Distributed Optimization Control Method and System for MTDC System Connected to Offshore Wind Farm

Abstract

This invention discloses a distributed optimization control method and system for an MTDC system connected to an offshore wind farm. The method includes establishing an incremental state space prediction model for the entire system, establishing an optimization mathematical model for the MTDC system connected to the offshore wind farm, and determining its constraints; decomposing the optimization mathematical model into two standard QP problem mathematical models: the optimization mathematical model for the MTDC system controller and the optimization mathematical model for the wind farm controller; and iteratively solving the two standard QP problem mathematical models using a distributed optimization solution method based on the alternating direction multiplier method to update the current voltage reference instructions for the grid-side converter and the wind farm-side converter, as well as the active and reactive power instructions for the wind turbines. This invention can improve the stability of the entire system and reduce the computational burden of traditional centralized control methods.

Description

Technical Field

[0001] The present invention relates to the field of power system operation control, and in particular to a distributed optimization control method and system for an MTDC system connected to an offshore wind farm. Background Art

[0002] Wind power is a clean energy source and the most technologically mature clean energy source for development and use worldwide. According to the Global Wind Energy Council, wind power generation is increasing annually, and the installed capacity of offshore wind power is climbing year by year. Offshore wind power will become a key development focus of the future wind power market. With the development of flexible direct current transmission technology based on voltage source converters (VSC-HVDC), many demonstration projects have been established to form multi-terminal direct current (MTDC) systems. Offshore wind farms are expanding in size and are increasingly located farther from shore. MTDC is one of the effective means of tackling the long-distance, high-capacity transmission of offshore wind power.

[0003] Large-scale offshore wind farms are being connected to MTDC systems and grids, and their impact on the power system is increasing. The safe, stable, and economical operation of the MTDC system connected to these large-scale offshore wind farms is crucial to the safe operation of the entire power system. Voltage control and economical operation of large-scale offshore wind farms improve wind farm efficiency and reduce operating costs. This, in turn, helps improve the economic benefits of MTDC systems.

[0004] Since the MTDC system connected to a large-scale offshore wind farm has a different structure from the traditional AC grid-connected wind farm, its control method is also different. The control method of the MTDC system connected to a large-scale offshore wind farm based on MPC mainly aims to stabilize the terminal voltage of the wind turbine and minimize the loss of the entire system, which can not only improve the utilization rate of the wind turbine but also reduce the loss of the entire system.

[0005] As offshore wind farms expand in size, the computational burden of traditional centralized control increases significantly. Furthermore, the MTDC system's droop control is unable to optimize its operation. These challenges present themselves in the operational control of MTDC systems connected to large-scale offshore wind farms. Therefore, the development of distributed control methods suitable for the operational control of MTDC systems connected to large-scale offshore wind farms is urgently needed. Advances in artificial intelligence have led to rapid developments in fields such as distributed optimization and parallel computing, laying the foundation for real-time distributed optimal control. Summary of the Invention

[0006] The technical problem to be solved by the present invention is as follows: In response to the above-mentioned problems of the prior art, a distributed optimization control method and system for an MTDC system connected to an offshore wind farm are provided. The present invention can utilize the active power, loss, reactive power, and voltage control capabilities of the grid-side converter, the wind farm-side converter WFVSC, and the wind turbine to achieve voltage stability and loss reduction for the entire system, thereby improving the stability of the entire system and reducing the computational burden of traditional centralized control methods, thereby meeting the optimized operation control requirements of the MTDC system connected to a large-scale offshore wind farm.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A distributed optimization control method for an MTDC system connected to an offshore wind farm comprises:

[0009] 1) Establish an incremental state space prediction model for the entire system, including wind turbines, wind farm-side converters, and grid-side converters. Based on this incremental state space prediction model, establish an optimal mathematical model for the MTDC system connected to the offshore wind farm and determine its constraints.

[0010] 2) Decompose the established optimization mathematical model of the MTDC system connected to the offshore wind farm into two standard QP problem mathematical models: the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller;

[0011] 3) A distributed optimization solution method based on the alternating direction multiplier method iteratively solves two standard QP problem mathematical models: the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller, to update the current voltage reference instructions of the grid-side converter and the wind farm-side converter, and the active and reactive instructions of the wind turbine.

[0012] Optionally, the function expression of the incremental state space prediction model of the entire system established in step 1) is:

[0013] Δx(k+1)=A d Δx(k)+B d Δu(k),

[0014] Δy(k)=C d Δx(k),

[0015] In the above formula, Δx(k+1) and Δx(k) are the state variables of the entire system including the wind turbine, wind farm side converter and grid side converter at time k+1 and time k respectively. The state variables include the voltage of the grid side converter and the wind farm side converter and the active and reactive power of the wind turbine. Δu(k) is the control variable of the entire system including the wind turbine, WFSVC and GSVSC at time k, including the voltage reference instructions of the grid side converter and the wind farm side converter and the active and reactive instructions of the wind turbine. A d 、B d and C d are the discretized state equation matrices, Δy(k) is the output variable of the entire system including the wind turbine, the wind farm side converter and the grid side converter at time k, and the output variables include the voltages of the grid side converter and the wind farm side converter and the active and reactive power of the wind turbine.

[0016] Optionally, the function expression of the optimization mathematical model of the MTDC system connected to the offshore wind farm established in step 1) is:

[0017]

[0018] In the above formula, f(Δu dc ,Δu C ,Δu W ) represents the optimal mathematical model of the MTDC system connected to the offshore wind farm, Δu dc is the control variable vector composed of the DC side voltage increments of each VSC in the MTDC system, Δu C is the voltage reference value of the WFSVC controller, Δu W is the reference vector of active power and reactive power of each wind turbine in the wind farm, N p is the number of sampling steps in the prediction period, M is the number of wind farms, N W is the number of wind turbines, and V ref are the measured value and reference voltage of the terminal voltage of wind turbine i in the m-th wind farm at time k, respectively; λ1, λ2, λ3, λ4 are weight parameters, is the active power loss of the m-th wind farm at the current time k, V dc (k) is the voltage increment vector of each VSC DC side in the MTDC system at time k, G dc is the node admittance matrix of the MTDC system, is the difference between the active power of the wind turbine at the current moment k in the m-th wind farm and the allocated power reference value; the constraints of the optimal mathematical model of the MTDC system connected to the offshore wind farm are:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] In the above formula, P Wi (k) is the active power of the i-th wind turbine at the current time k, is the available active power of the i-th wind turbine; Q Wi (k) is the reactive power output of the i-th wind turbine at the current time k, and are the minimum and maximum limits of reactive output of the i-th wind turbine respectively; G dc (i,j) is the node admittance between the i-th and j-th grid-side converters, V dci (k) and V dcj (k) are the predicted values of the DC bus voltage of the i-th and j-th grid-side converters at the current time k, is the rated active power on the DC cable line of the i-th and j-th grid-side converters, and are the minimum and maximum limits of the DC bus voltage of the i-th grid-side converter; V C,m (k) is the measured value of the AC bus voltage of the m-th wind farm-side converter at the current time k, and are the minimum and maximum limits of the AC bus voltage of the m-th wind farm-side converter respectively; is the actual command value of the power output of the i-th grid-side converter at the current moment k, V dci (0) and V dcj(0) are the measured values of the DC bus voltage of the i-th and j-th grid-side converters at the initial moment, P loss is the active power loss of the wind farm, P W is the active power output by the wind turbine, ΔP W is the active power increment vector output by each wind turbine in the wind farm, Q W is the reactive power output by the wind turbine, ΔQ W is the reactive power increment vector output by each wind turbine in the wind farm, V C is the AC side voltage of the wind farm side converter, ΔV C is the AC side voltage increment of the wind farm side converter, is the intermediate variable, V i is the voltage of node i in the network, V j is the voltage of node j in the network, G ij is the conductance between nodes i and j, θ ij is the voltage phase angle difference between nodes i and j, is the voltage measurement value of the i-th wind turbine at the initial moment, V Wi is the voltage prediction value of the i-th wind turbine, P Wi (0) is the measured active power value of the i-th wind turbine at the initial moment, ΔP Wi is the predicted value of the active power increment of the i-th wind turbine at the initial moment, α i is the wind power utilization rate of the i-th wind turbine, P dck (k) is the active power input to the MTDC system by the kth WFVSC at time k, P dci (k) is the active power input from the i-th wind farm-side converter to the MTDC system at time k, and n is the total number of grid-side converters in the MTDC system.

[0032] Optionally, the function expression of the optimized mathematical model of the MTDC system controller in step 2) is:

[0033]

[0034] In the above formula, f(Δu dc ,z g ) r+1 represents the r+1th iteration of the optimization mathematical model of MTDC system control, Δu dc is the control variable vector composed of the DC side voltage increments of each VSC in the MTDC system, z g is a global variable, λ3 is a weight coefficient, V dc (k) is the DC side voltage prediction vector of each grid-side converter at time k, G dc is the node admittance matrix of the MTDC system, y T is an auxiliary variable, is the local variable of the rth step of the optimization mathematical model of MTDC system control, and ρ is the penalty factor.

[0035] Optionally, the function expression of the optimization mathematical model of the wind farm controller in step 2) is:

[0036]

[0037] In the above formula, f(Δu C,m ,Δu W,m ,z l,m ) r+1 represents the r+1th iteration of the optimization mathematical model for wind farm control, Δu C,m is the AC bus voltage increment control variable of the m-th wind farm side converter, Δu W,m is the active and reactive power increment control variable of each wind turbine in the m-th wind farm, z l,m are local variables, λ1, λ2, λ4 are weight parameters, N p is the number of sampling steps in the prediction period, N W is the number of wind turbines, and V ref are the terminal voltage and reference voltage of wind turbine i in the m-th wind farm, is the active power loss of the m-th wind farm at the current time k, is the difference between the active power of the m-th wind farm at the current time k and the allocated power reference value, y m To update the auxiliary variables of the optimal mathematical model for wind farm control, It is the local variable of the r+1th step of the optimization mathematical model of wind farm control.

[0038] Optionally, in step 2), the function expressions of the two standard QP problem mathematical models, namely, the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller, are:

[0039]

[0040] St.x∈Ω

[0041] In the above formula, Φ(x) is the objective function, H and g are the Hessian matrix and coefficient matrix respectively, and Ω is the domain of the decision variable x.

[0042] Optionally, step 3) includes:

[0043] 3.1) Initialization step r = 0 of the optimization mathematical model of the MTDC system controller for local variables Auxiliary variable y when the value is 0 and r = 0 [0]= is 0 and the value of k at the current moment is 0; it is the local variable of the optimization mathematical model of the wind farm controller when the initialization step r=0 Auxiliary variable y when the value is 0 and r = 0 [0] = is 0 and the value of step r is 0;

[0044] 3.2) Determine whether step r is greater than 1 and the preset convergence conditions are met at the same time; if not, jump to the next step; otherwise, add 1 to the current moment k, and the MTDC system controller updates the global variable of the r+1 step of its optimization mathematical model And send it to the controller of the wind farm side converter; update the local variables of the wind farm controller's optimization mathematical model in the r+1 step through the wind farm side converter and auxiliary variables And send it to the MTDC system controller; jump to step 3.2);

[0045] 3.3) The MTDC system controller issues an iteration end instruction, and the MTDC system controller and the wind farm side converter controller issue instructions to update the current grid side converter, wind farm side converter voltage reference instructions and wind turbine active and reactive power instructions.

[0046] Optionally, the function expression of the convergence condition preset in step 3.2) is:

[0047]

[0048] In the above formula, R [r] and S [r] is an intermediate variable, is the local variable of the rth step of the optimization mathematical model of MTDC system control, is the global variable of the rth step of the optimization mathematical model of MTDC system control, It is the global variable of the r+1th step of the optimization mathematical model of MTDC system control.

[0049] In addition, the present invention also provides a distributed optimization control system for an MTDC system connected to an offshore wind farm, comprising a microprocessor and a memory connected to each other, wherein the microprocessor stores steps programmed or configured to execute the distributed optimization control method for the MTDC system connected to an offshore wind farm.

[0050] In addition, the present invention also provides a computer-readable storage medium storing a computer program for being executed by a computer device to implement the distributed optimization control method of the MTDC system connected to an offshore wind farm.

[0051] Compared with the prior art, the present invention mainly has the following advantages:

[0052] 1. The present invention fully utilizes the active power, loss, reactive power and voltage regulation capabilities of the wind farm side converter, grid side converter and wind turbine generator set, and coordinates and optimizes the control;

[0053] 2. Compared with traditional centralized optimization control, the present invention decomposes large-scale complex constraint optimization problems into simple constraint optimization problems and multiple parallel small-scale simple constraint optimization problems, which greatly reduces the computational burden of the central controller and is more suitable for the optimization control of MTDC systems connected to large-scale offshore wind farms.

[0054] 3. Compared with separate optimization control, the present invention optimizes and controls the MTDC system connected to a large-scale offshore wind farm as a whole to achieve global optimization. This design greatly reduces the active power loss of the entire system and enhances the stability and flexibility of the entire system.

[0055] 4. The present invention takes into account the multi-step optimization of the system's dynamic process, further improving the optimization control effect of the MTDC system connected to a large-scale offshore wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0057] Figure 2 FIG. 1 is a schematic diagram of the structure of an MTDC system connected to an offshore wind farm according to an embodiment of the present invention.

[0058] Figure 3 Detailed flowchart of the method according to the embodiment of the present invention.

[0059] Figure 4 This is a control principle diagram of an MTDC system connected to an offshore wind farm in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention application. Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those commonly understood by those of ordinary skill in the art to which the present invention application belongs. It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0061] like Figure 1 As shown, the distributed optimization control method of the MTDC system connected to the offshore wind farm in this embodiment includes:

[0062] 1) Establish an incremental state space prediction model for the entire system, including wind turbines, wind farm side converters (WFSVCs), and grid side converters (GSVSCs). Based on this incremental state space prediction model, establish an optimal mathematical model for the MTDC system connected to the offshore wind farm and determine its constraints.

[0063] 2) Decompose the established optimization mathematical model of the MTDC system connected to the offshore wind farm into two standard QP problem mathematical models: the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller;

[0064] 3) A distributed optimization solution method based on the alternating direction multiplier method iteratively solves two standard QP problem mathematical models: the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller, to update the current voltage reference instructions of the grid-side converter and the wind farm-side converter, and the active and reactive instructions of the wind turbine.

[0065] Figure 2 This is a schematic diagram of the MTDC system structure connected to an offshore wind farm in this embodiment, including multiple wind farm-side converters. The input end of each wind farm-side converter is connected to a 33kV feeder through a 155kV feeder and a 33kV / 155kV transformer. The 33kV feeder is connected to multiple groups of wind turbines. The distance between adjacent wind turbines in the figure is 4km; the output end of the wind farm-side converter is connected to multiple grid-side converters on shore through a 400kV transmission cable. Figure 4 This is a control schematic diagram of the MTDC system connected to an offshore wind farm in an embodiment of the present invention, in which the dispatching department is used to issue dispatching instructions to each wind farm. To the MTDC system controller, the MTDC system controller executes and sends the global variables of the r+1th step of the optimization mathematical model Each wind farm controller updates its local variables of the optimization mathematical model at step r+1. and auxiliary variables And sent to the MTDC system controller, each wind farm controller executes the distributed optimization control method of this embodiment, and finally obtains a reference instruction for each wind turbine. and reactive reference instructions And control the operation of wind turbines.

[0066] Figure 4 FIG1 is a control principle diagram of an MTDC system connected to an offshore wind farm in an embodiment of the present invention. Figure 4As shown, when it is at the control time node, the MTDC controller is triggered, the MTDC controller obtains the voltage measurement value related to the MTDC system, and the wind farm controller obtains the wind farm operation status information, including the active power P of the wind turbine. l 、Reactive Q l Measured values, voltages at various nodes in the wind farm Measured value, WFVSC bus voltage V C Based on the wind farm measurement information obtained, the voltage sensitivity coefficient of node i to the active and reactive power injected into node l is:

[0067]

[0068]

[0069] In the above formula, is the voltage phasor of each node in the wind farm, V i yes The complex conjugate of V i is the amplitude of the voltage phasor, θ i is the phase angle of the voltage phasor, P l ,Q l are the active and reactive power input to the wind turbine, respectively. Re(·) and Im(·) represent the real and imaginary parts of the complex number, respectively. The active loss sensitivity coefficient of the node i voltage is:

[0070]

[0071] In the above formula, G ij is the conductance between node i and node j, θ ij =θ i -θ j is the phase angle between node i and node j, N is the number of wind farm nodes, P loss is the line network loss of the wind farm. The sensitivity coefficient of node i to the WFVSC bus voltage is:

[0072]

[0073] In the above formula, The sensitivity coefficient is updated in each control cycle and sent to the wind farm controller. The sensitivity coefficient is used to convert the independent variable in the objective function into the control variable of the system, which facilitates the conversion of the optimization objective function into a function of the system control variable.

[0074] The function expression of the incremental state space prediction model of the entire system established in step 1) of this embodiment is:

[0075] Δx(k+1)=A d Δx(k)+B d Δu(k),

[0076] Δy(k)=C d Δx(k),

[0077] In the above formula, Δx(k+1) and Δx(k) are the state variables of the entire system including the wind turbine, wind farm side converter and grid side converter at time k+1 and time k respectively. The state variables include the voltage of the grid side converter and the wind farm side converter and the active and reactive power of the wind turbine. Δu(k) is the control variable of the entire system including the wind turbine, WFSVC and GSVSC at time k, including the voltage reference instructions of the grid side converter and the wind farm side converter and the active and reactive instructions of the wind turbine. A d 、B d and C d are the discretized state equation matrices, and Δy(k) is the output variable of the entire system at time k, including the wind turbine, wind farm side converter, and grid side converter. The output variables include the voltages of the grid side converter and wind farm side converter, and the active and reactive power of the wind turbine. Figure 3 As shown, the establishment process is as follows:

[0078] 1. Establish prediction models for the wind turbine, wind farm-side converter, and grid-side converter respectively. In this embodiment, the prediction model for the wind farm-side converter is:

[0079]

[0080]

[0081]

[0082] In the above formula, the state variables of the wind farm side converter are Control variables of wind farm side converter ΔV C and are the increment and increment reference value of the controlled bus voltage of the wind farm side converter respectively; A C and B C They are the state equation matrices of the converter on the wind farm side; are the voltage loop time constants of the 1st to Mth wind farm side converters respectively; M is the number of wind farm side converters. The active and reactive linear control prediction model of the wind turbine is:

[0083]

[0084]

[0085]

[0086] In the above formula, the state variable of the wind turbine [Δx W ] T =[ΔP W ,ΔQ W ] T Wind turbine control variables ΔP W and are the increment of active power output of wind turbine and the increment of active power output reference value, ΔQ W and are the reactive output increment and reactive output reference value increment of wind turbines respectively; A W and B W They are the state equation matrices of the wind turbine; is the time constant of active power control of each wind turbine, is the time constant of reactive power control of each wind turbine. The prediction model of the grid-side converter is:

[0087]

[0088] A dc =diag(-1 / T dc,1 ,-1 / T dc,2 ,...,-1 / T dc,n )

[0089] B dc =diag(1 / T dc,1 ,1 / T dc,2 ,...,1 / T dc,n )

[0090] In the above formula, the state variable of the grid-side converter [Δx dc ] T =[V dc ] T , the control variables of the grid-side converter V dc and are the increment and increment reference value of the controlled DC bus voltage of the grid-side converter respectively; A dc and B dc They are the state equation matrices of the grid-side converter, T dc,1 ~T dc,n Measure the converter voltage outer loop time constant for each power grid.

[0091] 2. According to the system parameter values and measurement information, establish the entire system (including multiple wind farms, WFVSC and GSVSC, each wind farm with N W Typhoon turbine generator set) incremental state space model:

[0092]

[0093] In the above formula, the state variable Δx, the control variable Δu and the output variable Δy are:

[0094] Δx=[Δx dc ,Δx C ,Δx W ] T ;

[0095] Δu=[Δu dc ,Δu C ,Δu W ] T ;

[0096]

[0097] In the above formula, Δx dc ,Δx C ,Δx W are the state variables of the grid-side converter, wind farm-side converter, and wind turbine generator set, respectively. dc ,Δu C ,Δu W are the control variables of the grid-side converter, wind farm-side converter, and wind turbine generator set, respectively. dc1 ~V dcn is the DC bus voltage of the 1st to nth grid-side converters VSC, They are the Nth wind farm in the Mth wind farm connected to the MTDC system. W The active power increase of typhoon generators, They are the Nth wind farm in the Mth wind farm connected to the MTDC system. W The reactive power increase of typhoon generators, is the AC bus voltage of the M-th wind farm side converter connected to the MTDC system, and the state equation matrix is:

[0098]

[0099] In the above formula, A dc ,A C ,A W ,B dc ,B C and B W are the state equation matrices of the grid-side converter, wind farm-side converter, and wind turbine generator set, respectively.

[0100] 3. The entire system (including multiple wind farms, WFVSC and GSVSC, each wind farm with N W By discretizing the incremental state space model of the typhoon turbine group), the function expression of the incremental state space prediction model of the entire system established in step 1) of this embodiment can be obtained.

[0101] In this embodiment, the function expression of the PI controller of the active power of the grid-side converter is:

[0102]

[0103] In the above formula, ΔP dci The active power increment transmitted to the AC grid by the grid-measuring converter is: is the active power increment transmitted from the grid converter to the AC grid at the initial moment, β is the coefficient of the PI controller, is the instruction value issued by the system operator, is the measured value of the power output of the i-th grid-side converter, is the actual command value of the power output of the i-th grid-side converter.

[0104] An optimal mathematical model (global centralized mathematical model) for the control of an MTDC system connected to a large-scale multi-wind farm based on MPC is established, including decision variables (active and reactive power of wind turbines, voltage reference value of WFVSC, voltage reference value of GSVSC), objective function (reducing active power loss, reducing voltage offset, reducing active output fluctuation), constraints (active and reactive output constraints of wind turbines, voltage control constraints of WFVSC, voltage control constraints of GSVSC, active output constraints of GSVSC), etc. Specifically, the functional expression of the optimal mathematical model of the MTDC system connected to the offshore wind farm established in step 1) of this embodiment is:

[0105]

[0106] In the above formula, f(Δu dc ,Δu C ,Δu W ) represents the optimal mathematical model of the MTDC system connected to the offshore wind farm, Δu dc is the control variable vector composed of the DC side voltage increments of each VSC in the MTDC system, Δu C is the voltage reference value of the WFSVC controller, Δu W is the reference vector of active power and reactive power of each wind turbine in the wind farm, N p is the number of sampling steps in the prediction period, M is the number of wind farms, N W is the number of wind turbines, and Vref are the measured value and reference voltage of the terminal voltage of wind turbine i in the m-th wind farm at time k, respectively; λ1, λ2, λ3, λ4 are weight parameters, is the active power loss of the m-th wind farm at the current time k, V dc (k) is the voltage increment vector of each VSC DC side in the MTDC system at time k, G dc is the node admittance matrix of the MTDC system, is the difference between the active power of the wind turbine at the current moment k in the m-th wind farm and the allocated power reference value; the constraints of the optimal mathematical model of the MTDC system connected to the offshore wind farm are:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] In the above formula, P Wi (k) is the active power of the i-th wind turbine at the current time k, is the available active power of the i-th wind turbine; Q Wi (k) is the reactive power output of the i-th wind turbine at the current time k, and are the minimum and maximum limits of reactive output of the i-th wind turbine respectively; G dc (i,j) is the node admittance between the i-th and j-th grid-side converters, V dci (k) and V dcj (k) are the predicted values of the DC bus voltage of the i-th and j-th grid-side converters at the current time k, is the rated active power on the DC cable line of the i-th and j-th grid-side converters, and are the minimum and maximum limits of the DC bus voltage of the i-th grid-side converter; V C,m (k) is the measured value of the AC bus voltage of the m-th wind farm-side converter at the current time k, and are the minimum and maximum limits of the AC bus voltage of the m-th wind farm-side converter respectively; is the actual command value of the power output of the i-th grid-side converter at the current moment k, V dci (0) and V dcj (0) are the measured values of the DC bus voltage of the i-th and j-th grid-side converters at the initial moment, P loss is the active power loss of the wind farm, P W is the active power output by the wind turbine, ΔP W is the active power increment vector output by each wind turbine in the wind farm, Q W is the reactive power output by the wind turbine, ΔQ W is the reactive power increment vector output by each wind turbine in the wind farm, V C is the AC side voltage of the wind farm side converter, is the sensitivity coefficient of wind turbine active power to wind farm active power loss, is the sensitivity coefficient of wind turbine reactive power to wind farm active power loss, is the sensitivity coefficient of the AC side voltage of the wind farm side converter to the active power loss of the wind farm, ΔV C is the AC side voltage increment of the wind farm side converter, is the intermediate variable, V i is the voltage of node i in the network, V j is the voltage of node j in the network, G ij is the conductance between nodes i and j, θ ij is the voltage phase angle difference between nodes i and j, is the voltage measurement value of the i-th wind turbine at the initial moment, V Wi is the voltage prediction value of the i-th wind turbine, is the sensitivity coefficient matrix of the active power of each wind turbine in the wind farm to the terminal voltage of the i-th wind turbine, is the sensitivity coefficient matrix of the reactive power of each wind turbine in the wind farm to the terminal voltage of the i-th wind turbine, P is the sensitivity coefficient of the AC side voltage of the wind farm side converter to the terminal voltage of the i-th wind turbine, Wi (0) is the measured active power value of the i-th wind turbine at the initial moment, ΔP Wi is the predicted value of the active power increment of the i-th wind turbine at the initial moment, αi is the wind power utilization rate of the i-th wind turbine, P dck (k) is the active power input to the MTDC system by the kth WFVSC at time k, P dci (k) is the active power input from the i-th wind farm-side converter to the MTDC system at time k, and n is the total number of grid-side converters in the MTDC system.

[0120] In this embodiment, the function expression of the optimized mathematical model of the MTDC system controller in step 2) is:

[0121]

[0122] In the above formula, f(Δu dc ,z g ) r+1 represents the r+1th iteration of the optimization mathematical model of MTDC system control, Δu dc is the control variable vector composed of the DC side voltage increments of each VSC in the MTDC system, z g is a global variable, λ3 is a weight coefficient, V dc (k) is the DC side voltage prediction vector of each grid-side converter at time k, G dc is the node admittance matrix of the MTDC system, y T is an auxiliary variable, is the local variable of the rth step of the optimization mathematical model of MTDC system control, and ρ is the penalty factor.

[0123] In this embodiment, the function expression of the optimized mathematical model of the wind farm controller in step 2) is:

[0124]

[0125] In the above formula, f(Δu C,m ,Δu W,m ,z l,m ) r+1 represents the r+1th iteration of the optimization mathematical model for wind farm control, Δu C,m is the AC bus voltage increment control variable of the m-th wind farm side converter, Δu W,m is the active and reactive power increment control variable of each wind turbine in the m-th wind farm, z l,m are local variables, λ1, λ2, λ4 are weight parameters, N p is the number of sampling steps in the prediction period, N W is the number of wind turbines, and V ref are the terminal voltage and reference voltage of wind turbine i in the m-th wind farm, is the active power loss of the m-th wind farm at the current time k, is the difference between the active power of the m-th wind farm at the current time k and the allocated power reference value, y m To update the auxiliary variables of the optimal mathematical model for wind farm control, It is the local variable of the r+1th step of the optimization mathematical model of wind farm control.

[0126] In this embodiment, the function expressions of the two standard QP problem mathematical models, namely the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller in step 2), are:

[0127]

[0128] St.x∈Ω

[0129] In the above formula, Φ(x) is the objective function, H and g are the Hessian matrix and coefficient matrix respectively, and Ω is the domain of the decision variable x.

[0130] According to the two standard QP problem mathematical models, the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller, a distributed optimization solution method based on the ADMM framework can be established. The distributed solution method based on ADMM is implemented by alternating calculation iterations of the MTDC controller and the wind farm controller, decomposing the large-scale complex constraint optimization problem into a simple constraint optimization problem and multiple parallel small-scale simple constraint optimization problems; wherein the simple constraint optimization problem only requires relevant information of the MTDC, which is completed in the MTDC controller and used to update the original variable x; the small-scale simple constraint optimization problem only requires relevant information of the wind farm, which is completed in the wind farm controller and used to update the auxiliary variable z; at the same time, the dual variable y is updated in the wind farm controller. Specifically, step 3) in this embodiment includes:

[0131] 3.1) Initialization step r = 0 of the optimization mathematical model of the MTDC system controller for local variables Auxiliary variable y when the value is 0 and r = 0 [0] = is 0 and the value of k at the current moment is 0; it is the local variable of the optimization mathematical model of the wind farm controller when the initialization step r=0 Auxiliary variable y when the value is 0 and r = 0 [0] = is 0 and the value of step r is 0;

[0132] 3.2) Determine whether step r is greater than 1 and the preset convergence conditions are met at the same time; if not, jump to the next step; otherwise, add 1 to the current moment k, and the MTDC system controller updates the global variable of the r+1 step of its optimization mathematical model And send it to the controller of the wind farm side converter; update the local variables of the wind farm controller's optimization mathematical model in the r+1 step through the wind farm side converter and auxiliary variables And send it to the MTDC system controller; jump to step 3.2);

[0133] 3.3) The MTDC system controller issues an iteration end instruction, and the MTDC system controller and the wind farm side converter controller issue instructions to update the current grid side converter, wind farm side converter voltage reference instructions and wind turbine active and reactive power instructions.

[0134] In this embodiment, the function expression of the convergence condition preset in step 3.2) is:

[0135]

[0136] In the above formula, R [r] and S [r] is an intermediate variable, is the local variable of the rth step of the optimization mathematical model of MTDC system control, is the global variable of the rth step of the optimization mathematical model of MTDC system control, It is the global variable of the r+1th step of the optimization mathematical model of MTDC system control.

[0137] In summary, this embodiment includes establishing an incremental state space prediction model of the entire system including a wind turbine, a wind farm-side converter, and a grid-side converter; establishing an optimization mathematical model of the MTDC system connected to the offshore wind farm based on the incremental state space prediction model of the entire system and determining its constraints; decomposing the established optimization mathematical model of the MTDC system connected to the offshore wind farm into two standard QP problem mathematical models: an optimization mathematical model of the MTDC system controller and an optimization mathematical model of the wind farm controller; and iteratively solving the two standard QP problem mathematical models: the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller using a distributed optimization solution method based on model prediction (MPC) and alternating direction multiplier method (ADMM) to update the current voltage reference instructions of the grid-side converter and the wind farm-side converter and the active and reactive instructions of the wind turbine. This embodiment can utilize the active power, loss, reactive power, and voltage control capabilities of the grid-side converter, the wind farm-side converter WFVSC, and the wind turbines to achieve voltage stability and loss reduction for the entire system, thereby improving the stability of the entire system and reducing the computational burden of traditional centralized control methods to meet the optimized operation control requirements of MTDC systems connected to large-scale offshore wind farms.

[0138] Furthermore, this embodiment provides a distributed optimization control system for an MTDC system connected to an offshore wind farm, comprising an interconnected microprocessor and a memory, wherein the microprocessor stores therein steps programmed or configured to execute the aforementioned distributed optimization control method for an MTDC system connected to an offshore wind farm. Furthermore, this embodiment provides a computer-readable storage medium storing therein a computer program for execution by a computer device to implement the aforementioned distributed optimization control method for an MTDC system connected to an offshore wind farm.

[0139] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0140] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A distributed optimization control method for an MTDC system connected to an offshore wind farm, characterized in that: include: 1) Establish an incremental state space prediction model for the entire system, including wind turbines, wind farm-side converters, and grid-side converters. Based on this incremental state space prediction model, establish an optimal mathematical model for the MTDC system connected to the offshore wind farm and determine its constraints. 2) Decompose the established optimization mathematical model of the MTDC system connected to the offshore wind farm into two standard QP problem mathematical models: the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller; 3) A distributed optimization solution based on the alternating direction multiplier method is used to iteratively solve two standard QP problem mathematical models: the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller, to update the current voltage reference instructions of the grid-side converter and the wind farm-side converter, as well as the active and reactive power instructions of the wind turbine. The function expression of the optimal mathematical model of the MTDC system controller in step 2) is: ， In the above formula, represents the r+1th iteration of the optimization mathematical model of MTDC system control, is the control variable vector composed of the DC side voltage increments of each VSC in the MTDC system, is a global variable, is the weight coefficient, N p is the number of sampling steps in the prediction period, for k The DC side voltage prediction vector of each grid-side converter at the moment, is the node admittance matrix of the MTDC system, is an auxiliary variable, The first part of the optimization mathematical model for MTDC system control r Local variables of the step, is the penalty factor; The function expression of the optimized mathematical model of the wind farm controller in step 2) is: ， In the above formula, The first part represents the optimal mathematical model for wind farm control. r +1 iteration, is the AC bus voltage increment control variable of the m-th wind farm side converter, is the active and reactive power increment control variable of each wind turbine in the m-th wind farm, is a local variable, , , is the weight parameter, N p is the number of sampling steps in the prediction period, N W is the number of wind turbines, and are the wind turbines in the m-th wind farm i The terminal voltage and reference voltage, is the current moment of the m-th wind farm k Active power loss, is the current moment of the m-th wind farm k The difference between the active power of the allocated power and the reference power value, To update the auxiliary variables of the optimal mathematical model for wind farm control, The optimal mathematical model for wind farm control r +1 step local variables.

2. The distributed optimization control method for the MTDC system connected to the offshore wind farm according to claim 1, characterized in that: The function expression of the incremental state space prediction model of the entire system established in step 1) is: ， ， In the above formula, and The entire system including wind turbines, wind farm side converters and grid side converters k +1 moment and k The state variables at the moment include the voltage of the grid-side converter and the wind farm-side converter and the active and reactive power of the wind turbine generator set. For the entire system including wind turbines, WFSVC and GSVSC k The control variables at the moment include the voltage reference instructions of the grid-side converter and the wind farm-side converter, and the active and reactive instructions of the wind turbine. 、 and are the discretized state equation matrices, For the entire system including wind turbines, wind farm side converters and grid side converters k The output variables at the time include the voltages of the grid-side converter and the wind farm-side converter and the active and reactive power of the wind turbine.

3. The distributed optimization control method for the MTDC system connected to the offshore wind farm according to claim 2, characterized in that: The function expression of the optimization mathematical model of the MTDC system connected to the offshore wind farm established in step 1) is: ， In the above formula, The optimal mathematical model of the MTDC system connected to the offshore wind farm is represented. is the control variable vector composed of the DC side voltage increments of each VSC in the MTDC system, is the voltage reference value of the WFSVC controller, is the reference vector of active power and reactive power of each wind turbine in the wind farm, N p is the number of sampling steps in the prediction period, M is the number of wind farms, N W is the number of wind turbines, and are the wind turbines in the m-th wind farm i Terminal voltage k The measured value and reference voltage value at each moment, , , , is the weight parameter, is the current moment of the m-th wind farm k Active power loss, for The DC side voltage increment vector of each VSC in the MTDC system at time t, is the node admittance matrix of the MTDC system, is the current moment of the wind turbine in the mth wind farm k The difference between the active power and the allocated power reference value; the constraints of the optimization mathematical model of the MTDC system connected to the offshore wind farm are: ， ， ， ， ， ， ， ， ， ， ， ， In the above formula, For the i Typhoon turbine current time k The active power, For the i The available active power of the typhoon turbines; For the i Typhoon turbine current time k The reactive power output, and Respectively i Minimum and maximum limits for reactive output of typhoon turbines; For the i 、 j Node admittance between grid-side converters, and Respectively i 、 j The DC bus voltage of the grid-side converter at the current moment k The predicted value of For the i 、 j The rated active power on the DC cable line of the grid-side converter, and Respectively i The minimum and maximum limits of the DC bus voltage of the grid-side converter; is the AC bus voltage of the m-th wind farm side converter at the current moment k The measured value of and are the minimum and maximum limits of the AC bus voltage of the m-th wind farm-side converter respectively; For the i The power of the grid-side converter at the current moment k The actual command value output, and Respectively i 、 j The measured value of the DC bus voltage of the grid-side converter at the initial moment, is the active power loss of the wind farm, is the active power output by the wind turbine, is the incremental active power vector output by each wind turbine in the wind farm, is the reactive power output by the wind turbine, is the reactive power increment vector output by each wind turbine in the wind farm, is the AC side voltage of the wind farm side converter, is the AC side voltage increment of the wind farm side converter, is an intermediate variable, Nodes in the network i The voltage, Nodes in the network j The voltage, For nodes i 、 j The conductance between For nodes i 、 j The voltage phase angle difference between For the i The voltage measurement value of the typhoon generator at the initial moment, For the i The voltage forecast value of the typhoon generator, The current moment of the wind turbine k The difference between the active power of the allocated power and the reference power value, For the i The measured active power value of the typhoon generator at the initial moment, For the i The predicted value of the active power increment of the typhoon generator at the initial moment, For the i Wind power utilization rate of typhoon turbines, for k Moment k The active power input from each WFVSC to the MTDC system, for k Moment i The active power input from the wind farm side converter to the MTDC system, n is the total number of grid-side converters in the MTDC system.

4. The distributed optimization control method for the MTDC system connected to an offshore wind farm according to claim 1, characterized in that: The function expressions of the two standard QP problem mathematical models of the optimization mathematical model of the MTDC system controller and the optimization mathematical model of the wind farm controller in step 2) are: ， St. , In the above formula, is the objective function, and are the Hessian matrix and the coefficient matrix respectively, is the decision variable The domain of definition.

5. The distributed optimization control method for the MTDC system connected to the offshore wind farm according to claim 4 is characterized in that: Step 3) includes: 3.1) Initialization step for the optimization mathematical model of the MTDC system controller r= Local variables at time 0 The value of is 0, r= Auxiliary variable y at time 0 [0] The value is 0 and the current time k The value is 0; it is the initialization step of the optimization mathematical model of the wind farm controller r= Local variables at time 0 The value of is 0, r= Auxiliary variable y at time 0 [0] The value is 0 and the step r The value of is 0; 3.2) Judgment step r Is greater than 1, and are the preset convergence conditions met at the same time? If not, jump to the next step; otherwise, the current moment k Add 1, and the MTDC system controller updates the first r+ Global variables for step 1 , and sent to the controller of the wind farm side converter; the wind farm side converter updates the optimized mathematical model of the wind farm controller. r +1 step local variables and auxiliary variables and sent to the MTDC system controller; jump to step 3.2); 3.3) The MTDC system controller issues an iteration end instruction, and the MTDC system controller and the wind farm side converter controller issue instructions to update the current voltage reference instructions of the grid side converter and the wind farm side converter and the active and reactive power instructions of the wind turbine.

6. The distributed optimization control method for the MTDC system connected to the offshore wind farm according to claim 5, characterized in that: The function expression of the convergence condition preset in step 3.2) is: ， ， ； In the above formula, and is an intermediate variable, The first part of the optimization mathematical model for MTDC system control r Local variables of the step, The first part of the optimization mathematical model for MTDC system control r Global variables of the step, The first part of the optimization mathematical model for MTDC system control r+ Global variables for step 1, is the set threshold parameter.

7. A distributed optimization control system for an MTDC system connected to an offshore wind farm, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor stores therein steps programmed or configured to execute the steps of the distributed optimization control method for the MTDC system connected to the offshore wind farm as claimed in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program for being executed by a computer device to implement the distributed optimization control method for an MTDC system connected to an offshore wind farm according to any one of claims 1 to 6.

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