State prediction method of new energy microgrid group considering wind and light disturbance

Abstract

The invention provides a new energy microgrid group state prediction method based on segmented trajectory linearization, and the method comprises the steps: firstly carrying out the linearization of a new energy microgrid group system through a segmented trajectory linearization method, and obtaining a small signal state space model of the new energy microgrid group system based on segmented trajectory linearization; secondly, representing wind-light disturbance by adopting a chinonol polyhedron set legally, adding the wind-light disturbance into the model, and carrying out reachable set calculation on the model to obtain a reachable set of each time period; and calculating an error between the reachable set of each time period and a real state of the corresponding time period of the new energy microgrid group, adding the errors of all time periods to obtain an accumulative error, performing repeated iteration to enable the accumulative error to be smaller than a preset threshold value, and obtaining a final reachable set of each time period as a state prediction result. The method can be used for solving all possible moving trajectories of the key state quantity of the new energy micro-grid group system under the condition that various uncertain factors are considered, and guiding significance is provided for safe and stable operation of the high-proportion new energy micro-grid group.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid system stability and control, and more specifically, to a state prediction method for a new energy microgrid group considering wind and solar disturbances. Background Art

[0002] With the rapid development of renewable energy technology and the rise of the concept of smart grid, microgrids have been widely promoted as a new type of power system architecture. Microgrids are small power generation and distribution systems composed of multiple distributed power sources, distributed energy storage, energy conversion devices, loads, etc. It is an autonomous system that can achieve self-control, protection and management. In order to provide greater flexibility under normal and extreme conditions, microgrids can be interconnected to form microgrid groups. However, in microgrid groups, a large number of distributed power sources are connected to the system through power electronic converters. The low inertia characteristics of these power electronic interfaces make microgrid groups more susceptible to random disturbances, and phenomena such as over-limit and oscillation instability occur. Therefore, it is of great practical significance to study the dynamic operation characteristics of microgrid group systems under uncertain factors.

[0003] In order to fully understand the dynamic characteristics of microgrid systems under uncertain factors, it is necessary to establish an accurate microgrid system model. However, the current modeling methods all assume that the irradiance or wind speed is constant, and are all based on single-point linearization for approximation, which can only guarantee the local validity of the model. Therefore, it is necessary to further study dynamic modeling based on multi-point linearization and considering source-side uncertainty disturbances.

[0004] At present, the main methods for dynamic analysis of uncertain disturbances can be divided into in-domain probability analysis method, time domain simulation method, and Monte Carlo method. However, these methods cannot verify the infinite number of scenarios that may occur in real systems, so it is urgent to find a new method to represent uncertain disturbance analysis. Summary of the invention

[0005] In order to overcome the defect of the above-mentioned prior art that the uncertainty disturbance defect cannot be expressed to ensure the global validity of the model, the present invention provides a state prediction method for a new energy microgrid group considering wind and solar disturbances.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] The present invention proposes a state prediction method for a new energy microgrid group considering wind and solar disturbance, comprising the following steps:

[0008] S1: Set the simulation step size and use the state spacing selection algorithm to select multiple linearization points for the real state of the constructed new energy microgrid in multiple time periods;

[0009] S2: Linearize the non - linear model of the new - energy micro - grid group by using the multiple linearization points to obtain a small - signal state - space model of the new - energy micro - grid group system based on piece - wise trajectory linearization;

[0010] S3: Represent the wind - solar perturbation of the new - energy micro - grid group system by using the zonotope - based method;

[0011] S4: Add the wind - solar perturbation of the new - energy micro - grid group system to the small - signal state - space model of the new - energy micro - grid group system based on piece - wise trajectory linearization to obtain a small - signal state - space model of the new - energy micro - grid group system with wind - solar perturbation based on piece - wise trajectory linearization, and calculate the reachable set of this model to obtain the reachable set for each time period;

[0012] S5: Calculate the error between the reachable set for each time period and the true state of the new - energy micro - grid group corresponding to the time period, sum up the errors for all time periods to obtain the cumulative error. If the cumulative error is less than the preset threshold, obtain the final reachable set for each time period; otherwise, update the simulation step size and return to S1.

[0013] Preferably, use an algorithm based on state distance to select linearization points, perform multi - point linearization on the non - linear model of the new - energy micro - grid group, and obtain several linearization points, including:

[0014] The new - energy micro - grid group is represented as a set of non - linear equations, and the general expression of the non - linear equations is as follows:

[0015]

[0016] where, is the system state vector, is the perturbation vector, and r is the simulation step size;

[0017] Use the state - distance selection algorithm to select linearization points for Equation (1). First, select the initial state z0 as the first linearization point and let q = 0; then calculate the next moment Screen the next linearization point. When d min < δ, select z q+1 = z as the (q + 1) - th linearization point and let q = q + 1 until q < k - 1, and the selection of linearization points is completed. k represents the number of selected linearization points; let d = ||z T - z0|| / ||z0||, z T is the final state of the system. If ||z0|| = 0, then d = ||z T ||, and δ is taken as d / 5. δ is the specific interval for controlling the selection of linearization points; d min is the change amount.

[0018] Preferably, the nonlinear model of the new energy microgrid group is linearized using the multiple linearization points, and the expression is:

[0019]

[0020] Among them, f is the nonlinear equation of the state variables of the new energy microgrid system, x(t) is the set of system state vectors at time t, and t k is the number of k linearization points selected at time t, α is the linearization point selected according to the state spacing selection algorithm, α k-1 are the k-1 linearization points selected by the state spacing selection algorithm, ξ k-1 are k-1 test error linearization points.

[0021] Preferably, formula (2) is applied to the microgrid group system to obtain a small signal state space model of the new energy microgrid group system, and the small signal state space model of the new energy microgrid group system is:

[0022]

[0023] Where ΔX MG (t) is the state variable of the sub-microgrid at time t, = ΔX MG The first derivative of (t), ΔX MMG (t) is the state variable of the microgrid group at time t, = ΔX MMG The first derivative of (t), ΔX MG is the state variable of the microgrid, A MG is the state matrix of the sub-microgrid, ΔX MMG is the state variable of the microgrid, ΔX MGn is the state variable of the nth microgrid, ΔX TL is the state variable of the tie line, A MMG is the state matrix of the microgrid group, Δx WT is the fan state variable, Δx PV is the photovoltaic state variable, Δx BESS is the energy storage state variable, Δx FD is the tie line state variable, Δx LD is the load state variable.

[0024] Preferably, in step S4, the method for determining the small signal state space model of the new energy microgrid system based on piecewise trajectory linearization including wind and solar disturbances comprises:

[0025]

[0026] Among them, ΔU MG(t) is the wind and solar disturbance of the microgrid at time t represented by the Chino polyhedron, ΔU MMG (t) is the wind and solar disturbance of the microgrid group at time t represented by the Chino polyhedron, B MG is the input matrix of the sub-microgrid, ΔU MG is the wind-solar disturbance of the microgrid represented by the Chino polyhedron, ΔU MMG is the wind-solar disturbance of the microgrid group represented by the Chino polyhedron, B MMG is the input matrix of the microgrid group, which includes multiple sub-microgrids, ΔI ph is the short-circuit current of the photovoltaic components of the microgrid, ΔI phn is the short-circuit current of the PV module of the nth microgrid, ΔV wind is the wind speed of the microgrid group, ΔV windn is the wind speed of the nth sub-microgrid.

[0027] Preferably, in S4, performing reachable set calculation on the model to obtain a reachable set for each time period includes:

[0028] First calculate the reachable set R(t) at time t and the homogeneous solution R of the differential equation (4) h (t), then calculate R(t) and R h (t) and then calculate the convex hull ΓR(τ) from the uncertain input B MMG ΔU MMG The special solution R * (t), and finally calculate the convex hull as k Above the approximately reachable set R(τ);

[0029] The reachable set R(t k+1 )for:

[0030]

[0031] Among them, r represents the simulation step size, ⊕ represents Minkowski addition, R h (t) is the homogeneous solution of the differential equation of formula (4) at the current time t;

[0032] If A MMG Irreversible, then for formula (5) Perform Taylor expansion:

[0033]

[0034] Where η represents the number of Taylor series expansion terms, i represents the order of the Taylor series expansion term, Indicates A MMG The i-th power, Q(r) is the upper bound of the interval matrix, [-Q(r)r,Q(r)r] represents the set of points with a step length of r between -Q(r)r and Q(r)r;

[0035] The expanded convex hull is shown in formula (7):

[0036]

[0037] Among them, u c is a constant,

[0038] The reachable set in time period τ is shown in formula (8):

[0039]

[0040] Where Ar represents A MMG The product of and r, CH(·) represents the convex hull calculation, which is a method of calculating the reachable set of Chino polyhedron;

[0041] The reachable set of each time interval is shown in formula (9):

[0042]

[0043] According to formula (9), the reachable set based on the linearization of the piecewise trajectory is obtained, as shown in formula (10):

[0044]

[0045] Preferably, in step S5, updating the simulation step size includes calculating the center point of the error set, and adjusting the simulation step size by setting an expansion factor according to the center point of the error set.

[0046] The present invention proposes a state prediction system for a new energy microgrid group considering wind and solar disturbances, which is used to implement the state prediction method for a new energy microgrid group considering wind and solar disturbances, comprising:

[0047] The linearization point acquisition module is used to set the simulation step size and select multiple linearization points for the real state of the constructed new energy microgrid group in multiple time periods based on the state spacing selection algorithm;

[0048] A state space model acquisition module, used to linearize the nonlinear model of the new energy microgrid group using the multiple linearization points to obtain a small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization;

[0049] A wind-solar disturbance acquisition module, used for expressing the wind-solar disturbance of the new energy microgrid system by using the Chino polyhedron set method;

[0050] A reachable set calculation module is used to add the wind-solar disturbance of the new energy microgrid group system to the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization, obtain the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization containing wind-solar disturbance, and perform reachable set calculation on the model to obtain a reachable set for each time period;

[0051] The state prediction module is used to calculate the error between the reachable set of each time period and the actual state of the new energy microgrid group in the corresponding time period, and add the errors of all time periods to obtain the cumulative error. If the cumulative error is less than the preset threshold, the final reachable set of each time period is obtained, otherwise the simulation step size is updated and the linearization point acquisition module is returned.

[0052] The present invention also provides a computer-readable storage medium on which a computer program is stored, wherein the computer program implements the steps of the above-mentioned method when executed by a processor.

[0053] The present invention further proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the above-mentioned method when executing the computer program.

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

[0055] The present invention proposes a state prediction method for a new energy microgrid group based on piecewise trajectory linearization. First, the new energy microgrid group system is linearized by the piecewise trajectory linearization method to obtain a small signal state space model of the new energy microgrid group system based on the piecewise trajectory linearization. Then, the wind and light disturbances are represented by the Chino polyhedron set method and added to the model, and the reachable set calculation is performed on the model to obtain the reachable set for each time period; the error between the reachable set of each time period and the true state of the new energy microgrid group in the corresponding time period is calculated, and the errors of all time periods are added to obtain the cumulative error, and repeated iterations are performed to make the cumulative error less than the preset threshold, and the final reachable set for each time period is obtained. The present invention can be used to solve all possible operating trajectories of key state quantities of the new energy microgrid group system under consideration of multiple uncertain factors, and provide guidance for the safe and stable operation of high-proportion new energy microgrid groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of the state prediction method of a new energy microgrid group considering wind and solar disturbances as described in Example 1;

[0057] Figure 2 This is a schematic diagram of the structure of the new energy microgrid system described in Example 2;

[0058] Figure 3This is a schematic diagram of the state spacing selection algorithm described in Example 2;

[0059] Figure 4 The Chino polyhedron represents an uncertain input flow chart as described in Example 2;

[0060] Figure 5 This is a schematic diagram of the state prediction method described in Example 2;

[0061] Figure 6 This is a schematic diagram of the device structure described in Example 2;

[0062] Figure 7 This is a structural diagram of the state prediction system of the new energy microgrid group considering wind and solar disturbances as described in Example 3. DETAILED DESCRIPTION

[0063] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0064] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0065] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0066] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0067] Example 1

[0068] This embodiment provides a state prediction method for a new energy microgrid group considering wind and solar disturbances. Figure 1 As shown, the following steps are included:

[0069] S1: Set the simulation step size and use the state spacing selection algorithm to select multiple linearization points for the real state of the constructed new energy microgrid in multiple time periods;

[0070] S2: linearizing the nonlinear model of the new energy microgrid group using the multiple linearization points to obtain a small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization;

[0071] S3: Using the Chino polyhedron set method to represent the wind and solar disturbance of the new energy microgrid system;

[0072] S4: adding the wind-solar disturbance of the new energy microgrid group system to the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization, obtaining the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization containing the wind-solar disturbance, and performing reachable set calculation on the model to obtain the reachable set for each time period;

[0073] S5: Calculate the error between the reachable set of each time period and the actual state of the new energy microgrid group in the corresponding time period, add the errors of all time periods to get the cumulative error, if the cumulative error is less than the preset threshold, get the final reachable set of each time period, otherwise update the simulation step size and return to S1.

[0074] In the specific implementation process, firstly, based on the state spacing selection algorithm, multiple linearization points are selected for the real state of the constructed new energy microgrid group in multiple time periods, and then the small signal state space model is obtained, and the wind and solar disturbances are added to the small signal state space model. Then, the reachable set calculation is performed to obtain the reachable set of each time period, and finally, the error between the reachable set of each time period and the real state of the new energy microgrid group in the corresponding time period is calculated, and it is iterated to obtain the final reachable set of each time period.

[0075] Example 2

[0076] This embodiment provides a state prediction method for a new energy microgrid group considering wind and solar disturbances, comprising the following steps:

[0077] S1: Set the simulation step size and use the state spacing selection algorithm to select multiple linearization points for the real state of the constructed new energy microgrid in multiple time periods;

[0078] like Figure 1 As shown, it is a structural schematic diagram of the new energy microgrid group system described in this embodiment. The system is a 380V three-phase low-voltage AC system. Sub-microgrid 1, sub-microgrid 2, and sub-microgrid n (hereinafter represented by letters "MG1", "MG2", and "MGn" in the figure) are in parallel structure. All sub-microgrids are connected to the key bus through tie lines, and then connected to the external power grid through off / on-grid switch S1 and 10kV / 0.4kV transformer. Because this article only focuses on the autonomous operation mode, S1 is in the open state. By closing the tie line switches S11, S12, ..., S1n, multiple adjacent new energy microgrids in a certain area can be interconnected to form a new energy microgrid group system. In each sub-microgrid, there are many different types of distributed power sources, including doubly fed induction generators (DFIG), photovoltaic generators (PV), and battery energy storage systems (BESS). All of the above distributed power sources use power electronic converters as interfaces, are connected to LCL filters, and transmit power to the point of common coupling (PCC) to ensure the power supply of local loads.

[0079] The new energy microgrid group is represented as a set of non - linear equations, and the general expression of the non - linear equations is as follows:

[0080]

[0081] Wherein, is the system state vector, is the perturbation vector, and r is the simulation step size;

[0082] Use the state - spacing selection algorithm to linearly select points for Equation (1). First, select the initial state z0 as the first linearization point and let q = 0; then calculate the next moment Screen the next linearization point. When d min <δ, select z q+1 = z as the (q + 1) - th linearization point and let q = q + 1 until q < k - 1, and the selection of linearization points is completed. k represents the number of selected linearization points; let d = ||z T - z0|| / ||z0||, z T is the final state of the system. If ||z0|| = 0, then d = ||z T ||, and δ is taken as d / 5. δ is the specific selection interval for controlling the linearization points; d min is the change amount.

[0083] S2: Linearize the non - linear model of the new energy microgrid group by using the multiple linearization points to obtain a small - signal state - space model of the new energy microgrid group system based on piece - wise trajectory linearization;

[0084] Linearize the non - linear model of the new energy microgrid group by using the multiple linearization points, and express

[0085]

[0086] Wherein, f is the non - linear equation of the state variables of the new energy microgrid system, x(t) is the set of system state vectors at time t, t k is the number of k linearization points selected at time t, α is the linearization point selected according to the state - spacing selection algorithm, α k-1 is the k - 1 linearization points selected according to the state - spacing selection algorithm, and ξ k-1 is the linearization point of k - 1 test errors.

[0087] Apply Equation (2) to the microgrid group system to obtain a small - signal state - space model of the new energy microgrid group system. The small - signal state - space model of the new energy microgrid group system is:

[0088]

[0089] Wherein, ΔX MG(t) is the state variable of the sub-microgrid at time t, = ΔX MG The first derivative of (t), ΔX MMG (t) is the state variable of the microgrid group at time t, = ΔX MMG The first derivative of (t), ΔX MG is the state variable of the microgrid, A MG is the state matrix of the sub-microgrid, ΔX MMG is the state variable of the microgrid, ΔX MGn is the state variable of the nth microgrid, ΔX TL is the state variable of the tie line, A MMG is the state matrix of the microgrid group, Δx WT is the fan state variable, Δx PV is the photovoltaic state variable, Δx BESS is the energy storage state variable, Δx FD is the tie line state variable, Δx LD is the load state variable.

[0090] S3: Using the Chino polyhedron set method to represent the wind and solar disturbance of the new energy microgrid system;

[0091]

[0092] Among them, Z represents the wind and light disturbance, c is the center of the Chino polyhedron, β s is the generator of the Chino polyhedron, and p is the number of generators.

[0093] The small signal state space model of the new energy microgrid system based on piecewise trajectory linearization including wind and solar disturbance is:

[0094]

[0095] Among them, ΔU MG (t) is the wind and solar disturbance of the microgrid at time t represented by the Chino polyhedron, ΔU MMG (t) is the wind and solar disturbance of the microgrid group at time t represented by the Chino polyhedron, B MG is the input matrix of the sub-microgrid, ΔU MG is the wind-solar disturbance of the microgrid represented by the Chino polyhedron, ΔU MMG is the wind-solar disturbance of the microgrid group represented by the Chino polyhedron, B MMG is the input matrix of the microgrid group, which includes multiple sub-microgrids, ΔI ph is the short-circuit current of the photovoltaic components of the microgrid, ΔI phn is the short-circuit current of the PV module of the nth microgrid, ΔV windis the wind speed of the microgrid group, ΔV windn is the wind speed of the nth sub-microgrid.

[0096] S4: adding the wind-solar disturbance of the new energy microgrid group system to the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization, obtaining the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization containing the wind-solar disturbance, and performing reachable set calculation on the model to obtain the reachable set for each time period;

[0097] First calculate the reachable set R(t) at time t and the homogeneous solution R of the differential equation (4) h (t), then calculate R(t) and R h (t) and then calculate the convex hull ΓR(τ) from the uncertain input B MMG ΔU MMG The special solution R * (t), and finally calculate the convex hull as k Above the approximately reachable set R(τ);

[0098] The reachable set R(t k+1 )for:

[0099]

[0100] Among them, r represents the simulation step size, ⊕ represents Minkowski addition, R h (t) is the homogeneous solution of the differential equation of formula (4) at the current time t;

[0101] If A MMG Irreversible, then for formula (5) Perform Taylor expansion:

[0102]

[0103] Where η represents the number of Taylor series expansion terms, i represents the order of the Taylor series expansion term, Indicates A MMG The i-th power, Q(r) is the upper bound of the interval matrix, [-Q(r)r,Q(r)r] represents the set of points with a step length of r between -Q(r)r and Q(r)r;

[0104] The expanded convex hull is shown in formula (7):

[0105]

[0106] Among them, u c is a constant,

[0107] The reachable set in time period τ is shown in formula (8):

[0108]

[0109] Where Ar represents A MMG The product of and r, CH(·) represents the convex hull calculation, which is a method of calculating the reachable set of Chino polyhedron;

[0110] The reachable set of each time interval is shown in formula (9):

[0111]

[0112] According to formula (9), the reachable set based on the linearization of the piecewise trajectory is obtained, as shown in formula (10):

[0113]

[0114] like Figure 5 As shown, this is a schematic diagram of the state prediction method.

[0115] S5: Calculate the error between the reachable set of each time period and the actual state of the new energy microgrid group in the corresponding time period, add the errors of all time periods to get the cumulative error, if the cumulative error is less than the preset threshold, get the final reachable set of each time period, otherwise update the simulation step size and return to S1.

[0116] Calculate the center point of the error set and adjust the simulation step size according to the expansion factor set based on the center point of the error set.

[0117] This embodiment further provides a computer-readable storage medium on which a computer program is stored, wherein the computer program implements the steps of the above-mentioned method when executed by a processor.

[0118] This embodiment also provides a computer device, such as Figure 6 As shown, it includes a memory and a processor, the memory stores a computer program, and is characterized in that the steps of the above-mentioned method are implemented when the processor executes the computer program.

[0119] Example 3

[0120] This embodiment provides a state prediction system for a new energy microgrid group considering wind and solar disturbances, which is used to implement the method described in Embodiment 1 or 2, such as Figure 7 As shown, including:

[0121] The linearization point acquisition module is used to set the simulation step size and select multiple linearization points for the real state of the constructed new energy microgrid group in multiple time periods based on the state spacing selection algorithm;

[0122] A state space model acquisition module, used to linearize the nonlinear model of the new energy microgrid group using the multiple linearization points to obtain a small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization;

[0123] A wind-solar disturbance acquisition module, used for expressing the wind-solar disturbance of the new energy microgrid system by using the Chino polyhedron set method;

[0124] A reachable set calculation module is used to add the wind-solar disturbance of the new energy microgrid group system to the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization, obtain the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization containing wind-solar disturbance, and perform reachable set calculation on the model to obtain a reachable set for each time period;

[0125] The state prediction module is used to calculate the error between the reachable set of each time period and the actual state of the new energy microgrid group in the corresponding time period, and add the errors of all time periods to obtain the cumulative error. If the cumulative error is less than the preset threshold, the final reachable set of each time period is obtained as the state prediction result, otherwise the simulation step size is updated and the linearization point acquisition module is returned.

[0126] The same or similar reference numerals correspond to the same or similar components;

[0127] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0128] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A state prediction method for a new energy microgrid group considering wind and solar disturbances, characterized in that: The steps include: S1: Set the simulation step size and use the state spacing selection algorithm to select multiple linearization points for the real state of the constructed new energy microgrid in multiple time periods; S2: linearizing the nonlinear model of the new energy microgrid group using the multiple linearization points to obtain a small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization; S3: Using the Chino polyhedron set method to represent the wind and solar disturbance of the new energy microgrid system; S4: adding the wind-solar disturbance of the new energy microgrid group system to the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization, obtaining the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization containing the wind-solar disturbance, and performing reachable set calculation on the model to obtain the reachable set for each time period; S5: Calculate the error between the reachable set of each time period and the actual state of the new energy microgrid group in the corresponding time period, add the errors of all time periods to get the cumulative error, if the cumulative error is less than the preset threshold, obtain the final reachable set of each time period as the state prediction result, otherwise update the simulation step size and return to S1.

2. According to claim 1, a state prediction method for a new energy microgrid group considering wind and solar disturbances is characterized in that: The linearization point selection algorithm based on state spacing is used to perform multi-point linearization on the nonlinear model of the new energy microgrid group, and several linearization points are obtained, including: The new energy microgrid group is represented as a set of nonlinear equations. The generalized expression of the nonlinear equations is as follows: in, is the system state vector, is the disturbance vector, r is the simulation step length; Use the state spacing selection algorithm to linearly select points for Equation (1). First, select the initial state \(z_0\) as the first linearization point and set \(q = 0\). Then calculate the next moment Screen the next linearization point. When d min < \(\delta\), select \(z q+1 = z\) as the \((q + 1)\)-th linearization point and set \(q = q + 1\) until \(q < k - 1\). The selection of linearization points is completed, where \(k\) represents the number of selected linearization points. Let \(d=\frac{\|z T -z_0\|}{\|z_0\|}\), \(z T is the final state of the system. If \(\|z_0\| = 0\), then \(d=\|z T \|\), and \(\delta\) is taken as \(d / 5\). \(\delta\) is the specific interval for controlling the selection of linearization points. \(d min is the change amount.

3. A state prediction method for a new energy microgrid group considering wind and solar disturbance according to claim 2, characterized in that: The nonlinear model of the new energy microgrid group is linearized using the multiple linearization points, and the expression is: Among them, f is the nonlinear equation of the state variables of the new energy microgrid system, x(t) is the set of system state vectors at time t, and t k is the number of k linearization points selected at time t, α is the linearization point selected according to the state spacing selection algorithm, α k-1 are the k-1 linearization points selected by the state spacing selection algorithm, ξ k-1 are k-1 test error linearization points.

4. The state prediction method of a new energy microgrid group considering wind and solar disturbance according to claim 3 is characterized in that: Applying formula (2) to the microgrid group system, the small signal state space model of the new energy microgrid group system is obtained. The small signal state space model of the new energy microgrid group system is: Where ΔX MG (t) is the state variable of the sub-microgrid at time t, = ΔX MG The first derivative of (t), ΔX MMG (t) is the state variable of the microgrid group at time t, = ΔX MMG The first derivative of (t), ΔX MG is the state variable of the microgrid, A MG is the state matrix of the sub-microgrid, ΔX MMG is the state variable of the microgrid, ΔX MGn is the state variable of the nth microgrid, ΔX TL is the state variable of the tie line, A MMG is the state matrix of the microgrid group, Δx WT is the fan state variable, Δx PV is the photovoltaic state variable, Δx BESS is the energy storage state variable, Δx FD is the tie line state variable, Δx LD is the load state variable.

5. The state prediction method of a new energy microgrid group considering wind and solar disturbance according to claim 4 is characterized in that: In step S4, the method for determining the small signal state space model of the new energy microgrid system based on piecewise trajectory linearization including wind and solar disturbances includes: ΔX MG =[Δx WT ,Δx PV ,Δx BESS ,Δx FD ,Δx LD ] T ΔU MG =[ΔI ph ,ΔV wind ] T ΔX MMG =[ΔX MG1 ,ΔX MG2 ,…,ΔX MGn ,ΔX TL ] T ΔU MMG =[ΔI ph1 ,ΔI ph2 ,…,ΔI phn ,ΔV wind1 ,ΔV wind2 ,…,ΔV windn ] T Among them, ΔU MG (t) is the wind and solar disturbance of the microgrid at time t represented by the Chino polyhedron, ΔU MMG (t) is the wind and solar disturbance of the microgrid group at time t represented by the Chino polyhedron, B MG is the input matrix of the sub-microgrid, ΔU MG is the wind-solar disturbance of the microgrid represented by the Chino polyhedron, ΔU MMG is the wind-solar disturbance of the microgrid group represented by the Chino polyhedron, B MMG is the input matrix of the microgrid group, which includes multiple sub-microgrids, ΔI ph is the short-circuit current of the photovoltaic components of the microgrid, ΔI phn is the short-circuit current of the PV module of the nth microgrid, ΔV wind is the wind speed of the microgrid group, ΔV windn is the wind speed of the nth sub-microgrid.

6. The state prediction method of a new energy microgrid group considering wind and solar disturbance according to claim 5 is characterized in that: In S4, the step of calculating the reachable set of the model to obtain the reachable set of each time period includes: First calculate the reachable set R(t) at time t and the homogeneous solution R of the differential equation (4) h (t), then calculate R(t) and R h (t) and then calculate the convex hull ΓR(τ) from the uncertain input B MMG ΔU MMG The special solution R * (t), and finally calculate the convex hull as k Above the approximately reachable set R(τ); The reachable set R(t k+1 )for: Where r represents the simulation step size, represents Minkowski addition, R h (t) is the homogeneous solution of the differential equation of formula (4) at the current time t; If A MMG Irreversible, then for formula (5) Perform Taylor expansion: Where η represents the number of Taylor series expansion terms, i represents the order of the Taylor series expansion term, Indicates A MMG The i-th power, Q(r) is the upper bound of the interval matrix, [-Q(r)r,Q(r)r] represents the set of points with a step length of r between -Q(r)r and Q(r)r; The expanded convex hull is shown in formula (7): Among them, u c is a constant, The reachable set in time period τ is shown in formula (8): Where Ar represents A MMG The product of and r, CH(·) represents the convex hull calculation, which is a method of calculating the reachable set of Chino polyhedron; The reachable set of each time interval is shown in formula (9): According to formula (9), the reachable set based on the linearization of the piecewise trajectory is obtained, as shown in formula (10):

7. A state prediction method for a new energy microgrid group considering wind and solar disturbance according to claim 6, characterized in that: In step S5, updating the simulation step size includes calculating the center point of the error set, and adjusting the simulation step size by setting an expansion factor according to the center point of the error set.

8. A state prediction system for a new energy microgrid group considering wind and solar disturbances, used to implement a state prediction method for a new energy microgrid group considering wind and solar disturbances as described in any one of claims 1 to 7, characterized in that: include: The linearization point acquisition module is used to set the simulation step size and select multiple linearization points for the real state of the constructed new energy microgrid group in multiple time periods based on the state spacing selection algorithm; A state space model acquisition module, used to linearize the nonlinear model of the new energy microgrid group using the multiple linearization points to obtain a small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization; A wind-solar disturbance acquisition module, used for expressing the wind-solar disturbance of the new energy microgrid system by using the Chino polyhedron set method; A reachable set calculation module is used to add the wind-solar disturbance of the new energy microgrid group system to the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization, obtain the small signal state space model of the new energy microgrid group system based on piecewise trajectory linearization containing wind-solar disturbance, and perform reachable set calculation on the model to obtain a reachable set for each time period; The state prediction module is used to calculate the error between the reachable set of each time period and the actual state of the new energy microgrid group in the corresponding time period, and add the errors of all time periods to obtain the cumulative error. If the cumulative error is less than the preset threshold, the final reachable set of each time period is obtained as the state prediction result, otherwise the simulation step size is updated and the linearization point acquisition module is returned.

9. 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.

10. 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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