Power transmission network extension planning method, system, equipment and medium

Abstract

The invention discloses a power transmission network extension planning method, system and device and a medium, and the method comprises the steps: firstly depicting the uncertainty of new energy output through employing a typical scene set; meanwhile, the influence of the extension planning scheme on the new energy abandoned power quantity and load shedding quantity, the influence on the system line load uniformity and the influence on the system power electronic equipment small interference stability are considered, a multi-target optimization model is established, and a multi-target genetic algorithm is used for solving the multi-target optimization model to obtain a power transmission network extension planning scheme. The method is easy to implement, the requirement of the double-high characteristic for the power transmission network is met more comprehensively and accurately, and the bearing capacity of new energy of the system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system planning and operation, and in particular to a transmission network expansion planning method, system, equipment and medium. Background Art

[0002] The new power system has the characteristics of "double high", that is, a high proportion of renewable energy and a high proportion of power electronic equipment. The "double high" characteristics of the new power system put forward higher requirements for transmission network planning. The output of new energy represented by wind and solar power is uncertain, which makes the system flow change in a large range, requiring the transmission network to adapt to this change. New energy represented by wind and solar power is also power electronic equipment. Studies have shown that the higher the proportion of power electronic equipment, the greater the "intensity" of the transmission network is required, which can provide support for the operation of large-scale power electronic equipment. Therefore, it is of great significance to study the transmission network planning method of the new power system. Transmission network expansion planning is one of the important contents of transmission network planning. The current transmission network expansion planning method considering new energy has the problem of incomplete consideration. It often only considers the impact of random volatility of new energy output, only from the economic consideration, and does not consider the requirement of the transmission network to provide support for the operation of large-scale power electronic equipment. Summary of the invention

[0003] The present application provides a transmission network expansion planning method, system, equipment and medium for comprehensively meeting the requirements of the "double high" characteristics for the transmission network and improving the system's new energy carrying capacity.

[0004] In view of this, the first aspect of the present application provides a transmission network expansion planning method, the method comprising:

[0005] Obtain basic information about the planned transmission network to be expanded, including: information about the lines to be expanded, information about new energy sources connected to the transmission network system, and typical scenarios for new energy output;

[0006] Based on the basic information, a multi-objective optimization model is established by taking into account the economic efficiency of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small disturbance stability of the system power electronic equipment;

[0007] A multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a transmission network expansion planning scheme.

[0008] Optionally, the multi-objective optimization model is established based on the basic information while considering the economic efficiency of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small disturbance stability of the system power electronic equipment, including:

[0009] Based on the basic information, a first sub-objective function of the construction cost of the transmission network expansion plan considering the influence of the amount of abandoned electricity and load shedding of new energy is constructed, a second sub-objective function of the line load rate balance of the system after the transmission network expansion plan is implemented is constructed, and the support strength of the transmission network system for new energy is analyzed by running the generalized short-circuit ratio analysis method, so as to construct a third sub-objective function of the support strength of the system for new energy power electronic equipment after the transmission network expansion plan is implemented;

[0010] The multi-objective optimization model is established according to the first sub-objective function, the second sub-objective function and the third sub-objective function.

[0011] Optionally, the expression of the first sub-objective function is:

[0012] ;

[0013] In the formula, c k is the cost of building a single-circuit line for the kth channel, u k is the number of lines built for the kth channel, u k is [0,n max_k ] integer, n max_k is the maximum number of lines that can be constructed for the kth channel, n max_k is an integer not less than 1; α is the penalty factor, M is the number of ways to examine typical scenarios, ΔP k , Δr k They are respectively the renewable energy curtailment amount and the minimum load shedding amount under the j-th mode.

[0014] Optionally, the expression of the second sub-objective function is:

[0015] ;

[0016] Where N L The total number of lines after expansion. is the mean load rate of the system line under the jth mode after the given transmission network expansion planning scheme, M is the number of modes for the typical scenario, δ j is the probability of the jth way, is the active power flowing through the kth line in the jth mode, Plmax k is the maximum allowable power of active power flowing through the kth line.

[0017] Optionally, the expression of the third sub-objective function is:

[0018]

[0019] In the formula, is the number of line N-1 faults in all channels, is the line N-1 fault of all channels in the jth mode (a total of N F The generalized short-circuit ratio of the operation under the condition of i=0,1,...,N F , where i=0 represents full wiring mode, i=1,...,N F The line N-1 of the i-th line representing all channels fails.

[0020] Optionally, the expression of the multi-objective optimization model is:

[0021] ;

[0022] In the formula, The first sub-objective function, The second sub-objective function, is the third sub-objective function.

[0023] Optionally, the multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a transmission network expansion planning scheme, including:

[0024] S31, initializing system parameters of the planned transmission network to be expanded and parameters of the typical scenario;

[0025] S32, using a multi-objective genetic algorithm, dividing the population into a main population, a slave population and an external reserve set, and optimizing each sub-objective function in the multi-objective optimization model;

[0026] S33, performing selection-crossover-mutation operations on the master population and each of the slave populations respectively;

[0027] S34, updating the external reserve set, including: retaining non-dominated individuals and removing other individuals;

[0028] S35, repeating steps S33 and S34 until a preset termination condition is met, and obtaining a non-inferior solution set in the external reserve set as a candidate transmission network expansion planning scheme.

[0029] A second aspect of the present application provides a transmission network expansion planning system, the system comprising:

[0030] An acquisition unit is used to acquire basic information of the planned transmission network to be expanded, including: information of the lines to be expanded, information of new energy sources connected to the transmission network system, and typical scenarios of new energy output;

[0031] A construction unit is used to establish a multi-objective optimization model based on the basic information, taking into account the economic efficiency of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small disturbance stability of the system power electronic equipment;

[0032] The solving unit is used to solve the multi-objective optimization model by using a multi-objective genetic algorithm to obtain a transmission network expansion planning scheme.

[0033] A third aspect of the present application provides a transmission network expansion planning device, the device comprising a processor and a memory:

[0034] The memory is used to store program codes and transmit the program codes to the processor;

[0035] The processor is used to execute the steps of the transmission network expansion planning method as described in the first aspect according to the instructions in the program code.

[0036] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the transmission network expansion planning method described in the first aspect above.

[0037] It can be seen from the above technical solutions that this application has the following advantages:

[0038] The present application provides a transmission network expansion planning method, which first uses a typical scenario set to characterize the volatility of renewable energy output. On this basis, the economic feasibility of the expansion planning scheme is considered at the same time, including: the impact of renewable energy abandonment and load shedding, the impact on the uniformity of system line load, and the impact on the small disturbance stability of system power electronic equipment. A multi-objective optimization model is established, and the multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a transmission network expansion planning scheme. The method of the present application is simple to implement, and more comprehensively and accurately meets the requirements of the "double high" characteristics for the transmission network, and improves the carrying capacity of the system's renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of a flow chart of a transmission network expansion planning method provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the structure of a transmission network expansion planning system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] See also Figure 1, a transmission network expansion planning method provided in an embodiment of the present application includes:

[0043] Step 101: Obtain basic information of the planned transmission network to be expanded, including: information of the lines to be expanded, information of new energy sources connected to the transmission network system, and typical scenarios of new energy output.

[0044] It should be noted that for the power grid with a high proportion of renewable energy under study, it is assumed that the set of line channels for the transmission network expansion is , there are N candidate channels in total, and the maximum number of extended lines for the i-th channel is n max_i . Assume that there are N new energy sources connected to the system w The rated installed capacity is: Pe1, Pe2, ..., Pe Nw, The typical scenario of considering renewable energy output is {S1,S2,...,S M}, M is the number of ways to examine, and the corresponding probability of each scenario is {δ1, δ2, ..., δ M}. The output of the new energy station in the jth mode is P 1_j ,P 2_j ,...,P Nw_j . Assume that there are 1+N power plants in total g The output limit is Pgmin k 、Pgmax k ,k=0,..,N g , where k=0 represents a balancing machine, and the output in the jth mode is Pg 1_j ,Pg 2_j ,...,Pg Ng_j Assume that there are N load nodes in total d The load powers in the jth mode are Pd 1_j ,Pd 2_j ,...,Pd Nd_j .

[0045] Step 102: Based on the basic information, a multi-objective optimization model is established by taking into account the economic efficiency of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small-disturbance stability of the system power electronic equipment.

[0046] Step 103: Use a multi-objective genetic algorithm to solve the multi-objective optimization model to obtain a transmission network expansion planning solution.

[0047] In one embodiment, step 102 includes:

[0048] Based on the basic information, the first sub-objective function of the construction cost of the transmission network expansion plan considering the impact of the amount of abandoned electricity and load shedding of renewable energy is constructed, the second sub-objective function of the line load rate balance of the system after the implementation of the transmission network expansion plan is constructed, and the support strength of the transmission network system for renewable energy is analyzed by using the generalized short-circuit ratio analysis method, so as to construct the third sub-objective function of the support strength of the system for renewable energy and other power electronic equipment after the implementation of the transmission network expansion plan;

[0049] A multi-objective optimization model was established based on the first sub-objective function, the second sub-objective function and the third sub-objective function.

[0050] It should be noted that based on the basic information in step 101, the following multi-objective optimization model for transmission network expansion planning with a high proportion of renewable energy is established:

[0051] (1)

[0052] The sub-objective function f1 represents the construction cost of the transmission network expansion planning scheme, as shown in the following formula:

[0053] (2)

[0054] Where c k is the cost of building a single-circuit line for the kth channel, u k is the number of lines built for the kth channel, u k is [0,n max_k ] integer, n max_k is the maximum number of lines that can be constructed for the kth channel, n max_k is an integer not less than 1; α is the penalty factor, which is a large positive real number, such as 10000; ΔP k , Δr k are the amount of renewable energy abandoned and the minimum load shedding under the j-th mode respectively, and the solution method is as follows:

[0055] For a given transmission network expansion plan {u1,...,u N}, the total number of all lines after expansion is N L Considering the N-1 failure of all channels after expansion (a total of N F ), under the jth mode and the lth N-1 fault, the amount of renewable energy abandoned power and load shedding is:

[0056] (3)

[0057] (4)

[0058] Where, Pl l,jis the active power flow array of the line under the jth mode and the lth N-1 fault, and the element is Pl k,l,j ,k=1,...,N L ;X j Pg is the active power correlation matrix of the system node injected power line under the lth N-1 fault; l,j is the power plant output array under the jth mode and the lth N-1 fault, and the element is Pg k,l,j ,k=1,...,N g ; Pd j is the node active load array under the jth mode, and the element is Pd k,j ,k=1,...,N d ;P j is the new energy output array under the jth mode, and the element is P k,j ,k=1,...,N w ; ΔP l,j is the array of renewable energy curtailment under the j-th mode and the l-th N-1 fault, and the element is ΔP k,l,j ,k=1,...,N w . Δr l,j is the load shedding array for the jth mode and the lth N-1th fault, and the element is Δr k,l,j ,k=1,...,N d The above problem is a typical linear programming problem, which can be solved by mature commercial software (such as CPLEX). For a given planning scheme, the total amount of renewable energy abandoned power and load shedding under the jth mode is:

[0059] (5)

[0060] In summary, we can get:

[0061] (6)

[0062] The smaller f1 is, the more cost-effective and reliable the expansion planning scheme is.

[0063] Sub-objective function f2 represents the line load balance of the system after the transmission network expansion plan is implemented. For a given transmission network expansion plan {u1,...,u N}, the total number of all lines after expansion is N L f2 is as follows:

[0064] (7)

[0065] In the formula is the average load rate of the system line under the jth mode after the given transmission network expansion planning scheme, that is:

[0066] (8)

[0067] The smaller f2 is, the better the uniformity of the line load rate of the system under the considered scenario under the extended planning scheme.

[0068] Sub-objective function f3 represents the support strength of the system for new energy and other power electronic equipment after the transmission network expansion plan is implemented. For a given transmission network expansion plan {u1,...,u N}, the total number of all lines after expansion is N L The specific method of using the generalized short-circuit ratio analysis system to analyze the strength of new energy support, that is, small disturbance stability, is as follows:

[0069] According to the AC grid impedance parameters and topological structure, the equivalent admittance matrix is ​​obtained, and then according to N w The number of new energy access points is obtained through the Schur complement operation. w The power frequency node admittance matrix B of the Thevenin equivalent network looking at the grid from a feed point m In the jth mode, the capacity diagonal matrix SB is composed of the new energy output of each new energy feed-in point j , For SB j The inverse matrix of . Then the generalized short-circuit ratio gSCR of the fully connected power system with multiple renewable energy feed-in is 0,j for:

[0070] (9-1)

[0071] in, (9-2)

[0072] In the formula, is the matrix eigenvalue, gSCR 0，j The larger the value, the greater the support intensity of the system for new energy under a given transmission network expansion plan. i After disconnection, the equivalent admittance matrix is ​​obtained according to the AC grid impedance parameters and the latest topological structure, and then according to N w The number of new energy access points is obtained through the Schur complement operation. w The power frequency node admittance matrix B of the Thevenin equivalent network looking at the grid from a feed point m,i For a given transmission network expansion plan {u1,...,u N}, calculate the line N-1 fault (a total of N) in all modes, full wiring and all channels according to the above method. F The generalized short-circuit ratio of the operation under the condition of (a), f3 is as follows:

[0073] (10)

[0074] is the line N-1 fault of all channels in the jth mode (a total of N F The generalized short-circuit ratio of operation under the condition of i=0 represents full connection, and i≠0 represents the i-th line L i The smaller f3 is, the greater the support for new energy under the expansion planning scheme is.

[0075] According to the above method, a multi-objective optimization model for transmission network expansion planning to improve renewable energy carrying capacity was established.

[0076] In one embodiment, step 103 includes:

[0077] S31. Initialize system parameters of the planned transmission network to be expanded and parameters of typical scenarios.

[0078] It should be noted that the parameters of the system to be studied are read in, including system parameters (rated installed capacity of new energy stations, minimum and maximum output of generator sets, system network parameters), M scenario parameters considered (new energy output, generator output, load level, probability of scenario), etc.

[0079] S32. Using a multi-objective genetic algorithm, the population is divided into a main population, a slave population and an external reserve set, and each sub-objective function in the multi-objective optimization model is optimized.

[0080] It should be noted that the population in the genetic algorithm is divided into 1 master population, 3 slave populations, and 1 external reserve set. Slave population 1 is optimized with the goal of minimizing f1, slave population 2 is optimized with the goal of minimizing f2, and slave population 3 is optimized with the goal of minimizing f3. Initialize the size of the master and slave populations (assuming that they are all N p ), initialize each individual of each group: randomly generate u i =(u 1_i ,...,u N_i ) is [0,n max_j ] is a random integer. The fitness value f1, f2, f3 of each individual is calculated according to the above method.

[0081] S33. Perform selection-crossover-mutation operations on the master population and each slave population respectively.

[0082] It should be noted that for each slave population, optimization is performed separately according to its own optimization goal. Take slave population 1 as an example. First, retain the best individual (that is, the individual with the smallest f1) to enter the evolved population. Use the tournament strategy to select 2 individuals as parent individuals (such as randomly selecting 3 individuals, among which the individual with the smallest f1 is used as the first parent individual, and then randomly selecting 3 individuals, among which the individual with the smallest f1 is used as the second parent individual). The two parent individuals perform crossover (such as single-point crossover) to generate a new individual. The newly generated individual is determined to mutate according to a certain probability. If it mutates, the mutation operation is performed to enter the evolved population (such as generating a random number rand in [0,1]. If rand is less than 0.1, the new individual randomly selects one of the 1~N bits for mutation operation. If the kth bit is selected, it randomly produces [0,n max_k ] and replace the original k-th value). If it does not mutate, it will directly enter the evolved population. Repeat the above selection-crossover-mutation operation until the size of the generated new individuals is N. p , and merge the non-dominated individuals into the external reserve set. The same operation is performed from populations 2 and 3.

[0083] For the main population, first retain the non-dominated individuals in the evolved population, then randomly select two non-dominated individuals in the population as parent individuals, and the rest of the crossover and mutation operations are the same as above. Repeat the above selection-crossover-mutation operations until the size of the generated new individuals is N p , and merge non-dominated individuals into the external reserve set.

[0084] S34. Update the external reserve set, including: retaining non-dominated individuals and removing the remaining individuals.

[0085] It should be noted that when updating the external reserve set, non-dominated individuals are retained and the rest are removed. Individuals distributed in sparse areas are selected from the external reserve set to randomly replace an individual in the main population, and each slave population replaces the individual with the worst sub-goal in the external reserve set with the best individual corresponding to its own sub-goal.

[0086] S35, repeating steps S33 and S34 until a preset termination condition is met, and obtaining a non-inferior solution set in the external reserve set as a candidate transmission network expansion planning scheme.

[0087] It should be noted that steps S33 and S34 are repeated until the termination condition is met, and the non-inferior solution set in the external reserve set is the Pareto optimal solution set, that is, the candidate transmission network expansion planning scheme.

[0088] The embodiment of the present application provides a transmission network expansion planning method for improving the carrying capacity of new energy. First, a typical scenario set is used to characterize the volatility of new energy output. On this basis, the economic efficiency of the expansion planning scheme is considered at the same time, including: the impact of new energy abandonment and load shedding, the impact on the uniformity of system line load, and the impact on the small disturbance stability of system power electronic equipment. A multi-objective optimization model is established, and the multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a transmission network expansion planning scheme. The method of the present application is simple to implement, and more comprehensively and accurately meets the requirements of the "double high" characteristics on the transmission network, and improves the carrying capacity of the system's new energy.

[0089] The above is a transmission network expansion planning method provided in an embodiment of the present application, and the following is a transmission network expansion planning system provided in an embodiment of the present application.

[0090] See also Figure 2 , a transmission network expansion planning system provided in an embodiment of the present application includes:

[0091] The acquisition unit 201 is used to acquire basic information of the planned transmission network to be expanded, including: information of the lines to be expanded, information of new energy sources connected to the transmission network system, and typical scenarios of new energy output.

[0092] The construction unit 202 is used to establish a multi-objective optimization model based on the basic information while considering the economy of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small disturbance stability of the system power electronic equipment.

[0093] The solving unit 203 is used to solve the multi-objective optimization model using a multi-objective genetic algorithm to obtain a transmission network expansion planning solution.

[0094] Furthermore, an embodiment of the present application also provides a transmission network expansion planning device, the device including a processor and a memory:

[0095] The memory is used to store program codes and transmit the program codes to the processor;

[0096] The processor is used to execute the steps of the transmission network expansion planning method as described in the above method embodiment according to the instructions in the program code.

[0097] Furthermore, a computer-readable storage medium is provided in an embodiment of the present application, and the computer-readable storage medium is used to store program code, and the program code is used to execute the method described in the above method embodiment.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0099] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0100] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0101] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0104] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0105] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A transmission network expansion planning method, characterized in that: include: Obtain basic information about the planned transmission network to be expanded, including: information about the lines to be expanded, information about new energy sources connected to the transmission network system, and typical scenarios for new energy output; Based on the basic information, a multi-objective optimization model is established by taking into account the economic efficiency of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small disturbance stability of the system power electronic equipment; A multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a transmission network expansion planning scheme.

2. The transmission network expansion planning method according to claim 1, characterized in that: Based on the basic information, a multi-objective optimization model is established by taking into account the economic efficiency of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small disturbance stability of the system power electronic equipment, including: Based on the basic information, a first sub-objective function of the construction cost of the transmission network expansion plan considering the influence of the amount of abandoned power and load shedding of new energy is constructed, a second sub-objective function of the line load rate balance of the system after the transmission network expansion plan is implemented is constructed, and the support strength of the transmission network system for new energy is analyzed by running the generalized short-circuit ratio analysis method, so as to construct a third sub-objective function of the support strength of the system for new energy power electronic equipment after the transmission network expansion plan is implemented; The multi-objective optimization model is established according to the first sub-objective function, the second sub-objective function and the third sub-objective function.

3. The transmission network expansion planning method according to claim 2, characterized in that: The expression of the first sub-objective function is: ； In the formula, c k is the cost of building a single-circuit line for the kth channel, u k is the number of lines built for the kth channel, u k is [0,n max_k ] integer, n max_k is the maximum number of lines that can be constructed for the kth channel, n max_k is an integer not less than 1; α is the penalty factor, M is the number of ways to examine typical scenarios, ΔP k , Δr k They are respectively the renewable energy curtailment amount and the minimum load shedding amount under the j-th mode.

4. The transmission network expansion planning method according to claim 3, characterized in that: The expression of the second sub-objective function is: ； Where N L The total number of lines after expansion. is the average load rate of the system line under the jth mode after the given transmission network expansion planning scheme, M is the number of modes for investigating typical scenarios, is the active power flowing through the kth line in the jth mode, Plmax k is the maximum allowable power of active power flowing through the kth line.

5. The transmission network expansion planning method according to claim 4, characterized in that: The expression of the third sub-objective function is: ； In the formula, is the number of line N-1 faults in all channels, For full wiring and line N-1 failure of all channels (total N F The generalized short-circuit ratio of the operation under the condition of i=0,1,...,N F , where i=0 represents full wiring mode, i=1,...,N F The line N-1 of the i-th line representing all channels fails.

6. The transmission network expansion planning method according to claim 5, characterized in that: The expression of the multi-objective optimization model is: ； In the formula, The first sub-objective function, The second sub-objective function, is the third sub-objective function.

7. The transmission network expansion planning method according to claim 1, characterized in that: The multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a transmission network expansion planning scheme, including: S31, initializing system parameters of the planned transmission network to be expanded and parameters of the typical scenario; S32, using a multi-objective genetic algorithm, dividing the population into a main population, a slave population and an external reserve set, and optimizing each sub-objective function in the multi-objective optimization model; S33, performing selection-crossover-mutation operations on the master population and each of the slave populations respectively; S34, updating the external reserve set, including: retaining non-dominated individuals and removing other individuals; S35, repeating steps S33 and S34 until a preset termination condition is met, and obtaining a non-inferior solution set in the external reserve set as a candidate transmission network expansion planning scheme.

8. A transmission network expansion planning system, characterized in that: include: An acquisition unit is used to acquire basic information of the planned transmission network to be expanded, including: information of the lines to be expanded, information of new energy sources connected to the transmission network system, and typical scenarios of new energy output; A construction unit is used to establish a multi-objective optimization model based on the basic information, taking into account the economic efficiency of the expansion planning scheme, the impact on the load balance of the system lines, and the impact on the small disturbance stability of the system power electronic equipment; The solving unit is used to solve the multi-objective optimization model by using a multi-objective genetic algorithm to obtain a transmission network expansion planning scheme.

9. A transmission network expansion planning device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the transmission network expansion planning method according to any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the transmission network expansion planning method according to any one of claims 1-7.

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