Micro-grid control method, computer equipment, storage medium and micro-grid system

[0023]In order to make the objectives, technical solutions and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0024]It can be understood that the terms "first", "second", etc. used in this application can be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of this application, the first xx script can be referred to as the second xx script, and similarly, the second xx script can be referred to as the first xx script.

[0025]figure 1 It is a schematic structural diagram of a micro-grid system provided by an embodiment of the present invention. The micro-grid system includes: a micro-grid terminal and a large-grid terminal, and the micro-grid terminal and the large-grid terminal are connected through network communication;

[0026]The microgrid terminal is used to obtain the microgrid state model and send the first trigger state information to the large power grid terminal. The microgrid state model is the state information relationship model of the current value and the voltage value at different moments on the microgrid terminal. The first trigger state information is the state information of the current value and the voltage value corresponding to the moment when the first event model is triggered by the microgrid terminal;

[0027]In the embodiment of the present invention, the specific network connection structure between the large power grid and the microgrid is not limited. For example, the large power grid and the microgrid can be connected through an Ethernet network. Among them, the state model of the microgrid can be replaced by the state model of the photovoltaic inverter, so that the microgrid end can include a photovoltaic inverter, a sampler, and a first trigger unit, where the photovoltaic inverter, a sampler, and the first trigger unit Connected in sequence, where the first trigger unit may be a server or a terminal, the first trigger unit is provided with a first event model, and the large power grid terminal may include a prediction unit, a second trigger unit and a controller connected in sequence, wherein the prediction unit and the second trigger unit The second trigger unit can be an independent server or terminal, or can be set on the same server or terminal, and the prediction unit, the second trigger unit, and the controller can also be set on the same server or terminal, but it is not limited to this.

[0028]In the embodiment of the present invention, the microgrid uses photovoltaic inverters instead of description, such asfigure 2 Shown is a schematic diagram of the circuit structure of the photovoltaic inverter on the microgrid side. The microgrid state model takes the relationship model of the current and voltage of the photovoltaic inverter as an example. According to Kirchhoff's law, the state model of the microgrid can be expressed X=[if vo vc]T. When the control signal g is selected as u(t), that is, u(t)=g; voIs the system output, ifIs the current value of the LC filter, vcIs the voltage value of the LC filter, the mathematical model of the microgrid photovoltaic inverter system is:

[0029]

[0030]The embodiment of the present invention does not limit the specific modeling process of the microgrid photovoltaic inverter system, for example, the modeling disclosed in the article named A Novel Sliding Mode Estimation for Microgrid Control with Communication Time Delays in the journal IEEE TRANSACTIONS ON SMART GRID process. The microgrid side can obtain the discrete time i of the microgrid state model by setting the samplerf, Vo, VcThe state information between the first event triggers the model to process the state of the microgrid at discrete moments. The resulting discretized state model:

[0031]

[0032]The process of discretization of the mathematical model of the microgrid photovoltaic inverter system is as follows: Since the discretization is mainly the state equation describing the dynamic characteristics of the system, the output equation is a static algebraic equation, which should remain unchanged after discretization, namely:

[0033]C(T)=C

[0034]The formula for solving the state equation of the continuous system is as follows, the general solution of (1) is:

[0035]

[0036]Take t0=kT,t=(k+1)T, it can be seen that u(τ) maintains the function u(kT) at the last moment in the holder, so

[0037]

[0038]Do the variable substitution υ=(k+1)T-τ, then the above formula can be expressed as

[0039]x((k+1)T)=eAT x(kT)+∫0TeAυ dυBu(kT) (5)

[0040]And then expressed as

[0041]x((k+1)T)=G(T)x(kT)+H(T)u(kT) (6)

[0042]among them,

[0043]In the embodiment of the present invention, the first event model may be:

[0044][x(k+r)-x(k)]TΦ[x(k+r)-x(k)]-μx(k+r)T(Φ+σI)x(k+r)>0, (7)

[0045]Where t0=0, μ>0 is a given preset parameter, x(k) is the state of the microgrid at the moment when the first event is triggered, x(k+r) is the state of the microgrid at the current latest moment, and Φ is a pending solution The symmetric positive definite matrix of, σ is the parameter triggered by the adaptive adjustment event. When tkRepresents the moment when the first event model is triggered, then x(k) means that at the trigger moment tkThe state of the microgrid, x(k+r) represents the current state of the microgrid. For example, the sampling time is from t1 to t5. When the state of the microgrid at time t1 triggers the first event model, the state of the microgrid at time t2 is the microgrid. The current state of the grid. For example, take tk+1Represents the next moment when the first event model is triggered, then

[0046]

[0047]Among them, the first event model adaptively adjusts the parameter model, and the process of adaptive parameter adjustment is:

[0048]Step 1: For sampling time tn(n=1, 2, 3,...n), select appropriate adaptive adjustment event trigger parameter σM,σmi And σm, Where σm mi M. Given that σ=σ at the initial time t=0mi , The maximum operating time is among them Is a scalar. Define NuRepresents the number of data packets that are not triggered continuously, NcIs the number of data packets triggered continuously, let Nu= 0 and Nc=1, select NuAnd NcThe upper bounds are with Assume that the first measured value can be passed to the controller.

[0049]Step 2: If the event trigger mechanism is not triggered, skip to step 3; otherwise, let t=t+h,Nc= 0 and Nu=Nu+1. in case Skip directly to step 4; if And σ>σm, Then let Nu=0, σ=σ+λ (λ is the step increment of σ), and then skip to step 4.

[0050]Step 3: Let t=t+h,Nu= 0 and Nc=Nc+1. in case Go directly to step 4; if Nc=Nc, And σ M, Let Nc=0 and σ=σ-λ, then skip to step 4; if And σ≥σM, Let Nu=0,NcT=NcT+1 and keep σ unchanged, then skip to step 4;

[0051]Step 4: If t M, Skip to step 2; otherwise, the algorithm ends. The process of adaptive parameter adjustment can also be described as if there are continuous a sampling packets to be transmitted, a smaller adaptive adjustment parameter is needed to transmit less information to ensure the performance of the system, and σ needs to be reduced; if there is continuous If b sample packets are not delivered, a larger event trigger adjustment parameter is needed to increase the amount of data transferred, and σ needs to be increased, so as to achieve adaptive adjustment of event trigger parameters, thereby saving network resources.

[0052]The large power grid terminal is used to receive the first trigger state information, predict the first non-triggered state information through a predictive model, and control the current value and voltage value of the microgrid terminal according to the first trigger state information and the first non-triggered state information State, the first non-triggered state information is state information of the current value and the voltage value corresponding to the moment when the first event is not triggered on the microgrid terminal.

[0053]In the embodiment of the present invention, since the first event model is set to save network resources, only the state information that triggers the first event model can be transmitted to the large power grid through the network. If the large power grid only uses the received microgrid The first trigger status information of the terminal controls the status information of the microgrid, and there will be a control deviation. The first trigger status information does not represent the status information at all sampling moments. By setting the prediction model, the status of the microgrid at the time of the non-triggering first event model can be predicted Information, for example, the sampling time is t1-t5, t1 and t5 are the time when the first event model is triggered, and the state information of the microgrid at t2, t3, and t4 needs to be predicted through the prediction model, so that the large power grid can obtain the microgrid The state information of all sampling moments t1-t5 at the end. In this embodiment, the state information at all sampling moments at the microgrid end refers to the state information at the moment when the first event model is triggered and the prediction model predicts the state at the time when the first event model is not triggered. information.

[0054]In the embodiment of the present invention, the prediction model may be among them, Represents the first non-trigger state information predicted by the large power grid, Is the control signal vector, K is the feedback matrix to be designed, Is a constant, Is the matrix model obtained from A and B,

[0055]Use predictive control model to predict the moment of packet loss, available To show that for every moment The triggered state of the event trigger mechanism 2 is regarded as the predicted state of the microgrid, which is When using x(ki) Represents the trigger state that meets the trigger condition of event trigger mechanism 1, and can be successfully transmitted to the control end of the large power grid, then use x(ki) Predict the next moment of the microgrid, that is, at Working all the time, the first predicted state is: Then the next prediction state can be obtained by the following iterative method:

[0056]

[0057]

[0058]The corresponding predictive control signal can be expressed as

[0059]

[0060]Pack these predictive microgrid control signals into a data packet Then through the communication network, the control function is transmitted to the microgrid. The control effect of the previous moment will be maintained.

[0061]In the embodiment of the present invention, the micro-grid system can also be provided with a zero-order keeper. The control signal sent by the controller of the large power grid is sent to the zero-order keeper through the connection network, and then the zero-order keeper inputs the control signal to the photovoltaic inverter. In this way, the control effect of a control signal is maintained for a certain period of time.

[0062]In the microgrid system provided by the embodiment of the present invention, by setting the first event model and the prediction model, the transmission data between the microgrid terminal and the large power grid can be reduced, network resources can be saved, and the microgrid terminal can be obtained through the prediction model. State information at all sampling moments, so that the large power grid can control the current and voltage status information of the microgrid based on the state information of all sampling moments on the microgrid side, and the large power grid can be based on the state information of all sampling moments on the microgrid side To determine the power of the microgrid side, when the power of the microgrid side cannot meet its power supply and distribution needs, the large grid side needs to increase the power of the microgrid side by controlling and adjusting the current and voltage values ​​of the microgrid side. In addition to the demand for power supply and distribution, there is a surplus. The large power grid can reduce the power of the microgrid by controlling and adjusting the current and voltage values ​​of the microgrid, and determine the microgrid's power based on the state information of all sampling moments on the microgrid. Compared with determining the power of the microgrid based only on the state information at the trigger time of the microgrid, the power effectively improves the judgment of the power of the microgrid, thereby improving the accuracy of the control of the microgrid by the large grid.

[0063]Such asFigure 4 As shown, in another embodiment of the present invention, controlling the state of the current value and the voltage value of the microgrid terminal according to the first trigger state information and the first non-trigger state information includes:

[0064]Step S302: Process the first trigger state information and the first non-trigger state information through a second event model to determine second trigger state information, where the second trigger state information is the moment when the second event model is triggered The corresponding current value and voltage value status information;

[0065]Step S304: Control the state of the current value and voltage value of the microgrid terminal according to the second trigger state information.

[0066]In the embodiment of the present invention, if the large power grid determines the power of a microgrid according to each sampling moment of the microgrid, and then sends a corresponding control information to the microgrid based on the power of the microgrid at each sampling moment, no doubt, It will also increase the information transmission between the large grid and the microgrid, thereby increasing the network transmission load. By setting the second event model, the second event model can be used to process the state information at all sampling moments of the microgrid, which will trigger the second event model The status information is sent to the large-scale grid-side controller, so that the large-scale grid-side controller determines the power of the micro-grid according to the state information that triggers the second event model, and sends corresponding control information to the micro-grid side, thereby reducing the large-scale grid side The amount of data sent by the microgrid saves network resources.

[0067]In the embodiment of the present invention, the second event model may be:

[0068]

[0069]among them, To predict the state of the microgrid at the moment of triggering the second event, Predict the state of the microgrid at the latest time, i∈{1, 2, ...∞}, m∈{0, 1, 2, ...∞}, μ>0, which is a Given preset parameters, Φ is a symmetric positive definite matrix to be solved, and σ is a parameter triggered by an adaptive adjustment event. The adaptive parameter adjustment process of the second event model is the same as the process of adaptive parameter adjustment of the first event model, and will not be repeated here.

[0070]In the embodiment of the present invention, preferably, the output terminal of the large power grid-side controller is also connected to the input terminal of the prediction unit, so as to realize feedback control.

[0071]In the microgrid system provided by the embodiment of the present invention, by setting the first event model and the second event model, and setting the first event model and the second event model as adaptive parameter adjustment models, the large power grid can be When the microgrid side controls, the trigger parameters are adjusted according to the actual amount of information, so as to save network resources while ensuring system performance.

[0072]The microgrid system provided by the above-mentioned embodiment of the present invention establishes constraint conditions. Under the condition of zero input, for a given positive number δ, ε, and δ +, Positive definite matrix R, the system is called stable in finite time with respect to (δ,ε,R,N), satisfying The large power grid can predict the state information of the microgrid within a limited time. The system represented by the prediction model is stable without considering the noise input and sensor measurement noise; under the condition of zero input, for a given positive number δ, ε, And δ +, Positive definite matrix R, the system is called stable in finite time with respect to (δ,ε,R,N), satisfying

[0073]Lyapunov stability analysis method is used to establish the linear matrix inequality that makes the prediction model meet the above constraints;

[0074]The prediction model for the large power grid is exactly the same as the state model of the microgrid (can be accurately known), namely

[0075](a.1) For some given constant μ∈(0,1), h≥1,δ,ε>0 and adaptive adjustment parameter σM, If there is a positive definite weight matrix P>0, Φ>0, R>0, matrix Y and variable v, and satisfy the following linear matrix inequality:

[0076]

[0077]among them, with Respectively represent the matrix The maximum and minimum eigenvalues. Therefore, system (2) is said to be asymptotically stable with respect to (δ, ε, R, N) in a finite time.

[0078](a.2) The prediction model for the large power grid is not exactly the same as the state model of the microgrid (cannot be accurately known), namely

[0079]The discrete system model of the microgrid is:

[0080]

[0081]Assume that there is packet loss in the communication network between the microgrid and the large power grid, and the maximum packet loss is P. In the process of microgrid network communication, a discrete random variable w (0 or 1) is introduced to describe the probability of network packet loss. For a given event trigger parameter μ∈(0,1), the parameter σ is adjusted adaptivelyMAnd the constant μ∈(0,1h),≥δ,ε,α,β,>γ, if there is a positive definite weight matrix P>0,Φ>0,R>0, matrix Y and variable v, and satisfy the following linearity Matrix inequality:

[0082]

[0083]

[0084]It is said that the system is asymptotically stable with respect to (δ, ε, R, N) finite time.

[0085]among them

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]definition for Then sum it from 0 to t-1, we can get V(t)-V(0)<0, that is

[0093]

[0094]

[0095]

[0096]So there is,

[0097]

[0098]xT(k)Rx(k)≤ε2

[0099]

[0100]According to the literature [J.Song,Y.Niu,Y.Zou.Finite-time stabilization via slidingmode control[J].IEEE Transactions on Automatic Control,2017,62(3):1478-1483.], there can be with So you can get

[0101]In summary, Therefore, the system (2) is stable with respect to (δ, ε, R, N). The proof is over.

[0102]Such asFigure 5 As shown, in another embodiment of the present invention, a microgrid control method is also provided, which is applied to the large power grid side, and the microgrid control method includes:

[0103]Step S202, acquiring first state information of the microgrid;

[0104]In the embodiment of the present invention, the first state information of the microgrid refers to the state information of the photovoltaic inverter at the microgrid side at all sampling moments sent to the large power grid through the first trigger unit.

[0105]Step S204: Identify the time of packet loss, and predict the second state information of the microgrid, where the second state information is the state information of the microgrid at the time of packet loss;

[0106]In the embodiment of the present invention, since the microgrid end of the microgrid system is provided with the first trigger unit, the first trigger unit can only send the state information of the photovoltaic inverter to the large power grid at the moment when the first event model is triggered. The time in the sampling time that does not trigger the state information of the first event model cannot be sent to the large power grid, and the corresponding time that does not trigger the state information of the first event model is the packet loss time. This embodiment does not limit the specific process of identifying the moment of packet loss. For example, the sampling moment corresponding to the first state information can be determined according to the received first state information, and the first state information is compared with the preset sampling time range. By comparison, the time of packet loss can be obtained.

[0107]In the embodiment of the present invention, a prediction model may be used to predict the second state information. For example, the prediction model may be: among them, Represents the first non-trigger state information predicted by the large power grid, Is the control signal vector, K is the feedback matrix to be designed, Is a constant, It is the matrix model obtained by A and B. By identifying the moment of packet loss, the large power grid can use the predictive model to obtain the second state information of the microgrid. The first state information and the second state information obtained by the large power grid are the state information at all sampling moments of the microgrid, so that the large power grid The terminal obtains the status information of all sampling moments of the microgrid.

[0108]Step S206: Process the first state information and the second state information through the second event model to determine second trigger state information, where the second trigger state information is the microgrid state information corresponding to the moment when the second event model is triggered;

[0109]In the embodiment of the present invention, in the embodiment of the present invention, if the large power grid determines the power of a microgrid according to each sampling time of the microgrid, then the power of the microgrid is sent to the microgrid according to the power of the microgrid at each sampling time. The corresponding control information will undoubtedly increase the information transmission between the large grid and the microgrid, thereby increasing the network transmission load. By setting the second event model, the second event model can be used to process the status information of all sampling moments of the microgrid , Send the state information that triggers the second event model to the large power grid controller, so that the large power grid controller determines the power of the microgrid according to the state information that triggers the second event model, and sends corresponding control information to the microgrid side, This can reduce the amount of data sent from the large grid to the microgrid and save network resources.

[0110]In the embodiment of the present invention, the second event model may be:

[0111]

[0112]among them, To predict the state of the microgrid at the moment of triggering the second event, Predict the state of the microgrid at the latest time, i∈{1, 2, ...∞}, m∈{0, 1, 2, ...∞}, μ>0, which is a Given preset parameters, Φ is a symmetric positive definite matrix to be solved, and σ is a parameter triggered by an adaptive adjustment event. The adaptive parameter adjustment process of the second event model is the same as the process of adaptive parameter adjustment of the first event model, and will not be repeated here.

[0113]Step S208 controls the state of the current value and voltage value of the microgrid terminal according to the second trigger state information.

[0114]In the embodiment of the present invention, in order to save network resources, the large-grid-side controller can determine the power of the photovoltaic inverter on the micro-grid side according to the second trigger state information. When the power of the micro-grid side cannot meet its power supply and distribution needs, the large-grid side It is necessary to control and adjust the current and voltage values ​​of the microgrid to increase the power of the microgrid. When the power of the microgrid exceeds its power supply and distribution needs, the large grid can adjust the current value of the microgrid through control And the voltage value to reduce the power of the microgrid end, so as to realize the state of controlling the current value and voltage value of the microgrid end.

[0115]The microgrid control method provided by the embodiment of the present invention predicts the second state information of the microgrid, and processes the first state information and the second state information through the second event model, so that the large power grid can obtain the microgrid The state information at all sampling moments of the power grid can also reduce the amount of information transmitted from the large power grid to the microgrid and save network resources.

[0116]Image 6 Shows an internal structure diagram of a computer device in an embodiment. The computer device may specifically be a server provided with a measurement unit, a second trigger unit, and a controller at the large power grid. Such asImage 6 As shown, the computer equipment includes the computer equipment including a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can realize the microgrid control method. A computer program may also be stored in the internal memory. When the computer program is executed by the processor, the processor can execute the microgrid control method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, or a button, trackball or touch pad set on the housing of the computer equipment. It can be an external keyboard, touchpad, or mouse.

[0117]Those skilled in the art can understand,Image 6 The structure shown in is only a block diagram of part of the structure related to the solution of the application, and does not constitute a limitation on the computer equipment to which the solution of the application is applied. The specific computer equipment may include more or Fewer parts, or combine some parts, or have a different arrangement of parts.

[0118]In one embodiment, a computer device is proposed. The computer device includes a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor executes the computer The following steps are implemented in the program:

[0119]Step S202, acquiring first state information of the microgrid;

[0120]Step S204: Identify the time of packet loss, and predict the second state information of the microgrid, where the second state information is the state information of the microgrid at the time of packet loss;

[0121]Step S206: Process the first state information and the second state information through the second event model to determine second trigger state information, where the second trigger state information is the microgrid state information corresponding to the moment when the second event model is triggered;

[0122]Step S208, controlling the state of the current value and the voltage value of the microgrid terminal according to the second trigger state information.

[0123]In one embodiment, a computer-readable storage medium is provided, and a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the processor is caused to perform the following steps:

[0124]Step S202, acquiring first state information of the microgrid;

[0125]Step S204: Identify the time of packet loss, and predict the second state information of the microgrid, where the second state information is the state information of the microgrid at the time of packet loss;

[0126]Step S206: Process the first state information and the second state information through the second event model to determine second trigger state information, where the second trigger state information is the microgrid state information corresponding to the moment when the second event model is triggered;

[0127]Step S208, controlling the state of the current value and the voltage value of the microgrid terminal according to the second trigger state information.

[0128]It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless specifically stated in this article, the execution of these steps is not strictly restricted in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The order of execution is not necessarily performed sequentially, but may be performed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0129]A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium. Here, when the program is executed, it may include the procedures of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and/or volatile memory. Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not a limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0130]The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the various technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, All should be considered as the scope of this specification.

[0131]The above-mentioned embodiments only express several implementation modes of the present invention, and their description is relatively specific and detailed, but they should not be understood as a limitation to the patent scope of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0132]The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the spirit and principle of the present invention shall be included in the protection of the present invention. Within range.