intelligent tuning of the core parameters of the distributed collaborative optimization algorithm is achieved by adaptively updating the dual variables based on the original residuals and historical penalty coefficients, and dynamically adjusting the penalty coefficients based on the relative magnitudes of the dual and original residuals. This mechanism can automatically identify the imbalance between equality constraint relaxation and the target gradient during the current optimization process, thereby coordinating the convergence speed and convergence accuracy in real time during algorithm iteration: when the dual residuals are dominant, the penalty coefficient is reduced to accelerate the update of the dual variables and improve convergence efficiency; when the original residuals are dominant, the penalty coefficient is increased to strengthen the satisfaction of equality constraints. This effectively avoids problems such as oscillation, divergence, or slow convergence that may be caused by traditional fixed-parameter methods, and significantly improves the adaptability, stability, and overall optimization performance of the algorithm in complex multi-microgrid collaborative operation scenarios.

[0159] It should be noted that, in order to further obtain the global optimal solution for micro-allocation cooperative operation, the objective function weights are dynamically updated. Define the contributions of distribution network targets and multi-microgrid targets respectively. , As shown in the following formula (30):

[0160] (30)

[0161] in, This is the set of optimization variables for all microgrids.

[0162] It should be noted that the actual objective function percentage is calculated in the k-th iteration. for: Define candidate adjustment values ​​for the current weights. : ,in, To correct the step size, This is a pre-set adjustment coefficient. To avoid oscillations in the distributed algorithm caused by weight jumps, the candidate adjustment values ​​are... Perform exponential smoothing: ;in, This refers to a pre-defined smoothing coefficient. Projecting the smoothed weights onto the effective range yields dynamically updated weights. : ;in, This is the projection operator.

[0163] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0164] Based on the same inventive concept, this application also provides a power adjustment device for implementing the power adjustment method of the converter described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the power adjustment device for the converter provided below can be found in the limitations of the power adjustment method for the converter described above, and will not be repeated here.

[0165] In one exemplary embodiment, such as Figure 5 As shown, a power adjustment device for a converter is provided, comprising: an acquisition module 501, a determination module 502, and a target module 503, wherein:

[0166] The acquisition module 501 is used to acquire the current-side power of the converter in at least one microgrid connected to the distribution network;

[0167] The determination module 502 is used to determine the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration process based on the power adjustment model and the historical current-side power and historical adjustment parameters of each converter in the previous iteration process for each iteration process; in the first iteration process, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters;

[0168] The target module 503 is used to take the current current-side power as the historical current-side power in the next iteration process and the current adjustment parameter as the historical adjustment parameter in the next iteration process when the iteration stop condition is not met in the current iteration round, until the current iteration round meets the iteration stop condition, and the current current-side power is taken as the target current-side power of the corresponding converter; the iteration stop condition is that the target residual of all converters is less than the preset residual threshold or the current iteration round reaches the preset round threshold.

[0169] In some embodiments, the determining module 502 is further configured to determine the current current-side power in the current iteration process based on the historical current-side power and historical adjustment parameters in the previous iteration process; for each converter, determine the target residual of the converter based on the historical current-side power and current current-side power of the converter; adjust the historical adjustment parameters of the converter based on the target residual of the converter to obtain the current adjustment parameters of the converter; wherein, the historical current-side power includes the historical AC-side power and the historical DC-side power; the current current-side power includes the current AC-side power and the current DC-side power; the target residual includes the dual residual and the original residual; and the historical adjustment parameters include the historical dual variable and the historical penalty coefficient.

[0170] In some embodiments, the determining module 502 is further configured to, for each converter, adjust the historical DC-side power of the converter according to each historical AC-side power and historical adjustment parameters to obtain the current DC-side power of the converter; and, for each converter, adjust the historical AC-side power of the converter according to each historical DC-side power and historical adjustment parameters to obtain the current AC-side power of the converter.

[0171] In some embodiments, the determining module 502 is further configured to use the difference between the current AC side power and the current DC side power as the original residual of the converter; and to determine the dual residual of the converter based on the difference between the current DC side power and the historical DC side power, and the historical penalty coefficient.

[0172] In some embodiments, the determining module 502 is further configured to adjust the historical dual variable of the converter according to the historical penalty coefficient, the original residual and the historical dual variable to obtain the current dual variable of the converter; and to adjust the historical penalty coefficient of the converter according to the magnitude relationship between the dual residual and the original residual to obtain the current penalty coefficient of the converter.

[0173] In some embodiments, the determining module 502 is further configured to: decrease the historical penalty coefficient to obtain the current penalty coefficient of the converter when the dual residual is greater than the product of the original residual and the preset compensation coefficient; increase the historical penalty coefficient to obtain the current penalty coefficient of the converter when the original residual is greater than the product of the dual residual and the preset compensation coefficient; and use the historical penalty coefficient as the current penalty coefficient of the converter when the dual residual is not greater than the product of the original residual and the preset compensation coefficient, and the original residual is not greater than the product of the dual residual and the preset compensation coefficient.

[0174] The various modules in the power regulation device of the aforementioned converter can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0175] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a power regulation method for an inverter.

[0176] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0177] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0178] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0179] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0180] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0181] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power adjustment method for a converter, characterized in that, The method includes: Obtain the current-side power of the converter in at least one microgrid connected to the distribution network; For each iteration, based on the power adjustment model, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration are determined according to the historical current-side power and historical adjustment parameters of each converter in the previous iteration. In the first iteration, the historical current-side power is the current-side power, and the historical adjustment parameters are the preset adjustment parameters. If the iteration stop condition is not met in the current iteration round, the current current-side power is used as the historical current-side power in the next iteration process, and the current adjustment parameter is used as the historical adjustment parameter in the next iteration process, until the current iteration round meets the iteration stop condition, and the current current-side power is used as the target current-side power of the corresponding converter; the iteration stop condition is that the target residual of all converters is less than the preset residual threshold or the current iteration round reaches the preset round threshold.

2. The method according to claim 1, characterized in that, The power adjustment model determines the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration based on the historical current-side power and historical adjustment parameters of each converter in the previous iteration, including: Based on the historical current-side power and historical adjustment parameters from the previous iteration, determine the current-side power in the current iteration. For each converter, the target residual is determined based on the historical current-side power and current current-side power of the converter. Based on the target residual of the converter, the historical adjustment parameters of the converter are adjusted to obtain the current adjustment parameters of the converter; Wherein, the historical current-side power includes historical AC-side power and historical DC-side power; the current current-side power includes current AC-side power and current DC-side power; the target residual includes dual residual and original residual; and the historical adjustment parameters include historical dual variables and historical penalty coefficients.

3. The method according to claim 2, characterized in that, The step of determining the current current-side power in the current iteration process based on the historical current-side power and historical adjustment parameters from the previous iteration process includes: For each converter, the historical DC-side power of the converter is adjusted based on the historical AC-side power and historical adjustment parameters to obtain the current DC-side power of the converter; and, For each converter, the historical AC power of the converter is adjusted according to the historical DC power and historical adjustment parameters to obtain the current AC power of the converter.

4. The method according to claim 2, characterized in that, The step of determining the target residual of the converter based on its historical current-side power and current current-side power includes: The difference between the current AC-side power and the current DC-side power is used as the original residual of the converter; and, The dual residual of the converter is determined based on the difference between the current DC-side power and the historical DC-side power, and the historical penalty coefficient.

5. The method according to claim 2, characterized in that, The step of adjusting the historical adjustment parameters of the converter based on the target residual of the converter to obtain the current adjustment parameters of the converter includes: Based on the historical penalty coefficient, the original residual, and the historical dual variable, the historical dual variable of the converter is adjusted to obtain the current dual variable of the converter; and, Based on the relationship between the dual residual and the original residual, the historical penalty coefficient of the converter is adjusted to obtain the current penalty coefficient of the converter.

6. The method according to claim 5, characterized in that, The step of adjusting the historical penalty coefficient of the converter based on the relationship between the dual residual and the original residual to obtain the current penalty coefficient of the converter includes: If the dual residual is greater than the product of the original residual and the preset compensation coefficient, the historical penalty coefficient is reduced to obtain the current penalty coefficient of the converter. If the original residual is greater than the product of the dual residual and the preset compensation coefficient, the historical penalty coefficient is increased to obtain the current penalty coefficient of the converter. If the dual residual is not greater than the product of the original residual and the preset compensation coefficient, and the original residual is not greater than the product of the dual residual and the preset compensation coefficient, the historical penalty coefficient is used as the current penalty coefficient of the converter.

7. A power adjustment device for a converter, characterized in that, The device includes: The acquisition module is used to acquire the current-side power of the converter in at least one microgrid connected to the distribution network; The determination module is used to determine, for each iteration process, the current current-side power, current adjustment parameters, and target residual of the corresponding converter in the current iteration process based on the power adjustment model and the historical current-side power and historical adjustment parameters of each converter in the previous iteration process; in the first iteration process, the historical current-side power is the current-side power, and the historical adjustment parameters are preset adjustment parameters; The target module is used to use the current current-side power as the historical current-side power in the next iteration process and the current adjustment parameter as the historical adjustment parameter in the next iteration process when the current iteration does not meet the iteration stop condition, until the current iteration meets the iteration stop condition, and then use the current current-side power as the target current-side power of the corresponding converter; the iteration stop condition is that the target residual of all converters is less than a preset residual threshold or the current iteration reaches a preset threshold.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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