Node electricity price composition analysis method and system of electric power spot market, and medium

Abstract

The invention discloses a node electricity price composition component analysis method and system for an electric power spot market and a medium, and the method comprises the steps: determining a corresponding LPC model based on obtained electric power market load data, and carrying out the analysis of the LPC model, and obtaining a corresponding first basis matrix; determining a constraint right value variation, and obtaining a corresponding basis variable variation matrix based on a target basis matrix and the constraint right value variation, the target basis matrix being obtained by converting the first basis matrix; extracting a unit output disturbance quantity from the basic variable change matrix, and constructing a corresponding unit output change matrix based on the unit output disturbance quantity; and based on the unit output change matrix, obtaining a marginal unit corresponding to each node and a component analysis result. Compared with the prior art, an analysis model does not need to be additionally established, and the analysis accuracy and the analysis efficiency of component analysis can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power dispatching, and in particular to a method, system and medium for analyzing the components of node electricity prices in the electric power spot market. Background Art

[0002] The component analysis method is usually used to parse and determine the various components and their contents in complex systems or substances, while the component analysis method of node electricity prices is a method used to analyze and determine the components of electricity prices at different nodes in the electricity market. The specific components of node electricity prices are determined by analyzing the supply and demand relationship in the electricity market, the physical constraints of the power grid, and various cost factors in the power transmission process.

[0003] The existing technology generally analyzes the components of node electricity prices by establishing power flow calculation models and marginal cost calculation models, but there are limitations in the analysis of node electricity price components in the electricity spot market. Among them, the power flow calculation model often ignores parameters such as resistance and reactive power during the construction process, resulting in deviations between the calculation results and the actual situation. At the same time, it involves a large amount of data and complex calculation processes, so the calculation complexity is large. The marginal cost calculation model often requires accurate acquisition of generator cost data and market demand data, and these data are difficult to obtain in real time or are uncertain, which in turn affects the accuracy and practicality of the analysis results.

[0004] Application Contents

[0005] The present application provides a method, system and medium for analyzing the components of node electricity prices in the electricity spot market. Compared with the prior art, the present application not only does not require the establishment of an additional analysis model, but also can improve the analysis accuracy and efficiency of the component analysis.

[0006] In a first aspect, the present application provides a method for analyzing the components of node electricity prices in a spot electricity market, including:

[0007] Determine a corresponding LPC model based on the acquired power market load data, and analyze the LPC model to obtain a corresponding first basis matrix;

[0008] Determine the constraint right value change amount, and obtain the corresponding basis variable change matrix based on the target basis matrix and the constraint right value change amount, wherein the target basis matrix is ​​obtained by transforming the first basis matrix;

[0009] Extracting the unit output disturbance from the basic variable change matrix, and constructing a corresponding unit output change matrix based on the unit output disturbance;

[0010] Based on the unit output change matrix, the marginal unit and component analysis results corresponding to each node are obtained.

[0011] The embodiment of the present application determines the corresponding LPC model based on the acquired power market load data, and can obtain the LPC model reflecting the power market, which is convenient for extracting the corresponding information from the LPC model later; by analyzing the LPC model, the information in the corresponding LPC model can be accurately obtained, which is convenient for the subsequent analysis of the components of node electricity prices; by extracting the unit output disturbance from the basic variable change matrix and constructing the unit output change matrix, the output disturbance corresponding to each unit at each moment when each node is disturbed can be accurately grasped, and the marginal unit and component analysis results corresponding to each node can be obtained in real time according to the acquired output disturbance; based on the unit output change matrix, the marginal unit and component analysis results corresponding to each node can be accurately obtained. Compared with the existing component analysis method, the present application not only does not need to establish an additional analysis model, but also can improve the analysis accuracy and analysis efficiency of the component analysis.

[0012] Furthermore, the LPC model is analyzed to obtain a corresponding first basis matrix, which is specifically:

[0013] Obtaining a coefficient matrix corresponding to the LPC model, wherein the coefficient matrix includes basis variables and non-basis variables;

[0014] Based on the basis variables and non-basis variables, the LPC model is iteratively calculated until the test numbers of all variables are non-negative, thereby obtaining the optimal basis variables and the first basis matrix corresponding to the LPC model, wherein the test numbers are coefficients of non-basis variables.

[0015] In this way, by solving the LPC model corresponding to the electricity spot market, the information in the corresponding LPC model can be accurately obtained, which is convenient for subsequent understanding of the causes of node electricity prices, and then accurately obtaining the marginal units and component analysis results corresponding to each node.

[0016] Furthermore, the calculation formula for iteratively calculating the LPC model is:

[0017]

[0018] x B =B -1 b, X N =0;

[0019] f min =C B B -1 b;

[0020] Where B is the first basis matrix, which means the matrix containing the coefficients of the basis variables in the coefficient matrix A; N is the non-basis matrix, which means the matrix containing the coefficients of the non-basis variables in the coefficient matrix A; x Brepresents the optimal basic variable set; x N is a set of non-basic variables; b is the right value of the constraint in the LPC model; B -1 is the matrix obtained by inverting the first basis matrix B; f min is the objective function expression; c B is the corresponding basic variable coefficient in the cost coefficient c.

[0021] Furthermore, the corresponding basis variable change matrix is ​​obtained based on the target basis matrix and the constraint right value change amount, which is specifically:

[0022] x′ B =x B +△x b =B -1 (b+△b);

[0023]

[0024] In the formula, x′ B is the actual basis variable; x B represents the optimal basic variable set; B -1 represents the first basis matrix after inversion; b is the right value of the constraint in the LPC model; Δx b is the base variable change matrix; is the target basis matrix; Δb is the change in the right value of the constraint.

[0025] Furthermore, the target basis matrix is ​​obtained by converting the first basis matrix, specifically:

[0026] Inverting the first basis matrix, and transposing the inverted first basis matrix to obtain a second basis matrix;

[0027] The variables in the second basis matrix are placed into a zero matrix according to their original positions in the coefficient matrix to obtain a target basis matrix.

[0028] In this way, by converting the first basis matrix into a target basis matrix that matches the dimension of the original coefficient matrix, the calculation of the subsequent unit output change matrix and the analysis of the subsequent node electricity price components can be facilitated.

[0029] Furthermore, the extraction of the unit output disturbance from the basic variable change matrix is ​​specifically as follows:

[0030] Based on the variable sequence of the coefficient matrix, determining the position information corresponding to the unit output variable coefficient in the coefficient matrix;

[0031] The unit output disturbance corresponding to the position information is extracted from the basic variable change matrix.

[0032] In this way, by extracting the unit output disturbance from the basic variable change matrix and constructing the unit output change matrix, we can accurately grasp the output disturbance of each unit corresponding to each node when a disturbance occurs at each moment, and obtain the marginal unit and component analysis results corresponding to each node in real time according to the obtained output disturbance.

[0033] Furthermore, after obtaining the marginal units and component analysis results corresponding to each node based on the unit output change matrix, the method further includes:

[0034] Obtaining the declared price corresponding to the marginal unit;

[0035] Based on the declared price, the marginal units and the component analysis result, the node electricity price corresponding to each node is obtained, wherein each node includes a plurality of the marginal units.

[0036] In this way, by analyzing the marginal units and component analysis results, a new and more accurate node electricity price calculation formula can be obtained.

[0037] Furthermore, the calculation formula for obtaining the node electricity price corresponding to each node based on the declared price, the marginal unit and the component analysis result is specifically:

[0038]

[0039] Where, LMP n,t represents the node electricity price; η u,t is the component analysis result of the marginal unit u in time period t; c u is the declared price corresponding to the marginal unit u.

[0040] In a second aspect, the present application provides a node electricity price component analysis system for an electricity spot market, comprising: an acquisition module, a first processing module, a second processing module, and an analysis module;

[0041] The acquisition module is used to determine the corresponding LPC model based on the acquired power market load data, and analyze the LPC model to obtain the corresponding first basis matrix;

[0042] The first processing module is used to determine the constraint right value change amount, and obtain the corresponding basis variable change matrix based on the target basis matrix and the constraint right value change amount, wherein the target basis matrix is ​​obtained by transforming the first basis matrix;

[0043] The second processing module is used to extract the unit output disturbance from the basic variable change matrix, and construct a corresponding unit output change matrix based on the unit output disturbance;

[0044] The analysis module is used to obtain marginal units and component analysis results corresponding to each node based on the unit output change matrix.

[0045] The embodiment of the present application determines the corresponding LPC model based on the acquired power market load data, and can obtain the LPC model reflecting the power market, which is convenient for extracting the corresponding information from the LPC model later; by analyzing the LPC model, the information in the corresponding LPC model can be accurately obtained, which is convenient for the subsequent analysis of the components of node electricity prices; by extracting the unit output disturbance from the basic variable change matrix and constructing the unit output change matrix, the output disturbance corresponding to each unit at each moment when each node is disturbed can be accurately grasped, and the marginal unit and component analysis results corresponding to each node can be obtained in real time according to the acquired output disturbance; based on the unit output change matrix, the marginal unit and component analysis results corresponding to each node can be accurately obtained. Compared with the existing component analysis method, the present application not only does not need to establish an additional analysis model, but also can improve the analysis accuracy and analysis efficiency of the component analysis.

[0046] In a third aspect, the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the node electricity price component analysis method of the electricity spot market as described in the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of an embodiment of a method for analyzing the components of node electricity prices in the electricity spot market provided by the present application;

[0048] Figure 2 It is a structural diagram of an embodiment of a node electricity price component analysis system for the electricity spot market provided by the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0051] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0052] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0053] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0054] The component analysis method is often used to analyze the composition and content of complex systems or substances. The node electricity price component analysis method is specifically used to analyze the electricity price composition of different nodes in the power market, and determines the specific components of the node electricity price by examining the supply and demand relationship, grid constraints and transmission costs.

[0055] Existing technologies mainly analyze node electricity prices by establishing power flow calculation and marginal cost calculation models. However, these methods have limitations in the electricity spot market. Power flow calculation models often ignore parameters such as resistance and reactive power, resulting in deviations between the calculation results and the actual situation and complex calculations; while marginal cost models require accurate acquisition of generator costs and market demand data, which are difficult to obtain in real time and have uncertainties, thus affecting the accuracy of the analysis results.

[0056] Next, the nouns involved in this application are analyzed:

[0057] The LPC (Locational Marginal Pricing) model is a pricing mechanism in the electricity spot market, which takes into account the supply and demand balance of the electricity market, the physical constraints of the power grid, and the differences in electricity value in different geographical locations.

[0058] Based on this, the embodiments of the present application provide a method, system and medium for analyzing the component components of node electricity prices in the electricity spot market. Compared with the existing component analysis method, the present application not only does not require the establishment of an additional analysis model, but also can improve the analysis accuracy and efficiency of the component analysis.

[0059] The node electricity price component analysis method, system and medium for the electricity spot market provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the node electricity price component analysis method for the electricity spot market in the embodiments of the present application is described.

[0060] The node electricity price component analysis method of the electricity spot market provided in the embodiment of the present application relates to the field of power dispatching. The node electricity price component analysis method of the electricity spot market provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the node electricity price component analysis method of the electricity spot market, etc., but is not limited to the above forms.

[0061] The present application can also be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0062] Embodiment 1

[0063] Please refer to Figure 1 , Figure 1 It is a flow chart of an embodiment of a method for analyzing the components of node electricity prices in the electricity spot market provided by the present application, including steps S101 to S104;

[0064] Step S101, determining a corresponding LPC model based on the acquired power market load data, and analyzing the LPC model to obtain a corresponding first basis matrix;

[0065] It is understandable that the load data of the power market can be obtained in real time through, but not limited to, a data acquisition system, and then the collected load data needs to be preprocessed to obtain data preprocessing results, and based on the data preprocessing results, since the quotations of the generator sets, the output levels of the generator sets, the physical parameters of the power grid (such as the capacity and impedance of the transmission lines), and load demand and other factors all have a crucial impact on the accuracy and reliability of the LPC model, it is necessary to jointly consider the data preprocessing results, the quotations of the generator sets, the output levels of the generator sets and the physical parameters of the power grid to construct the corresponding LPC model.

[0066] It should be noted that data preprocessing includes but is not limited to data cleaning (removing outliers, filling missing values, etc.), data conversion (such as converting data into a format suitable for model input) and data normalization (scaling data to the same scale). Since data preprocessing is not the focus of this application, it will not be expanded here.

[0067] It should be noted that the construction of the LPC model is not the focus of this application, so it will not be expanded here.

[0068] After the LPC model is determined, the LPC model can be analyzed, specifically: first, the coefficient matrix corresponding to the LPC model is obtained, wherein the coefficient matrix includes basis variables and non-basis variables. This process only requires converting the LPC model into the standard form of a mathematical model, and then solving it to obtain the corresponding coefficient matrix A, wherein the mathematical standard form of the LPC model is:

[0069]

[0070] z=c1x1+c2x2+…+c n x n ;

[0071] a 11 x1+a 12 x2+…+a 1n x n ≤b1;

[0072] a 21 x1+a 22 x2+…+a 2n x n ≤b2;

[0073] …

[0074] a m1 x1+a m2 x2+…+a mn x n ≤ b m ;

[0075] Wherein, the dimension of the LPC model is MxN, including N variables and M constraints; A is the coefficient matrix, which represents the matrix composed of the coefficients of the variables in the constraints, with a dimension of MxN, and the element A of the coefficient matrix A is i,j is the coefficient of the jth variable of the LPC model in the i-th constraint; c is the cost coefficient vector, indicating that the value of the i-th element c1 is the coefficient of the i-th variable in the objective function expression; x is a vector containing all variables; b is a vector containing all constraint constant terms, indicating that the i-th element b i The value of the constant term of the i-th constraint is usually called the right value of the constraint.

[0076] Then, based on the basis variables and non-basis variables, the LPC model is iteratively calculated until the test numbers of all variables are non-negative, and the optimal basis variables and the first basis matrix corresponding to the LPC model are obtained, wherein the test numbers are the coefficients of the non-basis variables. Specifically, first, an m-order non-singular square matrix is ​​found in the coefficient matrix A of the constraint equation group as the initial basis matrix, wherein the variables corresponding to the initial basis matrix are the initial basis variables; secondly, the initial non-basis variables are set to zero, and then the constraint equations are solved to obtain the solution of the process basis variables, at which point the process basis variables are the initial basis feasible solutions, but not the optimal basis variables; then, the non-basis variables are used to represent the objective function, and the test numbers of each non-basis variable are calculated, wherein the test number is the objective function. The coefficient of the non-basic variable in the target function represents the change in the value of the objective function when the non-basic variable increases by one unit. In the problem of maximizing the objective function, the non-basic variable with the largest test number is selected as the incoming basis variable. According to the minimum ratio principle, the ratio of the coefficient of the incoming basis variable in each constraint equation to the constant term is calculated. The original basis variable in the constraint equation where the minimum ratio is located is selected as the outgoing basis variable. Through elementary row transformation, the outgoing basis variable is replaced with the incoming basis variable to obtain a new basis matrix B and a new basis feasible solution. The steps of calculating the test number, selecting the incoming basis variable, selecting the outgoing basis variable and the basis transformation are repeated until the test numbers of all variables are non-negative, then the optimal basis variable and the first basis matrix corresponding to the LPC model can be obtained. Among them, the calculation formula for iterative calculation of the LPC model is:

[0077]

[0078] x B =B -1 b,x N =0;

[0079] f min =C B B -1 b;

[0080] Where B is the first basis matrix, which means the matrix containing the coefficients of the basis variables in the coefficient matrix A, with rows representing constraints and columns representing variables; N is the non-basis matrix, which means the matrix containing the coefficients of the non-basis variables in the coefficient matrix A; x B represents the optimal basic variable set; x N is a set of non-basic variables; b is the right value of the constraint in the LPC model; B -1 is the matrix obtained by inverting the first basis matrix B; f min is the objective function expression; c B is the corresponding basic variable coefficient in the cost coefficient c.

[0081] In this way, by solving the LPC model corresponding to the electricity spot market, the information in the corresponding LPC model can be accurately obtained, which is convenient for subsequent understanding of the causes of node electricity prices, and then accurately obtaining the marginal units and component analysis results corresponding to each node.

[0082] Step S102, determining the constraint right value variation, and obtaining the corresponding basis variable variation matrix based on the target basis matrix and the constraint right value variation, wherein the target basis matrix is ​​obtained by transforming the first basis matrix;

[0083] It can be understood that the constraint right value change Δb can be determined by the calculation expression of the node electricity price, where, without considering the network loss, the general calculation expression of the node electricity price is: Where, LMP n,t is the node electricity price; SMP t is the system energy price, which represents the shadow price of the system load balance constraint; τ l,t and τ s,t represents the shadow price of network security constraints in the congestion price; G l-n and G s-n In order to obtain information such as marginal units in the future, since the shadow prices of other constraints do not affect the calculation results of node electricity prices, we only need to consider the right-hand value changes of constraints directly related to node electricity prices, namely load balance constraints and network security constraints. According to the characteristics of constraints and the definition of transfer distribution factors, the right-hand value change of constraints Δb can be expressed as: Δb = [0…1…0…G l-n …G s-n ] T ; Among them, the 0 vector represents the change of other constraints in the model except load balance and safety constraints. Because it has no effect on the electricity price, the change is set to 0; and the first item in the non-zero element is a vector with all elements 1, indicating that in the load balance constraint, the coefficient of the unit output variable contained in it is 1, that is, the change corresponding to the load balance constraint is equal to the increase of the unit power value of a certain unit; G l-nIndicates that in the network security constraint, the coefficient of the unit output variable is the transfer distribution factor of the node where it is located to the line or section; G s-n It means that when the unit increases unit power, the change in line and section safety constraints is the value of the transfer distribution factor corresponding to the unit.

[0084] It should be noted that the shadow price reflects the impact of relaxing a certain constraint by a unit value on the cost of the objective function.

[0085] After determining the constraint right value change Δb, the first basis matrix needs to be transformed to obtain the target basis matrix. Specifically, first, the first basis matrix is ​​inverted, and the inverted first basis matrix is ​​transposed to obtain the second basis matrix (B -1 ) T , where rows represent variables and columns represent constraints. Assuming that for the LPC model with M=6 and N=4, the second basis matrix is ​​in the form of Then, the variables in the second basis matrix are placed in the zero matrix according to their original positions in the coefficient matrix A to obtain the target basis matrix Among them, the target basis matrix The form is

[0086] In this way, by converting the first basis matrix into a target basis matrix that matches the dimension of the original coefficient matrix, the calculation of the subsequent unit output change matrix and the analysis of the subsequent node electricity price components can be facilitated.

[0087] When the target basis matrix is ​​obtained After the constraint right value change Δb, since the node electricity price is defined as the cost required by the system when the current node increases one unit of power injection, and the marginal unit is defined as the generator set that can respond to the unit of injected power at the current node, in order to calculate the marginal unit, it is necessary to characterize the relationship between the unit output change and the power disturbance in the system, that is, the relationship between the marginal unit and the node electricity price. Therefore, based on the target basis matrix And the constraint right value change Δb, the corresponding basis variable change matrix Δx can be obtained b , the relevant formula is:

[0088] x′ B =x B +Δx b =B -1 (b+Δb);

[0089]

[0090] In the formula, x′ B is the actual basis variable; x B represents the optimal basic variable set; B-1 represents the first basis matrix after inversion; b is the right value of the constraint in the LPC model; Δx b is the base variable change matrix; is the target basis matrix; Δb is the change in the right value of the constraint.

[0091] Step S103, extracting the unit output disturbance from the basic variable change matrix, and constructing a corresponding unit output change matrix based on the unit output disturbance;

[0092] It can be understood that when the basis variable change matrix Δx is obtained b Afterwards, in order to obtain the power variation of the generator set at each time period and each node, it is necessary to obtain the basic variable variation matrix Δx b The unit output disturbance is extracted from the coefficient matrix by: firstly, based on the variable order of the coefficient matrix, determining the position information corresponding to the unit output variable coefficient in the coefficient matrix; secondly, from the basic variable change matrix Δx b The unit output disturbance corresponding to the position information is extracted. And the corresponding unit output change matrix Δx is constructed based on the extracted unit output disturbance unit , which represents the disturbance of each unit output corresponding to each node at each moment. For example, let the i-th column to the j-th column in the coefficient matrix A be the position of the unit output variable coefficient, then from the basic variable change matrix Δx b The unit output disturbance is taken from the i-th row to the j-th row, and the unit output change matrix Δx is formed unit .

[0093] In this way, by extracting the unit output disturbance from the basic variable change matrix and constructing the unit output change matrix, we can accurately grasp the output disturbance of each unit corresponding to each node when a disturbance occurs at each moment, and obtain the marginal unit and component analysis results corresponding to each node in real time according to the obtained output disturbance.

[0094] Step S104, based on the unit output change matrix, obtain the marginal unit and component analysis results corresponding to each node.

[0095] It can be understood that after obtaining the unit output change matrix Δx unit Afterwards, due to the unit output change matrix Δx unit The element α at position (i,j) i,j It represents the response of the uth unit to the unit change of the power injected into the nth node during the t period. Therefore, when α i,j When it is 0, it means that the unit is not a marginal unit and does not participate in the pricing of this period. i,jWhen it is not 0, it means that the unit responds to the disturbance of the node injection power and participates in pricing as a marginal unit. (round up), u = i%T, n = j%T, % is the modulo operator. Therefore, by Δx unit The marginal units participating in pricing in each node in each period can be determined, and α i,j While characterizing the power response, it also represents the component analysis results, so the marginal units and component analysis results corresponding to each node are obtained.

[0096] It is understandable that after obtaining the marginal units and component analysis results corresponding to each node, the calculation formula of the node electricity price can be redefined based on the obtained information. Specifically, the declared price c corresponding to the marginal unit can be obtained from the clearing condition. u ; and based on the declared price c u , the marginal unit and the component analysis result η u,t , obtain the node electricity price corresponding to each node, wherein each node includes a number of said marginal units. Wherein, the calculation formula for obtaining the node electricity price corresponding to each node based on the said declared price, the said marginal units and the said component analysis result is specifically:

[0097]

[0098] Where, LMP n,t represents the node electricity price; η u,t is the component analysis result of the marginal unit u in time period t; c u is the declared price corresponding to the marginal unit u.

[0099] In this way, by analyzing the marginal units and component analysis results, a new and more accurate node electricity price calculation formula can be obtained.

[0100] The embodiment of the present application determines the corresponding LPC model based on the acquired power market load data, and can obtain the LPC model reflecting the power market, which is convenient for extracting the corresponding information from the LPC model later; by analyzing the LPC model, the information in the corresponding LPC model can be accurately obtained, which is convenient for the subsequent analysis of the components of node electricity prices; by extracting the unit output disturbance from the basic variable change matrix and constructing the unit output change matrix, the output disturbance corresponding to each unit at each moment when each node is disturbed can be accurately grasped, and the marginal unit and component analysis results corresponding to each node can be obtained in real time according to the acquired output disturbance; based on the unit output change matrix, the marginal unit and component analysis results corresponding to each node can be accurately obtained. Compared with the existing component analysis method, the present application not only does not need to establish an additional analysis model, but also can improve the analysis accuracy and analysis efficiency of the component analysis.

[0101] Embodiment 2

[0102] Please refer to Figure 2 , Figure 2 It is a structural diagram of an embodiment of a node electricity price component analysis system of the power spot market provided by the present application, including an acquisition module 100, a first processing module 200, a second processing module 300 and an analysis module 400;

[0103] The acquisition module 100 is used to determine the corresponding LPC model based on the acquired power market load data, and analyze the LPC model to obtain the corresponding first basis matrix;

[0104] The first processing module 200 is used to determine the constraint right value change amount, and obtain the corresponding basis variable change matrix based on the target basis matrix and the constraint right value change amount, wherein the target basis matrix is ​​obtained by transforming the first basis matrix;

[0105] The second processing module 300 is used to extract the unit output disturbance from the basic variable change matrix, and construct a corresponding unit output change matrix based on the unit output disturbance;

[0106] The analysis module 400 is used to obtain marginal units and component analysis results corresponding to each node based on the unit output change matrix.

[0107] Since the information interaction, execution process, etc. between the modules within the above-mentioned node electricity price component analysis system based on the electricity spot market are based on the same concept as the embodiment of the node electricity price component analysis method of the electricity spot market in the first aspect of the present invention, the technical effects achieved are basically the same. For specific contents, please refer to the description in the first embodiment of the method of the present invention, and will not be repeated here.

[0108] The device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, i.e., may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0109] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for analyzing the component parts of node electricity prices in the electricity spot market as described in the first embodiment above is implemented.

[0110] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-monitorable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0111] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application.

[0112] It is particularly pointed out that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for analyzing the components of node electricity prices in the electricity spot market, characterized in that: include: Determine a corresponding LPC model based on the acquired power market load data, and analyze the LPC model to obtain a corresponding first basis matrix; Determine the constraint right value change amount, and obtain the corresponding basis variable change matrix based on the target basis matrix and the constraint right value change amount, wherein the target basis matrix is ​​obtained by transforming the first basis matrix; Extracting the unit output disturbance from the basic variable change matrix, and constructing a corresponding unit output change matrix based on the unit output disturbance; Based on the unit output change matrix, the marginal unit and component analysis results corresponding to each node are obtained.

2. The method for analyzing the components of node electricity prices in the electricity spot market according to claim 1, characterized in that: The LPC model is analyzed to obtain the corresponding first basis matrix, which is specifically: Obtaining a coefficient matrix corresponding to the LPC model, wherein the coefficient matrix includes basis variables and non-basis variables; Based on the basis variables and non-basis variables, the LPC model is iteratively calculated until the test numbers of all variables are non-negative, thereby obtaining the optimal basis variables and the first basis matrix corresponding to the LPC model, wherein the test numbers are coefficients of non-basis variables.

3. The method for analyzing the components of node electricity prices in the electricity spot market according to claim 2, characterized in that: The calculation formula for iterative calculation of the LPC model is: x B =B -1 b,x N =0; f min =C B B -1 b; Where B is the first basis matrix, which means the matrix containing the coefficients of the basis variables in the coefficient matrix A; N is the non-basis matrix, which means the matrix containing the coefficients of the non-basis variables in the coefficient matrix A; x B represents the optimal basic variable set; x N is a set of non-basic variables; b is the right value of the constraint in the LPC model; B -1 is the matrix obtained by inverting the first basis matrix B; f min is the objective function expression; c B is the corresponding basic variable coefficient in the cost coefficient c.

4. The method for analyzing the components of node electricity prices in the electricity spot market according to claim 1, characterized in that: Based on the target basis matrix and the constraint right value variation, the corresponding basis variable variation matrix is ​​obtained, which is specifically: x′ B =x B +Δx b =B -1 (b+Δb); In the formula, x′ B is the actual basis variable; x B represents the optimal basic variable set; B -1 represents the first basis matrix after inversion; b is the right value of the constraint in the LPC model; Δx b is the base variable change matrix; is the target basis matrix; Δb is the change in the right value of the constraint.

5. The method for analyzing the components of node electricity prices in the electricity spot market according to claim 2, characterized in that: The target basis matrix is ​​obtained by transforming the first basis matrix, specifically: Inverting the first basis matrix, and transposing the inverted first basis matrix to obtain a second basis matrix; The variables in the second basis matrix are placed into a zero matrix according to their original positions in the coefficient matrix to obtain a target basis matrix.

6. The method for analyzing the components of node electricity prices in the electricity spot market according to claim 2, characterized in that: The extraction of the unit output disturbance from the basic variable change matrix is ​​specifically: Based on the variable sequence of the coefficient matrix, determining the position information corresponding to the unit output variable coefficient in the coefficient matrix; The unit output disturbance corresponding to the position information is extracted from the basic variable change matrix.

7. The method for analyzing the components of node electricity prices in the electricity spot market according to claim 1, characterized in that: After obtaining the marginal units and component analysis results corresponding to each node based on the unit output change matrix, the method further includes: Obtaining the declared price corresponding to the marginal unit; Based on the declared price, the marginal units and the component analysis result, the node electricity price corresponding to each node is obtained, wherein each node includes a plurality of the marginal units.

8. The method for analyzing the components of node electricity prices in the electricity spot market according to claim 7, characterized in that: The calculation formula for obtaining the node electricity price corresponding to each node based on the declared price, the marginal unit and the component analysis result is specifically: Where, LMP n,t represents the node electricity price; η u,t is the component analysis result of the marginal unit u in time period t; c u is the declared price corresponding to the marginal unit u.

9. A node electricity price component analysis system for the electricity spot market, characterized in that: include: An acquisition module, a first processing module, a second processing module and an analysis module; The acquisition module is used to determine the corresponding LPC model based on the acquired power market load data, and analyze the LPC model to obtain the corresponding first basis matrix; The first processing module is used to determine the constraint right value change amount, and obtain the corresponding basis variable change matrix based on the target basis matrix and the constraint right value change amount, wherein the target basis matrix is ​​obtained by transforming the first basis matrix; The second processing module is used to extract the unit output disturbance from the basic variable change matrix, and construct a corresponding unit output change matrix based on the unit output disturbance; The analysis module is used to obtain marginal units and component analysis results corresponding to each node based on the unit output change matrix.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the node electricity price component analysis method of the electricity spot market as described in any one of claims 1 to 8.

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