Voltage optimization method, device, equipment and medium for wind power access power system

Abstract

The invention discloses a voltage optimization method, device, equipment and medium for a wind power access power system, and relates to the field of voltage optimization control, and the key points of the technical scheme are as follows: obtaining parameter information of a fan, a first topological relation of a wind power plant and a second topological relation of a wind power plant group; according to the parameter information of the fans and the second topological relation, constructing a distributed optimization model of the wind power plant group; constructing a centralized distributed optimization model of the wind power plant according to the parameter information of the fans and the first topological relation; constructing an upper-layer voltage optimization model and a lower-layer voltage optimization model based on the decentralized distributed optimization model and the centralized distributed optimization model; and performing adaptive optimization on a penalty factor of the asynchronous alternating direction multiplier method by using a multi-objective function, and under a preset constraint condition, solving the upper-layer voltage optimization model and the lower-layer voltage optimization model by using the asynchronous alternating direction multiplier method after adaptive optimization of the penalty factor to obtain a control instruction of a voltage optimal scheme. And controlling voltage output of the wind power plant group based on the control instruction.

Description

Technical Field

[0001] The present application relates to the field of voltage optimization control of wind power clusters and wind power access to power systems, and more specifically, it relates to voltage optimization methods, devices, equipment and media for wind power access to power systems. Background Art

[0002] Wind farms (WF) have become the focus of new energy development due to their zero carbon emissions and renewability. From a cost-effectiveness perspective, voltage source converter high voltage direct current (VSC-HVDC) is considered the preferred transmission technology for long-distance wind farms. As the scale and number of wind farms continue to increase, their impact on the power system is also increasing. The intermittent and uncertain nature of wind power generation poses a major challenge to transmission system operators. In order to ensure system voltage stability, grid operation control requires wind farms to have certain voltage and reactive power support capabilities. Therefore, in order to alleviate voltage fluctuations at the wind turbine (WT) end, it is necessary to develop more efficient and reasonable voltage control strategies for increasingly large-scale wind farm clusters (WFC).

[0003] In response to the above problems, many control schemes have been proposed to effectively regulate the voltage and reactive power of wind farm groups. However, the existing methods mainly focus on synchronous updates, without considering the control paralysis caused by communication delays or node failures between wind turbine controllers and wind farm controllers. In addition, the study did not comprehensively analyze the characteristics of wind farm groups and the advantages and disadvantages of decentralized control and global control. It adopted a single-layer distributed control, or a two-layer distributed control with both decentralized and global architectures, and did not select the best control method. For example, global control can achieve global optimization and easy maintenance, but has poor fault response capabilities. Decentralized control has high flexibility and strong anti-interference ability, but high cost and poor global problem handling capabilities. Therefore, it is impossible to provide a reasonable voltage and reactive power control strategy for wind farm groups. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for optimizing the voltage of wind power access to a power system. The present application considers the characteristics of a wind farm group and the advantages and disadvantages of decentralized control and global control to establish an upper and lower two-layer voltage optimization model, and uses an asynchronous alternating direction multiplier method to decompose the upper and lower two-layer voltage optimization model into a wind power controller and a wind farm controller for independent solution, and through the information exchange iteration between the wind farm controller and the wind farm controller and the information exchange iteration between the wind farm controller and each wind turbine controller, and the information exchange process controller can directly iterate when the received information reaches a preset value, without receiving information from all controllers, effectively dealing with the communication delay problem caused by inconsistent computing power and communication speed between the actual wind turbine controller and the wind farm controller. Therefore, the method provided by the present application not only achieves the voltage optimization effect, but also uses a multi-objective function to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method, thereby accelerating the solution speed of the asynchronous alternating direction multiplier method, thereby achieving the stability of the voltage output of the wind farm group and ensuring the safe and stable operation of the entire wind farm group.

[0005] In a first aspect of the present application, a method for optimizing voltage of a wind power access power system is provided, the method comprising:

[0006] Obtaining parameter information of wind turbines, a first topological relationship of wind farms, and a second topological relationship of wind farm groups, wherein the first topological relationship refers to wind turbines controlled by the wind farms, and the second topological relationship refers to wind farms controlled by the wind farm groups;

[0007] According to the parameter information of wind turbines and the second topological relationship, a decentralized distributed optimization model of wind farm groups is constructed;

[0008] Constructing a centralized distributed optimization model of the wind farm according to the parameter information of the wind turbine and the first topological relationship;

[0009] Construct upper and lower voltage optimization models based on decentralized distributed optimization models and centralized distributed optimization models;

[0010] The penalty factor of the asynchronous alternating direction multiplier method is adaptively optimized using a multi-objective function. Under preset constraints, the asynchronous alternating direction multiplier method with the adaptively optimized penalty factor is used to solve the upper and lower voltage optimization models, and the control instructions for the optimal voltage solution are obtained. The voltage output of the wind farm group is controlled based on the control instructions.

[0011] In one implementation, a decentralized distributed optimization model of a wind farm group is constructed based on the parameter information of the wind turbines and the second topological relationship, including:

[0012] According to the incremental operation algorithm, a first incremental state space model of the wind farm group converter, a second incremental state space model of the wind turbine, and a third incremental state space model of the influence of the wind farm group converter and the wind farm on the bus voltage increment are established;

[0013] Obtaining a fourth incremental state space model of the wind farm group according to the first incremental state space model, the second incremental state space model and the third incremental state space model;

[0014] Discretizing the fourth incremental state space model to obtain a discrete model of the wind farm group converter;

[0015] Constructing a first objective function that minimizes the voltage deviation at the wind farm connection point;

[0016] Construct a cost function based on a discrete model;

[0017] Converting the first objective function into a first function with respect to the input variables, and converting the cost function into a quadratic programming function according to the first function;

[0018] The quadratic programming function is formulated as an asynchronous alternating direction multiplier method problem, and a decentralized distributed optimization model for wind farm groups is constructed.

[0019] In one implementation, a centralized distributed optimization model of a wind farm is constructed according to the parameter information of the wind turbine and the first topological relationship, including:

[0020] Establishing a fifth incremental state space model of the i-th wind farm;

[0021] The second objective function of minimizing the sum of the bus voltage deviation of the wind farm, the voltage deviation of the wind turbine and the reactive power deviation of the wind turbine is derived according to the fifth incremental state space model;

[0022] Construct the cost function based on the fifth incremental state space model

[0023] Converting the second objective function into a second function with respect to the input variables, and converting the cost function into a quadratic programming function according to the second function;

[0024] The quadratic programming function is expressed as an asynchronous alternating direction multiplier method problem, and a centralized and distributed optimization model of the wind farm is constructed.

[0025] In one implementation, a multi-objective function is used to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method, specifically:

[0026] A set of primal value and dual residual data is selected for the primal value and dual residual during the iteration of the asynchronous alternating direction multiplier method by a polynomial fitting method to obtain a fitting equation of a dynamically changing penalty factor; wherein the fitting equation represents a functional relationship between the penalty factor and the primal residual and the dual residual;

[0027] According to the fitting equation, the multi-objective function is determined.

[0028] In one implementation, the expression of the multi-objective function is:

[0029]

[0030] ρ represents the penalty parameter, e represents a small power of 10, represents the original staggered fitting function, represents the dual staggered fitting function.

[0031] In one implementation scheme, an asynchronous alternating direction multiplier method after an adaptive optimization penalty factor is used to solve the upper and lower voltage optimization models. Specifically, the asynchronous alternating direction multiplier method after an adaptive optimization penalty factor is run in the wind turbine controller and the wind farm controller to solve the decentralized distributed optimization model and the centralized distributed optimization model respectively, and through iterations between wind farm controllers and between wind farm controllers and each wind turbine controller, information exchange iteration is achieved until a predefined stop condition is reached, thereby obtaining control instructions for the optimal voltage solution.

[0032] In a second aspect of the present application, a device for optimizing voltage of a wind power access power system is provided, the device comprising:

[0033] An information acquisition module, used to acquire parameter information of wind turbines, a first topological relationship of wind farms, and a second topological relationship of wind farm groups, wherein the first topological relationship refers to wind turbines controlled by the wind farms, and the second topological relationship refers to wind farms controlled by the wind farm groups;

[0034] A first model building module is used to build a decentralized distributed optimization model of the wind farm group according to the parameter information of the wind turbine and the second topological relationship;

[0035] A second model building module, used to build a centralized distributed optimization model of the wind farm according to the parameter information of the wind turbine and the first topological relationship;

[0036] An optimization model building module is used to build upper and lower voltage optimization models based on a decentralized distributed optimization model and a centralized distributed optimization model;

[0037] The solution optimization module is used to use multi-objective functions to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method. Under preset constraints, the asynchronous alternating direction multiplier method after adaptively optimizing the penalty factor is used to solve the upper and lower voltage optimization models, obtain the control instructions for the optimal voltage solution, and control the voltage output of the wind farm group based on the control instructions.

[0038] In one implementation, solving the optimization module includes:

[0039] A fitting equation determination module is used to select a set of original value and dual residual data for the original value and dual residual during the iteration of the asynchronous alternating direction multiplier method by a polynomial fitting method to obtain a fitting equation for a dynamically changing penalty factor; wherein the fitting equation represents a functional relationship between the penalty factor and the original residual and the dual residual;

[0040] The multi-objective function determination module is used to determine the multi-objective function according to the fitting equation.

[0041] The third aspect of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a method for optimizing the voltage of wind power access to a power system as provided in the first aspect of the present application are implemented.

[0042] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a method for optimizing the voltage of wind power access to a power system as provided in the first aspect of the present application are implemented.

[0043] Compared with the prior art, this application has the following beneficial effects:

[0044] In a method for optimizing the voltage of a wind power access power system provided in an embodiment of the present application, the present application considers the characteristics of a wind farm group and the advantages and disadvantages of decentralized control and global control to establish an upper and lower voltage optimization model, and uses an asynchronous alternating direction multiplier method to decompose the upper and lower voltage optimization model into a wind power controller and a wind farm controller for independent solution, and through the information exchange iteration between the wind farm controller and the wind farm controller and the information exchange iteration between the wind farm controller and each wind turbine controller, and the information exchange process controller can directly iterate when the received information reaches a preset value, without receiving information from all controllers, effectively dealing with the communication delay problem caused by inconsistent computing power and communication speed between the actual wind turbine controller and the wind farm controller. Therefore, the method provided by the present application, while achieving the voltage optimization effect, uses a multi-objective function to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method, thereby accelerating the solution speed of the asynchronous alternating direction multiplier method, thereby achieving the stability of the voltage output of the wind farm group and ensuring the safe and stable operation of the entire wind farm group. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings:

[0046] Figure 1 A schematic diagram of a flow chart of a method for optimizing voltage of a wind power access power system provided in an embodiment of the present application;

[0047] Figure 2 A schematic diagram of a second topological relationship of a wind farm group provided in an embodiment of the present application.

[0048] Figure 3 A control diagram of a wind farm converter provided in an embodiment of the present application.

[0049] Figure 4 A block diagram of a control scheme provided in an embodiment of the present application.

[0050] Figure 5 An information flow diagram between the present application and the prior art provided for an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0052] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the function, operation or element applied for, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0053] In various embodiments of the present application, the expression "or" or "at least one of B or / and C" includes any combination or all combinations of the words listed at the same time. For example, the expression "B or C" or "at least one of B or / and C" may include B, may include C, or may include both B and C.

[0054] It should be understood that terms such as "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly indicate the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0055] Please refer to Figure 1 , Figure 1 A flow chart of a method for optimizing voltage of a wind power access power system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0056] S101, obtaining parameter information of a wind turbine, a first topological relationship of a wind farm, and a second topological relationship of a wind farm group, wherein the first topological relationship refers to a wind turbine controlled by the wind farm, and the second topological relationship refers to a wind farm controlled by the wind farm group.

[0057] In this embodiment, if Figure 2 As shown, Figure 2 It is a topological relationship between a wind farm group and a wind farm. It can be understood that a wind farm group is composed of multiple wind farms, and a wind farm is composed of multiple wind turbines. This is a topological relationship familiar to people in the technical field, and this embodiment will not make redundant elaboration.

[0058] Secondly, the parameter information of the wind turbine includes power electronic parameters, capacity, input and output of each module, voltage, current, active power, reactive power and other information at the key connection points with the power system. Power electronic parameters refer to some control links of the wind turbine, such as PID control link, whose power electronic parameters include the size of time constant, proportional gain, integral gain and so on.

[0059] S102: constructing a decentralized distributed optimization model of the wind farm group according to the parameter information of the wind turbines and the second topological relationship.

[0060] In this embodiment, the specific implementation method of constructing a decentralized distributed optimization model is as follows:

[0061] S1021, according to the incremental operation algorithm, establish a first incremental state space model of the wind farm group converter, a second incremental state space model of the wind turbine, and a third incremental state space model of the impact of the wind farm group converter and the wind farm on the bus voltage increment.

[0062] Specifically, Figure 3 As shown in the figure, it is a PID control framework diagram based on the wind farm group converter WFCVSC. Figure 3 The control process shown can determine the first incremental state space model of WFCVSC, namely In the formula, is the system state variable, Δu v =ΔV c ref is the control variable, and represents the coefficient matrix, where V c is the AC bus voltage, V c ref is the AC bus reference voltage, V c and The integral of the error between f is the capacitance, K p ,K i are proportional gain and integral gain respectively, T i is the time constant, and Δ represents the incremental operation method.

[0063] Taking into account the communication system delay and dynamic reactive power response of the wind turbine, the second incremental state space model of the wind turbine is established, and its expression is: In the formula, Q wt Represents the reactive power output of the fan, Indicates the reactive power of the fan.

[0064] Considering the bus voltage increment ΔV caused by the converter and wind farm cThe influence of ΔV is established by using the first-order Taylor formula to approximate the calculation. c The third incremental state space model of is expressed as:

[0065] Among them, V s is the terminal voltage of the converter, Q wf,i is the reactive power of the i-th wind farm.

[0066] S1022, obtaining a fourth incremental state space model of the wind farm group according to the first incremental state space model, the second incremental state space model and the third incremental state space model;

[0067] Specifically, according to the above equations (1), (2) and (3), the fourth incremental state space model of the entire wind farm group is obtained, and its expression is: Where Δx=(Δx v ,Δx w ),Δu=(Δu v ,Δu w );Δy=(ΔV s ,ΔQ wf,1 ,…,ΔQ wf,N );

[0068]

[0069] S1023, discretizing the fourth incremental state space model to obtain a discrete model of the wind farm group converter;

[0070] Specifically, since the wind farm group contains N f wind farms, discretize (4) to obtain the discrete model of the incremental state space model of the wind farm controller, which is expressed as:

[0071] In the formula, Δx wv =(Δx v ,ΔQ wf,i )′,

[0072]

[0073] in, is the reactive injection sensitivity of the bus voltage relative to the i-th wind farm.

[0074] S1024, constructing a first objective function that minimizes the voltage deviation at the connection point of the wind farm;

[0075] Specifically, the expression of the first objective function is: vsc =min||Vpoc (k)-V rate || 2 ; In the formula, Where V poc is the voltage at the connection point.

[0076] S1025, constructing a cost function according to the discrete model;

[0077] In this embodiment, the MPC problem of the discrete model is rearranged into a compact QP form, that is, the cost function is constructed using (5). The expression of the cost function is as follows:

[0078]

[0079] (7); where Δx(k+i|k) is the k+i-th system variable predicted during the k-th iteration, Δu(k+i|k) is the k+i-th state variable predicted during the k-th iteration, and Q, R, and F are diagonal matrices.

[0080] S1026, converting the first objective function into a first function about the input variables, and converting the cost function into a quadratic programming function according to the first function; expressing the quadratic programming function as an asynchronous alternating direction multiplier method problem, and constructing a decentralized distributed optimization model of the wind farm group.

[0081] In this embodiment, since the optimal solution of the cost function involves the input variable Δu, the objective function is converted into a first function about Δu, that is,

[0082] In the formula, X k =(Δx(k|k),Δx(k+1|k),…,Δx(k+N|k));

[0083] U k =(Δu(k|k),Δu(k+1|k),…,Δu(k+N-1|k)); A, B are coefficient matrices, Δx k is the initial value.

[0084] Therefore, the cost function (7) can be converted into a quadratic programming function, which is expressed as:

[0085]

[0086] in

[0087] The above optimization problem (9) is expressed as an ADMM problem, and a cost function is constructed to convert it into a function, and a decentralized distributed optimization model is obtained, which is expressed as:

[0088] in

[0089] S103: constructing a centralized distributed optimization model of the wind farm according to the parameter information of the wind turbine and the first topological relationship.

[0090] In this embodiment, the steps of constructing a centralized distributed optimization model of a wind farm are as follows:

[0091] S1031, establishing a fifth incremental state space model of the ith wind farm.

[0092] In this embodiment, the expression of the fifth incremental state space model is:

[0093] In formula (11),

[0094] Where V c ref(v) represents the reference voltage of the ith wind farm, Q wt,i is the reactive power output of the i-th wind turbine in the wind farm, is the total reactive power reference output by the i-th wind turbine optimized in the j-th wind farm controller.

[0095] S1032, deriving, based on the fifth incremental state space model, a second objective function that minimizes the sum of the bus voltage deviation of the wind farm, the voltage deviation of the wind turbine, and the reactive power deviation of the wind turbine.

[0096] In this embodiment, the second objective function is derived based on the formula (11) described above, and its expression is:

[0097]

[0098] The constraints are:

[0099] S1033: Construct a cost function according to the fifth incremental state space model.

[0100] In this embodiment, for the MPC problem of the fifth incremental state-space model, it is still rearranged into a compact QP form for solution. It should be noted that the way of constructing the cost function in this embodiment is consistent with that recorded in step S1025 above, and will not be repeated here.

[0101] S1034, converting the second objective function into a second function about the input variable, and converting the cost function into a quadratic programming function according to the second function.

[0102] In this embodiment, the method of converting the quadratic programming function is similarly consistent with the description of step S1026 above, and is not described in detail in this embodiment.

[0103] S1035, expressing the quadratic programming function as an asynchronous alternating direction multiplier method problem, and constructing a centralized distributed optimization model of the wind farm.

[0104] In this embodiment, the expression of the centralized distributed optimization model is:

[0105] In the formula,

[0106] x i =(Δu i (k|k),Δu i (k+1|k),…,Δu i (k+N-1|k)),i∈{1,2,…,N f +1}.

[0107] S104, constructing upper and lower voltage optimization models based on the decentralized distributed optimization model and the centralized distributed optimization model.

[0108] In this embodiment, if Figure 4 As shown, a two-layer voltage optimization model is established by combining the decentralized distributed optimization model of formula (10) and the centralized distributed optimization model of formula (14), namely:

[0109] S105, using a multi-objective function to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method, under preset constraints, using the asynchronous alternating direction multiplier method after adaptively optimizing the penalty factor to solve the upper and lower voltage optimization models, obtain control instructions for the optimal voltage solution, and control the voltage output of the wind farm group based on the control instructions.

[0110] In this embodiment, if Figure 5 As shown, for the asynchronous decentralized alternating direction multiplier method, the active set and the inactive set are first defined. The coupling element x in the active set i ,x jThe following conditions must be met: j is any element that has a communication relationship with i, represented by {i, j}∈ψ. In the active set, worker i represents the active component, and l∈ψ. The sub-WF controllers in the active set can communicate, transfer the global optimization variable z and the local optimization variable x, ρ, to achieve variable optimization and update, while the variables in the inactive set can be updated with their historical data, and finally achieve overall optimization control. Define the clock S, when the number of active components reaches S, the component is updated. Set the global variable τ, when the number of non-updates of the inactive component reaches τ, all components will be updated once. According to formulas (6) and (10), in addition to the active set and the inactive set, the asynchronous decentralized ADMM iteration can be described as: Assumption D i Indicates getting x from matrix D i The i-th column of H l It means getting the lth row from the matrix H. The active value of the kth iteration can be expressed as:

[0111] Using formula (10), the augmented Lagrangian function is obtained:

[0112]

[0113] Where F s :R n →R and G s :R n →R is a convex function, X and Z are R n and R m In order to solve the above optimization problem, an asynchronous decentralized consistency ADMM iterative algorithm is established. The steps are as follows:

[0114]

[0115]

[0116] (2) Centralized distributed control of sub-WF

[0117] In the asynchronous global consistency ADMM, the Sub-WF controller node is the master node responsible for updating the global variable z, while the WT nodes are called worker nodes, each of which minimizes its local objective f based on its data subset. i , and the updated target x i and λ tThis process is regulated by the master clock. When the amount of data transmitted from the worker reaches the set value S of the master clock, the transmitted data z is updated in the worker. In order to keep the global variable z fresh in the worker program, a global variable τ is established. When the slowest worker has not updated the number of times τ i When τ is reached, all workers perform an update and use equation (14) to obtain the augmented Lagrangian function:

[0118]

[0119] In order to solve the above optimization problem, an asynchronous global consistency ADMM iterative algorithm is established. The steps are as follows:

[0120]

[0121]

[0122] In some embodiments, the penalty factor of the asynchronous alternating direction multiplier method is adaptively optimized using a multi-objective function, specifically: a set of original value and dual residual data is selected for the original value and the dual residual during the iteration of the asynchronous alternating direction multiplier method by a polynomial fitting method to obtain a fitting equation of a dynamically changing penalty factor; wherein the fitting equation represents a functional relationship between the penalty factor and the original residual and the dual residual;

[0123] According to the fitting equation, the multi-objective function is determined.

[0124] Specifically, the expression of the multi-objective function is:

[0125]

[0126] ρ represents the penalty parameter, e represents a small power of 10, and g sk (x) represents the original staggered fitting function, g rk (ρ) represents the dual staggered fitting function.

[0127] In one implementation scheme, an asynchronous alternating direction multiplier method after an adaptive optimization penalty factor is used to solve the upper and lower voltage optimization models. Specifically, the asynchronous alternating direction multiplier method after an adaptive optimization penalty factor is run in the wind turbine controller and the wind farm controller to solve the decentralized distributed optimization model and the centralized distributed optimization model respectively, and through iterations between wind farm controllers and between wind farm controllers and each wind turbine controller, information exchange iteration is achieved until a predefined stop condition is reached, thereby obtaining control instructions for the optimal voltage solution.

[0128] The embodiment of the present application also provides a device for optimizing voltage of a wind power access power system, the device comprising:

[0129] An information acquisition module, used to acquire parameter information of wind turbines, a first topological relationship of wind farms, and a second topological relationship of wind farm groups, wherein the first topological relationship refers to wind turbines controlled by the wind farms, and the second topological relationship refers to wind farms controlled by the wind farm groups;

[0130] A first model building module is used to build a decentralized distributed optimization model of the wind farm group according to the parameter information of the wind turbine and the second topological relationship;

[0131] A second model building module, used to build a centralized distributed optimization model of the wind farm according to the parameter information of the wind turbine and the first topological relationship;

[0132] An optimization model building module is used to build upper and lower voltage optimization models based on a decentralized distributed optimization model and a centralized distributed optimization model;

[0133] The solution optimization module is used to use multi-objective functions to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method. Under preset constraints, the asynchronous alternating direction multiplier method after adaptively optimizing the penalty factor is used to solve the upper and lower voltage optimization models, obtain the control instructions for the optimal voltage solution, and control the voltage output of the wind farm group based on the control instructions.

[0134] Accordingly, a voltage optimization device for accessing a wind power system provided in this embodiment considers the characteristics of a wind farm group and the advantages and disadvantages of decentralized control and global control to establish an upper and lower voltage optimization model, and uses an asynchronous alternating direction multiplier method to decompose the upper and lower voltage optimization models into a wind power controller and a wind farm controller for independent solution, and through the information exchange iteration between the wind farm controller and the wind farm controller and the information exchange iteration between the wind farm controller and each wind turbine controller, and the information exchange process controller can directly iterate when the received information reaches a preset value, without receiving information from all controllers, effectively dealing with the communication delay problem caused by inconsistent computing power and communication speed between the actual wind turbine controller and the wind farm controller. Therefore, the method provided in this application not only achieves the voltage optimization effect, but also uses a multi-objective function to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method, thereby accelerating the solution speed of the asynchronous alternating direction multiplier method, thereby achieving the stability of the voltage output of the wind farm group and ensuring the safe and stable operation of the entire wind farm group.

[0135] In some embodiments, solving the optimization module includes:

[0136] A fitting equation determination module is used to select a set of original value and dual residual data for the original value and dual residual during the iteration of the asynchronous alternating direction multiplier method by a polynomial fitting method to obtain a fitting equation for a dynamically changing penalty factor; wherein the fitting equation represents a functional relationship between the penalty factor and the original residual and the dual residual;

[0137] The multi-objective function determination module is used to determine the multi-objective function according to the fitting equation.

[0138] A voltage optimization device for accessing a wind power system in an embodiment of the present application is similar to the above Figure 1 The voltage optimization method for wind power access to the power system shown is a technical solution based on the same inventive concept. Through the detailed description of the voltage optimization method for wind power access to the power system provided in the above embodiment, technical personnel in this field can clearly understand the implementation process of a wind power access to the power system voltage optimization device in this embodiment, so for the sake of brevity of the specification, it will not be repeated here.

[0139] An embodiment of the present application also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a method for optimizing the voltage of wind power access to a power system as described in the above embodiment are implemented.

[0140] It should be noted that the specific implementation of each operation of the electronic device can be as described above. Figure 1 The corresponding description of the method embodiment shown can be used to implement a method for optimizing the voltage of wind power access to a power system in the above method embodiment of the present application, and will not be described in detail here.

[0141] In the embodiments of the present disclosure, a computer-readable storage medium is also provided, and the computer-readable storage medium is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of a method for optimizing the voltage of a wind power access power system in the above embodiment. Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0142] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for optimizing voltage of wind power access power system, characterized in that: include: Obtaining parameter information of wind turbines, a first topological relationship of wind farms, and a second topological relationship of wind farm groups, wherein the first topological relationship refers to wind turbines controlled by the wind farms, and the second topological relationship refers to wind farms controlled by the wind farm groups; According to the parameter information of wind turbines and the second topological relationship, a decentralized distributed optimization model of wind farm groups is constructed; Constructing a centralized distributed optimization model of the wind farm according to the parameter information of the wind turbine and the first topological relationship; Construct upper and lower voltage optimization models based on decentralized distributed optimization models and centralized distributed optimization models; The penalty factor of the asynchronous alternating direction multiplier method is adaptively optimized using a multi-objective function. Under preset constraints, the asynchronous alternating direction multiplier method with the adaptively optimized penalty factor is used to solve the upper and lower voltage optimization models, and the control instructions for the optimal voltage solution are obtained. The voltage output of the wind farm group is controlled based on the control instructions.

2. The method according to claim 1, characterized in that According to the parameter information of the wind turbine and the second topological relationship, a decentralized distributed optimization model of the wind farm group is constructed, including: According to the incremental operation algorithm, a first incremental state space model of the wind farm group converter, a second incremental state space model of the wind turbine, and a third incremental state space model of the influence of the wind farm group converter and the wind farm on the bus voltage increment are established; Obtaining a fourth incremental state space model of the wind farm group according to the first incremental state space model, the second incremental state space model and the third incremental state space model; Discretizing the fourth incremental state space model to obtain a discrete model of the wind farm group converter; Constructing a first objective function that minimizes the voltage deviation at the wind farm connection point; Construct a cost function based on a discrete model; Converting the first objective function into a first function with respect to the input variables, and converting the cost function into a quadratic programming function according to the first function; The quadratic programming function is formulated as an asynchronous alternating direction multiplier method problem, and a decentralized distributed optimization model for wind farm groups is constructed.

3. The method according to claim 1, characterized in that According to the parameter information of the wind turbine and the first topological relationship, a centralized distributed optimization model of the wind farm is constructed, including: Establishing a fifth incremental state space model of the i-th wind farm; The second objective function of minimizing the sum of the bus voltage deviation of the wind farm, the voltage deviation of the wind turbine and the reactive power deviation of the wind turbine is derived according to the fifth incremental state space model; Construct the cost function based on the fifth incremental state space model Converting the second objective function into a second function with respect to the input variables, and converting the cost function into a quadratic programming function according to the second function; The quadratic programming function is expressed as an asynchronous alternating direction multiplier method problem, and a centralized and distributed optimization model of the wind farm is constructed.

4. The method according to claim 1, characterized in that The penalty factor of the asynchronous alternating direction multiplier method is adaptively optimized using a multi-objective function, specifically: Selecting a set of primal value and dual residual data for the primal value and dual residual during iterations of the asynchronous alternating direction multiplier method by a polynomial fitting method to obtain a fitting equation for a dynamically changing penalty factor; The fitting equation represents the functional relationship between the penalty factor and the original residual and the dual residual; According to the fitting equation, the multi-objective function is determined.

5. The method according to claim 4, characterized in that The expression of the multi-objective function is: Among them, ρ represents the penalty parameter, e represents a small power of 10, and g sk (x) represents the original staggered fitting function, g rk (ρ) represents the dual staggered fitting function.

6. The method according to claim 1, characterized in that: The asynchronous alternating direction multiplier method after adaptive optimization penalty factor is used to solve the upper and lower voltage optimization models. Specifically, the asynchronous alternating direction multiplier method after adaptive optimization penalty factor is run in the wind turbine controller and the wind farm controller to solve the decentralized distributed optimization model and the centralized distributed optimization model respectively, and through the iteration between the wind farm controller and the wind farm controller and the iteration between the wind farm controller and each wind turbine controller, the information exchange iteration is realized until the predefined stop condition is reached, and the control instructions of the voltage optimal solution are obtained.

7. A voltage optimization device for wind power access to a power system, characterized in that: The device includes: An information acquisition module, used to acquire parameter information of wind turbines, a first topological relationship of wind farms, and a second topological relationship of wind farm groups, wherein the first topological relationship refers to wind turbines controlled by the wind farms, and the second topological relationship refers to wind farms controlled by the wind farm groups; A first model building module is used to build a decentralized distributed optimization model of the wind farm group according to the parameter information of the wind turbine and the second topological relationship; A second model building module, used to build a centralized distributed optimization model of the wind farm according to the parameter information of the wind turbine and the first topological relationship; An optimization model building module is used to build upper and lower voltage optimization models based on a decentralized distributed optimization model and a centralized distributed optimization model; The solution optimization module is used to use multi-objective functions to adaptively optimize the penalty factor of the asynchronous alternating direction multiplier method. Under preset constraints, the asynchronous alternating direction multiplier method after adaptively optimizing the penalty factor is used to solve the upper and lower voltage optimization models, obtain the control instructions for the optimal voltage solution, and control the voltage output of the wind farm group based on the control instructions.

8. The device according to claim 7, characterized in that Solving optimization modules, including: A fitting equation determination module is used to select a set of original value and dual residual data for the original value and dual residual during the iteration of the asynchronous alternating direction multiplier method by a polynomial fitting method to obtain a fitting equation for a dynamically changing penalty factor; wherein the fitting equation represents a functional relationship between the penalty factor and the original residual and the dual residual; The multi-objective function determination module is used to determine the multi-objective function according to the fitting equation.

9. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a method for optimizing the voltage of wind power access to a power system as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of a method for optimizing the voltage of wind power access to a power system according to any one of claims 1 to 7 are implemented.

Images