Load frequency coordination optimization control method

Abstract

The invention discloses a load frequency coordination optimization control method, and belongs to the technical field of power system load frequency control technologies. The method comprises the following steps: obtaining a frequency response state space model of a hybrid energy storage-containing two-region interconnected power system through mechanism analysis, performing discretization processing, converting the model into a discretization model, and further establishing a discretization prediction model of the system; analyzing constraints existing in the system, and converting the constraints into a linear matrix inequality; and finally, establishing an objective function of the system, performing online rolling optimization on the control quantity, and designing a multi-region controller, thereby realizing a load frequency coordination optimization control effect of the interconnected power system containing hybrid energy storage. The problems of insufficient flexible resources and poor adjusting characteristics of frequency modulation of an electric power system are solved, coordination and cooperation of battery energy storage, pumped storage and a traditional thermal power generating unit are achieved through coordination control, the frequency response performance of the system is improved, the nonlinear constraint problem existing in the system is solved, and potential safety hazards caused by overload work of equipment are avoided.

Description

Technical Field

[0001] This invention relates to a load frequency coordination and optimization control method, belonging to the field of power system load frequency control technology. Background Technology

[0002] With the large-scale integration of renewable energy, the problems of insufficient frequency regulation resources and poor regulation characteristics in power systems are becoming increasingly prominent. In the future frequency regulation of power systems, how to incorporate more clean energy is a major challenge. Energy storage, with its advantages of bidirectional power regulation and flexible configuration, has received widespread attention in the field of power system load frequency control. Currently, energy storage forms applied to power system frequency regulation include pumped hydro storage, battery energy storage, and supercapacitors. Among these, pumped hydro storage technology is mature, safe to operate, low-carbon and green, and can provide inertia support for the system, making it the most reliable and flexible regulation resource for building new power systems. However, pumped hydro storage power plants have drawbacks such as difficult site selection and slow response speed. Therefore, combining pumped hydro storage power plants with fast energy storage such as battery energy storage to form a wide-area hybrid energy storage system is one of the important ways to solve the above-mentioned shortcomings. It also provides a framework for applying multiple energy storage types to power system frequency regulation.

[0003] Distributed model predictive control can be applied to the optimization control of large-scale systems. It divides the system into multiple coupled subsystems with independent local controllers. These subsystems need to consider not only their own state and control variables, but also the correlation between the coupled subsystems. The optimization goal of the system is achieved through coordination algorithms. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a load frequency coordinated optimization control method that achieves coordinated optimization control of hybrid energy storage in two regions through an optimized distributed model predictive control algorithm and the processing of system constraints.

[0005] To achieve the above objectives / to solve the above technical problems, the present invention is implemented using the following technical solution: A load frequency coordination and optimization control method, the method comprising: A load frequency control model was established, and a continuous-time state-space model was obtained through mechanism analysis. Obtain the initial information values ​​of the load frequency control model; Discretize the continuous-time state-space model to establish a predictive model for the load frequency control model; Based on the prediction error and initial information values, an optimization objective function for the load frequency control model of the hybrid energy storage interconnected power system is constructed; where the prediction error is the difference between the predicted value output by the prediction model and the set prediction value. Solving the nonlinear constraint problem in the hybrid energy storage interconnected power system is transformed into a constrained nonlinear programming problem by combining linear matrix inequalities and the optimization objective function, resulting in the optimal control variables for two regions with step sizes. The optimal control variables are processed to obtain the control signal, which is then input into the hybrid energy storage interconnected power system to predict the output y(k); If the relevant variables of the output quantity y(k) are zero, the control of the hybrid energy storage interconnected power system is completed. If the relevant variables of the output quantity y(k) are not zero, the output quantity y0 of the initial information is reacquired until the maximum running time of the operating load frequency control module is reached.

[0006] The above technical solutions can not only effectively improve the frequency response characteristics of power systems with hybrid energy storage interconnection and coordinate and optimize unit output, but also achieve good mutual synergy among energy storage systems.

[0007] Optionally, the discretization of the continuous-time state-space model to establish a predictive model for the load frequency control model includes: Read the set sampling time value, discretize the continuous-time state-space model, and obtain the discretized state-space model; The discretized state-space models of the two regions are simplified into a new state-space model, thus obtaining the prediction model.

[0008] The above technical solution, which adopts a distributed predictive model for predictive control, can greatly reduce the computational load of the system, make the system more stable, and better coordinate and optimize the control of various energy storage systems.

[0009] Optionally, the process of obtaining the control signal from the optimal control variables includes: The optimal control variable is processed by a coordinated optimization controller to obtain the control signal. .

[0010] The above technical solution, by limiting the control variables within the constraints, avoids the system from exceeding the safe or stable operating boundaries due to excessively large or small control quantities, thus ensuring the smooth dynamic process of the system.

[0011] Optionally, the expression for the continuous-time state-space model is: (1); In the formula, , , , , For the first Each region's state vector, For the first Individual area control input, For the first Regional disturbance input, For the first Each region outputs a vector. Represents a time variable. , or ,and , , , , Adjacent areas state vector, Adjacent areas control input vector and Adjacent areas The perturbation input vector. , This represents the state matrix of a hybrid energy storage interconnected power system under continuous-time conditions. , This represents the input matrix of a hybrid energy storage interconnected power system under continuous-time conditions. , The interference matrix of a hybrid energy storage interconnected power system under continuous time conditions. This is the output matrix of a hybrid energy storage interconnected power system under continuous-time conditions.

[0012] The above technical solution: By adopting a continuous-time state-space model, charging and discharging strategies can be coordinated in real time, thereby improving system efficiency.

[0013] Optionally, the first The vector expression for each region is: State vector: ; Control input: ; Disturbance input: ; Output vector: ; The adjacent areas The vector expression is: State vector: ; Control input vector: ; Perturbation input vector: ; Output vector: ; in, Indicates frequency deviation. This indicates the change in the position of the speed controller valve in zone 1. , These represent the changes in active power of the tie lines in regions 1 and 2, respectively. This indicates the change in the position of the control valve in zone 1. This refers to the change in the position of the regulating valve in Zone 2. This is expressed as the output power of BESS; Area 1 system control signals, This indicates the BESS control signal; , These represent the load disturbance changes in Region 1 and Region 2, respectively. Indicates the deviation in energy storage power; This indicates the change in position of the servo motor regulating valve in area 2. For the control signal of the area 1 system This is the control signal for the Region 2 system.

[0014] The above technical solution achieves load frequency coordination and optimization control of interconnected power systems with hybrid energy storage by designing a multi-region controller.

[0015] Optionally, the discretization of the continuous-time state-space model to establish a predictive model for the load frequency control model includes: Discretize the formula of the continuous-time state-space model to obtain the discretized model, as shown in the formula: (2); By iterating over formula (2), the state-space expression of the prediction model in the prediction time domain is obtained: (3); Simplifying formula (3) yields the expression: (4); Transform equation (4) by multiplying both sides by an invertible matrix. Equation (5) is obtained: (5); Divide the system into two subsystems. The state-space model of subsystem i is represented as: (6); In the formula, , , , , , , The matrix is ​​respectively , , Obtained by dividing into blocks; in, Indicates the region within the prediction time domain The future state vector, Indicates the current state Influence coefficient matrix on predicted state Represents the control input sequence Influence coefficient matrix on predicted state Indicates the perturbation sequence For the predicted state Influence coefficient matrix Indicates the discrete time step. , , , Indicates the area For the region The influence coefficients of the predicted state correspond to the cross-regional coupling terms of the current state, future state, control, and disturbance, respectively.

[0016] The above technical solution utilizes distributed model predictive control technology to handle nonlinear factors such as generator power and energy storage limitations, transforming them into linear matrix inequalities. These inequalities, combined with the designed objective function, form a constrained nonlinear programming problem. The solution is obtained through rolling optimization within a finite time frame, yielding the optimal control signal for the system. This constraint handling effectively avoids safety hazards caused by equipment overload.

[0017] Optionally, the expression for the optimization objective function is: (7); in, This represents the input-output weighting matrix. Represents the control weighting matrix. Represents the parameter trajectory. Indicates the first The actual output vector at time step 1. Indicates the first The control vector at any given time.

[0018] The above technical solution establishes the objective function of the system, performs online rolling optimization of the control quantity, and designs a multi-region controller, thereby achieving load frequency coordination and optimization control effect for interconnected power systems with hybrid energy storage.

[0019] Optionally, the constraints of the optimization objective function are as follows: (8); in, The system's output power, This is the minimum output power of the system. This represents the system's maximum output power. This represents the discrete time step.

[0020] The above technical solution effectively avoids safety hazards caused by equipment overload through constraint conditions.

[0021] Optionally, the linear matrix inequality is expressed as: st ; in, Indicates the first The control vector at time t, Represents a constant vector. Indicates the area The constraint matrix.

[0022] The above technical solution can achieve rolling optimization within a limited time to obtain the optimal control signal for the system.

[0023] Optionally, the two regions obtain optimal control variables of several step sizes, including: Area 1: , ; Area 2: , ; By multiplying the optimal control sequence by a matrix The optimal control variables for the first step size are obtained.

[0024] The above technical solution avoids centralized solutions to high-dimensional coupled problems, reducing communication burden and computational complexity.

[0025] Optionally, the calculation of the optimal control sequence includes: Real-time acquisition of frequency deviation of hybrid energy storage interconnected power system Variables of state change and load disturbance changes ; frequency deviation Variables of state change and load disturbance changes Input prediction model, predict output ; According to the output Solving for the future using distributed model predictive control algorithms The optimal control sequence for thermal power units, pumped storage units, and battery energy storage at each moment.

[0026] The above technical solution achieves coordinated operation between battery energy storage, pumped hydro storage, and traditional thermal power units through coordinated control, improves the system's frequency response performance, solves the nonlinear constraint problem in the system, and avoids safety hazards caused by equipment overload.

[0027] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: This invention employs a distributed predictive model for predictive control, which significantly reduces the computational load of the system and makes it more stable. It also enables better coordination and optimization of various energy storage controls.

[0028] This invention utilizes distributed model predictive control (MMCC) technology to handle nonlinear factors such as generator power and energy storage limitations, transforming them into linear matrix inequalities. These inequalities, combined with the designed objective function, form a constrained nonlinear programming problem. The problem is solved through rolling optimization within a finite time frame, yielding the optimal control signal for the system. This constraint handling effectively avoids safety hazards caused by equipment overload.

[0029] The coordinated optimization control strategy proposed in this invention can not only effectively improve the frequency response characteristics of power systems with hybrid energy storage interconnection and coordinate and optimize unit output, but also achieve good mutual synergy among energy storage systems. Attached Figure Description

[0030] Figure 1 This is a structural diagram of a load frequency coordination optimization algorithm for an interconnected power system with hybrid energy storage, provided in an embodiment of the present invention. Figure 2 This invention provides a block diagram model of the load frequency control transfer function for an interconnected power system with hybrid energy storage, as provided in an embodiment of the invention. Figure 3 A flowchart of load frequency control for an interconnected power system with hybrid energy storage provided for an embodiment of the present invention. Detailed Implementation

[0031] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0032] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0033] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0034] Example 1, as Figures 1-3 As shown, a load frequency coordination optimization control method is disclosed, the method comprising: Step S1: Establish a load frequency control model for a hybrid energy storage interconnected power system containing battery energy storage, pumped storage, and traditional thermal power units, and obtain a continuous-time state-space model through mechanism analysis; Step S2: Set the operating parameters for the load frequency control model, including the sampling time T. s Predictive compensation N p Control step size N c Input-output weighting matrix Q i Control weighting matrix R i Parameter trajectory Y i and maximum running time T max ; Step S3: Obtain the initial information values ​​of the load frequency control model; Step S4: Discretize the continuous-time state-space model and establish a predictive model for the load frequency control model of the hybrid energy storage interconnected power system; Step S5: Based on the prediction error and initial information values, construct the optimization objective function of the load frequency control model of the hybrid energy storage interconnected power system; where the prediction error is the difference between the predicted value output by the prediction model and the set prediction value, and the initial information values ​​are the output quantity y0, state quantity x0, control signal u0 and disturbance signal w0 output by the load frequency control model. Step S6: Transform the nonlinear constraint problem in the power system with hybrid energy storage into a linear matrix inequality, and jointly optimize the objective function into a constrained nonlinear programming problem; Step S7: Solve the constrained nonlinear programming problem to obtain two regions and N. c The optimal control variable u for each step size pi (k); Step S8: Set the optimal control variable u pi (k) The control signal u(k) is obtained by the coordinated optimization controller; Step S9: Input the control signal u(k) into the hybrid energy storage interconnected power system, output the quantity y(k), the state quantity x(k), and the disturbance quantity w(k), and upload them; Step S10: Determine whether the relevant variables in the system output y(k) are zero; if the detection result is yes, the frequency of the power system with hybrid energy storage is stable, and the load frequency control of the system is completed; if the detection result is no, execute S3 until the maximum running time T is reached. max .

[0035] Example 2, based on Example 1, firstly, at the sampling time... At that time, the system will separately analyze the frequency deviation between the two regions. Variables of state change and load disturbance changes Information is collected, fed into the built prediction model, and then the output is obtained through calculation. ; Solving for the future using the Distributed Model Predictive Control (DMPC) algorithm The optimal control sequence for thermal power units, pumped storage units, and battery energy storage at any given time. The obtained values ​​are processed by the coordination optimization controller to obtain the control signal. And it is applied to a wide-area hybrid energy storage interconnected power system to obtain output. When the sampling time is At that time, the control variable is the first set of data obtained from the previous time step, and the other steps are the same as above. In this way, the system can perform cyclic rolling optimization within a finite time.

[0036] The hybrid energy storage interconnected power system includes a battery energy storage subsystem, a pumped hydro storage subsystem, and a thermal power generation subsystem; the flowchart of the load frequency coordination and optimization control method for the hybrid energy storage interconnected power system is as follows: Figure 3 As shown, the method specifically includes the following steps: Step S1: By simplifying the battery energy storage subsystem, pumped hydro storage subsystem, and thermal power generation subsystem into their corresponding transfer functions through model equivalence, the model of the load frequency control system is obtained. The model of the load frequency control system is as follows: Figure 2 As shown, the continuous-time state-space model of the system obtained through mechanism analysis is shown in Equation (1); (1); In the formula, , , , The first Each region's state vector, control input, disturbance input, and output vector. , or ,and . , , Adjacent regions The state vector, control input vector, and disturbance input vector are expressed in the following specific ways:

[0037] , , , , , , , ; , , , , , , Let be the state matrix, input matrix, disturbance matrix, and output matrix of the system in continuous time, respectively. Their specific expressions are as follows:

[0038] , , , , , , , .

[0039] In this embodiment, the hybrid energy storage interconnected power system includes a battery energy storage subsystem, a pumped storage subsystem, and a thermal power generation subsystem. The thermal power plant system comprises two modules: a speed governor and a steam turbine. Each module can be converted into a combination of corresponding inertial and proportional elements using an equivalent method. , , , , , , , , These are the equivalent transfer function models for each subsystem. , These represent the system frequency changes in region 1 and region 2, respectively. , These are the power system gain coefficients for Region 1 and Region 2, respectively. , Power system time constants for Region 1 and Region 2, respectively. , These represent the changes in generator output power in regions 1 and 2, respectively. , These represent the load disturbance changes in Region 1 and Region 2, respectively. , These represent the changes in active power of the tie lines in regions 1 and 2, respectively. This refers to the output power of BESS. This refers to the unit adjustment coefficient of the energy storage battery; For BESS control signals; is the time constant of the energy storage battery. The change in the position of the speed controller valve in Zone 1; For the control signal of the area 1 system; The time constant of the speed controller in region 1; For the speed regulation coefficient of the speed controller in zone 1, —Time constant of turbine in region 1 —Change in position of the servo motor regulating valve in area 2 , , The time constant of the hydropower unit in region 2; This refers to the speed adjustment coefficient of the speed controller in Zone 2. For the control signal of the area 2 system; This refers to the change in the position of the regulating valve in Zone 2. The start time for water supply to Zone 2. —The energy storage time constant of a pumped storage unit. and ; This represents the inter-regional interconnection gain coefficient. ; This is the regional frequency deviation factor.

[0040] Step S2: Set the operating parameters in the computer monitoring and management system, including the sampling time T. s Predicted compensation N p Control step size N c Input-output weighted matrix Q i and control weighting matrix R i Parameter trajectory Y i Maximum running time T max And set the initial value k=1.

[0041] Step S3: Obtain the initial information values ​​of the load frequency control model of the power system with hybrid energy storage interconnection, including the load frequency control model output y0, state variable x0, control signal u0 and disturbance signal w0; Step S4: Establish a load frequency control model for a power system with hybrid energy storage interconnection. The system reads the set sampling time value and discretizes the model in equation (1) to obtain the discretized model, as shown in equation (2): (2); By iterating over formula (2), the state-space expression (3) in the prediction time domain is obtained.

[0042] (3); in, , , , .

[0043] Simplifying formula (3) yields a new expression: (4); in, , , , .

[0044] , , , ,

[0045] , , , Transform equation (4) by multiplying both sides by an invertible matrix. Equation (5) is obtained: (5); in, , , .

[0046] Dividing the entire system into two subsystems, the state-space model of subsystem i can be represented as: (6); In the formula, , , , , , , The matrix is ​​respectively , , Obtained by dividing into blocks.

[0047] Step S5: Based on equation (6), establish the system's optimization objective function as shown in equation (7): (7); among which, This represents the input-output weighting matrix. Represents the control weighting matrix. Represents the parameter trajectory. Indicates the first The actual output vector at time step 1. Indicates the first The control vector at any given time.

[0048] Step S6: To avoid safety hazards caused by overloaded equipment, the nonlinear constraint problem in the power system with hybrid energy storage is processed and transformed into a linear matrix inequality.

[0049] Due to the inherent physical characteristics of energy storage batteries, constraints are imposed on their energy storage output when they participate in frequency regulation in order to extend their cycle life, as shown below: (8); definition: , , , , .

[0050] because ,so: (9); From equations (8) and (9), we can obtain the following about The constraint equations are: (10); In formula (6) Substituting into equation (10), we get: ; definition: , .

[0051] Simplifying the above inequality yields equation (11). (11);

[0052] Considering practical realities, certain environmental factors can affect equipment safety; therefore, the rate of change in the power generation of thermal power units must be limited (typically [value missing]). Typical values ​​for hydropower plants Its value cannot exceed the specified range.

[0053] Based on the above description, we can obtain equation (12): (12); In the formula, .

[0054] ,Right now .

[0055] count , It is a region The Given several state variables, we obtain a new relation (13): (13); In the formula, .

[0056] Equation (13) can be used to derive The relationship between time and time: (14); definition: ; Within the prediction time domain: (15); (16); In the formula, , .

[0057] at the same time The constraints are: (17);

[0058] In the formula, , .

[0059] because ,so: (18); In the formula, taking region 1 as an example , .

[0060] According to equations (16), (17), and (18), the new constraint equations can be obtained: (19); In formula (6) Substituting into equation (19), we get: ; definition: , .

[0061] Simplifying the above inequality, we get equation (20): (20); Step S8: The problem of solving for the control signal is transformed into a constrained nonlinear programming problem with constraints, as follows: Area 1: , .

[0062] (twenty one); Area 2: , .

[0063] (twenty two); Solving the constrained nonlinear programming problem yields N in both regions. c The optimal control variable U for each step size pi (k), U pi (k) multiplied by matrix Obtain the optimal control variable u for the first step size pi (k); S9. The control signal u(k) is transmitted to the interconnected power system containing hybrid energy storage through the computer monitoring and management system to suppress system frequency fluctuations; at the same time, the monitoring system collects the system output y(k), state quantity x(k), and disturbance quantity w(k) and uploads them to the computer management terminal.

[0064] S10. Determine if the relevant variables in the system output y(k) are zero. If the result is yes, the frequency of the power system with hybrid energy storage interconnection is stable, and the load frequency control of the system is completed. If the result is no, execute S3 until the maximum running time T is reached. max .

[0065] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A load frequency coordination optimization control method characterized by, The method comprises: establishing a load frequency control model, and obtaining a continuous-time state space model through mechanism analysis; obtaining an initial information value of the load frequency control model; discretizing the continuous-time state space model to establish a prediction model of the load frequency control model; constructing an optimization objective function of the load frequency control model of the hybrid energy storage interconnected power system according to a prediction error and the initial information value, wherein the prediction error is a difference between a predicted value output by the prediction model and a set predicted value; solving a nonlinear constraint problem in the hybrid energy storage interconnected power system to transform a linear matrix inequality and the optimization objective function into a constraint nonlinear programming problem, and obtaining optimal control variables of two regions at a step length; processing the optimal control variables to obtain control signals, and inputting the control signals to the hybrid energy storage interconnected power system to predict an output quantity y(k); in response to a relevant variable of the output quantity y(k) being zero, completing control of the hybrid energy storage interconnected power system, and in response to the relevant variable of the output quantity y(k) not being zero, reacquiring an initial information output quantity y0 until a maximum running time of the load frequency control model is reached.

2. The load frequency coordination optimization control method according to claim 1, characterized by, The discretizing of the continuous-time state space model to establish the prediction model of the load frequency control model comprises: reading a set sampling time value, discretizing the continuous-time state space model, and obtaining a discretized state space model; simplifying the discretized state space models of the two regions into a new state space model to obtain the prediction model.

3. The load frequency coordination optimization control method according to claim 1, characterized by, The processing of the optimal control variables to obtain the control signals comprises: The optimal control variables are processed by the coordinated optimal controller to obtain control signals .

4. The load frequency coordination optimization control method according to claim 1, characterized by, an expression of the continuous-time state space model is: （1） wherein , , , , is the state vector of the z-th region, is the control input of the z-th region, is the disturbance input of the z-th region, is the output vector of the z-th region, denotes the time variable, , or , and , , , , is the state vector of the adjacent region is the control input vector of the adjacent region is the disturbance input vector of the adjacent region , , , , , , , , is the state matrix of the hybrid energy storage interconnected power system in continuous time state, , is the input matrix of the hybrid energy storage interconnected power system in continuous time state, , is the disturbance matrix of the hybrid energy storage interconnected power system in continuous time state, is the output matrix of the hybrid energy storage interconnected power system in continuous time state. The vector expression of the first region is: State vector: ; Control input: ; Disturbance input: ; Output vector: ; the adjacent regions The vector expression is: State vector: ; Control input vector: ; Disturbance input vector: ; Output vector: ; wherein, represents a frequency deviation, represents a change in the speed governor valve position of region 1, are respectively the tie-line active power change of region 1, region 2; represents a change in the regulating valve position of region 1, is the regulating valve position change of region 2; represents the output power of the BESS; is the system control signal of region 1, represents the BESS control signal; are respectively the load disturbance change of region 1, region 2; represents the energy storage power deviation; represents the regulating valve position change of region 2 servo motor, is the system control signal of region 1 is the system control signal of region 2.​​ 5. The load frequency coordination optimization control method according to claim 1, characterized by, The discretizing of the continuous-time state space model to establish the prediction model of the load frequency control model comprises: discretizing a formula of the continuous-time state space model to obtain a discretized model, as shown in the formula: （2）； iteratively processing the formula (2) to obtain a state space expression in a prediction time domain of the prediction model: （3）； simplifying the formula (3) to obtain an expression: （4）； Transforming equation (4) by multiplying both sides by the invertible matrix , we obtain equation (5): （5）； dividing the system into two subsystems, and representing a state space model of the subsystem i as: (6)； wherein , , , , , , are obtained by partitioning the matrix , , , respectively; wherein, represents a future state vector of the region in the prediction horizon, represents a current state a matrix of influence coefficients of the prediction state, represents a control input sequence a matrix of influence coefficients of the prediction state, represents a disturbance sequence a matrix of influence coefficients of the prediction state , represents a discrete time step, , , , represents a region a matrix of influence coefficients of the prediction state of the region corresponding to the cross-region coupling terms of the current state, future state, control, disturbance, respectively.​ 6. The load frequency coordination optimization control method according to claim 1, characterized by, an expression of the optimization objective function is: （7）； wherein, represents an input-output weighting matrix, represents a control weighting matrix, represents a parameter trajectory, represents an actual output vector at a time instant, represents a control amount vector at a time instant.

7. The load frequency coordination optimization control method according to claim 1, characterized by, constraint conditions of the optimization objective function are as follows: (8)； wherein, Pmin is the minimum output power of the system, Pmin is the minimum output power of the system, Pmax is the maximum output power of the system, denotes a discrete time step.

8. The load frequency coordination optimization control method according to claim 1, characterized by, the linear matrix inequality is represented as: s.t. ; wherein, represents the control vector at the time instant represents a constant vector, represents a constraint matrix of the region .​ 9. The load frequency coordination optimization control method according to claim 1, characterized by, the optimal control variables of the two regions at the step length comprise: Region 1: , ; Region 2: , ; optimal control sequence multiplied by matrix results in the optimal control variable for the first step.

10. The load frequency coordination optimization control method according to claim 9, characterized by, the calculation of the optimal control sequence comprises: Real-time acquisition of frequency deviation of hybrid energy storage interconnected power system , state change variable and load disturbance change ; inputting a frequency deviation , a state change variable , and a load disturbance change amount , inputting a prediction model, predicting an output ; According to the output The optimal control sequences of thermal power units, pumped storage units and battery energy storage at future times are solved by using a distributed model predictive control algorithm.

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