Virtual synchronous machine energy storage frequency modulation method and system based on model prediction and adaptive control

Abstract

The invention relates to the field of power system frequency stability control, and discloses a virtual synchronous machine energy storage frequency modulation method and system based on model prediction and adaptive control, and the method comprises the steps: constructing a prediction model of a virtual synchronous machine, wherein a virtual inertia coefficient and a virtual damping coefficient are included; designing a cost function, wherein the cost function is a weighted sum of squares of the frequency increment and the input power increment; solving the optimal control sequence by using quadratic programming, and further correcting the input power of the VSG in real time; and in combination with the dynamic characteristics of the energy storage device, the virtual inertia coefficient and the damping coefficient are adaptively adjusted according to the frequency deviation and the frequency change rate. The method focuses on the combination of model prediction control and adaptive control, is applied to the energy storage frequency modulation of the virtual synchronous machine, and aims to effectively solve the problem of frequency stability caused by the access of high-proportion renewable energy to a power system.

Description

Technical Field

[0001] The present invention relates to the field of power system frequency stability control, and particularly to a control method and system for an energy storage virtual synchronous generator (VSG) that combines model predictive control (MPC) with adaptive virtual inertia damping regulation. Background Art

[0002] In recent years, new energy sources represented by wind power and photovoltaic power have been widely developed and utilized globally. However, new energy power generation is characterized by intermittency, volatility, and uncertainty. When a large amount of new energy is connected to the power system, it will cause a significant decrease in the equivalent inertia and damping of the system. Traditional synchronous generators rely on the inertia of their rotors and mechanical damping to cope with power imbalances in the system, thereby maintaining frequency stability. However, most new energy power generation equipment is connected to the grid through power electronic interfaces and lacks the inertia and damping characteristics similar to synchronous generators. This makes the frequency change rate increase and the frequency deviation intensify when the power system faces disturbances such as sudden load changes and new energy power fluctuations, seriously threatening the safe and stable operation of the power system.

[0003] Limitations of existing virtual synchronous generator control technologies:

[0004] Insufficiency of fixed-parameter control: Traditional virtual synchronous generator control methods usually adopt fixed virtual inertia coefficients and virtual damping coefficients. This fixed-parameter control method is designed based on specific operating conditions and system parameters. Once the system operating conditions change, such as sudden load changes or large fluctuations in new energy output, the fixed parameters cannot be adjusted in real time to adapt to the new conditions. For example, when there is a large power deficit in the system, the fixed virtual inertia may not be able to provide sufficient inertial support, resulting in too rapid a frequency drop; while when the system has excess power, the frequency may recover slowly due to excessive virtual inertia.

[0005] Defects of adaptive control: Although some existing adaptive control methods attempt to dynamically adjust the virtual inertia and damping coefficients according to the operating state of the system, these methods often have problems such as slow response speed and low adjustment accuracy. For example, some adaptive control methods only adjust the parameters based on the frequency deviation and do not fully consider the influence of the frequency change rate, resulting in ineffective adjustment in a rapidly changing system. In addition, some adaptive control algorithms may experience parameter mutations during the parameter adjustment process, which will have an adverse impact on the stability of the system.

[0006] Lack of real-time optimization mechanism: Most traditional virtual synchronous generator control methods are based on the current system state for control, lacking prediction and optimization of future system states. In the power system, the changes in load and new energy output have certain randomness and uncertainty. It is difficult to cope with complex and changing working conditions by relying solely on the information at the current moment. Therefore, a method that can predict the future state of the system in real time and perform optimal control is needed.

[0007] Analysis of related research status

[0008] Research on model predictive control: Existing research has proposed a virtual synchronous generator control method based on model predictive control (MPC). This method builds a prediction model of the system, predicts the system state in the future for a period of time, and solves the optimal control sequence according to the preset cost function, so as to realize the real-time optimization of the input power of the virtual synchronous generator. However, most of these studies do not fully consider the adaptive adjustment of virtual inertia and damping coefficients, and cannot dynamically optimize the inertia and damping characteristics of the virtual synchronous generator according to the real-time operating state of the system.

[0009] Research on adaptive virtual inertia and damping control: There is also research focusing on adaptive virtual inertia and damping control, which dynamically adjusts the virtual inertia and damping coefficients according to the frequency deviation and frequency change rate by designing an adaptive control strategy. However, these studies often lack the prediction of the future state of the system and cannot respond to system disturbances in advance, resulting in limited control effects. Summary of the invention

[0010] The main object of the present invention is to provide a virtual synchronous generator energy storage frequency modulation method and system based on model prediction and adaptive control to overcome the deficiencies of the prior art. By combining model predictive control and adaptive control, the real-time optimization adjustment of the virtual inertia coefficient, virtual damping coefficient and input power of the virtual synchronous generator is realized, so as to improve the frequency stability and anti-interference ability of the power system in the face of various disturbances.

[0011] To achieve the above object, the present invention provides a virtual synchronous generator energy storage frequency modulation method based on model prediction and adaptive control, including the following steps:

[0012] (1) Construct a prediction model of the virtual synchronous generator (VSG), which is based on the discretized state equation and output equation and includes the virtual inertia coefficient and virtual damping coefficient;

[0013] (2) Design a cost function, which is the weighted sum of squares of the frequency increment and the input power increment;

[0014] (3) Use quadratic programming to solve the optimal control sequence, and then real-time correct the input power of the VSG;

[0015] (4) Combine the dynamic characteristics of the energy storage device and adaptively adjust the virtual inertia coefficient and damping coefficient according to the frequency deviation and the rate of change of frequency.

[0016] Preferably, the establishment process of the prediction model includes:

[0017] Discretize the VSG frequency characteristic equation;

[0018] Define the system state variable as the frequency increment;

[0019] Define the control input variable as the input power increment;

[0020] Consider the load disturbance as a measurable disturbance quantity.

[0021] Preferably, the design principle of the cost function is:

[0022] Take the frequency increment and the input power increment as the optimization objectives;

[0023] Set the weight coefficient to balance the frequency response speed and the change amplitude of the control quantity;

[0024] Include a rolling optimization mechanism with a prediction step of 3 steps.

[0025] Preferably, the strategy for adaptively adjusting the virtual inertia coefficient is:

[0026] When the frequency deviation and the rate of change of frequency have the same sign, increase the virtual inertia coefficient;

[0027] When the frequency deviation and the rate of change of frequency have different signs, decrease the virtual inertia coefficient;

[0028] Introduce an inertia link to smooth the inertia adjustment process.

[0029] Preferably, the strategy for adaptively adjusting the virtual damping coefficient is:

[0030] When the rate of change of frequency is positive, adjust the damping coefficient according to the frequency deviation;

[0031] When the rate of change of frequency is negative, adjust the damping coefficient according to both the frequency deviation and the rate of change of frequency;

[0032] Set a threshold value to avoid parameter oscillation caused by small fluctuations.

[0033] Preferably, the steps for real-time correcting the input power include:

[0034] Solve the optimal control sequence through quadratic programming;

[0035] Calculate the power compensation amount to offset the active power deficit of the system;

[0036] Limit the amplitude and the rate of change of the power compensation amount.

[0037] Preferably, the method is applied to a microgrid system including a thermal power unit, a doubly-fed wind turbine, and an energy storage device, and includes the following coordinated control steps:

[0038] The thermal power unit maintains stable output through an exciter and a regulator;

[0039] The doubly-fed wind turbine adopts a variable speed constant frequency control strategy;

[0040] The energy storage device provides frequency support through the method of the present invention.

[0041] Preferably, the method further includes:

[0042] Set the initial values of the virtual inertia coefficient and the damping coefficient;

[0043] Determine the weight coefficient and the adjustment parameter through experiments or simulations;

[0044] Introduce safety constraint conditions to ensure the stable operation of the system.

[0045] The present invention also provides a virtual synchronous generator energy storage frequency modulation system based on model prediction and adaptive control, including:

[0046] A prediction model construction module for establishing a discretized prediction model of the VSG;

[0047] A cost function design module for generating a weighted optimization target of the frequency increment and the input power increment;

[0048] A quadratic programming solution module for calculating the optimal control sequence;

[0049] An adaptive adjustment module for adjusting the virtual inertia and the damping coefficient according to the frequency deviation and the change rate.

[0050] Preferably, the system further includes:

[0051] A data acquisition module for real-time acquisition of frequency, power, and voltage signals;

[0052] A safety protection module for monitoring the system operation status and triggering protection actions;

[0053] A human-machine interaction module for parameter setting and operation status display.

[0054] The technical solution of the present invention has the following beneficial effects compared with the prior art:

[0055] Frequency stability improvement: Through model predictive control, it is possible to predict the future state of the system in advance and adjust the input power of the virtual synchronous machine in real time according to the prediction results, effectively suppressing the rate of change of frequency. The adaptive virtual inertia and damping control dynamically adjusts the virtual inertia and damping coefficients according to the real-time operating state of the system, further improving the frequency stability of the system. In the simulation experiment, compared with the traditional method, the rate of change of frequency of the method of the present invention can be reduced by more than 70%, and the frequency offset can be reduced by more than 50%.

[0056] Enhanced anti-interference ability: The method of the present invention can quickly respond to various disturbances in the system, such as sudden load changes, new energy power fluctuations, etc. In the face of complex and changeable working conditions, the stability of the system is maintained by real-time optimizing the control strategy. For example, when the wind power suddenly drops, the energy storage device can quickly supplement the power to maintain the frequency stability of the system.

[0057] Improved system adaptability: The adaptive control strategy enables the virtual synchronous machine to automatically adjust parameters according to different operating conditions of the system, improving the adaptability of the system. Whether in the island operation or grid-connected operation mode, the method of the present invention can effectively play a role to ensure the frequency stability of the system.

[0058] Equipment collaborative optimization: In the multi-device collaborative control, the thermal power unit, doubly-fed wind turbine and energy storage device can achieve complementary advantages, improving the operating efficiency and reliability of the entire microgrid system. The fast response ability of the energy storage device makes up for the deficiency of the slow adjustment speed of the thermal power unit and doubly-fed wind turbine, while the thermal power unit and doubly-fed wind turbine provide a stable operating environment for the energy storage device. Specific implementation manners

[0059] The following details the specific implementation manners of the present invention. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] The technical solution of the present invention is as follows:

[0061] (1) Prediction model establishment

[0062] First, based on the frequency characteristic equation of the virtual synchronous machine, which describes the relationship between the moment of inertia, input power, output power and frequency of the virtual synchronous machine. By discretizing this equation, the continuous-time system model is transformed into a discrete-time state-space model. During the discretization process, the influence of the sampling period on the dynamic characteristics of the system is considered.

[0063] Define the state variable of the system as the frequency increment, which reflects the deviation between the actual frequency of the system and the rated frequency. The control input variable is defined as the input power increment. By adjusting the input power increment, the output power of the virtual synchronous machine can be changed, thereby affecting the frequency of the system. At the same time, the load disturbance is considered as a measurable disturbance quantity in the model because the change of the load is one of the main reasons for the frequency fluctuation of the system.

[0064] Specifically, the discretized state equation describes the change relationship of the system state variable between adjacent moments, which includes the dynamic characteristics of the system and the influence of the control input. The output equation relates the state variable of the system to the measurable output quantity (such as frequency). By establishing such a prediction model, the state of the system in the future period can be predicted.

[0065] (2) Model Predictive Control (MPC)

[0066] Designing the cost function is one of the key steps in model predictive control. The cost function of the present invention is the weighted sum of squares of the frequency increment and the input power increment. The frequency increment reflects the degree of deviation of the system frequency from the rated value. By optimizing its weighted sum of squares, the system can minimize the frequency deviation and improve the frequency stability during the control process. The input power increment is related to the change of the control quantity. Weighting it can balance the change amplitude of the control quantity and avoid the adverse impact of the drastic change of the control quantity on the system.

[0067] The selection of the weight coefficient is an important part of the cost function design. Through experiments and simulations, the value of the weight coefficient can be adjusted according to different system requirements and operating conditions to achieve the best control effect. For example, when more attention is paid to frequency stability, the weight coefficient of the frequency increment can be increased; when it is necessary to limit the change amplitude of the control quantity, the weight coefficient of the input power increment can be increased.

[0068] The quadratic programming method is used to solve the optimal solution of the cost function. Quadratic programming is an optimization method for solving the minimum value of a quadratic objective function under constraint conditions. During the solution process, various constraint conditions of the system are considered, such as the upper and lower limits of the input power and the allowable deviation range of the frequency. By solving the optimal control sequence, the input power increment in the future period can be obtained, thereby realizing the real-time correction of the input power of the virtual synchronous machine.

[0069] (3) Adaptive Virtual Inertia and Damping Control

[0070] Adaptive virtual inertia control strategy: When the frequency deviation and the rate of change of frequency have the same sign, it indicates that the system is in the stage of intensifying power imbalance. At this time, increasing the virtual inertia coefficient can provide greater inertial support and suppress the further increase of the rate of change of frequency. For example, when there is a power deficit in the system, the frequency drops and the rate of change of frequency is negative. At this time, increasing the virtual inertia can slow down the rate of frequency drop. When the frequency deviation and the rate of change of frequency have different signs, it indicates that the power imbalance of the system is being alleviated. At this time, reducing the virtual inertia coefficient can accelerate the frequency recovery speed. In order to avoid sudden changes in the virtual inertia coefficient, an inertia link is introduced to make the inertia adjustment process smoother.

[0071] Adaptive virtual damping control strategy: When the rate of change of frequency is positive, it indicates that the system frequency is rising. At this time, adjusting the damping coefficient according to the frequency deviation can suppress the excessive rise of the frequency. When the rate of change of frequency is negative, it indicates that the system frequency is falling. At this time, adjusting the damping coefficient according to both the frequency deviation and the rate of change of frequency can more effectively suppress the frequency drop. Setting a threshold can avoid frequent adjustment of the damping coefficient caused by small frequency fluctuations and improve the stability of the system.

[0072] (4) Multi-device collaborative control

[0073] In a microgrid system including thermal power units, doubly-fed wind turbines, and energy storage devices, the method of the present invention realizes the collaborative control of multiple devices. The thermal power unit maintains stable output power and voltage through the exciter and regulator. It has large inertia and strong regulation ability and can provide basic power support in the system. The doubly-fed wind turbine adopts a variable-speed constant-frequency control strategy and can adjust the output power in real time according to the change of wind speed to improve the utilization efficiency of wind energy. The energy storage device quickly responds to the frequency change of the system through the model prediction and adaptive control method of the present invention and provides frequency support. When there is a power deficit in the system, the energy storage device can quickly release energy to supplement the active power of the system; when there is power surplus in the system, the energy storage device can absorb the excess energy to maintain the power balance of the system.

[0074] (5) Simulation experiment platform construction

[0075] To verify the effectiveness of the method of the present invention, a simulation experiment platform based on MATLAB / Simulink is built. On this platform, a microgrid system model including a virtual synchronous machine, an energy storage device, a thermal power unit, and a doubly-fed wind turbine is established. The virtual synchronous machine is simulated by a power electronic converter, and its control strategy is realized by the model prediction and adaptive control algorithm of the present invention. The energy storage device adopts a battery energy storage model, considering the charge and discharge characteristics and capacity limitations of the battery. The models of the thermal power unit and the doubly-fed wind turbine are built according to the actual physical characteristics, including main components such as generators, governors, and exciters.

[0076] In the specific implementation process, the experimental conditions are set as follows:

[0077] (1) Load mutation condition: After the system operates stably for a period of time, a certain load power is suddenly increased or decreased to simulate the common load mutation situation in the system. For example, at t = 2s, the system load suddenly increases by 10%, and the frequency response of the system and the power output changes of each device are observed.

[0078] (2) New energy power fluctuation condition: The wind speed of the doubly-fed wind turbine is set to change according to a certain rule to simulate the fluctuation of wind power. For example, the wind speed rises from 5m / s to 10m / s within a certain period of time and then drops to 3m / s, and the frequency stability and control effect of the system under the condition of wind power fluctuation are observed.

[0079] Analysis of experimental results

[0080] Results of the load mutation condition: Under the load mutation condition, due to the use of fixed parameters in the traditional virtual synchronous machine control method, the frequency change rate is relatively large and the frequency deviation is also obvious. However, the method of the present invention predicts the change of the load in advance through model predictive control, and adjusts the input power of the virtual synchronous machine in real time, and adaptively adjusts the virtual inertia and damping coefficient at the same time. After the load increases, the virtual inertia coefficient increases rapidly, providing sufficient inertial support and suppressing the increase of the frequency change rate; as the frequency deviation decreases, the virtual inertia coefficient gradually decreases, accelerating the frequency recovery. The experimental results show that under the load mutation condition, the frequency change rate of the method of the present invention is reduced by 75%, and the frequency deviation is reduced by 60%, significantly improving the frequency stability of the system.

[0081] Results of the new energy power fluctuation condition: Under the new energy power fluctuation condition, the traditional method is difficult to quickly respond to the change of wind power, resulting in large fluctuations in the system frequency. The method of the present invention monitors the frequency and power changes of the system in real time, and uses model predictive control and adaptive control strategies to enable the energy storage device to quickly adjust the output power to compensate for the fluctuation of wind power. During the process of wind speed change, the energy storage device releases or absorbs energy in a timely manner according to the system requirements, maintaining the power balance of the system. The experimental results show that under the new energy power fluctuation condition, the frequency fluctuation range of the method of the present invention is reduced by 80%, effectively improving the adaptability of the system to the new energy power fluctuation.

[0082] In practical applications, the technical solutions covered by the claims have broad applicability and scalability. For the establishment of the prediction model, although the present invention adopts a specific discretization method, in different systems, appropriate discretization algorithms and model parameters can be selected according to the actual situation. The design of the cost function can also be adjusted according to different control objectives and system requirements. For example, the consideration of other state variables can be increased to further optimize the control effect. The adaptive virtual inertia and damping control strategy can be improved according to different system characteristics and operating conditions to better adapt to various complex situations. In terms of multi-device cooperative control, it can be further extended to include more types of distributed power sources and energy storage devices to achieve more efficient energy management and system control.

[0083] Industrial applicability

[0084] The method and system of the present invention have strong industrial applicability and can be widely applied to various power system scenarios:

[0085] Distributed energy microgrid: In a distributed energy microgrid, due to the intermittency and volatility of new energy generation, the problem of system frequency stability is relatively prominent. The method of the present invention can effectively improve the frequency stability of the microgrid and ensure the reliable operation of the microgrid under various working conditions. For example, in some independent microgrids in remote areas, by applying the technology of the present invention, local renewable energy can be fully utilized to reduce the dependence on the traditional power grid.

[0086] Large-scale new energy grid-connected system: With the continuous increase in the installed capacity of new energy generation, the large-scale grid connection of new energy poses a huge challenge to the frequency stability of the power system. The method of the present invention can provide effective frequency support during the new energy grid connection process and improve the system's acceptance capacity for new energy. For example, when large-scale wind farms and photovoltaic power stations are grid-connected, the technology of the present invention can reduce the impact on the power grid and improve the stability of the power grid.

[0087] Smart grid: In the construction of a smart grid, it is necessary to achieve precise control and optimized operation of the power system. The model prediction and adaptive control method of the present invention can provide real-time frequency control strategies for the smart grid and improve the intelligent level and operation efficiency of the grid. For example, in the energy management system of a smart grid, applying the technology of the present invention can achieve the cooperative control of distributed power sources and energy storage devices and optimize the power distribution and operating state of the grid.

[0088] In summary, by combining model predictive control and adaptive control, the present invention proposes an efficient virtual synchronous machine energy storage frequency modulation method and system. This method has significant advantages in improving the frequency stability, anti-interference ability, and system adaptability of the power system, and has broad application prospects and industrial value.

[0089] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited thereto. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, including any other suitable combination of each technical feature. These simple modifications and combinations should also be regarded as the content disclosed by the present invention and fall within the protection scope of the present invention.

Claims

1. A virtual synchronous machine energy storage frequency modulation method based on model prediction and adaptive control, characterized in that: The following steps are involved: (1) A prediction model of a virtual synchronous machine (VSG) is constructed based on the discretized state equation and output equation, which contains virtual inertia coefficient and virtual damping coefficient. (2) Design a cost function, which is the weighted square sum of the frequency increment and the input power increment; (3) Use quadratic programming to solve the optimal control sequence and then correct the input power of the VSG in real time; (4) In combination with the dynamic characteristics of the energy storage device, the virtual inertia coefficient and the damping coefficient are adaptively adjusted according to the frequency deviation and the frequency change rate.

2. The method according to claim 1, characterized in that: The process of establishing the prediction model includes: Discretize the VSG frequency characteristic equation; Define the system state variable as the frequency increment; The control input variable is defined as the input power increment; Load disturbances are considered as measurable disturbance quantities.

3. The method according to claim 1 or 2, characterized in that: The design principle of the cost function is: The frequency increment and input power increment are used as optimization targets; Set the weight coefficient to balance the frequency response speed and the control amount change amplitude; Contains a rolling optimization mechanism with a prediction step of 3 steps.

4. The method according to any one of claims 1 to 3, characterized in that: The strategy for adaptively adjusting the virtual inertia coefficient is: When the frequency deviation and the frequency change rate have the same sign, increase the virtual inertia coefficient; When the frequency deviation and the frequency change rate have opposite signs, reduce the virtual inertia coefficient; An inertia link is introduced to smooth the inertia adjustment process.

5. The method according to any one of claims 1 to 4, characterized in that: The strategy for adaptively adjusting the virtual damping coefficient is: When the frequency change rate is positive, the damping coefficient is adjusted according to the frequency deviation; When the frequency change rate is negative, the damping coefficient is adjusted according to the frequency deviation and the frequency change rate at the same time; A threshold is set to avoid parameter oscillation caused by small fluctuations.

6. The method according to any one of claims 1 to 5, characterized in that: The step of correcting the input power in real time comprises: Solve the optimal control sequence through quadratic programming; Calculate the power compensation amount to offset the system active power shortage; Limit the amplitude and rate of change of power compensation.

7. The method according to any one of claims 1 to 6, characterized in that: The method is applied to a microgrid system including a thermal power unit, a double-fed wind turbine and an energy storage device, and includes the following coordinated control steps: Thermal power units maintain stable output through exciters and regulators; The double-fed wind turbine adopts a variable speed constant frequency control strategy; The energy storage device provides frequency support through the method of the present invention.

8. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: Set the initial values ​​of virtual inertia coefficient and damping coefficient; Determine weight coefficients and adjustment parameters through experiments or simulations; Safety constraints are introduced to ensure stable operation of the system.

9. A virtual synchronous machine energy storage frequency modulation system based on model prediction and adaptive control, characterized in that: include: Prediction model building module, used to build a discretized prediction model for VSG; A cost function design module, used to generate a weighted optimization target of frequency increment and input power increment; Quadratic programming solver module, used to calculate the optimal control sequence; The adaptive adjustment module is used to adjust the virtual inertia and damping coefficient according to the frequency deviation and the rate of change.

10. The system according to claim 9, characterized in that The system further comprises: Data acquisition module, used to obtain frequency, power and voltage signals in real time; Safety protection module, used to monitor the system operation status and trigger protection actions; Human-computer interaction module, used for parameter setting and operation status display.