Modeling method, controller and storage medium of energy internet system

Abstract

Disclosed are a modeling method, controller, and storage medium for an energy internet system. The modeling method for the energy internet system includes: establishing a differential equation system for the energy internet system regarding system state variables, system control variables, and the rate of change of the system state variables; setting initial values and a predetermined time period for the system state variables; randomly discretizing the predetermined time period in a first dimension to obtain a first set of time points, and solving a first solution set of the system state variables of the differential equation system at the first set of time points based on the initial values; randomly discretizing the predetermined time period in a second dimension to obtain a second set of time points; determining a second solution set of the system state variables of the differential equation system at the second set of time points based on the first solution set; determining characteristic basis functions arranged in descending order of eigenvalues based on the second solution set; selecting the first M of the characteristic basis functions as intrinsic orthogonal basis functions; and performing a dimensionality reduction transformation on the system state variables in the differential equation system using the intrinsic orthogonal basis functions.

Description

Technical Field

[0001] The present disclosure belongs to the field of energy internet, and specifically relates to a modeling method, a controller, and a storage medium for an energy internet system. Background Art

[0002] Mathematical models of energy internet systems are key to describing their operational status. With the integration of new energy sources such as photovoltaics and wind power, the scale of energy internet systems continues to grow, and their models are also evolving towards complex networks characterized by high dimensions and nonlinearity. Consequently, conventionally constructed system models often have high order. These high-dimensional models impose heavy computational burdens and long solution times, posing significant challenges to state monitoring, real-time control, and simulation analysis in energy internet system engineering applications.

[0003] Model reduction technology can appropriately reduce the order of system models while ensuring that the deviation between the reduced model and the original system model is within a controllable range, which can, to a certain extent, solve the problem of difficult-to-calculate systems. Currently, widely used model reduction methods such as Pade approximation, Arnoldi reduction, and multi-point fitting model reduction require sampling the original system before reduction. However, the Energy Internet has a wide distribution range and a large number of nodes, and the factors that need to be considered for each node are also relatively complex. Therefore, when applied to the Energy Internet model, these order reduction techniques cannot solve the problem that the cost of sampling the complex original system is too high, which reduces the practicality of the system model.

[0004] Therefore, existing energy internet modeling methods have failed to effectively solve the problem of solving the established model within an acceptable time while keeping the error with the actual system under control. Summary of the Invention

[0005] The present disclosure is provided to solve the above-mentioned problems existing in the prior art.

[0006] There is a need for a method that can take into account the high dimensionality and complexity of the energy internet, perform mathematical modeling on it, and use order reduction technology to achieve order reduction of the system model at a lower sampling cost of the original system samples, while ensuring that the error between the reduced-order system model and the original model is within a controllable range. The present disclosure provides a method for modeling an energy internet with multiple microgrid nodes as a set of high-dimensional differential equations, combining a compressed sample sampling method and an intrinsic orthogonal decomposition method, extracting the basis functions of the system model at a lower sampling cost of the original model, and performing order reduction transformation on the original system model. The energy internet modeling method disclosed in the present disclosure can further improve the efficiency of order reduction without changing the accuracy of order reduction, and improve the problems of large amount of calculation and difficulty in solving in the modeling and order reduction process. The reduced-order system model has the advantages of fast calculation speed and controllable system accuracy.

[0007] According to a first embodiment of the present disclosure, a modeling method for an energy internet system is provided, wherein the energy internet system includes a plurality of microgrids as nodes, and the modeling method includes: establishing a differential equation system for the energy internet system regarding system state variables, system control variables, and the rate of change of the system state variables, wherein the order N of the differential equation system is associated with the number of the nodes; setting an initial value and a predetermined time period of the system state variables; performing random discretization of the predetermined time period in a first dimension to obtain a first set of time points, and solving a first solution of the differential equation system for the system state variables at the first set of time points based on the initial value; set; randomly discretize the predetermined time period in a second dimension to obtain a second set of time points, wherein the second dimension is larger than the first dimension; determine a second solution set of the system state variables of the differential equation group at the second set of time points based on the first solution set of the system state variables; determine characteristic basis functions arranged in descending order of eigenvalues based on the second solution set of the system state variables; according to the order M to which the differential equation group is to be reduced, select the first M of the arranged characteristic basis functions as eigenorthogonal basis functions; and perform a dimensionality reduction transformation on the system state variables in the differential equation group using the eigenorthogonal basis functions.

[0008] According to a second embodiment of the present disclosure, a controller for modeling an energy internet system is provided, wherein the energy internet system includes a plurality of microgrids as nodes, and the controller implements the following steps when executing the modeling of the energy internet system: establishing a differential equation system for the energy internet system regarding system state variables, system control variables, and the rate of change of system state variables, wherein the order N of the differential equation system is associated with the number of nodes; setting an initial value and a predetermined time period of the system state variables; performing random discretization of the predetermined time period in a first dimension to obtain a first set of time points, and solving the differential equation system at the first set of time points based on the initial value. a first solution set of system state variables; randomly discretizing the predetermined time period in a second dimension to obtain a second set of time points, wherein the second dimension is larger than the first dimension; determining a second solution set of system state variables of the differential equation group at the second set of time points based on the first solution set of the system state variables; determining characteristic basis functions arranged in descending order of eigenvalues based on the second solution set of the system state variables; selecting the first M of the arranged characteristic basis functions as intrinsic orthogonal basis functions according to the order M to which the differential equation group is to be reduced; and performing a dimensionality reduction transformation on the system state variables in the differential equation group using the intrinsic orthogonal basis functions.

[0009] According to a third embodiment of the present disclosure, a non-transitory computer storage medium is provided, on which computer-executable instructions are stored. When the computer-executable instructions are executed by a controller for modeling an energy internet system, the steps of the above-mentioned modeling method for an energy internet system are implemented.

[0010] The modeling methods, controllers, and storage media of the energy internet system according to various embodiments of the present disclosure enable modeling of large-scale, high-dimensional, complex energy internet systems comprising multiple microgrid nodes. By combining the compressed sample sampling method with the intrinsic orthogonal decomposition method, the basis functions of the system model are extracted at a relatively low sampling cost for the original model samples, and the original system model is reduced in order. This solves the problems of existing energy internet system high-dimensional models being difficult to solve, high sampling costs for model reduction, and low efficiency in order reduction. The reduced-order energy internet system model has the advantages of fast computational speed and controllable system accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In the drawings, which are not necessarily drawn to scale, like reference numerals may describe similar components in different views. The drawings illustrate various embodiments generally by way of example and not limitation, and together with the description and claims, serve to illustrate the disclosed embodiments. Where appropriate, like reference numerals are used throughout the drawings to refer to like or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive of the present controller or method.

[0012] Figure 1 A schematic diagram showing the structure of an energy internet system according to an embodiment of the present disclosure;

[0013] Figure 2 A flowchart showing a modeling method for an energy internet system according to an embodiment of the present disclosure; and

[0014] Figure 3 A schematic diagram showing the composition of a controller for modeling an energy internet system according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the present disclosure is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present disclosure are further described in detail below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present disclosure. For the various steps described herein, if there is no necessity for a contextual relationship between each other, the order in which they are described as examples herein should not be regarded as a limitation. Those skilled in the art should know that they can be adjusted in order as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0016] Furthermore, persons of ordinary skill in the art will appreciate that the figures provided herein are for illustration purposes only and are not necessarily drawn to scale.

[0017] Unless the context clearly requires otherwise, throughout the specification and claims, the words "include," "comprising," and similar words should be construed in an inclusive sense rather than an exclusive or exhaustive sense; that is, in the sense of "including but not limited to."

[0018] In the description of the present disclosure, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, "plurality" means two or more.

[0019] Figure 1 A schematic diagram showing the structure of the energy internet system according to an embodiment of the present disclosure is shown. As an example, Figure 1 The energy internet system shown includes multiple nodes 100 and a controller 207. In some embodiments, the controller 207 can be implemented as a central controller or a distributed controller. In some embodiments, the controller 207 can be centrally located or distributed in the cloud.

[0020] Specifically, each node 100 may include an edge energy router 201 and energy generation / consumption equipment, and the energy generation / consumption equipment may adopt different configurations depending on the actual situation. For example, each node 100 may include an edge energy router 201 for performing energy exchange between nodes, a terminal device 202, a traditional power generation device 203, an energy storage device 204, a renewable energy power generation device 205, and a common load 206. However, according to the actual situation, some nodes 100 may also include only an edge energy router 201 and a terminal device 202 (such as Figure 1 ), or it may include only traditional power generation equipment 203, energy storage equipment 204, and ordinary load 206 (as shown in the node 100 in the lower left corner of FIG. Figure 1 100 in the lower right corner), etc., which will not be described here.

[0021] Each node 100 can exchange energy through an energy transmission line 101 (shown by a solid line) and exchange information through an information communication path 102 (shown by a dotted line). Taking into account the geographical relationship between adjacent nodes or the logical relationship set as needed, a pair of nodes can transmit energy only through the energy transmission line 101, can communicate only through the information communication path 102, can transmit energy through the energy transmission line 101 and communicate simultaneously through the information communication path 102, or can have neither an energy transmission line 101 nor an information communication path 102. However, it should be noted that for any node 100, it usually has an energy transmission line 101 and / or an information communication path 102 with at least one adjacent node in the energy internet system, thereby becoming an integral part of the energy internet system. In some embodiments, the controller 207 can be configured to perform control on each edge energy router 201 in the energy internet system, including, for example, determining the control parameters (also referred to as system control variables) of each node 100 by modeling the energy internet system, and transmitting them to the edge energy router 201 of each node 100. After receiving the above control parameters, the edge energy router 201 of each node 100 performs corresponding control on the corresponding energy generation / consumption equipment according to the control parameters. As the number of nodes 100 increases, the order of the model will also increase significantly. In some embodiments, the controller 207 can be further configured to implement reduced-order modeling of the energy internet system, and solve the system state variables and system control variables based on the reduced-order model, and transmit the solved system state variables and system control variables to each node 100 to perform global monitoring and control of the entire energy internet system.

[0022] The applicant has found that the modeling of an energy internet system of any structure can be transformed into a differential equation system represented by the following formula (1) (an example of the transformed expression will be given below):

[0023]

[0024] Among them, x(t) represents the system state variable, u(t) represents the system control variable, represents the rate of change of the system state variables. The order N of the differential equation system is associated with the number of nodes 100. Given that each node 100 may have a variety of energy generation / consumption devices, as well as energy transmission lines 101 and / or information communication paths 102 that may exist between nodes 100, as the number of nodes 100 increases, the parameters of the differential equation system, especially the order of the system state variables and their rates of change, will also increase significantly. In some embodiments, the differential equation system shown in formula (1) can be any of a linear ordinary differential equation system, a nonlinear ordinary differential equation system, a linear partial differential equation system, and a nonlinear partial differential equation system.

[0025] In some embodiments, the system control variables of the energy internet system may be constant or time-varying.

[0026] The system control variables include at least one or a combination of the parameters of the loads of the microgrids (e.g., ordinary loads 206), the parameters of the renewable energy power generation equipment 205, the parameters of the traditional power generation equipment 203, and the parameters of the energy storage equipment 204. They may also include at least one or a combination of the relevant parameters for balance and operational stability between the various nodes 100, such as but not limited to parameters associated with frequency stability, parameters associated with power balance, etc., which are not elaborated here.

[0027] The system state variables include at least one of the power changes of the loads of the microgrids (such as the ordinary load 206), the power generation changes of the renewable energy power generation equipment 205, the power generation changes of the traditional power generation equipment 203, and the energy storage changes of the energy storage equipment 204, or a combination thereof, and may also include but are not limited to at least one of the changes in energy transmission between other microgrids (other nodes 100) and the bus frequency changes, or a combination thereof.

[0028] The modeling method according to various embodiments of the present disclosure can be used to reduce the high-order differential equations shown in formula (1) to a lower-order differential equations shown in the following formula (1'):

[0029]

[0030] in, represents the system state variable of the reduced low-order system, u(t) represents the system control variable of the reduced low-order system, The applicant has found that the efficient order reduction of the entire differential equation system can be achieved by performing a dimensionality reduction transformation on the system state variable x(t).

[0031] Figure 2 A flowchart of a modeling method for an energy internet system according to an embodiment of the present disclosure is shown.

[0032] In step S201 , a differential equation group regarding system state variables, system control variables and the rate of change of system state variables is established for the energy internet system. The order N of the differential equation group is associated with the number of nodes of the energy internet.

[0033] It is worth noting that although the energy internet system can be modeled in many different ways, the system operation mechanisms of traditional fossil power generation systems including thermal power generation and renewable energy power generation systems including hydropower, wind power, and solar power generation can be modeled in the form of differential equations or a system of differential equations composed of multiple differential equations. As an energy internet system composed of the above systems, it can also be modeled as a system of differential equations accordingly, and the order N of the established system of differential equations is related to the number of nodes in the system.

[0034] When modeling an Energy Internet system, different modeling objectives can be employed, and the corresponding differential equations constructed can take various forms, including, for example, linear ordinary differential equations, nonlinear ordinary differential equations, linear partial differential equations, and nonlinear partial differential equations. In some embodiments, the Energy Internet system can be modeled as an ordinary differential equation, or converted into an ordinary differential equation through certain modifications, substitutions, or approximations, but this is not limited to this. As an example, a natural gas energy system, which is a traditional fossil power generation system, is more suitable for modeling as a partial differential equation.

[0035] As an example, when the nodes in the energy internet system have traditional power generation equipment represented by thermal power generation, the differential equations of the thermal power generation equipment can be expressed as shown in formula (2): Where, i = 1, 2, ..., p, j = 1, 2, ..., q Formula (2) Formula (2) includes the first set of equations in the first row and the second equation system F on the second line j (x_F i (t), u_F j (t))=0.

[0036] Specifically, the first set of equations is a differential equation, where x_F1(t), x_F2(t), ..., x_F p(t) and u_F1(t), u_F2(t),…,u_F q (t) are all operating parameters related to the thermal power generation system, among which x_F1(t), x_F2(t), ..., x_F p (t) is used as a state variable in the differential equation system, They are state variables x_F1(t), x_F2(t), ..., x_F p The rate of change of (t), u_F1(t), u_F2(t), ..., u_F q (t) is a control parameter in the thermal power generation system, that is, a control variable. The second set of equations is an algebraic equation set that characterizes the relationship between the control variables and the state variables of the thermal power generation system.

[0037] As described above, the differential equation system can be any of a linear ordinary differential equation system, a nonlinear ordinary differential equation system, a linear partial differential equation system, and a nonlinear partial differential equation system. In the embodiment described in conjunction with thermal power generation equipment, for example, in formula (2), depending on the number of independent variables contained in the differential equations actually included in the first differential equation system, the first differential equation system can be an ordinary differential equation or a partial differential equation. In particular, when each of all the differential equations in the first differential equation system contains only one independent independent variable and is independent of the other independent variables, the first differential equation system in formula (2) above is an ordinary differential equation system.

[0038] Similarly, although the first set of equations in formula (2) is a set of linear differential equations, this is only for example; in other embodiments, the energy internet system can also be modeled as a set of nonlinear differential equations, which is not limited here.

[0039] In some embodiments, when x_F is used as the state variable of the system i (t) and the system input variable u_F j (t) are subject to small disturbances, according to the principle of small oscillation method, they can be replaced by their steady-state values x_F i0 and u_F j0 (i=1,2,…,p,j=1,2,…,q) and the offset Δx_F caused by the interference i (t) and Δu-F j (t) is used to represent the transformed form, which is shown in formula (3):

[0040] Where, i = 1, 2, ..., p, j = 1, 2, ..., q Formula (3)

[0041] Substitute equation (3) into equation (2), and then convert the resulting system of equations into the steady-state value x_F i0 and u_F j0By expanding it into a Taylor series and ignoring the quadratic and higher-order terms of the offset, we can obtain the "first-order approximation" equation of formula (2), as shown in the following formula (4):

[0042]

[0043]

[0044] Where, i = 1, 2, ..., p, j = 1, 2, ..., q Formula (4)

[0045] Further eliminate the variable Δu_F j (t), j = 1, 2, ..., q, that is, after eliminating the algebraic equations, a set of linear differential equations with constant coefficients can be obtained, as shown in the following formula (5):

[0046] That is: where i = 1, 2, ..., p Formula (5)

[0047] Where A is the constant coefficient matrix in formula (5). It can be seen that formula (5) is the same as formula (1) A specific implementation method, specifically, Δx_F k (t) corresponds to x(t), Corresponding to

[0048] However, the above is only an example. It should be noted that in other embodiments, the differential equations obtained by modeling the energy internet system may not be a system of differential equations with constant coefficients. For example, it may be a system of differential equations with variable coefficients, which is also a specific implementation of formula (1). The differential equations obtained by modeling energy internet systems of various structures can be unified under the basic framework of formula (1).

[0049] In some embodiments, such as in the model of the thermal power generation equipment at the above-mentioned node, as an example only, the state variable x_F1(t) can represent the power generation power of the thermal power generation equipment at the node, x_F2(t) can represent the energy output by the thermal power generation equipment at the node to another node in the energy Internet system, and the control variable u_F1(t) can be the frequency on the AC bus when the thermal power generation equipment is connected to the power grid, which is associated with frequency stability.

[0050] In some embodiments, the selection of state variables and control variables in the energy Internet system can be jointly determined based on the system's functions, structure, monitoring / control objectives, etc., including but not limited to parameters based on the load of each microgrid node, parameters of renewable energy power generation equipment, parameters of traditional power generation equipment, parameters of energy storage equipment, as well as parameters of energy transmission between microgrids, bus frequency of the energy Internet system and other parameters, to determine the state variables and control variables of the entire energy Internet system.

[0051] Without loss of generality, the differential equations obtained by modeling the energy internet system can be expressed as the following formula (6):

[0052]

[0053] Among them, x(t) is the state variable, u(t) is the control variable, is the rate of change of the state variable. After a transformation similar to that in step S201, the differential equations in formula (6) can be converted into a high-order ordinary differential equation of order N, or can be converted into the form of the above formula (1).

[0054] As the order N of the differential equation system increases, the difficulty of solving it will also increase rapidly. In fact, due to the high time cost and computing resource cost, the system model may not have practical application value. Therefore, in the subsequent steps, the N-order differential equation system in formula (7) will be transformed into a lower-order M-order (where M is less than N) reduced-order model through order reduction transformation, that is, the following formula (8):

[0055]

[0056] Next, in step S202, the initial value of the system state variable x0∈R is first set. N , where the vector x0 contains N components, each of which is the value of a state variable at the initial time. Next, a predetermined time period [0, T] is determined as a unit measurement and calculation cycle. In some embodiments, T can be 1 minute, 1 hour, 1 day, etc., and can be set based on the solution requirements of the system.

[0057] In step S203, the predetermined time period [0, T] is randomly discretized into a first dimension m to obtain a first set of time points (t1, t2, ..., t m ), and based on the initial value x0, solve the m-dimensional numerical solution of a set of state variables under the initial value x0, that is, the first solution set of the system state variables, which is expressed as the following formula (9):

[0058]

[0059] in, The sample set is composed of the sample values of each component of the state variable x(t) at m discrete points in a predetermined continuous time period [0, T]. In some embodiments, the first solution set of the m-dimensional system state variable can be solved by the difference method based on the initial value x0 of the system state variable. In some embodiments, the value of m takes into account the time and computing resource cost of solving the numerical solution corresponding to each time point based on the initial value, and is limited to being able to solve the solution quickly under certain computing infrastructure conditions.

[0060] In step S204, the predetermined time period [0, T] is randomly discretized into a second dimension n to obtain a second set of time points (t1, t2, ..., t n ), where n is greater than m, and typically n is much greater than m. In some embodiments, for example, n = 100m or n = 50m can be set, but this is not limited thereto. It should be noted that both the first and second sets of time points are randomly selected, and therefore the distributions of the time points in the two sets are independent of each other.

[0061] In other embodiments, the values of the second dimension n and the first dimension m are selected in association so that the exponentials of the logarithms of the first dimension m and the second dimension n are multiples. As an example, the first dimension m and the second dimension n can be selected as described in formula (A):

[0062] m=|klog 4 n|, where k∈[1, 10] Formula (A)

[0063] Wherein, log n represents the logarithm of n with base 10, |·| represents an integer, and k is a constant between 1 and 10. For example, when n = 10000, according to formula (A), the value range of m can be [log 4 10000, 10*log 4 10000], that is, [256, 2560], and so on, which will not be repeated here.

[0064] The applicant has found that in the modeling of the energy internet system, the relationship between n and m set as above can ensure that in the subsequent steps, the sampling of the state variables of the lower first dimension m is used. Solve the sampling of the state variable with a higher second dimension n without distortion At the same time, it has high model reduction efficiency.

[0065] In step S205, the first solution set based on the system state variable x(t) is Determine the second solution set of the system state variables of the differential equations in formula (7) at the second set of time points in, is an n-dimensional discrete variable, which can be expressed as the following formula (10):

[0066]

[0067] Relative to the first solution set In terms of The dimension is much higher than Therefore, it is not possible to use the difference method or other methods to solve it conveniently, or the time cost or computational cost of solving it is too high. Therefore, in this embodiment, the first solution set of low dimension that has been obtained will be used. Through a series of transformations and operations to solve the high-dimensional sample set The specific steps include:

[0068] First, according to the random discretization of the first dimension m and the random discretization of the second dimension n for the predetermined period [0, T], it is determined that the first solution set and the second solution set The associated information matrix Ψ, that is, the information matrix Ψ that satisfies the following formula (11) is solved:

[0069]

[0070] Among them, the second solution set The n-dimensional discrete variable x(t) as the state variable can be sparsely converted by Fourier transform, for example, to obtain The sparse expression of is shown in formula (12):

[0071]

[0072] where F is, for example, the basis function of the discrete Fourier transform, Represents state variables The sparse coefficients of will be solved in the following steps. The transformation method used for sparsification is not limited to Fourier transform, but may also be any other applicable sparse transform, such as wavelet transform.

[0073] Next, the sparse coefficients corresponding to the basis function F are solved by the following equation as shown in formula (13):

[0074]

[0075] Among them, n is much smaller than n, so formula (13) is an underdetermined system of equations. Theoretically, there are infinite sets of solutions, but due to The sparsity of can be solved by solving the l1 optimization problem defined by the following formula (B) to obtain the unique solution of formula (13):

[0076] Satisfaction in, is the sparse coefficient that needs to be solved, m is the dimension of the first solution set that has been obtained, is the first solution set of dimension m for which numerical solutions have been obtained, and F is The basis function of the sparse transformation, Ψ is the first solution set and the second solution set The associated information matrix, Representation matrix 1-norm of , Representation matrix 2-norm, ε represents a threshold parameter, which is a preset constant such as close to 0, and min represents finding the minimum value.

[0077] The 1-norm in formula (B) is the matrix norm, defined by formula (B-1):

[0078]

[0079] That is, formula (B-1) defines a column sum norm whose value is all matrix column vectors The maximum sum of absolute values.

[0080] The 2-norm in formula (B) is the matrix norm, defined by formula (B-2):

[0081]

[0082] Formula (B-2) defines a spectral norm (Euclidean norm), where for The conjugate transposed matrix of , λ1 is All eigenvalues λ i The maximum value in , the 2-norm is the square root of λ1.

[0083] When solving the optimization problem defined by formula (B), any applicable sparse reconstruction algorithm, including but not limited to the matching pursuit algorithm, can be used to obtain a unique solution to the optimization problem. In other embodiments, other applicable methods can also be used to solve the sparse coefficients. No restrictions here.

[0084] After determining the sparse coefficient After that, we can use formula (12) based on the basis function F and the sparse coefficients Obtain the second solution set

[0085] Through the above steps, the embodiment of the present disclosure does not need to perform a sample set (e.g., ) for direct sampling, but instead can make full use of the sample set of lower dimension (such as dimension m) that has been obtained (such as ), the transformation matrix between the high-dimensional sample set and the low-dimensional sample set (such as the information matrix Ψ) and the basis function of the sparse transformation of the state variable (such as the basis function F), are solved at a lower cost to obtain In the above steps, given that The sparsity (sparsity), and when the first dimension m-dimensional discretization and the second dimension n-dimensional discretization of the predetermined time period [0, T] are both random discretization, so the sample set obtained by sampling is and Has incoherence, so using The sample set of high-dimensional sampling of state variables can be solved without distortion And significantly reduced the computational load.

[0086] Next, through steps S206-S208, the second solution set that has been solved is used. Will As the n-dimensional sample set required for the intrinsic orthogonal decomposition of the state variable x(t), the state variable x(t) is subjected to a dimensionality reduction transformation that meets certain accuracy requirements through the intrinsic orthogonal decomposition method.

[0087] In step S206, based on the second solution set of the system state variables obtained in step S205 Determine the characteristic basis functions arranged in descending order of eigenvalues. The specific steps are as follows:

[0088] First, in some embodiments, based on the second solution set Calculate the correlation matrix according to formula (14):

[0089]

[0090] The correlation matrix X obtained by the above method is a positive definite matrix. Next, the correlation matrix X is subjected to eigendecomposition, as shown in the following formula (15):

[0091] X=QAQ -1 Formula (15)

[0092] In the above formula, Λ is a diagonal matrix, and the elements Λ on the diagonal are i，i is the i-th eigenvalue of the correlation matrix X, Q∈R N×Nis an N-dimensional standard orthogonal basis composed of characteristic basis functions, where i，i The eigenvector corresponding to (i∈1, 2, ..., N) is the i-th column of the matrix Q, denoted as Q ：，i From formula (14), we can see that the correlation matrix X is a positive definite matrix, so after eigendecomposition, each eigenvalue Λ i，i All are positive numbers.

[0093] For the eigenvalue Λ i，i Arrange them in descending order according to size, and adjust the order of the corresponding characteristic basis functions accordingly, so as to obtain a new matrix Q with the characteristic basis functions arranged in descending order. N .

[0094] Then, in step S207, according to the order reduction requirements of the energy internet system model, for example, the order of the system model with order N is reduced to order M, and the matrix Q N The first M characteristic basis functions are selected as the intrinsic orthogonal basis functions of the energy Internet system model after reduction, that is: Q M ∈R N×M .

[0095] In some embodiments, the order M of the reduced system model can be determined by comprehensively considering the desired solution time and computational cost of the energy internet system model, as well as the system's tolerance for precision loss of state variables and control variables. The order M can be set in conjunction with the system accuracy requirements of the energy internet system, such that the higher the system accuracy requirements, the larger the order M. Without excessively compromising system measurement and control accuracy, the system model is reduced as much as possible to achieve the highest possible cost-effectiveness. As an example, if the relative error requirement (as a representation of the system accuracy requirement) of the energy internet system is less than 0.05, then the sum of the first M selected eigenvalues and the sum of all eigenvalues must be greater than 0.95. In other embodiments, M eigenvalues can also be selected based on other principles, including but not limited to ensuring that the ratio of M to N is between [0.01, 0.5]. More specifically, for example, a more typical M value can be selected where the ratio of M to N is between [0.05, 0.2], etc.

[0096] In some embodiments, the order M can also be determined in combination with the distribution of the eigenvalues. When the distribution of the eigenvalues is sparser, the reduced order M can be smaller. i，i The first r terms in (i∈1, 2, ..., N), i.e., Λ i，i (i∈1, 2, ..., r) are non-zero positive numbers that decrease in order but are comparable in magnitude, and starting from the r+1th term to the Nth term, that is, Λ i，i(i∈r+1, r+2, ..., N) is Λ r，r If the value of M is 1 / 10 or smaller, it can be considered that the characteristic basis function corresponding to the eigenvalue starting from the r+1th term has little influence on the system model with order N. Therefore, when reducing the order of the system model, only the first r terms can be intercepted, that is, r is taken as the value of M to determine the intrinsic orthogonal basis function Q of the energy Internet system model after reduction. M It can be understood that in the process of determining the intrinsic orthogonal basis functions based on the truncation of eigenvalues, the discarding of non-zero eigenvalues will inevitably lead to a certain degree of loss in system accuracy. In some embodiments, when the system model is reduced in order, the maximum M value that can control the reduction in the above-mentioned accuracy within the range allowed by the system can be selected through numerical calculation or experimental data fitting as the order of the reduced system.

[0097] In step S208, the intrinsic orthogonal basis function Q determined in step S207 is used M , perform dimensionality reduction transformation on the state variables in the differential equation group as shown in formula (16), and obtain the system state variables after dimensionality reduction:

[0098]

[0099] Finally, in step S209, the system state variables after dimensionality reduction obtained by formula (16) are substituted into the high-order differential equation group in formula (1), and the low-order system model of the energy Internet with a reduced order of M represented by the low-order differential equation group in formula (1') can be obtained.

[0100] In the disclosed embodiment, through the above-mentioned modeling method for the energy internet system, after the large-scale, high-dimensional, and complex system of the energy internet including multiple microgrid nodes is modeled as a high-dimensional differential equation system, in order to address the difficulties of solving the high-dimensional differential equation system and the high time cost and computational cost of system sampling, a low-dimensional numerical solution of a set of differential equation state variables is first obtained at a relatively low sampling and solution cost. On this basis, the original high-dimensional sampling sample set of the state variables is obtained, and the high-dimensional sampling sample set is used to calculate and implement model reduction of the energy internet system based on eigenvalue sorting. The above-mentioned method avoids sampling the high-dimensional state variables of the energy internet system, reduces the cost of solving the eigenorthogonal basis functions, greatly improves the efficiency of modeling, and at the same time, achieves model reduction under controllable system accuracy.

[0101] The following are embodiments of the controller disclosed herein, which can be used to execute the method embodiments disclosed herein. For details not disclosed in the controller embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0102] Figure 3A schematic diagram illustrating the components of a controller for modeling an energy internet system according to an embodiment of the present disclosure. In some embodiments, the controller 300 for modeling an energy internet system can be a dedicated device or a general-purpose device. For example, the controller 300 can be a computer customized for the energy internet system modeling task, or a cloud-based server. For example, the controller 300 can also be integrated into other operation, maintenance, and control devices of the energy internet system.

[0103] As an example, the controller 300 for modeling an energy internet system may be implemented as including a memory 301 , a processor 302 , and computer executable instructions 303 for modeling an energy internet system stored in the memory and executable on the processor.

[0104] In some embodiments, the controller 300 may further include a communication interface 304 for obtaining various parameters associated with energy internet system modeling and order reduction, including but not limited to the desired model dimension M after order reduction, or the system accuracy requirements of the model reduction. In other embodiments, after the modeling is completed, the controller 300 for modeling the energy internet system may send the established system model to other operation and maintenance management and control devices in the energy internet system via the communication interface 304. Figure 3 In some embodiments, for example, the communication interface 304 can be connected to the microgrid via a communication cable, a wireless local area network (WLAN), a wide area network (WAN), a wireless network (such as via radio waves, a cellular or telecommunications network, and / or a local or short-range wireless network (e.g., Bluetooth). TM )) or other communication methods to receive and send relevant data.

[0105] In some embodiments, the memory 301 may include read-only memory (ROM), flash memory, random access memory (RAM), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM, static memory (e.g., flash memory, static random access memory), etc., on which computer executable instructions are stored in any format.

[0106] In some embodiments, the processor 302 may include one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), and the like. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that runs other instruction sets, or a processor that runs a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), and the like. The processor 302 may be communicatively coupled to the memory 301 and configured to execute computer-executable instructions 303 for modeling an energy internet system stored thereon, to perform methods for modeling an energy internet system, such as those according to various embodiments of the present disclosure. In some embodiments, the computer-executable instructions may be accessed by the processor 302, read from a ROM or any other suitable storage location, and loaded into a RAM for execution by the processor 302, to implement the methods for modeling an energy internet system according to various embodiments of the present disclosure. The disclosed embodiments are not limited to any type of processor or processor circuit that is otherwise configured to meet the computational requirements of executing methods such as those for modeling an energy internet system according to various embodiments of the present disclosure. Furthermore, the term "processor" or "image processor" may include more than one processor, for example, a multi-core design or multiple processors, each of which has a multi-core design. Processor 302 may execute computer program instructions 303 stored in memory 301 to perform the various operations, processes, and methods disclosed herein.

[0107] In some embodiments, the controller 300 for modeling an energy internet system may additionally include at least one of an input / output 305 and a display 306 .

[0108] The input / output 305 may include a keyboard and mouse for allowing user input, and may be configured to allow the controller 300 for modeling the energy internet system to receive and / or send data required for modeling and order reduction. For example, input means may be provided for aspects of the system modeling process that require user interaction.

[0109] The display 306 may be an LCD, CRT, or LED display, etc. The display 306 is configured to display any information related to the modeling of the energy internet system, including system accuracy requirements associated with system modeling, or graphics, images, text, and data of system modeling results.

[0110] Embodiments of the present disclosure also provide a non-transitory computer storage medium having computer-executable instructions stored thereon. When the computer-executable instructions are executed by a controller for modeling an energy internet system, the aforementioned modeling method for the energy internet system is implemented. The storage medium may include a read-only memory (ROM), a flash memory, a random access memory (RAM), a dynamic random access memory (DRAM) such as a synchronous DRAM (SDRAM) or a Rambus DRAM, a static memory (e.g., a flash memory, a static random access memory), etc., on which the computer-executable instructions may be stored in any format.

[0111] Furthermore, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present disclosure with equivalent elements, modifications, omissions, combinations (e.g., solutions that intersect various embodiments), adaptations, or changes. The elements in the claims are to be interpreted broadly based on the language employed in the claims and are not limited to the examples described in this specification or during the prosecution of this application, which examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, with the true scope and spirit being indicated by the following claims and the full scope of their equivalents.

[0112] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more schemes thereof) can be used in combination with each other. For example, a person of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present disclosure. This should not be interpreted as an intention that a disclosed feature that is not required to be protected is necessary for any claim. On the contrary, the subject matter of the present disclosure may be less than all the features of a specific disclosed embodiment. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to which these claims are entitled.

[0113] The above embodiments are merely exemplary embodiments of the present disclosure and are not intended to limit the present invention. The scope of protection of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the spirit and scope of protection of the present disclosure, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present invention.

[0114] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present disclosure with equivalent elements, modifications, omissions, combinations (e.g., solutions that intersect various embodiments), adaptations, or changes. The elements in the claims are to be interpreted broadly based on the language employed in the claims and are not limited to the examples described in this specification or during the prosecution of this application, which examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, with the true scope and spirit being indicated by the claims and the full scope of their equivalents.

[0115] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more schemes thereof) can be used in combination with each other. For example, a person of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present disclosure. This should not be interpreted as an intention that a disclosed feature that is not required to be protected is necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of a specific disclosed embodiment. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to which these claims are entitled.

Claims

1. A modeling method for an energy internet system, wherein the energy internet system includes a plurality of microgrids as nodes, characterized in that: The modeling method comprises: Establishing a differential equation system for the energy internet system regarding system state variables, system control variables, and rates of change of system state variables, wherein the order N of the differential equation system is associated with the number of nodes; Setting the initial value and predetermined time period of the system state variable; Randomly discretizing the predetermined time period into a first dimension m to obtain a first set of time points, and solving a first solution set of system state variables of the differential equations at the first set of time points based on the initial values; Randomly discretizing the predetermined time period into a second dimension n to obtain a second set of time points, wherein the second dimension is greater than the first dimension; Determining a second solution set of the system state variables of the differential equation group at the second set of time points based on the first solution set of the system state variables; determining characteristic basis functions arranged in descending order of eigenvalues based on a second solution set of the system state variables; According to the order M to which the differential equation system is to be reduced, the first M of the arranged characteristic basis functions are selected as eigenorthogonal basis functions; Using the intrinsic orthogonal basis functions, performing a dimensionality reduction transformation on the system state variables in the differential equation group; The system control variables include at least one of the parameters of the load of each microgrid, the parameters of the renewable energy power generation equipment, the parameters of the traditional power generation equipment, the parameters of the energy storage equipment, the parameters associated with frequency stability, and the parameters associated with power balance, or a combination thereof; the system state variables include at least one of the power change of the load of each microgrid, the power generation change of the renewable energy power generation equipment, the power generation change of the traditional power generation equipment, the energy storage change of the energy storage equipment, the change in energy transmission between other microgrids, and the bus frequency change, or a combination thereof.

2. The modeling method according to claim 1, characterized in that The system control variables of the energy internet system are constant or time-varying.

3. The modeling method according to claim 1 or 2, characterized in that: The order M is determined according to the distribution of the eigenvalues, so that the sparser the distribution of the eigenvalues, the smaller the order M.

4. The modeling method according to claim 1 or 2, characterized in that: The order M is set in association with the system accuracy requirement of the energy internet system, so that the higher the system accuracy requirement, the larger the order M.

5. The modeling method according to claim 1 or 2, characterized in that: The values of the second dimension and the first dimension are selected in association with each other so that the exponents of the logarithms of the first dimension and the second dimension are in a multiple relationship.

6. The modeling method according to claim 1, characterized in that Determining a second solution set of the system state variables of the differential equation group at the second set of time points based on the first solution set of the system state variables specifically includes: determining an information matrix associating the first solution set with the second solution set based on the random discretization of the first dimension and the random discretization of the second dimension performed on the predetermined time period; Based on the first solution set, the information matrix, and a basis function for sparsely representing the second solution set, solving for sparse coefficients corresponding to the basis function; Based on the basis functions and the sparse coefficients, the second solution set is determined.

7. The modeling method according to claim 6, characterized in that: Solving the sparse coefficients corresponding to the basis function specifically includes solving the sparse coefficients that satisfy the following formula (B): in, is the sparse coefficient, m is the first dimension, is the first solution set with dimension m, F is the basis function, Ψ is the information matrix, Representation matrix 1-norm of , Representation matrix 2-norm, ε represents the threshold parameter, which is a preset constant, and min represents the minimum value.

8. The modeling method according to claim 1 or 2, characterized in that: Solving the first solution set specifically includes, based on the initial values of the system state variables, using a difference method to solve the numerical solution of the first dimension of the system state variables as the first solution set.

9. The modeling method according to claim 1 or 2, characterized in that: Determining the characteristic basis functions arranged in descending order of eigenvalues based on the second solution set of the system state variables specifically includes: Based on the second solution set, calculating a correlation matrix; Performing eigendecomposition on the correlation matrix to obtain the eigenvalues and the eigenbasis functions corresponding to the eigenvalues, and arranging the corresponding eigenbasis functions in descending order according to the magnitude of the eigenvalues.

10. The modeling method according to claim 1 or 2, characterized in that: The differential equations are any one of linear ordinary differential equations, nonlinear ordinary differential equations, linear partial differential equations and nonlinear partial differential equations.

11. A controller for modeling an energy internet system, wherein the energy internet system includes a plurality of microgrids as nodes, characterized in that: The controller implements the following steps when executing the modeling of the energy internet system: Establishing a differential equation system for the energy internet system regarding system state variables, system control variables, and rates of change of system state variables, wherein the order N of the differential equation system is associated with the number of nodes; Setting the initial value and predetermined time period of the system state variable; Performing random discretization of the predetermined time period in a first dimension to obtain a first set of time points, and solving a first solution set of system state variables of the differential equation group at the first set of time points based on the initial values; randomly discretizing the predetermined time period into a second dimension to obtain a second set of time points, wherein the second dimension is greater than the first dimension; Determining a second solution set of the system state variables of the differential equation group at the second set of time points based on the first solution set of the system state variables; determining characteristic basis functions arranged in descending order of eigenvalues based on a second solution set of the system state variables; According to the order M to which the differential equation system is to be reduced, the first M of the arranged characteristic basis functions are selected as eigenorthogonal basis functions; Using the intrinsic orthogonal basis functions, performing a dimensionality reduction transformation on the system state variables in the differential equation group; The system control variables include at least one of the parameters of the load of each microgrid, the parameters of the renewable energy power generation equipment, the parameters of the traditional power generation equipment, the parameters of the energy storage equipment, the parameters associated with frequency stability, and the parameters associated with power balance, or a combination thereof; the system state variables include at least one of the power change of the load of each microgrid, the power generation change of the renewable energy power generation equipment, the power generation change of the traditional power generation equipment, the energy storage change of the energy storage equipment, the change in energy transmission between other microgrids, and the bus frequency change, or a combination thereof.

12. A non-transitory computer storage medium, characterized in that Computer-executable instructions are stored thereon, and when the computer-executable instructions are executed by a controller for modeling an energy internet system, the modeling method for an energy internet system according to any one of claims 1 to 10 is implemented.

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