Wind storage automatic power generation control optimization method

Abstract

The invention discloses a wind storage automatic power generation control optimization method, and relates to the technical field of power system stability control, and the method comprises the steps: constructing an automatic power generation control model containing a wind turbine generator and an energy storage system, introducing a per-unit coefficient by the wind turbine generator through droop adjustment and a virtual inertia response strategy, and building an open-loop transfer function, the energy storage system adopts a first-order inertial model; constructing an improved MPC control model fused with the PIDN, connecting a feedforward control component of the MPC with a feedback control component of the PIDN in parallel, and generating a unified control signal to drive a wind turbine generator and an energy storage system to perform power adjustment; and carrying out global optimization on model predictive control MPC parameters by adopting an STOA algorithm and taking ITAE indexes of frequency deviation and tie line power deviation as optimization targets. According to the method, the system frequency stability and the power regulation precision are effectively improved, and the dynamic performance and the robustness of wind and storage combined participation in automatic power generation control are enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system stability control technology, and in particular to an optimization method for automatic wind and energy storage generation control. Background Technology

[0002] With the deepening of global energy structure transformation and the "dual carbon" goal, the penetration rate of renewable energy, represented by wind power, in the power system continues to increase. However, wind power output has significant randomness, volatility, and intermittency. Large-scale grid connection leads to a decrease in the system's equivalent rotational inertia, and the frequency regulation capability of traditional synchronous units is relatively insufficient, posing a severe challenge to grid frequency stability. Automatic generation control (AGC), as a key means to maintain system power balance and frequency stability, has long relied mainly on the regulation capabilities of conventional units such as thermal power plants. Its response speed is slow, making it difficult to effectively track the rapid power fluctuations caused by new energy sources.

[0003] To enhance system regulation capabilities, wind turbines participate in frequency regulation through droop control and virtual inertia control, while energy storage systems, with their millisecond-level fast response characteristics, complement wind power. However, wind-storage joint frequency regulation systems are characterized by multivariable, nonlinear, strongly coupled, and complex operational constraints. Traditional control strategies, such as PID control, often struggle to balance dynamic performance and robustness when dealing with such complex systems. Model predictive control (MPC) shows potential in wind-storage joint frequency regulation due to its ability to handle multivariable constraints, perform rolling optimization, and look-ahead control; however, traditional MPC still has significant limitations in practical applications: First, its control performance heavily relies on an accurate system model, and the uncertainties of wind-storage systems lead to a high risk of model mismatch; second, the tuning process for MPC parameters (such as prediction time domain, control time domain, and weight matrix) is complex, lacking universal and efficient tuning methods, relying heavily on empirical trial and error, making it difficult to guarantee global optimality; finally, online rolling optimization involves large computational loads, which may affect the real-time performance of control in grid frequency control scenarios requiring millisecond-level response.

[0004] Therefore, there is an urgent need in the existing technology for an intelligent AGC optimization method that can effectively integrate wind and energy storage characteristics and take into account both response speed and stability. While giving full play to the advantages of MPC, it should overcome its shortcomings such as strong model dependence, difficulty in parameter tuning and insufficient real-time calculation, so as to meet the higher requirements of frequency control quality for high-proportion new energy power systems. Summary of the Invention

[0005] To address the above problems, this invention proposes an optimization method for automatic wind-storage power generation control.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] A wind-storage automatic power generation control optimization method, characterized by comprising:

[0008] Construct an automatic power generation control model that includes wind turbine generators and energy storage systems:

[0009] An improved MPC control model integrating PIDN is constructed, and the improved MPC control model is used to generate control signals to drive the automatic generation control model for power adjustment.

[0010] The STOA algorithm is used to optimize the parameters of the improved MPC control model globally, with the ITAE index of system frequency deviation and tie-line power deviation as the optimization target.

[0011] As a preferred embodiment of the present invention, the wind turbine in the automatic generation control model is based on a droop control and virtual inertia response control strategy to construct an open-loop transfer function that participates in grid frequency regulation. The expression of the open-loop transfer function is as follows:

[0012] ;

[0013] In the formula, This refers to the power adjustment of the wind turbine unit. For system frequency deviation, For proportional gain stage, The inertia coefficient, For the Laplace operator, This is the per-unit coefficient.

[0014] As a preferred embodiment of the present invention, the energy storage system in the automatic generation control model adopts a first-order inertial model, and the transfer function of the energy storage system is constructed. The expression of the transfer function is as follows:

[0015] ;

[0016] In the formula, For the power adjustment of the energy storage system, The frequency regulation gain coefficient of the energy storage unit; This represents the inertial response coefficient of the energy storage unit.

[0017] As a preferred embodiment of the present invention, the automatic power generation control model further includes: obtaining a power frequency characteristic formula and regional control error parameters based on the wind turbine, energy storage system, and other parameters; including:

[0018] The power frequency characteristic formula is used to output the system frequency deviation. The dynamic change process is expressed as:

[0019] ;

[0020] In the formula, The time inertia constant of the synchronizing machine, The damping coefficient is... For power adjustment between tie lines, This refers to the load disturbance power.

[0021] The expression for calculating the area control error parameter is as follows:

[0022] ;

[0023] In the formula, is the frequency response coefficient of the region.

[0024] As a preferred embodiment of the present invention, the construction of the improved MPC control model incorporating PIDN includes: MPC is based on the system state-space model, and by optimizing the control sequence in the future prediction time domain, it processes multivariable constraints and generates feedforward control components, including:

[0025] The system state-space model is as follows:

[0026] Equations of state:

[0027] ;

[0028] Output equation:

[0029] ;

[0030] In the formula, , , and Represents the system state space matrix. and These represent the output and input diagonal arrays, respectively. A dimensionless vector representing the input variables. Indicates at time The system state variables, Indicates at time +1 system state variable, Indicates at time The controller output, Indicates at time The actual output of the system;

[0031] Construct the optimization objective function:

[0032] ;

[0033] In the formula, Indicates at discrete time step Minimum control increment, Indicates at discrete time step Control increments on top This represents the loop variable for summation. Indicates the current discrete time step. Indicates the control range. Represents the control weight matrix. Indicates the prediction range, where 1≤ ≤ , Indicates the sampling time. Indicates the output error. This represents the loop variable for summation. This represents the output weight matrix;

[0034] The optimization objective function follows the multivariate constraints of a regional power system containing wind turbines and energy storage systems. The multivariate constraints include input constraints and output constraints.

[0035] Based on the aforementioned objective function, feedforward control components are generated:

[0036] ;

[0037] In the formula, Indicates the current time The absolute control quantity, Indicates the current time -1 is the absolute control quantity.

[0038] As a preferred embodiment of the present invention, PIDN generates feedback control components by introducing feedback coefficients to adjust system damping and dynamic response; including:

[0039] Transfer function:

[0040] ;

[0041] Output function:

[0042] ;

[0043] In the formula, For the transfer function of the PIDN controller, For feedback control components, This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... For feedback coefficients, This refers to the area control error parameter.

[0044] As a preferred embodiment of the present invention, the step of generating a control signal for power adjustment using an improved MPC control model to drive an automatic power generation control model includes: obtaining the final control signal u based on the feedforward control component and the feedback control component, with the expression:

[0045] ;

[0046] In the formula, For feedforward control components, This is the feedback control component.

[0047] As a preferred embodiment of the present invention, the optimization target for the ITAE index of system frequency deviation and tie-line power deviation includes:

[0048] ;

[0049] In the formula, For time variables, The derivative of the system frequency deviation, This is the derivative of the tie line power deviation.

[0050] As a preferred embodiment of the present invention, the MPC parameters of the improved MPC control model include prediction time domain P, control time domain M, output weight matrix Q, control weight matrix R, and sampling time T, with the corresponding value ranges as follows:

[0051] P: ;

[0052] M: ;

[0053] Q: ;

[0054] R: ;

[0055] T: ;

[0056] The MPC is powered by three inputs: a reference signal, a frequency deviation, and a load disturbance signal.

[0057] As a preferred embodiment of the present invention, the iterative optimization of the MPC parameters using the STOA algorithm includes:

[0058] S1. Based on the constraints of the MPC parameters, generate a population of Black-eared Terns, where the position of each Black-eared Tern in the population represents a set of MPC parameters. ;

[0059] S2. Configure each set of MPC parameters into the improved MPC control model. Under a load disturbance scenario, run the automatic generation control model containing wind turbines and energy storage systems, and calculate the ITAE index values ​​of system frequency and tie-line power. The ITAE index values ​​are the fitness of the current MPC parameters.

[0060] S3. The STOA algorithm updates the population position through the migration phase and the attack phase;

[0061] S4. Repeat S2 and S3 until the termination condition is met, and output the optimal parameter set found during the search process.

[0062] The beneficial effects of this invention are as follows: By merging the MPC feedforward control component and the PIDN feedback control component in parallel, a collaborative control mechanism with look-ahead optimization and rapid correction capabilities is constructed, effectively improving the dynamic response speed and stability of the system. The U-Tern optimization algorithm is adopted, using ITAE as the optimization target to achieve global automatic tuning of key MPC parameters, overcoming the engineering bottleneck of traditional MPC parameters relying on experience. A first-order inertial model is used, significantly reducing computational complexity while ensuring accuracy, ensuring the feasibility of millisecond-level real-time control. The control architecture exhibits strong robustness to non-ideal operating conditions such as communication delays, providing a reliable frequency regulation solution for high-proportion renewable energy power grids. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart of the wind-storage automatic power generation control optimization method of the present invention; Figure 2 This is a schematic diagram of the wind-storage frequency control model in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the principle of the model predictive control algorithm in an embodiment of the present invention; Figure 4 This is a schematic diagram of the improved MPC control model incorporating PIDN in an embodiment of the present invention; Figure 5 This is a schematic diagram of the parameter optimization process based on the SPOA algorithm in an embodiment of the present invention; Figure 6 This is a schematic diagram comparing frequency modulation performance indicators under load disturbance scenarios in an embodiment of the present invention; Figure 7 This is a schematic diagram comparing frequency modulation performance indicators under communication delay scenarios in embodiments of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0065] like Figures 1-5 As shown, this is an embodiment of the present invention, which provides a wind-storage automatic power generation control optimization method, including:

[0066] S1. Construct an automatic power generation control model including wind turbine generators and energy storage systems, including wind turbine generators using droop adjustment control and virtual inertia response control strategies, and introducing per-unit coefficients. An open-loop transfer function is constructed; the energy storage system adopts a first-order inertial model, and the transfer function of the energy storage system is constructed; based on the wind turbine, energy storage system, and other parameters, the power frequency characteristic formula and the regional control error parameters are obtained.

[0067] Considering the application of variable speed wind turbines (WTGs) in grid frequency regulation, it is deconstructed into two core strategies: droop control simulating traditional units and virtual inertia response. First, the droop control strategy is simplified equivalently to a combination of a dead-time element and a proportional gain element, where the proportional gain... The sensitivity of the wind turbine's output power response after sensing a frequency deviation is directly determined; similarly, the virtual inertia control strategy is equivalent to a differential element capable of sensing the rate of frequency change, the strength of which is determined by the inertia coefficient. This coefficient reflects the wind turbine's ability to simulate the rotor inertia of a synchronous generator. Furthermore, to accurately reflect the actual contribution of wind turbines to the power grid, a key scaling factor is introduced into the model. This factor, expressed as a per-unit value, represents the share of frequency regulation undertaken by wind power within the available frequency regulation resources.

[0068] In summary, by integrating all the above-mentioned links, a complete open-loop transfer function for wind turbines participating in grid frequency regulation is constructed, as shown in the following equation. This provides a theoretical basis for subsequent analysis of the frequency response characteristics of wind power under grid disturbances, evaluation of its frequency regulation effect, and optimization of control parameters.

[0069] ;

[0070] In the formula, This refers to the power adjustment of the wind turbine unit. For system frequency deviation, For the Laplace operator.

[0071] The equivalent model of an energy storage system is crucial for research and simulation. The first-order inertial model and the Thevenin equivalent circuit model are two commonly used models. The first-order inertial model treats the battery as an energy source with inertial characteristics and can simulate the charging and discharging process of the battery. However, it simplifies the description of the complex chemical reactions inside the battery. The Thevenin equivalent circuit model provides a more detailed description of the internal characteristics of the battery. Its advantage is that it can more accurately reflect the dynamic behavior of the battery under different operating conditions. However, its complexity leads to an increase in the computational load of the simulation process and a decrease in the simulation speed.

[0072] In practical research on AGC for new energy systems, since the main focus is on the rapid response capability of the energy storage system to grid frequency fluctuations, a first-order inertial model is sufficient to meet the simulation requirements. Furthermore, the simplification of the first-order inertial model reduces its computational burden in the real-time control system, improving the system's real-time response capability. The transfer function of the energy storage unit used is shown in the following equation:

[0073] ;

[0074] In the formula, For the power adjustment of the energy storage system, For system frequency deviation, The frequency regulation gain coefficient of the energy storage unit; The inertial response coefficient of the energy storage unit. For the Laplace operator.

[0075] This invention takes a power system containing wind turbines and energy storage as the analysis scenario, and analyzes the power frequency characteristics of the power system containing wind turbines and energy storage. The expression is as follows:

[0076] ;

[0077] In the formula, The time inertia constant of the synchronizing machine, For the Laplace operator, The damping coefficient is... , , These are power regulation for wind turbines, energy storage power systems, and interconnecting lines; This refers to the load disturbance power of the system.

[0078] Area Control Error (ACE) is a key parameter in Automatic Generation Control (AGC) systems for achieving power coordination and frequency stability between control zones. It primarily reflects the deviation between the operating state and the planned operating state within the controlled area. When ACE is zero, it indicates that generation and load in the area are in balance; however, when ACE deviates from zero, it indicates a power imbalance in the area, thus affecting the stability of the system frequency. To maintain safe system operation, ACE needs to be controlled within a certain range. This is typically achieved by the AGC system automatically adjusting the output of units within the area to eliminate deviations and maintain frequency stability. Its calculation expression is shown below:

[0079] ;

[0080] In the formula, is the frequency response coefficient of the region.

[0081] The wind-storage integrated frequency control model constructed based on the above analysis consists of multiple equivalent modules, including wind turbines, energy storage systems, synchronous machines, loads, and MPC load frequency controllers. These modules are interconnected. Figure 2 As shown.

[0082] S2. Construct an improved MPC control model for PIDN, including MPC based on the system state-space model, which optimizes the control sequence in the future prediction time domain, handles multivariable constraints, and generates feedforward control components; PIDN adjusts system damping and dynamic response by introducing feedback coefficients to generate feedback control components; connect PIDN and MPC in parallel; obtain the final control signal u based on the feedforward control components and feedback control components, which is used to simultaneously drive the wind turbine and energy storage system for power adjustment;

[0083] Model predictive control (MPC) provides a method for predicting future behavior and planning ahead. It can handle multivariable control problems, simultaneously optimizing multiple control objectives and considering input and output constraints to achieve precise control. Its algorithm principle is as follows: Figure 3 As shown; however, due to its high computational complexity, MPC requires frequent optimization problems and has high computational resource requirements. In addition, its control strategy depends on an accurate mathematical model. If the model is inaccurate, the control performance may be affected. Figure 3 In the diagram, yd is the reference signal; yr(k+p) is the optimal reference value; yc(k+p) is the corrected output; η(k) is the control input; ym(k+p) is the predicted value; y(k) is the system output; k is the sampling time; and p is the prediction step size.

[0084] In the MPC control model, the inputs and outputs of the wind turbine and energy storage system can be equivalently represented by the following system state space model:

[0085] State equation: ;

[0086] Output equation: ;

[0087] Wherein, , , and represent the system state space matrices, and represent the output and input diagonal arrays respectively, represents the dimensionless vector of the input variable, represents the system state variable at time , represents the system state variable at time +1, represents the controller output at time , represents the actual system output at time .

[0088] Construct the optimization objective function:

[0089] ;

[0090] Wherein, represents the minimum control increment at the discrete time step , represents the control increment at the discrete time step , represents the summation loop variable, represents the current discrete time step, represents the control range, represents the control weight matrix, represents the prediction range, where 1 ≤ ≤ , represents the sampling time, represents the output error, represents the summation loop variable, represents the output weight matrix.

[0091] The optimization objective function needs to comply with the multivariable constraints of the system, including: input constraints and output constraints;

[0092] Based on the optimization objective function, generate the feedforward control component:

[0093] ;

[0094] In the formula, Indicates the current time The absolute control quantity, Indicates the current time -1 is the absolute control quantity.

[0095] The PIDN controller introduces a feedback coefficient to optimize the dynamic performance of a system based on the traditional PID architecture. This controller includes four key linear parameters: proportional gain, integral gain, derivative gain, and the newly added feedback coefficient. The PIDN controller constructs an internal feedback path through the feedback coefficient, which acts on the derivative term, thereby adjusting the strength of the internal signal. This allows for flexible changes to the controller's equivalent transfer function, thus dynamically adjusting the system's damping ratio and response characteristics. This mechanism enables the PIDN controller to retain all the advantages of PID control while more effectively balancing the system's speed and stability, resulting in a superior dynamic response curve. Its transfer function expression is as follows:

[0096] Transfer function:

[0097] ;

[0098] Output function:

[0099] ;

[0100] In the formula, For the transfer function of the PIDN controller, For feedback control components, This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... For feedback coefficients, This refers to the area control error parameter.

[0101] To further improve the overall control performance of the AGC control model, this section combines the advantages of MPC and PIDN strategies and connects them in parallel, proposing an improved MPC control model that integrates PIDN, such as... Figure 4As shown in the diagram, MPC provides the ability to handle complex systems and consider constraints, while PIDN control takes nonlinearity into account to improve control performance. Furthermore, the proposed improved MPC controller offers flexibility in adjusting control parameters, while the PIDN component allows the use of traditional tuning methods that are widely recognized and understood. The MPC component achieves specific performance requirements by adjusting the prediction range, control range, weights in the cost function, and other tuning parameters. Therefore, the improved MPC controller combines the predictive power, optimal control, and robustness of MPC with the simplicity and adjustability of PIDN control, thereby improving system stability, performance, and economical operation.

[0102] Based on the aforementioned feedforward and feedback control components, the final control signal u is obtained. This control signal u is used to simultaneously drive the wind turbine and energy storage system for power adjustment, including:

[0103] ;

[0104] In the formula, For feedforward control components, This is the feedback control component.

[0105] S3. The STOA algorithm is used to perform global optimization of the MPC, including using the ITAE index of system frequency deviation and tie-line power deviation as the optimization target; within the constraints of the preset MPC parameter set, the STOA algorithm is used to iteratively optimize the MPC parameter set, which includes prediction time domain P, control time domain M, output weight matrix Q, control weight matrix R, and sampling time T; the optimized MPC parameter set is applied to the improved MPC control model.

[0106] The St. Tern Optimization Algorithm (STOA) is a metaheuristic optimization algorithm that simulates the migration and attack behavior of St. Tern flocks. Its core principle is built upon two key behavioral mechanisms: coordinated group attack during migration and precise strikes when attacking prey. During the migration phase, STOA simulates the behavior of a flock following the strongest individual in a large-scale movement. It updates the position of each individual using mathematical formulas, allowing the flock to explore a vast search space while avoiding collisions, thus ensuring the algorithm's global search capability. Once the flock discovers a potential optimal region, the algorithm enters the attack phase. This phase simulates the process of terns spiraling down from the air to catch prey. Individuals perform intense, spiraling local searches around the current optimal solution. This mechanism greatly enhances the algorithm's local exploitation capability, enabling it to precisely discover and lock in the global optimum. The entire optimization process balances global search and local optimization by dynamically switching between migration and attack behaviors. STOA continuously updates the global optimum by evaluating the fitness value of each individual's position, guiding the entire flock towards it. This dual-behavioral mechanism enables the Black-and-White tern optimization algorithm to effectively avoid getting trapped in local optima while ensuring fast convergence speed and high solution accuracy when dealing with complex multimodal optimization problems. The update expression during the migration process is described as follows:

[0107] ;

[0108] ;

[0109] In the formula, Indicates the location of the black tern. Indicates the current target location in the search. Indicates the current iteration number. It refers to the movement of the target within a specific search area, while It is control The variables set, This indicates the maximum number of iterations.

[0110] Search for the best neighbor according to the following equation, and converge with it after avoiding collisions:

[0111] ;

[0112] ;

[0113] In the formula, Indicates the different positions of the target after the update; Indicates the optimal objective. Represents a random variable. This represents a random number within the interval [0,1]. The optimization algorithm searches for and refreshes its position relative to the optimal target.

[0114] ;

[0115] In the formula, This represents the difference between the current position and the optimal position. When attacking prey, the target changes its speed and exhibits spiral behavior, defined as follows:

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] In the formula, This represents the radius of each spiral turn. This represents the helix angle, which is variable within the range [0, 2π]. , , Represents the coordinate components of the new position in three-dimensional space. and The constant representing the spiral form, Indicates the radial component of the helix. This represents a positive logarithm. The target updates its position according to the following equation.

[0121] ;

[0122] In the formula, Represents a three-dimensional displacement vector. This is the optimal solution saved after updating the position.

[0123] This embodiment uses the STOA algorithm to identify MPC parameters. The purpose of the STOA algorithm is to minimize ITAEs caused by tie-line frequency and power fluctuations, as shown below:

[0124] ;

[0125] In the formula, For time variables, The derivative of the system frequency deviation, This is the derivative of the tie-line power deviation;

[0126] The constraints on the MPC parameters are chosen as follows: Prediction time domain P: Control time domain M: Output weight matrix Q: Control weight matrix R: Sampling time T: ;

[0127] The MPC is powered by three inputs: a reference signal, a frequency offset, and a load disturbance signal. The ITAE is calculated based on the current distortion of frequency and power in the tie line and then fed to the proposed STOA. By minimizing the ITAE, the STOA can determine the optimal parameters of the MPC, which are then fed back to the aforementioned improved MPC. Figure 5 The optimized process of STOA in wind and energy storage frequency regulation control is demonstrated.

[0128] like Figures 6-7 As shown, another embodiment of the present invention provides a verification effect on the wind-storage automatic power generation control optimization method, including:

[0129] The simplified model is analyzed to show the trend of frequency fluctuations and verify the effectiveness of the optimization scheme proposed in this invention. The system is converted to per-unit values, and an equivalent model is built in Matlab / Simulink. The sampling step size of MPC is 0.1s, the prediction time domain is 4s, and the control time domain is 3s. The simulation system parameters are shown in Table 1.

[0130] Table 1 Simulation System Parameters

[0131]

[0132] To verify the effectiveness of the frequency control proposed in this invention, a step response simulation was performed. When the load step disturbance signal was 0.1 pu, the frequency simulation curves of three control scenarios—PID, MPC, and improved MPC—were compared. According to the "Notice on Organizing and Carrying Out Simulation Operation of Guangdong Frequency Regulation Auxiliary Service Market" issued by the Southern Energy Market Supervision Bureau, the calculation formula for the comprehensive frequency regulation performance index k of AGC is as follows:

[0133] ;

[0134] In the formula: To adjust the rate; For response time; To adjust the precision.

[0135] The regulation rate reflects the unit's ability to adjust output per unit time and is an important indicator of its rapid response capability. Response time measures the unit's reaction speed to dispatch commands, reflecting its transient regulation capability in the face of system frequency fluctuations. The response time of traditional units is approximately 10 seconds, while that of energy storage systems is approximately 1 second. Regulation accuracy reflects the accuracy of the unit's output adjustment and is a key factor in ensuring grid frequency stability. Combining the above calculation methods, the k-values ​​under various regulation strategies are obtained as follows: Figure 6 As shown.

[0136] from Figure 6The comparative data shows that the improved MPC has a comprehensive performance index k value of 1.36, which is better than the traditional MPC's 1.13, achieving a 36% performance improvement. Specifically, this performance stems from the comprehensive optimization of its sub-indicators: In terms of regulation rate (k1), the improved MPC reaches 1.36, which is 36% higher than PID and 16.2% higher than the traditional MPC, indicating that the algorithm can adjust the unit output at a faster speed and respond quickly to the power shortage in the grid; in terms of response time (k2), the values ​​of all strategies are not significantly different, but the improved MPC's 1.02 is still the best among the three, indicating that it responds most quickly to dispatch commands and is crucial for suppressing the initial frequency drop; in terms of regulation accuracy (k3), the improved MPC control model is 23% and 6% higher than PID and the traditional MPC, respectively, indicating that the improved algorithm can more accurately adjust the unit output to the target value, effectively reducing over-adjustment and under-adjustment, thereby maintaining the stability of the grid frequency. In summary, the improved MPC, by integrating the feedback advantages of PIDN with the predictive capabilities of MPC, achieves control performance with fast response, precise control, and high regulation rate, verifying its application value and competitive advantage in the modern power system frequency regulation ancillary service market.

[0137] To further verify the applicability of the proposed improved MPC control model, considering the scenario of communication delay, a delay of 0.2s was set during command transmission. The wind-storage frequency fluctuation response curves under different control strategies were compared as follows: Figure 7 As shown.

[0138] from Figure 7The comparative data shows that under ideal disturbances without delay, the control strategies mainly compare the response speed and accuracy to known disturbances. However, the introduction of communication delay shifts the focus of frequency modulation control to the ability to compensate for and suppress uncertainties and hysteresis. Simulation data shows that, facing communication delay, the improved MPC control model achieves a comprehensive performance index k-value of 1.26, higher than PID control and traditional MPC, indicating good robustness. Meanwhile, compared to the delay-free scenario, the standard MPC's comprehensive index drops from 1.13 to 1.10, showing a significant performance degradation, indicating its sensitivity to model mismatch and delay. The improved MPC control model's comprehensive index only drops from 1.36 to 1.26, a relatively small decrease, while still maintaining a performance level far exceeding other strategies. Looking at the sub-indicators, the improved MPC achieves a k-value of 1.34 for the critical response time (k2), higher than the traditional MPC's 1.23. This directly reflects the unique advantages of its PIDN feedback loop in combating delay and quickly correcting deviations, effectively compensating for the impact of information lag. Meanwhile, its regulation rate (k3) and regulation accuracy (k3) are still higher than those of PID and traditional MPC control, ensuring that frequency stability can be restored quickly and accurately even under command delay. Therefore, the improved MPC control model can still maintain the stability of frequency regulation control under communication delay scenarios, providing a more solid and reliable theoretical basis for its engineering application in real power grids.

[0139] In summary, this invention constructs an automatic power generation control model incorporating wind turbines and energy storage systems. The energy storage system employs a first-order inertial model to balance fast response and computational efficiency. An improved controller integrating PIDN and MPC is used, combining the foresight and optimality of MPC in handling multivariable and constrained optimization problems with the advantages of PIDN controllers' simple structure and ease of tuning, thereby enhancing the controller's flexibility and robustness. The Black-and-White optimization algorithm is introduced, using the ITAE index of system frequency deviation and tie-line power deviation as the optimization objective to globally optimize key parameters of MPC, further improving the frequency control performance of the new energy system. This invention overcomes the limitations of traditional MPC in terms of model dependence, parameter tuning flexibility, and real-time computation, designing a more intelligent and efficient improved control strategy. This allows MPC control to fully leverage its theoretical advantages in wind-storage frequency regulation scenarios with high dynamic and time-sensitive requirements, enhancing the system's dynamic frequency response and anti-disturbance performance.

[0140] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any other combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0141] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0142] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A wind-storage automatic power generation control optimization method, characterized in that, include: Construct an automatic power generation control model that includes wind turbine generators and energy storage systems: Construct an improved MPC control model that integrates PIDN; The STOA algorithm is adopted, with the ITAE index of system frequency deviation and tie-line power deviation as the optimization target. The MPC parameters of the improved MPC control model are globally optimized, and the optimized improved MPC control model is used to generate control signals to drive the automatic generation control model for power adjustment.

2. The wind-storage automatic power generation control optimization method according to claim 1, characterized in that, In the automatic generation control model, the wind turbine is based on droop control and virtual inertia response control strategies to construct an open-loop transfer function that participates in grid frequency regulation. The expression of the open-loop transfer function is as follows: ； In the formula, This refers to the power adjustment of the wind turbine unit. For system frequency deviation, For proportional gain stage, The inertia coefficient, For the Laplace operator, This is the per-unit coefficient.

3. The wind-storage automatic power generation control optimization method according to claim 2, characterized in that, In the automatic generation control model, the energy storage system adopts a first-order inertial model. The transfer function of the energy storage system is constructed, and its expression is as follows: ； In the formula, For the power adjustment of the energy storage system, The frequency regulation gain coefficient of the energy storage unit; This represents the inertial response coefficient of the energy storage unit.

4. The wind-storage automatic power generation control optimization method according to claim 3, characterized in that, The automatic power generation control model also includes: obtaining power frequency characteristic formulas and regional control error parameters based on the wind turbine, energy storage system, and other parameters; including: The power frequency characteristic formula is used to output the system frequency deviation. The dynamic change process is expressed as: ； In the formula, The time inertia constant of the synchronizing machine, The damping coefficient is... For power adjustment between tie lines, This refers to the load disturbance power. The expression for calculating the area control error parameter is as follows: ； In the formula, is the frequency response coefficient of the region.

5. The wind-storage automatic power generation control optimization method according to claim 4, characterized in that, The construction of the improved MPC control model integrating PIDN includes: MPC is based on the system state-space model, and by optimizing the control sequence in the future prediction time domain, it handles multivariable constraints and generates feedforward control components, including: The system state-space model is as follows: Equations of state: ； Output equation: ； In the formula, , , and Represents the system state space matrix. and These represent the output and input diagonal arrays, respectively. A dimensionless vector representing the input variables. Indicates at time The system state variables, Indicates at time +1 system state variable, Indicates at time The controller output, Indicates at time The actual output of the system; Construct the optimization objective function: ； In the formula, Indicates at discrete time step Minimum control increment, Indicates at discrete time step Control increments on top This represents the loop variable for summation. Indicates the current discrete time step. Indicates the control range. Represents the control weight matrix. Indicates the prediction range, where 1≤ ≤ , Indicates the sampling time. Indicates the output error. This represents the loop variable for summation. This represents the output weight matrix; The optimization objective function follows the multivariate constraints of a regional power system containing wind turbines and energy storage systems. The multivariate constraints include input constraints and output constraints. Based on the aforementioned objective function, feedforward control components are generated: ； In the formula, Indicates the current time The absolute control quantity, Indicates the current time -1 is the absolute control quantity.

6. The wind-storage automatic power generation control optimization method according to claim 5, characterized in that, PIDN generates feedback control components by introducing feedback coefficients to adjust system damping and dynamic response; including: Transfer function: ； Output function: ； In the formula, For the transfer function of the PIDN controller, For feedback control components, This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... For feedback coefficients, This refers to the area control error parameter.

7. The wind-storage automatic power generation control optimization method according to claim 6, characterized in that, The method of generating control signals for power adjustment using an improved MPC control model to drive an automatic power generation control model includes: obtaining the final control signal u based on the feedforward control component and the feedback control component, with the expression: ； In the formula, For feedforward control components, This is the feedback control component.

8. The wind-storage automatic power generation control optimization method according to claim 7, characterized in that, The optimization target for the ITAE index, which is the system frequency deviation and tie-line power deviation, includes: ； In the formula, For time variables, The derivative of the system frequency deviation, This is the derivative of the tie line power deviation.

9. The wind-storage automatic power generation control optimization method according to claim 8, characterized in that, The improved MPC control model's MPC parameters include prediction time domain P, control time domain M, output weight matrix Q, control weight matrix R, and sampling time T, with the following value ranges: P： ； M： ； Q： ； R： ； T： ； The MPC is powered by three inputs: a reference signal, a frequency deviation, and a load disturbance signal.

10. The wind-storage automatic power generation control optimization method according to claim 9, characterized in that, The iterative optimization of the MPC parameters using the STOA algorithm includes: S1. Based on the constraints of the MPC parameters, generate a population of Black-eared Terns, where the position of each Black-eared Tern in the population represents a set of MPC parameters. ; S2. Configure each set of MPC parameters into the improved MPC control model. Under a load disturbance scenario, run the automatic generation control model containing wind turbines and energy storage systems, and calculate the ITAE index values ​​of system frequency and tie-line power. The ITAE index values ​​are the fitness of the current MPC parameters. S3. The STOA algorithm updates the population position through the migration phase and the attack phase; S4. Repeat S2 and S3 until the termination condition is met, and output the optimal parameter set found during the search process.

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