Electric power system automatic power generation control method and system supporting online parameter identification and distributed model prediction

Abstract

The invention provides a power system automatic power generation control method and system supporting online parameter identification and distributed model prediction. According to the method, in order to solve the problem that the frequency modulation capacity of traditional power generation resources is gradually insufficient, wind power, photovoltaic and battery energy storage systems are added into an electric power system and cooperatively output with a traditional thermal power generating unit for common frequency modulation. Considering that a new energy unit has no limitation of power generation rate compared with a traditional thermal power unit and can quickly respond to load disturbance to adjust output, a distributed model predictive control (DMPC) method is designed, the quick frequency modulation capability of a quick frequency modulation unit can be fully exerted, and frequency fluctuation can be quickly suppressed. Due to the fact that the uncertainty of key parameters of the system is enhanced due to improvement of the new energy permeability, an online parameter identification method is designed in order to adopt real system key parameters during calculation of a DMPC controller in the control system, and dynamic parameter updating of the control system is achieved when the key parameters of the system change greatly.

Description

Technical Field

[0001] The present invention belongs to the technical field of novel power system AGC, and in particular relates to a power system automatic generation control method and system supporting online parameter identification and distributed model prediction. Background Art

[0002] With the increasing penetration of power electronics and new energy in new power systems, the inertia of the system is getting lower and lower, and the uncertainty of parameters is gradually increasing. At the same time, the proportion of traditional frequency regulation resources is getting lower and lower. At this time, if we only rely on traditional frequency regulation resources, there will be a problem of insufficient frequency regulation capacity. Therefore, new frequency regulation resources are urgently needed to coordinate the output of traditional resources. At present, centralized PI control is often used for power system AGC (automatic generation control, AGC), but it has the following problems:

[0003] 1) It has a heavy communication and computational burden and is easily affected by single point failures;

[0004] 2) The unit output weight factor of each generator set is fixed, which makes it difficult to give full play to the rapid frequency regulation potential of new frequency regulation resources and cannot adapt to the development trend of new power systems.

[0005] Distributed PI control configures a PI controller for each unit. Compared with centralized PI control, it is an improvement and is no longer affected by single-point failures. However, during frequency regulation in response to load disturbances, its control law is fixed and cannot guarantee that the control signal for each control cycle is optimal. Therefore, more advanced AGC methods are still needed to achieve optimal coordinated output and give full play to the frequency regulation potential of each unit.

[0006] In order to solve the above problems, many scholars have been seeking an ideal technical solution. Summary of the invention

[0007] The purpose of the present invention is to adapt to the development of new power systems and solve the shortcomings of the existing technology. A method and system for automatic power generation control of power systems that supports online parameter identification and distributed model prediction is proposed, which can improve the frequency regulation performance and stability of new power systems.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is:

[0009] First, in response to the increasingly insufficient frequency regulation capacity of traditional power generation resources, wind power, photovoltaic and battery energy storage systems are added to the power system to coordinate output with traditional thermal power units and jointly regulate frequency. Considering that new energy units have no power generation rate restrictions compared to traditional thermal power units and have the advantage of quickly responding to control signals to adjust output, a distributed model predictive control (DMPC) method is designed to obtain the optimal control signal for each control cycle, give full play to the fast frequency regulation capacity of fast frequency regulation units, and quickly suppress frequency fluctuations. In addition, due to the increase in the penetration rate of new energy, the uncertainty of the key parameters of the system has increased. In order to make the DMPC controller in the control system use the real key system parameters when calculating, an online parameter identification method is designed, which can realize the online dynamic update of the control system parameters when the key system parameters change greatly, and improve the accuracy of the calculation results of the DMPC controller in the control system.

[0010] Specifically, the first aspect of the present invention provides a method for automatic power generation control of a power system supporting online parameter identification and distributed model prediction, characterized in that it includes the following steps:

[0011] Establish a new heterogeneous multi-source power system in the two regions to add wind power, photovoltaic and battery energy storage systems to the power system, coordinate output with traditional thermal power units, and jointly regulate frequency;

[0012] A distributed model predictive control (DMPC) control method is designed. The DMPC controller samples the required state information. Under the premise of meeting the system constraints, the objective function of the DMPC controller is solved and the solution is output as the optimal control signal for each control cycle to adjust the frequency of the generator sets in the power system.

[0013] An online parameter identification model is designed to achieve online dynamic update of key system parameters when the key system parameters change greatly, so that the DMPC controller uses real key system parameters when calculating, thus improving the accuracy of the calculation results of the DMPC controller;

[0014] When a disturbance occurs on the load side of the power system, the DMPC controller uses the set initial values ​​of the key parameters of the power system to calculate and adjust the output of each generator set in the early stage of response, that is, before the end of parameter identification. At the same time, it continuously collects and stores relevant status information of the power system, and performs parameter identification on the key parameters of the power system based on the designed parameter identification model.

[0015] During the parameter identification process, when the results before and after a certain identification cycle meet the parameter identification termination condition, the sampling and storage of system-related state information is stopped, and the previously stored state information is cleared, and the parameter identification is stopped. The current identification result is the final parameter identification result;

[0016] When the final parameter identification result meets the parameter update condition, that is, when the key parameter changes greatly, the key parameter value in the control system is updated, and when the key parameter changes slightly, the control system still uses the set initial value of the key parameter;

[0017] After stopping parameter identification, the DMPC controller uses the final key parameter values ​​in the control system for calculation to obtain the optimal control signal for the current power system to adjust the output of each generator set until the power system returns to stability.

[0018] A second aspect of the present invention provides an automatic power generation control system for an electric power system supporting online parameter identification and distributed model prediction, characterized in that it includes:

[0019] Two-region heterogeneous multi-source new power system model module, used to establish two-region heterogeneous multi-source new power system module;

[0020] A regional interconnected system state space model module is used to establish a regional interconnected system state space model;

[0021] DMPC model prediction iterative calculation module, used to establish DMPC model for model prediction iterative calculation;

[0022] The optimal control output module is used to design the objective function and system constraints for rolling optimization to obtain the optimal control output;

[0023] Parameter identification model module, used to design online parameter identification methods and parameter identification models related to key system parameters;

[0024] An update module is used to design event-triggered control to control the parameter identification of the online parameter identification system and the update of key parameters in the control system;

[0025] The two-region heterogeneous multi-source new power system model module, the regional interconnected system state space model module, the DMPC model prediction iterative calculation module, the optimal control output module, the parameter identification model module and the update module are respectively connected to realize the power system automatic power generation control method supporting online parameter identification and distributed model prediction.

[0026] A third aspect of the present invention provides an automatic power generation controller for an electric power system, comprising:

[0027] one or more processors;

[0028] a memory for storing one or more programs,

[0029] When the one or more programs are executed by the one or more processors, the one or more processors execute the steps of the method for automatic power generation control of a power system supporting online parameter identification and distributed model prediction.

[0030] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for automatic power generation control of an electric power system supporting online parameter identification and distributed model prediction as described above.

[0031] The present invention has the following beneficial effects and advantages:

[0032] 1. By introducing new energy as frequency modulation resources, the problem of insufficient frequency modulation capacity of traditional frequency modulation resources caused by the increasing penetration rate of new energy and the decreasing penetration rate of traditional resources is effectively solved.

[0033] 2. The distributed model predictive control of the present invention can give full play to the rapid frequency regulation potential of the rapid frequency regulation unit and significantly improve the frequency regulation performance of the system.

[0034] 3. The parameter identification method of the present invention can quickly complete parameter identification, and the result is basically the same as the true value of the parameter, and can ensure that the parameter update of the control system is completed when the key parameters of the system change significantly.

[0035] 4. The automatic power generation control method of the present invention can ensure that when the key system parameters change greatly, the DMPC controller can still use the real key system parameters for calculation, thereby ensuring the accuracy of its output optimal control signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a structural diagram of the heterogeneous multi-source new power system AGC designed in the present invention.

[0037] Figure 2 It is a flowchart of the working principle of the method of the present invention. DETAILED DESCRIPTION

[0038] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0039] Example 1

[0040] This embodiment provides a method for automatic power generation control of a power system supporting online parameter identification and distributed model prediction, which specifically includes the following steps:

[0041] 1) The method of establishing a two-region heterogeneous multi-source new power system is:

[0042] 1-1) Define the thermal power unit model:

[0043]

[0044] Among them, G g (s) is the speed regulator transfer function, G t (s) is the non-reheat steam turbine transfer function, T g is the speed regulator time constant, T t is the turbine time constant;

[0045] 1-2) Define wind power, photovoltaic and battery energy storage system models:

[0046]

[0047] Among them, T w is the wind turbine generator time constant, T p is the time constant of the photovoltaic generator set, T b is the time constant of the battery energy storage system;

[0048] 1-3) Define a control model:

[0049]

[0050] Where Δf i (t) is the frequency deviation of region i at time t, and R n are the primary control output command and droop coefficient of power generation unit n respectively;

[0051] 1-4) Define the regional control deviation in the AGC system:

[0052] ACE i (t) = β i Δf i (t)+Δp tie,i (t)

[0053] Among them, β i , Δp tie,i (t) are the frequency deviation factor and tie line power deviation of region i respectively.

[0054] 2) Establish a power loop super local linear model state observer;

[0055] 2-1) Establish the following regional interconnection system frequency control model:

[0056]

[0057] where Δp g,i , Δp t,i , Δpw,i , Δp v,i and Δp b,i They are the speed governor output deviation of thermal power units, output power deviation of thermal power units, output power deviation of wind turbine units, output power deviation of photovoltaic units and output power deviation of battery energy storage system in regional system i respectively; and are the control instructions for the thermal power unit, wind power unit, photovoltaic unit and battery energy storage system of regional system i respectively; Δp d,i is the load disturbance of regional control system i; H i and D i are the equivalent inertia time constant and equivalent damping coefficient of control system i respectively; T ij is the synchronization coefficient of the tie line between regional control systems i and j, η i For ACE i The integral of

[0058] 2-2) Establish the following regional interconnected system state space model:

[0059]

[0060] Among them, A i , B i 、F i and C i are the state matrix, control matrix, disturbance matrix and output matrix of regional control system i respectively; A ij is the state interaction matrix between regional control systems i and j;

[0061] x i (t) = [Δp g,i ,Δp w,i ,Δp v,i ,Δp b,i ,Δp t,i ,Δf i ,Δp tie,i ,η i ] T is the state vector;

[0062] is the control vector;

[0063] w i (t) = [Δp d,i ] T is the perturbation vector, y i (t) = [ACE i ,η i ] T is the output vector;

[0064] 3) Establish a DMPC model to perform iterative calculations for model prediction;

[0065] 3-1) Discretize the state space model of the regional interconnected system:

[0066]

[0067] Among them, x i (k),u i (k), w i (k) and y i (k) are the state vector, control vector, disturbance vector and output vector after discretization respectively; A d,i , B d,i 、F d,i , C d,i and A d,ij are the corresponding matrices after discretization processing;

[0068] 3-2) Create the following expansion vector:

[0069] X i =[x i (k+1|k) T ,x i (k+2|k) T ,…,x i (k+N p |k) T ] T

[0070] U i =[u i (k|k) T ,u i (k+1|k) T ,…,u i (k+N c -1|k) T ] T

[0071] W i =[w i (k|k) T ,w i (k+1|k) T ,…,w i (k+N p -1|k) T ] T

[0072] X j =[x j (k|k) T ,x j (k+1|k) T ,…,x j (k+Np -1|k) T ] T

[0073] Where N p is the prediction time domain, N c (N c ≤N p ) is the control time domain;

[0074] 3-3) Establish the following DMPC model:

[0075]

[0076] Among them, U i It is the optimal control sequence obtained by DMPC by optimizing the objective function, and its first element will be applied to control the system at the current moment;

[0077] 4) Design objective function and system constraints for rolling optimization to obtain the optimal control output;

[0078] 4-1) Design the following DMPC stage cost function L1:

[0079]

[0080] Among them, Q i , R i is the corresponding diagonal weighting coefficient matrix;

[0081] 4-2) Design the following DMPC terminal penalty function L2:

[0082] L2=x(k+N p |k) T Hx(k+N p |k)

[0083] Where H is the corresponding diagonal weighting coefficient matrix;

[0084] 4-3) Design the following DMPC final objective function:

[0085]

[0086] 4-4) Design the following thermal power unit climbing constraints and active output constraints:

[0087]

[0088] in and They represent the minimum and maximum values ​​allowed for the ramp rate of thermal power units respectively; and They represent the lower and upper limits allowed for the active output of thermal power units respectively;

[0089] 4-5) Design the following wind turbine active output constraints:

[0090]

[0091] in and They represent the lower and upper limits allowed for the active output of wind turbines respectively;

[0092] 4-6) Design the following active output constraints of the PV unit:

[0093]

[0094] in and Respectively represent the lower and upper limits of the active output of the photovoltaic unit. 4-7) Design the following active output constraints of the battery energy storage system:

[0095]

[0096] in and They represent the lower and upper limits allowed for the active output of the battery energy storage system respectively.

[0097] 5) Design online parameter identification methods and parameter identification models related to key system parameters;

[0098] 5-1) Define the linear model as follows:

[0099] y=a1x1+a2x2+…+a n x n +b

[0100] Among them, a i and b are the parameters to be determined, x i is the input variable, y is the output variable;

[0101] 5-2) Define the following linear equations corresponding to different data points of the linear model:

[0102]

[0103] 5-3) Simplify the linear equations in 5-2) as follows:

[0104] Y=XA

[0105] in is the data matrix of the input variables and is the data matrix of the output variables, is the parameter vector;

[0106] 5-4) Given the following cost function for the least squares problem:

[0107]

[0108] 5-5) Define the following cost function gradient expression:

[0109]

[0110] 5-6) Taking the gradient as zero, we get the following unknown parameter expression:

[0111] A=(X T X) -1 X T Y

[0112] 5-7) Design the parameter identification model of the following key system parameters:

[0113]

[0114] Among them, H, D and p d are the system equivalent inertia time constant, equivalent damping coefficient and load disturbance, which are all parameters to be identified. total is the sum of the output of each unit and the output of the regional tie line, Δf is the frequency deviation, is the differential information of frequency deviation, which is the input information of parameter identification system;

[0115] 5-8) The parameter identification model is simplified as follows:

[0116] y=a1x1+a2x2+b

[0117] 5-9) The expression of the system parameters to be identified is as follows:

[0118]

[0119] 6) Design event-triggered control to control the parameter identification of the online parameter identification system and the update of key parameters in the control system;

[0120] 6-1) The event trigger control of the designed parameter identification is as follows:

[0121]

[0122] where φ k is the parameter identification result at the current k moment, φ k-1is the parameter identification result of the previous parameter identification cycle at time k-1, ρ1 is the trigger threshold of event trigger control I (parameter identification termination condition), if the parameter identification results of all key parameters at time k meet the event trigger condition, then stop sampling and transmitting the state information to the parameter identification system, stop parameter identification at the same time and clear the data matrix storing the state information required for identification;

[0123] 6-2) The designed control system key parameter update event trigger control is as follows:

[0124]

[0125] where φ pre is the parameter value of the system before parameter identification begins, φ n is the final parameter identification result at the end of parameter identification, ρ2 is the trigger threshold of event-triggered control II (parameter update condition), and if the key parameters of the system change significantly and meet the trigger condition, the parameters are updated to the output result of the online parameter identification system.

[0126] like Figure 1 As shown, the power system designed by the present invention includes three parts: primary control, secondary control and frequency modulation unit. In addition to the traditional coal-fired units, the frequency modulation unit part also introduces three new frequency modulation resources, namely wind turbines, photovoltaic generators and battery energy storage systems, which work together with the traditional frequency modulation resources to jointly modulate the frequency; the primary control part adopts droop control, which can negatively feedback the system frequency deviation change to adjust the unit output, which is a differential regulation method, and for this purpose, it is necessary to introduce secondary control; the secondary control part is the core technical part of the present invention, which includes a parameter identification system and a control system, which can respond to the system load side disturbance, output control signals to adjust the output of each generator set, suppress frequency fluctuations, and restore the system to stability as soon as possible.

[0127] like Figure 2 As shown, the parameter identification system and the control system of the present invention work together to respond to changes in the system load side and optimally adjust the generator sets in the system. The specific implementation steps are as follows:

[0128] First, when a disturbance occurs on the load side of the system, the DMPC controller designed in the control system uses the initial values ​​of key parameters set by the control system to calculate and adjust the output of each unit in the early stage of response, that is, before the end of parameter identification. At the same time, the parameter identification system continuously collects and stores system-related state information, and uses the designed parameter identification method to calculate and identify the key parameters of the system based on the designed parameter identification model.

[0129] Then, when the results before and after a certain identification cycle of the parameter identification system meet the parameter identification termination condition, the sampling and storage of the system-related state information will be stopped, and the previously stored state information will be cleared, and the parameter identification will be stopped. The current identification result is the final output result of the parameter identification system;

[0130] Then, when the output result of the parameter identification system meets the parameter update condition, that is, when the key parameter of the system changes greatly, the key parameter value in the control system is updated, and when the key parameter of the system changes slightly, the control system still uses the set initial value of the key parameter;

[0131] Finally, after stopping parameter identification, the DMPC controller in the control system uses the final key parameter values ​​in the control system to calculate to obtain the optimal control signal of the current system to adjust the output of each unit until the system returns to stability.

[0132] Example 2

[0133] This embodiment provides an automatic power generation control system for an electric power system supporting online parameter identification and distributed model prediction, characterized in that it includes:

[0134] Two-region heterogeneous multi-source new power system model module, used to establish two-region heterogeneous multi-source new power system module;

[0135] A regional interconnected system state space model module is used to establish a regional interconnected system state space model;

[0136] DMPC model prediction iterative calculation module, used to establish DMPC model for model prediction iterative calculation;

[0137] The optimal control output module is used to design the objective function and system constraints for rolling optimization to obtain the optimal control output;

[0138] Parameter identification model module, used to design online parameter identification methods and parameter identification models related to key system parameters;

[0139] An update module is used to design event-triggered control to control the parameter identification of the online parameter identification system and the update of key parameters in the control system;

[0140] The two-region heterogeneous multi-source new power system model module, the regional interconnected system state space model module, the DMPC model prediction iterative calculation module, the optimal control output module, the parameter identification model module and the update module are respectively connected to implement the power system automatic power generation control method supporting online parameter identification and distributed model prediction as described in claim 1.

[0141] It should be noted that the system embodiment of this embodiment is similar to the method embodiment of Embodiment 1, so the description is relatively simple, and the relevant parts can refer to the method of Embodiment 1.

[0142] Example 3

[0143] The embodiment of the present application also provides an automatic power generation controller for an electric power system, comprising:

[0144] one or more processors;

[0145] a memory for storing one or more programs,

[0146] When the one or more programs are executed by the one or more processors, the one or more processors execute the steps of the method for automatic power generation control of an electric power system supporting online parameter identification and distributed model prediction as described in Example 1.

[0147] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the method for automatic power generation control of an electric power system supporting online parameter identification and distributed model prediction as disclosed in Example 1 of the present application are implemented.

[0148] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0149] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, systems, devices, storage media, and 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, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] The methods, systems, devices, media and products provided by the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the methods and core ideas of the present application. At the same time, for those skilled in the art, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for automatic power generation control of a power system supporting online parameter identification and distributed model prediction, characterized in that: The following steps are involved: Establish a new heterogeneous multi-source power system in the two regions to add wind power, photovoltaic and battery energy storage systems to the power system, coordinate output with traditional thermal power units, and jointly regulate frequency; A distributed model predictive control (DMPC) control method is designed. The DMPC controller samples the required state information. Under the premise of meeting the system constraints, the objective function of the DMPC controller is solved and the solution is output as the optimal control signal for each control cycle to adjust the frequency of the generator sets in the power system. An online parameter identification model is designed to achieve online dynamic update of key system parameters when the key system parameters change greatly, so that the DMPC controller uses real key system parameters when calculating, thus improving the accuracy of the calculation results of the DMPC controller; When a disturbance occurs on the load side of the power system, the DMPC controller uses the set initial values ​​of the key parameters of the power system to calculate and adjust the output of each generator set in the early stage of response, that is, before the end of parameter identification; At the same time, the relevant status information of the power system is continuously collected and stored, and the key parameters of the power system are identified based on the designed parameter identification model; During the parameter identification process, when the results before and after a certain identification cycle meet the parameter identification termination condition, the sampling and storage of system-related state information is stopped, and the previously stored state information is cleared, and the parameter identification is stopped. The current identification result is the final parameter identification result; When the final parameter identification result meets the parameter update condition, that is, when the key parameter changes greatly, the key parameter value in the control system is updated, and when the key parameter changes slightly, the control system still uses the set initial value of the key parameter; After stopping parameter identification, the DMPC controller uses the final key parameter values ​​in the control system for calculation to obtain the optimal control signal for the current power system to adjust the output of each generator set until the power system returns to stability.

2. The method for automatic power generation control of a power system supporting online parameter identification and distributed model prediction according to claim 1, characterized in that: The specific steps include: 1) The method of establishing a two-region heterogeneous multi-source new power system is: 1-1) Define the thermal power unit model: Among them, G g (s) is the speed regulator transfer function, G t (s) is the non-reheat steam turbine transfer function, T g is the speed regulator time constant, T t is the turbine time constant; 1-2) Define wind power, photovoltaic and battery energy storage system models: Among them, T w is the wind turbine generator time constant, T p is the time constant of the photovoltaic generator set, T b is the time constant of the battery energy storage system; 1-3) Define a control model: Where Δf i (t) is the frequency deviation of region i at time t, and R n are the primary control output command and droop coefficient of power generation unit n respectively; 1-4) Define the regional control deviation in the AGC system: ACE i (t)=β i Δf i (t)+Δp tie,i (t) Among them, β i , Δp tie,i (t) are the frequency deviation factor and tie line power deviation of region i respectively. 2) Establish a power loop super local linear model state observer; 2-1) Establish the following regional interconnection system frequency control model: Where Δp g,i , Δp t,i , Δp w,i , Δp v,i and Δp b,i They are the speed governor output deviation of thermal power units, output power deviation of thermal power units, output power deviation of wind turbine units, output power deviation of photovoltaic units and output power deviation of battery energy storage system in regional system i respectively; and are the control instructions for the thermal power unit, wind power unit, photovoltaic unit and battery energy storage system of regional system i respectively; Δp d,i is the load disturbance of regional control system i; H i and D i are the equivalent inertia time constant and equivalent damping coefficient of control system i respectively; T ij is the synchronization coefficient of the tie line between regional control systems i and j, η i For ACE i The integral of 2-2) Establish the following regional interconnected system state space model: Among them, A i , B i 、F i and C i are the state matrix, control matrix, disturbance matrix and output matrix of regional control system i respectively; A ij is the state interaction matrix between regional control systems i and j; x i (t) = [Δp g,i ,Δp w,i ,Δp v,i ,Δp b,i ,Δp t,i ,Δf i ,Δp tie,i ,η i ] T is the state vector; is the control vector; w i (t) = [Δp d,i ] T is the perturbation vector, y i (t) = [ACE i ,η i ] T is the output vector; 3) Establish a DMPC model to perform iterative calculations for model prediction; 3-1) Discretize the state space model of the regional interconnected system: Among them, x i (k) and u i (k), w i (k) and y i (k) are the state vector, control vector, disturbance vector and output vector after discretization respectively; A d,i , B d,i 、F d,i , C d,i and A d,ij are the corresponding matrices after discretization processing; 3-2) Create the following expansion vector: X i =[x i (k+1|k) T ,x i (k+2|k) T ,…,x i (k+N p |k) T ] T YOU i =[u i (k|k) T ,you i (k+1|k) T ,…,you i (k+N c -1|k) T ] T W i =[w i (k|k) T ,w i (k+1|k) T ,…,w i (k+N p -1|k) T ] T X j =[x j (k|k) T ,x j (k+1|k) T ,…,x j (k+N p -1|k) T ] T Where N p is the prediction time domain, N c (N c ≤N p ) is the control time domain; 3-3) Establish the following DMPC model: Among them, U i It is the optimal control sequence obtained by DMPC by optimizing the objective function, and its first element will be applied to control the system at the current moment; 4) Design objective function and system constraints for rolling optimization to obtain the optimal control output; 4-1) Design the following DMPC stage cost function L1: Among them, Q i , R i is the corresponding diagonal weighting coefficient matrix; 4-2) Design the following DMPC terminal penalty function L2: L2=x(k+N p |k) T Hx(k+N p |k) Where H is the corresponding diagonal weighting coefficient matrix; 4-3) Design the following DMPC final objective function: 4-4) Design the following thermal power unit climbing constraints and active output constraints: in and They represent the minimum and maximum values ​​allowed for the ramp rate of thermal power units respectively; and They represent the lower and upper limits allowed for the active output of thermal power units respectively; 4-5) Design the following wind turbine active output constraints: in and They represent the lower and upper limits allowed for the active output of wind turbines respectively; 4-6) Design the following active output constraints of the PV unit: in and Respectively represent the lower and upper limits of the active output of the photovoltaic unit 4-7) Design the following active output constraints for the battery energy storage system: in and They represent the lower and upper limits allowed for the active output of the battery energy storage system respectively. 5) Design online parameter identification methods and parameter identification models related to key system parameters; 5-1) Define the linear model as follows: y=a1x1+a2x2+…+a n x n +b Among them, a i and b are the parameters to be determined, x i is the input variable, y is the output variable; 5-2) Define the following linear equations corresponding to different data points of the linear model: 5-3) Simplify the linear equations in 5-2) as follows: Y=XA in is the data matrix of the input variables and is the data matrix of the output variables, is the parameter vector; 5-4) Given the following cost function for the least squares problem: 5-5) Define the following cost function gradient expression: 5-6) Taking the gradient as zero, we get the following unknown parameter expression: A=(X T X) -1 X T Y 5-7) Design the parameter identification model of the following key system parameters: Among them, H, D and p d are the system equivalent inertia time constant, equivalent damping coefficient and load disturbance, which are all parameters to be identified. total is the sum of the output of each unit and the output of the regional tie line, Δf is the frequency deviation, is the differential information of frequency deviation, which is the input information of parameter identification system; 5-8) The parameter identification model is simplified as follows: y=a1x1+a2x2+b 5-9) The expression of the system parameters to be identified is as follows: 6) Design event-triggered control to control the parameter identification of the online parameter identification system and the update of key parameters in the control system; 6-1) The event trigger control of the designed parameter identification is as follows: where φ k is the parameter identification result at the current k moment, φ k-1 is the parameter identification result of the previous parameter identification cycle at time k-1, ρ1 is the trigger threshold of event trigger control I. If the parameter identification results of all key parameters at time k meet the event trigger condition, the sampling and transmission of state information to the parameter identification system are stopped, and the parameter identification is stopped at the same time, and the data matrix storing the state information required for identification is cleared; 6-2) The designed control system key parameter update event trigger control is as follows: where φ pre is the parameter value of the system before parameter identification begins, φ n is the final parameter identification result at the end of parameter identification, ρ2 is the trigger threshold of event-triggered control II. If the key parameters of the system change greatly and meet the trigger conditions, the parameters are updated to the output results of the online parameter identification system.

3. An automatic power generation control system supporting online parameter identification and distributed model prediction, characterized in that: include: Two-region heterogeneous multi-source new power system model module, used to establish two-region heterogeneous multi-source new power system module; A regional interconnected system state space model module is used to establish a regional interconnected system state space model; DMPC model prediction iterative calculation module, used to establish DMPC model for model prediction iterative calculation; The optimal control output module is used to design the objective function and system constraints for rolling optimization to obtain the optimal control output; Parameter identification model module, used to design online parameter identification methods and parameter identification models related to key system parameters; An update module is used to design event-triggered control to control the parameter identification of the online parameter identification system and the update of key parameters in the control system; The two-region heterogeneous multi-source new power system model module, the regional interconnected system state space model module, the DMPC model prediction iterative calculation module, the optimal control output module, the parameter identification model module and the update module are respectively connected to implement the power system automatic power generation control method supporting online parameter identification and distributed model prediction as described in claim 1 or 2.

4. An automatic power generation controller for an electric power system, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the steps of the method for automatic power generation control of an electric power system supporting online parameter identification and distributed model prediction as described in claim 1 or 2.

5. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the steps of the method for automatic power generation control of an electric power system supporting online parameter identification and distributed model prediction as described in claim 1 or 2 are implemented.

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