Transmission Grid Line Planning Method, Device, Medium and Equipment

Abstract

The present application discloses a transmission network line planning method, device, medium and equipment. The method includes: obtaining a set of candidate slack power nodes based on the network topology index, structural vulnerability index and critical load index of each node, and obtaining a set of candidate slack lines based on the system congestion curtailment power, line load rate and power flow betweenness of each line; generating slack constraints based on the set of candidate slack power nodes and the set of candidate slack lines, and generating adjustable line constraints based on the critical load index and the system congestion curtailment power index; constructing a transmission network planning model based on the comprehensive cost minimum objective function, the slack constraints and the adjustable line constraints; and solving the transmission network planning model to obtain a line adjustment result. By identifying vulnerable nodes and lines, determining the set of candidate slack constraints and the set of candidate adjustable lines, and introducing the slack constraints and the adjustable line constraints into the power grid planning model, the obtained line adjustment result effectively improves the flexibility of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line planning, and particularly to a transmission grid line planning method, device, medium and equipment. Background Art

[0002] With the continuous improvement of the functions of modern power systems, the system structure has become increasingly complex, the number of components has continued to increase, and the degree of automation has been continuously improved. The power system is developing towards high voltage, long distance, and large capacity. Under such circumstances, ensuring the safe, reliable, economic and flexible operation of the power system has become a basic requirement in the industry. Facing the growing power demand and the large-scale access of renewable energy, how to optimize grid expansion by scientifically planning the existing transmission corridors and reasonably introducing adjustable lines to meet the needs of future development has become an important issue.

[0003] Currently, current research methods considering slack constraints and adjustable lines in grid expansion planning mainly include multi-objective hierarchical optimization, two-layer model construction, etc. For example, Cheng Haozhong et al. proposed a hierarchical optimization method based on multi-objective grid planning, effectively reducing the computational complexity; Yang Qian et al. designed line overload rate constraints and improved DC power flow constraints for the annual statistical index constraints in the two-stage model, and established a two-layer grid planning model; Lian Xiaoyang considered the problem of insufficient system flexibility in his research and proposed a collaborative planning model including maintenance factors. These research results provide new ideas and technical support for the optimization of power systems.

[0004] However, although the above research has made certain progress, they mainly focus on specific types of constraint conditions, such as peak shaving capacity constraints, regulation rate constraints, and risk constraints, etc. This results in that in practical applications, grid expansion planning may not fully meet the development needs of the power system and it is difficult to achieve the stable and efficient operation of the system. Especially when it comes to issues such as how to balance system stability and expansion cost, and how to dynamically adjust the network structure according to the actual operating conditions, the existing solutions are often not flexible enough or difficult to cover all possible scenarios comprehensively. Summary of the Invention

[0005] In view of this, the present invention provides a transmission grid line planning method, device, medium and equipment, mainly aiming to solve the problem that the existing transmission grid line planning methods are not flexible enough and difficult to cover many possible scenarios.

[0006] According to one aspect of the present application, a transmission grid line planning method is provided, and the method includes:

[0007] Obtain the basic power grid operation data of the target area. Based on the basic power grid operation data, calculate the network topology index, structural vulnerability index, critical load index of each node in the target area, and the system curtailment blocking power, line load rate, and current flow betweenness of each line.

[0008] Based on the network topology index, structural vulnerability index, and critical load index of each node, calculate the first comprehensive index corresponding to each node. Based on the system curtailment blocking power, line load rate, and current flow betweenness of each line, calculate the second comprehensive index corresponding to each line. Based on the arrangement order of the first comprehensive index, obtain the set of candidate slack power nodes. Based on the arrangement order of the second comprehensive index, obtain the set of candidate slack lines.

[0009] Based on the basic power grid operation data, the set of candidate slack power nodes, and the set of candidate slack lines, generate slack constraints. Based on the critical load index and the system curtailment blocking power index, generate adjustable line constraints.

[0010] Taking the minimum comprehensive cost as the objective function, and taking the slack constraints, the adjustable line constraints, node power balance constraints, existing line DC power flow constraints, candidate line DC power flow constraints, existing line capacity constraints, candidate unslacked line capacity constraints, unslacked generator output constraints, load shedding amount constraints, unit power generation constraints, candidate line status constraints, and balance node phase angle constraints as constraint conditions, construct a transmission network planning model.

[0011] Solve the transmission network planning model to obtain the line adjustment result.

[0012] Optionally, the calculating the first comprehensive index corresponding to each node based on the network topology index, structural vulnerability index, and critical load index of each node includes:

[0013] Based on the network topology index, structural vulnerability index, and critical load index of each node, generate a decision matrix, and normalize the vectors in the decision matrix to obtain an alternative matrix.

[0014] Based on the extreme values in each column vector of the alternative matrix, obtain the first ideal matrix and the second ideal matrix. Based on the alternative matrix, the first ideal matrix, and the second ideal matrix, calculate the first grey correlation degree and the first proximity distance between the alternative scheme of each node and the first ideal scheme, and the second grey correlation degree and the second proximity distance between the alternative scheme of each node and the second ideal scheme.

[0015] Based on the first proximity distance and the second proximity distance, calculate the comprehensive proximity degree of each node.

[0016] Based on the first grey correlation degree, the first proximity distance, the second grey correlation degree, the second proximity distance and the comprehensive proximity degree of each node, the first comprehensive index corresponding to each node is obtained.

[0017] Optionally, the obtaining the first comprehensive index corresponding to each node based on the first grey correlation degree, the first proximity distance, the second grey correlation degree, the second proximity distance and the comprehensive proximity degree of each node includes:

[0018] Taking the first grey correlation degree, the first proximity distance, the second grey correlation degree, the second proximity distance and the comprehensive proximity degree as a vulnerability index respectively;

[0019] Based on each vulnerability index of each node, the system entropy corresponding to each vulnerability index of each node is obtained;

[0020] Based on the system entropy corresponding to each vulnerability index of each node, the index entropy weight corresponding to each vulnerability index of each node is obtained;

[0021] Based on each vulnerability index of each node and its corresponding index entropy weight, the first comprehensive index corresponding to each node is obtained.

[0022] Optionally, the following method is used to calculate the first grey correlation degree, the second grey correlation degree, the first proximity distance and the second proximity distance of each node:

[0023]

[0024] Wherein, is the first grey correlation degree of the i-th node, is the second grey correlation degree of the i-th node, is the first grey correlation coefficient of the j-th index of the i-th node, is the second grey correlation coefficient of the j-th index of the i-th node, m is the number of column vectors in the alternative matrix, n is the number of row vectors in the alternative matrix, is the maximum value in the j-th column vector of the alternative matrix, is the minimum value in the j-th column vector of the alternative matrix, x ij is the vector of the i-th row and j-th column in the alternative matrix, ξ is the correlation coefficient, is the first proximity distance, is the second proximity distance, is the Euler distance between the alternative plan and the first ideal plan, is the Euler distance between the alternative plan and the second ideal plan, α is the position coefficient, β is the shape coefficient.

[0025] Optionally, the second comprehensive index corresponding to each line obtained by the system based on the blocked power, line load rate, and power flow betweenness of each line includes:

[0026] Taking the blocked power, line load rate, and power flow betweenness of the system as a line index respectively;

[0027] Based on the blocked power, line load rate, and power flow betweenness of each line, generating a line index evaluation matrix, and normalizing the vectors in the line index evaluation matrix to obtain a line index standard matrix;

[0028] Based on the line index standard matrix, obtaining the entropy of each line index, and based on the entropy of each line index, obtaining the weight coefficient corresponding to each line index;

[0029] Based on each line index and the corresponding weight coefficient, obtaining the second comprehensive index corresponding to each line.

[0030] Optionally, generating an adjustable line constraint based on the critical load index and the blocked power index of the system, including:

[0031] Sorting the nodes according to the critical load index, determining the vulnerable nodes in the system, analyzing the operating conditions and influence of the vulnerable nodes, and based on the analysis results, determining the in-series form lines in the set of candidate adjustable lines;

[0032] Identifying the vulnerable lines according to the blocked power of the system, obtaining the critical nodes around the vulnerable lines, and taking the lines connected to the critical nodes around the vulnerable lines as the out-series form lines in the set of candidate adjustable lines;

[0033] Generating an adjustable line constraint based on the in-series form lines and the out-series form lines in the set of candidate adjustable lines.

[0034] Optionally, the transmission network planning model is:

[0035] Objective function: Node power balance constraint: DC power flow constraint of existing lines: DC power flow constraint of candidate lines: Capacity constraint of existing lines: Capacity constraint of candidate unrelaxed lines: Capacity constraint of candidate relaxed lines: Output constraint of unrelaxed generators: Output constraint of relaxed generators: Load shedding amount constraint: Power generation constraint of units: The constraint on the status of candidate lines is a constraint condition: Adjustable line constraint: Balanced node phase angle constraint: θ0 = 0

[0036] Among them, τ + is the set of candidate lines, c li is the cost of the newly built transmission line li, z i is the construction status of generator i, 0 means not constructed, 1 means constructed, ρ s is the probability of scenario s occurring, N is the number of scenarios, Ω includes Ω - the set of candidate non-relaxed power node sets and Ω + the set of candidate relaxed power node sets, O pk is the unit production cost of conventional units, P G,s,k is the output of generator k under scenario s, Ψ is the set of buses, C pb is the unit load shedding cost, R s,b is the load shedding amount of bus b under scenario s, ψ b represents bus b, f s,mn(i) is the active power flow of line i, m and n are the bus numbers at both ends of line i, P s,b is the load of bus b under scenario s, r mn(i) is the susceptance value of line i, θ s,m and θ s,n are the phase angles of buses m and n under scenario s respectively, τ - is the set of existing lines, P Li,max is the capacity of line i, λ2 is the ratio of the capacity of the relaxed line to the capacity of the non-relaxed line, P G,k,min is the minimum output of generator k, P G,k,max is the maximum output of generator k, λ1 is the ratio of the capacity of the relaxed power node to the capacity of the non-relaxed power node, ε d is the maximum allowable load shedding amount, T G,k is the equivalent annual utilization hours of generator k, P w,s,k is the output of generator k at node w under output scenario s, is the in-series form line in the candidate set of adjustable lines, is the out-series form line in the candidate set of adjustable lines, l s , l t , l v Lines s, t, and v represent each group of adjustable lines.

[0037] According to another aspect of the present application, a transmission network line planning device is provided, including:

[0038] An index data acquisition module, configured to acquire the basic power grid operation data of a target area, and calculate, based on the basic power grid operation data, the network topology index, the structural vulnerability index, the critical load index of each node in the target area, and the system curtailment blocking power, the line load rate, and the current flow betweenness of each line;

[0039] A candidate set generation module, configured to calculate, based on the network topology index, the structural vulnerability index, and the critical load index of each node, a first comprehensive index corresponding to each node, calculate, based on the system curtailment blocking power, the line load rate, and the current flow betweenness of each line, a second comprehensive index corresponding to each line, obtain a slack power node candidate set based on the arrangement order of the first comprehensive index, and obtain a slack line candidate set based on the arrangement order of the second comprehensive index;

[0040] A constraint generation module, configured to generate slack constraints based on the basic power grid operation data, the slack power node candidate set, and the slack line candidate set, and generate adjustable line constraints based on the critical load index and the system curtailment blocking power index;

[0041] A power grid planning model acquisition module, configured to construct a transmission grid planning model with the minimum comprehensive cost as the objective function and with the slack constraints, the adjustable line constraints, the node power balance constraints, the existing line DC power flow constraints, the candidate line DC power flow constraints, the existing line capacity constraints, the candidate unslacked line capacity constraints, the unslacked generator output constraints, the load shedding amount constraints, the generator power generation constraints, the candidate line status constraints, and the balance node phase angle constraints as the constraint conditions;

[0042] A line adjustment result acquisition module, configured to solve the transmission grid planning model to obtain a line adjustment result.

[0043] Optionally, the candidate set generation module is further configured to:

[0044] Generate a decision matrix based on the network topology index, the structural vulnerability index, and the critical load index of each node, and perform normalization processing on the vectors in the decision matrix to obtain an alternative matrix;

[0045] Obtain a first ideal matrix and a second ideal matrix based on the extreme values in each column vector of the alternative matrix, and calculate the first grey correlation degree and the first proximity distance between the alternative scheme of each node and the first ideal scheme, and the second grey correlation degree and the second proximity distance between the alternative scheme of each node and the second ideal scheme based on the alternative matrix, the first ideal matrix, and the second ideal matrix;

[0046] Calculate the comprehensive proximity degree of each node based on the first proximity distance and the second proximity distance;

[0047] Based on the first grey relational degree, the first proximity distance, the second grey relational degree, the second proximity distance and the comprehensive proximity degree of each node, a first comprehensive index corresponding to each node is obtained.

[0048] Optionally, the candidate set generation module is further configured to:

[0049] Take the first grey relational degree, the first proximity distance, the second grey relational degree, the second proximity distance and the comprehensive proximity degree as a vulnerability index respectively;

[0050] Based on each vulnerability index of each node, the system entropy corresponding to each vulnerability index of each node is obtained;

[0051] Based on the system entropy corresponding to each vulnerability index of each node, the index entropy weight corresponding to each vulnerability index of each node is obtained;

[0052] Based on each vulnerability index of each node and its corresponding index entropy weight, a first comprehensive index corresponding to each node is obtained.

[0053] Optionally, the following method is used to calculate the first grey relational degree, the second grey relational degree, the first proximity distance and the second proximity distance of each node:

[0054]

[0055] Wherein, is the first grey relational degree of the i-th node, is the second grey relational degree of the i-th node, is the first grey relational coefficient of the j-th index of the i-th node, is the second grey relational coefficient of the j-th index of the i-th node, m is the number of column vectors in the alternative matrix, n is the number of row vectors in the alternative matrix, is the maximum value in the j-th column vector of the alternative matrix, is the minimum value in the j-th column vector of the alternative matrix, x ij is the vector of the i-th row and j-th column in the alternative matrix, ξ is the correlation coefficient, is the first proximity distance, is the second proximity distance, is the Euler distance between the alternative plan and the first ideal plan, is the Euler distance between the alternative plan and the second ideal plan, α is the position coefficient, β is the shape coefficient.

[0056] Optionally, the candidate set generation module is further configured to:

[0057] Take the system's blocked power reduction, line load rate and power flow betweenness as a line index respectively;

[0058] Based on the system power curtailment for blocking, line load rate, and power flow betweenness of each line, a line index evaluation matrix is generated, and the vectors in the line index evaluation matrix are normalized to obtain a line index standard matrix;

[0059] Based on the line index standard matrix, the entropy of each line index is obtained, and based on the entropy of each line index, the weight coefficient corresponding to each line index is obtained;

[0060] Based on each line index and the corresponding weight coefficient, the second comprehensive index corresponding to each line is obtained.

[0061] Optionally, the constraint generation module is further configured to:

[0062] Sort the nodes according to the key load indicators, determine the vulnerable nodes in the system, analyze the operating conditions and influence of the vulnerable nodes, and based on the analysis results, determine the in-series form lines in the set of candidate adjustable lines;

[0063] Identify the vulnerable lines according to the system power curtailment for blocking, obtain the key nodes around the vulnerable lines, and use the lines connected to the key nodes around the vulnerable lines as the out-series form lines in the set of candidate adjustable lines;

[0064] Based on the in-series form lines and the out-series form lines in the set of candidate adjustable lines, an adjustable line constraint is generated.

[0065] Optionally, the transmission network planning model is:

[0066] Objective function: Node power balance constraint: DC power flow constraint of existing lines: DC power flow constraint of candidate lines: Capacity constraint of existing lines: Capacity constraint of candidate unrelaxed lines: Capacity constraint of candidate relaxed lines: Output constraint of unrelaxed generators: Output constraint of relaxed generators: Load shedding amount constraint: Generator power generation constraint: The candidate line status constraint is a constraint condition: Adjustable line constraint: Balanced node phase angle constraint: θ0 = 0

[0067] where τ + is the set of candidate lines, c li is the cost of the newly built transmission line li, zi is the construction status of generator i, where 0 indicates not constructed and 1 indicates constructed, ρ s is the probability of scenario s occurring, N is the number of scenarios, and Ω includes Ω - the set of candidate sets of unrelaxed power nodes and Ω + the set of candidate sets of relaxed power nodes, O pk is the unit production cost of a conventional unit, P G,s,k is the output of generator k under scenario s, Ψ is the set of buses, C pb is the unit load shedding cost, R s,b is the load shedding amount of bus b under scenario s, ψ b represents bus b, f s,mn(i) is the active power flow of line i, m and n are the bus numbers at both ends of line i, P s,b is the load of bus b under scenario s, r mn(i) is the susceptance value of line i, θ s,m and θ s,n are the phase angles of buses m and n under scenario s respectively, τ - is the set of existing lines, P Li,max is the capacity of line i, and λ2 is the ratio of the capacity of the relaxed line to the capacity of the unrelaxed line, P G,k,min is the minimum output of generator k, P G,k,max is the maximum output of generator k, and λ1 is the ratio of the capacity of the relaxed power node to the capacity of the unrelaxed power node, ε d is the maximum allowable load shedding amount, T G,k is the equivalent annual utilization hours of generator k, P w,s,k is the output of generator k at node w under output scenario s, is the in-series form line in the candidate set of adjustable lines, is the out-series form line in the candidate set of adjustable lines, l s , l t , l v Lines s, t, and v represent each group of adjustable lines.

[0068] According to another aspect of the present application, a storage medium is provided, in which at least one executable instruction is stored, and the executable instruction causes the processor to perform the operations corresponding to the above transmission network line planning method.

[0069] According to another aspect of the present application, a computer device is provided, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0070] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the above-mentioned transmission grid line planning method.

[0071] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages:

[0072] A transmission grid line planning method, device, equipment and medium provided by the present application, based on the basic data of power grid operation, calculates the network topology index, structural vulnerability index, critical load index of each node in the target area, and the system curtailment blocking power, line load rate and current flow betweenness of each line. Based on the network topology index, structural vulnerability index and critical load index of each node, a first comprehensive index corresponding to each node is calculated. Based on the arrangement order of the first comprehensive index, a set of candidate slack power nodes is obtained. Based on the system curtailment blocking power, line load rate and current flow betweenness of each line, a second comprehensive index corresponding to each line is calculated. Based on the arrangement order of the second comprehensive index, a set of candidate slack lines is obtained. Based on the set of candidate slack power nodes and the set of candidate slack lines, slack constraints are generated. Based on the critical load index and the system curtailment blocking power index, adjustable line constraints are generated. Taking the minimum comprehensive cost as the objective function and taking slack constraints, adjustable line constraints, node power balance constraints, etc. as constraint conditions, a transmission grid planning model is constructed, the transmission grid planning model is solved to obtain the line adjustment result. By identifying vulnerable nodes and lines, introducing a set of candidate slack constraints and a set of candidate adjustable lines, and introducing slack constraints and adjustable lines into the power grid planning model, the obtained line adjustment result effectively improves the flexibility of the power grid, covers many possible scenarios, enhances the adaptability of the system to load fluctuations and faults, enables the system to achieve efficient allocation of resources under wider conditions, and reduces investment and operation costs.

[0073] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically gives the specific implementation manners of the present invention. Brief Description of the Drawings

[0074] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0075] Figure 1 A flowchart showing a transmission grid line planning method provided by an embodiment of the present application is shown;

[0076] Figure 2 The power transmission network structure diagram of a power transmission network line planning method provided by an embodiment of the present application is shown;

[0077] Figure 3 The structural block diagram of a power transmission network line planning device provided by an embodiment of the present application is shown.

[0078] Among them,

[0079] Figure 3 In: 302 - Index data acquisition module; 304 - Candidate set generation module; 306 - Constraint generation module; 308 - Power grid planning model acquisition module; 310 - Line adjustment result acquisition module; Specific embodiments

[0080] The present invention will be described in detail below with reference to the drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0081] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following describes in detail the specific embodiments, structures, features, and effects of the present invention application in accordance with the drawings and preferred embodiments. In the following description, different "one embodiment" or "embodiment" does not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0082] Aiming at the problem that the existing power transmission network line planning method is not flexible enough and difficult to cover many possible scenarios, an embodiment of the present application provides a power transmission network line planning method, as Figure 1 shown, the method includes:

[0083] 102: Obtain the basic power grid operation data of the target area, and based on the basic power grid operation data, calculate the network topology index, structural vulnerability index, critical load index of each node in the target area, and the system reduction blocking power, line load rate, and current flow betweenness of each line;

[0084] 104: Based on the network topology index, structural vulnerability index, and critical load index of each node, calculate the first comprehensive index corresponding to each node. Based on the system reduction blocking power, line load rate, and current flow betweenness of each line, calculate the second comprehensive index corresponding to each line. Based on the arrangement order of the first comprehensive index, obtain the candidate set of slack power nodes, and based on the arrangement order of the second comprehensive index, obtain the candidate set of slack lines;

[0085] 106: Generate relaxation constraints based on the basic power grid operation data, the set of candidate slack power nodes, and the set of candidate slack lines, and generate adjustable line constraints based on the key load indicators and the system's blocked power reduction indicator;

[0086] 108: With the minimum comprehensive cost as the objective function, and with relaxation constraints, adjustable line constraints, node power balance constraints, existing line DC power flow constraints, candidate line DC power flow constraints, existing line capacity constraints, candidate un-relaxed line capacity constraints, un-relaxed generator output constraints, load shedding amount constraints, generator power generation constraints, candidate line status constraints, and balance node phase angle constraints as the constraint conditions, construct a transmission grid planning model;

[0087] 110: Solve the transmission grid planning model to obtain the line adjustment result.

[0088] Specifically, the basic power grid operation data includes stored load data, power source data, and transmission grid data. The load data is the load magnitude of each node in the system. The power source data includes parameters such as generator installed capacity, upper and lower output limits, and generator status. The transmission grid data includes parameters such as the transmission grid topology structure, line length, line admittance, and line capacity. Based on complex network theory, appropriate evaluation indicators are selected to calculate the nodes and lines of the target power grid. The identification indicators for important nodes are selected as network topology indicators, structural vulnerability indicators, and key load indicators, and the identification indicators for important lines are selected as the system's blocked power reduction, line load rate, and current flow betweenness. Based on the basic power grid operation data, the network topology indicators, structural vulnerability indicators, key load indicators, the system's blocked power reduction, line load rate, and current flow betweenness are calculated.

[0089] The network topology indicators in the identification indicators for important nodes include the three most commonly used and representative evaluation indicators: degree, betweenness, and node central proximity. The structural vulnerability indicators include two indicators: the maximum support load rate of the power grid and the transmission efficiency of the power grid. Based on the network topology indicators, structural vulnerability indicators, and key load indicators of each node, the first comprehensive indicator of each node is calculated, and the first comprehensive indicators are sorted. According to the sorting results, the set of candidate slack power nodes can be obtained. According to the system's blocked power reduction, line load rate, and current flow betweenness of each line, the second comprehensive indicator of each line is calculated, and the second comprehensive indicators are sorted. According to the sorting results, the set of candidate slack lines can be obtained. Based on the set of candidate slack power nodes and the set of candidate slack lines, relaxation constraints are generated.

[0090] Based on the key load index, determine the candidate set of series-out lines in the candidate set of adjustable lines to relieve the system load pressure and improve the stability of nodes. Based on the system congestion reduction power index, determine the candidate set of series-in lines in the candidate set of adjustable lines to increase the transmission capacity of the lines, optimize the network structure, and alleviate the line congestion problem. Based on the candidate set of series-out lines in the candidate set of adjustable lines and the candidate set of series-in lines in the candidate set of adjustable lines, generate line adjustment constraints.

[0091] Comprehensively consider the power source investment cost, transmission line investment cost, operation cost, and load shedding cost. With the minimum comprehensive cost as the objective, and taking the slack constraint, adjustable line constraint, node power balance constraint, DC power flow constraint of existing lines, DC power flow constraint of candidate lines, capacity constraint of existing lines, capacity constraint of unslacked candidate lines, unslacked generator output constraint, load shedding amount constraint, unit power generation constraint, candidate line status constraint, and balance node phase angle constraint as constraint conditions, construct a transmission network planning model and establish a transmission network planning model considering slack power source and transmission corridor constraints.

[0092] The model can be solved using the YALMIP toolbox and the Gurobi solver to obtain the line adjustment result.

[0093] This application provides a method for transmission network line planning. Compared with the prior art, based on the basic power grid operation data, calculate the network topology index, structural vulnerability index, key load index of each node in the target area, and the system congestion reduction power, line load rate, and current transfer distribution factor of each line. Based on the network topology index, structural vulnerability index, and key load index of each node, calculate the first comprehensive index corresponding to each node. Based on the sorting order of the first comprehensive index, obtain the candidate set of slack power nodes. Based on the system congestion reduction power, line load rate, and current transfer distribution factor of each line, calculate the second comprehensive index corresponding to each line. Based on the sorting order of the second comprehensive index, obtain the candidate set of slack lines. Based on the candidate set of slack power nodes and the candidate set of slack lines, generate the slack constraint. Based on the key load index and the system congestion reduction power index, generate the adjustable line constraint; with the minimum comprehensive cost as the objective function, and taking the slack constraint, adjustable line constraint, node power balance constraint, etc. as constraint conditions, construct a transmission network planning model, solve the transmission network planning model to obtain the line adjustment result. By identifying vulnerable nodes and lines, introducing the candidate set of slack constraints and the candidate set of adjustable lines, introducing slack constraints and adjustable lines into the power grid planning model, the obtained line adjustment result effectively improves the flexibility of the power grid, covers many possible scenarios, enhances the system's adaptability to load fluctuations and faults, enables the system to achieve efficient resource allocation under a wider range of conditions, and reduces investment and operation costs.

[0094] In one embodiment, based on the network topology index, structural vulnerability index, and critical load index of each node, a first comprehensive index corresponding to each node is calculated, including:

[0095] Based on the network topology index, structural vulnerability index, and critical load index of each node, a decision matrix is generated, and the vectors in the decision matrix are normalized to obtain an alternative matrix;

[0096] Based on the extreme values in each column vector of the alternative matrix, a first ideal matrix and a second ideal matrix are obtained. Based on the alternative matrix, the first ideal matrix, and the second ideal matrix, the first grey correlation degree and the first proximity distance between the alternative solution of each node and the first ideal solution, and the second grey correlation degree and the second proximity distance between the alternative solution of each node and the second ideal solution are calculated;

[0097] Based on the first proximity distance and the second proximity distance, the comprehensive proximity degree of each node is calculated;

[0098] Based on the first grey correlation degree, the first proximity distance, the second grey correlation degree, the second proximity distance, and the comprehensive proximity degree of each node, a first comprehensive index corresponding to each node is obtained.

[0099] Specifically, the network topology index includes three most commonly used and representative evaluation indexes: degree, betweenness, and node central proximity. The degree of node i reflects the number of connections between the node in the network and other nodes in the network, which can be obtained by calculating how many edges are connected to a node, as shown in the following formula:

[0100] In the formula, when the path l contains node i, δ l takes the value of 1, and E is the set of all edges in the network. The node betweenness is a parameter used to quantify the status of each node in the network, that is, the number of times each node acts as a bridge between other two nodes, reflecting the degree to which it is on the critical path of the system, as shown in the following formula.

[0101] In the formula, N jk represents the total number of the shortest paths between node j and k; N jk (i) represents the total number of the shortest paths between node j and k passing through node i. From this formula, the normalized betweenness index of each node in the network can be calculated. The larger its value, the higher the status of the corresponding node in the network, and the higher its importance relative to other nodes in the same network.

[0102] The closeness centrality of a node reflects the comprehensive closeness between a certain node in the network and other nodes. The reciprocal of the sum of the shortest path distances from a node to all other nodes is used to represent the closeness centrality. The closeness centrality of a node is considered from the importance of the load node in the network topology. The principle is that in a complex network environment, the smaller the shortest path from a node to all reachable nodes in the network, the closer the node is to the center of the network, and the greater its role. For node i, its closeness is defined as follows: Where: N is the number of power grid nodes, and dij is the number of edges included in the shortest path from node i to node j.

[0103] The structure vulnerability index includes two indicators: the maximum support load rate of the power grid and the transmission efficiency of the power grid. The maximum support load rate index of the power grid is used to describe the level of power supply and power supply reliability of the power grid before and after a power failure occurs at the key node. Each node in the system exits the operation in turn, and the ratio of the maximum load that can be borne after the exit to the initial load is calculated. The calculation formula is as follows:

[0104] maxλ=ΣP lo ′ ad / ΣP load0

[0105] s.t.∑P f,ij +∑P G =P lo ′ ad.i -P R,i

[0106] P f,ij -γ ij (θ i -θ j )=0

[0107] -P ij,max ≤P f,ij ≤P ij,max

[0108] P G,min ≤P G ≤P G,max

[0109] Where: P load0 represents the initial load level of the power grid before a power failure occurs at the key node of the power network; P lo ′ ad represents the maximum load level that the power grid can still support after a power failure occurs at the key node of the power grid. The four constraint conditions are respectively the node power balance constraint, the power flow equation constraint, the line capacity constraint, and the generator output constraint.

[0110] Measured by the power grid transmission efficiency index, which comprehensively considers the global and local importance of key nodes. The definition of this index is the ratio of the sum of the reciprocals of the shortest arrival paths between all generator nodes and all load nodes in the largest sub-network after the power grid is islanded to the sum of the reciprocals of the shortest arrival paths between all generator nodes and all load nodes in the power grid before the power failure of the power source. After calculating the successive failure of nodes to exit the operation, calculate the ratio of the sum of the reciprocals of the shortest paths between all generator nodes and all load nodes in the network at this time to that in normal operation. The calculation formula is as follows:

[0111]

[0112] In the formula: Ω GB is the set of all generator nodes in the largest sub-network after the power failure of the power grid nodes, and Ω LB is the set of all load nodes in the largest sub-network after the power failure of the power grid nodes. dij represents the shortest path between generator node i and load node j in the largest sub-network. d0-ij respectively represent the set of all generator nodes, the set of all load nodes in the initial power grid, and the shortest path between generator node i and load node j.

[0113] The critical load index fully considers the influence of the power grid load level, as well as the corresponding power generation capacity of the power grid and its distribution, etc., and incorporates the load quantity into the evaluation of node importance.

[0114] First, normalize the load, as shown in the following formula: In the formula: is the injection power of node k, and S base is the base power of the system. Define the load quantity importance of any node k in the network to reflect the critical load level, as follows: In the formula: w and w′ are any two different nodes in the network; k ww′ is a 0-1 variable for the shortest path between node w and node w′ passing through any node k, that is, if the shortest path does not pass through node k, then k ww′ = 0, and if the shortest path passes through node k, then k ww′ = 1.

[0115] Then, for any network G, the following formula can be obtained c ww′ (k) represents the number of times the shortest path between node w and w′ passes through node k, and then the critical load index is obtained as shown in formula (9):

[0116]

[0117] Therefore, the importance of a node can be measured by the importance of the node load. From the above formula, it can be seen that 0 < C q(k) < 1.

[0118] The system curtailment blocking power in the identification index of important lines can be used to evaluate the inadequacy of the power grid transmission capacity and reflect potential transmission capacity problems in system transmission. It is defined as the following formula: LUC = P (trade)ij - P (max)ij , where P (trade)ij represents the power that line ij needs to pass through; P (max)ij represents the limit value of the transmission power of line ij. Generally, we consider static blocking. In fact, dynamic blocking may also need to be considered in the research. At this time, P (max)ij represents the steady-state limit power of the line. When calculating, the magnitudes of the thermal limit power and the steady-state limit power can be compared, and the smaller of the two can be taken as P (max)ij .

[0119] The branch load rate is defined as the branch load level under the maximum load condition in the normal mode. As follows In the formula: L ij is the load rate of branch i, j; P ij is the actual operating active power of this branch; is the maximum allowable active power of this branch.

[0120] According to the index results calculated by the important node and line identification module, it is necessary to determine the comprehensive index of node and line indicators. Three different types of indicators are proposed for important node identification indicators, namely network topology indicators, structural vulnerability indicators, and critical load indicators. First, a decision matrix is constructed based on the important node identification indicators of each node: A decision matrix X = (x ij ) n×m is constructed from n nodes and m indicators. Nodes and indicators can be regarded as alternative solutions and attributes respectively. Then, the decision matrix X is normalized to eliminate the influence of different dimensions and obtain the alternative matrix.

[0121] The benefit-type indicators are normalized by the following method: The cost-type indicators are normalized by the following method:

[0122] Next, calculate the first ideal matrix corresponding to the ideal decision-making scheme, also called the positive ideal matrix, and calculate the second ideal matrix corresponding to the ideal decision-making scheme, also called the negative ideal matrix. The positive and negative ideal matrices are respectively taken as the maximum and minimum values of each column vector of the alternative matrix, that is, the positive ideal matrix is The negative ideal matrix is where Based on the alternative matrix, the first ideal matrix, and the second ideal matrix, the Euler distances between the alternative solutions and the first ideal solution and the second ideal solution are solved as follows:

[0123] Then, calculate the grey relational degree between the alternative solution of each node and the ideal solution. The grey relational coefficients between solution i and the positive and negative ideal solutions (also called the first grey relational coefficient and the second grey relational coefficient) are:

[0124] The positive and negative grey relational degrees (also called the first grey relational degree and the second grey relational degree) are

[0125] where ξ is the discrimination coefficient, and generally takes a value of 0.5.

[0126] Combine the Euler distance and the grey relational degree to calculate the closeness of the alternative solutions of each node to the positive and negative ideal solutions. Set the position coefficient α and the shape coefficient β, both of which are taken as 0.5 here, to obtain the first closeness distance and the second closeness distance:

[0127]

[0128] According to the calculation results of the closeness distance, the comprehensive closeness can be obtained:

[0129] In an embodiment of the present invention, based on the first grey relational degree, the first closeness distance, the second grey relational degree, the second closeness distance, and the comprehensive closeness of each node, the first comprehensive index corresponding to each node is obtained, including:

[0130] Take the first grey relational degree, the first closeness distance, the second grey relational degree, the second closeness distance, and the comprehensive closeness as a vulnerability index respectively;

[0131] Based on each vulnerability index of each node, obtain the system entropy corresponding to each vulnerability index of each node;

[0132] Based on the system entropy corresponding to each vulnerability index of each node, obtain the index entropy weight corresponding to each vulnerability index of each node;

[0133] Based on each vulnerability index of each node and its corresponding index entropy weight, obtain the first comprehensive index corresponding to each node.

[0134] Specifically, the sorting results of the comprehensive proximity degree of each alternative, i.e., the node, can be used as the sorting results of its vulnerability degree. The proximity degree can reflect the degree of closeness between the vulnerability value of the node and the positive ideal solution, and the positive ideal solution can be used as the maximum value of the vulnerability in the set of network nodes. Therefore, it can be considered that the greater the proximity degree of the node, the higher the node vulnerability. So far, the m decision attributes of the n alternatives have been transformed into five vulnerability indicators of the n alternatives: the comprehensive proximity degree of the node, the positive and negative proximity distances, and the positive and negative grey relational degrees.

[0135] The weighted entropy of each vulnerability indicator is calculated by weighting the entropy, and according to the sequence of vulnerability indicator values of each alternative: Y = [Y1, Y2,..., Y N , the system entropy corresponding to each vulnerability indicator of each node can be defined as In the formula, where l k represents the number of standardized vulnerability indicator values included in the interval (Y k , Y k+1 ); μ(k) is the average value of the vulnerability indicators in the interval (Y k , Y k+1 ).

[0136] According to the idea of multi-attribute decision-making, the nodes in the power grid can be regarded as decision-making alternatives, and each vulnerability indicator of the node can be regarded as a decision-making attribute. Then each decision-making alternative has 5 decision-making attributes to measure its vulnerability degree. To measure the role of each attribute in the measurement, the index entropy weight of vulnerability indicator i can be defined as:

[0137]

[0138] In the formula, 0 < α i < 1, and When the index entropy weight is larger, it indicates that the index can better reflect the differences between system nodes and plays a greater role in decision-making. According to the above steps, the index entropy weight of each index of each node is calculated. For each node, the value of each index is multiplied by the index entropy weight of the index, and then the products of the values of each index and the index entropy weight are added to obtain the first comprehensive index. Sort the first comprehensive index, and the candidate set of slack power nodes can be obtained according to the sorting results.

[0139] In one embodiment, based on the system congestion reduction power, line load rate, and power flow betweenness of each line, a second comprehensive index corresponding to each line is obtained, including:

[0140] The system congestion reduction power, line load rate, and power flow betweenness are respectively used as a line index;

[0141] Based on the system power congestion curtailment, line load rate, and power flow betweenness of each line, generate a line index evaluation matrix, and normalize the vectors in the line index evaluation matrix to obtain a line index standard matrix;

[0142] Based on the line index standard matrix, obtain the entropy of each line index, and based on the entropy of each line index, obtain the weight coefficient corresponding to each line index;

[0143] Based on each line index and its corresponding weight coefficient, obtain the second comprehensive index corresponding to each line.

[0144] Specifically, the comprehensive index of the relaxed line is determined by the entropy method, and its main steps are as follows:

[0145] First, take the system power congestion curtailment, line load rate, and power flow betweenness as a line index respectively; according to the system power congestion curtailment, line load rate, and power flow betweenness of each line, construct a line index evaluation matrix with p lines and q items of indicators:

[0146] U=(u ij ) p×q , i = 1, 2,..., p; j = 1, 2,..., q, where u ij is the attribute value of the i-th line with respect to the j-th indicator.

[0147] Then, standardize the line index matrix to obtain a line index standard matrix:

[0148]

[0149] P ij That is, the contribution degree of the i-th object with respect to the j-th indicator.

[0150] Then, calculate the entropy E of each line index j

[0151]

[0152] It can be seen that 0 ≤ E j ≤ 1, and the entropy E j also represents the total contribution of all objects with respect to the j-th indicator.

[0153] Finally, calculate the weight coefficient h of each line index j

[0154]

[0155] The larger the entropy weight coefficient h, the greater the amount of information represented by this indicator, indicating that this indicator plays a greater role in the comprehensive evaluation of the line. The entropy weight coefficient h = (h1, h2,..., h p)That is, the coefficient of variation vector. According to the above steps, the weight coefficients of each line index are calculated. For each line, multiply the value of each line index by the corresponding weight coefficient of that line index, and then sum up the products of the values of all line indices and the weight coefficients to obtain the second comprehensive index. Sort the second comprehensive index, and according to the sorting result, the candidate set of relaxed transmission corridors can be obtained, which is also called the candidate set of relaxed lines.

[0156] The candidate set of relaxed power source nodes and the candidate set of relaxed transmission corridors together constitute the candidate set of relaxed constraints. According to the candidate set of relaxed constraints and different relaxation multiples, this module can give the following relaxed generator output constraints and candidate relaxed line capacity constraints.

[0157]

[0158] In the formula, Ω + represents the set of relaxed units, that is, the candidate set of relaxed power source nodes, and τ ++ is the candidate relaxed line, that is, the candidate set of relaxed transmission corridors. P G,s,k is the output of generator k under scenario s, f s,mn(i) , P s,b are the active power flows of line i (where the subscripts m and n are the bus numbers at both ends of line i) and the load of bus b under scenario s, respectively. λ1 is the ratio of the relaxed power source capacity to the unrelaxed power source capacity, and λ2 is the ratio of the relaxed transmission corridor capacity to the unrelaxed transmission corridor capacity.

[0159] In one embodiment, based on the critical load index and the system congestion reduction power index, adjustable line constraints are generated, including:

[0160] Sort the nodes according to the critical load index, determine the vulnerable nodes in the system, analyze the operating conditions and influence of the vulnerable nodes, and based on the analysis results, determine the in-series form lines in the candidate set of adjustable lines;

[0161] According to the system congestion reduction power, identify the vulnerable lines, obtain the critical nodes around the vulnerable lines, and take the lines connecting the critical nodes around the vulnerable lines as the out-of-series form lines in the candidate set of adjustable lines;

[0162] Based on the in-series form lines and the out-of-series form lines in the candidate set of adjustable lines, adjustable line constraints are generated.

[0163] Specifically, nodes are sorted according to key load indicators to identify vulnerable nodes in the system. For example, nodes with key load indicators greater than the load threshold are regarded as vulnerable nodes. By analyzing the operating conditions and influence ranges of these vulnerable nodes, for example, vulnerable nodes with poor operating status and small influence ranges are included in the candidate set for possible string-out operations to relieve the system load pressure and improve the stability of nodes. At the same time, according to the index results of the system for reducing blocked power, vulnerable lines are identified. For example, lines with the system reducing blocked power greater than the block threshold are regarded as vulnerable lines. Combining with the key nodes around them, the relevant lines are included in the candidate set for possible string-in operations to increase the transmission capacity of the lines, optimize the network structure, and relieve the line congestion problem.

[0164] Finally, the line status is adjusted according to this candidate set to form the specific adjustable line constraints as shown below.

[0165]

[0166] In the formula, is the line in the string-in form in the candidate set of adjustable lines, is the line in the string-out form in the candidate set of adjustable lines; among them, s, t, and v refer to each group of adjustable lines.

[0167] In one embodiment, the transmission network planning model is:

[0168] Objective function: Node power balance constraint: DC power flow constraint of existing lines: DC power flow constraint of candidate lines: Existing line capacity constraint: Capacity constraint of candidate unrelaxed lines: Capacity constraint of candidate relaxed lines: Unrelaxed generator output constraint: Relaxed generator output constraint: Load shedding amount constraint: Generator set power generation constraint: The candidate line status constraint is the constraint condition: Adjustable line constraint: Balanced node phase angle constraint: θ0 = 0

[0169] Among them, τ + is the set of candidate lines, c li is the cost of the newly built transmission line li, z i is the construction status of generator i, 0 means not constructed, 1 means constructed, ρ s is the probability of the occurrence of scenario s, N is the number of scenarios, and Ω contains Ω -Set of candidate unrelaxed power nodes and Ω + Set of candidate relaxed power nodes, O pk Unit production cost of conventional units, P G,s,k Output of generator k under scenario s, Ψ is the set of buses, C pb Unit load shedding cost, R s,b Load shedding amount of bus b under scenario s, ψ b Denotes bus b, f s,mn(i) Active power flow of line i, m and n are the bus numbers at both ends of line i, P s,b Load of bus b under scenario s, r mn(i) Susceptance value of line i, θ s,m And θ s,n Phase angles of buses m and n under scenario s respectively, τ - Set of existing lines, P Li,max Capacity of line i, λ2 is the ratio of the capacity of the relaxed line to the capacity of the unrelaxed line, P G,k,min Minimum output of generator k, P G,k,max Maximum output of generator k, λ1 is the ratio of the capacity of the relaxed power node to the capacity of the unrelaxed power node, ε d Allowed maximum load shedding amount, T G,k Equivalent annual utilization hours of generator k, P w,s,k Output of generator k at node w under output scenario s, In-series form lines in the set of candidate adjustable lines, Out-series form lines in the set of candidate adjustable lines, l m , l n , l q Lines m, n, q represent each group of adjustable lines.

[0170] Taking a certain area as an example, the installed capacity of thermal power units is 48704.25 MW, the maximum load is 45421 MW, the number of nodes is 45, and the number of lines is 59. As Figure 2 shown, the investment cost per unit capacity of thermal power units is 5,000,000 yuan / MW, the investment cost per unit length of lines is 6,000,000 yuan / km, the fuel cost is 300 yuan / MWh, and the load shedding cost is 25,000 yuan / MWh. The calculation results of important node and line identification indexes are shown in Tables 1 and 2; the relaxed candidate set and the set of candidate adjustable lines after determining the comprehensive indexes are shown in Table 3; the relaxation multiples in Planning Schemes 1 to 3 are 1, 1.1, and 1.3 respectively. The model is solved using the YALMIP toolbox and the Gurobi solver, and the solution accuracy is set to 0.5%, and the solution results are shown in Table 4.

[0171] Table 1 Calculation results of important node indexes

[0172]

[0173]

[0174] Table 2 Calculation Results of Important Line Indicators

[0175]

[0176]

[0177] Table 3 Slack Candidate Set and Adjustable Line Candidate Set

[0178]

[0179] Table 4 Model Solution Results

[0180]

[0181] It can be seen from the solution results of the embodiments that as the slack multiple increases, the total cost of the system shows an obvious downward trend. This is because the introduction of slack constraints enables the power grid planning scheme to more flexibly cope with load demands and line capacity limitations, thereby reducing unnecessary investment and operating expenses. In addition, the number of adjustable lines required also decreases, indicating that under the condition of a higher slack multiple, the system can optimize the configuration of lines and nodes, reduce the dependence on line adjustment operations, and further improve the economy and operating efficiency of power grid expansion.

[0182] Furthermore, as an implementation of the above Figure 1 shown method, an embodiment of the present invention provides a transmission grid line planning device, as Figure 3 shown, the device includes:

[0183] An index data acquisition module 302, configured to acquire the basic power grid operation data of the target area, and based on the basic power grid operation data, calculate the network topology index, structural vulnerability index, critical load index of each node in the target area, and the system curtailment blocking power, line load rate, and current flow betweenness of each line;

[0184] A candidate set generation module 304, configured to calculate a first comprehensive index corresponding to each node based on the network topology index, structural vulnerability index, and critical load index of each node, calculate a second comprehensive index corresponding to each line based on the system curtailment blocking power, line load rate, and current flow betweenness of each line, obtain a slack power node candidate set based on the arrangement order of the first comprehensive index, and obtain a slack line candidate set based on the arrangement order of the second comprehensive index;

[0185] A constraint generation module 306 is configured to generate relaxation constraints based on the basic power grid operation data, the set of candidate slack power nodes, and the set of candidate slack lines, and generate adjustable line constraints based on the critical load index and the system congestion power reduction index;

[0186] A power grid planning model acquisition module 308 is configured to construct a transmission grid planning model with the minimum comprehensive cost as the objective function and with relaxation constraints, adjustable line constraints, node power balance constraints, existing line DC power flow constraints, candidate line DC power flow constraints, existing line capacity constraints, candidate un-relaxed line capacity constraints, un-relaxed generator output constraints, load shedding amount constraints, generator power generation constraints, candidate line status constraints, and balance node phase angle constraints as the constraint conditions;

[0187] A line adjustment result acquisition module 310 is configured to solve the transmission grid planning model to obtain a line adjustment result.

[0188] Compared with the prior art, the transmission grid line planning device of the present application calculates the network topology index, structure vulnerability index, critical load index of each node in the target area, and the system congestion power reduction, line load rate, and current flow betweenness of each line based on the basic power grid operation data, calculates the first comprehensive index corresponding to each node based on the network topology index, structure vulnerability index, and critical load index of each node, obtains the set of candidate slack power nodes based on the arrangement order of the first comprehensive index, calculates the second comprehensive index corresponding to each line based on the system congestion power reduction, line load rate, and current flow betweenness of each line, obtains the set of candidate slack lines based on the arrangement order of the second comprehensive index, generates relaxation constraints based on the set of candidate slack power nodes and the set of candidate slack lines, and generates adjustable line constraints based on the critical load index and the system congestion power reduction index; constructs a transmission grid planning model with the minimum comprehensive cost as the objective function and with relaxation constraints, adjustable line constraints, node power balance constraints, etc. as the constraint conditions, solves the transmission grid planning model to obtain a line adjustment result, effectively improves the flexibility of the power grid by identifying vulnerable nodes and lines, introducing the set of candidate relaxation constraints and the set of candidate adjustable lines, introducing relaxation constraints and adjustable lines into the power grid planning model, enhances the system's adaptability to load fluctuations and faults, enables the system to achieve efficient resource allocation under a wider range of conditions, and reduces investment and operation costs.

[0189] In one embodiment, the candidate set generation module is further configured to:

[0190] generate a decision matrix based on the network topology index, structure vulnerability index, and critical load index of each node, and perform normalization processing on the vectors in the decision matrix to obtain an alternative matrix;

[0191] Based on the extreme values in each column vector of the alternative matrix, the first ideal matrix and the second ideal matrix are obtained. Based on the alternative matrix, the first ideal matrix and the second ideal matrix, the first grey correlation degree and the first proximity distance between the alternative solutions of each node and the first ideal solution are calculated, as well as the second grey correlation degree and the second proximity distance between the alternative solutions of each node and the second ideal solution;

[0192] Based on the first proximity distance and the second proximity distance, the comprehensive proximity degree of each node is calculated;

[0193] Based on the first grey correlation degree, the first proximity distance, the second grey correlation degree, the second proximity distance and the comprehensive proximity degree of each node, the first comprehensive index corresponding to each node is obtained.

[0194] In one embodiment, the candidate set generation module is further configured to:

[0195] Take the first grey correlation degree, the first proximity distance, the second grey correlation degree, the second proximity distance and the comprehensive proximity degree as a vulnerability index respectively;

[0196] Based on each vulnerability index of each node, the system entropy corresponding to each vulnerability index of each node is obtained;

[0197] Based on the system entropy corresponding to each vulnerability index of each node, the index entropy weight corresponding to each vulnerability index of each node is obtained;

[0198] Based on each vulnerability index of each node and its corresponding index entropy weight, the first comprehensive index corresponding to each node is obtained.

[0199] In one embodiment, the following method is used to calculate the first grey correlation degree, the second grey correlation degree, the first proximity distance and the second proximity distance of each node:

[0200]

[0201] Wherein, is the first grey correlation degree of the i-th node, is the second grey correlation degree of the i-th node, is the first grey correlation coefficient of the j-th index of the i-th node, is the second grey correlation coefficient of the j-th index of the i-th node, m is the number of column vectors in the alternative matrix, n is the number of row vectors in the alternative matrix, is the maximum value in the j-th column vector of the alternative matrix, is the minimum value in the j-th column vector of the alternative matrix, x ij is the i-th row and j-th column vector in the alternative matrix, ξ is the correlation coefficient, is the first proximity distance, is the second proximity distance, is the Euler distance between the alternative plan and the first ideal plan, is the Euler distance between the alternative plan and the second ideal plan, α is the position coefficient, and β is the shape coefficient.

[0202] In one embodiment, the candidate set generation module is further configured to:

[0203] Take the system curtailment of blocked power, line load rate, and power flow betweenness as a line index respectively;

[0204] Based on the system curtailment of blocked power, line load rate, and power flow betweenness of each line, generate a line index evaluation matrix, and perform normalization processing on the vectors in the line index evaluation matrix to obtain a line index standard matrix;

[0205] Based on the line index standard matrix, obtain the entropy of each line index, and based on the entropy of each line index, obtain the weight coefficient corresponding to each line index;

[0206] Based on each line index and the corresponding weight coefficient, obtain the second comprehensive index corresponding to each line.

[0207] In one embodiment, the constraint generation module is further configured to:

[0208] Sort the nodes according to the key load index, determine the vulnerable nodes in the system, analyze the operating conditions and influence of the vulnerable nodes, and based on the analysis results, determine the in-series form lines in the candidate set of adjustable lines;

[0209] Identify the vulnerable lines according to the system curtailment of blocked power, obtain the key nodes around the vulnerable lines, and take the lines connected to the key nodes around the vulnerable lines as the out-series form lines in the candidate set of adjustable lines;

[0210] Based on the in-series form lines and the out-series form lines in the candidate set of adjustable lines, generate adjustable line constraints.

[0211] In one embodiment, the transmission network planning model is:

[0212] Objective function: Node power balance constraint: DC power flow constraint of existing lines: DC power flow constraint of candidate lines: Capacity constraint of existing lines: Capacity constraint of candidate unrelaxed lines: Capacity constraint of candidate relaxed lines: Unrelaxed generator output constraint: Relaxed generator output constraint: Load shedding amount constraint: Generator power generation constraint: The constraint of the status of candidate lines is the constraint condition: Adjustable line constraint: Balanced node phase angle constraint: θ0 = 0

[0213] Among them, τ + is the set of candidate lines, c li is the cost of the newly built transmission line li, z i is the construction status of generator i, 0 means not constructed, 1 means constructed, ρ s is the probability of scenario s occurring, N is the number of scenarios, Ω contains Ω - the set of candidate non-relaxed power node sets and Ω + the set of candidate relaxed power node sets, O pk is the unit production cost of conventional units, P G,s,k is the output of generator k under scenario s, Ψ is the set of buses, C pb is the unit load shedding cost, R s,b is the load shedding amount of bus b under scenario s, ψ b represents bus b, f s,mn(i) is the active power flow of line i, m and n are the bus numbers at both ends of line i, P s,b is the load of bus b under scenario s, r mn(i) is the susceptance value of line i, θ s,m and θ s,n are the phase angles of buses m and n under scenario s respectively, τ - is the set of existing lines, P Li,max is the capacity of line i, λ2 is the ratio of the capacity of the relaxed line to the capacity of the non-relaxed line, P G,k,min is the minimum output of generator k, P G,k,max is the maximum output of generator k, λ1 is the ratio of the capacity of the relaxed power node to the capacity of the non-relaxed power node, ε d is the maximum allowable load shedding amount, T G,k is the equivalent annual utilization hours of generator k, P w,s,k is the output of generator k at node w under output scenario s, is the in-series form line in the candidate set of adjustable lines, is the out-series form line in the candidate set of adjustable lines, l s , l t , l v Lines s, t, and v represent each group of adjustable lines.

[0214] According to an embodiment of the present invention, a storage medium is provided. The storage medium stores at least one executable instruction, and the computer executable instruction can execute the transmission network line planning method in any of the above method embodiments.

[0215] According to an embodiment of the present invention, a computer device is provided, including: a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete mutual communication through the communication bus;

[0216] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above transmission network line planning method.

[0217] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. In one embodiment, they can be implemented by program code executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the present invention is not limited to any specific combination of hardware and software.

[0218] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of the present application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present application.

Claims

1. A method for planning a transmission network line, characterized in that: include: Obtaining basic power grid operation data of the target area, and calculating network topology indicators, structural vulnerability indicators, key load indicators of each node in the target area, and system reduction blocking power, line load rate, and power flow betweenness of each line based on the basic power grid operation data; Based on the network topology index, structural vulnerability index and key load index of each node, a first comprehensive index corresponding to each node is calculated; based on the system blocking power reduction, line load rate and power flow betweenness of each line, a second comprehensive index corresponding to each line is calculated; based on the arrangement order of the first comprehensive index, a candidate set of slack power supply nodes is obtained; based on the arrangement order of the second comprehensive index, a candidate set of slack lines is obtained; Generate a relaxed constraint based on the basic power grid operation data, the relaxed power source node candidate set and the relaxed line candidate set, and generate an adjustable line constraint based on the key load index and the system blocking power reduction index; Taking the minimum comprehensive cost as the objective function, taking the relaxation constraint, the adjustable line constraint, the node power balance constraint, the existing line DC power flow constraint, the to-be-selected line DC power flow constraint, the existing line capacity constraint, the to-be-selected unrelaxed line capacity constraint, the unrelaxed generator output constraint, the load shedding constraint, the unit power generation constraint, the to-be-selected line state constraint and the balance node phase angle constraint as constraint conditions, a transmission network planning model is constructed; The transmission network planning model is solved to obtain a line adjustment result.

2. The transmission network line planning method according to claim 1, characterized in that: The first comprehensive index corresponding to each node is calculated based on the network topology index, structural vulnerability index and key load index of each node, including: Based on the network topology index, structural vulnerability index and key load index of each node, a decision matrix is ​​generated, and the vectors in the decision matrix are normalized to obtain an alternative matrix; Based on the extreme values ​​in each column vector in the alternative matrix, a first ideal matrix and a second ideal matrix are obtained, and based on the alternative matrix, the first ideal matrix and the second ideal matrix, a first grey correlation degree and a first close distance between the alternative solution of each node and the first ideal solution, and a second grey correlation degree and a second close distance between the alternative solution of each node and the second ideal solution are calculated; Calculate the comprehensive proximity of each node based on the first close distance and the second close distance; Based on the first grey relational degree, the first close distance, the second grey relational degree, the second close distance and the comprehensive proximity of each node, a first comprehensive index corresponding to each node is obtained.

3. The transmission network line planning method according to claim 2, characterized in that: The first comprehensive index corresponding to each node is obtained based on the first grey correlation degree, the first close distance, the second grey correlation degree, the second close distance and the comprehensive closeness of each node, including: The first grey relational degree, the first close distance, the second grey relational degree, the second close distance and the comprehensive closeness are taken as a vulnerability index respectively; Based on each vulnerability index of each node, the system entropy corresponding to each vulnerability index of each node is obtained; Based on the system entropy corresponding to each vulnerability indicator of each node, the indicator entropy weight corresponding to each vulnerability indicator of each node is obtained; Based on each vulnerability indicator of each node and its corresponding indicator entropy weight, a first comprehensive indicator corresponding to each node is obtained.

4. The transmission network line planning method according to claim 3, characterized in that: The following method is used to calculate the first grey relational degree, the second grey relational degree, the first close distance and the second close distance of each node: in, is the first grey relational degree of the i-th node, is the second grey relational degree of the i-th node, is the first grey correlation coefficient of the jth index of the i-th node, is the second grey correlation coefficient of the jth indicator of the ith node, m is the number of column vectors in the candidate matrix, n is the number of row vectors in the candidate matrix, is the maximum value of the j-th column vector in the candidate matrix, is the minimum value of the j-th column vector in the candidate matrix, x ij is the i-th row and j-th column vector in the candidate matrix, ξ is the correlation coefficient, For the first close distance, For the second closest distance, is the Euler distance between the alternative solution and the first ideal solution, is the Euler distance between the alternative solution and the second ideal solution, α is the position coefficient, and β is the shape coefficient.

5. The transmission network line planning method according to claim 1, characterized in that: The second comprehensive index corresponding to each line is obtained based on the system blocking power reduction, line load rate and power flow betweenness of each line, including: The system blocking power reduction, line load factor and power flow betweenness are used as a line index respectively; Based on the system blocking power reduction, line load rate and power flow betweenness of each line, a line index evaluation matrix is ​​generated, and the vectors in the line index evaluation matrix are normalized to obtain a line index standard matrix; Based on the line indicator standard matrix, the entropy of each line indicator is obtained, and based on the entropy of each line indicator, the weight coefficient corresponding to each line indicator is obtained; Based on each line indicator and the corresponding weight coefficient, a second comprehensive indicator corresponding to each line is obtained.

6. The transmission network line planning method according to claim 1, characterized in that: The generating of adjustable line constraints based on the key load index and the system reduction blocking power index comprises: Sort the nodes according to the key load indicators, determine the vulnerable nodes in the system, analyze the operating conditions and influence of the vulnerable nodes, and determine the string-in-place lines in the adjustable line selection set based on the analysis results; According to the system's reduction of blocked power, vulnerable lines are identified, key nodes around the vulnerable lines are obtained, and lines connected to the key nodes around the vulnerable lines are used as out-of-line lines in the adjustable line selection set; Based on the in-string form lines in the adjustable line candidate set and the out-string form lines in the adjustable line candidate set, an adjustable line constraint is generated.

7. The method for planning a transmission network line according to any one of claims 1 to 6, characterized in that: The transmission network planning model is: Objective function: Node power balance constraints: Existing line DC power flow constraints: DC power flow constraints of the selected lines: Existing line capacity constraints: The capacity constraints of the lines to be selected without relaxation are: Candidate relaxed line capacity constraints: Generator output constraints are not relaxed: Relax the generator output constraint: Load shedding constraint: Unit power generation constraints: The state constraints of the candidate lines are as follows: Adjustable line constraints: Balance node phase angle constraint: θ0 = 0 Among them, τ + is the set of candidate routes, c li is the cost of the new transmission line li, z i is the construction status of generator i, 0 means not in construction, 1 means in construction, ρ s is the probability of scene s occurring, N is the number of scenes, and Ω contains Ω - The set of unrelaxed power nodes to be selected and Ω + The set of relaxed power nodes to be selected, O pk is the unit production cost of conventional units, P G,s,k is the output of generator k in scenario s, Ψ is the busbar set, C pb is the unit load shedding cost, R s,b is the load shedding of busbar b under scenario s, ψ b Indicates busbar b, f s,mn(i) is the active power flow of line i, m and n are the bus numbers at both ends of line i, P s,b is the load of busbar b under scenario s, r mn(i) is the susceptance value of line i, θ s,m and θ s,n are the phase angles of busbars m and n under scenario s, τ - is the set of existing lines, P Li,max is the capacity of line i, λ2 is the ratio of the relaxed line capacity to the unrelaxed line capacity, P G,k,min is the minimum output of generator k, P G,k,max is the maximum output of generator k, λ1 is the ratio of the capacity of the relaxed power node to the capacity of the unrelaxed power node, ε d is the maximum allowable load shedding capacity, T G,k is the equivalent annual utilization hours of generator k, P w,s,k is the output of generator k at node w in output scenario s, It is a string-input type line in the adjustable line selection set. is the out-of-line form line in the adjustable line selection set, l s , l t , l v Lines s, t, and v represent the adjustable lines of each group.

8. A transmission network line planning device, characterized in that: include: An index data acquisition module is used to acquire basic grid operation data of a target area, and based on the basic grid operation data, calculate network topology indexes, structural vulnerability indexes, key load indexes, and system reduction blocking power, line load rate, and power flow betweenness of each line in the target area; A candidate set generation module is used to calculate the first comprehensive index corresponding to each node based on the network topology index, structural vulnerability index and key load index of each node, calculate the second comprehensive index corresponding to each line based on the system blocking power reduction, line load rate and power flow betweenness of each line, obtain the slack power source node candidate set based on the arrangement order of the first comprehensive index, and obtain the slack line candidate set based on the arrangement order of the second comprehensive index; A constraint generation module, configured to generate a relaxed constraint based on the basic power grid operation data, the relaxed power source node candidate set and the relaxed line candidate set, and to generate an adjustable line constraint based on the key load index and the system blocking power reduction index; A power grid planning model acquisition module is used to take the minimum comprehensive cost as the objective function, and take the relaxation constraint, the adjustable line constraint, the node power balance constraint, the existing line DC power flow constraint, the to-be-selected line DC power flow constraint, the existing line capacity constraint, the to-be-selected unrelaxed line capacity constraint, the unrelaxed generator output constraint, the load shedding constraint, the unit power generation constraint, the to-be-selected line state constraint and the balance node phase angle constraint as constraint conditions to construct a transmission network planning model; The line adjustment result acquisition module is used to solve the transmission network planning model to obtain the line adjustment result.

9. The transmission network line planning device according to claim 8, characterized in that: The to-be-selected set generation module is also used for: Based on the network topology index, structural vulnerability index and key load index of each node, a decision matrix is ​​generated, and the vectors in the decision matrix are normalized to obtain an alternative matrix; Based on the extreme values ​​in each column vector in the alternative matrix, a first ideal matrix and a second ideal matrix are obtained, and based on the alternative matrix, the first ideal matrix and the second ideal matrix, a first grey correlation degree and a first close distance between the alternative solution of each node and the first ideal solution, and a second grey correlation degree and a second close distance between the alternative solution of each node and the second ideal solution are calculated; Calculate the comprehensive proximity of each node based on the first close distance and the second close distance; Based on the first grey relational degree, the first close distance, the second grey relational degree, the second close distance and the comprehensive proximity of each node, a first comprehensive index corresponding to each node is obtained.

10. The transmission network line planning device according to claim 9, characterized in that: The to-be-selected set generation module is also used for: The first grey relational degree, the first close distance, the second grey relational degree, the second close distance and the comprehensive closeness are taken as a vulnerability index respectively; Based on each vulnerability index of each node, the system entropy corresponding to each vulnerability index of each node is obtained; Based on the system entropy corresponding to each vulnerability indicator of each node, the indicator entropy weight corresponding to each vulnerability indicator of each node is obtained; Based on each vulnerability indicator of each node and its corresponding indicator entropy weight, a first comprehensive indicator corresponding to each node is obtained.

11. The transmission network line planning device according to claim 10, characterized in that: The following method is used to calculate the first grey relational degree, the second grey relational degree, the first close distance and the second close distance of each node: in, is the first grey relational degree of the i-th node, is the second grey relational degree of the i-th node, is the first grey correlation coefficient of the jth index of the i-th node, is the second grey correlation coefficient of the jth indicator of the ith node, m is the number of column vectors in the candidate matrix, n is the number of row vectors in the candidate matrix, is the maximum value of the j-th column vector in the candidate matrix, is the minimum value of the j-th column vector in the candidate matrix, x ij is the i-th row and j-th column vector in the candidate matrix, ξ is the correlation coefficient, For the first close distance, For the second closest distance, is the Euler distance between the alternative solution and the first ideal solution, is the Euler distance between the alternative solution and the second ideal solution, α is the position coefficient, and β is the shape coefficient.

12. The transmission network line planning device according to claim 8, characterized in that: The to-be-selected set generation module is also used for: The system blocking power reduction, line load factor and power flow betweenness are used as a line index respectively; Based on the system blocking power reduction, line load rate and power flow betweenness of each line, a line index evaluation matrix is ​​generated, and the vectors in the line index evaluation matrix are normalized to obtain a line index standard matrix; Based on the line indicator standard matrix, the entropy of each line indicator is obtained, and based on the entropy of each line indicator, the weight coefficient corresponding to each line indicator is obtained; Based on each line indicator and the corresponding weight coefficient, a second comprehensive indicator corresponding to each line is obtained.

13. The transmission network line planning device according to claim 8, characterized in that: The constraint generation module is also used for: Sort the nodes according to the key load indicators, determine the vulnerable nodes in the system, analyze the operating conditions and influence of the vulnerable nodes, and determine the string-in-place lines in the adjustable line selection set based on the analysis results; According to the system's reduction of blocked power, vulnerable lines are identified, key nodes around the vulnerable lines are obtained, and lines connected to the key nodes around the vulnerable lines are used as out-of-line lines in the adjustable line selection set; Based on the in-string form lines in the adjustable line candidate set and the out-string form lines in the adjustable line candidate set, an adjustable line constraint is generated.

14. The transmission network line planning device according to any one of claims 8 to 13, characterized in that: The transmission network planning model is: Objective function: Node power balance constraints: Existing line DC power flow constraints: DC power flow constraints of the selected lines: Existing line capacity constraints: The capacity constraints of the lines to be selected without relaxation are: Candidate relaxed line capacity constraints: Generator output constraints are not relaxed: Relax the generator output constraint: Load shedding constraint: Unit power generation constraints: The state constraints of the candidate lines are as follows: Adjustable line constraints: Balance node phase angle constraint: θ0 = 0 Among them, τ + is the set of candidate routes, c li is the cost of the new transmission line li, z i is the construction status of generator i, 0 means not in construction, 1 means in construction, ρ s is the probability of scene s occurring, N is the number of scenes, and Ω contains Ω - The set of unrelaxed power nodes to be selected and Ω + The set of relaxed power nodes to be selected, O pk is the unit production cost of conventional units, P G,s,k is the output of generator k in scenario s, Ψ is the busbar set, C pb is the unit load shedding cost, R s,b is the load shedding of busbar b under scenario s, ψ b Indicates busbar b, f s,mn(i) is the active power flow of line i, m and n are the bus numbers at both ends of line i, P s,b is the load of busbar b under scenario s, r mn(i) is the susceptance value of line i, θ s,m and θ s,n are the phase angles of busbars m and n under scenario s, τ - is the set of existing lines, P Li,max is the capacity of line i, λ2 is the ratio of the relaxed line capacity to the unrelaxed line capacity, P G,k,min is the minimum output of generator k, P G,k,max is the maximum output of generator k, λ1 is the ratio of the capacity of the relaxed power node to the capacity of the unrelaxed power node, ε d is the maximum allowable load shedding capacity, T G,k is the equivalent annual utilization hours of generator k, P w,s,k is the output of generator k at node w in output scenario s, It is a string-input type line in the adjustable line selection set. is the out-of-line form line in the adjustable line selection set, l s , l t , l v Lines s, t, and v represent the adjustable lines of each group.

15. A storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to execute operations corresponding to the transmission network line planning method according to any one of claims 1 to 7.

16. A computer device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the transmission network line planning method according to any one of claims 1-7.

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