Island isolated network model driven cluster frequency control method

Abstract

The invention discloses a cluster frequency control method driven by an island isolated network model, and relates to the field of power grids. Comprising the following steps: establishing a state-space equation of an automatic generation control (AGC) model considering transmission time delay; discretizing the state-space equation to obtain a discrete state-space equation; establishing an optimization model containing a control effect and a control cost based on the discrete state space equation; executing online rolling optimization control in a fixed control period TC, measuring a current value of an AGC model state vector in each control period, and obtaining a control input vector of a previous control period and a power disturbance vector of a future Np step; and solving the optimization model to obtain a control input vector and issuing the control input vector to a corresponding unit. According to the invention, coordination of primary frequency modulation and secondary frequency modulation can be realized, the effect of the control method is better, and powerful guarantee can be provided for power balance and safe operation of the island power grid.

Description

Technical Field

[0001] The invention relates to the technical field of power grids, and in particular to a cluster frequency control method driven by an island isolated grid model. Background Art

[0002] With the continuous development of new energy technologies, the power system has gradually built a low-carbon and clean energy system based on the access of various forms of power sources such as wind power and photovoltaic power to the power grid. However, the intermittent and random nature of renewable energy has brought new challenges to the safety and stability of the power grid.

[0003] Traditional control methods such as droop control, PI control, and PID control combined with optimization algorithms such as particle swarm optimization [1] have alleviated the frequency fluctuations caused by intermittent renewable energy to a certain extent. However, the control strategy does not consider the impact of the real-time status of renewable energy on the control strategy, and does not optimize the use of power generation resources.

[0004] In order to further improve the frequency control effect, two frequency control technology routes have been developed in recent years: model-driven and data-driven. The model-driven method considers the operating status of the synchronous machine and the inverter, and realizes the control of frequency fluctuations based on model calculation [2]. At the same time, on this basis, some studies have taken economic benefits into consideration to achieve optimal control of control effect and economic benefits [3]. However, the model does not take into account the negative impact of the transmission delay of the control signal input on the control response, and does not take the power generation fluctuation as the model disturbance, which also has an adverse effect on the control effect. The data-driven method introduces tools such as reinforcement learning and neural networks to control the coordinated output of multiple energy sources without building a physical model to achieve frequency stability control [4-5]. However, in the complex environment of the South China Sea, both the amount of data training and feature extraction face challenges. There are also studies that combine data-driven and physical model-driven, introduce data-driven algorithms for data enhancement, improve the diversity and robustness of historical samples, and improve the efficiency of the initial iteration process of the existing model-driven power grid frequency regulation strategy [6]. The essence of this method is to use data-driven related methods to improve the global convergence speed and accuracy of the physical model-driven method. The problems existing in the above-mentioned model-driven method have not been fundamentally solved. Summary of the invention

[0005] In view of the problems existing in the existing power grid, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to achieve real-time optimization of the operating status of the microgrid system to ensure the power balance and frequency safety of the island power grid.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a cluster frequency control method driven by an island isolated grid model, which includes establishing a state space equation of an automatic generation control AGC model taking into account transmission delay;

[0009] discretizing the state space equation to obtain a discrete state space equation;

[0010] Establishing an optimization model including control effect and control cost based on the discrete state space equation;

[0011] Perform online rolling optimization control with a fixed control period TC, measure the current value of the AGC model state vector in each control period, obtain the control input vector of the previous control period and the power disturbance vector of the next Np steps;

[0012] Solve the optimization model to obtain the control input vector and send it to the corresponding unit.

[0013] As a preferred solution of the cluster frequency control method driven by the island isolated network model of the present invention, the state space equation is:

[0014]

[0015] Where x(t) is the state vector of the AGC model at time t, is the control input vector after the transmission delay at time t, w(t) is the disturbance vector of the AGC model at time t, A is the state matrix of the AGC model, B u is the control input matrix of the AGC model, B w is the disturbance input matrix of the AGC model.

[0016] As a preferred solution of the cluster frequency control method driven by the island isolated network model of the present invention, the state vector x(t) includes: system frequency deviation f(t), synchronous unit state vector P G (t) and the inverter unit state vector P E (t), where P G (t) includes the valve opening increment P of each synchronous unit vi (t) and mechanical power increment P mi (t), P E (t) includes the electric power increment P of each inverter unit mj (t).

[0017] As a preferred solution of the cluster frequency control method driven by the island isolated network model of the present invention, wherein: the control input vector Including: Synchronous unit control input vector P cG (t) and the inverter unit control input vector P cE( t), where P cG (t) and P cE (t) The transmission delays τi and τj of each unit are taken into account respectively.

[0018] As a preferred solution of the cluster frequency control method driven by the island isolated network model of the present invention, the discrete state space equation is:

[0019]

[0020] Where A is the discrete time state matrix, B u is the current control input matrix, is the control input matrix of the previous cycle, B w is the discrete-time perturbation input matrix.

[0021] As a preferred solution of the cluster frequency control method driven by the island isolated network model of the present invention, the objective function of the optimization model is:

[0022]

[0023] Among them, the objective function J is to minimize the sum of the control effect and the control cost, the C matrix only considers the frequency deviation state quantity, γ is the cost coefficient, and R is the control cost matrix, whose diagonal elements are the inverse of the adjustable capacity of the corresponding unit.

[0024] As an optimal solution of the cluster frequency control method driven by the island isolated network model described in the present invention, the optimization model includes the following constraints: system frequency deviation limit constraint, control input upper and lower limit constraints, unit output upper and lower limit constraints and unit climbing rate constraints.

[0025] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the cluster frequency control method driven by the island isolated network model as described in the first aspect of the present invention are implemented.

[0026] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the cluster frequency control method driven by the island isolated network model as described in the first aspect of the present invention are implemented.

[0027] The beneficial effects of the present invention are as follows: the key technology of the present invention is to establish a measurement and model-driven system-cluster frequency control method suitable for island microgrids. The model takes the transmission delay of the control input vector into consideration, quantifies the model disturbance based on the prediction of the disturbance power of renewable energy, refines the model from both the model input and disturbance aspects to improve the response accuracy, and considers the differences in the dynamic responses of synchronous units and inverter units. By taking into account the optimal frequency control strategy of cost control and system frequency stability, the optimal balance point of control effect and control cost is found, thereby realizing real-time optimization of the operating status of the microgrid system, ensuring the power balance and frequency safety of the island power grid, and efficient use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 Automatic power generation control model diagram;

[0030] Figure 2 Automatic generation control signal considering transmission delay; DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1

[0035] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a cluster frequency control method driven by an island isolated network model, comprising:

[0036] In this embodiment, the system hardware consists of a measurement unit, a data transmission module, a control center, an execution unit, etc., and a complete frequency control closed loop is constructed through the data transmission and feedback adjustment module. The functions of each module and their mutual cooperation are described in detail as follows:

[0037] Measuring unit:

[0038] Each power unit (wind turbine, photovoltaic inverter, etc.) is equipped with independent power sensors, frequency meters and other equipment to measure the grid parameters on the generator and load sides, including power output, frequency, load demand, etc.

[0039] The measurement data is uploaded to the control center through the communication module to ensure that the frequency control data is updated every second.

[0040] Data transmission module:

[0041] Optical fiber or wireless communication network is used to transmit the data collected by the measurement unit to the control center.

[0042] The system compensates and corrects communication delays and packet loss to avoid the impact of data transmission delays on control accuracy.

[0043] Control Center:

[0044] Equipped with high-performance processors and multi-core computer clusters, it is mainly responsible for the execution of frequency control optimization algorithms.

[0045] The control center is equipped with automatic generation control (AGC) algorithm and model predictive control (MPC) algorithm, which can analyze the power balance status of the island power grid in real time and generate control signals.

[0046] Execution Unit:

[0047] It includes inverter control module and synchronous generator regulation module. Each unit adjusts the power generation through control signals to maintain the system frequency at the set value.

[0048] The execution unit also has a feedback device that can provide real-time feedback on the adjustment effect and provide closed-loop feedback to the control center.

[0049] In frequency control, the real-time and accuracy of data directly affect the control effect. This embodiment ensures the high accuracy and continuity of the measured data through data preprocessing and delay compensation mechanism.

[0050] Data collection and preliminary processing

[0051] Real-time data such as power output and frequency fluctuation of each power unit are collected through the measurement unit. Data processing is divided into the following steps:

[0052] Data cleaning and denoising: After the control center receives the data, it uses a Kalman filter to filter out random noise and uses the neighboring mean to fill in the mutation data.

[0053] Anomaly detection and correction: Set reasonable thresholds for frequency and power. If the threshold is exceeded, the data is considered an outlier. The system corrects the outlier by interpolating the most recent data.

[0054] Data transmission and delay correction

[0055] Since long-distance data transmission may introduce a tiny delay, this embodiment uses a delay prediction correction method to compensate for the delay of the control signal input:

[0056] Linear interpolation compensation: For signals with transmission delay, linear interpolation is used to estimate the data closest to the actual value.

[0057] Delay prediction: The system predicts the possible deviation at the current moment based on the data of the previous cycle to ensure the continuity of the frequency control input signal.

[0058] The MPC algorithm is the core of this embodiment. Through this algorithm, future power disturbances are predicted, and dynamic adjustment of frequency control is achieved. The system performs frequency control optimization calculations based on real-time collected power output and load demand in each cycle.

[0059] Construction of automatic power generation control model

[0060] The automatic generation control model is based on the state space equation, which is expressed as follows:

[0061]

[0062] State variables: including frequency deviation, power output of synchronous units and inverter units.

[0063] Control input matrix: The control input matrix Bu describes the regulation effect of the control signal on frequency and power.

[0064] Disturbance input matrix: The disturbance matrix Bw describes the impact of power fluctuations (such as wind speed changes) on the system.

[0065] Control objectives and optimization models

[0066] The control objectives include frequency stability and efficient use of power. The specific optimization objective function is:

[0067]

[0068] The Q matrix is ​​used to constrain the frequency deviation, and the R matrix controls the regulation cost. The optimization problem is solved by MPC in a rolling manner within a fixed period to obtain the optimal control input.

[0069] In each control cycle, the system predicts the power fluctuations in the next few cycles. The control center uses historical data and current disturbance conditions to generate a disturbance vector w(t)w(t)w(t) to make the prediction more consistent with the actual changes. The process mainly includes:

[0070] Historical data enhancement: Synthesize past environmental data with real-time data to generate possible future fluctuation trends.

[0071] Real-time update of disturbances: Using real-time data of wind speed and light, the disturbances in the prediction model are corrected, making the prediction accuracy of the control model higher.

[0072] The generation and distribution of control signals is a key step in frequency control. The system generates and optimizes control signals in each cycle and controls the power output of different units through feedback regulation.

[0073] Control signal generation: Based on the output value of the optimization model, the system generates control instructions for the synchronous units and inverter units and issues them in sequence. The control signal generation process takes into account the response time differences of each unit to ensure synchronous signal transmission.

[0074] Signal transmission and regulation: The control signal is transmitted to the execution unit through the transmission channel. The execution unit receives the signal through the power regulator and frequency regulator and adjusts the power output.

[0075] Feedback regulation and adaptive control: The system monitors the control effect in real time, performs feedback analysis on the adjusted frequency deviation and power change, and adaptively adjusts the control signal based on the feedback results to optimize the frequency control effect.

[0076] In practical applications, this embodiment tests the adaptability and control effect of the system in a variety of complex scenarios, including multiple indicators such as power fluctuation response speed, frequency deviation adjustment range, and control cost.

[0077] Frequency stability: When the frequency fluctuates greatly, the control signal can respond in time to ensure that the system frequency deviation is controlled within the preset range, avoiding grid instability caused by frequency fluctuations.

[0078] Control cost optimization: Through MPC rolling optimization, the system can dynamically select the optimal control path to ensure frequency control while reducing control costs.

[0079] Adaptability to complex environments:

[0080] Under extreme conditions such as high temperature and high humidity on the island, the system exhibits good anti-interference performance.

[0081] In the case of rapid wind speed fluctuations, the adaptability of the control model is improved through historical data enhancement, allowing the system to return to a balanced state in a shorter time.

[0082] This embodiment successfully solves the problems of poor stability and slow response speed of traditional frequency control technology in an isolated network environment through methods such as model predictive control and delay correction. Its main advantages include:

[0083] Accurate frequency control: The frequency deviation is controlled within the set range, ensuring the safety and stability of the system.

[0084] Efficient resource utilization: The optimization model maximizes the use of the island’s limited power generation resources while regulating the frequency.

[0085] Strong adaptive adjustment capability: able to cope with disturbances caused by different climatic conditions and improve the applicability and stability of the system.

[0086] Summarize

[0087] Through the integration of measurement, data transmission, model prediction and feedback regulation, this embodiment provides a cluster frequency control solution for island isolated network environment. In high-frequency disturbances and complex environments, the system shows significant frequency control effect and has strong application promotion value.

[0088] This embodiment solves the shortcomings of the prior art in a high-frequency disturbance environment through a frequency control optimization method, thereby ensuring the stable operation of the island microgrid system.

[0089] This embodiment also provides a computer device, which is suitable for the cluster frequency control method driven by an island isolated network model, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the cluster frequency control method driven by the island isolated network model proposed in the above embodiment.

[0090] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0091] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the cluster frequency control method driven by the island isolated network model proposed in the above embodiment is implemented.

[0092] In summary, traditional AGC methods (such as PI control) do not consider the impact of the primary frequency modulation loop on the secondary frequency modulation and the differences between heterogeneous units, and there is a hidden danger of frequency exceeding the limit when power fluctuations are frequent. In the environment of three highs and three strongs, when the power fluctuations of island microgrids with a high proportion of new energy penetration are frequent, there is a greater risk of frequency exceeding the limit. The present invention (a cluster frequency control method driven by an island isolated grid model) takes into account the differences in dynamic processes between heterogeneous units, considers the primary frequency modulation loop in the AGC control, and incorporates the disturbance power prediction at the same time, which can achieve the coordination of primary and secondary frequency modulation. The control method is more effective and can provide a strong guarantee for the power balance and safe operation of the island power grid.

[0093] Example 2

[0094] Reference Figure 1-Figure 2 , which is the second embodiment of the present invention, and provides a cluster frequency control method driven by an island isolated network model.

[0095] The state space equation of the Automatic Generation Control (AGC) model is established. The expression is shown in (1). The AGC model is as follows: Figure 1 shown.

[0096]

[0097] Where x(t) represents the state vector of the AGC model at time t, represents the differential of the state vector at time t with respect to time t, u(t) represents the control input vector of the AGC model at time t, represents the control input vector after the transmission delay at time t, and w(t) represents the disturbance vector of the AGC model at time t. represents the state matrix of the AGC model, represents the control input matrix of the AGC model, Represents the perturbation input matrix of the AGC model.

[0098] Variables x(t), u(t), w(t) belongs to the continuous time domain and is defined as follows:

[0099] x(t)=[Δf(t) ΔP G (t) T ΔP E (t) T ] T

[0100] ΔP G (t)=[…ΔP vi (t) ΔP mi (t) …] T

[0101] ΔP E (t)=[…ΔP mj (t) …] T

[0102] u(t)=[ΔP cG (t) T ΔP cE (t) T ] T

[0103]

[0104] ΔP cG (t)=[…ΔP ci (t) …] T

[0105] ΔP cE (t)=[…ΔP cj (t) …] T

[0106]

[0107] w(t)=ΔP e (t)

[0108]

[0109] Where Δf(t) represents the frequency deviation of the island power system at time t, ΔP G (t) represents the state vector of the synchronous unit at time t, ΔP E (t) represents the state vector of the inverter unit at time t, ΔP vi (t), ΔP mi (t) respectively represent the valve opening increment and mechanical power increment of the ith synchronous unit at time t, ΔP mj (t) represents the power increment of the jth inverter unit at time t, ΔP cG (t), ΔP cE (t) represent the control input vectors of the synchronous unit and the inverter unit at time t, They represent the control input vectors of the synchronous unit and the inverter unit after the transmission delay at time t, ΔP ci (t), ΔP cj (t) represent the control input variables of the i-th synchronous unit and the j-th inverter unit at time t, They represent the control input variables of the i-th synchronous unit and the j-th inverter unit after the transmission delay at time t, τ i , τ j They represent the transmission delay of the i-th synchronous unit and the j-th inverter unit, ΔP e (t) represents the disturbance power of the island power system at time t. Ng is the total number of synchronous units, Ne is the total number of inverter units, H sys Denotes the total inertia of the island power system, D sys represents the load damping factor of the island power system, R i , T Gi , T Ti 、F i They represent the droop coefficient, governor time constant, turbine time constant, and turbine proportional coefficient of the i-th synchronous unit respectively. j , T Ej They represent the droop coefficient and equivalent speed regulator time constant of the j-th inverter unit respectively, and 0m×n represents an m×n zero matrix. They respectively represent the sub-matrix corresponding to the synchronous unit and the sub-matrix corresponding to the inverter unit in the control input matrix.

[0110] The state space equation of the above AGC model is discretized.

[0111] The automatic power generation control system usually runs in discrete mode. The dispatching control center performs sampling calculations at a fixed period and completes the issuance of instructions. The control period of AGC is TC. Based on the zero-order hold method, the state control equation of the above AGC model is discretized, and the discrete state space equation can be obtained as follows:

[0112]

[0113] In the formula, x n 、u n 、w n They represent the AGC system state vector, control input vector, and disturbance vector at the nth discrete moment, respectively. The subscript n represents the discrete moment number. Due to the existence of transmission delay, the control input vector u of the previous control cycle (corresponding to the previous discrete moment) n-1 It will also affect the system vector x at the next moment n+1 A represents the discrete time state matrix, B u They respectively represent the last control input vector u n-1 The control input matrix and the corresponding current control input vector u n The control input matrix, B w represents the discrete-time disturbance input matrix, which can be calculated as follows:

[0114]

[0115] B u =[B G B E ]

[0116]

[0117]

[0118] In the formula, Respectively represent matrices The sub-matrix corresponding to the synchronous unit and the sub-matrix corresponding to the inverter unit, B G , B E Represents the matrix B u The sub-matrix corresponding to the synchronous unit and the sub-matrix corresponding to the inverter unit, I represents the unit matrix, Representation Matrix The inverse matrix of .

[0119] Model predictive control is essentially an online optimization problem. The automatic power generation control optimization model consists of an objective function and a series of constraints, as shown below:

[0120]

[0121] -Δf lim ≤(Δf) n ≤Δf lim ,n=1,…,N p

[0122]

[0123] -Rd i ≤(ΔP mi ) n -(ΔP mi ) n-1 ≤Ru i ,n=1,…,N p

[0124] -Rd j ≤(ΔP mj ) n -(ΔP mj ) n-1 ≤Ru j ,n=1,…,N p

[0125] In the formula, the objective function J is to minimize the sum of control effect and control cost, matrix:

[0126] The control effect only considers the frequency deviation state quantity, γ is the cost coefficient, R is the control cost matrix, which is a diagonal matrix, and each diagonal element is the inverse of the adjustable capacity of the corresponding unit. p is the number of prediction steps in model predictive control, N C is the number of control steps in model predictive control, and the two generally satisfy Represents the system state vector a or control input vector a at the nth step (i.e., the nth discrete moment). Δf lim is the maximum frequency deviation allowed in the island power system, are the upper and lower bounds of the control input of the ith synchronous unit, are the upper and lower bounds of the control input of the jth inverter unit, are the upper and lower bounds of the mechanical power increment of the i-th synchronous unit, are the upper and lower bounds of the power increment of the jth inverter unit, Ru i , Rd i are the maximum up and down climbing amount of the i-th synchronous unit, Ru j , Rd j They are the maximum up and down climbing amounts of the j-th inverter unit respectively.

[0127] The proposed automatic power generation control method is an online rolling time domain optimization control technology, which starts the calculation and completes the instruction issuance with a fixed control cycle TC. In each control cycle, the control algorithm includes the following steps:

[0128] 1) Measure the current value x0 of the AGC model state vector and obtain the control input vector u of the previous control cyclen-1 And the future N p The power perturbation vector of the step

[0129] 2) Solve the optimization model. The optimization model can be quickly solved by calling a commercial solver (such as Gurobi, Cplex, etc.).

[0130] 3) The solution result is obtained, and the control input vector u0 of the first step in the optimization result is obtained. Each element of the vector (including the control input vectors of the synchronous unit and the inverter unit at time t) is taken out and sent to the corresponding unit to realize the automatic power generation control of the current control cycle.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A cluster frequency control method driven by an island isolated network model, characterized in that: include, Establish the state space equation of the automatic generation control AGC model considering transmission delay; discretizing the state space equation to obtain a discrete state space equation; Establishing an optimization model including control effect and control cost based on the discrete state space equation; Perform online rolling optimization control with a fixed control period TC, measure the current value of the AGC model state vector in each control period, obtain the control input vector of the previous control period and the power disturbance vector of the next Np steps; Solve the optimization model to obtain the control input vector and send it to the corresponding unit.

2. The cluster frequency control method driven by the island isolated network model as claimed in claim 1, characterized in that: The state space equation is: Where x(t) is the state vector of the AGC model at time t, is the control input vector after the transmission delay at time t, w(t) is the disturbance vector of the AGC model at time t, A is the state matrix of the AGC model, B u is the control input matrix of the AGC model, B w is the disturbance input matrix of the AGC model.

3. The cluster frequency control method driven by the island isolated network model as claimed in claim 2, characterized in that: The state vector x(t) includes: system frequency deviation f(t), synchronous unit state vector P G (t) and the inverter unit state vector P E (t), where P G (t) includes the valve opening increment P of each synchronous unit vi (t) and mechanical power increment P mi (t), P E (t) includes the electric power increment P of each inverter unit mj (t).

4. The cluster frequency control method driven by the island isolated network model as claimed in claim 3, characterized in that: The control input vector Including: Synchronous unit control input vector P cG (t) and the inverter unit control input vector P cE( t), where P cG (t) and P cE (t) The transmission delays τi and τj of each unit are taken into account respectively.

5. The cluster frequency control method driven by the island isolated network model as claimed in claim 4, characterized in that: The discrete state space equation is: Where A is the discrete time state matrix, B u is the current control input matrix, is the control input matrix of the previous cycle, B w is the discrete-time perturbation input matrix.

6. The cluster frequency control method driven by the island isolated network model as claimed in claim 5, characterized in that: The objective function of the optimization model is: Among them, the objective function J is to minimize the sum of the control effect and the control cost, the C matrix only considers the frequency deviation state quantity, γ is the cost coefficient, and R is the control cost matrix, whose diagonal elements are the inverse of the adjustable capacity of the corresponding unit.

7. The cluster frequency control method driven by the island isolated network model as claimed in claim 6, characterized in that: The optimization model includes the following constraints: system frequency deviation limit constraint, control input upper and lower limit constraints, unit output upper and lower limit constraints and unit climbing rate constraints.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the cluster frequency control method driven by the island isolated network model described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cluster frequency control method driven by an island isolated network model described in any one of claims 1 to 7 are implemented.

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