Optical storage cooperation method, system and device for autonomous operation of power grid, and medium

Abstract

The invention belongs to the technical field of new energy grid-connected converter analysis and control technologies, and discloses a power grid autonomous operation optical storage cooperation method, system, device and medium, the power grid autonomous operation optical storage cooperation method comprises the following steps: determining the influence of the moment of inertia and the damping coefficient of a virtual synchronous generator on the active power response; a self-adaptive control function is constructed according to a rotational inertia and damping coefficient selection principle; the future error between the predicted output and the reference value is minimized through a preset cost function, and the optimal control signal of the next sampling time is selected; and the output power and the energy storage strategy of the optical storage system are adjusted in real time. According to the method, the network construction capability of system operation is improved, the rapidity and stability of the system can be considered by adaptively adjusting inertia and damping parameters, the robustness of the system in the transient process is enhanced, and the robustness of the optical storage system in the transient process is enhanced by introducing an improved inertia and damping parameter dynamic adjustment control algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis and control of new energy grid-connected converters, and in particular to a photovoltaic-storage coordination method, system, equipment and medium for autonomous operation of a power grid. Background Art

[0002] As an important means of grid-connected converter grid-type control, the virtual synchronous generator (VSG) introduces rotational inertia and damping coefficient, gets rid of the shortcomings of low damping, low inertia, and weak frequency support capability of grid-connected inverters, provides transient support for the grid, and helps the grid to operate autonomously. Traditional VSG grid-connected inverters use constant inertia and damping control, and their robustness is poor when the system is disturbed.

[0003] At present, most of the different proposed methods for grid-connected photovoltaic and energy storage control are based on proportional-integral control, which has problems such as poor multi-objective coordination, slow dynamic response, and limited control bandwidth. In order to overcome the limitations of classical control, model predictive control (MPC) has been widely studied due to its fast dynamic response and the ability to add definable constraints to the evaluation function. However, there are many studies on the application of MPC in grid-connected inverters, and few studies have been conducted on solving the problem of coordinated control of photovoltaic and energy storage systems based on grid-connected control.

[0004] Therefore, how to provide a photovoltaic-storage collaborative method, system, equipment and medium for autonomous operation of the power grid is an urgent problem to be solved. Summary of the invention

[0005] The embodiments of the present invention provide a photovoltaic-storage coordination method, system, device and medium for autonomous operation of a power grid to solve the problems in the prior art.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a photovoltaic-storage coordination method for autonomous operation of a power grid is provided.

[0008] In one embodiment, the photovoltaic-storage coordination method for autonomous operation of a power grid includes the following steps:

[0009] Based on the power frequency characteristics of the virtual synchronous generator when it is connected to the grid, the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response is determined, and an adaptive control function is constructed according to the principle of selecting the moment of inertia and damping coefficient.

[0010] By building a discrete time model, the future evolution of the controlled parameters within a preset time window is predicted, and the optimal control signal for the next sampling time is selected by minimizing the future error between the predicted output and the reference value through a pre-set cost function;

[0011] According to the adaptive control function and the optimal control signal, the output power and energy storage strategy of the photovoltaic energy storage system are adjusted in real time.

[0012] In one embodiment, based on the power frequency characteristics of the virtual synchronous generator when it is connected to the grid, determining the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response, and constructing an adaptive control function according to the moment of inertia and damping coefficient selection principle includes the following steps:

[0013] According to the system topology diagram of the photovoltaic storage unit, the connection relationship between the photovoltaic storage system, the grid-building converter, the filter, the load and the power grid is identified, and the active power and reactive power functions output by the virtual synchronous generator are constructed;

[0014] According to the two-order model of the synchronous generator, a mathematical model of the virtual synchronous generator is constructed, wherein the mathematical model of the virtual synchronous generator includes a motion equation of a virtual synchronous rotor, a frequency modulation function and an excitation regulation function of the virtual synchronous generator;

[0015] Based on the active power and reactive power functions output by the virtual synchronous generator, the motion equation of the virtual synchronous rotor and the closed-loop transfer function of the active link of the virtual synchronous generator, the natural oscillation angular frequency and damping coefficient are obtained;

[0016] Based on the virtual synchronous rotor motion equation, the moment of inertia and damping coefficient are adaptively controlled according to the static stability regulation principle of the synchronous generator, and the adaptive control function of the moment of inertia and damping coefficient is established through the selection principle.

[0017] In one embodiment, based on the virtual synchronous rotor motion equation, the moment of inertia and the damping coefficient are adaptively controlled according to the static stability regulation principle of the synchronous generator, and the adaptive control function of the moment of inertia and the damping coefficient is established by selecting the principle, including the following steps:

[0018] Based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, the angular velocity change rate and angular velocity deviation of the virtual synchronous rotor are calculated by formula conversion;

[0019] According to the rate of change of the virtual synchronous rotor angular velocity, the oscillation process is divided into intervals, and a control strategy is formulated for each interval according to the over-selection principle;

[0020] The adaptive control functions of the moment of inertia and damping coefficient are established, and the optimal second-order system method is used to adjust the moment of inertia and damping coefficient.

[0021] In one embodiment, the expressions of the adaptive control functions of the moment of inertia and the damping coefficient are:

[0022]

[0023]

[0024] Where J and D represent the adaptive control functions of the moment of inertia and damping coefficient, respectively; J0 and D0 represent the moment of inertia and damping coefficient of the virtual synchronous generator when it is running stably; K J0 and K d The threshold value of Δω change, K J1 represents the threshold value of dω / dt change, Δω represents the angular velocity change, dω / dt represents the angular velocity change rate, T1 and T2 both represent the inertia time constant; k a , k b , k c , k d , k e , k f Represents the adjustment coefficient.

[0025] In one embodiment, by establishing a discrete time model, predicting the future evolution of the controlled parameter within a preset time window, and minimizing the future error between the predicted output and the reference value through a preset cost function, selecting the optimal control signal for the next sampling time includes the following steps:

[0026] The state space equation is discretized using the output current mathematical model of the AC side determined by Kirchhoff's law to obtain a discrete time model;

[0027] According to the principle that capacitor voltage or inductor current cannot change suddenly, the switch state of the next sampling period is predicted through the discrete time model, and the predicted value of the controlled variable in the next sampling period corresponding to each switch state is calculated;

[0028] A cost function group is constructed, and the cost function value of each switch state is calculated according to the cost function group, and the control signal of the switch state corresponding to the minimum error between the cost function and the reference value is selected as the optimal control signal of the switch state in the next control cycle.

[0029] In one embodiment, the state space equation expression of the discrete time model is:

[0030]

[0031] In the formula, x(k) represents the controlled variable of the system at the current time k, x(k+1) represents the controlled variable of the system at the next time, that is, time k+1, e represents a natural constant, A represents the state matrix of the system, I represents the unit matrix, B represents the input matrix of the system, y(k) represents the input at the current time k, and T S Indicates the sampling period.

[0032] The expressions of the system's state matrix and input matrix are:

[0033]

[0034] Where C, L and R represent the capacitance, inductance and resistance of the grid-connected side of the system.

[0035] In one embodiment, constructing the cost function group includes the following steps:

[0036] Calculate the capacitor voltage at the common connection point at a preset time based on the state space equation expression of the discrete time model, and construct a cost function for capacitor voltage control;

[0037] According to the improved MPPT method, the cost function of the photovoltaic side voltage control is constructed by scanning the voltage step;

[0038] The output power of the battery is calculated based on the working mode of battery charging and discharging, and the cost function of DC side voltage control is constructed according to the principle of maintaining power balance between the battery and the photovoltaic system;

[0039] The cost function of capacitor voltage control, the cost function of photovoltaic side voltage control and the cost function of DC side voltage control are combined to obtain a cost function group.

[0040] According to a second aspect of an embodiment of the present invention, a photovoltaic-storage cooperative system for autonomous operation of a power grid is provided;

[0041] In one embodiment, the photovoltaic energy storage coordinated system for autonomous operation of the power grid includes:

[0042] An adaptive control function building unit, used to determine the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response based on the power frequency characteristics of the virtual synchronous generator when it is connected to the grid, and to build an adaptive control function according to the principle of selecting the moment of inertia and damping coefficient;

[0043] An optimal control signal calculation unit, used to predict the future evolution process of the controlled parameter within a preset time window by establishing a discrete time model, and to minimize the future error between the predicted output and the reference value through a preset cost function, and select the optimal control signal for the next sampling time;

[0044] The photovoltaic storage system adjustment unit is used to adjust the output power and energy storage strategy of the photovoltaic storage system in real time according to the adaptive control function and the optimal control signal.

[0045] In one embodiment, the adaptive control function building unit includes:

[0046] Active power and reactive power function construction module, used to identify the connection relationship between the photovoltaic storage system, grid-building converter, filter, load and power grid according to the system topology diagram of the photovoltaic storage unit, and to construct the active power and reactive power function output by the virtual synchronous generator;

[0047] A mathematical model building module, used to build a mathematical model of a virtual synchronous generator according to a two-order model of the synchronous generator, wherein the mathematical model of the virtual synchronous generator includes a motion equation of a virtual synchronous rotor, a frequency modulation function and an excitation regulation function of the virtual synchronous generator;

[0048] A natural oscillation angular frequency and damping coefficient calculation module is used to obtain the natural oscillation angular frequency and damping coefficient based on the active power and reactive power functions output by the virtual synchronous generator, the virtual synchronous rotor motion equation and the closed-loop transfer function of the active link of the virtual synchronous generator;

[0049] The adaptive control function establishment module is used to adaptively control the moment of inertia and damping coefficient based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, and to establish the adaptive control function of the moment of inertia and damping coefficient through the selection principle.

[0050] In one embodiment, based on the virtual synchronous rotor motion equation, the moment of inertia and the damping coefficient are adaptively controlled according to the static stability regulation principle of the synchronous generator, and the adaptive control function of the moment of inertia and the damping coefficient is established by selecting the principle including:

[0051] Based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, the angular velocity change rate and angular velocity deviation of the virtual synchronous rotor are calculated by formula conversion;

[0052] According to the rate of change of the virtual synchronous rotor angular velocity, the oscillation process is divided into intervals, and a control strategy is formulated for each interval according to the over-selection principle;

[0053] The adaptive control functions of the moment of inertia and damping coefficient are established, and the optimal second-order system method is used to adjust the moment of inertia and damping coefficient.

[0054] In one embodiment, the expressions of the adaptive control functions of the moment of inertia and the damping coefficient are:

[0055]

[0056] Where J and D represent the adaptive control functions of the moment of inertia and damping coefficient, respectively; J0 and D0 represent the moment of inertia and damping coefficient of the virtual synchronous generator when it is running stably; K J0 and K d The threshold value of Δω change, K J1 represents the threshold value of dω / dt change, Δω represents the angular velocity change, dω / dt represents the angular velocity change rate, T1 and T2 both represent the inertia time constant; k a , k b , k c , k d , k e , k f Represents the adjustment coefficient.

[0057] In one embodiment, the optimal control signal calculation unit includes:

[0058] A discrete time model building module is used to discretize the state space equation using the output current mathematical model of the AC side determined by Kirchhoff's law to obtain a discrete time model;

[0059] The predicted value prediction module of the controlled variable is used to predict the switch state of the next sampling period through a discrete time model according to the principle that the capacitor voltage or the inductor current cannot change suddenly, and calculate the predicted value of the controlled variable in the next sampling period corresponding to each switch state;

[0060] The optimal control signal selection module is used to construct a cost function group, calculate the cost function value of each switch state according to the cost function group, and select the control signal of the switch state corresponding to the minimum error between the cost function and the reference value as the optimal control signal of the switch state in the next control cycle.

[0061] In one embodiment, the state space equation expression of the discrete time model is:

[0062]

[0063] In the formula, x(k) represents the controlled variable of the system at the current time k, x(k+1) represents the controlled variable of the system at the next time, that is, time k+1, e represents a natural constant, A represents the state matrix of the system, I represents the unit matrix, B represents the input matrix of the system, y(k) represents the input at the current time k, and T S Indicates the sampling period.

[0064] The expressions of the system's state matrix and input matrix are:

[0065]

[0066] Where C, L and R represent the capacitance, inductance and resistance of the grid-connected side of the system.

[0067] In one embodiment, constructing the cost function set includes:

[0068] Calculate the capacitor voltage at the common connection point at a preset time based on the state space equation expression of the discrete time model, and construct a cost function for capacitor voltage control;

[0069] According to the improved MPPT method, the cost function of the photovoltaic side voltage control is constructed by scanning the voltage step;

[0070] The output power of the battery is calculated based on the working mode of battery charging and discharging, and the cost function of DC side voltage control is constructed according to the principle of maintaining power balance between the battery and the photovoltaic system;

[0071] The cost function of capacitor voltage control, the cost function of photovoltaic side voltage control and the cost function of DC side voltage control are combined to obtain a cost function group.

[0072] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0073] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0074] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0075] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0076] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0077] The present invention proposes and studies a photovoltaic-storage coordination method based on inertia damping control to facilitate the autonomous operation of the power grid. The strategy optimizes the coordination ability of the photovoltaic-storage system, reduces the impact caused by system frequency changes, and improves the network-building ability of the system operation. By adaptively adjusting the inertia and damping parameters, it is possible to take into account both the rapidity and stability of the system and enhance its robustness under transient processes. By introducing an improved inertia and damping parameter dynamic adjustment control algorithm, the robustness of the photovoltaic-storage system under transient processes is enhanced. The strategy proposed in the present invention better solves the impact problem existing in the network-building photovoltaic-storage system when it is applied, and provides a feasible control method for its future application.

[0078] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0080] Figure 1 is a flow chart of a photovoltaic-storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0081] Figure 2 is a principle block diagram of a photovoltaic storage cooperative system for autonomous operation of a power grid according to an exemplary embodiment;

[0082] Figure 3 is a system topology diagram including a photovoltaic storage unit in a photovoltaic storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0083] Figure 4 is a control block diagram of a virtual synchronous generator in a photovoltaic-storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0084] Figure 5 It is one of the output active power dynamic response diagrams under different rotation inertia and damping systems in a photovoltaic energy storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0085] Figure 6 This is a second output active power dynamic response diagram under different moments of inertia and damping systems in a photovoltaic energy storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0086] Figure 7 A photovoltaic side DC-DC converter structure in a photovoltaic storage coordination method for autonomous operation of a power grid is shown according to an exemplary embodiment;

[0087] Figure 8is a current flow diagram of a photovoltaic storage system in a photovoltaic storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0088] Fig. 9 It is a waveform diagram of active power output from a photovoltaic storage system side after frequency and light intensity change in a photovoltaic storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0089] Fig.10 is a frequency waveform diagram of a photovoltaic storage system side after frequency and light intensity change in a photovoltaic storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0090] Fig.11 is a waveform diagram of output voltage on the photovoltaic storage system side after frequency and light intensity change in a photovoltaic storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0091] Fig.12 It is a dynamic diagram of power flow of a photovoltaic storage system after frequency and light intensity change in a photovoltaic storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0092] Fig.13 is a dynamic diagram of power flow of a photovoltaic storage system in a photovoltaic storage coordination method for autonomous operation of a power grid according to an exemplary embodiment;

[0093] Fig.14 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0094] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0095] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document 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 operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0096] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0097] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0098] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0099] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0100] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0101] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0102] Figure 1 An embodiment of the photovoltaic-storage coordination method for autonomous operation of a power grid of the present invention is shown.

[0103] In this optional embodiment, the photovoltaic-storage coordination method for autonomous operation of the power grid includes the following steps:

[0104] Step S101, based on the power frequency characteristics of the virtual synchronous generator when it is connected to the grid, determine the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response, and construct an adaptive control function according to the principle of selecting the moment of inertia and damping coefficient;

[0105] Step S103, predicting the future evolution of the controlled parameter within a preset time window by establishing a discrete time model, and minimizing the future error between the predicted output and the reference value through a preset cost function, and selecting the optimal control signal for the next sampling time;

[0106] Step S105, adjusting the output power and energy storage strategy of the photovoltaic energy storage system in real time according to the adaptive control function and the optimal control signal.

[0107] In this optional embodiment, based on the power frequency characteristics of the virtual synchronous generator when connected to the grid, the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response is determined, and an adaptive control function is constructed according to the principle of selecting the moment of inertia and damping coefficient. The connection relationship between the photovoltaic storage system, the grid-connected converter, the filter, the load and the power grid can be identified according to the system topology diagram of the photovoltaic storage unit, and the active power and reactive power functions output by the virtual synchronous generator are constructed; according to the two-order model of the synchronous generator, a mathematical model of the virtual synchronous generator is constructed, and the mathematical model of the virtual synchronous generator includes the virtual synchronous rotor motion equation, the frequency modulation function and the excitation regulation function of the virtual synchronous generator; based on the active power and reactive power functions output by the virtual synchronous generator, the virtual synchronous rotor motion equation and the closed-loop transfer function of the active link of the virtual synchronous generator are calculated to obtain the natural oscillation angular frequency and damping coefficient; based on the virtual synchronous rotor motion equation, the moment of inertia and the damping coefficient are adaptively controlled according to the static stability regulation principle of the synchronous generator, and an adaptive control function of the moment of inertia and the damping coefficient is established through the selection principle.

[0108] In this optional embodiment, based on the virtual synchronous rotor motion equation, the moment of inertia and the damping coefficient are adaptively controlled according to the static stability regulation principle of the synchronous generator, and an adaptive control function of the moment of inertia and the damping coefficient is established through the selection principle. Based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, the virtual synchronous rotor angular velocity change rate and the angular velocity deviation can be calculated through formula conversion; the oscillation process is divided into intervals according to the virtual synchronous rotor angular velocity change rate, and a control strategy is formulated for each interval according to the selection principle; an adaptive control function of the moment of inertia and the damping coefficient is established, and the moment of inertia and the damping coefficient are adjusted using the optimal second-order system method.

[0109] In this optional embodiment, when predicting the future evolution of the controlled parameters within a preset time window by establishing a discrete time model, and minimizing the future error between the predicted output and the reference value through a pre-set cost function to select the optimal control signal for the next sampling time, the state space equation can be discretized using the output current mathematical model of the AC side determined by Kirchhoff's law to obtain a discrete time model; based on the principle that the capacitor voltage or the inductor current cannot change suddenly, the switching state of the next sampling period is predicted through the discrete time model, and the predicted value of the controlled variable in the next sampling period corresponding to each switching state is calculated; a cost function group is constructed, and the cost function value of each switching state is calculated based on the cost function group, and the control signal of the switching state corresponding to the minimum error between the cost function and the reference value is selected as the optimal control signal of the switching state for the next control period.

[0110] In this optional embodiment, when constructing the cost function group, the capacitor voltage at the common connection point at a preset time can be calculated based on the state space equation expression of the discrete time model, and the cost function of the capacitor voltage control can be constructed; according to the improved MPPT method, the cost function of the photovoltaic side voltage control is constructed by scanning the voltage step; the output power of the battery is calculated based on the battery charging and discharging working mode, and the cost function of the DC side voltage control is constructed according to the principle of maintaining power balance between the battery and the photovoltaic system; the cost function of the capacitor voltage control, the cost function of the photovoltaic side voltage control and the cost function of the DC side voltage control are combined to obtain a cost function group.

[0111] Figure 2 An embodiment of a photovoltaic-storage cooperative system for autonomous operation of a power grid according to the present invention is shown.

[0112] In this optional embodiment, the photovoltaic energy storage coordinated system for autonomous operation of the power grid includes:

[0113] The adaptive control function construction unit 201 is used to determine the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response based on the power frequency characteristics of the virtual synchronous generator when it is connected to the grid, and to construct an adaptive control function according to the moment of inertia and damping coefficient selection principle;

[0114] The optimal control signal calculation unit 203 is used to predict the future evolution process of the controlled parameter within a preset time window by establishing a discrete time model, and to minimize the future error between the predicted output and the reference value through a preset cost function, and select the optimal control signal for the next sampling time;

[0115] The photovoltaic energy storage system adjustment unit 205 is used to adjust the output power and energy storage strategy of the photovoltaic energy storage system in real time according to the adaptive control function and the optimal control signal.

[0116] In this optional embodiment, the adaptive control function construction unit 201 includes: an active power and reactive power function construction module (not shown in the figure), a mathematical model construction module (not shown in the figure), a natural oscillation angular frequency (not shown in the figure) and a damping coefficient calculation module and an adaptive control function establishment module (not shown in the figure), wherein the active power and reactive power function construction module is used to identify the connection relationship between the photovoltaic storage system, the grid-forming converter, the filter, the load and the power grid according to the system topology diagram of the photovoltaic storage unit, and construct the active power and reactive power functions output by the virtual synchronous generator; the mathematical model construction module is used to construct the virtual synchronous generator according to the two-order model of the synchronous generator. The mathematical model of the virtual synchronous generator includes the virtual synchronous rotor motion equation, the frequency modulation function and the excitation regulation function of the virtual synchronous generator; a natural oscillation angular frequency and damping coefficient calculation module, which is used to obtain the natural oscillation angular frequency and the damping coefficient based on the active power and reactive power functions output by the virtual synchronous generator, the virtual synchronous rotor motion equation and the closed-loop transfer function of the active link of the virtual synchronous generator; an adaptive control function establishment module, which is used to adaptively control the moment of inertia and the damping coefficient based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, and establish an adaptive control function of the moment of inertia and the damping coefficient through a selection principle.

[0117] In this optional embodiment, the adaptive control function establishing module performs adaptive control on the moment of inertia and the damping coefficient based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, and establishes the adaptive control function of the moment of inertia and the damping coefficient through the selection principle. Based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, the angular velocity change rate and the angular velocity deviation of the virtual synchronous rotor can be calculated through formula conversion; the oscillation process can be divided into intervals according to the angular velocity change rate of the virtual synchronous rotor, and a control strategy can be formulated for each interval according to the selection principle; an adaptive control function of the moment of inertia and the damping coefficient is established, and the moment of inertia and the damping coefficient are adjusted using the optimal second-order system method.

[0118] In this optional embodiment, the control signal calculation unit 203 includes: a discrete time model building module (not shown in the figure), a predicted value prediction module of a controlled variable (not shown in the figure) and an optimal control signal selection module (not shown in the figure); wherein the discrete time model building module is used to discretize the state space equation using the output current mathematical model of the AC side determined by Kirchhoff's law to obtain a discrete time model; the predicted value prediction module of the controlled variable is used to predict the switch state of the next sampling period through the discrete time model based on the principle that the capacitor voltage or the inductor current cannot change suddenly, and calculate the predicted value of the controlled variable in the next sampling period corresponding to each switch state; the optimal control signal selection module is used to construct a cost function group, and calculate the cost function value of each switch state according to the cost function group, and select the control signal of the switch state corresponding to the minimum error between the cost function and the reference value as the optimal control signal of the switch state of the next control period.

[0119] In this optional embodiment, when constructing the cost function group, the capacitor voltage at the common connection point at a preset time can be calculated based on the state space equation expression of the discrete time model, and the cost function of the capacitor voltage control can be constructed; according to the improved MPPT method, the cost function of the photovoltaic side voltage control is constructed by scanning the voltage step; the output power of the battery is calculated based on the battery charging and discharging working mode, and the cost function of the DC side voltage control is constructed according to the principle of maintaining power balance between the battery and the photovoltaic system; the cost function of the capacitor voltage control, the cost function of the photovoltaic side voltage control and the cost function of the DC side voltage control are combined to obtain a cost function group.

[0120] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are further described below from the perspective of architecture and principle, as follows:

[0121] A photovoltaic-storage coordination method based on inertia damping control to facilitate the autonomous operation of the power grid is proposed. The power frequency characteristics of VSG when connected to the grid are analyzed, and it is found that the rotational inertia J of VSG determines the oscillation frequency in the output active power response process. The selection of J is determined by both the change amount and the change rate of the virtual rotor angular velocity, while the damping coefficient D determines the oscillation attenuation rate of the active power response. The selection of D is only determined by the change amount of the virtual rotor angular velocity. Therefore, under different conditions, according to the selection principles of J and D, an adaptive control strategy of J and D can be designed. On the basis of adaptive control, an improved dynamic adjustment control algorithm of inertia and damping parameters is introduced, and then an adaptive grid control strategy for photovoltaic-storage coordination is proposed.

[0122] Model predictive control is to establish a discrete time model of the system and predict the future evolution of the controlled parameters within a certain time window. The future error between the predicted output and the reference value is minimized by using a predefined cost function, and the optimal control signal for the next sampling time is selected. The cost function of capacitor voltage control is derived by Kirchhoff's current law and state space equations. The control task is achieved by an improved MPPT method. MPPT scanning step V step Adaptive changes according to the rate of change of power. In the stage of sudden irradiance change, the reference voltage changes with a larger step size to find the approximate interval of MPPT at a faster speed. The cost function of the photovoltaic side voltage control can be obtained according to the improved MPPT method. The output power of the battery is calculated according to the working mode of battery charging and discharging, and the power required for the battery to maintain power balance with the photovoltaic system should be provided by the battery through the boost converter, so the cost function of the DC side voltage control is obtained.

[0123] Specifically, in Example 1, the VSG control algorithm includes the following steps:

[0124] Step 1: The system topology consists of the main circuit and the control system. The main circuit includes the photovoltaic storage system, grid-forming converter, filter, load and grid. The control algorithm includes the VSG control algorithm and model predictive control. The overall block diagram of the system topology including photovoltaic storage unit is as follows: Figure 3 As shown. Among them, Rs and Ls simulate the stator armature resistance and synchronous inductance of the synchronous generator respectively; abc =[e a ,e b ,e c ] T 、u abc =[u a ,u b ,u c ] T 、i abc =[i a ,i b ,i c ] T are the VSG induced electromotive force, three-phase output terminal voltage and grid-connected current respectively; Pe and Q are the active and reactive power output by VSG respectively; P ref , Q ref , U ref ,ω ref They are the input reference active power, reactive power, reference voltage and rated electrical angular velocity respectively; U represents the output terminal voltage; E represents the reference excitation electromotive force; ω represents the mechanical angular velocity; σ represents the virtual power angle of VSG.

[0125] Step 2: Based on the two-order model of the traditional synchronous generator, the rotor motion equation of the equivalent virtual synchronous generator is shown in equation (1). For ease of analysis, it is assumed that the number of pole pairs of the synchronous generator is 1, and the mechanical angular velocity ω and the electrical angular velocity of the synchronous generator are the same.

[0126]

[0127] Where J represents the moment of inertia of the synchronous generator, in kg·m 2 , D represents the damping coefficient of the synchronous generator corresponding to the damping torque, the unit is N·m·s / rad, ω ref It represents the synchronous angular velocity of the power grid, that is, the rated angular velocity of the synchronous generator, in rad / s, T m , T e and T D They represent the mechanical torque, electromagnetic torque and damping torque of the synchronous generator respectively, and the unit is N·m. θ is the power angle of the synchronous generator.

[0128] The VSG controller includes an active power-frequency droop control link and a link to simulate the synchronous motor rotor motion characteristics. Its frequency modulation function can be expressed as follows:

[0129] P m =P ref +ΔP=P ref -D p (ω-ω ref ) (2)

[0130] Where P m Indicates mechanical power, D p represents the frequency modulation coefficient, ω represents the mechanical angular velocity, ΔP represents the power change, P ref Indicates the input reference active power.

[0131] The reactive power-voltage control of VSG builds a virtual excitation controller to simulate the excitation regulation of the synchronous motor. Its excitation regulation can be expressed as follows:

[0132] E=K u (U ref -U)+K p (Q ref -U)+E0 (3)

[0133] Where, E represents the reference excitation electromotive force, K p and K u Respectively represent the reactive voltage droop coefficient and voltage regulation coefficient, U ref represents the reference voltage, U represents the output terminal voltage, Q ref It represents the input reference reactive power, and E0 represents the rated value of the excitation electromotive force.

[0134] The control block diagram of the virtual synchronous generator is as follows: Figure 4 As shown in the figure, formula (4) is as follows:

[0135]

[0136] Where, E represents the reference excitation electromotive force, e a ,e b ,e c Represent the output port electromotive force of phases a, b and c respectively, Indicates the phase angle of the output voltage.

[0137] Step 3: According to Figure 3 As shown, the active power and reactive power output by VSG can be obtained as follows:

[0138]

[0139] Where: P e represents the active power output by VSG, Q represents the reactive power output by VSG, and Z represents the impedance of the filter circuit. Indicates the corresponding impedance angle, E indicates the reference excitation electromotive force, U indicates the output terminal voltage, σ indicates the phase angle of the grid voltage, when the inductor resistance value is X s Much larger than the resistance R s When P e It can be expressed as:

[0140]

[0141] When VSG is connected to the grid, its output angular frequency is clamped by the grid. By referring to the small signal model analysis method of synchronous generators in traditional power systems, the closed-loop transfer function of the active link of VSG can be calculated according to equations (1), (2) and (5):

[0142]

[0143] Where G2(s) represents the closed-loop transfer function of the VSG active link, X s represents the inductance resistance, E represents the reference excitation electromotive force, U represents the output terminal voltage, D represents the damping coefficient of the synchronous generator corresponding to the damping torque, J represents the moment of inertia of the synchronous generator, ω ref represents the grid synchronization angular velocity, and s represents a complex variable.

[0144] The natural oscillation angular frequency ω of the corresponding second-order model is obtained from formula (7): n And the damping coefficient ξ is:

[0145]

[0146] When 0<ξ<1, the error band is ±5%. In this paper, the error band Δ=2% is selected. Then the corresponding overshoot σ% and adjustment time t of the second-order system are s for:

[0147]

[0148] When the active and reactive commands are given, the dynamic performance of the second-order system is determined by the moment of inertia J, the damping coefficient D and the adjustment coefficient. Generally, the adjustment coefficient is constant. When D is constant, the larger J is, the smaller ξ is, the larger the overshoot σ% is, and the adjustment time t s When J is constant, the larger D is, the larger ξ is, the smaller the overshoot σ% is, and the adjustment time t s The shorter the time. The following conclusions can be drawn: the moment of inertia J of the virtual synchronous generator determines the oscillation frequency during the output active power response process, while the damping coefficient D determines the oscillation attenuation rate of the active power response. Figure 5-6 As shown in Figure 3, it is the system output active dynamic response under different moments of inertia and damping coefficients.

[0149] Step 4: Adaptively control J and D based on the static stability regulation principle of synchronous generators. From formula (1), we can get:

[0150]

[0151] Where dω / dt represents the rate of change of angular velocity, P m Represents mechanical power, P e represents electromagnetic power, ω ref represents the synchronous angular velocity of the power grid, D represents the damping coefficient of the synchronous generator corresponding to the damping torque, ω represents the mechanical angular velocity of the synchronous generator, J represents the moment of inertia of the synchronous generator, T m and T e They represent the mechanical torque and electromagnetic torque of the synchronous generator respectively, Δω represents the change in angular velocity, and ΔT represents the change in torque.

[0152] From formula (10), we can see that when ΔT-DΔω is constant, the larger J is, the greater the inertia of VSG is, and the smaller |dω / dt| is; from formula (11), we can see that when ΔT-Jdω / dt is constant, the larger D is, the greater the damping effect of the system is, and the smaller |Δω| is; according to the different signs of Δω and dω / dt, the change of D also has a certain influence on dω / dt. For the convenience of analysis, when the moment of inertia and damping coefficient are constant, a typical oscillation process is divided into 4 intervals, namely, the intervals [t0-t1], [t1-t2], [t2-t3], and [t3-t4] are represented by the serial numbers ①②③④.

[0153] Step 5. In interval ①, the virtual rotor angular velocity of the virtual synchronous generator is greater than the grid angular velocity and gradually increases. The rate of change of the angular velocity dω / dt first increases suddenly and then gradually decreases. Therefore, in interval ①, the moment of inertia J and the damping coefficient D need to be increased to prevent dω / dt and Δω from being too large, thereby constraining the increase of the rotor angular velocity; in interval ②, the rate of change of the virtual rotor angular velocity dω / dt is less than 0, and it enters the deceleration stage. ω gradually decreases from the maximum value, but the virtual rotor angular velocity is still greater than the grid angular velocity. In this stage, a smaller moment of inertia should be used to speed up the process of the angular velocity returning to the rated value. At the same time, when Δω is large, the damping coefficient should be increased to further suppress the deviation of the angular velocity; in intervals ③ and ④, the selection principles of the moment of inertia and the damping coefficient are similar to those in intervals ① and ②.

[0154] Through the above analysis, the selection of the moment of inertia is determined by both the change amount and the change rate of the virtual rotor angular velocity, while the selection of the damping coefficient is only determined by the change amount of the virtual rotor angular velocity. The selection principles of the moment of inertia and damping coefficient under different conditions are shown in Table 1.

[0155] Table 1 Selection principles of moment of inertia and damping coefficient

[0156] Interval dω / dt Δω (dω / dt)Δω J Changes D changes ① >0 >0 >0 Increase Appropriate increase ② <0 >0 <0 Reduce Appropriate increase ③ <0 <0 >0 Increase Appropriate increase ④ >0 <0 <0 Reduce Appropriate increase

[0157] Step 6. The selection of the moment of inertia is determined by both the change amount and the change rate of the virtual rotor angular velocity, while the selection of the damping coefficient is determined only by the change amount of the virtual rotor angular velocity. The active power response oscillation attenuation rate is determined, and the selection of D is determined only by the change amount of the virtual rotor angular velocity. Therefore, under different conditions, the adaptive control of J and D can be designed according to the selection principle of J and D. The expression of the adaptive control function of the moment of inertia and the damping coefficient is:

[0158]

[0159] Where J and D represent the adaptive control functions of the moment of inertia and damping coefficient, respectively; J0 and D0 represent the moment of inertia and damping coefficient of the virtual synchronous generator when it is running stably; K J0 and K d The threshold value of Δω change, K J1 represents the threshold value of dω / dt change, Δω represents the angular velocity change, dω / dt represents the angular velocity change rate, T1 and T2 both represent the inertia time constant; k a , k b , k c , k d , k e , k f It represents the adjustment coefficient, which takes into account both the speed and stability of system operation through real-time adjustment of J and D.

[0160] Step 7: For the moment of inertia J0 and damping coefficient D0 of the virtual synchronous generator during stable operation, adjust them according to the method of "optimal second-order system". When the system is running stably, dω / dt=0, and D can be adjusted according to the following formula:

[0161]

[0162] Where: T mmax Represents the maximum mechanical torque of the system; ω max Indicates the maximum permissible angular frequency deviation, ω ref represents the grid synchronization angular velocity, T e Represents the electromagnetic torque of the synchronous generator.

[0163] In order to fully utilize the output power capacity of the inverter, the virtual moment of inertia J should meet the following conditions:

[0164]

[0165] Where: P max is the maximum active power output by VSG, ω represents the mechanical angular velocity of the synchronous generator, and dω / dt represents the rate of change of angular velocity.

[0166] Embodiment 2 is as shown in the above embodiment 1, except that a network control method of model predictive control is proposed in order to further improve the system frequency stability and enhance the robustness of the photovoltaic storage system;

[0167] Among them, the implementation steps of the model predictive control strategy are:

[0168] 1. Based on the principle that capacitor voltage or inductor current cannot change suddenly, predict the finite number of possible switch states in the next sampling period.

[0169] 2. Calculate the predicted value of the controlled variable in the next sampling period corresponding to each switching state.

[0170] 3. Calculate the cost function value corresponding to each switch state.

[0171] 4. The switch state corresponding to the minimum value of the replacement function is used as the switch state of the next control cycle.

[0172] The main idea behind model predictive control is to build a discrete-time model of the system and predict the future evolution of the controlled parameters within a certain time window. The future error between the predicted output and the reference value is minimized using a predefined cost function and the optimal control signal for the next sampling time is selected.

[0173] According to Kirchhoff's law, the mathematical model of the output current on the AC side can be expressed as:

[0174]

[0175] In the formula, v c represents the capacitor voltage at the common connection point, i f represents the filter inductor current, i o represents the grid current at the grid-connected location, dx / dt represents the mathematical model of the output current on the AC side, x represents the system controlled variable, A represents the system state matrix, I represents the unit matrix, B represents the system input matrix, and y represents the system input. C, L, and R represent the capacitance, inductance, and resistance values ​​on the grid-connected side of the system.

[0176] The state space equation is discretized using formula (16), and the discretized state space equation is expressed as:

[0177]

[0178] Where, T S represents the sampling period, x(k) represents the system controlled variable at the current time k. x(k+1) represents the system controlled variable at the next time, that is, time k+1. e represents a natural constant. A represents the state matrix of the system, I represents the identity matrix, B represents the input matrix of the system, y(k) represents the input at the current time k, T S Indicates the sampling period.

[0179] The capacitor voltage v at the common connection point at time (k+1) c (k+1) can be used to calculate the predicted value through equation (18). The cost function equation of capacitor voltage control can be expressed as:

[0180]

[0181] In the formula, J v represents the cost function of capacitor voltage control, j represents the three phases a, b, and c, and v cj (k+1) represents the predicted capacitor voltage value of phases a, b, and c at time k+1, v* cj (k+1) represents the capacitor voltage reference value of the three phases a, b, and c at time k+1.

[0182] Among them, in the photovoltaic system, if the temperature or irradiance changes, the maximum power point voltage will deviate from the original position. A suitable MPPT controller is needed to scan the optimal photovoltaic voltage. The most commonly used MPPT method is the P&O method because it is simple and easy to apply. However, the fixed search steps in the standard P&O method may cause power oscillations under rapidly changing light irradiance. Therefore, an improved MPPT method is proposed to achieve the control task. The MPPT scanning step size V stepAdaptive changes according to the rate of change of power. In the stage of sudden change of irradiance, the reference voltage changes with a larger step size to find the approximate interval of MPP at a faster speed. After the scanning process is over, the step size is reduced to obtain better scanning accuracy. If the change of instantaneous power is within the disturbance limit ξ, the MPPT will be deactivated. If the solar irradiance changes significantly, the MPPT will restart. The output value V of the MPPT MPPT It can be described as:

[0183]

[0184] Where V step represents the scanning step length, V MPPT represents the output value of MPPT, λ represents the scaling factor related to the photovoltaic system parameters, which is used to adjust the scanning step size, P PV Represents the photovoltaic side power, V PV represents the PV side voltage, k represents the current moment, ΔP represents the power change, ΔV represents the voltage change, and ξ represents the disturbance limit.

[0185] The photovoltaic side DC-DC converter topology is as follows: Figure 7 As shown, C dc Indicates the DC side capacitance, L PV Indicates the inductance of the photovoltaic side DC-DC converter, U PV Indicates the battery terminal voltage. PV ,I INV and I C They represent photovoltaic current, inverter current and capacitor current respectively, S represents switch, T s Represents the sampling period. The voltage of the next control period of the photovoltaic system can be expressed as:

[0186]

[0187] The cost function equation of photovoltaic side voltage control can be expressed as:

[0188] J PV =[V PV (k+1)-V MPPT (k+1)] 2 (twenty three)

[0189] In the formula, J PV represents the cost function of PV side voltage control, V PV Represents photovoltaic voltage, V MPPT Indicates the MPPT controller reference voltage.

[0190] Among them, the energy storage device can make up for the power gap caused by changes in light intensity. Since the power output or absorbed by the energy storage device is actually controlled by the switching boost converter, a reasonable control strategy can be adopted to enable it to switch smoothly between the boost and buck modes to ensure the stable and reliable operation of the photovoltaic storage system. Figure 8 shown.

[0191] When the photovoltaic output power in the system is less than the rated power of the load, the battery starts to discharge, the system controls switch G2 to work, and switch G1 is always in the off state. The bidirectional DC / DC converter is in the Boost working mode at this time. When the photovoltaic output power is greater than the rated power of the load, the battery starts to be charged, the system controls switch G1 to work, and switch G2 is always off. At this time, the bidirectional DC / DC converter is in the Buck working mode.

[0192] The prediction model of the energy storage inductor current in Boost mode is:

[0193]

[0194] The prediction model of the energy storage inductor current in Buck mode is:

[0195]

[0196] By applying Kirchhoff's current law, the relationship between the currents is expressed as:

[0197] I BES =I PV -I DC -I C (26)

[0198] Therefore, the power at time (k+1) required for the energy storage system to maintain internal power balance can be calculated as:

[0199]

[0200] Assuming that the capacitor voltage changes linearly within the control period and the capacitor current does not change much, we have:

[0201]

[0202] Considering that the battery voltage changes relatively slowly, when the power balance changes, it is planned to complete the control within N control cycles, that is,

[0203]

[0204] Substituting formula (29) into formula (26), we obtain:

[0205] I BES(k+1)=I PV (k)-I DC (k)-I C (k+1) (30)

[0206] The output power of the battery can be calculated as:

[0207] P BES (k+1)=I BES (k+1)·V dc (k) (31)

[0208] The power required for the energy storage device to maintain power balance with the photovoltaic system should be provided by the energy storage device through the boost converter. Therefore, the following cost function should be minimized:

[0209]

[0210] In the formula, i B (k+1) represents the prediction model of the energy storage inductor current, T s represents the sampling period, V dc Indicates the DC bus voltage, L B Represents the inductance of the energy storage device, V BES Indicates the energy storage voltage value, i B (k) represents the current value flowing through the inductor in the energy storage device at time k, I BES Indicates the output current value of the energy storage device, I PV Represents the photovoltaic side current, I DC Indicates the DC side current value, I C Indicates the capacitance current value, It represents the power reference value at time (k+1) required for the energy storage system to maintain internal power balance, I C (k+1) represents the capacitor current reference value at time (k+1), C dc Indicates the capacitance value, V dc(k) represents the DC voltage value at time k, N represents the number of cycles required to complete the control, Indicates the DC voltage reference value, P BES Indicates the output power of the battery, J BES represents the cost function.

[0211] In this way, the energy storage system can respond quickly to changes in the output of the photovoltaic system, while providing power support for the grid-connected converter, improving the stability and response speed of the system.

[0212] In order to verify the effectiveness of the model predictive control photovoltaic storage collaborative adaptive networking control strategy proposed in the present invention, the following is verified in combination with the embodiment. A grid-connected model of a networking photovoltaic storage system is built on the MATLAB / Simulink simulation platform for simulation experimental analysis. The main circuit parameters are shown in Table 2. The control step of the model predictive control system is 100 microseconds. At the operating time t=1.4s, the system frequency suddenly increased from 50Hz to 50.2Hz, and recovered at t=2.0s. At t=1.7s, the light intensity increased from 1000W / m 2 Suddenly dropped to 400W / m 2 .

[0213] Table 2 Main circuit parameters

[0214]

[0215]

[0216] Fig. 9 is the active power curve output by the photovoltaic storage system during the simulation process, Fig.10 is the corresponding frequency curve. As can be seen from the figure, when the system frequency suddenly changes, the curve obtained by using the proposed control strategy is very smooth, there is no overshoot, and the time to enter the steady state is short. This shows that the proposed adaptive network control strategy based on model predictive control has a certain inhibitory effect on the change of frequency.

[0217] Fig.11 and Fig.12 is the output waveform of voltage and current. As can be seen from the figure, the current waveform fluctuates with the fluctuation of active power, while the output voltage hardly changes. And during the frequency mutation process, the current and voltage still maintain sinusoidal waveforms without significant distortion.

[0218] To verify the synergy between the photovoltaic system and the energy storage system, Fig.13 The power flow dynamics of the photovoltaic storage system under changes in system frequency and light intensity are demonstrated. At t=1.4s, the system frequency changes, and the total output power decreases under the control of the virtual synchronous machine. At this time, the photovoltaic system is still operating at the maximum power point and outputs maximum power, while the energy storage system reduces its output in response to changes in the given active power value. At t=1.7s, the light intensity changes suddenly, and the output power of the photovoltaic array drops sharply. At this time, the energy storage system responds in time to compensate for the missing power. At t=2.0s, the system frequency is restored. Since the light conditions remain unchanged at this time, the shortfall in the given output power is still compensated by the energy storage system. From Fig.10It can be seen that the power distribution between the photovoltaic system and the energy storage system has little effect on the external power output. The impact of sudden changes in light conditions on the system frequency output is suppressed. That is, the proposed model predictive control method improves the multi-objective control capability and dynamic response performance of the photovoltaic storage system, and improves the coordinated operation level of the energy storage device and the photovoltaic system, which plays an important role in providing frequency support for the system in conjunction with the grid-based control strategy.

[0219] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.14 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0220] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0221] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0222] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0223] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0224] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A photovoltaic-storage coordination method for autonomous operation of a power grid, characterized in that: The photovoltaic and storage coordination method for autonomous operation of the power grid includes the following steps: Based on the power frequency characteristics of the virtual synchronous generator when it is connected to the grid, the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response is determined, and an adaptive control function is constructed according to the principle of selecting the moment of inertia and damping coefficient. By building a discrete time model, the future evolution of the controlled parameters within a preset time window is predicted, and the optimal control signal for the next sampling time is selected by minimizing the future error between the predicted output and the reference value through a pre-set cost function; According to the adaptive control function and the optimal control signal, the output power and energy storage strategy of the photovoltaic energy storage system are adjusted in real time.

2. The photovoltaic and energy storage coordination method for autonomous operation of a power grid according to claim 1 is characterized in that: The method of determining the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response based on the power frequency characteristics of the virtual synchronous generator when connected to the grid, and constructing an adaptive control function according to the principle of selecting the moment of inertia and damping coefficient includes the following steps: According to the system topology diagram of the photovoltaic storage unit, the connection relationship between the photovoltaic storage system, the grid-building converter, the filter, the load and the power grid is identified, and the active power and reactive power functions output by the virtual synchronous generator are constructed; According to the two-order model of the synchronous generator, a mathematical model of the virtual synchronous generator is constructed, wherein the mathematical model of the virtual synchronous generator includes a motion equation of a virtual synchronous rotor, a frequency modulation function and an excitation regulation function of the virtual synchronous generator; Based on the active power and reactive power functions output by the virtual synchronous generator, the motion equation of the virtual synchronous rotor and the closed-loop transfer function of the active link of the virtual synchronous generator, the natural oscillation angular frequency and damping coefficient are obtained; Based on the virtual synchronous rotor motion equation, the moment of inertia and damping coefficient are adaptively controlled according to the static stability regulation principle of the synchronous generator, and the adaptive control function of the moment of inertia and damping coefficient is established through the selection principle.

3. The photovoltaic and energy storage coordination method for autonomous operation of a power grid according to claim 2 is characterized in that: The method of adaptively controlling the moment of inertia and the damping coefficient based on the virtual synchronous rotor motion equation and according to the static stability regulation principle of the synchronous generator, and establishing the adaptive control function of the moment of inertia and the damping coefficient by selecting the principle comprises the following steps: Based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, the angular velocity change rate and angular velocity deviation of the virtual synchronous rotor are calculated by formula conversion; According to the rate of change of the virtual synchronous rotor angular velocity, the oscillation process is divided into intervals, and a control strategy is formulated for each interval according to the over-selection principle; The adaptive control functions of the moment of inertia and damping coefficient are established, and the optimal second-order system method is used to adjust the moment of inertia and damping coefficient.

4. The photovoltaic and energy storage coordination method for autonomous operation of a power grid according to claim 3 is characterized in that: The expressions of the adaptive control functions of the moment of inertia and damping coefficient are: Where J and D represent the adaptive control functions of the moment of inertia and damping coefficient, respectively; J0 and D0 represent the moment of inertia and damping coefficient of the virtual synchronous generator when it is running stably; K J0 and K d The threshold value of Δω change, K J1 represents the threshold value of dω / dt change, Δω represents the angular velocity change, dω / dt represents the angular velocity change rate, T1 and T2 both represent the inertia time constant; k a , k b , k c , k d , k e , k f Represents the adjustment coefficient.

5. The photovoltaic and energy storage coordination method for autonomous operation of a power grid according to claim 1 is characterized in that: The method of establishing a discrete time model, predicting the future evolution of the controlled parameter within a preset time window, and minimizing the future error between the predicted output and the reference value through a preset cost function, and selecting the optimal control signal for the next sampling time includes the following steps: The state space equation is discretized using the output current mathematical model of the AC side determined by Kirchhoff's law to obtain a discrete time model; According to the principle that capacitor voltage or inductor current cannot change suddenly, the switch state of the next sampling period is predicted through the discrete time model, and the predicted value of the controlled variable in the next sampling period corresponding to each switch state is calculated; A cost function group is constructed, and the cost function value of each switch state is calculated according to the cost function group, and the control signal of the switch state corresponding to the minimum error between the cost function and the reference value is selected as the optimal control signal of the switch state in the next control cycle.

6. The photovoltaic and energy storage coordination method for autonomous operation of a power grid according to claim 5 is characterized in that: The state space equation expression of the discrete time model is: In the formula, x(k) represents the controlled variable of the system at the current time k, x(k+1) represents the controlled variable of the system at the next time, that is, time k+1, e represents a natural constant, A represents the state matrix of the system, I represents the unit matrix, B represents the input matrix of the system, y(k) represents the input at the current time k, and T S Indicates the sampling period; The expressions of the system's state matrix and input matrix are: Where C, L and R represent the capacitance, inductance and resistance of the grid-connected side of the system.

7. The photovoltaic and energy storage coordination method for autonomous operation of a power grid according to claim 5 is characterized in that: The construction of the cost function group comprises the following steps: Calculate the capacitor voltage at the common connection point at a preset time based on the state space equation expression of the discrete time model, and construct a cost function for capacitor voltage control; According to the improved MPPT method, the cost function of the photovoltaic side voltage control is constructed by scanning the voltage step; The output power of the battery is calculated based on the working mode of battery charging and discharging, and the cost function of DC side voltage control is constructed according to the principle of maintaining power balance between the battery and the photovoltaic system; The cost function of capacitor voltage control, the cost function of photovoltaic side voltage control and the cost function of DC side voltage control are combined to obtain a cost function group.

8. A photovoltaic and energy storage cooperative system for autonomous operation of a power grid, characterized in that: The grid's autonomous photovoltaic and storage synergy system includes: An adaptive control function building unit, used to determine the influence of the moment of inertia and damping coefficient of the virtual synchronous generator on the active power response based on the power frequency characteristics of the virtual synchronous generator when it is connected to the grid, and to build an adaptive control function according to the principle of selecting the moment of inertia and damping coefficient; An optimal control signal calculation unit, used to predict the future evolution process of the controlled parameter within a preset time window by establishing a discrete time model, and to minimize the future error between the predicted output and the reference value through a preset cost function, and select the optimal control signal for the next sampling time; The photovoltaic storage system adjustment unit is used to adjust the output power and energy storage strategy of the photovoltaic storage system in real time according to the adaptive control function and the optimal control signal.

9. The photovoltaic and energy storage coordinated system for autonomous operation of the power grid according to claim 8 is characterized in that: The adaptive control function building unit comprises: Active power and reactive power function construction module, used to identify the connection relationship between the photovoltaic storage system, grid-building converter, filter, load and power grid according to the system topology diagram of the photovoltaic storage unit, and to construct the active power and reactive power function output by the virtual synchronous generator; A mathematical model building module, used to build a mathematical model of a virtual synchronous generator according to a two-order model of the synchronous generator, wherein the mathematical model of the virtual synchronous generator includes a motion equation of a virtual synchronous rotor, a frequency modulation function and an excitation regulation function of the virtual synchronous generator; A natural oscillation angular frequency and damping coefficient calculation module is used to obtain the natural oscillation angular frequency and damping coefficient based on the active power and reactive power functions output by the virtual synchronous generator, the virtual synchronous rotor motion equation and the closed-loop transfer function of the active link of the virtual synchronous generator; The adaptive control function establishment module is used to adaptively control the moment of inertia and damping coefficient based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, and to establish the adaptive control function of the moment of inertia and damping coefficient through the selection principle.

10. The photovoltaic and energy storage coordinated system for autonomous operation of the power grid according to claim 9 is characterized in that: Based on the virtual synchronous rotor motion equation, the moment of inertia and the damping coefficient are adaptively controlled according to the static stability regulation principle of the synchronous generator, and the adaptive control function of the moment of inertia and the damping coefficient is established by selecting the principle, including: Based on the virtual synchronous rotor motion equation and the static stability regulation principle of the synchronous generator, the angular velocity change rate and angular velocity deviation of the virtual synchronous rotor are calculated by formula conversion; According to the rate of change of the virtual synchronous rotor angular velocity, the oscillation process is divided into intervals, and a control strategy is formulated for each interval according to the over-selection principle; The adaptive control function of the moment of inertia and damping coefficient is established, and the optimal second-order system method is used to adjust the moment of inertia and damping coefficient.

11. The photovoltaic and energy storage coordinated system for autonomous operation of the power grid according to claim 10, characterized in that: The expressions of the adaptive control functions of the moment of inertia and damping coefficient are: Where J and D represent the adaptive control functions of the moment of inertia and damping coefficient, respectively; J0 and D0 represent the moment of inertia and damping coefficient of the virtual synchronous generator when it is running stably; K J0 and K d The threshold value of Δω change, K J1 represents the threshold value of dω / dt change, Δω represents the angular velocity change, dω / dt represents the angular velocity change rate, T1 and T2 both represent the inertia time constant; k a , k b , k c , k d , k e , k f Represents the adjustment coefficient.

12. The photovoltaic and energy storage coordinated system for autonomous operation of the power grid according to claim 8, characterized in that: The optimal control signal calculation unit comprises: A discrete time model building module is used to discretize the state space equation using the output current mathematical model of the AC side determined by Kirchhoff's law to obtain a discrete time model; The predicted value prediction module of the controlled variable is used to predict the switch state of the next sampling period through a discrete time model according to the principle that the capacitor voltage or the inductor current cannot change suddenly, and calculate the predicted value of the controlled variable in the next sampling period corresponding to each switch state; The optimal control signal selection module is used to construct a cost function group, calculate the cost function value of each switch state according to the cost function group, and select the control signal of the switch state corresponding to the minimum error between the cost function and the reference value as the optimal control signal of the switch state in the next control cycle.

13. The photovoltaic and energy storage coordinated system for autonomous operation of the power grid according to claim 12 is characterized in that: The state space equation expression of the discrete time model is: In the formula, x(k) represents the controlled variable of the system at the current time k, x(k+1) represents the controlled variable of the system at the next time, that is, time k+1, e represents a natural constant, A represents the state matrix of the system, I represents the unit matrix, B represents the input matrix of the system, y(k) represents the input at the current time k, and T S Indicates the sampling period; The expressions of the system's state matrix and input matrix are: Where C, L and R represent the capacitance, inductance and resistance of the grid-connected side of the system.

14. The photovoltaic and energy storage coordinated system for autonomous operation of the power grid according to claim 12, characterized in that: The construction cost function group includes: Calculate the capacitor voltage at the common connection point at a preset time based on the state space equation expression of the discrete time model, and construct a cost function for capacitor voltage control; According to the improved MPPT method, the cost function of the photovoltaic side voltage control is constructed by scanning the voltage step; The output power of the battery is calculated based on the working mode of battery charging and discharging, and the cost function of DC side voltage control is constructed according to the principle of maintaining power balance between the battery and the photovoltaic system; The cost function of capacitor voltage control, the cost function of photovoltaic side voltage control and the cost function of DC side voltage control are combined to obtain a cost function group.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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