Group search method, system and medium for calculating minimum load margin of power system

Abstract

The present invention discloses a group search method for calculating the minimum load margin of an electric power system, and discloses a system and a storage medium having the group search method for calculating the minimum load margin of an electric power system. The group search method for calculating the minimum load margin of an electric power system calculates the CSNBP using a group search algorithm, provides a new CSNBP calculation method, extends the calculation methods in other fields to the field of electric power systems, and successfully provides a new calculation idea.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a group search method and system for calculating minimum load margin of a power system. Background Art

[0002] In the power injection space of the power flow equation, the distance (i.e., load margin) between the current system operating point and the saddle-node bifurcation point (SNBP) of the power flow equation generally varies along different load and power generation change directions. There exists a direction of load and power generation change that minimizes the load margin at the current point. This direction is defined as the direction closest to the static voltage stability limit. At the same time, the SNBP corresponding to the power flow equation is called the closest saddle-node bifurcation point (CSNBP), and the corresponding load margin is called the minimum load margin.

[0003] Calculating the CSNBP (Constant Power Flow) equation is crucial, especially with the widespread integration of renewable energy sources like wind power into the grid, which has made the direction of power generation and load fluctuations even more uncertain. Calculating the CSNBP can determine the direction of the system's worst-case power fluctuations, enabling preventive control measures to maintain safe system operation.

[0004] Intelligent optimization adopts a probabilistic search mechanism and is a heuristic optimization calculation method that can easily handle various complex optimization problems (such as objective functions that have no clear analytical expression, multiple peaks, multiple objectives, etc.).

[0005] There are few use cases of probabilistic search mechanisms in the existing technology when calculating the load margin of power systems. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a group search method for calculating the minimum load margin of a power system, which can calculate the CSNBP using a group search algorithm, providing a new solution.

[0007] The present invention also provides a system and a medium having the above-mentioned group search method for calculating the minimum load margin of the power system.

[0008] The group search method for calculating the minimum load margin of a power system according to the first embodiment of the present invention is characterized by comprising the following steps:

[0009] Set the control parameters of the group search optimization method and reset the number of iterations;

[0010] Obtain the power flow solution of the current operation mode of the power system and randomly generate several initial individuals;

[0011] Calculating the saddle-node bifurcation points corresponding to the plurality of initial individuals under the power flow solution of the current operation mode based on an asymptotic numerical method, and calculating the minimum load margin;

[0012] Based on the characteristics of the group search optimization method and the preset control parameters, updating the positions of the leader, followers and wanderers;

[0013] Based on the group search optimization method, the process of calculating the minimum load margin is iterated. If the iterative result does not meet the requirements, the minimum compliance margins of the initial individuals are recalculated. If the requirements are met, the iterative result of the minimum load margin is obtained.

[0014] The present invention applies the group search optimization method to the calculation process of the minimum margin of the power system, can apply emerging technologies to the field of power systems, and provides a new solution to problems existing in the power system.

[0015] Furthermore, the group search optimization method includes:

[0016] Identify the leader;

[0017] Conducting scanning sampling based on the leader and identifying discoverers, followers, and wanderers;

[0018] Select joiners based on the distribution of discoverers, followers, and wanderers, and enable the joiners to continue to increase the resources discovered by the joiners;

[0019] Make the remaining wanderers use a search strategy that involves random walks and systematically locating resources efficiently.

[0020] Furthermore, the asymptotic numerical method includes:

[0021] Solve the system based on the quasi-arc length method to determine the solution curve of the parametric power flow equation;

[0022] Based on the solution curve of the parameterized tidal flow equation, the saddle-node bifurcation point and the maximum calculation step size of the asymptotic numerical method are calculated.

[0023] Furthermore, when calculating the saddle-node bifurcation point, the minimum load margin is also calculated. The minimum load margin L i The calculation formula is:

[0024]

[0025] in, is the smallest modulus among all m individuals, ΔP i,n Represents the active power deviation corresponding to individual i among n nodes, ΔQ i,nRepresents the reactive deviation corresponding to individual i among n nodes.

[0026] Furthermore, the method for calculating the saddle-node bifurcation point based on the solution curve is to calculate the saddle-node bifurcation points corresponding to several initial individuals under the current operating mode, and calculate the minimum load margin.

[0027] A group search system for calculating a minimum load margin in a power system according to an embodiment of a second aspect of the present invention is characterized by comprising:

[0028] Initialization module, which can set the control parameters of the group search optimization method and reset the number of iterations;

[0029] The power system monitoring module is used to obtain the power flow solution of the current operation mode of the power system and randomly generate several initial individuals;

[0030] A minimum load margin calculation module is capable of calculating the saddle-node bifurcation points corresponding to the plurality of initial individuals under the power flow solution of the current operation mode based on an asymptotic numerical method, and calculating the minimum load margin;

[0031] A status update module capable of updating the positions of the leader, followers, and wanderers based on the characteristics of the group search optimization method and the preset control parameters;

[0032] The iterative calculation module can iterate the calculation process of the minimum load margin based on the group search optimization method. If the requirements are not met, the minimum compliance margin of the initial individuals is recalculated. If the requirements are met, the iterative result of the minimum load margin is obtained.

[0033] Furthermore, the minimum load margin calculation module includes:

[0034] A leader determination component, used to determine the leader;

[0035] a sampling component capable of performing scanning sampling based on the leader and determining discoverers, followers, and wanderers;

[0036] A resource weighting component that selects joiners based on the distribution of discoverers, followers, and wanderers, and enables the joiners to continue to increase the resources discovered by the joiners;

[0037] The wandering search component enables the remaining wanderers to conduct a search strategy that includes random walks and systematically locates resources efficiently.

[0038] Furthermore, the asymptotic numerical method includes:

[0039] Solve the system based on the quasi-arc length method to determine the solution curve of the parametric power flow equation;

[0040] Based on the solution curve of the parameterized tidal flow equation, the saddle-node bifurcation point and the maximum calculation step size of the asymptotic numerical method are calculated.

[0041] Furthermore, the method for calculating the saddle-node bifurcation point based on the solution curve is to calculate the saddle-node bifurcation points corresponding to several initial individuals under the current operating mode, and calculate the minimum load margin.

[0042] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium stores computer-executable instructions for executing the above-mentioned group search method for calculating the minimum load margin of the power system.

[0043] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0045] Figure 1 A schematic diagram of the steps of a group search method for calculating a minimum load margin of an electric power system according to an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of a visual scanning area in a three-dimensional space of a GSO method in an embodiment of the present invention;

[0047] Figure 3 Flowchart of the GSO algorithm according to an embodiment of the present invention;

[0048] Figure 4 This is a structural block diagram of a group search system for calculating the minimum load margin of an electric power system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0050] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0051] Example 1

[0052] Reference Figure 1 The present application provides a group search method for calculating the minimum load margin of an electric power system, the method comprising the following steps:

[0053] Step S100: Set control parameters of the group search optimization method and reset the number of iterations.

[0054] Step S200: Obtain a power flow solution for the current operation mode of the power system.

[0055] Since the calculation of the saddle-node bifurcation point requires the current operating point of the system and the power flow equation, it is necessary to obtain conditional data in advance, and the specific acquisition method is the existing method.

[0056] Step S300: randomly generate several initial individuals.

[0057] Assume that the initial number of individuals generated is m.

[0058] Step S400: Calculate the saddle-node bifurcation points (SNBPs) corresponding to a number of initial individuals under the current operation mode based on an asymptotic numerical method, and calculate the minimum load margin.

[0059] The minimum load margin corresponds to the minimum value of Li,

[0060] in, is the smallest modulus among all m individuals, ΔP i,n Represents the active power deviation corresponding to individual i among n nodes, ΔQ i,n Represents the reactive deviation corresponding to individual i among n nodes.

[0061] Step S500: Based on the characteristics of the group search optimization method, the positions of the leader, followers and wanderers are updated.

[0062] Step S600: Iterate the system based on the group search optimization method. If the iteration requirements are not met, return to step S400 to recalculate the minimum load margin until the iteration requirements are met, and output the calculation results.

[0063] Among them, two methods are mentioned in the above steps, namely the group search optimization method and the asymptotic numerical method. In order to more clearly explain the specific process of the above methods, the principles of the methods mentioned in the above processes are further described.

[0064] The group search optimizer (GSO), proposed by S. He, Q. H. Wu, and J. R. Saunders in 2009, is an optimization algorithm inspired by animal foraging behavior. Its framework is primarily based on a search mechanism based on the Producer-Scrounger model. Testing on both high- and low-dimensional standard functions has demonstrated that the group search optimizer outperforms other randomized optimization algorithms in terms of accuracy and convergence speed, particularly for high-dimensional multimodal problems.

[0065] In the GSO algorithm, a group is composed of certain members, and the members of the group are called individuals. In an n-dimensional search space, the position of the i-th individual at the t-th iteration is angle The search direction of the i-th individual Through coordinate transformation we get:

[0066]

[0067] The group in the GSO algorithm consists of three types of members: the producer, the scounger, and the ranger. The GSO algorithm consists of the following four steps:

[0068] Step A1: Scan and sample.

[0069] Reference Figure 2 The finder will start scanning from 0 degrees and then gradually randomly sample 3 points in the scanning area. The new positions in three different directions, namely, in front, to the right, and to the left, are:

[0070]

[0071] Where r1 is a normally distributed random number with a mean of 0 and a standard deviation of 1. r2∈R n-1 Is a random number uniformly distributed in (0, 1). max is the maximum search angle. max is the maximum search distance. is the position of the finder at the tth iteration. Z 、X r 、X l are the positions found by the pth individual at 0 angle, right, and left directions respectively. If the search in the three directions is completed, the new fitness values ​​in the three directions are obtained and compared with the fitness value of the original position of the finder. If the new value is better than the original value, the new position of the finder will replace the old position; otherwise, the finder remains at the original position and then turns to a new angle determined by the following formula:

[0072]

[0073] Among them, α max is the maximum steering angle. If the finder cannot find it after m iterations, it will return to 0 degrees.

[0074]

[0075] Step A2: Select participants.

[0076] In a search iteration, some individuals in the group are selected as joiners. These joiners continue to search for opportunities to join the resources discovered by the discoverer. In the tth iteration, the region replication behavior of the i-th joiner is considered to be a random move towards the discoverer:

[0077]

[0078] in, is the position of the joiner in the tth iteration. r2∈R n is a random number uniformly distributed within (0, 1).

[0079] Step A3: Make the remaining wanderers perform a search strategy that includes random walks and systematically locating resources.

[0080] The remaining individuals are wanderers, whose search strategies include random walks and systematic search strategies to efficiently locate resources.

[0081]

[0082] Among them, l i =ar1l max , l i For random distance.

[0083] Step A4: Repeat

[0084] At the end of the tth iteration, all individual fitness values ​​are re-evaluated and a new iteration starts from (1) until the condition is met.

[0085] In summary, the basic process of the GSO algorithm can be used Figure 3 To express.

[0086] Furthermore, the asymptotic numerical method (ANM) is an effective method for determining how the solutions of nonlinear equations (such as partial differential equations and algebraic equations) vary with parameters. It is currently widely used in fields such as mechanics (nonlinear fluids, elasticity, and structures) and applied mathematics. ANM divides the solution curve of a nonlinear equation containing parameters into segments, and the solution curve of each segment can be analytically expressed in the form of a closed power series, where the power series parameters are also called perturbation parameters. By introducing perturbation parameters, ANM transforms a nonlinear problem into an infinite number of linear subproblems and approximates the solution of the nonlinear problem with the sum of the solutions of the previous linear subproblems. ANM can also be considered a continuous method that uses power series expansion for high-order predictions. Since the predicted solution is almost the same as the true solution, the ANM continuous process generally does not require a correction step, and the calculation step size can also be adaptively adjusted.

[0087] The specific form of the tidal flow equation in rectangular coordinates is:

[0088]

[0089] Where N is the number of nodes; e i ,f i are the real and imaginary parts of the voltage at node i; G ij ,B ij are the (i, j)th components of the node admittance matrix respectively; for PV nodes, Equation (12) is replaced by the node voltage equation.

[0090] Assume that the node load and generator output change in equal proportion, that is:

[0091]

[0092] in, represents the base load and power generation of node i, corresponding to λ = 0; K lpi , K lqi , K gpi , K gqi represents the load and power generation change rate of node i.

[0093] The above equations (11) and (12) with parameters can be decomposed into the following general form:

[0094] f(x,λ)=L(x)+q(x,x)+λF=0 (14)

[0095] Where x is the node voltage state variable; L and q are linear and bilinear operators, respectively, and are constant vectors. For power systems containing HVDC and FACTS components, the power flow equation can be transformed into a general form such as Equation (14) by adding auxiliary variables and additional equations.

[0096] The quasi-arc length method is used to solve the following system to determine the solution curve of the parametric power flow equation:

[0097]

[0098] Among them, s is the path parameter, (x j ,λ j ) is the current calculation point, is the tangent vector of the current point, and <·,·> represents the vector inner product. The points between step j and (j+1) are expanded into the following power series:

[0099]

[0100] Substitute equation (16) into equation (15), combine the same powers about s, set the coefficients to zero, and get the coefficients about the power series: The following series of equations:

[0101] When p=1

[0102]

[0103] When p ≥ 2

[0104]

[0105] Using the block elimination algorithm to solve equations (17) and (18), we obtain:

[0106] When p=1

[0107]

[0108] When p ≥ 2

[0109]

[0110] Equations (19) and (20) are a series of linear equations with the same tidal Jacobian matrix as the coefficient matrix. Only one triangular decomposition is required to calculate the coefficients of each order. The sign of the tangent vector in Equation (19) is determined by the direction of the solution curve. When the tangent vector component When the sign changes, it indicates that the solution curve along the specified load and power generation change direction passes through the SNBP.

[0111] The maximum calculation step size of ANM (i.e., the convergence radius of Equation (16)) is determined by the following formula:

[0112]

[0113] Among them, ε r is the calculation accuracy control parameter; K is the truncation order of the power series formula (16) used in the calculation. In actual calculations, since high accuracy is not required when calculating the initial value of SNBP using ANM, a larger number can be selected for the accuracy parameter.

[0114] Embodiment 2: Further, the embodiment of the present application provides a group search system for calculating the minimum load margin of the power system, such as Figure 4 The system 40 includes:

[0115] Initialization module 401, capable of setting control parameters of the group search optimization method and resetting the number of iterations;

[0116] The power system monitoring module 402 is used to obtain the power flow solution of the current operation mode of the power system and randomly generate a number of initial individuals;

[0117] The minimum load margin calculation module 403 can calculate the saddle-node bifurcation points corresponding to the initial individuals under the current operation mode based on an asymptotic numerical method, and calculate the minimum load margin;

[0118] A status update module 404 is capable of updating the positions of the leader, followers, and wanderers based on the characteristics of the group search optimization method and the preset control parameters;

[0119] The iterative calculation module 405 can iterate the system of the minimum load margin based on the group search optimization method. If the requirements are not met, the minimum compliance margin is recalculated. If the requirements are met, the iterative result of the minimum load margin is obtained.

[0120] Furthermore, the minimum load margin calculation module includes:

[0121] A leader determination component, used to determine the leader;

[0122] a sampling component capable of performing scanning sampling based on the leader and determining discoverers, followers, and wanderers;

[0123] A resource weighting component that selects joiners based on the distribution of discoverers, followers, and wanderers, and enables the joiners to continue to increase the resources discovered by the joiners;

[0124] The wandering search component enables the remaining wanderers to conduct a search strategy that includes random walks and systematically locates resources efficiently.

[0125] Example 3:

[0126] Another embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions for executing the above-mentioned Figure 1 The group search method for calculating the minimum load margin of the power system is shown.

[0127] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0128] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A group search method for calculating the minimum load margin of an electric power system, characterized in that: The following steps are involved: Set the control parameters of the group search optimization method and reset the number of iterations; Obtain the power flow solution of the current operation mode of the power system and randomly generate several initial individuals; Calculating, based on an asymptotic numerical method, saddle-node bifurcation points corresponding to the plurality of initial individuals under the power flow solution of the current operating mode, and calculating a minimum load margin; wherein the asymptotic numerical method includes: determining a solution curve of a parametric power flow equation by solving the system based on a quasi-arc length method; calculating, based on the solution curve of the parametric power flow equation, a saddle-node bifurcation point and a maximum calculation step size of the asymptotic numerical method; The minimum load margin L i The calculation formula is: in, is the smallest modulus among all m individuals, ΔP i,n Represents the active power deviation corresponding to individual i among n nodes, ΔQ i,n Represents the reactive deviation corresponding to individual i among n nodes; Based on the characteristics of the group search optimization method and the preset control parameters, updating the positions of the leader, followers and wanderers; Based on the group search optimization method, the process of calculating the minimum load margin is iterated. If the iterative result does not meet the requirements, the minimum compliance margins of the initial individuals are recalculated. If the requirements are met, the iterative result of the minimum load margin is obtained.

2. The method according to claim 1, characterized in that The group search optimization method includes: Identify the leader; Conducting scanning sampling based on the leader and identifying discoverers, followers, and wanderers; Select joiners based on the distribution of discoverers, followers, and wanderers, and enable the joiners to continue to increase the resources discovered by the joiners; Make the remaining wanderers use a search strategy that involves random walks and systematically locating resources efficiently.

3. The method according to claim 1, characterized in that The method for calculating the saddle-node bifurcation point based on the solution curve of the parameter-containing power flow equation is to calculate the saddle-node bifurcation points corresponding to several initial individuals under the current operating mode and calculate the minimum load margin.

4. A group search system for calculating minimum load margin of a power system, characterized in that: include: Initialization module, which can set the control parameters of the group search optimization method and reset the number of iterations; The power system monitoring module is used to obtain the power flow solution of the current operation mode of the power system and randomly generate several initial individuals; A minimum load margin calculation module is capable of calculating, based on an asymptotic numerical method, saddle-node bifurcation points corresponding to the plurality of initial individuals under the power flow solution of the current operating mode, and calculating the minimum load margin; wherein the asymptotic numerical method includes: determining a solution curve of a parametric power flow equation by solving the system based on a quasi-arc length method; and calculating, based on the solution curve of the parametric power flow equation, the saddle-node bifurcation point and the maximum calculation step size of the asymptotic numerical method; The minimum load margin L i The calculation formula is: in, is the smallest modulus among all m individuals, ΔP i,n Represents the active power deviation corresponding to individual i among n nodes, ΔQ i,n Represents the reactive deviation corresponding to individual i among n nodes; A status update module capable of updating the positions of the leader, followers, and wanderers based on the characteristics of the group search optimization method and the preset control parameters; The iterative calculation module can iterate the calculation process of the minimum load margin based on the group search optimization method. If the requirements are not met, the minimum compliance margin of the initial individuals is recalculated. If the requirements are met, the iterative result of the minimum load margin is obtained.

5. The system according to claim 4, characterized in that The minimum load margin calculation module includes: A leader determination component, used to determine the leader; a sampling component capable of performing scanning sampling based on the leader and determining discoverers, followers, and wanderers; A resource weighting component that selects joiners based on the distribution of discoverers, followers, and wanderers, and enables the joiners to continue to increase the resources discovered by the joiners; The wandering search component enables the remaining wanderers to conduct a search strategy that includes random walks and systematically locates resources efficiently.

6. The system according to claim 4, characterized in that The method for calculating the saddle-node bifurcation point based on the solution curve of the parameter-containing power flow equation is to calculate the saddle-node bifurcation points corresponding to several initial individuals under the current operating mode and calculate the minimum load margin. 7 . A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method of claim 1 .

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