Distributed power supply aggregation additional control method and related device

Abstract

The invention provides a distributed power supply aggregation additional control method and a related device. The distributed power supply aggregation additional control method comprises the following steps: acquiring a coupling variable in a system; determining an independent variable by using a relative gain matrix based on the coupling variable; constructing a control model based on internal model control and pipeline model prediction control; adapting the uncertainty of the voltage deviation based on the independent variables and control model predictive control; according to the distributed power supply aggregation additional control method provided by the invention, a controller with separated performance is designed, dynamic response and error compensation processes of aggregation additional control and aggregation robust control are separated, control decoupling is realized, and the problem that parameters of the additional controller are difficult to independently optimize is solved; meanwhile, the influence of an additional controller on the stability and dynamic performance of an original control system is restrained, and the uncertainty possibly causing more serious voltage deviation can be adapted through accurate prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to the technical field of distributed power systems, and in particular to a distributed power source aggregation additional control method and related devices. Background Art

[0002] With the vigorous development of new energy in recent years, distributed energy sources, loads and storage, such as rooftop photovoltaics, smart home appliances, electric vehicles and charging piles, have been connected to the power grid, which has put forward higher requirements for distributed energy regulation. Distributed energy has the characteristics of complex associations, small capacity and dispersed locations. It can be aggregated and regulated by third-party aggregators or power grids, or it can be autonomously regulated through the exchange of internal and external information.

[0003] The introduction of distributed power sources makes the operation of distribution networks more complicated, while the system-level control of distribution networks focuses on the system stability control over a longer time scale, and achieves global control optimization through the system control framework and solution algorithm design. System-level control includes centralized control, distributed control, and hierarchical and partitioned control, and has been widely used in the fields of economic optimization, voltage control, and frequency control of active distribution networks.

[0004] Conventional control design and control structure have difficulty in dealing with the contradiction between robustness and dynamics. In addition, in distribution systems with multiple uncertainties such as drastic power fluctuations of distributed power sources, random load access, unmodeled dynamics, parameter perturbations, etc., it is impossible to achieve good performance by relying solely on aggregate robust control.

[0005] Therefore, there are technical problems in the prior art that the existing control design and control structure are difficult to deal with the contradiction between robustness and dynamics, and good performance cannot be achieved by relying solely on aggregate robust control. Summary of the invention

[0006] The main purpose of the present invention is to provide a distributed power source aggregation additional control method and related devices to solve the technical problems in the prior art that the existing control design and control structure are difficult to deal with the contradiction between robustness and dynamics, and good performance cannot be achieved by relying solely on aggregated robust control.

[0007] In order to achieve the above-mentioned purpose, according to one aspect of the present invention, a distributed power generation aggregation additional control method is provided, comprising the following steps: obtaining coupling variables in the system; determining independent variables based on the coupling variables using a relative gain matrix; constructing a control model based on internal model control and pipeline model predictive control; and adapting to the uncertainty of voltage deviation based on the independent variables and control model predictive control to keep the voltage deviation in a safe and stable range.

[0008] In some embodiments, determining the independent variable based on the coupling variable using a relative gain matrix includes: constructing a relative gain matrix based on the coupling variable; determining decoupling information of the coupling variable based on the gain matrix; and decoupling the coupling variable using a compensation matrix based on the decoupling information.

[0009] In some embodiments, the relative gain matrix is:

[0010] in

[0011] p ij is the first magnification factor, indicating that r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification factor; q ij The second magnification factor is expressed in y r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification coefficient of the relative gain matrix Λ is 1 in each row or column.

[0012] In some implementations, determining the decoupling information of the coupling variables based on the gain matrix includes: determining the decoupling information of the coupling variables based on the strong and weak coupling between the control variables by λ ij The value is judged: 0.8<λ ij <1.2, indicating that other processes are weakly coupled to this process and do not need to be decoupled; ij ≈0, indicating that the regulation effect of this process is weak and is not suitable as the optimal regulation process; 0.3<λ ij <0.7 or λ ij >1.5, indicating that other processes are strongly coupled to this process and need to be decoupled.

[0013] In some embodiments, the compensation matrix is:

[0014]

[0015] Where G(s) is the transfer function matrix of the system, and the decoupling compensation matrix D can be expressed as the inverse matrix of the transfer matrix, K ij ; represents the gain of input uj to output yi, τ ij : represents the time constant of input uj to output yi, s: Laplace variable in the complex domain.

[0016] In some embodiments, constructing a control model based on the internal model control and the pipeline model predictive control includes determining an objective function of a nominal controller of the internal model control and the pipeline model predictive control, where the objective function is defined as:

[0017]

[0018] In the formula, Q N and R N H represents the weight matrix of the corresponding input and output of the nominal model predictive controller; p and H c denote the prediction time domain and the control time domain respectively; Δv controls the change in input, z(k+1)-z ref : state vector and reference state z ref The deviation between .

[0019] In some embodiments, the discrete state space equations for pipeline model predictive control are:

[0020] x[k+1]=Ax[k]+B u u[k]+B w w[k]

[0021] y[k]=Cx[k];

[0022] Where:

[0023]

[0024] Active power output of the 1st to nth PV systems at time k;

[0025] Reactive power output of the 1st to nth PV systems at time k;

[0026] Active power output of the 1st to nth energy storage systems at time k;

[0027] Reactive power output of the 1st to nth energy storage systems at time k;

[0028] Control inputs of the 1st to nth PV systems at time k;

[0029] Active power control input of the 1st to nth energy storage systems;

[0030] Reactive power control input of the 1st to nth energy storage systems;

[0031] Active power disturbances of the 1st to nth PV systems at time k due to the disturbance of the maximum power point tracking algorithm.

[0032] According to another aspect of the present invention, the present invention also provides a distributed power source aggregation additional control device, including: an acquisition module, used to acquire coupling variables in the system; a determination module, which determines independent variables based on the coupling variables using a relative gain matrix; a construction module, which constructs a control model based on internal model control and pipeline model predictive control; a prediction module, which adapts to the uncertainty of voltage deviation based on the independent variables and control model predictive control to keep the voltage deviation in a safe and stable range.

[0033] According to another aspect of the present invention, the present invention further provides a storage medium storing a computer program, wherein the computer program implements the steps of any of the aforementioned methods when executed by a processor.

[0034] According to another aspect of the present invention, the present invention also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the aforementioned methods when executing the computer program on the memory.

[0035] The distributed power generation aggregation additional control method and related devices provided by the present invention are applied to obtain the coupling variables in the system; and determine the independent variables based on the coupling variables using the relative gain matrix; and then build a control model based on internal model control and pipeline model predictive control; finally, based on the independent variables and control model predictive control to adapt to the uncertainty of voltage deviation, by designing a performance-separated controller, separating the dynamic response and error compensation process of the aggregated additional control and the aggregated robust control, to achieve "control decoupling", solve the problem that the additional controller parameters are difficult to optimize independently, and at the same time suppress the influence of the additional controller on the stability and dynamic performance of the original control system, so that the uncertainty that may cause more serious voltage deviation can be adapted through accurate prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0037] Figure 1 A flowchart showing the steps of the distributed power aggregation additional control method provided by the present disclosure is shown;

[0038] Figure 2 A flowchart of the steps of determining independent variables based on coupled variables using a relative gain matrix provided by the present disclosure is shown;

[0039] Figure 3 It is a structural block diagram of the distributed power aggregation additional control device provided by the present disclosure;

[0040] Figure 4It is a schematic diagram of the structure of the electronic device provided by the present disclosure. DETAILED DESCRIPTION

[0041] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, but are not intended to limit the present disclosure.

[0042] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the above description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0043] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0044] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0045] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0046] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0047] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0049] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0050] The present disclosure is further described below in conjunction with the accompanying drawings and specific embodiments.

[0051] In order to solve the technical problems in the prior art that the existing control design and control structure are difficult to deal with the contradiction between robustness and dynamics, and good performance cannot be achieved by relying solely on aggregated robust control, the present invention provides a distributed power source aggregation additional control method and related devices.

[0052] Figure 1 The following is a flowchart showing the steps of the distributed power aggregation additional control method provided by the present disclosure, as shown in Figure 1 As shown, the distributed power source aggregation additional control method includes the following steps:

[0053] S101, obtaining coupling variables in the system.

[0054] In this step, the coupling variables in the system are obtained.

[0055] Specifically, in the power aggregation additional control process, the coupling variable provided by the system is obtained, where the coupling variable can be different functions, for example, the active power P of the photovoltaic system PV , the reactive power Q of the energy storage system ES , voltage setting value V set , current setting value I set wait.

[0056] S102, determining independent variables using a relative gain matrix based on the coupling variables.

[0057] After completing the above step S101, in this step, the independent variable is determined based on the coupling variable using the relative gain matrix.

[0058] Furthermore, the variable coupling between different controllers of the same control object is decoupled through decoupling compensation matrix design and control structure parameterization adjustment. After identifying the coupling variables in the system, the decoupling compensation matrix is ​​designed using the relative gain matrix method, and the controller parameters are adjusted to make them work independently in the decoupled system.

[0059] Specifically, Figure 2 The flowchart of the steps of determining the independent variables based on the coupling variables using the relative gain matrix provided by the present disclosure is shown as follows: Figure 2 As shown, determining the independent variable based on the coupling variable using the relative gain matrix includes the following steps:

[0060] S201, constructing a relative gain matrix based on the coupling variables.

[0061] In this step, a relative gain matrix is ​​constructed based on the coupling variables.

[0062] Assume that the n-dimensional input of the system is U = [u1 u2…u n ] T , the corresponding system n-dimensional output Y = [y1 y2…y n ] T , the relative gain matrix is:

[0063]

[0064] p ij is the first magnification factor, indicating that r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification factor of q ij The second magnification factor is expressed in y r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification coefficient of the relative gain matrix Λ is 1 in each row or column.

[0065] S202: Determine decoupling information of coupling variables based on the gain matrix.

[0066] After completing the above step S201, in this step, decoupling information of the coupling variables is determined based on the gain matrix.

[0067] Specifically, after the relative gain matrix is ​​constructed, the similarity gain matrix is ​​solved to obtain the relative gain of the coupled variables, that is, the decoupling information.

[0068] The decoupling information of coupling variables determined by the gain matrix includes: based on the strong and weak coupling between the control variables, the decoupling information of coupling variables is determined by λ ij The value is judged: 0.8<λ ij <1.2, indicating that other processes are weakly coupled to this process and do not need to be decoupled; ij ≈0, indicating that the regulation effect of this process is weak and is not suitable as the optimal regulation process; 0.3<λ ij <0.7 or λ ij >1.5, indicating that other processes are strongly coupled to this process and need to be decoupled.

[0069] S203: Decouple the coupling variables using a compensation matrix based on the decoupling information.

[0070] After completing the above step S202, in this step, the coupling variables are decoupled using a compensation matrix based on the decoupling information.

[0071] Specifically, for the coupling variables that need to be decoupled, decoupling is performed through a decoupling compensation matrix, wherein the compensation matrix is:

[0072]

[0073] Where G(s) is the transfer function matrix of the system, and the decoupling compensation matrix D can be expressed as the inverse matrix of the transfer matrix, K ij ; represents the gain of input uj to output yi, τ ij : represents the time constant of input uj to output yi, s: Laplace variable in the complex domain.

[0074] Through static decoupling, the coupling relationship between input variables and multiple output variables can be eliminated, thereby ensuring that each input variable only has a major impact on its target output.

[0075] For example, after static decoupling, the active power P of the photovoltaic system PV It mainly affects the frequency f of the power grid, but does not affect the bus voltage V and the reactive power Q of the energy storage system. ES The main purpose is to adjust the bus voltage V rather than the frequency.

[0076] S103, constructing a control model based on internal model control and pipeline model predictive control.

[0077] In this step, a control model is constructed based on internal model control and pipeline model predictive control.

[0078] Specifically, by combining generalized internal model control and pipeline model predictive control, the decoupled variables in step 1 are applied to the control structure to separate the dynamic response transmission process and the error compensation feedback process; wherein, a control model is constructed based on internal model control and pipeline model predictive control, and the discrete state space equation of the control model is as follows:

[0079] x[k+1]=Ax[k]+B u u[k]+B w w[k]

[0080] y[k]=Cx[k];

[0081] Where;

[0082]

[0083] Active power output of the 1st to nth PV systems at time k;

[0084] Reactive power output of the 1st to nth PV systems at time k;

[0085] Active power output of the 1st to nth energy storage systems at time k;

[0086] Reactive power output of the 1st to nth energy storage systems at time k;

[0087] Control inputs of the 1st to nth PV systems at time k;

[0088] Active power control input of the 1st to nth energy storage systems;

[0089] Reactive power control input of the 1st to nth energy storage systems;

[0090] Active power disturbances of the 1st to nth PV systems at time k due to the disturbance of the maximum power point tracking algorithm.

[0091] In this step, the active power change ΔP PV、 Reactive power change ΔQ ES、The dynamic adjustment of voltage setting Vset(t), the dynamic adjustment of frequency setting Δfset(t), etc. are used as input variables, the voltage error eV(t) = V(t)-Vref, the frequency error ef(t) = f(t)-fref, and the control input adjustment amount Δu(t) are used as output variables to realize dynamic decoupling of variables, which can eliminate steady-state errors and ensure that the system reaches the set reference value in long-term operation. For example, eV(t) and ef(t) are gradually eliminated, and the future state can be predicted, and the control input can be adjusted in a short time to deal with possible disturbances. For example, MPC predicts the future voltage and frequency state and adjusts the control input in advance.

[0092] S104, adapting to the uncertainty of voltage deviation based on the independent variables and the control model predictive control, so that the voltage deviation is maintained in a safe and stable range.

[0093] After completing the above step S103, in this step, the uncertainty of the voltage deviation is adapted to the predictive control based on the independent variables and the control model, so that the voltage deviation is kept in a safe and stable range.

[0094] Specifically, in order to ensure the dynamic control performance in the scenario of multiple renewable energy sources and low-voltage distribution networks, a nominal controller and an auxiliary controller are designed to coordinate distributed power sources to keep the voltage deviation in a safe and stable range, wherein the nominal controller is mainly responsible for optimizing the control strategy according to a predetermined model under ideal conditions, in the expectation that the power grid will operate stably as planned. It attempts to achieve or maintain specific power grid operation goals by predicting future states and optimizing control behaviors. The auxiliary controller dynamically adjusts to uncertainties encountered in actual operation, such as output fluctuations of renewable energy. Its role is to monitor and regulate deviations from the plan caused by these uncertainties, ensuring that the power grid remains in a safe and stable state even in the face of external disturbances.

[0095] Among them, the objective function of the nominal controller is:

[0096]

[0097] In the formula, Q N and R N H represents the weight matrix of the corresponding input and output of the nominal model predictive controller; p and H c denote the prediction time domain and the control time domain respectively; Δv controls the change in input, z(k+1)-z ref : state vector and reference state z ref The deviation between .

[0098] The objective function of the auxiliary controller is:

[0099]

[0100] Among them, y(k): the output variable of the system at the kth moment, y ref : The reference target value of the system, that is, the target output that the system is expected to track, Q A : Weight matrix, used to adjust the priority of different output variables; R A : Weight matrix, used to measure the cost of control input change, Δu(k) = u(k)-u(k-1): Change in control input, indicating the difference between the current control input and the input at the previous moment, γ: Balance coefficient, a trade-off between control error compensation and input stability, w(k): External disturbance, such as load change, photovoltaic power fluctuation and other unpredictable factors, P A : Weight matrix, measuring the priority of disturbance compensation, β: Weighting coefficient, used to adjust the importance of disturbance compensation, H c : Control time domain, that is, the number of control steps of the controller in the future time period.

[0101] In this step, the common input of the nominal controller is the active power P of the PV system PV , the reactive power Q of the energy storage system ES The nominal controller can maintain the voltage V(t) and frequency f(t) within the set range. The common inputs of the auxiliary controller are voltage deviation eV(t), frequency deviation ef(t), and power disturbance w(t). The auxiliary controller can quickly adjust the control input ΔP when a disturbance occurs. PV , ΔQ ES , so that the system can be restored to stability.

[0102] The distributed power aggregation additional control method provided by the present invention is applied to obtain the coupling variables in the system; and determine the independent variables based on the coupling variables using the relative gain matrix; and then build a control model based on internal model control and pipeline model predictive control; finally, adapt to the uncertainty of voltage deviation based on the independent variables and control model predictive control, and achieve "control decoupling" by designing a performance-separated controller to separate the dynamic response and error compensation process of the aggregated additional control and the aggregated robust control, thereby solving the problem that the additional controller parameters are difficult to optimize independently, and at the same time suppressing the influence of the additional controller on the stability and dynamic performance of the original control system, so that the uncertainty that may cause more serious voltage deviation can be adapted through accurate prediction.

[0103] In order to better implement the above method, the second aspect of the present disclosure further provides a distributed power source aggregation additional control device, which can be integrated on an electronic device.

[0104] For example, Figure 3As shown, the control device 200 may include: an acquisition module 210, a determination module 220, a construction module 230 and a prediction module 240, which are specifically as follows:

[0105] (1) An acquisition module 210, which is used to acquire coupling variables in the system.

[0106] (2) A determination module 220, wherein the determination module 220 is used to determine the independent variable based on the coupled variable using a relative gain matrix.

[0107] (3) A construction module 230, wherein the construction module 230 is used to construct a control model based on internal model control and pipeline model predictive control.

[0108] (4) A prediction module 240, which is used to adapt to the uncertainty of voltage deviation based on the independent variables and the control model prediction control, so as to keep the voltage deviation in a safe and stable range.

[0109] Furthermore, the determination module 220 includes a construction unit, a determination unit and a decoupling unit, wherein the construction unit is used to construct a relative gain matrix based on the coupling variables, the determination unit determines the decoupling information of the coupling variables based on the gain matrix, and the decoupling unit decouples the coupling variables using a compensation matrix based on the decoupling information.

[0110] Furthermore, the relative gain matrix is:

[0111] in

[0112] p ij is the first magnification factor, indicating that r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification factor of q ij The second magnification factor is expressed in y r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification coefficient of the relative gain matrix Λ is 1 in each row or column.

[0113] Further, determining the decoupling information of the coupling variables based on the gain matrix includes: determining the decoupling information of the coupling variables based on the strong and weak coupling between the control variables by λ ij The value is judged: 0.8<λ ij <1.2, indicating that other processes are weakly coupled to this process and do not need to be decoupled; ij ≈0, indicating that the regulation effect of this process is weak and is not suitable as the optimal regulation process; 0.3<λij <0.7 or λ ij >1.5, indicating that other processes are strongly coupled to this process and need to be decoupled.

[0114] In some embodiments, the compensation matrix is:

[0115]

[0116] Where G(s) is the transfer function matrix of the system, and the decoupling compensation matrix D can be expressed as the inverse matrix of the transfer matrix, K ij ; represents the gain of input uj to output yi, τ ij : represents the time constant of input uj to output yi, s: Laplace variable in the complex domain.

[0117] Further, constructing a control model based on the internal model control and the pipeline model predictive control includes determining an objective function of a nominal controller of the internal model control and the pipeline model predictive control, and the objective function is defined as:

[0118]

[0119] In the formula, Q N and R N H represents the weight matrix of the corresponding input and output of the nominal model predictive controller; p and H c denote the prediction time domain and the control time domain respectively; Δv controls the change in input, z(k+1)-z ref : state vector and reference state z ref The deviation between .

[0120] Furthermore, the discrete state space equations of pipeline model predictive control are:

[0121] x[k+1]=Ax[k]+B u u[k]+B w w[k]

[0122] y[k]=Cx[k];

[0123] Where;

[0124]

[0125]

[0126] Active power output of the 1st to nth PV systems at time k;

[0127] Reactive power output of the 1st to nth PV systems at time k;

[0128] Active power output of the 1st to nth energy storage systems at time k;

[0129] Reactive power output of the 1st to nth energy storage systems at time k;

[0130] Control inputs of the 1st to nth PV systems at time k;

[0131] Active power control input of the 1st to nth energy storage systems;

[0132] Reactive power control input of the 1st to nth energy storage systems;

[0133] Active power disturbances of the 1st to nth PV systems at time k due to the disturbance of the maximum power point tracking algorithm.

[0134] The distributed power aggregation additional control device provided by the present invention is applied to obtain the coupling variables in the system; and determine the independent variables based on the coupling variables using the relative gain matrix; and then build a control model based on internal model control and pipeline model predictive control; finally, the uncertainty of voltage deviation is adapted to the predictive control based on the independent variables and the control model, and the dynamic response and error compensation process of the aggregated additional control and the aggregated robust control are separated by designing a performance-separated controller to achieve "control decoupling", solve the problem that the additional controller parameters are difficult to optimize independently, and at the same time suppress the influence of the additional controller on the stability and dynamic performance of the original control system, so that the uncertainty that may cause more serious voltage deviation can be adapted through accurate prediction.

[0135] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0136] To this end, a third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the embodiment of the present disclosure is implemented, including the following steps S11 to S14:

[0137] S11, obtain the coupling variables in the system;

[0138] S12, determining independent variables using a relative gain matrix based on the coupled variables;

[0139] S13, constructing a control model based on internal model control and pipeline model predictive control;

[0140] S14, predicting and controlling the independent variables and the control model to adapt to the uncertainty of the voltage deviation, so that the voltage deviation is kept in a safe and stable range.

[0141] Furthermore, when the computer program is executed by a processor, other methods provided by any of the above embodiments of the present disclosure are implemented.

[0142] The distributed power aggregation additional control method provided by the present invention is applied to obtain the coupling variables in the system; and determine the independent variables based on the coupling variables using the relative gain matrix; and then build a control model based on internal model control and pipeline model predictive control; finally, adapt to the uncertainty of voltage deviation based on the independent variables and control model predictive control, and achieve "control decoupling" by designing a performance-separated controller to separate the dynamic response and error compensation process of the aggregated additional control and the aggregated robust control, thereby solving the problem that the additional controller parameters are difficult to optimize independently, and at the same time suppressing the influence of the additional controller on the stability and dynamic performance of the original control system, so that the uncertainty that may cause more serious voltage deviation can be adapted through accurate prediction.

[0143] A fourth embodiment of the present disclosure provides an electronic device, such as Figure 4 As shown, the electronic device at least includes a memory 310 and a processor 320. The memory 310 stores a computer program. The processor 320 implements the method provided by any embodiment of the present disclosure when executing the computer program on the memory 310. Exemplarily, the method executed by the electronic device computer program is as follows:

[0144] S21, obtain the coupling variables in the system;

[0145] S22, determining independent variables using a relative gain matrix based on the coupled variables;

[0146] S23, constructing a control model based on internal model control and pipeline model predictive control;

[0147] S24, predicting and controlling the independent variables and the control model to adapt to the uncertainty of the voltage deviation, so that the voltage deviation is kept in a safe and stable range.

[0148] In specific implementation, the acquisition module 210, the determination module 220, the construction module 230 and the prediction module 240 are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0149] The distributed power aggregation additional control method provided by the present invention is applied to obtain the coupling variables in the system; and determine the independent variables based on the coupling variables using the relative gain matrix; and then build a control model based on internal model control and pipeline model predictive control; finally, adapt to the uncertainty of voltage deviation based on the independent variables and control model predictive control, and achieve "control decoupling" by designing a performance-separated controller to separate the dynamic response and error compensation process of the aggregated additional control and the aggregated robust control, thereby solving the problem that the additional controller parameters are difficult to optimize independently, and at the same time suppressing the influence of the additional controller on the stability and dynamic performance of the original control system, so that the uncertainty that may cause more serious voltage deviation can be adapted through accurate prediction.

[0150] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0151] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0152] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0153] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a stand-alone software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0154] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0155] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0156] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0157] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0158] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0159] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0160] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0161] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0162] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A distributed power source aggregation additional control method, characterized in that: The steps include: Get the coupling variables in the system; Determining independent variables using a relative gain matrix based on the coupled variables; Construct control model based on internal model control and pipeline model predictive control; Based on the independent variables and the control model, predictive control is used to adapt to the uncertainty of voltage deviation, so that the voltage deviation is kept in a safe and stable range.

2. The distributed power source aggregation additional control method according to claim 1 is characterized in that: Determining the independent variable by using the relative gain matrix based on the coupled variable comprises: constructing a relative gain matrix based on the coupling variables; Determining decoupling information of coupling variables based on the gain matrix; The coupling variables are decoupled using a compensation matrix based on the decoupling information.

3. The distributed power source aggregation additional control method according to claim 2 is characterized in that: The relative gain matrix is: in p ij is the first magnification factor, indicating that r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification factor of q ij The second magnification factor is expressed in y r When (r≠i) remains unchanged, output y i For input u j The transfer relationship or static amplification coefficient of the relative gain matrix Λ is 1 in each row or column.

4. The distributed power source aggregation additional control method according to claim 2 is characterized in that: Determining the decoupling information of the coupling variables based on the gain matrix includes: determining the decoupling information of the coupling variables based on the strong and weak coupling between the control variables by λ ij The value is judged: 0.8<λ ij <1.2, indicating that other processes are weakly coupled to this process and do not need to be decoupled; ij ≈0, indicating that the regulation effect of this process is weak and is not suitable as the optimal regulation process; 0.3<λ ij <0.7 or λ ij >1.5, indicating that other processes are strongly coupled to this process and need to be decoupled.

5. The distributed power source aggregation additional control method according to claim 2 is characterized in that: The compensation matrix is: Where G(s) is the transfer function matrix of the system, and the decoupling compensation matrix D can be expressed as the inverse matrix of the transfer matrix, K ij ; represents the gain of input uj to output yi, τ ij : represents the time constant of input uj to output yi, s: Laplace variable in the complex domain.

6. The distributed power source aggregation additional control method according to claim 1, characterized in that: Building a control model based on internal model control and pipeline model predictive control includes determining the objective function of the nominal controller of internal model control and pipeline model predictive control, which is defined as: In the formula, Q N and R N H represents the weight matrix of the corresponding input and output of the nominal model predictive controller; p and H c denote the prediction time domain and the control time domain respectively; Δv controls the change in input, z(k+1)-z ref : state vector and reference state z ref The deviation between .

7. The distributed power source aggregation additional control method according to claim 1 is characterized in that: Discrete state space equations for the predictive control model: x[k+1]=Ax[k]+B u u[k]+B w w[k] y[k]=Cx[k]; where Active power output of the 1st to nth PV systems at time k; Reactive power output of the 1st to nth PV systems at time k; Active power output of the 1st to nth energy storage systems at time k; Reactive power output of the 1st to nth energy storage systems at time k; Control inputs of the 1st to nth PV systems at time k; Active power control input of the 1st to nth energy storage systems; Reactive power control input of the 1st to nth energy storage systems; Active power disturbances of the 1st to nth PV systems at time k due to the disturbance of the maximum power point tracking algorithm.

8. A distributed power source aggregation additional control device, characterized in that: include: An acquisition module is used to acquire coupling variables in the system; A determination module, which determines independent variables using a relative gain matrix based on the coupled variables; Building modules, building control models based on internal model control and pipeline model predictive control; The prediction module adapts to the uncertainty of voltage deviation based on the independent variables and the control model prediction control, so as to keep the voltage deviation in a safe and stable range.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the distributed power source aggregation additional control method described in any one of claims 1 to 7 are implemented.

10. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the distributed power aggregation additional control method described in any one of claims 1 to 7 when executing the computer program on the memory.

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