Load frequency control method of multi-region combined power system considering wind power integration

Abstract

The invention provides a load frequency control method for a multi-region combined power system considering wind power integration, and the method comprises the following steps: building a combined state space model of the multi-region combined power system, comprising a state space sub-model of a wind power region, a state space sub-model of a thermal power region and tie line power deviation constraint relationships among the regions; a combined control topology of the multi-area combined power system is established, the combined control topology comprises a sub-control topology of a wind power area, a sub-control topology of a thermal power area and a tie line power deviation feedback topology, and the tie line power deviation feedback topology comprises feedback devices which are the total number of the wind power area and the thermal power area; and controlling equipment in the wind power area and the thermal power area to carry out power grid load frequency adjustment by using a control instruction output by the combined control topology. According to the technical scheme, dynamic response speed characteristics of wind power and thermal power and comprehensive frequency modulation requirements are considered, and optimal load frequency adjustment is carried out on the multi-region power system.

Description

Technical Field

[0001] The present application belongs to the field of new energy control technology, and relates to wind power intelligent control technology. Specifically, it provides a load frequency control method for a multi-region joint power system considering wind power access. Background Art

[0002] Load frequency control (LFC) is a dynamic control process that automatically adjusts the power of generators within a control area to maintain a dynamic balance with load changes and regional switching plans, while also keeping the frequency of the interconnected grid within a specified range. LFC outputs power commands for the units participating in automatic generation control within the control area through data acquisition, computational processing, control judgment, and unit power allocation to meet the control area's deviation control requirements. In traditional centralized power systems dominated by thermal power, LFC strategies are relatively mature. Common methods include classic controllers such as proportional integral (PI) and proportional integral derivative (PID), which offer advantages such as simple structure and easy implementation. However, these methods are often based on linearized models and are difficult to adapt to the frequently changing operating conditions and structural complexity of modern power systems.

[0003] With a large number of distributed wind power equipment connected to multiple areas of the power system, it can not only effectively alleviate the power supply pressure in the area, but also because electronic converters or energy storage can respond within hundreds of milliseconds to a few seconds, the adjustment speed is much faster than thermal power equipment. Therefore, using the flexible characteristics of wind power to participate in grid frequency regulation can effectively share part of the load dynamic response from thermal power, thereby realizing rapid frequency regulation of wind power, and thermal power ensuring base load or slow frequency regulation, so that each equipment operates in a more optimal range, achieving the combined effect of extending life and reducing regulation costs.

[0004] However, in multi-regional, integrated power systems with wind power integration, wind power output fluctuates frequently and is difficult to predict, resulting in greater uncertainty and regulatory pressure for integrated frequency regulation. Furthermore, due to reduced system inertia, frequency drops rapidly when encountering large disturbances, leaving less buffer space for thermal power regulation. This places higher demands on frequency control and inertial support, placing greater pressure on the integrated power system. Therefore, a load frequency control method is needed that takes into account the dynamic response speed characteristics of wind and thermal power units and the requirements of integrated frequency regulation, enabling coordinated operation and optimal control of multi-regional, multi-energy systems. Summary of the Invention

[0005] The purpose of this application is to provide a load frequency control method for a multi-region joint power system, which achieves complementary advantages and overall system performance improvement by collaboratively controlling wind power areas and thermal power areas to adjust the grid load frequency.

[0006] The embodiments of the present application can be implemented through the following technical solutions: A load frequency control method for a multi-regional integrated power system considering wind power access, wherein the multi-regional integrated power system includes at least one wind power region and at least one thermal power region, each wind power region includes at least one wind turbine generator set, and each thermal power region includes at least one thermal turbine generator set. The method comprises the following steps: Establishing a joint state space model of a multi-region joint power system, wherein the joint state space model includes a state space submodel of a wind power region, a state space submodel of a thermal power region, and a tie line power deviation constraint relationship between the regions; Based on the joint state space model, a joint control topology of a multi-region joint power system is established, wherein the joint control topology includes a sub-control topology of each wind power region, a sub-control topology of each thermal power region, and a tie line power deviation feedback topology, wherein the tie line power deviation feedback topology includes A feedback device is used to feed back the tie line power deviation to each wind power area or thermal power area. is the total number of wind power areas and thermal power areas; The control instructions output by the joint control topology are used to control the equipment in the wind power area and the thermal power area to adjust the power grid load frequency.

[0007] The load frequency control method of the multi-region joint power system provided in the present application first establishes a wind power and thermal power state space model in a targeted manner based on the different state influencing factors and frequency response characteristics of the wind power region and the thermal power region, and establishes a constraint relationship through the interconnection line power deviation characteristics, thereby forming a multi-region joint state space model; then, the control architecture is optimized according to the joint state space model, and each wind power and thermal power region designs a local sub-controller according to its own characteristics, and a feedback exercise between each region is established through multiple feedback devices that generate interconnection line power deviations, and different control strategies are adopted for the wind power region and the thermal power region. Through this topology of independent control and mutual connection, effective control within the region and information interaction and collaborative optimization between regions can be achieved, taking into account the fluctuation of wind power output, the dynamic response speed of thermal power units and the comprehensive frequency regulation requirements, thereby achieving coordinated operation and optimal control of multi-region and multi-energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of a load frequency control method for a multi-regional joint power system provided according to an embodiment of the present application; Figure 2 A joint control topology for a multi-regional power system consisting of a wind power region and a thermal power region; Figure 3 The joint control topology for a multi-regional power system consisting of one wind power region and two thermal power regions; Figure 4 is a schematic diagram of a disturbance signal applied in a specific embodiment; Figure 5a is a schematic diagram of the frequency response of a wind power area when the penetration rate is 50% in a specific embodiment; Figure 5b is a schematic diagram of the frequency response of a thermal power area when the penetration rate is 50% in a specific embodiment; Figure 6a is a schematic diagram of the frequency response of a wind power area when the penetration rate is 25% in a specific embodiment; Figure 6b is a schematic diagram of a frequency response of a thermal power area under the condition of a penetration rate of 25% in a specific embodiment; Figure 6c is a schematic diagram of a second frequency response of a thermal power area under a penetration rate of 25% in a specific embodiment; Figure 6d is a schematic diagram of three-frequency responses of a thermal power area when the penetration rate is 25% in a specific embodiment; Figure 7 2 is a schematic diagram showing a comparison of effects of different control strategies when the permeability is 50% in a specific embodiment; Figure 8 FIG. 1 is a schematic diagram showing a comparison of effects of different control strategies when the permeability is 25% in a specific embodiment. DETAILED DESCRIPTION

[0009] Hereinafter, the present application will be further described based on preferred embodiments with reference to the accompanying drawings.

[0010] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "inner", "outer" and the like indicate an orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or are the orientation or positional relationship in which the products of the embodiments of the present application are usually placed when in use, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, in order to distinguish different units, words such as first and second are used in this specification, but these are not limited by the order of manufacture, nor can they be understood as indicating or implying relative importance. Their names may be different in the detailed description and claims of the present application.

[0011] The vocabulary in this specification is used to illustrate the embodiments of the present application, but is not intended to limit the present application. It should also be noted that, unless otherwise clearly specified and limited, the terms "disposed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, an indirect connection through an intermediate medium, or a communication between the two components. For those skilled in the art, the specific meanings of the above terms in this application can be specifically understood.

[0012] The present application provides a load frequency control method for a multi-regional joint power system considering wind power access, wherein the multi-regional joint power system includes at least one wind power region and at least one thermal power region, and each wind power region includes at least one wind turbine generator set, and each thermal power region includes at least one thermal turbine generator set.

[0013] In some embodiments, as Figure 1 As shown, the method includes the following steps: Step 100: Establish a joint state space model of a multi-region joint power system, wherein the joint state space model includes a state space sub-model of a wind power region, a state space sub-model of a thermal power region, and a tie line power deviation constraint relationship between the regions.

[0014] Step 200: Based on the joint state space model, a joint control topology of the multi-region joint power system is established, wherein the joint control topology includes a sub-control topology of each wind power region, a sub-control topology of each thermal power region, and a tie line power deviation feedback topology, wherein the tie line power deviation feedback topology includes A feedback device is used to feed back the tie line power deviation to each wind power area or thermal power area. is the total number of wind power areas and thermal power areas.

[0015] Step 300: Use the control instructions output by the joint control topology to control the equipment in the wind power area and the thermal power area to adjust the grid load frequency.

[0016] The specific implementation of steps 100, 200, and 300 is described in detail below.

[0017] <Step 100> Due to the characteristics of regional structural differences, diverse power source types, and complex interconnection line coupling, the frequency response characteristics of multi-regional power systems show significant dynamic differences and nonlinear characteristics. Especially in the context of a high proportion of wind power access, the system inertia level decreases significantly, and conventional frequency regulation methods cannot ensure the rapid recovery and stable operation of the system frequency. Therefore, constructing an accurate and representative regional state space model is a prerequisite for designing a high-performance controller.

[0018] In step 100 of the present application, based on the above analysis, in terms of modeling the wind power area, its low inertia characteristics and virtual inertia control strategy are taken into consideration, and a dynamic model including factors such as wind speed disturbance, pitch angle control, and energy storage system response is established to capture its key influencing mechanism in frequency regulation. For the thermal power area, on the basis of the classic LFC model, the interconnection line power interaction with the wind power area is introduced to establish a multi-area joint model.

[0019] 1) Wind power regional state space sub-model: The dynamic process of a wind turbine (such as a wind turbine) can be expressed as: (1).

[0020] The definitions of the variables in formula (1) are shown in Table 1.

[0021] Table 1 Definition of variables in the dynamic equation of wind turbine When measuring wind speed, there will be an error between the actual wind speed and the measured wind speed due to disturbance. Therefore, the actual wind speed can be Break it down into two parts: (2), in, is the measurement interval, where To measure wind speed, is the bounded perturbation error.

[0022] Next, we need to derive the frequency response of the wind power area. Similar to the process of thermal power plants participating in load frequency control, the frequency response of the wind power area can also be calculated using the tie line power , the output power of the wind turbine and disturbance power However, due to the differences in physical properties, other variables need to be introduced into the frequency response of wind power areas.

[0023] After the introduction of wind turbines, they will take on part of the frequency regulation tasks of traditional thermal power plants. However, due to the lower inertia of wind turbines, the inertial response capability of wind power areas cannot support the frequency regulation needs compared to thermal power plants. Therefore, virtual inertia control strategies can be incorporated into LFC to compensate for the inertia of wind turbines. Virtual inertia control (VIC) is a control strategy that simulates the rotating mass inertia power of wind turbine blades and provides inertial response according to the frequency change rate. Its implementation depends on the energy storage system (ESS) configured in the wind farm. For the first For a wind farm area (generally, each wind farm area includes at least one wind turbine), taking into account virtual inertia control and speed disturbance, its frequency response characteristics can be expressed as: (3), in, The frequency deviation (the actual frequency of the power grid in the wind power area and the rated frequency) , such as the deviation between 50Hz), 、 、 、 They are the output power deviation of the wind turbine, the virtual inertial support power deviation of the energy storage system (indicating the deviation of the injected power component output by the energy storage system configured in the wind power area), the tie line deviation and the disturbance power deviation. 、 are the frequency regulation proportional coefficient and frequency response time constant of the wind power area, 、 are the damping coefficient and inertia constant of the wind power area respectively.

[0024] Linearize (ignore the nonlinear terms) Then, combined with formula (3), we can get The spatial state equation of a wind power region (i.e., the wind power region state space sub-model): (4), in, Wind power area The state variables, is the control variable, is the interference term, is the limit of the interference term, Indicates the power change of the tie line between this area and other areas, is the regional control error (can be used express); 、 、 、 、 is a coefficient matrix, and in some specific embodiments, its form is shown in the following formula: , , , , , in, Wind power area The frequency offset coefficient of For the region For the region The tie line gain coefficient, 、 Wind power areas The virtual inertia control gain and virtual damping coefficient of is the time constant of the energy storage system, 、 、 exist Domain Satisfaction , is the Laplace operator; is the linearization working point, 、 They are 、 exist The value at .

[0025] In addition, through The expression can be obtained, Right now Include as well as part.

[0026] 2) Thermal power area state space sub-model: The state space submodel of the thermal power area can refer to the wind power area. For example, in some embodiments, the thermal power units in the thermal power area are regarded as a system consisting of a regulator, a prime mover (such as various turbines such as gas turbines) and a generator. By combining its motion equation with the frequency response characteristics, the thermal power area can be obtained as shown in the following formula: The state space submodel of: (5), In the above formula, is the output power deviation of the generator, is the position deviation of the regulating valve of the regulator, is the output power deviation of the prime mover, is the control instruction of the regulator, 、 、 、 Thermal power areas The state variables, control variables, disturbance terms and output terms of 、 、 、 、 is a coefficient matrix, and in some specific embodiments, its form is shown in the following formula: , , , , , in, 、 are the reheat gain and reheat time constant of the prime mover, 、 are the system gain and system time constant of the generator, is the time constant of the regulator; is the turbine time constant of the prime mover.

[0027] 3) Constraints between wind power areas and thermal power areas The above equations (4) and (5) are the state space sub-models of the wind power area and thermal power area respectively. When there are wind power areas and thermal power areas in the system at the same time, the two sub-models can be combined and the subscripts 、 , thus obtaining the joint state space model of the multi-regional joint power system as shown in the following formula: (6), (6) In the formula, each region The state variables, control variables, disturbance terms, output terms and coefficient matrices can be selected according to wind power and thermal power. It can be found that different wind power areas and thermal power areas are connected to each other through tie lines, and by merging and The corresponding terms in , we can get the following tie line power deviation constraint relationship: a. For any wind power area, its tie line power deviation is determined based on the difference between the load frequency deviation of that area and the load frequency deviations of other wind power areas, as well as the difference between the load frequency deviation of that area and the load frequency deviations of all thermal power areas; b. For any thermal power area, the tie line power deviation is determined based on the difference between the load frequency deviation of the area and the load frequency deviations of other thermal power areas, as well as the difference between the load frequency deviation of the area and the load frequency deviations of all wind power areas.

[0028] Based on the system memory Wind power areas and Thermal power areas ( ) as an example, let the sets of wind power areas and thermal power areas be 、 For any wind power area , its tie line power deviation exist The expression of the domain is shown as follows: (7), For any thermal power area , its tie line power deviation exist The domain expression is determined as follows: (8).

[0029] Through the above formulas, we can establish the constraints that the wind power areas and thermal power areas need to meet in the joint frequency regulation process, thereby linking each wind power area with the thermal power area.

[0030] <Step 200> By using the joint state space model established in step 100 and the tie line power deviation constraint relationship obtained, a joint control topology of a multi-region joint power system can be established. In the embodiment of the present application, the joint control topology includes a sub-control topology of each wind power area and a sub-control topology of each thermal power area. In addition, according to the tie line power deviation constraint relationship obtained in step 100, the joint control topology also includes a tie line power deviation feedback topology, wherein the tie line power deviation feedback topology includes Feedback ( is the total number of the wind power areas and thermal power areas), used to feed back the tie line power deviation to each wind power area or thermal power area.

[0031] Figure 2A specific embodiment of the joint control topology of a multi-region joint power system is shown. In this embodiment, the power system includes a wind power region (numbered 1) and a thermal power region (numbered 2), that is, the wind power penetration rate is 50%. Figure 2 As shown, the joint control topology includes a sub-control topology of a wind power area and a sub-control topology of a thermal power area.

[0032] Specifically, the wind turbine control topology in the upper part includes an MPC controller, a wind turbine model, a virtual inertia control model, and a frequency response model.

[0033] Specifically, the MPC controller is based on the wind power area of ​​the feedback input , the optimal control instructions for each wind turbine in the wind power area are generated through the MPC optimization strategy, where the wind power area of ​​the feedback input Based on the load frequency deviation of wind power area 1 and tie line power deviation Determine, its specific expression is The wind turbine model determines the output power deviation of the wind turbine based on the optimal control instructions generated by MPC , the input of the virtual inertial control model is ,based on 、 、 The transfer function is constructed to output the virtual inertial support power deviation of the wind turbine ;Frequency response model based on output power deviation , virtual inertial support power deviation , Tie line power deviation and disturbance power deviation (various forms of disturbance known to those skilled in the art may be used, such as random signals, quasi-periodic disturbances, step or sudden disturbances, etc.) to determine the load frequency deviation of the wind power area. .

[0034] The thermal power control sub-topology in the lower part includes PI controller, speed regulator model, turbine model, and generator model. Specifically, the PI controller is based on the feedback input of the thermal power area. , through the PI regulation strategy, and combined with the feedback to obtain the rate regulation , generates control instructions for the thermal power unit, wherein the feedback input of the thermal power area 2 Based on the load frequency deviation of the thermal power area 2 and tie line power deviation Determine; the control command is passed through the speed regulator model and the turbine model to obtain the output power deviation of the thermal power unit , the generator model is based on the output power deviation , Tie line power deviation and disturbance power deviation , determine the load frequency deviation of the thermal power area .

[0035] Furthermore, a tie line power deviation feedback topology is provided between the sub-control topology of wind power area 1 and the sub-control topology of thermal power area 2. Since there is only one wind power area and one thermal power area in this embodiment, the number of tie lines is one. Accordingly, the tie line power deviation feedback topology includes only one feedback device for feeding back the tie line power deviation to wind power area 1 and thermal power area 2. and .

[0036] Specifically, 、 It can be obtained from equations (7) and (8). Obviously, for the case of a tie line, , therefore, see Figure 2 , feedback device input 、 Then, take the difference between the two and multiply by and Then multiply by 1 and -1 respectively, as 、 Feedback to wind power areas and thermal power areas.

[0037] In addition, you can also and The difference multiplied by and Then multiply by 1 and -1 respectively, and use the result as 、 Feedback to the wind power area and thermal power area, obviously, since the feedback value in the two cases should be consistent, = .

[0038] Figure 3 The schematic diagram of the joint control topology for controlling a multi-region joint power system consisting of a wind power region (numbered 1) and two thermal power regions (numbered 2 and 3) is shown. Figure 3 As shown in the figure, in addition to the sub-control topologies corresponding to the wind power area and the thermal power area, the control topology also includes a tie line power deviation feedback topology composed of three feedback devices. As shown in the figure, the tie line power deviations fed back to area 1, area 2, and area 3 are respectively 、 、 .

[0039] pass Figure 2 、 3 The embodiment shown can determine that when the sum of the number of wind power areas and thermal power areas included in the multi-region combined power system is When the tie line power deviation feedback topology includes Feedback is used to realize feedback of the power deviation of the tie line between any two areas, where: Indicates from The number of combinations of any two numbers in the number, obviously, , and for any wind power area or thermal power area, the tie line power deviation can be uniformly determined by the following formula: (10).

[0040] <Step 300> In step 300, the joint control topology established in step 200 can be used to output optimized control instructions to the wind turbines and thermal turbines in the region through the MPC controller in the wind power region and the PI controller in the thermal power region, so as to adjust the load frequency of the power grid. At the same time, the changes in the load frequency of the power grid are continuously sampled in real time, and the corresponding frequency deviations of the interconnection lines between the regions are combined to obtain the corresponding frequency deviations of the wind power region and the thermal power region. And feed back to the controllers of each area, thus forming a closed-loop feedback.

[0041] Specifically, if Figure 2 As shown in the figure, the closed-loop feedback control for the thermal power area is carried out by the PI controller, and the closed-loop feedback control for the wind power area is carried out by the MPC controller. Since the PI controller has a simple structure and mature parameter setting, it can stably and quickly eliminate the frequency deviation for traditional thermal power units and meet their control requirements at a lower cost. Since wind power output is greatly affected by wind speed fluctuations and has strong adjustment flexibility, but high dynamic uncertainty and significant nonlinearity / multivariable coupling of the system itself, the use of MPC (model predictive control) can utilize real-time measurement / prediction models to optimize decisions on wind farm dynamics and constraint multi-objectives, thereby achieving more flexible, intelligent and personalized frequency distribution and inertia and damping control.

[0042] At the same time, the feedback constraints formed by the power deviation of the interconnection lines between wind power areas, between thermal power areas, and between wind power areas and thermal power areas can effectively ensure the safety of energy exchange between different types of power generation areas, and improve the coordinated efficiency and safety robustness of multi-energy complementarity and multi-regional power systems.

[0043] Specifically, the parameter tuning and control methods of PI controllers are well known to those skilled in the art. In some specific embodiments, the proportional coefficient and integral time constant can be optimized and adjusted based on the dynamic characteristics, response speed, and stability requirements of the thermal power unit through simulation analysis or field testing, using empirical formulas, critical proportion methods, Ziegler-Nichols tuning methods, and other methods to meet control performance requirements under different operating conditions. Furthermore, PI controllers can be combined with other control strategies (such as feedforward compensation and adaptive control) to further enhance the system's regulation accuracy and robustness.

[0044] The process of obtaining optimal control instructions in MPC is to use a preset objective function as the optimization target, utilize a prediction model to make a rolling prediction of the system's dynamic response within a certain prediction time domain, and obtain the optimal control instructions at the current moment in real time by solving a quadratic optimization problem involving input, output, and physical constraints.

[0045] Among them, the prediction model of the MPC controller can adopt the state space submodel of the wind power area established in the previous article, and its parameters and coefficient matrix can be determined by various methods known to technical personnel in this field. For example, a preliminary physical model can be established based on the aerodynamic theory of the wind turbine, the electrical characteristics of the generator and the converter, and then the system identification algorithm (such as least squares method, recursive least squares, online identification, etc.) is used to fit and optimize the dynamic response data of the wind power system under different wind speeds, loads and adjustment instructions, and then obtain the various parameters in the state space and transfer function model; in addition, the historical operation data of the wind turbine can also be used to estimate and correct the coefficient matrix of the model through data-driven methods (such as minimum mean square error, particle swarm optimization, genetic algorithm, etc.) to improve the modeling accuracy and robustness of the wind farm under different operating environments.

[0046] In some preferred embodiments, in order to minimize the system frequency deviation and reduce the fluctuation of the tie line power as much as possible, at any time , the MPC controller is determined based on the following objective function The optimal control instructions at each moment after time: (11), in, is the objective function, is the number of state prediction steps, To optimize the number of control steps and , 、 、 The time for separation Load frequency deviation, tie line power deviation and control instruction deviation; 、 、 The specific value of the weight coefficient can be initially set based on historical experience and further adjusted according to the actual operation results during the actual operation.

[0047] In addition, MPC generally needs to perform system state prediction and output of optimal control instructions in a rolling manner. In this process, the state prediction step size is It can be determined by comprehensively considering factors such as the dynamic characteristics of the system, the sampling period, and the predictability of future disturbances. For example, it can be 10-50. In practical applications, in the control instruction sequence obtained after optimization, usually only the first control instruction is executed, that is, is 1, and then at the next sampling moment, prediction and optimization are performed again to achieve rolling optimization control.

[0048] The constraints in the optimization process mainly include the physical constraints and operational constraints of the system, such as the active power output limit of the generator, the power transmission limit of the tie line, the allowable range of frequency deviation, etc. In some specific embodiments, the constraints of the MPC controller in determining the optimal control instructions are: (12), in, 、 Wind power areas Output power The lower and upper limits of 、 The interconnection line power in the wind power area is The lower and upper limits of is the maximum frequency deviation allowed, Wind power area The control instruction deviation, The maximum control instruction change allowed.

[0049] The MPC optimization problem can be solved using a variety of methods known to those skilled in the art, including commonly used quadratic programming (QP) algorithms, sequential quadratic programming (SQP), interior point methods, and the like. For example, in some embodiments, the optimization problem can be converted into a standard quadratic programming form: (13), in, is the control instruction sequence to be optimized, usually expressed as , is the quadratic weight matrix, is the state weight matrix, is the weighting matrix that controls the input changes, is the state transition matrix, 、 is the coefficient matrix and vector of the constraint relationship, is the state evolution matrix, is the state vector, is the perturbation transfer matrix, is the perturbation vector.

[0050] The solution to the optimization problem described in formula (13) can be achieved by writing an executable program or directly using commercial optimization solvers such as CPLEX, Gurobi, MOSEK, MATLAB Optimization Toolbox, etc.

[0051] Due to the existence of model errors, parameter changes and various uncertain interference factors in the power system, it is difficult to ensure the control performance of the system by relying solely on the prediction model for open-loop control. Therefore, in some preferred embodiments, the sub-control topology of the wind power area adopts a state estimation algorithm based on Kalman filtering to dynamically correct the input signal of the MPC controller, thereby realizing feedback correction.

[0052] Specifically, the feedback correction mechanism monitors the actual system output in real time and compares it with the output of the MPC prediction model to obtain a prediction error. This prediction error is then used to correct the prediction model to improve its prediction accuracy, thereby enabling the control strategy to better adapt to the actual system operation. Specific embodiment 1 Specific Example 1 is used to verify the effectiveness of the method provided by this application. First, a multi-region power system experimental test platform is built (the multi-region power system includes one wind power area and three thermal power areas). Then, a simulation test is performed using the load frequency control method provided by this application. The specific simulation test parameters are described as follows: 1) Wind power regional system parameters For general wind farms, the control object of the study is the wind turbine. Considering its complex internal structure, it is assumed that it is a turbine and a speed regulator. This not only satisfies the wind power area power generation and the speed regulation function of adapting to wind power, but also makes the design and calculation simpler. The transfer functions of the turbine and speed regulator are: and , and The parameters required for the wind power area are shown in Table 2 below.

[0054] Table 2 Wind power regional system parameters The interconnection gain coefficient between the wind power area and other wind power areas is 0.1, and the interconnection gain coefficient between the wind power area and the thermal power area is 1.3989.

[0055] 2) Thermal power regional system parameters Table 3 below shows the system parameters of the thermal power area.

[0056] The interconnection gains between thermal power plants are equal, with a value of 0.5, and the interconnection gains between each plant and the wind power area are also equal, with a value of 1.3989.

[0057] 3) MPC parameters For the MPC controller, the main parameters that need to be set are: sampling time , prediction interval , control interval And the weight matrix of the controlled object parameters and .

[0058] Prediction time domain It determines the prediction range of the model predictive control for the future state of the system. A longer prediction time domain can take into account the longer-term change trends of the system, provide more comprehensive information, and facilitate the formulation of more forward-looking control strategies. When the prediction time domain is long enough, the model predictive control can perceive large changes in load or fluctuations in renewable energy power generation in advance, thereby adjusting the output of the generator in advance to better maintain the stability of the system frequency. If the prediction time domain is too short, the model predictive control can only make decisions based on the recent state of the system and may not be able to respond to potential problems in the system in a timely manner, resulting in poor control effect. When the system load grows rapidly or renewable energy power generation fluctuates violently, a too short prediction time domain may prevent the controller from making adjustments in advance, resulting in an increase in the system frequency deviation.

[0059] Control time domain This determines the length of the control sequence derived from the optimization calculation. A larger control time domain means that more future control actions can be considered at each sampling moment, enabling more effective utilization of the system's dynamic information and achieving more refined control. Increasing the control time domain can improve the system's control accuracy and stability to a certain extent. However, if the control time domain is too large, the computational effort increases and the difficulty of solving the optimization problem, resulting in longer calculation times and impacting the real-time performance of the control. In practical applications, it is necessary to reasonably control the computational effort while ensuring effective control to meet the requirements of real-time control.

[0060] To determine appropriate prediction and control time-domain parameters, it is necessary to comprehensively consider factors such as the dynamic characteristics of the multi-regional power system, computing resources, and control requirements. A common approach is to evaluate the control effectiveness under different parameter combinations through simulation experiments. During the simulation process, various operating scenarios are set up, such as random load fluctuations and intermittent changes in renewable energy generation, and the changes in indicators such as system frequency deviation and tie-line power fluctuations are compared under different parameter combinations.

[0061] Combined with the system simulation results, set , , When the system faces various disturbances, the frequency deviation and the power fluctuation of the interconnection line can be kept within a small range, and the control effect is better.

[0062] For the weight matrix and , considering that among the state variables of the wind power area, the frequency change is considered to have the greatest impact, followed by the interconnection gain between the areas, and then the generator speed change, while the inertia compensation of the wind power area, the actual speed and pitch angle can be considered to be negligible. Therefore, the wind power system weight matrix is ​​set As shown in the following formula: .

[0063] Similarly, for the control variables, the role of pitch angle is almost decisive, but relative to the state variables, its influence is smaller than that of inertia compensation. Therefore, the weight matrix of the control variables is The settings are as follows: .

[0064] The load frequency control capabilities are evaluated when the wind power penetration rate is 50% (i.e., including one wind power area and one thermal power area) and 25% (i.e., one wind power area and three thermal power areas).

[0065] Figure 4 The disturbance signal applied to the power system is shown in FIG. 1 , which has an amplitude of 2 MW and a duration of 6 seconds. Figure 5a 、 Figure 5b The frequency responses of wind power areas and thermal power areas are shown respectively when the wind power penetration rate is 50%; Figures 6a to 6d The frequency responses of the wind power area and three thermal power areas are shown respectively when the wind power penetration rate is 25%.

[0066] The figures above show that in multi-regional power systems with wind power penetration rates of 50% and 25%, all using MPC controllers, after a transient drop of around 1 Hz, the MPC controller intervenes, rapidly recovering the frequency. The overshoot required for subsequent stabilization is around 0.3 Hz, within the system's acceptable range. This demonstrates that the MPC controller can effectively regulate frequency.

[0067] In the thermal power area, the time required for a system with a wind power penetration rate of 25% to reach stability is obviously longer than that of a system with a wind power penetration rate of 50%. This is because in a system with a lower wind power penetration rate, the frequency regulation capability of the wind power area cannot be fully utilized, and thermal power still occupies a relatively dominant position at this time.

[0068] At the same time, in order to verify the characteristics of the frequency after adjustment by the MPC controller compared with the traditional PI controller, the system frequency response comparison of the PI control and the MPC control is obtained by simulation. Figure 7 、 Figure 8 The comparisons are shown for penetration rates of 50% and 25%.

[0069] It can be seen that, regardless of whether the wind power penetration rate is high or low, the response speed and steady-state conditions of the system using MPC control are superior to those of the system using traditional PI control when facing the same disturbance. In a system with a wind power penetration rate of 50%, when the overshoot is similar, the system using MPC control stabilizes significantly faster and has less fluctuation in steady state. In a system with a wind power penetration rate of 25%, the systems using both control methods reach stability at similar speeds, but the instantaneous frequency drop of the system using MPC control is significantly smaller than that of the system using PI control, and it is more resistant to disturbances.

[0070] The above is a detailed introduction to the specific implementation methods of the present application. For those skilled in the art, several improvements and modifications can be made to the present application without departing from the principles of the present application. These improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A load frequency control method for a multi-regional integrated power system considering wind power access, wherein the multi-regional integrated power system includes at least one wind power region and at least one thermal power region, each wind power region includes at least one wind turbine generator set, and each thermal power region includes at least one thermal turbine generator set, characterized in that: The following steps are involved: Establishing a joint state space model of a multi-region joint power system, wherein the joint state space model includes a state space submodel of a wind power region, a state space submodel of a thermal power region, and a tie line power deviation constraint relationship between the regions; Based on the joint state space model, a joint control topology of a multi-region joint power system is established, wherein the joint control topology includes a sub-control topology of each wind power region, a sub-control topology of each thermal power region, and a tie line power deviation feedback topology, wherein the tie line power deviation feedback topology includes A feedback device is used to feed back the tie line power deviation to each wind power area or thermal power area. is the total number of wind power areas and thermal power areas; The control instructions output by the joint control topology are used to control the equipment in the wind power area and the thermal power area to adjust the power grid load frequency.

2. The load frequency control method for a multi-regional integrated power system considering wind power integration according to claim 1, characterized in that: The state space sub-model of the wind power area is determined based on the operating state equation of the wind turbine generator set and its frequency response characteristics under the virtual inertia control strategy.

3. The load frequency control method for a multi-regional joint power system considering wind power integration according to claim 1, characterized in that: The tie line power deviation constraint relationship between the various areas is: For any wind power area, the tie line power deviation is determined based on the difference between the load frequency deviation of the area and the load frequency deviations of other wind power areas, and the difference between the load frequency deviation of the area and the load frequency deviations of various thermal power areas. For any thermal power area, its interconnection line power deviation is determined based on the difference between the load frequency deviation of the area and the load frequency deviations of other thermal power areas, and the difference between the load frequency deviation of the area and the load frequency deviations of each wind power area.

4. The load frequency control method for a multi-regional joint power system considering wind power integration according to claim 1, characterized in that: The control sub-topology of the wind power area includes: MPC controller, wind turbine model, virtual inertia control model, and frequency response model; The MPC controller generates optimal control instructions for each wind turbine in the wind power area through an MPC optimization strategy based on the regional control error of the wind power area input by feedback, wherein the regional control error of the wind power area input by feedback is determined based on the load frequency deviation and the tie line power deviation of the wind power area; The wind turbine model determines the output power deviation of the wind turbine based on the optimal control instruction; The virtual inertia control model is used to output the virtual inertia support power deviation of the wind turbine generator set; The frequency response model determines the load frequency deviation of the wind power area based on the output power deviation, the virtual inertial support power deviation, the tie line power deviation and the disturbance power deviation.

5. The load frequency control method for a multi-regional joint power system considering wind power integration according to claim 1, characterized in that: The thermal power generation unit in the thermal power area is composed of a speed governor, a turbine and a generator. The control sub-topology of the thermal power area includes: PI controller, speed regulator model, turbine model, generator model; The PI controller generates a control instruction for the thermal power unit based on the regional control error of the thermal power area input by feedback, through a PI adjustment strategy, and in combination with the rate adjustment amount obtained by feedback, wherein the regional control error of the thermal power area input by feedback is determined based on the load frequency deviation of the thermal power area and the tie line power deviation, and the rate adjustment amount obtained by feedback is the ratio of the load frequency deviation of the thermal power area to the adjustment parameter of the speed governor; The control instruction is passed through the speed governor model and the turbine model to obtain the output power deviation of the thermal power unit; The generator model determines the load frequency deviation of the thermal power area based on the output power deviation, the tie line power deviation and the disturbance power deviation.

6. The load frequency control method for a multi-regional joint power system considering wind power integration according to claim 4 or 5, characterized in that: For any wind power area or thermal power area, the tie line power deviation is determined by the following formula: ， in, is the Laplace transform operator, 、 is the number of the wind power area or thermal power area, For the region The tie line power deviation, 、 Respectively for regions 、 The load frequency deviation, For the region 、 The interconnection gain between For the region 、 The interconnection gain between them.

7. The load frequency control method for a multi-regional joint power system considering wind power integration according to claim 6, characterized in that: Each feedback device is connected to the transmission lines of any two different areas of the wind power area and the thermal power area, and is used to obtain the load frequency deviation of the two areas and feed back the tie line power deviation to the two areas.

8. The load frequency control method for a multi-regional joint power system considering wind power integration according to claim 4, characterized in that: At any time , the MPC controller is determined based on the following objective function The optimal control instructions at each moment after time: ， in, is the objective function, is the number of state prediction steps, To optimize the number of control steps and , 、 、 The time for separation Load frequency deviation, tie line power deviation and control instruction deviation; 、 、 is the weight coefficient.

9. The load frequency control method for a multi-regional integrated power system considering wind power integration according to claim 8, characterized in that: The constraints of the MPC controller in determining the optimal control instructions are: ， in, Wind power area The output power, 、 are their lower and upper limits respectively, is the interconnection line power in the wind power area, 、 are their lower and upper limits respectively, Wind power area The load frequency deviation, is the maximum frequency deviation allowed, Wind power area The control instruction deviation, The maximum control instruction change allowed.

10. The load frequency control method for a multi-regional integrated power system considering wind power integration according to claim 1, characterized in that: The sub-control topology of the wind power area adopts a state estimation algorithm based on Kalman filtering to dynamically correct the input signal of the MPC controller.

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