Regional source network load interactive regulation and control method and system considering double-layer sensitivity model

Abstract

The invention discloses a regional source network load interactive regulation and control method and system considering a double-layer sensitivity model, and relates to the technical field of regional source network load electric energy system interactive regulation and control, and the method comprises the steps: obtaining the initial data information of a regional source network load electric energy system; based on the initial data information, constructing a first-layer power branch sensitivity analysis model; based on the initial data information, constructing a second-layer adjustable resource sensitivity analysis model; and based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model, performing node power regulation and control on an overload line of the regional source network load power energy system. The technical problem that in the prior art, there is no source network load interaction regulation and control method is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of interactive regulation of regional source-grid-load electric energy systems, and in particular to a method and system for interactive regulation of regional source-grid-loads taking into account a double-layer sensitivity model. Background Art

[0002] With the continuous advancement of the "carbon neutrality and carbon peak" goals, the construction and development of new power systems have become the main battlefield and main force of electric energy. Building a new power system is an important part of building a new energy system. Coordinated optimization and regulation of sources, networks, loads and storage has become a key element. In-depth research on the characteristics of diversified adjustable resources and friendly interaction and precise regulation between sources and networks, networks and loads, and sources, networks and loads has become the current technical hotspot.

[0003] Traditional power grid dispatching mainly starts from the power supply side and performs "power dispatching". By dispatching conventional generators and tracking the power load curve, the real-time balance between the system's power generation and power load is achieved and maintained. However, the technology and resources in this area have been constantly being explored, and the room for improvement is getting smaller and smaller. The traditional dispatching operation mode has been unable to adapt to the challenges brought by the construction of a new power system with "double highs". It is urgent to transform the existing operation and control mode and break through the corresponding key technologies. The new dispatching technology focuses on the load side and carries out "load dispatching". When a power grid fails, it can achieve rapid coordination and control between power sources, power grids, and loads, fundamentally solving the problems of real-time balance of power supply and demand and operation safety in emergency situations. The existing technical achievements mainly focus on the role of coordinated interaction between source, grid, load, and storage in improving the consumption of new energy, exploring the demand-side regulation potential, improving fault defense capabilities, and analyzing power and electricity balance. There is a lack of relevant technical research on the sensitivity analysis of power system power nodes and adjustable resources aggregated under nodes from the perspective of source, grid, and load interaction, and high-sensitivity and precise control methods for source, grid, and grid-load interaction. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for regional source-grid-load interactive control taking into account a double-layer sensitivity model in order to solve at least one of the above technical problems.

[0005] In the first aspect, an embodiment of the present invention provides a regional source-grid-load interactive control method taking into account a double-layer sensitivity model, which is applied to a regional source-grid-load electric energy system; the method includes: obtaining initial data information of the regional source-grid-load electric energy system; based on the initial data information, constructing a first-layer power branch sensitivity analysis model; the first-layer power branch sensitivity analysis model is a model for calculating the power branch sensitivity matrix of the regional source-grid-load electric energy system; based on the initial data information, constructing a second-layer adjustable resource sensitivity analysis model; the second-layer adjustable resource sensitivity analysis model is a model for calculating the sensitivity of different adjustable load resources under each branch power node; based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model, node power control is performed on the overloaded lines of the regional source-grid-load electric energy system.

[0006] Furthermore, the initial data information includes: power system branch parameters, generator parameters, power plant parameters, power system network topology relationships, balancing node parameters, load resource types under each power node, characteristics of various types of load resources, data attributes of each load resource, effective judgment matrix, overloaded lines, and flow-exceeding sections.

[0007] Furthermore, the first-layer power branch sensitivity analysis model includes: a node injection power model, a branch active power model, a node injection power matrix model, a branch active power matrix model, and a branch power sensitivity model to node power; wherein the node injection power model includes:

[0008]

[0009] Where: P i SP is the injected power of the node in the regional source-grid-load electric energy system; P i Gen Inject active power into the node power supply; P i Load is the active power of the node load; P ij is the branch active power; θ i ,θ j are the voltage phase angles of node i and node j respectively; ij is the branch reactance; j∈i is the branch connected to the node i and the two ends of the branch are numbered i and j; the active power model of the branch includes:

[0010]

[0011] The matrix model of the injected power of the node includes:

[0012]

[0013] in:

[0014]

[0015] Where: An injection power matrix for nodes in the regional source-grid-load electric energy system; It is an improved susceptance matrix; is the voltage phase angle matrix of the node; for The inverse matrix of hang×lie The matrix M is a matrix with up rows and lie columns, and the element in the upth row and lieth column is represented by M hanglie Indicates that the regional source-grid-load power energy system has a total of n power nodes and b branches; the active power matrix model of the branches includes:

[0016]

[0017] in:

[0018]

[0019] Where: is the branch power flow matrix, numbered r1, r2, …, rb; is a diagonal matrix composed of branch susceptances, numbered r1, r2, …, rb; It is a matrix composed of the phase angle difference at both ends of the line branch; is a branch-node network association matrix; the sensitivity model of the branch power to the node power includes:

[0020]

[0021] Where: It is the active power sensitivity of the injected power of any node sp to the branch r or power flow section, referred to as branch sensitivity; To calculate partial derivatives.

[0022] Furthermore, the second-layer adjustable resource sensitivity analysis model includes: a data standard 0-1 transformation model, a hierarchical structure model, a judgment matrix, a hierarchical single sorting and consistency test, a hierarchical total sorting and consistency test, and calculation of the sensitivity of each adjustable resource; wherein, for benefit-type attribute data, the data standard 0-1 transformation model includes:

[0023]

[0024] Where: z his the hth data among N data after the standard 0-1 transformation; y h is the hth data among the first N data of the transformation; y min is the minimum value of the N data before transformation; y max is the maximum value among the N data before transformation; for cost attribute data, the data standard 0-1 transformation method model includes:

[0025]

[0026] The construction of the hierarchical structure model includes: using hierarchical analysis to group the factors involved, each group is set as a level, and the structural model of the hierarchical analysis is constructed; the construction of the judgment matrix includes: using the consistent matrix method when determining the weight factors between the factors at different levels, comparing all the factors with each other, and using relative scales; the hierarchical single sorting and its consistency test include: calculating the eigenvector of the maximum eigenvalue of the judgment matrix, the elements of the eigenvector are the ranking weights of the relative importance of the factors at the same level to the factors at the previous level; confirming the hierarchical single sorting and carrying out consistency test; the hierarchical total sorting and its consistency test include: proceeding from top to bottom layer by layer, and calculating the specific expression of the sorting model of n elements on the k-1th layer relative to the total target as follows:

[0027]

[0028] Where: w (k-1) is the sorting vector of n elements on the k-1th layer relative to the total target; w (k-1) The nth element of k The specific expression of the single sorting vector with the jth element on the k-1th layer as the criterion is as follows:

[0029]

[0030] Where: is the kth layer n k A single sorting vector with elements as the criterion for the jth element on the k-1th level; for The jth element of ; the weights of the elements not dominated by the jth element are taken as zero, and we get n k ×n-order matrix U (k) , the U (k) The specific expression is as follows:

[0031]

[0032] Where: U (k) The jth column in is the kth layer n kA single sorting vector with elements as the criterion for the jth element on the k-1th level; For U (k) The nth element of ; for The nth k elements; the specific expression of the total ranking vector of each element on the kth layer for the total target is as follows:

[0033]

[0034] Where: w (k) is the total sorting vector of each element on the kth layer for the total target; w (k) The nth element of ;

[0035] w (k) =U (k) w (k-1)

[0036] Where: w (k) is the total sorting vector of each element on the kth layer for the total target; U (k) The jth column in is the kth layer n k The single sorting vector with elements as the criterion for the jth element on the k-1th level; w (k-1) is the sorting vector of n elements on the k-1th layer relative to the overall target; the calculation of the sensitivity of each adjustable resource includes:

[0037]

[0038] Where: is the adjustable resource sensitivity vector; w (k) It is the total sorting vector of each element on the kth layer for the total target.

[0039] Furthermore, based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model, node power control is performed on the overloaded lines of the regional source-grid-load electric energy system, including: calculating the sensitivity matrix of the line power to the node power of the overloaded lines of the regional source-grid-load electric energy system based on the first-layer power branch sensitivity analysis model; determining the target control node in the regional source-grid-load electric energy system based on the sensitivity matrix; determining the target power adjustment amount based on a preset power adjustment amount calculation model; adjusting the power of the target control node based on the target power adjustment amount; if the target power adjustment amount is limited or insufficient, reducing or adjusting the adjustable resources of the system to eliminate line overload; wherein, reducing or adjusting the adjustable resources of the system includes: performing adjustable resource sensitivity analysis on the sorted nodes one by one based on the second-layer adjustable resource sensitivity analysis model, and calling the adjustable resources one by one according to the line load reduction demand.

[0040] Furthermore, the preset power adjustment amount calculation model includes:

[0041]

[0042] Where: ΔP gen P is the power adjustment of the generator or power plant; OL is the current actual power of the overloaded line; Capacity limitation for overloaded lines; is the sensitivity of the generator or power plant node m with the positive maximum sensitivity; is the sensitivity of the generator or power plant node n with the negative maximum sensitivity.

[0043] Furthermore, based on the first-layer power branch sensitivity analysis model, the sensitivity matrix of the line power of the overloaded line of the regional source-grid-load electric energy system to the node power is calculated, including: selecting a balancing node on the overloaded line as a voltage phase angle reference point; based on the network and topological relationship of the regional source-grid-load electric energy system, calculating the diagonal matrix of branch susceptance, the branch-node network association matrix and the inverse matrix of the improved susceptance matrix; based on the diagonal matrix of branch susceptance, the branch-node network association matrix and the inverse matrix of the improved susceptance matrix, calculating the sensitivity matrix.

[0044] In the second aspect, an embodiment of the present invention also provides a regional source-grid-load interactive control system taking into account a double-layer sensitivity model, which is applied to a regional source-grid-load electric energy system; comprising: an acquisition module, a first construction module, a second construction module and a control module; wherein the acquisition module is used to acquire the initial data information of the regional source-grid-load electric energy system; the first construction module is used to construct a first-layer power branch sensitivity analysis model based on the initial data information; the first-layer power branch sensitivity analysis model is a model for calculating the power branch sensitivity matrix of the regional source-grid-load electric energy system; the second construction module is used to construct a second-layer adjustable resource sensitivity analysis model based on the initial data information; the second-layer adjustable resource sensitivity analysis model is a model for calculating the sensitivity of different adjustable load resources under each branch power node; the control module is used to perform node power control on the overloaded lines of the regional source-grid-load electric energy system based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model.

[0045] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method described in the first aspect above is implemented.

[0047] The present invention provides a regional source-grid-load interactive control method and system taking into account a double-layer sensitivity model. When solving the multi-scenario application needs of the power grid, it can accurately select high-sensitivity power nodes and effectively sort high-sensitivity adjustable resources, and can provide reference and guidance for the precise control of flexible load resources, thereby alleviating the technical problem of the lack of source-grid-load interactive control methods in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flow chart of a regional source-grid-load interactive control method taking into account a double-layer sensitivity model provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a regional source-grid-load interactive control system taking into account a double-layer sensitivity model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Embodiment 1

[0053] Figure 1 The flowchart of a method for regional source-grid-load interactive control taking into account a double-layer sensitivity model provided according to an embodiment of the present invention is applied to a regional source-grid-load power energy system. Figure 1 As shown, the method specifically comprises the following steps:

[0054] Step S102, obtaining initial data information of the regional source-grid-load electric energy system.

[0055] Specifically, the initial data information includes: power system branch parameters, generator parameters, power plant parameters, power system network topology, balancing node parameters, load resource types under each power node, various load resource characteristics, data attributes of each load resource, effective judgment matrix, overloaded lines, and flow-exceeding sections.

[0056] Step S104, constructing a first-level power branch sensitivity analysis model based on the initial data information; the first-level power branch sensitivity analysis model is a model for calculating the power branch sensitivity matrix of the regional source-grid-load electric energy system.

[0057] Step S106, constructing a second-layer adjustable resource sensitivity analysis model based on the initial data information; the second-layer adjustable resource sensitivity analysis model is a model for calculating the sensitivity of different adjustable load resources under each branch power node.

[0058] Step S108, based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model, perform node power control on the overloaded lines of the regional source-grid-load power energy system.

[0059] Specifically, for a medium and high voltage power system, there are n power nodes and b branches, and the DC power flow method is used to solve the power system power flow. The first-level power branch sensitivity analysis model includes: node injection power model, branch active power model, node injection power matrix model, branch active power matrix model, and branch power sensitivity model to node power;

[0060] For any node, the node injection power model includes:

[0061]

[0062] Where: P i SP is the injected power of the node in the regional source-grid-load power energy system; P i Gen Inject active power into the node power supply; P i Load is the active power of the node load; P ij is the branch active power; θ i ,θ j are the voltage phase angles of node i and node j respectively; ij is the branch reactance; j∈i is the branch connected to node i and the two ends of the branch are numbered i and j;

[0063] For any branch, the active power model of the branch includes:

[0064]

[0065] For a power system, the node injection power matrix model includes:

[0066]

[0067] in:

[0068]

[0069] Where: An injection power matrix for nodes in a regional source-grid-load electric energy system; It is an improved susceptance matrix, the diagonal elements of the matrix are positive numbers, and the off-diagonal elements are negative numbers; is the voltage phase angle matrix of the node; for The inverse matrix of hang×lie The matrix M is a matrix with up rows and lie columns, and the element in the upth row and lieth column is represented by M hanglie Indicates that when the total number of columns is 1, 1 is omitted; the regional source-grid-load power energy system has a total of n power nodes and b branches;

[0070] For a power system, the active power matrix model of the branch includes:

[0071]

[0072] in:

[0073]

[0074] Where: is the branch power flow matrix, numbered r1, r2, …, rb; is a diagonal matrix composed of branch susceptances, numbered r1, r2, …, rb; It is a matrix composed of the phase angle difference at both ends of the line branch; is the branch-node network association matrix;

[0075] Specifically, for a power system, the sensitivity model of branch power or power flow section to node power includes:

[0076]

[0077] Where: It is the active power sensitivity of the injected power of any node sp to the branch r or power flow section, referred to as branch sensitivity; To calculate partial derivatives.

[0078] In an optional implementation provided by an embodiment of the present invention, the present invention also provides an algorithm implementation process based on the first-layer power branch circuit sensitivity analysis model. The details are as follows:

[0079] Step #1: Select the balance node as the voltage phase angle reference point;

[0080] Step #2: Based on the power system network and topology, we can obtain the diagonal matrix composed of branch susceptances: Branch-node network association matrix Improved Susceptance Matrix The inverse matrix

[0081] Step #3: According to the matrix matrix matrix Get the branch sensitivity angle

[0082] Step #4: Based on the branch sensitivity angle Sort the branch sensitivities by size and select the power nodes for priority resource adjustment;

[0083] Step #5: After calculating the first-layer branch sensitivity according to steps #1 to #4, proceed to the second-layer sensitivity calculation.

[0084] After calculating the sensitivity of the first-layer branches and when the specific node ranking of the adjustment is known, sensitivity analysis is performed on different adjustable load resources under the same node; when performing sensitivity analysis on adjustable load resources, the load regulation characteristics of the adjustable load resources are considered; a second-layer adjustable resource sensitivity calculation system model is established, with the load regulation characteristics as an important factor to be considered, and a standardized and unified evaluation of the load regulation characteristics is performed. The sensitivity calculation system model forms the resource-specific sensitivity corresponding to each adjustable resource, and finally the adjustable load resources are sorted and called according to the size of the adjustable resource sensitivity.

[0085] Specifically, the second-layer adjustable resource sensitivity analysis model includes: data standard 0-1 transformation method model, building a hierarchical structure model, constructing a judgment matrix, hierarchical single sorting and consistency test, hierarchical total sorting and consistency test, and calculating the sensitivity of each adjustable resource; among them,

[0086] For benefit attribute data, the larger the value, the better. The data standard 0-1 transformation method model includes:

[0087]

[0088] Where: z h is the hth data among N data after the standard 0-1 transformation; y h is the hth data among the first N data of the transformation; y min is the minimum value of the N data before transformation; y max is the maximum value of the N data before transformation;

[0089] For cost attribute data, the smaller the value, the better. The data standard 0-1 transformation model includes:

[0090]

[0091] Constructing a hierarchical model includes: using hierarchical analysis to group the factors involved, setting each group as a level, and constructing a hierarchical analysis structural model.

[0092] In an optional implementation provided by an embodiment of the present invention, the hierarchical structure model includes the following three categories:

[0093] The first category is the highest level, that is, the target level: there is only one element in the target level. In the second level sensitivity algorithm analysis, the target level refers to the sensitivity of the control resources;

[0094] The second category is the middle layer, also known as the criterion layer: the criterion layer includes the intermediate links involved in achieving the goal. It consists of a number of levels and contains the criteria and sub-criteria that need to be considered. In the second-layer sensitivity algorithm analysis, it refers to the load regulation characteristics of the adjustable load resources.

[0095] The third category is the lowest level, namely the plan level: the plan level is the various types of plans, modes, decisions, and measures that can be selected to achieve the goal. In the second-level sensitivity algorithm analysis, it refers to the various flexible and adjustable resources under a certain power system network node.

[0096] Constructing a judgment matrix includes: using the consistent matrix method to determine the weight factors between factors at different levels, comparing all factors in pairs, and using relative scales; specifically, after the relationship between the upper and lower layers is determined, determining the proportion of each element of the lower layer associated with an element of the upper layer (target Z) in the upper layer elements; factor A in level A k and factor B in the next level 1 , B 2 , …, B n If there is a connection or association, the constructed judgment matrix B; the element b in the judgment matrix B ij For A k Come on, B i To B jThe relative importance of the judgment matrix is ​​expressed as a numerical representation of the relative importance of the factors related to the previous level. The importance degree is scaled from 1 to 9, and the relative importance value is obtained by the expert scoring method. Finally, the judgment matrix B = (b ij ) n×n , if the ratio of the importance of element i to element j is b ij , then the importance ratio of element j to element i satisfies b ji b ij =1.

[0097] In an optional implementation provided by an embodiment of the present invention, hierarchical single sorting and its consistency check include: calculating the eigenvector of the maximum eigenroot of the judgment matrix, the elements of the eigenvector are the sorting weights of the relative importance of the factors at the same level to the factors at the previous level; confirming the hierarchical single sorting and conducting a consistency check.

[0098] Specifically, the hierarchical single sorting includes: for the pairwise comparison matrices that are inconsistent within the allowable range, that is, the positive and negative matrix A, the maximum characteristic root λ max The eigenvector of is used as the weight vector W, that is, the hierarchical single sorting can be summarized as calculating the eigenroot and eigenvector of the judgment matrix, that is, the specific expression satisfied by the judgment matrix B is as follows:

[0099] BW=λ max W

[0100] Where: B is the judgment matrix; λ max is the largest characteristic root of B; W is the corresponding λ max The normalized eigenvector of W; the component W i That is, the weight of the corresponding factor in a single ranking, that is, the sensitivity of the factor at this level.

[0101] The consistency check for hierarchical single sorting includes:

[0102] Corresponding to the maximum characteristic root λ of the judgment matrix max The characteristic vector of is recorded as W after normalization, and its elements are the ranking weights or weights of the relative importance of the factors at the same level to a factor at the previous level. This process is called the hierarchical single ranking process; confirm the hierarchical single ranking, and carry out consistency test. The consistency test refers to the pairwise comparison matrix, that is, A, to determine the allowable range of inconsistency;

[0103] Check the consistency of the matrix and calculate its consistency index CI. The specific expression of the consistency index model is as follows:

[0104]

[0105] Where: CI is the consistency index; λmax is the maximum characteristic root of B; B is the judgment matrix; n is the order of the matrix.

[0106] When the judgment matrix has complete consistency, CI = 0; λ max The larger the -n is, the larger the CI value is, and the worse the consistency of the judgment matrix is; CI = 0, there is complete consistency, and the judgment matrix A is a consistent matrix, which is the most ideal situation; the CI value is close to 0, and there is satisfactory consistency; the larger the CI value is, the more serious the inconsistency is.

[0107] To check whether the judgment matrix has satisfactory consistency, it is necessary to find the standard for measuring the consistency index CI of matrix B and introduce the random consistency index RI. The ratio of the consistency index CI of the judgment matrix to the average random consistency index RI of the same order is the random consistency ratio of the judgment matrix, denoted as CR. The specific expression of the random consistency ratio model is as follows:

[0108]

[0109] Where: CR is the random consistency ratio; CI is the consistency index; RI is the average random consistency index of the same order;

[0110] When the calculated CR<0 . 1 or calculated When , the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be readjusted.

[0111] In an optional implementation provided by an embodiment of the present invention, the hierarchical total sorting and its consistency check include: performing sequential operations layer by layer from top to bottom, and calculating the specific expression of the sorting model of n elements on the k-1th layer relative to the total target as follows:

[0112]

[0113] Where: w (k-1) is the sorting vector of n elements on the k-1th layer relative to the total target; w (k-1) The nth element of ;

[0114] Layer k n k The specific expression of the single sorting vector with the jth element on the k-1th layer as the criterion is as follows:

[0115]

[0116] Where: is the kth layer n k A single sorting vector with elements as the criterion for the jth element on the k-1th level; for The jth element of ;

[0117] The weights of elements not dominated by the jth element are taken as zero, resulting in n k ×n-order matrix U (k) , U (k) The specific expression is as follows:

[0118]

[0119] Where: U (k) The jth column in is the kth layer n k A single sorting vector with elements as the criterion for the jth element on the k-1th level; For U (k) The nth element of ; for The nth k elements;

[0120] The specific expression of the total sorting vector of each element on the kth layer for the total target is as follows:

[0121]

[0122] Where: w (k) is the total sorting vector of each element on the kth layer for the total target; w (k) The nth element of ;

[0123] Further:

[0124] w (k) =U (k) w (k-1)

[0125] Where: w (k) is the total sorting vector of each element on the kth layer for the total target; U (k) The jth column in is the kth layer n k The single sorting vector with elements as the criterion for the jth element on the k-1th level; w (k-1) is the sorting vector of n elements on the k-1th layer relative to the total target.

[0126] In the embodiment of the present invention, the consistency of the hierarchical total sorting calculation results is evaluated by calculating a test quantity similar to the single sorting; the high-level layer performs the test layer by layer; the consistency index of the single sorting of the jth element of the k-1th layer by some factors in the kth layer is The average random consistency index of the kth layer is The specific expression of the single sorting consistency ratio model of the jth element of the kth layer to the k-1th layer is as follows:

[0127]

[0128] Where: is the single sorting consistency ratio of the jth element of the kth layer to the k-1th layer; is the consistency index of the single ranking of the jth element in the k-1th layer by some factors in the kth layer; is the average random consistency index of the kth layer;

[0129] When calculated Or calculate When , the calculation results of hierarchical single sorting have satisfactory consistency;

[0130] The specific expression of the consistency ratio model of the total sorting of the kth layer is as follows:

[0131]

[0132] Where: CR (k) is the consistency ratio of the total sorting at the kth level; is the consistency index of the single ranking of the jth element in the k-1th layer by some factors in the kth layer; is the average random consistency index of the kth layer; w (k-1) The jth element of (k-1) is the sorting vector of n elements on the k-1th layer relative to the total target;

[0133] When CR (k) When ≤0.1, the calculation results of the total hierarchical order have satisfactory consistency.

[0134] Specifically, the sensitivity of each adjustable resource is calculated, including:

[0135]

[0136] Where: is the adjustable resource sensitivity vector; w (k) It is the total sorting vector of each element on the kth layer for the total target.

[0137] In the embodiment of the present invention, the adjustable resources are regulated and sorted according to the sensitivity of the adjustable resources to achieve accurate regulation of the adjustable resources.

[0138] In an optional implementation provided by the embodiment of the present invention, the embodiment of the present invention further provides an algorithm implementation process based on the second-layer adjustable resource sensitivity analysis model. The details are as follows:

[0139] Step #1: Build a hierarchical model;

[0140] Step #2: Construct the judgment (expert judgment, pairwise comparison) matrix;

[0141] Step #3: Hierarchical single sorting and consistency check;

[0142] Step #4: Total hierarchical ordering and consistency check;

[0143] Step #5: Calculate the sensitivity of each adjustable resource;

[0144] When the attribute values ​​of each alternative plan under each target are known, the hierarchical single ranking results of the plan layer can be directly calculated according to the attribute values; when the attribute values ​​of different alternative plans under the corresponding targets cannot be quantified, the weight factors of each plan under each target, that is, the priority, are obtained by comparing them pairwise under each target, and then the total weight factor of each plan is calculated, and the advantages and disadvantages of the plans are ranked according to the size of the overall total weight factor, that is, the sensitivity of resources is adjusted.

[0145] Specifically, step S108 also includes the following steps:

[0146] Step S1081, based on the first-layer power branch sensitivity analysis model, calculate the sensitivity matrix of the line power of the overloaded line of the regional source-grid-load power energy system to the node power.

[0147] Specifically, a balance node is selected on the overloaded line as a voltage phase angle reference point;

[0148] Based on the network and topological relationship of the regional source-grid-load power energy system, the diagonal matrix of branch susceptance, the branch-node network association matrix and the inverse matrix of the improved susceptance matrix are calculated;

[0149] The sensitivity matrix is ​​calculated based on the diagonal matrix composed of branch susceptances, the branch-node network association matrix and the inverse matrix of the improved susceptance matrix.

[0150] Step S1082, based on the sensitivity matrix, determine the target control node in the regional source-grid-load electric energy system.

[0151] Step S1083: determining a target power adjustment amount based on a preset power adjustment amount calculation model.

[0152] Optionally, the preset power adjustment amount calculation model includes:

[0153]

[0154] Where: ΔP gen P is the power adjustment of the generator or power plant; OL is the current actual power of the overloaded line; Capacity limitation for overloaded lines; is the sensitivity of the generator or power plant node m with the positive maximum sensitivity; is the sensitivity of the generator or power plant node n with the negative maximum sensitivity.

[0155] Step S1084: adjusting the power of the target control node based on the target power adjustment amount.

[0156] Step S1085: If the target power adjustment amount is limited or insufficient, the adjustable resource amount of the system is reduced or adjusted to eliminate line overload.

[0157] Among them, reducing or adjusting the amount of adjustable resources of the system includes: performing adjustable resource sensitivity analysis on the sorted nodes one by one based on the second-layer adjustable resource sensitivity analysis model, and calling the adjustable resources one by one according to the line load reduction demand.

[0158] Specifically, when a line or transformer branch or a flow section branch is overloaded, the grid dispatcher has a demand for adjusting power and applies sensitivity analysis to reduce the load within the system or adjust the active power injection of the power node to eliminate the overload of the power branch and restore the normal operation of the power system; to minimize the amount of load reduction or adjustment of the system, first eliminate the branch overload by adjusting the output of the generator or power plant, and then reduce or adjust the load of the system when the output adjustment potential of the generator or power plant is exhausted or limited.

[0159] The implementation process steps of step S108 are as follows:

[0160] Step #1: Select the line OL that is most overloaded;

[0161] Step #2: Determine the generator or power plant node m and node n to be adjusted; calculate the sensitivity matrix of line power to node power by the first-layer power branch sensitivity analysis model. When the line OL is overloaded in the positive direction: reduce or adjust the output of the generator or power plant node m with a positive maximum sensitivity value, and increase the output of the generator or power plant node n with a negative maximum sensitivity value; when the line OL is overloaded in the negative direction: reduce or adjust the output of the generator or power plant node m with a negative maximum sensitivity value, and increase the output of the generator or power plant node n with a positive maximum sensitivity value;

[0162] Step #3: Determine the power regulation ΔP of the generator or power plant at node m and node n gen ; To eliminate the overload on line OL, the power adjustment of the generator or power plant at node m and node n is ΔP gen The specific expression of the calculation model is as follows:

[0163]

[0164] Where: ΔP gen is the power adjustment of the generator or power plant; |P OL| is the current actual power of the overloaded line; Capacity limitation for overloaded lines; is the sensitivity of the generator or power plant node m with the positive maximum sensitivity; is the sensitivity of the generator or power plant node n with the negative maximum sensitivity;

[0165] Step #4: Adjust the output of the generator or power plant node m and node n; calculate the generator or power plant power adjustment ΔP gen After that, the various adjustments of the generator or power plant node m and node n can be adjusted according to the principles of equal proportion adjustment, priority of the absolute value of sensitivity, priority of the short electrical distance, etc., so that the total adjustment amount reaches ΔP gen ; The power adjustment of the generator or power plant cannot violate the upper and lower limit constraints of the generator or power plant output. If the calculated result exceeds, the actual adjustment value is adjusted according to the boundary constraints to reduce the adjustment amount of the generator or power plant ΔP gen If the adjustment amount is insufficient, it can be achieved by subsequent reduction or adjustment of adjustable resources;

[0166] Step #5: Calculate the power flow information, which can be simplified according to actual needs; according to the power adjustment amount of the generator or power plant, gradually adjust the power of the generator or power plant, calculate the power flow, and based on the updated power flow results, check the effect of adjusting the output of the generator or power plant on eliminating the line overload, until the adjustment of the generator or power plant is no longer effective or the adjustment of the generator or power plant is limited;

[0167] Step 6: Reduce or adjust the amount of adjustable resources in the system; when the power adjustment amount of the generator or power plant is limited or insufficient, the line overload is eliminated by reducing or adjusting the amount of adjustable resources in the system; the reduction or adjustment method is generally similar to the process of adjusting the output of the generator or power plant. The effect of the node adjustable resource adjustment change on the line load reduction is calculated according to the first-level power branch sensitivity analysis model. The specific expression of the calculation model of the node adjustable resource adjustment amount on the line load reduction amount is as follows:

[0168]

[0169] Where: ΔP OL Reduce line load; is the sensitivity of the injected power of node node to the active power of overloaded line OL; ΔP node The adjustable amount of resources for the node node;

[0170] Step #7: Sequentially control the adjustable resources aggregated under the node; based on the node sensitivity determined by the first-level power branch sensitivity analysis, the second-level adjustable resource sensitivity analysis calculation is performed, and the adjustable resource sensitivity analysis is performed on the sorted nodes one by one, and the adjustable resources are called one by one according to the line load reduction requirements;

[0171] Step #8: When the branch is overloaded, if it is not feasible or not to directly adjust the active power injection of the node using the sensitivity analysis method, directly enter the step of regulating adjustable resources to adjust the flexible resources to eliminate the branch overload and restore the system operation status to normal.

[0172] Optionally, after step S108, the method provided by the embodiment of the present invention further includes: constructing a technical platform logical architecture for applying the regional source-grid-load friendly interactive precise control method and outputting regional source-grid-load power energy system data information.

[0173] Specifically, the logical architecture of the technical platform for the application of the regional source-grid-load friendly interactive precise control method is constructed, including:

[0174] On the basis of clarifying the implementation process of adjusting node power by the two-layer sensitivity analysis method, the technical platform logical architecture of the application of the regional source-grid-load friendly interactive precise control method is constructed to realize the two-layer sensitivity analysis. The technical platform logical architecture of the application of the regional source-grid-load friendly interactive precise control method includes three layers: regional power grid dispatching, regional source-grid-load friendly interactive precise control platform, and local distribution network dispatching.

[0175] Regional power grid dispatching analyzes the application requirements of regional backbone networks and regional power grids, and interacts with the regional source-grid-load friendly interactive precision control platform;

[0176] The regional source-grid-load friendly interactive precise control platform supports the sensitivity of regional power grid dispatching and analysis nodes, explores and analyzes the application potential and sensitivity of local distribution network resources, and realizes the precise matching of power grid application needs and control resources;

[0177] The local distribution network dispatching analyzes the resource characteristics and resource topological distribution of the local distribution network, and exchanges information with the regional source-grid-load friendly interactive precise control platform;

[0178] Regional power grid dispatching issues specific actual application requirements such as line overload, transformer overload, and power flow section over-limit according to power grid flow information. On the basis of clear power grid application requirements, the first-level power branch sensitivity analysis is carried out. The first-level power branch sensitivity analysis calculation can adopt two methods: dispatching given method, i.e., post-analysis, and direct calculation method, i.e., pre-analysis.

[0179] The dispatching given method is also called post-analysis: the regional power grid dispatching sorts out the specific order of nodes to be adjusted according to the existing judgment and evaluation methods, and the regional power grid dispatching transmits the calling node sorting information to the regional source-grid-load friendly interactive precise control platform. The regional source-grid-load friendly interactive precise control platform obtains the flow results and the first-layer power branch sensitivity or the sorting information of the adjusted power network nodes from the regional power grid dispatching. Then, the regional source-grid-load friendly interactive precise control platform mainly conducts the second-layer adjustable resource sensitivity analysis based on the first-layer sensitivity results given by the regional power grid dispatching;

[0180] The direct calculation method is also known as ex ante analysis: the regional source-grid-load friendly interactive precise control platform obtains the grid topology distribution, load forecast information, power output, network parameters, etc. from the regional power grid dispatching. The regional source-grid-load friendly interactive precise control platform first performs the first-level power branch sensitivity analysis and calculation, and then performs the second-level sensitivity analysis after the first-level power branch sensitivity analysis and calculation is completed. Finally, the double-level sensitivity analysis results are fed back to the regional power grid dispatching to support the regional power grid dispatching to accurately analyze and issue precise control instructions.

[0181] Specifically, the output area source-grid-load power energy system data information includes power system flow information, the first-layer power branch sensitivity matrix, the order of power nodes in the power regulation system, each pairwise comparison matrix, the maximum eigenvalue of each pairwise comparison matrix, the eigenvector of each pairwise comparison matrix, the consistency index of each pairwise comparison matrix, the sensitivity of each adjustable resource, the priority ranking of adjustable resource regulation under the node, and the quantitative effect of precise regulation.

[0182] From the above description, it can be seen that the embodiment of the present invention provides a regional source-grid-load interactive control method taking into account a double-layer sensitivity model, which has the following technical effects compared with the prior art:

[0183] (1) The method provided by the present invention meets the practical application needs in solving scenarios such as regional power grid line overload, transformer overload, and power flow section crossing, and the regional source-grid-load friendly interactive precise control platform can realize and apply the proposed model and strategy;

[0184] (2) The first-layer power branch sensitivity analysis model constructed by the present invention can accurately select the most sensitive and effective power regulation nodes to solve the application needs of the regional power grid. The second-layer adjustable resource sensitivity analysis model constructed can uniformly quantify and regulate multiple types of flexible adjustable resources, thereby achieving accurate and quantifiable regulation of adjustable resources in response to specific application needs of the power grid;

[0185] (3) The technical platform logical architecture of the refined two-layer sensitivity analysis model and the regional source-grid-load friendly interactive precise control method established in the present invention coordinates the power supply, power grid and load together to carry out orderly, observable, measurable and controllable source-grid-load friendly interactive precise control, which can provide reference and guidance for the safe operation of the regional source-grid-load power energy system and the precise control analysis of flexible load resources.

[0186] Embodiment 2

[0187] Figure 2 Schematic diagram of a regional source-grid-load interactive control system taking into account a double-layer sensitivity model provided according to an embodiment of the present invention, which is applied to a regional source-grid-load power energy system. Figure 2 As shown, the system includes: an acquisition module 10, a first construction module 20, a second construction module 30 and a control module 40.

[0188] Specifically, the acquisition module 10 is used to obtain initial data information of the regional source-grid-load electric energy system;

[0189] A first construction module 20 is used to construct a first-level power branch sensitivity analysis model based on initial data information; the first-level power branch sensitivity analysis model is a model for calculating the power branch sensitivity matrix of the regional source-grid-load power energy system;

[0190] The second construction module 30 is used to construct a second-layer adjustable resource sensitivity analysis model based on the initial data information; the second-layer adjustable resource sensitivity analysis model is a model for calculating the sensitivity of different adjustable load resources under each branch power node;

[0191] The control module 40 is used to perform node power control on overloaded lines of the regional source-grid-load power energy system based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model.

[0192] Specifically, the control module 40 is further used for:

[0193] Based on the first-layer power branch sensitivity analysis model, the sensitivity matrix of the line power to the node power of the overloaded line of the regional source-grid-load power energy system is calculated;

[0194] Based on the sensitivity matrix, the target control nodes in the regional source-grid-load power energy system are determined;

[0195] Determining a target power adjustment amount based on a preset power adjustment amount calculation model;

[0196] Based on the target power adjustment amount, adjusting the power of the target control node;

[0197] If the target power adjustment amount is limited or insufficient, the amount of adjustable resources of the system is reduced or adjusted to eliminate line overload;

[0198] Among them, reducing or adjusting the amount of adjustable resources of the system includes: performing adjustable resource sensitivity analysis on the sorted nodes one by one based on the second-layer adjustable resource sensitivity analysis model, and calling the adjustable resources one by one according to the line load reduction demand.

[0199] The present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method in the above-mentioned embodiment 1 when executing the computer program.

[0200] The present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method in the above-mentioned embodiment 1 is implemented.

[0201] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0202] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A regional source-grid-load interaction control method taking into account a double-layer sensitivity model, characterized in that: Applied to a regional source-grid-load electric energy system; the method comprises: Obtaining initial data information of the regional source-grid-load electric energy system; Based on the initial data information, a first-layer power branch sensitivity analysis model is constructed; the first-layer power branch sensitivity analysis model is a model for calculating the power branch sensitivity matrix of the regional source-grid-load power energy system; Based on the initial data information, a second-layer adjustable resource sensitivity analysis model is constructed; the second-layer adjustable resource sensitivity analysis model is a model for calculating the sensitivity of different adjustable load resources under each branch power node; Based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model, node power control is performed on the overloaded lines of the regional source-grid-load power energy system.

2. The method according to claim 1, characterized in that: The initial data information includes: power system branch parameters, generator parameters, power plant parameters, power system network topology, balancing node parameters, load resource types under each power node, various load resource characteristics, data attributes of each load resource, effective judgment matrix, overloaded lines, and flow-exceeding sections.

3. The method according to claim 1, characterized in that: The first-layer power branch sensitivity analysis model includes: a node injection power model, a branch active power model, a node injection power matrix model, a branch active power matrix model, and a branch power to node power sensitivity model; wherein, The injection power model of the node includes: Where: P i SP is the injected power of the node in the regional source-grid-load electric energy system; P i Gen Inject active power into the node power supply; P i Load is the active power of the node load; P ij is the branch active power; θ i ,θ j are the voltage phase angles of node i and node j respectively; ij is the branch reactance; j∈i is the branch connected to node i and the two ends of the branch are numbered i and j; The active power model of the branch includes: The matrix model of the injected power of the node includes: in: Where: An injection power matrix for nodes in the regional source-grid-load electric energy system; It is an improved susceptance matrix; is the voltage phase angle matrix of the node; for The inverse matrix of hang×lie The matrix M is a matrix with up rows and lie columns, and the element in the upth row and lieth column is represented by M hanglie Indicates that the regional source-grid-load power energy system has a total of n power nodes and b branches; The active power matrix model of the branch includes: in: Where: is the branch power flow matrix, numbered r1, r2, …, rb; is a diagonal matrix composed of branch susceptances, numbered r1, r2, …, rb; It is a matrix composed of the phase angle difference at both ends of the line branch; is the branch-node network association matrix; The sensitivity model of the branch power to the node power includes: Where: It is the active power sensitivity of the injected power of any node sp to the branch r or power flow section, referred to as branch sensitivity; To calculate partial derivatives.

4. The method according to claim 1, characterized in that: The second-layer adjustable resource sensitivity analysis model includes: data standard 0-1 transformation method model, building a hierarchical structure model, constructing a judgment matrix, hierarchical single sorting and consistency test, hierarchical total sorting and consistency test, and calculating the sensitivity of each adjustable resource; wherein, For benefit-based attribute data, the data standard 0-1 transformation model includes: Where: z h is the hth data among the N data after the standard 0-1 transformation; yh is the hth data among the N data before the transformation; y min is the minimum value of the N data before transformation; y max is the maximum value of the N data before transformation; For cost-type attribute data, the data standard 0-1 transformation method model includes: The construction of the hierarchical structure model includes: using hierarchical analysis to group the factors involved, setting each group as a level, and constructing a hierarchical analysis structure model; The construction of the judgment matrix includes: using a consistent matrix method when determining weight factors between factors at different levels, comparing all factors in pairs, and using relative scales; The hierarchical single sorting and consistency test thereof include: calculating the eigenvector of the maximum eigenvalue of the judgment matrix, the elements of the eigenvector being the sorting weights of the relative importance of the factors at the same level to the factors at the previous level; confirming the hierarchical single sorting and conducting consistency test; The hierarchical total sorting and consistency check include: performing sequential operations layer by layer from top to bottom, and calculating the specific expression of the sorting model of n elements on the k-1th layer relative to the total target as follows: Where: w (k-1) is the sorting vector of n elements on the k-1th layer relative to the total target; w (k-1) The nth element of ; Layer k n k The specific expression of the single sorting vector with the jth element on the k-1th layer as the criterion is as follows: Where: is the kth layer n k A single sorting vector with elements as the criterion for the jth element on the k-1th level; for The jth element of ; The weights of elements not dominated by the jth element are taken as zero, resulting in n k ×n-order matrix U (k) , the U (k) The specific expression is as follows: Where: U (k) The jth column in is the kth layer n k A single sorting vector with elements as the criterion for the jth element on the k-1th level; For U (k) The nth element of ; for The nth k elements; The specific expression of the total sorting vector of each element on the kth layer for the total target is as follows: Where: w (k) is the total sorting vector of each element on the kth layer for the total target; w (k) The nth element of ; w (k) =U (k) w (k-1) Where: w (k) is the total sorting vector of each element on the kth layer for the total target; U (k) The jth column in is the kth layer n k The single sorting vector with elements as the criterion for the jth element on the k-1th level; w (k-1) is the sorting vector of n elements on the k-1th layer relative to the total target; The calculating of the sensitivity of each adjustable resource includes: Where: is the adjustable resource sensitivity vector; w (k) It is the total sorting vector of each element on the kth layer for the total target.

5. The method according to claim 1, characterized in that: Based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model, node power regulation is performed on the overloaded line of the regional source-grid-load power energy system, including: Based on the first-layer power branch sensitivity analysis model, calculate the sensitivity matrix of the line power of the overloaded line of the regional source-grid-load power energy system to the node power; Based on the sensitivity matrix, determining a target control node in the regional source-grid-load electric energy system; Determining a target power adjustment amount based on a preset power adjustment amount calculation model; Adjusting the power of the target control node based on the target power adjustment amount; If the target power adjustment amount is limited or insufficient, reducing or adjusting the adjustable resource amount of the system to eliminate line overload; The reducing or adjusting the amount of adjustable resources of the system includes: performing adjustable resource sensitivity analysis on the sorted nodes one by one based on the second-layer adjustable resource sensitivity analysis model, and calling the adjustable resources one by one according to the line load reduction demand.

6. The method according to claim 5, characterized in that: The preset power adjustment amount calculation model includes: Where: ΔP gen is the power adjustment of the generator or power plant; |P OL | is the current actual power of the overloaded line; Capacity limitation for overloaded lines; is the sensitivity of the generator or power plant node m with the positive maximum sensitivity; is the sensitivity of the generator or power plant node n with the negative maximum sensitivity.

7. The method according to claim 5, characterized in that: Based on the first-layer power branch sensitivity analysis model, calculating the sensitivity matrix of the line power of the overloaded line of the regional source-grid-load power energy system to the node power, including: Selecting a balance node on the overloaded line as a voltage phase angle reference point; Based on the network and topological relationship of the regional source-grid-load electric energy system, a diagonal matrix of branch susceptance, a branch-node network association matrix and an inverse matrix of the improved susceptance matrix are calculated; The sensitivity matrix is ​​calculated based on the diagonal matrix composed of the branch susceptances, the branch-node network association matrix and the inverse matrix of the improved susceptance matrix.

8. A regional source-grid-load interactive control system taking into account a double-layer sensitivity model, characterized in that: Applied to regional source-grid-load power energy system; including: acquisition module, first construction module, second construction module and control module; wherein, The acquisition module is used to acquire the initial data information of the regional source-grid-load electric energy system; The first construction module is used to construct a first-layer power branch sensitivity analysis model based on the initial data information; the first-layer power branch sensitivity analysis model is a model for calculating the power branch sensitivity matrix of the regional source-grid-load power energy system; The second construction module is used to construct a second-layer adjustable resource sensitivity analysis model based on the initial data information; the second-layer adjustable resource sensitivity analysis model is a model for calculating the sensitivity of different adjustable load resources under each branch power node; The control module is used to perform node power control on the overloaded lines of the regional source-grid-load electric energy system based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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