Regional source and load interaction regulation method and system considering double-layer sensitivity model

Abstract

The application discloses a regional source network load interaction regulation method and system considering a double-layer sensitivity model, relates to the technical field of regional source network load power energy system interaction regulation, and comprises the following steps: acquiring initial data information of a regional source network load power energy system; constructing a first-layer power branch sensitivity analysis model based on the initial data information; constructing a second-layer adjustable resource sensitivity analysis model based on the initial data information; and performing node power regulation on overload lines of the regional source network load power energy system based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model. The application alleviates the technical problem that the prior art lacks a source network load interaction regulation method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of regional source network load power energy system interaction regulation, in particular to a regional source network load interaction regulation method and system considering a double-layer sensitivity model. BACKGROUND

[0002] With the continuous promotion of the "carbon neutralization and carbon peak" goal, the construction and development of new power systems have become the main battlefield and main force of power energy, and the construction of new power systems is an important part of the construction of new energy systems. Source, network, load and storage collaborative optimization and regulation have become key elements, and in-depth study of the characteristics of diversified adjustable resources, source network, network load, source network load friendly interaction and precise regulation have become the current technical hotspot.

[0003] Traditional power grid dispatching mainly starts from the power supply side and implements "power dispatching", which tracks the power load curve by dispatching conventional generator units to realize and maintain real-time balance between system power generation and power consumption load. However, the technology and resources in this regard have been constantly improved and the space for improvement is becoming smaller. The traditional dispatching and operation mode has been difficult to adapt to the challenges brought by the construction of new power systems with "double high", and it is urgent to change the existing operation and regulation mode and break through the corresponding key technologies. The new dispatching technology focuses on the load side and carries out "load dispatching", which realizes the rapid coordination and control among power supply, power grid and load when the power grid fails, and fundamentally solves the real-time balance of power supply and demand and the operation safety problem in emergency state. However, the existing technical achievements mainly focus on the role of source network load, source network load storage coordination interaction in improving new energy consumption, tapping the potential of demand side regulation, improving fault defense capability and analyzing power and electricity balance in specific application scenarios, and lack of sensitivity analysis of adjustable resources at power system power nodes and aggregated nodes from the perspective of source network load interaction, and high-sensitivity precise regulation method of source network and network load interaction. SUMMARY

[0004] The present application aims to provide a regional source network load interaction regulation method and system considering a double-layer sensitivity model to solve at least one of the above technical problems.

[0005] In a first aspect, an embodiment of the present application provides a regional source network load interaction regulation method considering a double-layer sensitivity model, applied to a regional source network load power energy system; the method comprises: obtaining initial data information of the regional source network load power 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 a power branch sensitivity matrix of the regional source network 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 regulation is performed on an overload line of the regional source network load power energy system.

[0006] Further, the initial data information comprises: power system branch parameters, generator parameters, power plant parameters, power system network topology relationships, balance node parameters, load resource types under each power node, load resource characteristics of each type, data attributes of each load resource, an effective judgment matrix, an overloaded line, and a power flow out-of-limit section.

[0007] Further, the first-layer power branch sensitivity analysis model comprises: 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 node injection power model comprises:

[0008]

[0009] P i = P G i - P D i, P j = P G j - P D j, P ij = P G ij - P D ij, θ i = θ G i - θ D i, θ j = θ G j - θ D j, x ij = x G ij - x D ij, where P i SP P i = P G i - P D i, P j = P G j - P D j, P ij = P G ij - P D ij, θ i = θ G i - θ D i, θ j = θ G j - θ D j, x ij = x G ij - x D ij, where P i Gen P i = P G i - P D i, P j = P G j - P D j, P ij = P G ij - P D ij, θ i = θ G i - θ D i, θ j = θ G j - θ D j, x ij = x G ij - x D ij, where P i Load P i = P G i - P D i, P j = P G j - P D j, P ij = P G ij - P D ij, θ i = θ G i - θ D i, θ j = θ G j - θ D j, x ij = x G ij - x D ij, where P ij P i = P G i - P D i, P j = P G j - P D j, P ij = P G ij - P D ij, θ i = θ G i - θ D i, θ j = θ G j - θ D j, x ij = x G ij - x D ij, where P i , θ j are voltage phase angles of nodes i and j, respectively; x ij is a branch reactance; j∈i is a branch connected to node i and the branch is numbered as i and j at both ends; the branch active power model comprises:

[0010]

[0011] The node injection power matrix model comprises:

[0012]

[0013] Wherein:

[0014]

[0015] In the formula: is the injection power matrix of nodes in the regional source network load power energy system; is the improved admittance matrix; is the voltage phase angle matrix of nodes; is the inverse matrix of [Μ] hang×lie is the matrix M is a matrix with hang rows and lie columns, and the element in the hang row and the lie column is represented by M hanglie ; The regional source network load power energy system has n power nodes and b branches; The active power matrix model of the branch includes:

[0016]

[0017] Wherein:

[0018]

[0019] In the formula: is the branch power flow matrix, numbered r1, r2, …, rb respectively; is the diagonal matrix composed of branch admittance, numbered r1, r2, …, rb respectively; is the matrix composed of the phase angle difference between the two ends of the line branch; is the branch-node network association matrix; The sensitivity model of branch power to node power includes:

[0020]

[0021] In the formula: is the active power sensitivity of the injection power of any node sp to the active power of the branch r or the flow section, simply referred to as branch sensitivity; is the partial derivative operation.

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

[0023]

[0024] In the formula: z hYh is the hth data in N data after standard 0-1 transformation; y h Yh is the hth data in N data before transformation; y min Ymin is the minimum value in N data before transformation; y max Ymax is the maximum value in N data before transformation; for cost type attribute data, the data standard 0-1 transformation method model comprises:

[0025]

[0026] The hierarchical structure model comprises: grouping the factors involved by using analytic hierarchy process, setting each group as a level to construct the structure model of analytic hierarchy process; the judgment matrix comprises: using the consistent matrix method when determining the weight factors between the factors in different levels, comparing all factors with each other, and using the relative scale; the single ordering of the levels and the consistency test thereof comprises: calculating the characteristic vector of the maximum eigenvalue of the judgment matrix, the elements of the characteristic vector being the ordering weight of the relative importance of the factors in the same level to the factors in the upper level; confirming the single ordering of the levels and carrying out the consistency test; the total ordering of the levels and the consistency test thereof comprises: from top to bottom, sequentially calculating the specific expression of the ordering model of the n elements in the k-1th level to the total target as follows:

[0027]

[0028] In the formula, w (k-1) is the ordering vector of the n elements in the k-1th level to the total target; is the nth element of w (k-1) ; the specific expression of the single ordering vector of the n k elements in the kth level to the jth element in the k-1th level as a criterion is as follows:

[0029]

[0030] In the formula, is the single ordering vector of the n k elements in the kth level to the jth element in the k-1th level as a criterion; is the jth element of w ; the weight of the element not dominated by the jth element is zero, and an n k ×n order matrix U (k) is obtained, and the specific expression of U (k) is as follows:

[0031]

[0032] In the formula, the jth column in U (k) is the n kThe single sorting vector of the jth element on the k-1th layer as the criterion of the nth element on the kth layer; The nth element of U (k) The nth element of U The nth element of U The nth element of U k The specific expression of the total sorting vector of each element on the kth layer to the total target is as follows:

[0033]

[0034] In the formula, w (k) The total sorting vector of each element on the kth layer to the total target; The nth element of w (k) The nth element of w

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

[0036] In the formula, w (k) The total sorting vector of each element on the kth layer to the total target; U (k) The jth column in U k The single sorting vector of the jth element on the k-1th layer as the criterion of the nth element on the kth layer; w (k-1) The sorting vector of the nth element on the k-1th layer to the total target; the calculation of the sensitivity of each adjustable resource includes:

[0037]

[0038] In the formula, The sensitivity vector of the adjustable resource; w (k) The total sorting vector of each element on the kth layer to the total target.

[0039] Further, based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model, the node power of the overloaded line of the regional source-grid-load power energy system is regulated, including: 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 power energy system to the node power is calculated; based on the sensitivity matrix, the target regulation node in the regional source-grid-load power energy system is determined; based on the preset power adjustment amount calculation model, the target power adjustment amount is determined; based on the target power adjustment amount, the power of the target regulation node is adjusted; if the target power adjustment amount is limited or insufficient, the adjustable resource amount of the system is reduced or adjusted to eliminate the line overload; wherein, the reduction or adjustment of the adjustable resource amount of the system includes: based on the second-layer adjustable resource sensitivity analysis model, the adjustable resource sensitivity of the sorted nodes is analyzed one by one, and the adjustable resource is called one by one according to the line load reduction demand.

[0040] Further, the preset power adjustment amount calculation model comprises:

[0041]

[0042] ΔP = - (P - P) / (P - P) gen P is a power adjustment amount of a generator or a power plant; P is a current actual power of an overload line; OL P is a current actual power of an overload line; P is a capacity limit of an overload line; P is a sensitivity of a generator or a power plant node m with positive maximum sensitivity; P is a sensitivity of a generator or a power plant node n with negative maximum sensitivity.

[0043] Further, based on the first layer power branch sensitivity analysis model, a line power to node power sensitivity matrix of an overload line of the regional source network load power energy system is calculated, comprising: selecting a balance node on the overload line as a voltage phase angle reference point; based on the network and topological relationship of the regional source network load power energy system, calculating a diagonal matrix of branch admittance, a branch-node network association matrix and an inverse matrix of an improved admittance matrix; based on the diagonal matrix of branch admittance, the branch-node network association matrix and the inverse matrix of the improved admittance matrix, calculating the sensitivity matrix.

[0044] In a second aspect, the embodiments of the present application further provide a regional source network load interaction regulation system considering a double-layer sensitivity model, applied to a regional source network load power energy system; comprising: an acquisition module, a first construction module, a second construction module and a regulation module; wherein the acquisition module is used to acquire initial data information of the regional source network load power 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 used to calculate a power branch sensitivity matrix of the regional source network 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 used to calculate the sensitivity of different adjustable load resources under each power branch node; and the regulation module is used to regulate node power of an overload line of the regional source network load power 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 electronic device is provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method of the first aspect when executing the computer program.

[0046] In a fourth aspect, a computer readable storage medium is provided, which stores computer instructions, and the computer instructions implement the method of the first aspect when executed by a processor.

[0047] The application provides a regional source-grid-load interaction regulation method and system considering a double-layer sensitivity model, which can accurately select high-sensitivity power nodes and effectively sort high-sensitivity adjustable resources when solving the application requirements of multiple scenarios of a power grid, and can provide reference and guidance for accurate regulation of flexible load resources, thereby solving the technical problem of lack of source-grid-load interaction regulation methods in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 A flowchart of a regional source-grid-load interaction regulation method considering a double-layer sensitivity model provided by the embodiments of the present application;

[0050] Figure 2 A schematic diagram of a regional source-grid-load interaction regulation system considering a double-layer sensitivity model provided by the embodiments of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0052] Embodiment one

[0053] Figure 1 A flowchart of a regional source-grid-load interaction regulation method considering a double-layer sensitivity model provided by the embodiments of the present application, which is applied to a regional source-grid-load power energy system. As shown in the figure, the method specifically comprises the following steps: Figure 1 ​

[0054] Step S102, obtaining initial data information of the regional source network load power energy system.

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

[0056] Step S104, constructing 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 network load power 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, performing node power regulation on the overloaded lines of the regional source network load power energy system.

[0059] Specifically, for a medium and high voltage power system, there are n power nodes and b branches, and the direct current power flow method is used to solve the power flow of the power system. 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,

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

[0061]

[0062] P i SP is the injection power of the node in the regional source network load power energy system; P i Gen is the active power injected by 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; x ij is the branch reactance; j∈i is the branch connected to node i and the branch numbers at both ends are i and j.

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

[0064]

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

[0066]

[0067] Wherein:

[0068]

[0069] In the formula: is the injection power matrix of the node in the regional source network load power energy system; is an improved susceptance matrix, the diagonal elements of the matrix are positive numbers, and the non-diagonal elements are negative numbers; is the voltage phase angle matrix of the node; is the inverse matrix of [Μ] hang×lie is a matrix M with hang rows and lie columns, and the element in the hangth row and the lie th column is represented by M hanglie When the number of columns is 1, 1 is omitted; the regional source network load power energy system has n power nodes and b branches in total;

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

[0071]

[0072] Wherein:

[0073]

[0074] In the formula: is the branch flow matrix, numbered as r1, r2, …, rb; is a diagonal matrix composed of branch susceptance, numbered as r1, r2, …, rb; is a matrix composed of the phase angle difference between the two ends of the line branch; is a branch-node network association matrix;

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

[0076]

[0077] In the formula: The active power sensitivity of the injection power of any node sp to the branch r or the power flow section, referred to as branch sensitivity; The partial derivative is calculated.

[0078] In an optional embodiment of the embodiment of the present application, the present application further provides an algorithm implementation process based on the first layer power branch sensitivity analysis model. Specifically as follows:

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

[0080] Step #2: According to the power system network and topological relationship, the diagonal matrix composed of branch admittance is obtained Branch-node network association matrix Improved admittance matrix Inverse matrix of

[0081] Step #3: According to the matrix Matrix Matrix The branch sensitivity angle is obtained

[0082] Step #4: According to the branch sensitivity angle The branch sensitivity is sorted by size, and the power node of the resource priority adjustment is selected;

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

[0084] After calculating the first layer branch sensitivity, and the specific node sorting of adjustment is known, the sensitivity of different adjustable load resources under the same node is analyzed; when the sensitivity of the adjustable load resource is analyzed, the load adjustment characteristics of the adjustable load resource are considered; the second layer adjustable resource sensitivity calculation system model is established, the load adjustment characteristics are important factors considered, the load adjustment characteristics are standardized and uniformly evaluated, the sensitivity calculation system model forms the sensitivity corresponding to each adjustable resource, and finally the adjustable load resource is sorted and called according to the sensitivity of the adjustable resource.

[0085] Specifically, the second layer adjustable resource sensitivity analysis model includes: data standard 0-1 transformation method model, construction hierarchical structure model, construction judgment matrix, hierarchical single sorting and consistency test, hierarchical total sorting and consistency test, and calculation of the sensitivity of each adjustable resource; wherein,

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

[0087]

[0088] In the formula: z h is the hth data in N data after standard 0-1 transformation; y h is the hth data in N data before transformation; y min is the minimum value in N data before transformation; y max is the maximum value in N data before transformation;

[0089] For cost type attribute data, the smaller the value is, the better the data is, and the data standard 0-1 transformation method model comprises:

[0090]

[0091] The hierarchical structure model is constructed, comprising: grouping the factors involved by using analytic hierarchy process, setting each group as a level, and constructing the structure model of analytic hierarchy process.

[0092] In an optional embodiment provided in the embodiment of the application, the levels in the hierarchical structure model comprise the following three types:

[0093] The first type is the highest level, i.e. the target level: there is only one element in the target level, and in the second level sensitivity algorithm analysis, the target level refers to the sensitivity of the regulation resource;

[0094] The second type is the middle level, i.e. the criterion level: the criterion level comprises the intermediate links involved in achieving the target, and if the second level is composed of a plurality of levels, the criterion level comprises the criteria and sub-criteria needed to be considered, and in the second level sensitivity algorithm analysis, the criterion level refers to the load regulation characteristics of the load regulation resource;

[0095] The third type is the bottom level, i.e. the scheme level: the scheme level comprises various types of schemes, modes, decisions and measures available for selection to achieve the target, and in the second level sensitivity algorithm analysis, the scheme level refers to the flexible and adjustable resources under each power system network node.

[0096] The judgment matrix is constructed, comprising: when determining the weight factors between the factors in different levels, a consistent matrix method is used to compare all the factors with each other, and the relative scale is used; after the relationship between the upper and lower levels is determined, the proportion of each element in the lower level associated with a certain element (target Z) in the upper level is determined; the factor A in the A level k is associated with the factors B1, B2, …, B n in the next level, then the judgment matrix B is constructed; the element b ij in the judgment matrix B is relative to A k , and B i is relative to B jThe numerical representation of the relative importance of the judgment matrix represents the relative importance between the factors of the lower level related to a factor of the upper level; the importance degree is represented by a 1-9 importance scale, and the relative importance value is obtained by expert scoring method, and finally the judgment matrix B=(b ij ) n×n If the importance ratio of element i and element j is b ij , then the importance ratio relationship of element j and element i satisfies b ji b ij =1.

[0097] In an optional embodiment provided by the embodiment of the application, the hierarchical single ordering and consistency check thereof include: calculating the characteristic vector of the maximum eigenvalue of the judgment matrix, and the elements of the characteristic vector are the ordering weights of the relative importance of the factors of the same level to the factors in the upper level; confirming the hierarchical single ordering and carrying out consistency check.

[0098] Specifically, the hierarchical single ordering includes: for the pair comparison matrix which is not consistent within the allowed range, i.e. the reciprocal matrix A, the characteristic vector of the maximum eigenvalue λ max is taken as the weight vector W, i.e. the hierarchical single ordering can be summarized as calculating the eigenvalue and the characteristic vector of the judgment matrix, i.e. the specific expression of the judgment matrix B is as follows:

[0099] BW=λ max W

[0100] In the formula, B is the judgment matrix; λ max is the maximum eigenvalue of B; W is the normalized characteristic vector corresponding to λ max ; and W i , the component of W, is the weight value of the corresponding factor ordering, i.e. the sensitivity of the factor of the same level.

[0101] For the consistency check of the hierarchical single ordering, it includes:

[0102] The characteristic vector corresponding to the maximum eigenvalue λ max of the judgment matrix is normalized and recorded as W, and the elements of W are the ordering weights or weight values of the relative importance of the factors of the same level to a factor in the upper level, and this process is called the hierarchical single ordering process; the hierarchical single ordering is confirmed, and the consistency check is carried out, and the consistency check refers to determining the allowed range of the pair comparison matrix, i.e. A, which is not consistent;

[0103] The consistency of the judgment matrix is checked, and the consistency index CI is calculated, and the specific expression of the consistency index model is as follows:

[0104]

[0105] In the formula, CI is the consistency index; λmax is the maximum eigenvalue of B; B is the judgment matrix; n is the order of the matrix.

[0106] CI=0 when the judgment matrix has complete consistency; λ max The larger n is, the larger CI is, and the worse the consistency of the judgment matrix is; CI=0, complete consistency, which is the most ideal case; CI close to 0, satisfactory consistency; the larger CI is, the more serious the inconsistency is.

[0107] To test whether the judgment matrix has satisfactory consistency, a standard of the consistency index CI of the matrix B needs to be found, and a random consistency index RI is introduced; 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, and the specific expression of the random consistency ratio model is as follows:

[0108]

[0109] In the formula, 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 the calculated , the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be adjusted.

[0111] In an optional embodiment provided in the embodiment of the application, the hierarchical total ranking and consistency test comprises: from top to bottom, layer by layer, the specific expression of the ranking model of n elements on the k-1th layer relative to the total target is as follows:

[0112]

[0113] In the formula, w (k-1) is the ranking vector of n elements on the k-1th layer relative to the total target; is the nth element of w (k-1) ;

[0114] The specific expression of the single ranking vector of the kth n k element relative to the jth element on the k-1th layer as a criterion is as follows:

[0115]

[0116] In the formula, is the single ranking vector of the kth n k element relative to the jth element on the k-1th layer as a criterion; is the jth element on the k-1th layer; the jth element of U

[0117] The element weight not dominated by the jth element is set to zero, and the n k ×n order matrix U (k) , U (k) The specific expression of U

[0118]

[0119] In the formula, U (k) The jth column of U k is the single ordering vector of the jth element of the k-1th layer as a criterion; is the nth element of U (k) ; is the nth k element of U ;

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

[0121]

[0122] In the formula, w (k) is the total ordering vector of each element on the kth layer to the total target; is the nth element of w (k) ;

[0123] Further,

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

[0125] In the formula, w (k) is the total ordering vector of each element on the kth layer to the total target; U (k) The jth column of U k is the single ordering vector of the jth element of the k-1th layer as a criterion; w (k-1) is the ordering vector of the n elements on the k-1th layer to the total target.

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

[0127]

[0128] In the formula: The single-order consistency ratio of the j-th element in the k-th layer to the (k-1)-th layer; It is a consistency index for the single ranking of the j-th element in the (k-1)-th layer by certain factors in the k-th layer; The average random consistency index for the k-th layer;

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

[0130] The specific expression for the consistency ratio model of the overall ranking at level k is as follows:

[0131]

[0132] Where: CR (k) The consistency ratio of the total ranking at level k; It is a consistency index for the single ranking of the j-th element in the (k-1)-th layer by certain factors in the k-th layer; The average random consistency index for the k-th layer; For w (k-1) The j-th element; w (k-1) This is the sorting vector of the n elements in the (k-1)th layer relative to the total target;

[0133] When CR (k) When the value is ≤0.1, the calculation results of the hierarchical overall ranking have satisfactory consistency.

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

[0135]

[0136] In the formula: For adjustable resource sensitivity vectors; w (k) This is the overall sorting vector of each element in the k-th layer relative to the overall goal.

[0137] In this embodiment of the invention, the adjustable resources are sorted according to their sensitivity to achieve precise control over them.

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

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

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

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

[0142] Step #4: Hierarchical total ranking and its consistency check;

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

[0144] When the attribute values of each alternative under each target are known, the hierarchical single ranking result of the scheme layer can be directly calculated according to the attribute values; when the attribute values of different alternatives under the corresponding target cannot be quantified, the weight factor of each scheme under each target is obtained by pairwise comparison under each target, that is, priority, and then the total weight factor of each scheme is calculated, and the priority of the scheme is sorted according to the size of the total weight factor of the total body, that is, the sensitivity of the adjustable resource.

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

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

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

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

[0149] Based on the diagonal matrix composed of branch admittance, branch-node network association matrix and inverse matrix of improved admittance matrix, the sensitivity matrix is calculated.

[0150] Step S1082, based on the sensitivity matrix, determining the target control node in the regional source network load power energy system.

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

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

[0153]

[0154] In the formula: ΔP gen is the power adjustment amount of the generator or power plant; P OL is the current actual power of the overloaded line; is the capacity limit of the overloaded line; is the sensitivity of the generator or power plant node m with positive maximum sensitivity; The sensitivity of the generator or power plant node n with the negative maximum sensitivity.

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

[0156] Step S1085, if the target power adjustment amount is limited or insufficient, reducing or adjusting the adjustable resource amount of the system to eliminate the line overload.

[0157] Wherein, the reduction or adjustment of the adjustable resource amount of the system comprises: based on the second layer adjustable resource sensitivity analysis model, the sorted nodes are successively subjected to adjustable resource sensitivity analysis, and the adjustable resources are successively called according to the line load reduction amount demand.

[0158] Specifically, when the line or transformer branch or power flow section branch is overloaded, the power regulation of the power grid has a demand, the sensitivity analysis method is applied to reduce the load or adjust the active power injection of the power node in the system, eliminate the power branch overload, and restore the normal operation of the power system; the reduced or adjusted load amount of the system is minimized, the branch overload is first eliminated by adjusting the generator or power plant output, and in the case that the generator or power plant output adjustment potential is exhausted or limited, the load amount of the system is reduced or adjusted.

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

[0160] Step #1: select the most serious overloaded line OL;

[0161] Step #2: determine the adjusted generator or power plant node m, node n; the sensitivity matrix of line power to node power is calculated 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 the positive maximum sensitivity value, and increase the output of the generator or power plant node n with the 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 the negative maximum sensitivity value, and increase the output of the generator or power plant node n with the positive maximum sensitivity value;

[0162] Step #3: determine the generator or power plant power adjustment amount ΔP gen of the node m, node n for eliminating the overload on the line OL, the specific expression of the calculation model of the generator or power plant power adjustment amount ΔP gen of the node m, node n is as follows:

[0163]

[0164] In the formula: ΔP gen is the generator or power plant power adjustment amount; |P OL| the current actual power of the overload line; | the capacity limit of the overload line; | the sensitivity of generator or power plant node m with positive maximum sensitivity; | the sensitivity of generator or power plant node n with negative maximum sensitivity;

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

[0166] Step #5: Calculate the power flow information, which can be simplified according to actual needs; according to the generator or power plant power adjustment, gradually adjust the power of the generator or power plant, calculate the power flow, according to the refreshed power flow result, check the effect of adjusting the generator or power plant output on eliminating line overload, until the adjustment of the generator or power plant is not in effect or the capacity of the generator or power plant is limited;

[0167] Step 6: Reduce or adjust the adjustable resource amount of the system; when the generator or power plant power adjustment is limited or insufficient, reduce or adjust the adjustable resource amount of the system to eliminate line overload; the reduction or adjustment method is generally similar to the process of adjusting the generator or power plant output, according to the first layer power branch sensitivity analysis model, calculate the line load reduction effect of node adjustable resource adjustment change, and the specific expression of the calculation model of node adjustable resource adjustment amount to line load reduction amount is as follows:

[0168]

[0169] In the formula: ΔP OL is the line load reduction amount; is the active power sensitivity of the injection power of node node to overload line OL; ΔP node is the adjustable resource adjustment amount of node node;

[0170] Step #7: Adjusting the aggregated adjustable resources under the sequence control node; on the basis of the node sensitivity determined by the first layer power branch sensitivity analysis, the second layer adjustable resource sensitivity analysis is calculated, 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 shedding demand;

[0171] Step #8: When the branch is overloaded, the sensitivity analysis method is applied to adjust the active power injection of the node directly infeasible or not adopted, and the flexible resource adjustment is directly performed in the step of adjusting the adjustable resource to eliminate the branch overload and restore the system operation state to normal.

[0172] Optionally, after step S108, the method provided by the embodiment of the application further includes: constructing a technical platform logical architecture and outputting regional source network load power energy system data information for application of the regional source network load friendly interaction precise regulation method.

[0173] Specifically, the technical platform logical architecture for application of the regional source network load friendly interaction precise regulation method includes:

[0174] On the basis of the implementation process of the double-layer sensitivity analysis method for adjusting the node power, the technical platform logical architecture for application of the regional source network load friendly interaction precise regulation method realizes the double-layer sensitivity analysis, and the technical platform logical architecture for application of the regional source network load friendly interaction precise regulation method includes three layers of regional power grid dispatching, regional source network load friendly interaction precise regulation platform, and local distribution network dispatching.

[0175] The regional power grid dispatching analyzes the application demand of the regional main network and the regional power grid, and interacts with the regional source network load friendly interaction precise regulation platform;

[0176] The regional source network load friendly interaction precise regulation platform supports the regional power grid dispatching to analyze the node sensitivity, and excavates and analyzes the resource application potential and sensitivity of the local distribution network to realize the precise matching of the power grid application demand and the regulation resources.

[0177] The local distribution network dispatching analyzes the resource characteristics and resource topological distribution of the local distribution network, and interacts with the regional source network load friendly interaction precise regulation platform;

[0178] The regional power grid dispatching sends specific line overload, transformer overload, and power flow section out-of-limit actual application demands according to the power flow information of the power grid, and performs the first layer power branch sensitivity analysis on the basis of the clear power grid application demand. The first layer power branch sensitivity analysis can be calculated by using the dispatching given method, that is, the post-analysis, or the direct calculation method, that is, the pre-analysis.

[0179] The scheduling given method is also a post analysis: the regional power grid scheduling sorts out the specific node order to be adjusted according to the existing evaluation and evaluation method, and the regional power grid scheduling transmits the node sorting information to the regional source network load friendly interaction accurate regulation and control platform, the regional source network load friendly interaction accurate regulation and control platform obtains the power flow result and the first layer power branch sensitivity or the sorting information of the adjustable power network node from the regional power grid scheduling, and then the regional source network load friendly interaction accurate regulation and control platform mainly performs the second layer adjustable resource sensitivity analysis on the basis of the first layer sensitivity result given by the regional power grid scheduling;

[0180] The direct calculation method is also a prior analysis: the regional source network load friendly interaction accurate regulation and control platform obtains the power grid topology distribution, load prediction information, power output, network parameters and the like from the regional power grid scheduling, the regional source network load friendly interaction accurate regulation and control platform first performs the first layer power branch sensitivity analysis calculation, then performs the second layer sensitivity analysis after the first layer power branch sensitivity analysis calculation is completed, and finally feeds back the double-layer sensitivity analysis result to the regional power grid scheduling to support the regional power grid scheduling to accurately analyze and accurately issue the regulation and control instruction.

[0181] Specifically, the output regional source network load power energy system data information includes power system power flow information, a first layer power branch sensitivity matrix, an order of an adjustable power system power node, each pair comparison matrix, a maximum eigenvalue of each pair comparison matrix, an eigenvector of each pair comparison matrix, a consistency index of each pair comparison matrix, each adjustable resource sensitivity, a node adjustable resource regulation and control priority order, and a quantitative effect of accurate regulation and control.

[0182] As can be known from the above description, the embodiment of the present application provides a regional source network load interaction regulation and control method considering a double-layer sensitivity model, and compared with the prior art, has the following technical effects:

[0183] (1) The method provided by the present application meets the actual application requirements in the scenarios of regional power grid line overload, transformer overload, power flow section line crossing and the like, and the regional source network load friendly interaction accurate regulation and 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 application can accurately select the most sensitive and effective power adjustment node to meet the application requirements of the regional power grid, and the second layer adjustable resource sensitivity analysis model constructed by the present application can uniformly quantify and regulate and control the multiple types of flexible adjustable resources, so as to realize the accurate and quantifiable regulation and control of the adjustable resources responding to the specific application requirements of the power grid;

[0185] (3) The technical platform logical architecture of the fine double-layer sensitivity analysis model and the regional source network load friendly interaction precise regulation method established by the application integrates power supply, power grid and load together, and performs orderly, observable, measurable and controllable source network load friendly interaction precise regulation, thereby providing reference and guidance for regional source network load power energy system safe operation and flexible load resource precise regulation analysis.

[0186] Embodiment Two

[0187] Figure 2 is a schematic diagram of a regional source network load interaction regulation system considering a double-layer sensitivity model according to an embodiment of the application, and is applied to a regional source network load power energy system. Figure 2 As shown in the figure, the system comprises an acquisition module 10, a first construction module 20, a second construction module 30 and a regulation module 40.

[0188] Specifically, the acquisition module 10 is configured to acquire initial data information of the regional source network load power energy system.

[0189] The first construction module 20 is configured 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 a power branch sensitivity matrix of the regional source network load power energy system.

[0190] The second construction module 30 is configured 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 power node of a branch.

[0191] The regulation module 40 is configured to perform node power regulation on an overload line of the regional source network 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 regulation module 40 is further configured to:

[0193] calculate, based on the first-layer power branch sensitivity analysis model, a sensitivity matrix of line power to node power of the overload line of the regional source network load power energy system;

[0194] determine a target regulation node in the regional source network load power energy system based on the sensitivity matrix;

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

[0196] adjust the power of the target regulation node based on the target power adjustment amount.

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

[0198] The adjustable resource amount of the system is reduced or adjusted, including: performing adjustable resource sensitivity analysis on the sorted nodes one by one based on the second-layer adjustable resource sensitivity analysis model, and calling adjustable resources one by one according to the line load reduction amount requirement.

[0199] The application further provides an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the method in the above embodiment one when executing the computer program.

[0200] The application further provides a computer readable storage medium, which stores computer instructions, and the computer instructions are executed by a processor to implement the method in the above embodiment one.

[0201] It is obvious for those skilled in the art that the application is not limited to the details of the above exemplary embodiments, and the application can be implemented in other specific forms without departing from the spirit or essential characteristics of the application. Therefore, the embodiments should be regarded as exemplary and non-limiting, the scope of the application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the application. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0202] In addition, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be combined appropriately to form other embodiments that those skilled in the art can understand.

Claims

1. A method for regional source-grid-load interaction regulation considering a two-layer sensitivity model, characterized in that, Applied to regional power generation, grid, and load systems; the method includes: Obtain the initial data information of the regional power 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 used to calculate 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 used to calculate 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 regulation is performed on the 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, nodal power regulation is performed on the overloaded lines of the regional source-grid-load power energy system, including: Based on the sensitivity analysis model of the first-layer power branch, 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. Based on the sensitivity matrix, the target control node in the regional source-grid-load power energy system is determined; The target power adjustment amount is determined based on the preset power adjustment amount calculation model; Based on the target power adjustment amount, adjust the power of the target control node; If the target power adjustment is limited or insufficient, the adjustable resources of the system are reduced or adjusted to eliminate line overload. The adjustable resource quantity of the reduction or adjustment 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 adjustable resources one by one according to the line load reduction requirements.

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, slack node parameters, load resource types under each power node, characteristics of various load resources, data attributes of each load resource, judgment matrix of effective values, overloaded lines, and cross sections exceeding power flow limits.

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 sensitivity model to node power; wherein, The injected power model of the node includes: ； In the formula: The injected power of the nodes in the regional power grid-load energy system; Inject active power into the node power source; Active power of node load; The active power of the branch circuit; , Let be the voltage phase angles of nodes i and j, respectively. For branch circuit reactance; The branch is 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 injected power matrix model of the node includes: ； in: ； In the formula: The injected power matrix for the nodes in the regional source-grid-load power energy system; An improved susceptance matrix; Let be the voltage phase angle matrix of the node; for The inverse matrix; For matrix Yes OK A matrix of column elements, and the first column... Line number The elements of the column are used This indicates that the regional power grid-load power energy system has n power nodes and b branch lines. The active power matrix model of the branch includes: ； in: ； In the formula: The branch power flow matrix is ​​numbered as follows: ; The diagonal matrix is ​​composed of branch susceptances, and the numbers are respectively: ; This is a matrix composed of the phase angle differences between the two ends of a line branch; The branch-node network correlation matrix; The sensitivity model of branch power to node power includes: ； In the formula: For any node The sensitivity of the injected power to the active power of the branch r or the power flow section is referred to as the branch sensitivity. To perform partial derivative operations.

4. The method according to claim 1, characterized in that: The second-layer adjustable resource sensitivity analysis model includes: a data standard 0-1 transformation model, constructing a hierarchical structure model, constructing a judgment matrix, hierarchical single ranking and its consistency test, hierarchical overall ranking and its consistency test, and calculating the sensitivity of each adjustable resource; among which, For benefit-type attribute data, the data standard 0-1 transformation model includes: ； In the formula: This is the h-th data point among N data points after standard 0-1 transformation; This refers to the h-th data point among the first N data points; The minimum value among the N data points before the transformation; The maximum value among the N data points before the transformation; For cost-type attribute data, the data standard 0-1 transformation 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 the consistent matrix method to determine the weighting factors between factors at different levels, comparing all factors pairwise, and using a relative scale. The hierarchical single ranking and its consistency test include: calculating the eigenvector of the largest eigenvalue of the judgment matrix, wherein the elements of the eigenvector are the ranking weights of the relative importance of factors at the same level to factors at the previous level; confirming the hierarchical single ranking and conducting a consistency test. The hierarchical overall ranking and its consistency check include: proceeding sequentially from top to bottom, and calculating the specific expression of the ranking model of the n elements in the (k-1)th layer relative to the overall target as follows: ； In the formula: This is the sorting vector of the n elements in the (k-1)th layer relative to the total target; for The nth element; kth layer The specific expression for a single sorting vector with each element taking the j-th element at the (k-1)-th level as the criterion is as follows: ； In the formula: For the kth layer A single sorted vector with each element as the criterion for the j-th element at the (k-1)-th level; for The j-th element; The weights of elements not dominated by the j-th element are set to zero, resulting in... 1-th order matrix The The specific expression is as follows: ； In the formula: The j-th column is the k-th layer. A single sorted vector with each element as the criterion for the j-th element at the (k-1)-th level; for The nth element; for The One element; The specific expression for the overall sorting vector of each element in the k-th layer relative to the overall goal is as follows: ； In the formula: This is the overall sorting vector of each element in the k-th layer relative to the overall goal; for The nth element; ； In the formula: This is the overall sorting vector of each element in the k-th layer relative to the overall goal; The j-th column is the k-th layer. A single sorted vector with each element as the criterion for the j-th element at the (k-1)-th level; This is the sorting vector of the n elements in the (k-1)th layer relative to the total target; The calculation of the sensitivity of each adjustable resource includes: ； In the formula: This is an adjustable resource sensitivity vector; This is the overall sorting vector of each element in the k-th layer relative to the overall goal.

5. The method according to claim 1, characterized in that: The preset power adjustment calculation model includes: ； In the formula: For power adjustment of generators or power plants; This represents the current actual power of the overloaded line. Capacity limitations for overloaded lines; The sensitivity of node m in a generator or power plant with a positive maximum sensitivity; The sensitivity of node n in a generator or power plant with negative maximum sensitivity.

6. The method according to claim 1, characterized in that: Based on the sensitivity analysis model of the first-layer power branch, the sensitivity matrix of the line power to the node power of the overloaded line in the regional source-grid-load power energy system is calculated, including: A balance node is selected on the overloaded line as a voltage phase angle reference point; Based on the network and topology of the regional source-grid-load power energy system, calculate the diagonal matrix of branch susceptance, the branch-node network correlation matrix, and the inverse matrix of the improved susceptance matrix; The sensitivity matrix is ​​calculated based on the diagonal matrix composed of the branch susceptance, the branch-node network correlation matrix, and the inverse matrix of the improved susceptance matrix.

7. A regional source-grid-load interaction control system considering a two-layer sensitivity model, characterized in that, Applied to regional power energy systems; 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 power 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 used to calculate 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 used to calculate 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 power energy system based on the first-layer power branch sensitivity analysis model and the second-layer adjustable resource sensitivity analysis model. The control module is also used for: Based on the sensitivity analysis model of the first-layer power branch, 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. Based on the sensitivity matrix, the target control node in the regional source-grid-load power energy system is determined; The target power adjustment amount is determined based on the preset power adjustment amount calculation model; Based on the target power adjustment amount, adjust the power of the target control node; If the target power adjustment is limited or insufficient, the adjustable resources of the system are reduced or adjusted to eliminate line overload. The adjustable resource quantity of the reduction or adjustment 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 adjustable resources one by one according to the line load reduction requirements.

8. 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, when executing the computer program, implements the method as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-6.

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