Network construction wind storage system frequency modulation optimization method based on explicit model predictive control

Abstract

The invention provides a network construction wind storage system frequency modulation optimization method based on explicit model prediction control, and the method comprises the steps: obtaining the real-time state data of a wind storage system, the real-time state data comprises the energy storage working state, the system demand power, the wind power working state and the wind speed; determining a state variable, control input and interference input according to the real-time state data; obtaining an initial state space equation according to the action relationship of the state variable, the control input and the interference input on the wind storage system; discretizing the initial state space equation to obtain a discrete state space equation; obtaining a power tracking error prediction model according to the coefficient matrix of the discrete state space equation; obtaining an optimization control model according to the power tracking error prediction model in combination with the control quantity change range; and obtaining a wind power control strategy and an energy storage control strategy of the wind storage system according to the real-time state data and the optimization control model. The response efficiency, continuity and accuracy of frequency modulation of the wind storage system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation control technology, and in particular to a frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control. Background Art

[0002] As grid-connected renewable energy generation gradually replaces traditional synchronous power sources, wind power and energy storage systems must possess stronger active frequency support capabilities to address the increasingly prominent issues of inertia loss and frequency fluctuations in the power system. Existing control strategies face the following bottlenecks when applied to actual operating conditions: First, the charging and discharging efficiency of energy storage systems is significantly affected by the SOC state, making precise control difficult; second, the complex coupling between wind turbine speed and inertia makes it difficult to achieve effective frequency regulation across the entire wind speed range by relying on a single rotor adjustment mechanism; third, the variable frequency response requirements at multiple time scales make it difficult for traditional control methods to balance dynamic adaptability with control accuracy. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control, which can improve the response efficiency, sustainability and accuracy of the frequency regulation of the wind-storage system.

[0004] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0005] A frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control includes:

[0006] Acquire real-time status data of the wind-storage system, including energy storage operating status, system power demand, wind power operating status, and wind speed;

[0007] Determining state variables, control inputs, and disturbance inputs based on the real-time state data;

[0008] According to the relationship between the state variables, control inputs and disturbance inputs on the wind-storage system, an initial state space equation is obtained;

[0009] Discretizing the initial state-space equation to obtain a discrete state-space equation;

[0010] Obtaining a power tracking error prediction model according to a coefficient matrix of the discrete state space equation;

[0011] According to the power tracking error prediction model and the control variable variation range, an optimized control model is obtained;

[0012] According to the real-time status data and the optimized control model, a wind power control strategy and an energy storage control strategy of the wind-storage system are obtained.

[0013] Optionally, determining the state variables, control inputs, and interference inputs according to the real-time state data includes:

[0014] The energy storage charge state and system demand power in the real-time status data are used as state variables; wind power, energy storage power and frequency modulation power are used as control inputs, and system frequency and wind speed are used as interference inputs.

[0015] Optionally, the initial state space equation is obtained according to the relationship between the state variables, control inputs and disturbance inputs on the wind-storage system, including:

[0016] Obtaining an updated state according to the state variables, control input, disturbance input, and wind-storage system equipment coefficients;

[0017] According to the state variables, the output state of the wind-storage system is obtained;

[0018] According to the updated state and the output state of the wind-storage system, an initial state space equation is established.

[0019] Optionally, the initial state space equation is discretized to obtain a discrete state space equation, including:

[0020] The coefficient matrix of the initial state space equation is discretized according to a preset discrete time domain step size to obtain a discrete state space equation.

[0021] Optionally, obtaining a power tracking error prediction model according to a coefficient matrix of the discrete state space equation includes:

[0022] According to the coefficient matrix of the discrete state space equation, a state deviation matrix, a control deviation matrix and a disturbance deviation matrix are obtained;

[0023] According to the state deviation matrix, the control deviation matrix and the interference deviation matrix, in combination with the state increment, the control increment and the interference increment, a power tracking error prediction model is obtained.

[0024] Optionally, an optimized control model is obtained based on the power tracking error prediction model and the control variable variation range, including:

[0025] Obtaining a power deviation vector according to the power tracking error prediction model;

[0026] According to the initial state space equation, a control constraint matrix and a control cost matrix are obtained;

[0027] According to the power deviation vector, the control constraint matrix and the control cost matrix, combined with the control variable variation range, an optimized control model is obtained.

[0028] Optionally, obtaining a wind power control strategy and an energy storage control strategy based on the real-time status data and the optimized control model includes:

[0029] According to the discrete state space equation, the state variables of each discrete point are obtained;

[0030] The real-time state data and each discrete point state variable are input into the optimization control model to obtain the wind power control strategy and energy storage control strategy of the wind-storage system.

[0031] The above technical solution of the present invention has at least the following technical effects:

[0032] The above-mentioned explicit model predictive control-based frequency regulation optimization method for a grid-connected wind-storage system of the present invention obtains real-time status data of the wind-storage system, including the energy storage operating state, system power demand, wind power operating state, and wind speed; determines state variables, control inputs, and interference inputs based on the real-time status data; obtains an initial state-space equation based on the relationship between the state variables, control inputs, and interference inputs on the wind-storage system; discretizes the initial state-space equation to obtain a discrete state-space equation; obtains a power tracking error prediction model based on the coefficient matrix of the discrete state-space equation; obtains an optimized control model based on the power tracking error prediction model and the range of control variable variation; and obtains a wind power control strategy and an energy storage control strategy for the wind-storage system based on the real-time status data and the optimized control model. This method can improve the response efficiency, sustainability, and accuracy of frequency regulation of the wind-storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention;

[0034] Figure 2 Schematic diagram of the control framework of the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention;

[0035] Figure 3 Schematic diagram of wind power control strategy of the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention;

[0036] Figure 4 Schematic diagram of the energy storage control strategy of the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention;

[0037] Figure 5 Schematic diagram of a simulation system for a frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control according to the present invention;

[0038] Figure 6This is a frequency response comparison diagram of the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention and the existing control method;

[0039] Figure 7 This is a comparison chart of wind power output power between the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention and the existing control method;

[0040] Figure 8 This is a comparison chart of the energy storage output power between the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention and the existing control method;

[0041] Figure 9 This is a comparison chart of wind turbine speeds between the frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control of the present invention and the existing control method;

[0042] Figure 10 This is a comparison chart of the energy storage SOC between the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control of the present invention and the existing control method;

[0043] Figure 11 Schematic diagram of the invented frequency regulation optimization device for a grid-connected wind-storage system based on explicit model predictive control. DETAILED DESCRIPTION

[0044] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0045] like Figure 1 As shown, an embodiment of the present invention proposes a frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control, comprising:

[0046] Step S1, obtaining real-time status data of the wind-storage system, wherein the real-time status data includes energy storage working status, system required power, wind power working status and wind speed;

[0047] Step S2, determining state variables, control inputs, and interference inputs based on the real-time state data;

[0048] Step S3, obtaining an initial state space equation based on the relationship between the state variables, control inputs, and disturbance inputs on the wind-storage system;

[0049] Step S4, discretizing the initial state space equation to obtain a discrete state space equation;

[0050] Step S5, obtaining a power tracking error prediction model according to the coefficient matrix of the discrete state space equation;

[0051] Step S6, obtaining an optimized control model based on the power tracking error prediction model and the control variable variation range;

[0052] Step S7: obtaining a wind power control strategy and an energy storage control strategy of the wind-storage system based on the real-time status data and the optimized control model.

[0053] In this embodiment, Figure 1 As shown in the frequency regulation optimization method of the grid-connected wind-storage system based on explicit model predictive control, first, the real-time status data of the wind-storage system is obtained. The wind-storage system includes wind power and energy storage system. The real-time status data includes energy storage working status, system required power, wind power working status and wind speed. Specifically, the energy storage state of charge (SOC), system required power, wind power power, energy storage power and VSG (virtual synchronous generator) primary frequency regulation power, system frequency and wind speed are obtained, and the wind power frequency regulation output power P is obtained. add , energy storage SOC adjustment coefficient k soc , the proportion of the wind turbine's current adjustable kinetic energy relative to its maximum adjustable kinetic energy k r , the ratio of the wind turbine's current variable output power to its maximum variable output power k p , energy storage reference output droop coefficient k bess ; Then, the discrete state space equation and power tracking error prediction model of the wind-storage system are established to obtain the optimization control model; finally, the wind power control strategy and energy storage control strategy of the wind-storage system are obtained based on the real-time state data and the optimization control model.

[0054] like Figure 2 As shown, by modeling the wind-storage system state space, dynamically accounting for system disturbances and nonlinear characteristics, and introducing an explicit rolling optimization solution structure, this approach enables rapid control law lookup and real-time system response. This collaboratively optimizes wind power reserve power and the energy storage SOC (storage state of charge) trajectory, improving the sustainability and economy of system frequency regulation. This invention significantly reduces maximum frequency deviation and steady-state error, enhancing the safety margin and energy efficiency of system operation.

[0055] This paper proposes a comprehensive control strategy that integrates dynamic SOC management, rotor kinetic energy constraints, and energy storage feedback. It also enhances the frequency regulation capability of grid-based wind-storage systems through multi-timescale collaborative optimization. A frequency regulation control framework based on multi-objective optimization and predictive control is constructed, taking into account frequency regulation accuracy, equipment operating constraints, and economic efficiency. Through online model updating and disturbance estimation, the control accuracy and robustness of nonlinear, multivariable, and multi-timescale coupled systems can be improved. The frequency regulation control strategy for grid-based wind-storage systems optimizes the dynamic response and stability of frequency regulation through rolling optimization and feedback correction mechanisms, enhancing system robustness and adaptability. The multi-objective optimization framework comprehensively considers precise frequency tracking, wind turbine power reserve safety, and energy storage power economy. The frequency regulation output power of a wind power system is proportional to its reserve capacity. Optimizing wind power frequency regulation output can reduce power losses and improve wind energy utilization. By optimizing battery discharge trajectories and introducing SOC constraints, the energy storage system balances economy and frequency regulation capability, preventing a rapid decrease in the energy storage SOC. The explicit polyhedron piecewise affine system is solved offline, and the optimal control vector is quickly obtained through online state partitioning and control law, thereby achieving frequency support and regulation and improving the coordinated frequency regulation capability of the wind-storage system.

[0056] In an optional embodiment of the present invention, in step S2, determining the state variables, control inputs, and interference inputs based on the real-time state data includes:

[0057] In step S21, the energy storage charge state and system demand power in the real-time status data are used as state variables; wind power, energy storage power and frequency modulation power are used as control inputs, and system frequency and wind speed are used as interference inputs.

[0058] In this embodiment, the energy storage SOC and the system required power P are used as state variables ; Wind power, energy storage power and VSG primary frequency regulation power are control inputs ,in is the wind power, is the energy storage power, is the VSG primary frequency modulation power; system frequency and wind speed changes are interference inputs , where f is the system frequency and v is the wind speed.

[0059] In an optional embodiment of the present invention, in step S3, the initial state space equation is obtained according to the relationship between the state variables, control inputs, and interference inputs on the wind-storage system, including:

[0060] Step S31, obtaining an updated state according to the state variables, control input, interference input and wind-storage system equipment coefficients;

[0061] Step S32, obtaining the output state of the wind-storage system according to the state variables;

[0062] Step S33: establishing an initial state space equation based on the updated state and the output state of the wind-storage system.

[0063] In this embodiment, an updated state is obtained based on the state variables, control input, interference input, and wind-storage system equipment coefficients; a system output is obtained based on the state variables; and the updated state and system output are combined to establish a state space equation for the gridded wind-storage system:

[0064]

[0065] in, is the state variable at time t, is the updated state at time t, is the control input, is the interference input, is the output vector,

[0066] , k soc Indicates the energy storage SOC adjustment coefficient, k r Indicates the proportion of the wind turbine’s current adjustable kinetic energy relative to its maximum adjustable kinetic energy, k p Indicates the ratio of the fan's current variable output power to its maximum variable output power. Denotes the VSG inertia coefficient, k bess Indicates the energy storage reference output droop coefficient; represents the correlation coefficient between the variable rotor kinetic energy ratio and the variable output power ratio, Indicates the VSG primary frequency modulation coefficient, Indicates the proportion coefficient of wind power reserve power, represents the compensation coefficient, Indicates the currently available charge and discharge capacity of the energy storage. represents the system electrical angular frequency coefficient, represents the inertia constant of the wind turbine rotor, represents the discharge efficiency of energy storage, Indicates the charging efficiency of energy storage.

[0067]

[0068] , represents a diagonal matrix;

[0069]

[0070] In an optional embodiment of the present invention, in step S4, the initial state space equation is discretized to obtain a discrete state space equation, including:

[0071] Step S41 : discretizing the coefficient matrix of the initial state space equation according to a preset discrete time domain step size to obtain a discrete state space equation.

[0072] In this embodiment, in order to obtain the optimal control sequence of the wind-storage system in the prediction time domain, the initial state space equation is discretized as follows:

[0073]

[0074] in , represents the discretized matrix of C, is the discrete time domain step size.

[0075] In an optional embodiment of the present invention, in step S5, obtaining a power tracking error prediction model according to the coefficient matrix of the discrete state space equation includes:

[0076] Step S51, obtaining a state deviation matrix, a control deviation matrix, and an interference deviation matrix according to the coefficient matrix of the discrete state space equation;

[0077] Step S52: obtaining a power tracking error prediction model based on the state deviation matrix, the control deviation matrix and the interference deviation matrix, combined with the state increment, the control increment and the interference increment.

[0078] In this embodiment, the power tracking error prediction model in the prediction time domain is:

[0079]

[0080] in,

[0081]

[0082]

[0083]

[0084]

[0085] Where, It indicates the deviation between the actual power and the target power at the k+1th moment predicted based on the current state. , is the state increment, To control the increment, that is, the change value of the energy storage power, wind power and VSG primary frequency regulation power control quantity; is the interference increment, i.e. the system frequency change and wind speed change at the reference point; is the state deviation matrix, To control the deviation matrix, is the interference deviation matrix, p is the prediction time domain, and q is the control time domain.

[0086] In an optional embodiment of the present invention, in step S6, an optimized control model is obtained based on the power tracking error prediction model and the control variable variation range, including:

[0087] Step S61, obtaining a power deviation vector according to the power tracking error prediction model;

[0088] Step S62, obtaining a control constraint matrix and a control cost matrix according to the initial state space equation;

[0089] Step S63 , obtaining an optimized control model according to the power deviation vector, the control constraint matrix and the control cost matrix in combination with the control variable variation range.

[0090] In this embodiment, according to the power tracking error prediction model, the power tracking error is kept at the minimum value, and the power deviation vector at this time is obtained. , ,in is the energy storage charge reference value, is the power reference value required by the system. According to the coefficient matrix in the initial state space equation and the deviation matrix in the power tracking error prediction model, the control constraint matrix and the control cost matrix are obtained. The control constraint matrix controls the importance of input changes in time, and the control cost matrix weights the output at future moments, which helps to minimize the future output error. Specifically, the control constraint matrix , control cost matrix , combined with the energy storage output power, the economic efficiency of energy storage charging and discharging power, wind power operation stability and primary frequency regulation effectiveness are considered in the prediction time domain. The multi-objective optimization control problem is expressed as:

[0091]

[0092] in, represents the optimal goal, represents the energy storage charge optimization target, represents the power response optimization objective, represents the control cost optimization target, p is the prediction time domain, q is the control time domain, It represents the quadratic norm of the value L. represents the control quantity at the k+i discrete moment, represents the transposed matrix of u, represents the control increment at the k+i-1th discrete moment, Indicates the minimum value of the control increment, Indicates the maximum value of the control increment, Indicates the minimum value of energy storage charge, Indicates the maximum energy storage charge, E indicates the system required power, Indicates the minimum power required by the system. Indicates the maximum power required by the system. Indicates constraints.

[0093] In an optional embodiment of the present invention, in step S7, obtaining a wind power control strategy and an energy storage control strategy based on the real-time status data and the optimized control model includes:

[0094] Step S71, obtaining the state variables of each discrete point according to the discrete state space equation;

[0095] Step S72: input the real-time state data and each discrete point state variable into the optimization control model to obtain the wind power control strategy and energy storage control strategy of the wind-storage system.

[0096] In this embodiment, given the system state variable x(k), the explicit optimal control law is solved offline using a rolling horizon optimization strategy. The steps are as follows:

[0097] (1) Obtain the system state x(k) at time k

[0098] (2) Considering the discrete linear time-invariant system of discrete state space equations, using its linear transfer property, we can obtain:

[0099]

[0100] in, is the control quantity at the k+ij-1th discrete moment, is the interference amount at the k+ij-1th discrete moment, where j=0,…,i-1, represents the discretized matrix of A, represents the matrix after B is discretized, Represents the matrix after D is discretized;

[0101] The real-time state data and the state variables of each discrete point are input into the optimization control model, and the constraints of the linear transfer properties of the offline system are added to obtain the expression:

[0102]

[0103] Among them, F* is the optimal target, x(k) is the initial state at the kth moment, U(k) is the control sequence at the kth moment, represents the discretized matrix of U(k). F*, U(k), and x(k) are considered as the optimal value function, decision variable, and parameter vector, respectively, thus transforming the optimization problem into a standard multi-parameter programming problem.

[0104] (3) Applying the calculated first control variable to the multi-objective optimization control of wind-storage system frequency tracking;

[0105] (4) Shift the prediction time domain forward by one step and repeat the above process at time k+1 to obtain the optimal control law in the form of a piecewise affine function:

[0106]

[0107] in, Represents the state feedback gain coefficient matrix, reflecting the system state Directly drive the linear relationship of the control quantity; after completing the calculation of a control area, the system switches to the next area until the entire feasible solution space is covered. ref and E ref Discretized into 8 intervals, 64 groups of solutions are obtained, corresponding to different control strategies.

[0108] In this embodiment, the wind power control strategy is a control strategy under a preset energy threshold, and the energy storage control strategy is a control strategy under a preset energy storage charge threshold. Each group of control strategies is divided into four areas for analysis to provide support for subsequent optimization.

[0109] (1) E>E ref And SOC>SOC ref :The system power demand exceeds the reference value, and the frequency rises. The wind turbines absorb the surplus power faster, and the energy storage system is ref , the battery does not need to be charged.

[0110] (2) E>E ref And SOC <SOC ref :When the system frequency rises, the wind turbine absorbs the surplus power by accelerating the rotor, and the energy storage system charges to adjust the frequency, but overcharging should be avoided.

[0111] (3) E <E ref And SOC>SOC ref :The system frequency drops, the wind turbine rotor adjusts the frequency through inertial response, and the energy storage system (SOC>SOC ref ) provides power supplement during the primary frequency modulation stage.

[0112] (4) E <E ref And SOC <SOC ref:When the system frequency rises and the energy storage SOC is lower than the reference value, the system power shortage is mainly provided by the grid-connected wind power, which is responsible for system frequency regulation.

[0113] like Figure 3 As shown, the SOC ref 65%, E ref The control strategy of wind power when is 0; Figure 4 As shown, the SOC ref 65%, E ref The control strategy of energy storage power when it is 0.

[0114] Verification of the effectiveness of the solution of the present invention:

[0115] First, a simulation system is constructed. The simulation system consists of a grid-connected wind turbine, an energy storage system, a synchronous generator, and an AC load. Figure 5 The wind farm consists of 15 2MW doubly-fed wind turbines with an output voltage of 690V, which is boosted to 35kV via a step-up transformer. The accompanying energy storage system is rated at 10% of the wind turbine's rated power and has a capacity of 3MW / 3MWh. The simulation model also includes a 60MW synchronous generator and randomly fluctuating AC loads. The primary frequency regulation deadband is set to ±0.033Hz. The optimization strategy sets a prediction period of 40s and a sampling interval Δt of 1s.

[0116] To verify the effectiveness of the proposed strategy under variable wind speed conditions, a comparative analysis of the following three collaborative control strategies for the grid-type wind-storage system is conducted for step load disturbance conditions: ① improved droop control; ② traditional MPC control; and ③ multi-objective optimization control of this scheme.

[0117] The effectiveness of this strategy is verified under high wind speed conditions of 12m / s. The initial SOC is set to 50%, and a step load disturbance with an amplitude of 30MW is added at 5s. The change curves of grid frequency deviation, wind turbine output, wind turbine speed, energy storage system output and energy storage SOC are shown in the figure below. Figures 6-10 shown.

[0118] Under high wind speed conditions, the multi-objective optimization control of the present invention adaptively adjusts the power output of the wind-storage system operating conditions. Compared with the other two control methods, the maximum frequency deviation Δf max and 16.88% and 10.29% respectively; at the same time, compared with the traditional MPC control, the steady-state frequency deviation Δf s It has been reduced by 5.52%, and the frequency modulation effect has been continuously improved.

[0119] Table 1 Frequency regulation index results at high wind speed

[0120] Control strategy <![CDATA[Δf max / Hz]]> <![CDATA[Δf s / Hz]]> SOC / % <![CDATA[v SOC ]]> <![CDATA[r min ]]> Droop control 0.443 0.252 0.326 1.01 1.117 Traditional MPC control 0.418 0.229 0.343 1.06 1.110 Multi-objective optimization control 0.379 0.217 0.375 1.15 1.108

[0121] At the beginning of the frequency disturbance, the energy storage system quickly provided significant power support, then gradually reduced its output. Simultaneously, the grid-connected wind turbines actively participated in grid frequency regulation by fully releasing their rotor kinetic energy. At 21.07 seconds after the disturbance, the turbine speed dropped to its lowest point, 1.108 pu. Ultimately, the energy storage's state of charge (SOC) dropped to 0.375 pu. Compared with traditional MPC control strategies, the energy storage system's maximum output depth was reduced by approximately 9.32%. The synergistic effect of the energy storage system and the grid-connected wind turbines effectively slowed the rate of decline in system frequency.

[0122] Therefore, multi-objective optimization control dynamically optimizes the wind-storage output ratio of the network, fully utilizes the collaborative frequency regulation advantages of wind power and energy storage, achieves improved frequency regulation performance under high wind speed conditions and stable operation under low wind speed conditions, significantly enhances the operating stability and economy of the wind-storage combined system, and at the same time improves its sustainability and safety during the frequency regulation process.

[0123] This paper proposes a multi-objective rolling frequency modulation control method for a grid-based wind-storage system, effectively addressing the issues of response hysteresis, regulation rigidity, and insufficient economic efficiency in existing wind-storage systems. Based on system state modeling, this control strategy fully integrates the kinetic energy characteristics of wind turbines, energy storage state of charge management, and multi-timescale frequency perturbation mechanisms, achieving dynamic, coordinated optimization of the wind-storage system's frequency response capability, operational stability, and economic efficiency.

[0124] Simulation experiments demonstrate that this method significantly reduces the system's maximum frequency deviation and steady-state error under high wind speed disturbance conditions, improving frequency support accuracy and system robustness. Compared to traditional control strategies, the proposed method demonstrates superior dynamic response and adaptability, maintaining efficient and stable frequency modulation control, particularly in complex scenarios such as those with severe system power fluctuations or limited energy storage boundaries.

[0125] The present invention has significant advantages such as clear structure, efficient control, and strong adaptability. It can be widely used in grid-type wind power and energy storage combined operation systems. It has good engineering feasibility and industrial promotion value, and can provide core technical support for the stable operation of large-scale new energy power systems.

[0126] like Figure 11 As shown, an embodiment of the present invention further provides a frequency regulation optimization device 110 for a grid-connected wind-storage system based on explicit model predictive control, comprising:

[0127] An acquisition module 111 is configured to acquire real-time status data of the wind-storage system, including the energy storage operating status, system power demand, wind power operating status, and wind speed;

[0128] The processing module 112 is used to determine the state variables, control inputs and interference inputs based on the real-time state data; obtain the initial state space equation based on the relationship between the state variables, control inputs and interference inputs on the wind-storage system; discretize the initial state space equation to obtain a discrete state space equation; obtain a power tracking error prediction model based on the coefficient matrix of the discrete state space equation; obtain an optimized control model based on the power tracking error prediction model and the range of control quantity variation; obtain the wind power control strategy and energy storage control strategy of the wind-storage system based on the real-time state data and the optimized control model.

[0129] Optionally, determining the state variables, control inputs, and interference inputs according to the real-time state data includes:

[0130] The energy storage charge state and system demand power in the real-time status data are used as state variables; wind power, energy storage power and frequency modulation power are used as control inputs, and system frequency and wind speed are used as interference inputs.

[0131] Optionally, the initial state space equation is obtained according to the relationship between the state variables, control inputs and disturbance inputs on the wind-storage system, including:

[0132] Obtaining an updated state according to the state variables, control input, disturbance input, and wind-storage system equipment coefficients;

[0133] According to the state variables, the output state of the wind-storage system is obtained;

[0134] According to the updated state and the output state of the wind-storage system, an initial state space equation is established.

[0135] Optionally, the initial state space equation is discretized to obtain a discrete state space equation, including:

[0136] The coefficient matrix of the initial state space equation is discretized according to a preset discrete time domain step size to obtain a discrete state space equation.

[0137] Optionally, obtaining a power tracking error prediction model according to a coefficient matrix of the discrete state space equation includes:

[0138] According to the coefficient matrix of the discrete state space equation, a state deviation matrix, a control deviation matrix and a disturbance deviation matrix are obtained;

[0139] According to the state deviation matrix, the control deviation matrix and the interference deviation matrix, in combination with the state increment, the control increment and the interference increment, a power tracking error prediction model is obtained.

[0140] Optionally, an optimized control model is obtained based on the power tracking error prediction model and the control variable variation range, including:

[0141] Obtaining a power deviation vector according to the power tracking error prediction model;

[0142] According to the initial state space equation, a control constraint matrix and a control cost matrix are obtained;

[0143] According to the power deviation vector, the control constraint matrix and the control cost matrix, combined with the control variable variation range, an optimized control model is obtained.

[0144] Optionally, obtaining a wind power control strategy and an energy storage control strategy based on the real-time status data and the optimized control model includes:

[0145] According to the discrete state space equation, the state variables of each discrete point are obtained;

[0146] The real-time state data and each discrete point state variable are input into the optimization control model to obtain the wind power control strategy and energy storage control strategy of the wind-storage system.

[0147] It should be noted that all implementations in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0148] An embodiment of the present invention further provides a computing device comprising: one or more processors; and a storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control described in the present invention. All implementations in the aforementioned method embodiments are applicable to the embodiments of this computing device and can achieve the same technical effects.

[0149] An embodiment of the present invention further provides a computer-readable storage medium storing a program that, when executed by a processor, implements the frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control described in the present invention. All implementations described in the aforementioned method embodiments are applicable to the embodiments of this computer-readable storage medium and can achieve the same technical effects.

[0150] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0151] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0154] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for causing a computer device (such as a personal computer, server, or network device) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0156] In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but they do not necessarily need to be performed in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including a processor, storage medium, etc.) or a network of computing devices. This can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0157] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code for implementing the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0158] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control, characterized in that: include: Acquire real-time status data of the wind-storage system, including energy storage operating status, system power demand, wind power operating status, and wind speed; Determining state variables, control inputs, and disturbance inputs based on the real-time state data; According to the relationship between the state variables, control inputs and disturbance inputs on the wind-storage system, an initial state space equation is obtained; Discretizing the initial state-space equation to obtain a discrete state-space equation; Obtaining a power tracking error prediction model according to a coefficient matrix of the discrete state space equation; According to the power tracking error prediction model and the control variable variation range, an optimized control model is obtained; According to the real-time status data and the optimized control model, a wind power control strategy and an energy storage control strategy of the wind-storage system are obtained.

2. The frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control according to claim 1 is characterized in that: Determining state variables, control inputs, and disturbance inputs based on the real-time state data includes: The energy storage charge state and system demand power in the real-time status data are used as state variables; wind power, energy storage power and frequency modulation power are used as control inputs, and system frequency and wind speed are used as interference inputs.

3. The frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control according to claim 2 is characterized in that: According to the relationship between the state variables, control inputs and disturbance inputs on the wind-storage system, the initial state space equation is obtained, including: Obtaining an updated state according to the state variables, control input, disturbance input, and wind-storage system equipment coefficients; According to the state variables, the output state of the wind-storage system is obtained; According to the updated state and the output state of the wind-storage system, an initial state space equation is established.

4. The frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control according to claim 1 is characterized in that: The initial state space equation is discretized to obtain a discrete state space equation, including: The coefficient matrix of the initial state space equation is discretized according to a preset discrete time domain step size to obtain a discrete state space equation.

5. The frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control according to claim 1 is characterized in that: According to the coefficient matrix of the discrete state space equation, a power tracking error prediction model is obtained, including: According to the coefficient matrix of the discrete state space equation, a state deviation matrix, a control deviation matrix and a disturbance deviation matrix are obtained; According to the state deviation matrix, the control deviation matrix and the interference deviation matrix, in combination with the state increment, the control increment and the interference increment, a power tracking error prediction model is obtained.

6. The frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control according to claim 1, characterized in that: According to the power tracking error prediction model and in combination with the control variable variation range, an optimized control model is obtained, including: Obtaining a power deviation vector according to the power tracking error prediction model; According to the initial state space equation, a control constraint matrix and a control cost matrix are obtained; According to the power deviation vector, the control constraint matrix and the control cost matrix, combined with the control variable variation range, an optimized control model is obtained.

7. The frequency regulation optimization method for a grid-connected wind-storage system based on explicit model predictive control according to claim 1 is characterized in that: Based on the real-time status data and the optimized control model, a wind power control strategy and an energy storage control strategy are obtained, including: According to the discrete state space equation, the state variables of each discrete point are obtained; The real-time state data and each discrete point state variable are input into the optimization control model to obtain the wind power control strategy and energy storage control strategy of the wind-storage system.

Images