New energy microgrid state prediction method based on piecewise trajectory linearization

Abstract

The present invention proposes a state prediction method for a new energy microgrid cluster based on piecewise trajectory linearization. First, the new energy microgrid cluster system is linearized using the piecewise trajectory linearization method to obtain a small-signal state space model of the new energy microgrid cluster system based on piecewise trajectory linearization. Wind and solar disturbances are then represented using the Chino polyhedron set method and added to the model. A reachable set calculation is performed on the model to obtain a reachable set for each time period. The error between the reachable set for each time period and the actual state of the new energy microgrid cluster in the corresponding time period is calculated. The errors for all time periods are summed to obtain a cumulative error. Repeated iterations are performed to reduce the cumulative error to less than a preset threshold, and the final reachable set for each time period is obtained as the state prediction result. The present invention can be used to solve all possible operating trajectories of key state quantities of the new energy microgrid cluster system under consideration of multiple uncertainties, providing guidance for the safe and stable operation of high-proportion new energy microgrid clusters.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid system stability and control, and more specifically, to a new energy microgrid group state prediction method based on piecewise trajectory linearization. Background Art

[0002] With the rapid development of renewable energy technologies and the rise of the smart grid concept, microgrids have been widely adopted as a new type of power system architecture. A microgrid is a small-scale power generation and distribution system consisting of multiple distributed power sources, distributed energy storage, energy conversion devices, and loads. It is an autonomous system capable of self-control, protection, and management. To provide greater flexibility under both normal and extreme conditions, microgrids can be interconnected to form microgrid clusters. However, in a microgrid cluster, 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 the microgrid cluster more susceptible to random disturbances, resulting in over-limit and oscillatory instability. Therefore, studying the dynamic operating characteristics of microgrid clusters under uncertainties is of great practical significance.

[0003] To fully understand the dynamic characteristics of microgrid clusters under uncertainties, an accurate microgrid cluster model is necessary. However, current modeling methods all assume constant irradiance or wind speed and are based on single-point linearization approximations, which only guarantee local model validity. Therefore, further research is needed on dynamic modeling based on multi-point linearization and consideration of source-side uncertainty disturbances.

[0004] Currently, the main methods for dynamic analysis of uncertain perturbations can be divided into in-domain probabilistic analysis, time-domain simulation, and Monte Carlo methods. However, these methods cannot verify the infinite number of scenarios that may occur in real systems. Therefore, it is urgent to find a new method to represent uncertain perturbation analysis. Summary of the Invention

[0005] In order to overcome the defect of the above-mentioned prior art that it cannot represent the uncertainty disturbance defect and ensure the global validity of the model, the present invention provides a new energy microgrid group state prediction method based on piecewise trajectory linearization.

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

[0007] The present invention proposes a new energy microgrid group state prediction method based on piecewise trajectory linearization, which includes the following steps:

[0008] S1: Set the simulation step size and use the state interval 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, and 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, obtain a small - signal state - space model of the new - energy micro - grid group system based on piece - wise trajectory linearization including wind - solar perturbation, and calculate the reachable set for 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, r is the simulation step size; [[ID=二十六]]

[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 ,

[0017] , min , T , q+1 , , , T , T , min ,

[0018] <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 system final state. If ||z0|| = 0, then d = ||z T ||, δ is taken as d / 5, and 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 is linearized using the multiple linearization points, and the expression is:

[0019]

[0020] Among them, f is the nonlinear equation of the state variable 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 the k-1 linearization points of the test error.

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

[0022]

[0023] ΔX MG =[Δx WT ,Δx PV ,Δx BESS ,Δx FD ,Δx LD ] T

[0024] ΔX MMG =[ΔX MG1 ,ΔX MG2 ,…,ΔX MGn ,ΔX TL ] T

[0025] 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 group, 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 wind turbine state variable, ΔxPV 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.

[0026] 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 includes:

[0027]

[0028] ΔX MG =[Δx WT ,Δx PV ,Δx BESS ,Δx FD ,Δx LD ] T

[0029] ΔU MG =[ΔI ph ,ΔV wind ] T

[0030] ΔX MMG =[ΔX MG1 ,ΔX MG2 ,…,ΔX MGn ,ΔX TL ] T

[0031] ΔU MMG =[ΔI ph1 ,ΔI ph2 ,…,ΔI phn ,ΔV wind1 ,ΔV wind2 ,…,ΔV windn ] T

[0032] 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 and 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 group, ΔIphn 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.

[0033] Preferably, in S4, performing reachable set calculation on the model to obtain a reachable set for each time period includes:

[0034] 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) is the convex hull ΓR(τ), and then the uncertainty input B is calculated MMG ΔU MMG The special solution R * (t), and finally calculate the convex hull as the k The upper approximately reachable set R(τ);

[0035] The reachable set R(t k+1 )for:

[0036]

[0037] Among them, r represents the simulation step size, ⊕ represents Minkowski addition, R h (t) is the homogeneous solution of the differential equation (4) at the current time t;

[0038] If A MMG Irreversible, then for formula (5) Perform Taylor expansion:

[0039]

[0040] 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, represents the set of points with a step length of r between -Q(r)r and Q(r)r;

[0041] The expanded convex hull is shown in formula (7):

[0042]

[0043] Among them, u c is a constant,

[0044] The reachable set in time period τ is shown in formula (8):

[0045]

[0046] Wherein, Ar represents A MMG The product of and r, CH(·) represents the convex hull calculation, which is an operation method for calculating the reachable set of Chino polyhedron;

[0047] The reachable set of each time interval is shown in formula (9):

[0048]

[0049] According to formula (9), the reachable set based on the linearization of the segmented trajectory is obtained, as shown in formula (10):

[0050]

[0051] 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.

[0052] The present invention proposes a new energy microgrid group state prediction system based on piecewise trajectory linearization, which is used to implement the above-mentioned new energy microgrid group state prediction method based on piecewise trajectory linearization, including:

[0053] The linearization point acquisition module is used to set the simulation step size and select multiple linearization points based on the real state of the constructed new energy microgrid in multiple time periods using a state interval selection algorithm;

[0054] A state space model acquisition module is 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;

[0055] A wind-solar disturbance acquisition module, configured to represent the wind-solar disturbance of the new energy microgrid system using a Chino polyhedron set method;

[0056] a reachable set calculation module, configured to add the wind-solar disturbance of the new energy microgrid system to the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization, obtain the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization containing the wind-solar disturbance, and perform reachable set calculation on the model to obtain a reachable set for each time period;

[0057] The state prediction module is used to calculate the error between the reachable set in each time period and the actual state of the new energy microgrid group in the corresponding time period. The errors of all time periods are added 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.

[0058] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above-mentioned method when executed by a processor.

[0059] The present invention further provides 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.

[0060] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0061] The present invention proposes a state prediction method for a new energy microgrid cluster based on piecewise trajectory linearization. First, the new energy microgrid cluster system is linearized using the piecewise trajectory linearization method to obtain a small signal state space model of the new energy microgrid cluster system based on piecewise trajectory linearization. Then, the wind and solar disturbances are represented by the Chino polyhedron set method and added to the model. The model is then subjected to a reachable set calculation to obtain a reachable set for each time period. The error between the reachable set for each time period and the actual state of the new energy microgrid cluster in the corresponding time period is calculated. The errors for all time periods are summed to obtain a cumulative error. Repeated iterations ensure that the cumulative error is less than a 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 cluster system under consideration of multiple uncertainties, providing guidance for the safe and stable operation of high-proportion new energy microgrid clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a flow chart of the new energy microgrid group state prediction method based on piecewise trajectory linearization described in Example 1;

[0063] Figure 2 This is a structural diagram of the new energy microgrid system described in Example 2;

[0064] Figure 3 This is a schematic diagram of the state distance selection algorithm described in Example 2;

[0065] Figure 4 The Chino polyhedron represents an uncertain input flow chart as described in Example 2;

[0066] Figure 5 This is a schematic diagram of the state prediction method described in Example 2;

[0067] Figure 6 This is a schematic diagram of the device structure described in Example 2;

[0068] Figure 7 This is a structural diagram of the new energy microgrid group state prediction system based on piecewise trajectory linearization described in Example 3. DETAILED DESCRIPTION

[0069] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0070] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0071] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0072] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0073] Example 1

[0074] This embodiment provides a new energy microgrid group state prediction method based on segmented trajectory linearization, such as Figure 1 As shown, the following steps are included:

[0075] S1: Set the simulation step size and use the state interval selection algorithm to select multiple linearization points for the real state of the constructed new energy microgrid in multiple time periods;

[0076] S2: linearizing the nonlinear model of the new energy microgrid cluster using the multiple linearization points to obtain a small signal state space model of the new energy microgrid cluster system based on piecewise trajectory linearization;

[0077] S3: Using the Chino polyhedron set method to represent the wind and solar disturbance of the new energy microgrid system;

[0078] S4: adding the wind-solar disturbance of the new energy microgrid system to the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization to obtain the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization including the wind-solar disturbance, and performing reachable set calculation on the model to obtain a reachable set for each time period;

[0079] 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 obtain the cumulative error, and if the cumulative error is less than the preset threshold, obtain the final reachable set of each time period. Otherwise, update the simulation step size and return to S1.

[0080] In the specific implementation process, first, based on the state distance selection algorithm, multiple linearization points are selected for the actual 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 for each time period. Finally, 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 is calculated, and it is iterated to finally obtain the final reachable set for each time period.

[0081] Example 2

[0082] This embodiment provides a new energy microgrid group state prediction method based on piecewise trajectory linearization, including the following steps:

[0083] S1: Set the simulation step size and use the state interval selection algorithm to select multiple linearization points for the real state of the constructed new energy microgrid in multiple time periods;

[0084] like Figure 1 Figure 2 shows the schematic diagram of the new energy microgrid cluster system described in this embodiment. This system is a 380V three-phase low-voltage AC system. Sub-microgrid 1, sub-microgrid 2, and sub-microgrid n (hereinafter referred to as "MG1," "MG2," and "MGn" in the figure) are connected in parallel. All sub-microgrids are connected to the key busbar via tie lines, and then connected to the external power grid via the grid-connected / off-grid switch S1 and a 10kV / 0.4kV transformer. Because this article focuses only on the autonomous operation mode, S1 is open. By closing the tie line switches S11, S12, ..., S1n, multiple adjacent new energy microgrids within a given area can be interconnected to form a new energy microgrid cluster system. Each sub-microgrid contains a variety of 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.

[0085] The new energy microgrid group is represented as a set of nonlinear equations. The generalized expression of the nonlinear equations is as follows:

[0086]

[0087] in, is the system state vector, is the disturbance vector, r is the simulation step length;

[0088] 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 let \(q = 0\); then calculate the next moment Screen the next linearization point. When \(d min <\delta\), take \(z q+1 =z\) as the \((q + 1)\)-th linearization point and let \(q=q + 1\) until \(q<k - 1\). The selection of linearization points is completed, where \(k\) represents the number of selected linearization points; let \(d=\vert\vert z T -z_0\vert\vert / \vert\vert z_0\vert\vert\), \(z T is the final state of the system. If \(\vert\vert z_0\vert\vert = 0\), then \(d=\vert\vert z T \vert\vert\), and \(\delta\) is taken as \(d / 5\). \(\delta\) is the specific control interval for the selection of linearization points; \(d min is the change amount.

[0089] S2: Linearize the nonlinear 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;

[0090] Linearize the nonlinear model of the new - energy micro - grid group by using the multiple linearization points, and express

[0091]

[0092] where \(f\) is the nonlinear equation of the state variables of the new - energy micro - grid 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 \(t\), \(\alpha\) is the linearization point selected according to the state - spacing selection algorithm, \(\alpha k-1 is the \(k - 1\) linearization points selected according to the state - spacing selection algorithm, and \(\xi k-1 is the linearization point of \(k - 1\) test errors.

[0093] Apply Equation (2) to the micro - grid group system to obtain the small - signal state - space model of the new - energy micro - grid group system. The small - signal state - space model of the new - energy micro - grid group system is:

[0094]

[0095] \(\Delta X MG =[\Delta x WT ,\Delta x PV ,\Delta x BESS ,\Delta x FD ,\Delta x LD T

[0096] \(\Delta X MMG =[\Delta X MG1,ΔX MG2 ,…,ΔX MGn ,ΔX TL ] T

[0097] 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 group, 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 wind turbine 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.

[0098] S3: Using the Chino polyhedron set method to represent the wind and solar disturbance of the new energy microgrid system;

[0099]

[0100] Where 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.

[0101] The small signal state space model of the new energy microgrid system based on piecewise trajectory linearization including wind and solar disturbance is:

[0102]

[0103] ΔX MG =[Δx WT ,Δx PV ,Δx BESS ,Δx FD ,Δx LD ] T

[0104] ΔU MG =[ΔI ph ,ΔV wind ]T

[0105] ΔX MMG =[ΔX MG1 ,ΔX MG2 ,…,ΔX MGn ,ΔX TL ] T

[0106] ΔU MMG =[ΔI ph1 ,ΔI ph2 ,…,ΔI phn ,ΔV wind1 ,ΔV wind2 ,…,ΔV windn ] T

[0107] 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 and 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 group, 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.

[0108] S4: adding the wind-solar disturbance of the new energy microgrid system to the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization to obtain the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization including the wind-solar disturbance, and performing reachable set calculation on the model to obtain a reachable set for each time period;

[0109] 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) is the convex hull ΓR(τ), and then the uncertainty input B is calculated MMG ΔU MMG The special solution R * (t), and finally calculate the convex hull as the k The upper approximately reachable set R(τ);

[0110] The reachable set R(t k+1 )for:

[0111]

[0112] Among them, r represents the simulation step size, ⊕ represents Minkowski addition, R h (t) is the homogeneous solution of the differential equation (4) at the current time t;

[0113] If A MMG Irreversible, then for formula (5) Perform Taylor expansion:

[0114]

[0115] 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;

[0116] The expanded convex hull is shown in formula (7):

[0117]

[0118] Among them, u c is a constant,

[0119] The reachable set in time period τ is shown in formula (8):

[0120]

[0121] Wherein, Ar represents A MMG The product of and r, CH(·) represents the convex hull calculation, which is an operation method for calculating the reachable set of Chino polyhedron;

[0122] The reachable set of each time interval is shown in formula (9):

[0123]

[0124] According to formula (9), the reachable set based on the linearization of the segmented trajectory is obtained, as shown in formula (10):

[0125]

[0126] like Figure 5 Figure 2 is a schematic diagram of the state prediction method.

[0127] 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 obtain the cumulative error, and if the cumulative error is less than the preset threshold, obtain the final reachable set of each time period. Otherwise, update the simulation step size and return to S1.

[0128] 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.

[0129] 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-described method when executed by a processor.

[0130] 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 processor implements the steps of the above method when executing the computer program.

[0131] Example 3

[0132] This embodiment provides a new energy microgrid group state prediction system based on segmented trajectory linearization, which is used to implement the method described in embodiment 1 or 2, such as Figure 7 Shown, including:

[0133] The linearization point acquisition module is used to set the simulation step size and select multiple linearization points based on the real state of the constructed new energy microgrid in multiple time periods using a state interval selection algorithm;

[0134] A state space model acquisition module is 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;

[0135] A wind-solar disturbance acquisition module, configured to represent the wind-solar disturbance of the new energy microgrid system using a Chino polyhedron set method;

[0136] a reachable set calculation module, configured to add the wind-solar disturbance of the new energy microgrid system to the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization, obtain the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization containing the wind-solar disturbance, and perform reachable set calculation on the model to obtain a reachable set for each time period;

[0137] The state prediction module is used to calculate the error between the reachable set in each time period and the actual state of the new energy microgrid group in the corresponding time period. The errors of all time periods are added 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.

[0138] The same or similar reference numerals correspond to the same or similar components;

[0139] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0140] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A new energy microgrid state prediction method based on piecewise trajectory linearization is characterized by: The steps include: S1: Set the simulation step size and use the state interval 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 cluster using the multiple linearization points to obtain a small signal state space model of the new energy microgrid cluster 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 system to the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization to obtain the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization including the wind-solar disturbance, and performing reachable set calculation on the model to obtain a 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 obtain 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. The new energy microgrid group state prediction method based on piecewise trajectory linearization according to claim 1 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. 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 z0 as the first linearization point and set q = 0; then calculate the next moment Filter the next linearization point. When d min < δ, set z q+1 = z as the (q + 1)-th linearization point and set q = q + 1 until q < k - 1, at which point the selection of linearization points is complete, where k represents the number of selected linearization points; set d = ||z T - z0|| / ||z0||, and 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.

3. The new energy microgrid group state prediction method based on piecewise trajectory linearization according to claim 2 is 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 variable 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 the k-1 linearization points of the test error.

4. The new energy microgrid group state prediction method based on piecewise trajectory linearization according to claim 3 is characterized in that: Applying formula (2) to the microgrid cluster system, the small signal state space model of the new energy microgrid cluster system is obtained. The small signal state space model of the new energy microgrid cluster 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 group, 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 wind turbine 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 new energy microgrid group state prediction method based on piecewise trajectory linearization 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: 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 and 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 group, 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 new energy microgrid group state prediction method based on piecewise trajectory linearization according to claim 5 is characterized in that: In S4, the calculation of 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) is the convex hull ΓR(τ), and then the uncertainty input B is calculated MMG ΔU MMG The special solution R * (t), and finally calculate the convex hull as the k The upper 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 (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): Wherein, Ar represents A MMG The product of and r, CH(·) represents the convex hull calculation, which is an operation method for 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 segmented trajectory is obtained, as shown in formula (10):

7. The new energy microgrid group state prediction method based on piecewise trajectory linearization according to claim 6 is 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 new energy microgrid group state prediction system based on piecewise trajectory linearization, used to implement the new energy microgrid group state prediction method based on piecewise trajectory linearization according to 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 based on the real state of the constructed new energy microgrid in multiple time periods using a state interval selection algorithm; A state space model acquisition module is 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, configured to represent the wind-solar disturbance of the new energy microgrid system using a Chino polyhedron set method; a reachable set calculation module, configured to add the wind-solar disturbance of the new energy microgrid system to the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization, obtain the small-signal state-space model of the new energy microgrid system based on piecewise trajectory linearization containing the 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 in each time period and the actual state of the new energy microgrid group in the corresponding time period. The errors of all time periods are added 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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