Key line identification method and system influencing bearing capacity of new energy power grid

Abstract

The invention relates to the technical field of electric power systems, and discloses a key line identification method and system influencing the bearing capacity of a new energy power grid. Taking the average load rate of the line, the new energy abandoned electric quantity after the line N-1, the minimum load margin of the system line after the line N-1 and the new energy small interference stability margin of the system after the line N-1 as evaluation indexes of the power grid bearing capacity influence under the condition that the new energy output uncertainty factor is considered; and determining the total weight coefficient of the evaluation index of each candidate key line by using an analytic hierarchy process, thereby identifying the key line by using the comparison result of the total weight coefficient of each power grid line, comprehensively and accurately identifying the key line of the near-region power grid accessed by the large-scale new energy, and ensuring the safe and stable operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for identifying key lines that affect the carrying capacity of a new energy power grid. Background Art

[0002] With the advancement of the construction of new power systems, the proportion of new energy represented by wind and solar power will become higher and higher. For new energy systems with a high proportion, large-scale new energy is often connected to the power grid from multiple points, which has a great impact on the nearby power grid.

[0003] The grid structure of the local power grid is one of the main factors limiting the carrying capacity of renewable energy. In order to ensure the absorption of renewable energy, it is necessary to identify the key lines of the local power grid where large-scale renewable energy is connected, so as to focus on them in system operation and planning, and ensure the safe and stable operation of the local power grid connected to large-scale renewable energy.

[0004] There are many methods for identifying critical lines in power systems, mainly based on complex network theory and power flow distribution methods. The complex network theory method uses the betweenness of branches as the basis for judging the criticality of branches, but often does not consider the physical characteristics of power flow transmission in power systems, or simply considers the weight of power transmission between all pairs of generators and load nodes in the system line. The power flow distribution method only considers the impact of power flow distribution on the power grid after the power grid line is disconnected, that is, it only considers the impact on the steady-state characteristics of the system.

[0005] The current methods for near-region power grids with large-scale renewable energy access are difficult to comprehensively and accurately identify the key lines of near-region power grids with large-scale renewable energy access, resulting in poor decision support for the safe and stable operation of the system. Summary of the invention

[0006] In view of this, the present invention provides a method and system for identifying key lines that affect the carrying capacity of the new energy power grid, which solves the technical problem that the current method for the near-area power grid with large-scale new energy access is difficult to comprehensively and accurately identify the key lines of the near-area power grid with large-scale new energy access, resulting in poor decision support effect of the identification results on the safe and stable operation of the system.

[0007] A first aspect of the present invention provides a method for identifying key lines that affect the carrying capacity of a new energy power grid, comprising:

[0008] According to the nearby power grid line set, multiple power grid lines with transient stability are selected to form a candidate set of power grid lines;

[0009] Determine the evaluation index of the impact of the grid carrying capacity, and determine the evaluation index value of each grid line in the candidate set of grid lines, the evaluation index includes the average load rate of the line, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy in the system after line N-1;

[0010] The weight coefficient of each evaluation index value is determined by using a hierarchical analysis method, and the total weight coefficient corresponding to each power grid line in the power grid line candidate set is determined according to the weight coefficient of each evaluation index value;

[0011] The key lines are identified based on the comparison results of the total weight coefficients of each power grid line.

[0012] Preferably, the step of selecting a plurality of transiently stable power grid lines according to the near-area power grid line set to form a candidate set of power grid lines comprises:

[0013] Conduct fault simulation on the nearby power grid system according to the preset typical scenarios of renewable energy output, and select multiple transiently stable power grid lines based on the simulation results;

[0014] A candidate set of power grid lines is formed based on the screened multiple transiently stable power grid lines.

[0015] Preferably, the average load rate of the line is calculated as follows:

[0016]

[0017] In the formula, is the average load factor, For a typical scenario The probability of occurrence, is the number of typical scenes, is a typical scene index, is the active power flowing through the i-th circuit, is the maximum allowable value of active power flowing through the i-th line.

[0018] Preferably, the method for calculating the amount of abandoned new energy after line N-1 is:

[0019] For each nearby power grid system under a preset typical scenario, an objective function is constructed with the minimization of the amount of abandoned renewable energy as the optimization goal, and the constraints of the objective function are determined to construct an optimization problem for the amount of abandoned renewable energy;

[0020] The optimization problem of the amount of abandoned renewable energy is solved to obtain the minimum amount of abandoned renewable energy in the nearby power grid system under each preset typical scenario;

[0021] According to the occurrence probability of each preset typical scenario, the minimum value of the renewable energy curtailment amount of the nearby power grid system under each preset typical scenario is added up to obtain the renewable energy curtailment amount after line N-1.

[0022] Preferably, the calculation method of the minimum load margin of the system line after line N-1 is:

[0023]

[0024] In the formula, is the minimum load margin of the system line after line N-1, is the active power flowing through the kth line after the i-th line is disconnected, calculated by DC power flow. is the maximum allowable value of active power flowing through the kth line, To obtain the index of the line through DC power flow calculation, is the number of lines, This is the amount of abandoned electricity from renewable energy sources after line N-1.

[0025] Among them, lines i and k are both considered power grid lines, and lines i and k influence each other. Therefore, after the i-th line is disconnected, the active power flowing through the k-th line obtained by DC power flow calculation will also change compared with before the i-th line is disconnected.

[0026] Preferably, the calculation method of the new energy small disturbance stability margin of the system after line N-1 is:

[0027]

[0028] in,

[0029] In the formula, is the stability margin of the new energy small disturbance of the system after line N-1, is the generalized short-circuit ratio after the i-th line is disconnected under the j-th scenario mode operation, is the matrix eigenvalue, SB new,j is the capacity diagonal matrix composed of the new energy output of each new energy feeding point under the jth scenario, For SB new,j The inverse matrix of is the power frequency node admittance matrix of the Thevenin equivalent network of the renewable energy feeding point relative to the power grid after the i-th line is disconnected.

[0030] Preferably, the step of using the analytic hierarchy process to determine the weight coefficient of each evaluation index value, and determining the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of each evaluation index value, comprises:

[0031] Constructing a hierarchical structure based on the analytic hierarchy process, the hierarchical structure comprising a top layer, a middle layer and a bottom layer, wherein the top layer is a key line, the middle layer is an evaluation index, and the bottom layer is each power grid line in the power grid line candidate set;

[0032] Using a 9-level comparison scale to compare the adjacent upper and lower layers in the hierarchical structure in pairs, and constructing a judgment matrix between the adjacent upper and lower layers;

[0033] Performing a consistency check on the judgment matrix, and when the judgment matrix satisfies the consistency check, calculating a weight coefficient of an evaluation index of each power grid line in the power grid line candidate set by using the judgment matrix;

[0034] The total weight coefficients respectively corresponding to the power grid lines in the power grid line candidate set are determined according to the weight coefficients of the evaluation indicators of the power grid lines in the power grid line candidate set.

[0035] In a second aspect, the present invention further provides a key line identification system that affects the carrying capacity of a new energy power grid, comprising:

[0036] A line candidate module is used to select multiple transiently stable power lines according to a nearby power line set to form a power line candidate set;

[0037] An evaluation index determination module is used to determine the evaluation index affecting the grid carrying capacity, and determine the evaluation index value of each grid line in the grid line candidate set, wherein the evaluation index includes the average load rate of the line, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy in the system after line N-1;

[0038] A weight determination module, used to determine the weight coefficient of each evaluation index value by using a hierarchical analysis method, and determine the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of each evaluation index value;

[0039] The key line identification module is used to identify the key lines according to the comparison results of the total weight coefficients of each power grid line.

[0040] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for identifying key lines that affect the carrying capacity of the new energy power grid as described in the first aspect.

[0041] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for identifying key lines that affect the carrying capacity of the new energy power grid as described in the first aspect.

[0042] It can be seen from the above technical scheme that the present invention selects multiple transiently stable power grid lines as candidate key lines, and uses the average load rate of the line under the uncertainty factor of new energy output, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy in the system after line N-1 as evaluation indicators of the influence of power grid carrying capacity, and then uses the hierarchical analysis method to determine the total weight coefficient of the evaluation index of each candidate key line, so as to identify the key line by comparing the total weight coefficients of each power grid line, thereby improving the accuracy of key line identification of the nearby power grid, so as to comprehensively and accurately identify the key lines of the nearby power grid with large-scale new energy access, and ensure the safe and stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 An application environment diagram of a method for identifying key lines that affect the carrying capacity of a new energy power grid provided by an embodiment of the present invention;

[0044] Figure 2 A flowchart of a method for identifying key lines that affect the carrying capacity of a new energy power grid provided by an embodiment of the present invention;

[0045] Figure 3 A schematic diagram of a hierarchical structure provided by an embodiment of the present invention;

[0046] Figure 4 A structural schematic diagram of a key line identification system that affects the carrying capacity of a new energy power grid provided by an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0049] The key line identification method that affects the carrying capacity of the new energy power grid provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. The power grid system communicates with the server 102 via the network. The data storage system can store data that the server 102 needs to process. The data storage system can be integrated on the server 102, or placed on the cloud or other network servers. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0050] like Figure 2 As shown, the embodiment of the present application provides a method for identifying key lines that affect the carrying capacity of a new energy power grid, and the method is applied to Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S4. Among them:

[0051] Step S1: Filter out a plurality of transiently stable power grid lines according to a nearby power grid line set to form a candidate power grid line set.

[0052] Among them, local grid lines usually refer to the power transmission and distribution network within a specific geographical area, such as a city, industrial area or residential area. These lines are part of the entire power system and are responsible for transmitting electricity from substations to end users, or exchanging and optimizing electricity between different substations.

[0053] Among them, for the nearby power grid lines under the typical scenario set of renewable energy output, the influence of the lines on the transient stability (power angle stability, transient voltage stability) of the system can be analyzed by using methods such as power flow analysis, generalized short-circuit ratio analysis, and time domain transient simulation methods.

[0054] Specifically, the process of selecting multiple transiently stable power grid lines from the nearby power grid line set to form a candidate set of power grid lines includes:

[0055] Step S101: perform fault simulation on a nearby power grid system according to a preset typical scenario of renewable energy output, and select multiple transiently stable power grid lines according to the simulation results.

[0056] The preset typical scenarios of renewable energy output are based on the historical operating conditions of the power grid system, such as maximum output under ideal weather conditions, seasonal variation patterns, daytime fluctuations, extreme climate events, and multi-energy complementary configurations. In general, the fault simulation of the nearby power grid system under the preset typical scenarios can be performed by using the time domain transient simulation method. The simulation results include parameters such as voltage, and the simulation results are used to identify multiple power grid lines with transient stability.

[0057] Step S102: forming a candidate grid line set according to the screened multiple transiently stable grid lines.

[0058] For example, for a power grid with a high proportion of renewable energy, it is assumed that the set of nearby power grid lines connected to large-scale renewable energy is {L1, L2, ..., L N}, N is the number of lines. For the i-th line L i , set the fault set {F i,1 ,F i,2 ,...,F i,Ki}, F i,j is the jth fault to be tested on the i-th line, K i is the number of faults to be assessed for the i-th line. Since the output of renewable energy has random fluctuations, the typical scenario of renewable energy output is considered as {S1,S2,...,S M}, M is the number of typical scenarios, and each typical scenario includes AC power flow data and stable data, and DC power flow data for transient stability simulation. For the i-th line L, firstly, based on the typical scenario of renewable energy output and the fault set {F i,1 ,F i,2 ,...,F i,Ki}Fault simulation is performed, and multiple power grid lines without transient stability (no power angle instability or transient voltage instability) are screened out to form a candidate set of power grid lines.

[0059] It should be noted that for the fault set, the lines that cause transient instability of the system are screened out as part of the critical lines, while for the transiently stable lines, the critical lines need to be further screened and used as the candidate set of power grid lines.

[0060] At the same time, the present application also uses the probability density function to determine the occurrence probability of each typical scenario, that is, {δ1, δ2, ..., δ M}, where δ M is the occurrence probability of the typical scenario M.

[0061] Step S2, determine the evaluation index affecting the grid carrying capacity, and determine the evaluation index value of each grid line in the grid line candidate set, the evaluation index includes the average load rate of the line, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy of the system after line N-1.

[0062] Among them, for the average load rate of the line, the calculation method of the average load rate of the line is:

[0063]

[0064] In the formula, is the average load factor, For a typical scenario The probability of occurrence, is the number of typical scenes, is a typical scene index, is the active power flowing through the i-th circuit, is the maximum allowable value of active power flowing through the i-th line.

[0065] Average load factor The larger the value, the higher the line L i The smaller the load margin, the greater the risk. i The more critical.

[0066] For the amount of renewable energy abandoned after line N-1, for the i-th line L i , in some ways when, when line L i After disconnection, the nearby power grid lines of large-scale renewable energy deployment points may be overloaded, and part of the power of renewable energy needs to be abandoned. The embodiment of the present application determines the abandoned power of renewable energy after line N-1 based on the DC power flow model.

[0067] Specifically, the calculation method for the abandoned power of new energy after line N-1 is:

[0068] Step S201: for each near-area power grid system under a preset typical scenario, an objective function is constructed with the minimization of the amount of abandoned renewable energy as the optimization goal, and the constraints of the objective function are determined to construct an optimization problem for the amount of abandoned renewable energy.

[0069] Among them, the objective function is:

[0070]

[0071] In the formula, is the amount of energy abandoned by energy b in typical scenario j, b is the energy index, N w is the amount of new energy, is the output of new energy b in typical scenario j.

[0072] The constraints of the objective function are:

[0073]

[0074] In the formula, is the active power flow array of the line, and its element is Pl k,j ,k=1,...,N;X i For line L i After disconnection, the active power correlation matrix of the power line injected by the power grid system node; PG j is the power plant output array, the element is PG d,j ,d=1,...,N g ; Pd is the node active load array; Pnew jIt is the new energy output array, and its element is Pnew b,j ,b=1,...,N w ; ΔPnew j is the new energy abandoned power array, and its elements are ; Balance the output of the machine for the power plant; is the node index; is the number of nodes; is the active load of node g; d is the power plant index; N g is the number of power plants; is the output of power plant d under typical scenario j; is the output of new energy source b in typical scenario j; is the output of line k in typical scenario j; is the output upper limit of line k in typical scenario j; , They are respectively the lower and upper limits of the output of power plant d in typical scenario j.

[0075] Step S202: finding an optimal solution to the problem of optimizing the amount of abandoned renewable energy, and obtaining a minimum amount of abandoned renewable energy in the nearby power grid system under each preset typical scenario.

[0076] Among them, the optimization problem of abandoned power of new energy is a typical linear programming problem, which can be solved by mature commercial software.

[0077] Step S203: summing up the minimum values ​​of the amount of renewable energy abandoned in the nearby power grid system under each preset typical scenario according to the probability of occurrence of each preset typical scenario, and obtaining the amount of renewable energy abandoned after line N-1.

[0078] Among them, the amount of new energy abandoned after line N-1 is:

[0079]

[0080] In the formula, This is the amount of abandoned electricity from renewable energy sources after line N-1.

[0081] The calculation method for the minimum load margin of the system line after line N-1 is:

[0082]

[0083] In the formula, is the minimum load margin of the system line after line N-1, is the active power flowing through the kth line after the i-th line is disconnected, calculated by DC power flow. is the maximum allowable value of active power flowing through the kth line, To obtain the index of the line through DC power flow calculation, is the number of lines.

[0084] Among them, when I2=0, The smaller the value, the greater the risk of line system exceeding the limit after the line is disconnected, and the more critical the line is.

[0085] For the stability margin of the new energy small disturbance of the system after line N-1, the generalized short-circuit ratio is used to analyze the line L i After disconnection, the system's support strength for new energy, that is, small disturbance stability, is as follows:

[0086] Line L i After disconnection, the equivalent admittance matrix is ​​obtained according to the AC grid impedance parameters and topological structure, and then according to N new The number of new energy access points is obtained through the Schur complement operation. new The power frequency node admittance matrix of the Thevenin equivalent network looking at the grid from a feed-in point The capacity diagonal matrix is ​​composed of the rated installed capacity of each renewable energy feed-in point. , then the calculation method of the stability margin of new energy small disturbance of the system after line N-1 is:

[0087]

[0088] in,

[0089] In the formula, is the small disturbance stability margin of the new energy system after line N-1, is the generalized short-circuit ratio after the i-th line is disconnected under the j-th scenario mode operation, is the matrix eigenvalue, SB new,j is the capacity diagonal matrix composed of the new energy output of each new energy feeding point under the jth scenario, For SB new,j The inverse matrix of is the power frequency node admittance matrix of the Thevenin equivalent network of the renewable energy feeding point relative to the power grid after the i-th line is disconnected.

[0090] Among them, for the jth typical scenario, the new energy output of each new energy feeding point is {Pnew 1,j , ..., Pnew Nw,j}, SB new,j for:

[0091]

[0092] Among them, the system new energy small disturbance stability margin after line N-1 The smaller it is, the worse the stability of the system's new energy small interference is. i The more critical.

[0093] Step S3: using the analytic hierarchy process to determine the weight coefficient of each evaluation index value, and determining the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of each evaluation index value.

[0094] Specifically, the process of using the analytic hierarchy process to determine the weight coefficient of each evaluation index value and determining the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of each evaluation index value includes:

[0095] Step S301, construct a hierarchical structure based on the hierarchical analysis method, the hierarchical structure includes a top layer, a middle layer and a bottom layer, wherein the top layer is the key line, the middle layer is the evaluation index, and the bottom layer is each power grid line in the power grid line candidate set.

[0096] Among them, Figure 3 The hierarchical structure shown in the figure has the highest level (overall target level) for key line identification, the middle level (reference level) for the four types of indicators considered, and the lowest level (scheme level) for each grid line L in the grid line candidate set. i (i=1,…,N).

[0097] Step S302: Use a 9-level comparison scale to compare the adjacent upper and lower layers in the hierarchical structure in pairs, and construct a judgment matrix between the adjacent upper and lower layers.

[0098] Among them, a 9-level comparison scale is used, as shown in Table 1.

[0099] Table 1

[0100]

[0101] Among them, the 9-level comparison scale is used to compare the adjacent upper and lower layers in the hierarchical structure in pairs, and the judgment matrix A of the highest layer and the middle layer is obtained as follows:

[0102]

[0103] In the formula, the element a in the matrix A is ij Representative index I a with I c Similarly, according to the specific values ​​of the four types of evaluation indicators of each candidate line obtained above, the judgment matrices of the middle and lowest level indicators I1, I2, I3, and I4 are B1, B2, B3, and B4 respectively:

[0104]

[0105] Among them, the matrix Elements in Represented in index I a Next candidate line L k With L l The comparison results.

[0106] Step S303: performing a consistency check on the judgment matrix. When the judgment matrix satisfies the consistency check, the weight coefficient of the evaluation index of each power grid line in the power grid line candidate set is calculated through the judgment matrix.

[0107] Among them, according to the hierarchical analysis method, it is necessary to analyze the matrix A and the matrix (a=1,…,4) to perform consistency check. If the consistency check fails, it is necessary to check the matrix A and the matrix (a=1,…,4) and modify again until the consistency check is satisfied.

[0108] When the judgment matrix satisfies the consistency test, the weight coefficients of the evaluation indicators of each power grid line in the power grid line candidate set are calculated through the judgment matrix. That is, firstly, the weight coefficients of the four types of indicators (after normalization) are obtained through matrix A, which are: w A1 、w A2 、w A3 、w A4 , each candidate line L i (i=1,…,N) in index I a The weight coefficients (after normalization) of (a=1,…,4) are: w Bi_1 、w Bi_2 ,…,w Bi_4 .

[0109] Step S304: determining the total weight coefficients corresponding to the respective power grid lines in the power grid line candidate set according to the weight coefficients of the evaluation indicators of the respective power grid lines in the power grid line candidate set.

[0110] Among them, the total weight coefficient corresponding to each power grid line in the power grid line candidate set is calculated as:

[0111]

[0112] In the formula, The grid line L i is the total weight coefficient of , and a is the evaluation index index.

[0113] Step S4: identifying the key lines according to the comparison results of the total weight coefficients of the power grid lines.

[0114] Among them, for the power grid line L iThe larger the total weight coefficient is, the more critical the line is. Generally speaking, the power grid line with the maximum value is determined as the critical line based on the comparison result of the total weight coefficients of each power grid line.

[0115] It should be noted that the embodiment of the present application selects multiple transiently stable power grid lines as candidate key lines, and uses the average load rate of the lines under the uncertainty factor of new energy output, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy in the system after line N-1 as evaluation indicators of the impact of power grid carrying capacity, and then uses the hierarchical analysis method to determine the total weight coefficient of the evaluation index of each candidate key line, so as to identify the key line by comparing the total weight coefficients of each power grid line, thereby improving the accuracy of key line identification of the nearby power grid, so as to comprehensively and accurately identify the key lines of the nearby power grid with large-scale new energy access, and ensure safe and stable operation of the system.

[0116] Based on the same inventive concept, an embodiment of the present application also provides a critical line identification system that affects the carrying capacity of a new energy power grid, which is used to implement the above-mentioned critical line identification method that affects the carrying capacity of a new energy power grid.

[0117] The implementation solution for solving the problem provided by the system is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more embodiments of the key line identification system affecting the carrying capacity of the new energy power grid provided below can be referred to the limitations of the key line identification method affecting the carrying capacity of the new energy power grid in the above text, and will not be repeated here.

[0118] like Figure 4 As shown, the embodiment of the present application also provides a key line identification system that affects the carrying capacity of the new energy power grid, including:

[0119] A line candidate module 100 is used to select a plurality of transiently stable power lines from a nearby power line set to form a power line candidate set;

[0120] An evaluation index determination module 200 is used to determine the evaluation index of the grid carrying capacity, and determine the evaluation index value of each grid line in the grid line candidate set, the evaluation index includes the average load rate of the line, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy in the system after line N-1;

[0121] The weight determination module 300 is used to determine the weight coefficient of each evaluation index value by using the hierarchical analysis method, and determine the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of each evaluation index value;

[0122] The key line identification module 400 is used to identify the key lines according to the comparison result of the total weight coefficients of each power grid line.

[0123] In some embodiments, the line candidate module 100 is used to perform fault simulation on the nearby power grid system according to a preset typical scenario of new energy output, and screen out multiple transiently stable power grid lines based on the simulation results; and form a power grid line candidate set based on the screened multiple transiently stable power grid lines.

[0124] In some embodiments, the average load rate of the line is calculated as follows:

[0125]

[0126] In the formula, is the average load factor, For a typical scenario The probability of occurrence, is the number of typical scenes, is a typical scene index, is the active power flowing through the i-th circuit, is the maximum allowable value of active power flowing through the i-th line.

[0127] In some embodiments, the calculation method of the abandoned new energy power after line N-1 is:

[0128] For each nearby power grid system under a preset typical scenario, an objective function is constructed with the minimization of renewable energy curtailment as the optimization goal, and the constraints of the objective function are determined to construct a renewable energy curtailment optimization problem.

[0129] Find the optimal solution to the problem of optimizing the amount of renewable energy curtailment and obtain the minimum amount of renewable energy curtailment in the nearby power grid system under each preset typical scenario;

[0130] According to the occurrence probability of each preset typical scenario, the minimum value of the renewable energy curtailment amount of the nearby power grid system under each preset typical scenario is added up to obtain the renewable energy curtailment amount after line N-1.

[0131] In some embodiments, the calculation method of the minimum load margin of the system line after line N-1 is:

[0132]

[0133] In the formula, is the minimum load margin of the system line after line N-1, is the active power flowing through the kth line after the i-th line is disconnected, calculated by DC power flow. is the maximum allowable value of active power flowing through the kth line, To obtain the index of the line through DC power flow calculation, is the number of lines, This is the amount of abandoned electricity from renewable energy sources after line N-1.

[0134] In some embodiments, the calculation method of the new energy small disturbance stability margin of the system after line N-1 is:

[0135]

[0136] in,

[0137] In the formula, is the stability margin of the new energy small disturbance of the system after line N-1, is the generalized short-circuit ratio, is the matrix eigenvalue, The capacity diagonal matrix is ​​formed for the rated installed capacity of each renewable energy feed-in point. It is the power frequency node admittance matrix of the Thevenin equivalent network of the renewable energy feed-in point relative to the power grid.

[0138] In some embodiments, the weight determination module 300 includes:

[0139] A hierarchical structure construction module is used to construct a hierarchical structure based on the hierarchical analysis method. The hierarchical structure includes a top layer, a middle layer and a bottom layer. The top layer is the key line, the middle layer is the evaluation index, and the bottom layer is each power line in the power line candidate set.

[0140] A judgment module is used to compare the adjacent upper and lower layers in the hierarchical structure in pairs using a 9-level comparison scale and to construct a judgment matrix between the adjacent upper and lower layers;

[0141] A weight determination module is used to check the consistency of the judgment matrix. When the judgment matrix satisfies the consistency check, the weight coefficient of the evaluation index of each power grid line in the power grid line candidate set is calculated through the judgment matrix;

[0142] The total weight determination module is used to determine the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of the evaluation index of each power grid line in the power grid line candidate set.

[0143] like Figure 5 As shown, an embodiment of the present application also provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the key line identification method affecting the carrying capacity of the new energy power grid in any of the above embodiments.

[0144] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the method for identifying key lines that affect the carrying capacity of the new energy power grid as described in any of the above embodiments are implemented.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0147] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0148] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the method described in each embodiment of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying key lines that affect the carrying capacity of a new energy power grid, characterized in that: include: According to the nearby power grid line set, multiple power grid lines with transient stability are selected to form a candidate set of power grid lines; Determine the evaluation index of the impact of the grid carrying capacity, and determine the evaluation index value of each grid line in the candidate set of grid lines, the evaluation index includes the average load rate of the line, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy in the system after line N-1; A hierarchical analysis method is used to determine the weight coefficient of each evaluation index value, and a total weight coefficient corresponding to each power grid line in the power grid line candidate set is determined according to the weight coefficient of each evaluation index value; The key lines are identified based on the comparison results of the total weight coefficients of each power grid line.

2. The method for identifying key lines that affect the carrying capacity of a new energy power grid according to claim 1 is characterized in that: The step of selecting a plurality of transiently stable power grid lines from the near-area power grid line set to form a candidate power grid line set comprises: Conduct fault simulation on the nearby power grid system according to the preset typical scenarios of renewable energy output, and select multiple transiently stable power grid lines based on the simulation results; A candidate set of power grid lines is formed based on the screened multiple transiently stable power grid lines.

3. The method for identifying key lines that affect the carrying capacity of a new energy power grid according to claim 1 is characterized in that: The calculation method of the average load rate of the line is: In the formula, is the average load factor, For a typical scenario The probability of occurrence, is the number of typical scenes, is a typical scene index, is the active power flowing through the i-th circuit, is the maximum allowable value of active power flowing through the i-th line.

4. The method for identifying key lines that affect the carrying capacity of a new energy power grid according to claim 1 is characterized in that: The calculation method of the abandoned new energy power after line N-1 is as follows: For each nearby power grid system under a preset typical scenario, an objective function is constructed with the minimization of the amount of abandoned renewable energy as the optimization goal, and the constraints of the objective function are determined to construct an optimization problem for the amount of abandoned renewable energy; The optimization problem of the amount of abandoned renewable energy is solved to obtain the minimum amount of abandoned renewable energy in the nearby power grid system under each preset typical scenario; According to the occurrence probability of each preset typical scenario, the minimum value of the renewable energy curtailment amount of the nearby power grid system under each preset typical scenario is added up to obtain the renewable energy curtailment amount after line N-1.

5. The method for identifying key lines that affect the carrying capacity of a new energy power grid according to claim 1 is characterized in that: The calculation method of the minimum load margin of the system line after line N-1 is: In the formula, is the minimum load margin of the system line after line N-1, is the active power flowing through the kth line after the i-th line is disconnected, calculated by DC power flow. is the maximum allowable value of active power flowing through the kth line, To obtain the index of the line through DC power flow calculation, is the number of lines, This is the amount of abandoned electricity from renewable energy after line N-1.

6. The method for identifying key lines that affect the carrying capacity of a new energy power grid according to claim 1 is characterized in that: The calculation method of the new energy small disturbance stability margin of the system after line N-1 is: in, In the formula, is the stability margin of the new energy small disturbance of the system after line N-1, is the generalized short-circuit ratio after the i-th line is disconnected under the j-th scenario mode operation, is the matrix eigenvalue, SB new,j is the capacity diagonal matrix composed of the new energy output of each new energy feeding point under the jth scenario, For SB new,j The inverse matrix of is the power frequency node admittance matrix of the Thevenin equivalent network of the renewable energy feeding point relative to the power grid after the i-th line is disconnected.

7. The method for identifying key lines that affect the carrying capacity of a new energy power grid according to claim 1 is characterized in that: The step of using the analytic hierarchy process to determine the weight coefficient of each evaluation index value, and determining the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of each evaluation index value, comprises: Constructing a hierarchical structure based on the analytic hierarchy process, the hierarchical structure comprising a top layer, a middle layer and a bottom layer, wherein the top layer is a key line, the middle layer is an evaluation index, and the bottom layer is each power grid line in the power grid line candidate set; Using a 9-level comparison scale to compare the adjacent upper and lower layers in the hierarchical structure in pairs, and constructing a judgment matrix between the adjacent upper and lower layers; Performing a consistency check on the judgment matrix, and when the judgment matrix satisfies the consistency check, calculating a weight coefficient of an evaluation index of each power grid line in the power grid line candidate set by using the judgment matrix; The total weight coefficients respectively corresponding to the power grid lines in the power grid line candidate set are determined according to the weight coefficients of the evaluation indicators of the power grid lines in the power grid line candidate set.

8. A critical line identification system that affects the carrying capacity of a new energy power grid, characterized in that: include: A line candidate module is used to select multiple transiently stable power lines according to a nearby power line set to form a power line candidate set; An evaluation index determination module is used to determine the evaluation index affecting the grid carrying capacity, and determine the evaluation index value of each grid line in the grid line candidate set, wherein the evaluation index includes the average load rate of the line, the amount of new energy abandoned after line N-1, the minimum load margin of the system line after line N-1, and the small disturbance stability margin of new energy in the system after line N-1; A weight determination module, used to determine the weight coefficient of each evaluation index value by using a hierarchical analysis method, and determine the total weight coefficient corresponding to each power grid line in the power grid line candidate set according to the weight coefficient of each evaluation index value; The key line identification module is used to identify the key lines according to the comparison results of the total weight coefficients of each power grid line.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the method for identifying key lines that affect the carrying capacity of the new energy power grid as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the method for identifying key lines that affect the carrying capacity of the new energy power grid are implemented as described in any one of claims 1 to 7.

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