Power transmission network line planning method and device, medium and equipment

Abstract

The invention discloses a power transmission network line planning method and device, a medium and equipment, and the method comprises the steps: obtaining a to-be-selected set of relaxed power supply nodes based on the network topology index, the structure vulnerability index and the key load index of each node, and obtaining a to-be-selected set of relaxed lines based on the system cut-down blocked power, the line load rate and the power flow betweenness of each line; generating a relaxation constraint based on the relaxation power supply node to-be-selected set and the relaxation line to-be-selected set, and generating an adjustable line constraint based on a key load index and a system blocking power reduction index; constructing a power transmission network planning model based on the comprehensive cost minimum objective function, the relaxation constraint and the adjustable line constraint; and solving the power transmission network planning model to obtain a line adjustment result. The flexibility of a power grid is effectively improved by identifying fragile nodes and lines, determining a relaxation constraint to-be-selected set and an adjustable line to-be-selected set, and introducing relaxation constraints and adjustable line constraints into a power grid planning model to obtain a line adjustment result.

Description

Technical Field

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

[0002] With the continuous improvement of the functions of modern power systems, the system structure is becoming increasingly complex, the number of components is continuously increasing, and the degree of automation is constantly improving. The power system is developing towards high voltage, long distance, and large capacity. In this context, ensuring that the power system can operate safely, reliably, economically and flexibly has become a basic requirement in the industry. In the face of growing electricity demand and large-scale access to renewable energy, how to optimize the expansion of the power grid through scientific planning of existing transmission corridors and the reasonable introduction of adjustable lines to meet the needs of future development has become an important issue.

[0003] At present, the research methods that consider relaxation constraints and adjustable lines in power grid expansion planning mainly include multi-objective hierarchical optimization and two-layer model construction. For example, Cheng Haozhong et al. proposed a hierarchical optimization method based on multi-objective power grid planning, which effectively reduced the computational complexity; Yang Qin et al. designed line overload rate constraints and improved DC power flow constraints for the annual statistical indicator constraint problem in the two-stage model, and established a two-layer model for power grid planning; 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 studies have made some progress, they are more focused on specific types of constraints, such as peak load capacity constraints, regulation rate constraints, and risk constraints. As a result, in practical applications, grid expansion planning may not be able to fully adapt to the development needs of the power system and it is difficult to achieve stable and efficient operation of the system. In particular, when it comes to how to balance system stability and expansion costs, how to dynamically adjust the network structure according to actual operating conditions, etc., existing solutions often appear to be inflexible or difficult to fully cover all possible scenarios. Summary of the invention

[0005] In view of this, the present invention provides a transmission network line planning method, device, medium and equipment, the main purpose of which is to solve the problem that the existing transmission network line planning method is not flexible enough and difficult to cover many possible scenarios.

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

[0007] 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;

[0008] 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;

[0009] 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;

[0010] 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;

[0011] The transmission network planning model is solved to obtain a line adjustment result.

[0012] Optionally, the first comprehensive index corresponding to each node is calculated based on the network topology index, the structural vulnerability index and the key load index of each node, including:

[0013] 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;

[0014] 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;

[0015] Calculate the comprehensive proximity of each node based on the first close distance and the second close distance;

[0016] 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.

[0017] Optionally, obtaining the first comprehensive index corresponding to each node 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 includes:

[0018] 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;

[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 indicator of each node, the indicator entropy weight corresponding to each vulnerability indicator of each node is obtained;

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

[0022] Optionally, 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:

[0023]

[0024] 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.

[0025] Optionally, obtaining a second comprehensive indicator corresponding to each line based on the system blocking power reduction, line load rate and power flow betweenness of each line includes:

[0026] The system blocking power reduction, line load factor and power flow betweenness are used as a line index respectively;

[0027] 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;

[0028] 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;

[0029] Based on each line indicator and the corresponding weight coefficient, a second comprehensive indicator corresponding to each line is obtained.

[0030] Optionally, generating an adjustable line constraint based on the key load indicator and the system curtailment blocking power indicator includes:

[0031] 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;

[0032] 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;

[0033] 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.

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

[0035] 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

[0036] 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.

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

[0038] 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;

[0039] 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;

[0040] 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;

[0041] 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;

[0042] The line adjustment result acquisition module is used to solve the transmission network planning model to obtain the line adjustment result.

[0043] Optionally, the to-be-selected set generation module is further used for:

[0044] 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;

[0045] 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;

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

[0047] 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.

[0048] Optionally, the to-be-selected set generation module is further used for:

[0049] 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;

[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 indicator of each node, the indicator entropy weight corresponding to each vulnerability indicator of each node is obtained;

[0052] Based on each vulnerability indicator of each node and its corresponding indicator entropy weight, a first comprehensive indicator 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 close distance and the second close distance of each node:

[0054]

[0055] 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.

[0056] Optionally, the to-be-selected set generation module is further used for:

[0057] The system blocking power reduction, line load factor and power flow betweenness are used as a line index respectively;

[0058] 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;

[0059] 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;

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

[0061] Optionally, the constraint generation module is further used 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 determine the string-in-place lines in the adjustable line selection set based on the analysis results;

[0063] 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;

[0064] 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.

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

[0066] 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

[0067] Among them, τ + is the set of candidate routes, c li is the cost of the new transmission line li, zi 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.

[0068] According to another aspect of the present application, a storage medium is provided, 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 above-mentioned transmission network line planning method.

[0069] According to another aspect of the present application, a computer device is provided, 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;

[0070] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned transmission network 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] The present application provides a transmission network line planning method, device, equipment and medium. Based on the basic data of power grid operation, the network topology index, structural vulnerability index, key load index of each node in the target area and the system blocking power reduction, line load rate and flow betweenness of each line are calculated. Based on the network topology index, structural vulnerability index and key load index of each node, the first comprehensive index corresponding to each node is calculated. Based on the arrangement order of the first comprehensive index, a set of slack power supply nodes to be selected is obtained. Based on the system blocking power reduction, line load rate and flow betweenness of each line, the second comprehensive index corresponding to each line is calculated. Based on the arrangement order of the second comprehensive index, a set of slack lines to be selected is obtained. Based on the arrangement order of the slack power supply nodes to be selected and the slack power supply nodes to be selected, a set of slack power supply nodes to be selected is obtained. The relaxed line selection set is generated, and the relaxed constraints are generated. Based on the key load indicators and the system blocking power reduction indicators, the adjustable line constraints are generated; with the minimum comprehensive cost as the objective function, and the relaxed constraints, adjustable line constraints, node power balance constraints and other constraints as constraints, a transmission network planning model is constructed, and the transmission network planning model is solved to obtain the line adjustment results. By identifying vulnerable nodes and lines, the relaxed constraint selection set and the adjustable line selection set are introduced. Relaxed constraints and adjustable lines are introduced into the power grid planning model. The line adjustment results obtained effectively improve the flexibility of the power grid, cover a variety of possible scenarios, and enhance the system's adaptability to load fluctuations and faults, so that the system can achieve efficient resource allocation under a wider range of conditions and reduce investment and operating costs.

[0073] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0075] Figure 1 A flow chart of a transmission network line planning method provided by an embodiment of the present application is shown;

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

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

[0078] in,

[0079] Figure 3 In: 302-index data acquisition module; 304-selection set generation module; 306-constraint generation module; 308-grid planning model acquisition module; 310-line adjustment result acquisition module; DETAILED DESCRIPTION

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

[0081] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention application are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

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

[0083] 102: Obtain 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, key load index of each node in the target area and the system reduction blocking power, line load rate and power flow betweenness of each line;

[0084] 104: 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 source nodes is obtained; based on the arrangement order of the second comprehensive index, a candidate set of slack lines is obtained;

[0085] 106: Generate relaxation constraints based on basic power grid operation data, a relaxation power node candidate set, and a relaxation line candidate set, and generate adjustable line constraints based on key load indicators and system blocking power reduction indicators;

[0086] 108: Taking the minimum comprehensive cost as the objective function, and taking the relaxation constraint, adjustable line constraint, node power balance constraint, existing line DC power flow constraint, to-be-selected line DC power flow constraint, existing line capacity constraint, to-be-selected unrelaxed line capacity constraint, unrelaxed generator output constraint, load shedding constraint, unit power generation constraint, to-be-selected line state constraint and balance node phase angle constraint as constraint conditions, a transmission network planning model is constructed;

[0087] 110: Solve the transmission network planning model and obtain the line adjustment result.

[0088] Specifically, the basic data of power grid operation include storage load data, power supply data, and transmission network data. Load data refers to the load size of each node in the system. Power supply data includes parameters such as unit installed capacity, upper and lower limits of output, and unit status. Transmission network data includes parameters such as transmission network topology, 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 of important nodes include network topology indicators, structural vulnerability indicators, and key load indicators, and the identification indicators of important lines include system reduction of blocking power, line load rate, and flow betweenness. Based on the basic data of power grid operation, network topology indicators, structural vulnerability indicators, key load indicators, system reduction of blocking power, line load rate, and flow betweenness are calculated.

[0089] The network topology indicators in the identification indicators of important nodes include the three most commonly used and representative evaluation indicators, namely degree, betweenness, and node center proximity. The structural vulnerability indicators include two indicators: the maximum supported 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 indicator is sorted. According to the sorting results, the candidate set of relaxed power supply nodes can be obtained. According to the system blocking power reduction, line load rate and flow betweenness of each line, the second comprehensive indicator of each line is calculated, and the second comprehensive indicator is sorted. According to the sorting results, the candidate set of relaxed lines can be obtained. Based on the candidate set of relaxed power supply nodes and the candidate set of relaxed lines, relaxed constraints are generated.

[0090] Based on the key load indicators, the candidate set of out-of-line lines in the candidate set of adjustable lines is determined to reduce the system load pressure and improve the stability of the nodes. Based on the system blocking power reduction indicators, the candidate set of in-line lines in the candidate set of adjustable lines is determined to increase the transmission capacity of the lines, optimize the network structure, and alleviate the line congestion problem. Based on the candidate set of out-of-line lines in the candidate set of adjustable lines and the candidate set of in-line lines in the candidate set of adjustable lines, line adjustment constraints are generated.

[0091] Taking into account the power source investment cost, transmission line investment cost, operation cost and load shedding cost, with the goal of minimizing the comprehensive cost, and taking relaxation constraints, adjustable line constraints, node power balance constraints, existing line DC power flow constraints, to-be-selected line DC power flow constraints, existing line capacity constraints, to-be-selected unrelaxed line capacity constraints, unrelaxed generator output constraints, load shedding constraints, unit power generation constraints, to-be-selected line state constraints and balance node phase angle constraints as constraints, a transmission network planning model is constructed, and a transmission network planning model considering the relaxation of power sources and transmission corridor constraints is established.

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

[0093] The present application provides a transmission network line planning method. Compared with the prior art, the present application provides a transmission network line planning method. Based on the basic data of power grid operation, the network topology index, structural vulnerability index, key load index of each node in the target area and the system blocking power reduction, line load rate and flow betweenness of each line are calculated. Based on the network topology index, structural vulnerability index and key load index of each node, the first comprehensive index corresponding to each node is calculated. Based on the arrangement order of the first comprehensive index, a set of slack power supply nodes to be selected is obtained. Based on the system blocking power reduction, line load rate and flow betweenness of each line, the second comprehensive index corresponding to each line is calculated. Based on the arrangement order of the second comprehensive index, a set of slack lines to be selected is obtained. Based on the arrangement order of the slack power supply nodes to be selected and the slack power supply nodes to be selected, a set of slack power supply nodes to be selected is obtained. Line selection set, generate relaxation constraints, generate adjustable line constraints based on key load indicators and system blocking power reduction indicators; take the minimum comprehensive cost as the objective function, and take relaxation constraints, adjustable line constraints, node power balance constraints and other constraints as constraints to build a transmission network planning model, solve the transmission network planning model, and obtain line adjustment results. By identifying vulnerable nodes and lines, introducing relaxation constraint selection sets and adjustable line selection sets, introducing relaxation constraints and adjustable lines in the power grid planning model, the obtained line adjustment results effectively improve the flexibility of the power grid, cover a variety of possible scenarios, and enhance the system's adaptability to load fluctuations and faults, so that the system can achieve efficient resource allocation under a wider range of conditions and reduce investment and operating costs.

[0094] In one embodiment, 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, including:

[0095] 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;

[0096] Based on the extreme values ​​in each column vector in 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, 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;

[0097] Calculate the comprehensive proximity of each node based on the first proximity distance and the second proximity distance;

[0098] 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.

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

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

[0101] Where N jk represents the total number of shortest paths between nodes j and k; N jk (i) represents the total number of shortest paths between nodes j and k that pass through node i. This formula can be used to calculate the normalized betweenness index of each node in the network. The larger the value, the higher the status of the corresponding node in the network, and the more important it is relative to other nodes in the same network.

[0102] Node center proximity reflects the comprehensive proximity between a node and other nodes in the network. The reciprocal of the sum of the shortest path distances from a node to all other nodes represents the center proximity. Node center proximity is considered from the importance of the load node in the grid 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 in the network and the greater its role. For node i, its proximity is defined as follows: Where: N is the number of power grid nodes, dij is the number of edges included in the shortest path from node i to node j.

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

[0104] maxλ=ΣP lo ' ad / ΣP load0

[0105] st∑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 It represents the initial load level before the power failure of the key node of the power network; P lo ' ad It indicates the maximum load level that the key nodes of the power grid can support after a power failure. The four constraints are node power balance constraint, power flow equation constraint, line capacity constraint, and generator output constraint.

[0110] The power grid transmission efficiency index is used to measure the power grid transmission efficiency, which comprehensively considers the global importance and local importance of key nodes. This index is defined as the ratio of the sum of the reciprocals of the shortest paths from all generator nodes to all load nodes in the largest sub-network after the power grid is decoupled and the sum of the reciprocals of the shortest paths from all generator nodes to all load nodes in the power grid before the power failure. After the calculation nodes fail to operate one by one, the ratio of the sum of the reciprocals of the shortest paths from all generator nodes to all load nodes in the network at this time to the normal operation is calculated. The calculation formula is as follows:

[0111]

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

[0113] The key load indicators fully consider the impact of the grid load level, the corresponding grid power generation capacity and its distribution, and incorporate the load into the evaluation of node importance.

[0114] First, normalize the load as follows: Where: is the injected power of node k, S base is the base power of the system. The load importance of any node k in the network is defined to reflect the critical load level as follows: Where: w and w′ are any two different nodes in the network; k ww′ is a 0-1 variable indicating that the shortest path between nodes w and w′ passes through any node k, that is, if the shortest path does not pass through node k, then k ww′ = 0, the shortest path passes through node k, then k ww′ =1.

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

[0116]

[0117] Therefore, the node load importance can be used to measure the importance of the node, and from the above formula, we can know that 0 <C q(k) <1.

[0118] The system reduction blocking power in the identification index of important lines can be used to evaluate the insufficiency of the power grid transmission capacity and reflect the potential transmission capacity problems in the system transmission. It is defined as follows: LUC = P (trade)ij -P (max)ij , where P (trade)ij Indicates the power that line ij needs to pass; P (max)ij In general, we consider static blocking. In fact, stable blocking may also need to be considered in the study. In this case, P (max)ij Indicates the stable limit power of the line. When calculating, you can compare the thermal limit power and the stable limit power, and take the smaller of the two as P (max)ij .

[0119] The branch load rate is defined as the branch load level under normal conditions and maximum load. Where: L ij is the load rate of branch i, j; P ij is the actual operating active power of the branch; is the maximum allowable active power of the 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 the node and line indicators. The important node identification index proposes three different indicators, network topology index, structural vulnerability index and critical load index. First, a decision matrix is ​​constructed based on the important node identification index of each node: the decision matrix X = (x ij ) n×m , nodes and indicators can be regarded as alternatives and attributes respectively. Then the decision matrix X is normalized to eliminate the influence of different dimensions and the alternative matrix is ​​obtained.

[0121] The benefit-based indicators are normalized using the following method: Cost indicators are normalized using the following method:

[0122] Next, the first ideal matrix corresponding to the ideal decision solution is calculated, also called the positive ideal matrix, and the second ideal matrix corresponding to the ideal decision solution is calculated, also called the negative ideal matrix. The positive and negative ideal matrices are taken as the maximum and minimum values ​​of each column vector of the candidate matrix, respectively, that is, the positive ideal matrix is The negative ideal matrix is in Based on the alternative matrix, the first ideal matrix and the second ideal matrix, the Euler distance between the alternative solution and the first ideal solution and the second ideal solution is calculated as follows:

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

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

[0125] Among them, ξ is the resolution coefficient, which is generally taken as 0.5.

[0126] Combine the Euler distance with the grey correlation degree to calculate the proximity of the alternative solutions of each node to the positive and negative ideal solutions. Set the position coefficient α and shape coefficient β, both of which are taken as 0.5, and the first close distance and the second close distance can be obtained:

[0127]

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

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

[0130] 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;

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

[0132] 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;

[0133] Based on each vulnerability indicator of each node and its corresponding indicator entropy weight, a first comprehensive indicator corresponding to each node is obtained.

[0134] Specifically, the ranking results of the comprehensive proximity of each alternative solution, i.e., the node, can be used as the ranking results of its vulnerability. The proximity can reflect the degree of proximity between the node's vulnerability value and the positive ideal solution, while the positive ideal solution can be used as the maximum value of the vulnerability in the network node set. Therefore, it can be considered that the greater the proximity of the node, the higher the node vulnerability. So far, the m decision attributes of n solutions have been transformed into five vulnerability indicators of n solutions: comprehensive node proximity, positive and negative proximity distance, and positive and negative gray correlation.

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

[0136] According to the idea of ​​multi-attribute decision-making, the nodes in the power grid can be regarded as decision-making schemes, and each vulnerability index of the node can be regarded as a decision attribute. Then, each decision scheme has five decision attributes to measure its vulnerability. In order to measure the role of each attribute in the measurement, the index entropy weight of vulnerability index i can be defined as:

[0137]

[0138] Where 0<α i <1, and The larger the index entropy weight is, the better the index can reflect the differences between system nodes and the greater the role it plays 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, and then the value of each index is multiplied by the index entropy weight to obtain the first comprehensive index. The first comprehensive index is sorted, and the candidate set of slack power supply nodes can be obtained according to the sorting result.

[0139] In one embodiment, 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:

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

[0141] 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;

[0142] 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;

[0143] Based on each line indicator and the corresponding weight coefficient, a second comprehensive indicator corresponding to each line is obtained.

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

[0145] Firstly, the system blocking power reduction, line load rate and power flow betweenness are taken as a line index respectively; according to the system blocking power reduction, line load rate and power flow betweenness of each line, a line index evaluation matrix with p lines and q items of indicators is constructed:

[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 indicator matrix to obtain the line indicator standard matrix:

[0148]

[0149] P ij That is, the contribution of the i-th object 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, entropy E j It also represents the total contribution of all objects to the jth indicator.

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

[0154]

[0155] The larger the entropy weight coefficient h is, the greater the amount of information represented by the indicator is, which means that the indicator has a greater impact on the comprehensive evaluation of the line. The entropy weight coefficient h = (h1, h2, ..., h p) is the difference coefficient vector. According to the above steps, the weight coefficient of each line indicator is calculated. For each line, the value of each line indicator is multiplied by the weight coefficient corresponding to the line indicator, and then the product of the value of all line indicators and the weight coefficient is added to obtain the second comprehensive indicator. The second comprehensive indicator is sorted, and the relaxation transmission corridor candidate set can be obtained according to the sorting result, which is also called the relaxation line candidate set.

[0156] The relaxed power node candidate set and the relaxed transmission corridor candidate set together constitute the relaxed constraint candidate set. According to the relaxed constraint candidate set and different relaxation multiples, the module can give the relaxed generator output constraint and the candidate relaxed line capacity constraint as follows.

[0157]

[0158] Where Ω + It is represented as the set of relaxed units, that is, the set of relaxed power nodes to be selected, τ ++ is the candidate set of relaxed lines, i.e., the candidate set of relaxed transmission corridors. G,s,k is the output of generator k in scenario s, f s,mn(i) , P s,b are the active power flow of line i (where subscripts m and n are the bus numbers at both ends of line i) and the load of bus b under scenario s. λ1 is the ratio of the relaxed power capacity to the unrelaxed power 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 key load index and the system curtailment blocking power index, an adjustable line constraint is generated, including:

[0160] Sort the nodes according to the key load indicators, determine the vulnerable nodes in the system, and analyze the operating conditions and influence of the vulnerable nodes. Based on the analysis results, determine the string-in-place lines in the adjustable line selection set;

[0161] 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;

[0162] 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, the adjustable line constraints are generated.

[0163] Specifically, the nodes are sorted according to the key load indicators to determine the vulnerable nodes in the system. For example, the nodes with key load indicators greater than the load threshold are regarded as vulnerable nodes. The operating conditions and impact range of these vulnerable nodes are analyzed. For example, vulnerable nodes with poor operating conditions and small impact range are included in the candidate set for possible out-of-line operation to reduce the system load pressure and improve the stability of the nodes. At the same time, according to the indicator results of the system's reduction of blocking power, the vulnerable lines are identified. For example, the lines with system blocking power reduction greater than the blocking threshold are regarded as vulnerable lines. Combined with the key nodes around them, the relevant lines are included in the candidate set for possible in-line operation to increase the transmission capacity of the lines, optimize the network structure, and alleviate the problem of line congestion.

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

[0165]

[0166] In the formula, It is a string-input type line in the adjustable line selection set. It is a string-out form line in the adjustable line selection set; wherein s, t, and v represent the adjustable lines of each group.

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

[0168] 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

[0169] 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 m , l n , l q Lines m, n, q represent the adjustable lines of each group.

[0170] Take a certain area as an example, the installed capacity of thermal power plants is 48704.25MW, the maximum load is 45421MW, the number of nodes is 45, and the number of lines is 59. Figure 2 As shown, the unit capacity investment cost of thermal power units is 5,000,000 yuan / MW, the unit length investment cost 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 indicators are shown in Tables 1 and 2; the relaxation candidate set and the adjustable line candidate set after the comprehensive indicators are determined 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 toolkit and Gurobi solver, and the solution accuracy is set to 0.5%. The solution results are shown in Table 4.

[0171] Table 1 Calculation results of important node indicators

[0172]

[0173]

[0174] Table 2 Calculation results of important line indicators

[0175]

[0176]

[0177] Table 3 Relaxed 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 embodiment that with the increase of the relaxation multiple, the total cost of the system shows a clear downward trend. This is because the introduction of relaxation constraints enables the power grid planning scheme to respond more flexibly to load demand and line capacity limitations, thereby reducing unnecessary investment and operating expenses. In addition, the number of adjustable lines required is also reduced, which shows that under the condition of higher relaxation multiples, the system can reduce the dependence on line adjustment operations by optimizing the configuration of lines and nodes, thereby further improving the economy and operating efficiency of power grid expansion.

[0182] Furthermore, as a response to the above Figure 1 The embodiment of the present invention provides a transmission network line planning device, such as Figure 3 As shown, the device comprises:

[0183] The index data acquisition module 302 is used to acquire the basic data of the power grid operation in the target area, and based on the basic data of the power grid operation, calculate the network topology index, structural vulnerability index, key load index of each node in the target area and the system reduction blocking power, line load rate and power flow betweenness of each line;

[0184] The candidate set generation module 304 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 candidate set of slack power nodes based on the arrangement order of the first comprehensive index, and obtain the candidate set of slack lines based on the arrangement order of the second comprehensive index;

[0185] The constraint generation module 306 is used to generate relaxation constraints based on the basic data of power grid operation, the relaxation power source node candidate set and the relaxation line candidate set, and to generate adjustable line constraints based on the key load index and the system blocking power reduction index;

[0186] The power grid planning model acquisition module 308 is used to construct a transmission network planning model with the minimum comprehensive cost as the objective function and with the relaxation constraint, adjustable line constraint, node power balance constraint, existing line DC power flow constraint, to-be-selected line DC power flow constraint, existing line capacity constraint, to-be-selected unrelaxed line capacity constraint, unrelaxed generator output constraint, load shedding constraint, unit power generation constraint, to-be-selected line state constraint and balance node phase angle constraint as constraint conditions;

[0187] The line adjustment result acquisition module 310 is used to solve the transmission network planning model to obtain the line adjustment result.

[0188] Compared with the prior art, the transmission network line planning device of the present application calculates the network topology index, structural vulnerability index, key load index of each node in the target area and the system reduction blocking power, line load rate and flow betweenness of each line based on the basic data of power grid operation; calculates the first comprehensive index corresponding to each node based on the network topology index, structural vulnerability index and key load index of each node; obtains the set of slack power supply nodes to be selected based on the arrangement order of the first comprehensive index; calculates the second comprehensive index corresponding to each line based on the system reduction blocking power, line load rate and flow betweenness of each line; obtains the set of slack lines to be selected based on the arrangement order of the second comprehensive index; The relaxed constraint set and the relaxed line selection set are used to generate relaxed constraints. Based on the key load indicators and the system blocking power reduction indicators, the adjustable line constraints are generated. Taking the minimum comprehensive cost as the objective function, and taking the relaxed constraints, the adjustable line constraints, the node power balance constraints and other constraints as the constraints, the transmission network planning model is constructed, and the transmission network planning model is solved to obtain the line adjustment results. By identifying the vulnerable nodes and lines, the relaxed constraint selection set and the adjustable line selection set are introduced. The relaxed constraints and the adjustable lines are introduced into the power grid planning model. The line adjustment results obtained effectively improve the flexibility of the power grid, enhance the system's adaptability to load fluctuations and faults, and enable the system to achieve efficient resource allocation under a wider range of conditions and reduce investment and operating costs.

[0189] In one embodiment, the to-be-selected set generation module is further used to:

[0190] 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;

[0191] Based on the extreme values ​​in each column vector in 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, 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;

[0192] Calculate the comprehensive proximity of each node based on the first proximity distance and the second proximity distance;

[0193] 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.

[0194] In one embodiment, the to-be-selected set generation module is further used to:

[0195] 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;

[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 indicator of each node, the indicator entropy weight corresponding to each vulnerability indicator of each node is obtained;

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

[0199] In one embodiment, 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:

[0200]

[0201] 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.

[0202] In one embodiment, the to-be-selected set generation module is further used to:

[0203] The system blocking power reduction, line load factor and power flow betweenness are used as a line index respectively;

[0204] 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;

[0205] 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;

[0206] Based on each line indicator and the corresponding weight coefficient, a second comprehensive indicator corresponding to each line is obtained.

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

[0208] 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;

[0209] 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;

[0210] 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, the adjustable line constraints are generated.

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

[0212] 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

[0213] 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.

[0214] According to one embodiment of the present invention, a storage medium is provided, wherein 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 one embodiment of the present invention, a computer device is provided, 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;

[0216] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned 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 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 a program code executable by a computing device, so that 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 made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, 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 intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall 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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