A method and device for determining a multi-loop back-to-back flexible DC access scheme

Abstract

The application provides a kind of multi-loop back-to-back flexible DC access scheme determination method and related device, including obtaining the basic parameters of the researched receiving end power grid based on the pre-constructed evaluation system;Using multi-objective particle swarm optimization algorithm to solve the pre-established multi-objective optimization model, obtain several non-inferior solutions of multi-loop back-to-back flexible DC access scheme for receiving end power grid;Using analytic hierarchy process to select several multi-loop back-to-back flexible DC access schemes, obtain the final multi-loop back-to-back flexible DC access scheme;The bottom layer of analytic hierarchy process is several candidate multi-loop back-to-back flexible DC access schemes, the middle layer is several evaluation indexes, and the highest layer is the final DC access scheme.The application constructs the evaluation system containing multi-dimensional evaluation indexes, can determine the most suitable multi-loop back-to-back flexible DC access scheme from numerous candidate schemes, and helps to improve the comprehensive performance and operation benefit of receiving end power grid when accessing back-to-back flexible DC.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning and operation, and particularly relates to a method and device for determining a multi-circuit back-to-back flexible direct current access scheme. Background Art

[0002] For receiving grids fed by multiple conventional DC lines, the electrical distance between DC converter buses is often short. AC faults can easily cause simultaneous commutation failures at multiple converter stations. In severe cases, this can lead to multiple commutation failures or even outages, posing a serious threat to system safety. Furthermore, short-circuit currents in receiving grids are often high. To control short-circuit currents, grid structure adjustments (such as shutting down lines or removing them from the grid) are necessary. This can significantly reduce grid strength and increase the risk of system instability. Connecting back-to-back flexible DC lines at one or more appropriate locations in the receiving grid can effectively reduce the risk of simultaneous commutation failures among multiple conventional DC lines and reasonably reduce short-circuit currents, offering an effective approach to addressing these issues. Therefore, research on single or multiple back-to-back flexible DC connection schemes in receiving grids is of great practical significance. Currently, research on multiple back-to-back flexible DC connection schemes in receiving grids is lacking in the literature. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to propose a method and device for determining a multi-circuit back-to-back flexible DC access scheme, which can effectively reduce the risk of simultaneous commutation failure of multiple conventional DC circuits, and at the same time can reasonably reduce the short-circuit current. This method can simultaneously consider the economy of the access scheme and its impact on system safety and stability.

[0004] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0005] In a first aspect, the present invention provides a method for determining a multi-circuit back-to-back flexible direct current access solution, comprising the following steps:

[0006] Obtain the basic parameters of the receiving grid under study based on a pre-built evaluation system; the evaluation system includes several evaluation indicators for evaluating multi-circuit back-to-back flexible DC access solutions;

[0007] Based on basic parameters, a multi-objective particle swarm optimization algorithm is used to solve a pre-established multi-objective optimization model, obtaining several non-inferior solutions. Each non-inferior solution is a multi-circuit back-to-back flexible DC access scheme for the receiving power grid. The multi-objective optimization model is a mathematical model that optimizes several evaluation indicators of the multi-circuit back-to-back flexible DC access scheme as the optimization objective, and uses the rated capacity of each back-to-back flexible DC connection scheme as the optimization variable.

[0008] The hierarchical analysis method is used to select the multi-circuit back-to-back flexible DC access schemes corresponding to several non-inferior solutions to obtain the final multi-circuit back-to-back flexible DC access scheme; the bottom layer of the hierarchical analysis method is composed of several candidate multi-circuit back-to-back flexible DC access schemes, the middle layer is composed of several evaluation indicators, and the top layer is the final DC access scheme.

[0009] Furthermore, a multi-objective particle swarm optimization algorithm is used to solve the pre-established multi-objective optimization model, and several non-inferior solutions are obtained, including:

[0010] Set up and initialize the particle swarm; the particle swarm includes a master particle swarm, several slave particle swarms, and a reserve set; the several slave particle swarms correspond one-to-one with several evaluation indicators and are optimized as their respective goals;

[0011] Iteratively update the particle swarm; the iterative update operation includes randomly selecting particles from the reserve set as the global guiding particles of the master particle swarm, and each slave particle swarm uses the best particle in the reserve set corresponding to its respective optimization target as the global guiding particle. The master and slave particle swarms are iteratively updated based on their respective global guiding particles and local guiding particles.

[0012] Update the reserve set; the update operation includes adding the non-inferior extreme value particles obtained from the particle swarm to the main particle swarm after each iteration, adding the non-inferior particles obtained from the main and slave particle swarms to the reserve set, and updating the reserve set to remove particles in the reserve set that are not non-inferior solutions;

[0013] Repeat the iterative update operation of the particle swarm and the update operation of the reserve set until the termination condition is met, and obtain the non-inferior solution set in the reserve set. Each non-inferior solution in the solution set represents a back-to-back flexible DC access scheme.

[0014] Furthermore, in the iterative update of the master-slave particle swarm based on their respective global guide particles and local guide particles, the iterative search formula is as follows:

[0015]

[0016]

[0017] Where, 、 are the search speeds of the d-th variable of particle i at the k+1th and kth iterations, respectively; ω is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers uniformly distributed on [0, 1]; 、 、 They are respectively the current value of the d-th variable position of particle i in the k-th iteration, the value of the d-th variable position of the individual local guiding particle, and the value of the d-th variable position of the global guiding particle.

[0018] Furthermore, the evaluation index includes the risk index caused by conventional DC commutation failure. In a given test fault set {F1,,..,F R} and mode data, for the fault F in the test fault set j , the calculation expression of risk index is as follows:

[0019]

[0020] Where, It is a risk indicator caused by conventional DC commutation failure; is the number of faults in a given test fault set, is the fault number index; is the number of conventional DC circuits fed into the receiving grid, It is the conventional DC serial number index; Fault F j Under the commutation failure condition of the conventional DC in the i-th round, u i,j =1 means the ith DC current is at F j Commutation failure occurs under fault conditions, u i,j =0 means the Back to normal DC at fault F j No commutation failure occurs; is the transmission power of the i-th conventional DC.

[0021] Furthermore, the evaluation index also includes the maximum short-circuit current index of the system. For the multi-circuit back-to-back flexible DC access scheme to be evaluated, under given operating mode data, for the busbars {B1, B2, ..., B M}, the calculation expression of the system maximum short-circuit current index is as follows:

[0022]

[0023] Where, It is the maximum short-circuit current indicator of the system; To investigate the number of buses with short-circuit current, The bus number index for investigating short-circuit current; and For the The three-phase current and single-phase short-circuit current of the bus; I max and I min is the short-circuit current threshold.

[0024] Further, the evaluation index also includes the construction cost of the back-to-back flexible DC access scheme. For the multi-loop back-to-back flexible DC access scheme to be evaluated, the calculation expression of the construction cost of the back-to-back flexible DC access scheme is as follows:

[0025]

[0026] In the formula, is the construction cost of the back-to-back flexible DC access scheme; is the number of candidate channels that can access the back-to-back flexible DC, is the candidate channel serial number index, is the construction cost of the back-to-back flexible DC unit capacity of the i th channel, is the rated active power of the back-to-back flexible DC configured for the i th channel, is the upper limit value of the construction cost, is whether the i th channel is configured with the back-to-back flexible DC, represents configuration, represents no configuration.

[0027] Further, the evaluation index also includes the system stability level index. For the multi-loop back-to-back flexible DC access scheme to be evaluated, under the given fault set , ,.., } and operation mode data, the calculation expression of the system stability level index is as follows:

[0028]

[0029] In the formula, is the system stability level index, is the minimum number of faults that cause the system to lose stability, is the number of faults in the given test fault set.

[0030] Further, in the system stability level index, the minimum number of faults that cause the system to lose stability is determined by using the particle swarm algorithm. The solving process includes the following steps:

[0031] The objective function is set to , and K is the number of faults that cause the system to lose stability;

[0032] The particle swarm is set. The particle swarm has S particles in total. For particle i, , is the transmission power of the j th back-to-back flexible DC in the scheme to be evaluated, , is the rated capacity of the j th back-to-back flexible DC, The number of channels configured with back-to-back flexible DC;

[0033] For particle i, fault time domain simulation analysis is performed according to given operation mode data and fault set; the total number of faults leading to system instability is counted as the fitness value of the particle, and the smaller the fitness value, the better the particle, and is recorded as , n is the number of iterations, and the best result searched by the particle after the nth iteration is recorded as ;

[0034] Each particle is updated according to the following formula:

[0035]

[0036]

[0037]

[0038]

[0039] In the formula, , , , is a control parameter, n, N iter is the current iteration number and the total iteration number, and rand() is a random number between (0, 1); represents the value of the jth bit of the ith particle after the nth iteration, Ulocal i,j (n) is the value of the jth bit of the local optimal individual of the ith particle after the nth iteration, Uglobal j (n) is the value of the jth bit of the optimal individual of the entire particle group after the nth iteration; V i,j (n) represents the speed value of the jth bit of the ith particle after the nth iteration, , respectively represent the lower limit and the upper limit of V i,j (n); represents the best particle of the particle group after the nth iteration;

[0040] The above iteration process is repeated until the termination condition is met, and the number of faults leading to system instability corresponding to the optimal solution at this time is recorded as .

[0041] Further, the analytic hierarchy process is used to select a plurality of multi-back-to-back flexible DC connection schemes, including:

[0042] The first matrix and the second matrix are respectively a judgment matrix of the highest layer and the middle layer and a judgment matrix of the middle layer and the bottom layer.

[0043] The final weight coefficient of each multi-back-to-back flexible DC access scheme is calculated based on the first weight coefficient and the second weight coefficient, and the candidate scheme with the maximum final weight coefficient is selected as the final DC access scheme; the final weight coefficient is calculated according to the following formula:

[0044]

[0045] In the formula, is the final weight coefficient of the jth candidate scheme, is the number of evaluation indexes, is the first weight coefficient of the jth evaluation index after normalization, is the second weight coefficient of the jth evaluation index after normalization for the jth candidate scheme. The second aspect of the present application provides a multi-back-to-back flexible DC access scheme determination device, comprising:

[0046] The data acquisition module is configured to acquire the basic parameters of the researched receiving end power grid based on a pre-constructed evaluation system; the evaluation system comprises a plurality of evaluation indexes for evaluating the multi-back-to-back flexible DC access scheme.

[0047] The model solving module is configured to solve a pre-established multi-objective optimization model based on the basic parameters and using a multi-objective particle swarm algorithm to obtain a plurality of non-inferior solutions, each of which is a multi-back-to-back flexible DC access scheme for the receiving end power grid; the multi-objective optimization model is a mathematical model with the optimization of a plurality of evaluation indexes of the multi-back-to-back flexible DC access scheme as the optimization target and the rated capacity of each back-to-back flexible DC in each multi-back-to-back flexible DC access scheme as the optimization variable.

[0048] The scheme determination module is configured to select the multi-back-to-back flexible DC access schemes corresponding to the plurality of non-inferior solutions using the analytic hierarchy process to obtain the final multi-back-to-back flexible DC access scheme; in the analytic hierarchy process, the bottom layer is the plurality of candidate multi-back-to-back flexible DC access schemes, the middle layer is the plurality of evaluation indexes, and the highest layer is the final DC access scheme.

[0049] The third aspect of the present application provides a computer device, which comprises a processor and a memory.

[0050] The third aspect of the present application provides a computer device, which comprises a processor and a memory.

[0051] ​The memory is used to store computer programs and send instructions of the computer programs to the processor;

[0052] The processor executes the method for determining a multi-circuit back-to-back flexible direct current access scheme according to the instructions of the computer program.

[0053] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for determining a multi-circuit back-to-back flexible direct current access scheme according to the first aspect is implemented.

[0054] In summary, the present invention provides a method and related device for determining a multi-circuit back-to-back flexible DC access scheme, including obtaining basic parameters of the receiving power grid under study based on a pre-constructed evaluation system; based on the basic parameters, using a multi-objective particle swarm algorithm to solve a pre-established multi-objective optimization model to obtain several non-inferior solutions, each non-inferior solution being a multi-circuit back-to-back flexible DC access scheme for the receiving power grid; the multi-objective optimization model is a mathematical model with the optimization of several evaluation indicators of the multi-circuit back-to-back flexible DC access scheme as the optimization target, and the rated capacity of each back-to-back flexible DC in each multi-circuit back-to-back flexible DC access scheme as the optimization variable; using a hierarchical analysis method to select several multi-circuit back-to-back flexible DC access schemes to obtain a final multi-circuit back-to-back flexible DC access scheme; the bottom layer in the hierarchical analysis method is a plurality of candidate multi-circuit back-to-back flexible DC access schemes, the middle layer is a plurality of evaluation indicators, and the top layer is the final DC access scheme. The present invention comprehensively considers multiple factors and can scientifically and rationally determine the most suitable multi-circuit back-to-back flexible DC access scheme from numerous candidate schemes, which helps to improve the comprehensive performance and operating efficiency of the receiving power grid when connected to back-to-back flexible DC, and ensure the stable and efficient operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flowchart of a method for determining a multi-circuit back-to-back flexible DC access solution provided by an embodiment of the present invention;

[0057] Figure 2 A schematic diagram of determining the final multi-circuit back-to-back flexible DC access solution using the analytic hierarchy process provided in an embodiment of the present invention;

[0058] Figure 3A block diagram of a device for determining a multi-circuit back-to-back flexible DC access solution provided by an embodiment of the present invention;

[0059] Figure 4 A block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purposes, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 The embodiment of the present invention provides a method for determining a multi-circuit back-to-back flexible direct current access solution, comprising the following steps:

[0062] S11: Obtain basic parameters of the receiving power grid under study based on a pre-built evaluation system; the evaluation system includes several evaluation indicators for evaluating multi-circuit back-to-back flexible DC access solutions.

[0063] It's important to note that this step is based on a pre-established evaluation system. This system was established based on the specific evaluation requirements for multi-circuit back-to-back flexible DC connections. It includes multiple evaluation indicators determined by analyzing various factors, including the power system's technical requirements, economic feasibility, and environmental impact.

[0064] The basic parameters include numerous characteristic data related to the receiving power grid, such as parameters related to the conventional DC fed into the receiving power grid (such as the number of conventional DC loops, the rated power of each conventional DC loop, etc.), bus information for examining short-circuit current (number of busbars, busbar numbers, etc.), and parameters related to candidate channels that can be connected to back-to-back flexible DC (such as the maximum transmittable active power of back-to-back flexible DC that can be constructed for each channel). They may also involve existing operating mode data and fault-related data of the power grid. These parameters can reflect the basic structure and operating characteristics of the receiving power grid.

[0065] This step obtains various basic information related to the receiving power grid to form a basic data set for subsequent calculations and analysis.

[0066] S12: Based on basic parameters, a multi-objective particle swarm algorithm is used to solve a pre-established multi-objective optimization model to obtain several non-inferior solutions, each of which is a multi-circuit back-to-back flexible DC access scheme for the receiving power grid; the multi-objective optimization model is a mathematical model with the optimization of several evaluation indicators of the multi-circuit back-to-back flexible DC access scheme as the optimization objective, and the rated capacity of each back-to-back flexible DC in each multi-circuit back-to-back flexible DC access scheme as the optimization variable.

[0067] It should be noted that the particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. It simulates the mechanism used by individuals in foraging groups, such as bird flocks, to collaborate and share information to find the optimal solution. In this embodiment, it is used to search for an optimal combination of multiple possible back-to-back flexible DC access solutions.

[0068] A multi-objective optimization model is a mathematical model constructed by simultaneously considering multiple different objectives (such as ensuring stable grid operation while controlling construction costs) when determining a multi-circuit back-to-back flexible DC access scheme. Its objective function consists of multiple evaluation indicators and aims to find a set of solutions that can optimize these evaluation indicators as much as possible, rather than just the optimal solution for a single objective.

[0069] In multi-objective optimization problems, there is no solution that is absolutely superior to other solutions in all objectives. Non-inferior solutions (Pareto optimal solutions) refer to those that have no other solutions that can be better than them in all objectives. In other words, they are better solutions that achieve a relative balance between various objectives.

[0070] Evaluation indicators specifically refer to quantitative indicators in multiple dimensions that measure the quality of multi-circuit back-to-back flexible DC access solutions, such as the risk indicator caused by conventional DC commutation failure (reflecting the impact on conventional DC operation), the system's maximum short-circuit current indicator (reflecting the impact on the grid's short-circuit safety performance), the construction cost of the back-to-back flexible DC access solution (related to economic costs), and the system stability level indicator (assessing the impact on the overall stability of the grid). These indicators are calculated and determined based on the basic parameters mentioned above and factors such as the rated capacity of the back-to-back flexible DC.

[0071] This step uses the search capability of the particle swarm algorithm to find multiple non-inferior solutions that achieve a relative balance between different objectives in a complex solution space (numerous possible access solution combinations). This provides a rich set of candidates for subsequent screening of the final optimal access solution, avoids the situation where only considering a single objective leads to poor performance in other aspects, and fully takes into account the multi-faceted needs of power grid operation.

[0072] S13: A hierarchical analysis method is used to select the multi-circuit back-to-back flexible DC access schemes corresponding to several non-inferior solutions to obtain a final multi-circuit back-to-back flexible DC access scheme; the bottom layer of the hierarchical analysis method is a number of candidate multi-circuit back-to-back flexible DC access schemes, the middle layer is a number of evaluation indicators, and the top layer is the final DC access scheme.

[0073] It should be noted that the Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method that divides complex decision-making problems into layers, constructs a judgment matrix by comparing elements at each layer, calculates weight coefficients, and then comprehensively evaluates and determines the optimal decision solution. In this embodiment, it is used to select the final multi-circuit back-to-back flexible DC access solution from multiple non-inferior solutions obtained by the particle swarm algorithm.

[0074] The process of determining the final solution using the Analytic Hierarchy Process (AHP) involves placing multiple candidate multi-circuit back-to-back flexible DC access solutions corresponding to several non-inferior solutions at the bottom layer of the AHP, placing the previously determined evaluation indicators in the middle layer, and placing the final DC access solution at the top layer. Expert scoring and other methods are then used to determine the relative importance of each evaluation indicator. A judgment matrix is ​​then constructed, and the weights of each evaluation indicator are calculated. The candidate access solutions at the bottom layer are then comprehensively evaluated and ranked, ultimately selecting the solution with the best overall performance as the final multi-circuit back-to-back flexible DC access solution.

[0075] This step scientifically quantifies the relative importance of different evaluation indicators and candidate solutions, comprehensively considers multiple factors, and accurately selects the final access solution that best meets the overall requirements from multiple non-inferior solutions obtained by the particle swarm algorithm. This makes the selection of the final solution no longer rely on subjective conjecture, but is based on rigorous mathematical calculations and reasonable weight distribution.

[0076] This embodiment provides a method for determining a multi-circuit back-to-back flexible DC access scheme. This method utilizes a multi-objective optimization model to simultaneously consider multiple evaluation indicators critical to grid operation, such as technical performance (conventional DC commutation, short-circuit current, and system stability) and economic cost (construction costs). This method ensures that the final access scheme achieves a relatively optimal balance across multiple dimensions, better meeting the comprehensive requirements of complex grid operation. Secondly, the method organically combines the particle swarm algorithm (PSO) and the analytic hierarchy process (AHP). The PSO excels at performing a global search within a complex solution space to find multiple non-inferior solutions, while the AHP enables comprehensive evaluation and decision-making based on the importance of different indicators within multiple non-inferior solutions. This combination leverages the respective strengths of the two algorithms, effectively overcoming the limitations of a single algorithm in solving such complex multi-objective decision-making problems and improving the scientificity and rationality of the final access scheme determination.

[0077] The following makes some assumptions about the basic parameters of the receiving grid to facilitate the subsequent introduction of relevant evaluation indicators. dc The rated power of each circuit is P1, P2, ..., P Ndc There are M buses whose short-circuit current needs to be examined, which are denoted as B1, B2, ..., B M There are N candidate channels that can be connected to the back-to-back flexible DC. The maximum transmittable active power of the back-to-back flexible DC that can be constructed in the i-th channel is P max,i ,i=1,2,...,N;Assume that for back-to-back flexible DC, its rated reactive power is α times of the rated active power. For a given multi-back-to-back flexible DC access scheme {P e1 ,P e2 ,...,P eN}, P ei Rated active power of the back-to-back flexible DC link configured for the i-th channel.

[0078] In one embodiment of the present invention, the risk index Id1 caused by conventional DC commutation failure is defined as one of the evaluation indicators. For the multi-circuit back-to-back flexible DC solution to be evaluated, given a test fault set {F1,,..,F R} and mode data, for fault F j , N dc The commutation failure condition of the conventional DC is {u 1,j ,u 2,j ,...,u Ndc,j},u i,j =1 or 0, 1 represents commutation failure, 0 represents no commutation failure, i=1,2,...,N dc Then the indicator Id1 is:

[0079] (1)

[0080] Where, is a risk indicator caused by the conventional DC commutation failure; is the number of faults in a given test fault set, is the fault number index; is the number of conventional DC circuits fed into the receiving grid, It is the conventional DC serial number index; Fault F j Under the commutation failure condition of the conventional DC in the i-th round, u i,j =1 means the ith DC current is at F j Commutation failure occurs under fault conditions; is the transmission power of the i-th conventional DC.

[0081] In another embodiment of the present invention, the maximum short-circuit current index Id2 of the system is defined as one of the evaluation indicators. For the multi-circuit back-to-back flexible DC scheme to be evaluated, given the operating mode data, the busbars {B1, B2, ..., B M}, calculate the three-phase and single-phase short-circuit current of each bus, respectively denoted as I abc,i and I a,i ,i=1,2,...,M。I max , I min is the pre-set short-circuit current threshold, then the indicator Id2 is:

[0082] (2)

[0083] Where, is the maximum short-circuit current index of the system; is the number of buses with short-circuit current, The bus serial number index of the short-circuit current; and For the The three-phase current and single-phase short-circuit current of the bus; I max and I min is the short-circuit current threshold.

[0084] In another embodiment of the present invention, the back-to-back flexible DC access scheme construction cost Id3 is defined as one of the evaluation indicators. For the multi-circuit back-to-back flexible DC scheme to be evaluated, assuming that the i-th channel back-to-back flexible DC construction cost is C i Yuan / unit capacity. Then the indicator Id3 is:

[0085] (3)

[0086] Where, The construction cost of the back-to-back flexible DC access solution; is the number of candidate channels that can be connected to back-to-back flexible DC, is the candidate channel sequence index, is the construction cost of the back-to-back flexible DC network for the i-th channel, The rated active power of the back-to-back flexible DC power supply configured for the i-th channel, is the upper limit of construction cost, Whether the i-th channel is configured with back-to-back flexible DC, Indicates configuration, Indicates no configuration.

[0087] In another embodiment of the present invention, a system stability level indicator Id4 is defined. For a multi-circuit back-to-back flexible DC scheme to be evaluated, given operating mode data and a fault set { , ,.., }(common Faults) and analyze the system stability characteristics under a given set of faults through time-domain simulation. With the goal of minimizing the number of faults that cause the system to lose stability, the following optimization model is established and solved using the particle swarm optimization algorithm.

[0088] (4)

[0089] , i=1,...,N(5)

[0090] Where, is the system stability level indicator, The minimum number of failures that cause the system to lose stability, The number of faults in the fault set for a given test.

[0091] With the goal of minimizing the number of failures that cause the system to lose stability, the following optimization model is established and solved using the particle swarm optimization algorithm.

[0092] Objective function: , K is the number of failures that cause the system to lose stability (6)

[0093] The solution process using particle swarm algorithm is as follows:

[0094] The actual transmission power of the back-to-back flexible DC access scheme to be evaluated is used as the optimization variable to solve the problem. Assume that the particle swarm has S particles. For particle i, , It refers to the transmission power of the jth back-to-back flexible DC link in the configuration scheme to be evaluated. , is the rated capacity of the jth back-to-back flexible DC link, is the number of channels configured with back-to-back flexible DC. For particle i, a fault time domain simulation analysis is performed based on the given operating mode data and fault set. The total number of faults that cause the system to lose stability is counted as the fitness value of the particle. The smaller the fitness, the better the particle, which is recorded as , n is the number of iterations, and the best result found by the particle after the nth iteration is recorded as (with the minimum fitness value) .

[0095] Update each particle according to the following formula:

[0096] (7)

[0097] (8)

[0098] (9)

[0099] (10)

[0100] wherein, , , , is a control parameter, n, N iter is the current iteration number and the total iteration number, rand() is a random number between (0, 1). represents the value of the jth bit of the ith particle after the nth iteration, Ulocal i,j (n) is the value of the jth bit of the local optimal individual of the ith particle after the nth iteration, Uglobal j (n) is the value of the jth bit of the optimal individual of the entire particle group after the nth iteration. i,j (n) represents the speed value of the jth bit of the ith particle after the nth iteration, , respectively represent the lower limit and the upper limit of V i,j (n). represents the best particle (the smallest fitness) of the particle group after the nth iteration.

[0101] The above iteration process is repeated until the termination condition is met. The fault number corresponding to the optimal solution at this time that causes the system to lose stability is recorded as K min Id4 can be obtained according to formula (4).

[0102] When evaluating the multi-loop back-to-back flexible DC access scheme, the above four evaluation indexes are set. For each access scheme, the specific values of these evaluation indexes can be calculated by the index calculation method proposed in the above embodiment. To solve the multi-objective optimization problem that makes the scheme evaluated by these evaluation indexes reach the non-inferior state, the present application adopts a multi-objective particle swarm optimization algorithm. Through the iterative search and information sharing of the particle swarm, in the objective space composed of multiple evaluation indexes, the non-inferior solution frontier is constantly explored and approached, so as to determine a series of non-inferior schemes that meet the multi-objective optimization requirements.

[0103] According to the four evaluation indexes respectively provided in the above embodiments, the following multi-loop back-to-back flexible DC access scheme multi-objective optimization model can be established:

[0104] The objective function f = (f1, f2, f3, f4), wherein

[0105] The optimization variable is the rated capacity of the back-to-back flexible DC among the N candidate channels that can be connected to the back-to-back flexible DC (P e1 ,P e2 ,...,P eN ), P ei ≤P max,i , i=1,2,...,N. For multi-objective optimization problems, in one embodiment of the present invention, a method for solving a pre-established multi-objective optimization model using a multi-objective particle swarm algorithm is provided. The method sets the particle swarm as 1 master particle swarm, several slave particle swarms, and 1 reserve set; the several slave particle swarms correspond one-to-one to several evaluation indicators and are optimized as their respective objectives. Taking the multi-objective optimization problem composed of the above four evaluation indicators as an example, the method divides the particle swarm into 1 master particle swarm, 4 slave particle swarms, and 1 reserve set. The reserve set is used to store Pareto optimal solutions. Slave particle swarm 1 is optimized with f1 as the target, slave particle swarm 2 is optimized with f2 as the target, slave particle swarm 3 is optimized with f3 as the target, and slave particle swarm 4 is optimized with f4 as the target. Taking the above four evaluation indicators as an example, the specific process of the multi-group particle swarm algorithm is introduced as follows:

[0106] a. Initialization. Read the maximum transmittable active power P of the candidate channels that can be connected to the back-to-back flexible DC. max,i , i = 1, 2, ..., N. Read in all the data required to calculate the indicator.

[0107] b. Divide the particle swarm into one master particle swarm and four slave particle swarms. Optimize slave particle swarm 1 with f1 as the target, slave particle swarm 2 with f2 as the target, slave particle swarm 3 with f3 as the target, and slave particle swarm 4 with f4 as the target. Initialize the particle swarm parameters, including the master and slave swarm sizes, learning factors, inertia weights, upper and lower limits for particle positions and velocities, and the maximum number of iterations. Initialize each particle swarm (randomly generate particle positions) and particle velocities, calculate the four metrics for each particle, and save the non-inferior solutions from the master and slave particle swarms to an external storage set.

[0108] c. Randomly select a particle from the external reserve as the global guiding particle for the master particle swarm. Each slave particle swarm uses the particle from the external reserve that best corresponds to its respective optimization objective as the global guiding particle. The master and slave particle swarms are iteratively updated according to the particle iterative search formula (such as Equations 11 and 12 in the subsequent examples), calculating the fitness value of each particle. For the master particle swarm, if a newly generated particle is not dominated by its local optimal solution, it becomes the local guiding particle of the original particle.

[0109] d. After each iteration, the non-inferior extreme value particles obtained from the particle swarm are added to the main particle swarm, and the non-inferior particles obtained from the master and slave particle swarms are added to the external reserve set. The reserve set is updated and the particles in the reserve set that are not non-inferior solutions are removed.

[0110] e. Repeat steps c and d until the termination condition is met. The non-inferior solution set in the external reserve is the Pareto optimal solution set. Assume that there are N best , which are recorded as S1, S2, ..., S Nbest , each non-inferior solution represents a back-to-back flexible DC access scheme.

[0111] Based on the process of solving the multi-objective optimization model by the particle swarm algorithm provided in the above embodiment, in a further embodiment of the present invention, particles in the particle swarm perform iterative search in the solution space according to individual local guiding particles and global guiding particles, and the formula is as follows:

[0112] (11)

[0113] (12)

[0114] Where, 、 are the search speeds of the d-th variable of particle i at the k+1th and kth iterations, respectively. , V max,d 、V min,d are the upper and lower limits of the search speed of the d-th variable; ω is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers that obey a uniform distribution on [0, 1]; 、 、 are the current value of the d-th variable position of particle i in the k-th iteration, the value of the d-th variable position of the individual local guide particle, and the value of the d-th variable position of the global guide particle. The global guide particle is given by the external reserve set. After each iteration, the best particles are passed to the external reserve set.

[0115] In one embodiment, the analytic hierarchy process is used to select several multi-circuit back-to-back flexible direct current access schemes, including:

[0116] S uses a first matrix and a second matrix to respectively calculate first weight coefficients of several evaluation indicators and second weight coefficients of several candidate multi-circuit back-to-back flexible DC access schemes under several evaluation indicators; the first matrix and the second matrix are judgment matrices for the top layer and the middle layer, and the middle layer and the bottom layer, respectively;

[0117] The final weight coefficient of each multi-circuit back-to-back flexible DC access scheme is calculated based on the first weight coefficient and the second weight coefficient, and the candidate scheme with the largest final weight coefficient is selected as the final DC access scheme; the final weight coefficient is calculated according to the following formula:

[0118] (13)

[0119] Where, For the The final weight coefficient of each candidate solution is is the number of evaluation indicators, is the first weight coefficient after normalization of the j-th evaluation index, For the The second weight coefficient of the candidate solution after normalization under the jth evaluation index.

[0120] In a further embodiment, the values ​​of the elements of the first matrix and the second matrix are determined using a 9-level comparison scale.

[0121] The following introduces the selection of the final DC access solution by the analytic hierarchy process using the four indicators proposed in the above embodiment as an example. Figure 2 The hierarchical structure shown in the figure has the following features: the top layer (overall target layer) is the final multi-circuit back-to-back flexible DC access scheme; the middle layer (baseline layer) is the four types of indicators considered; the bottom layer (scheme layer) is the candidate multi-circuit back-to-back flexible DC access schemes (i=1,…,N best ).

[0122] Pairwise comparison analysis was conducted on adjacent upper and lower layers (the highest layer and the middle layer, and the middle layer and the lowest layer) using a 9-level comparison scale, as shown in Table 1:

[0123]

[0124] The judgment matrix A of the highest layer and the middle layer is calculated as follows:

[0125] (14)

[0126] Element a in matrix A ij Representative indicator Id i With Id j The comparison results are the same. According to the specific values ​​of the four types of indicators of each candidate line obtained above, the judgment matrices of the indicators Id1, Id2, Id3, and Id4 in the middle layer and the bottom layer are B1, B2, B3, and B4 respectively:

[0127] ,j=1,2,3,4(15)

[0128] Matrix B j Elements in Represents the indicator Id j Next candidate line S k With S l According to the hierarchical analysis method, matrix A and matrix B need to be compared. j (j=1,…,4) to perform consistency check. If the consistency check fails, it is necessary to check the matrix A and matrix B. j (j=1,…,5) and re-modify until the consistency check is satisfied.

[0129] When matrix A and matrix B j When the consistency test is satisfied (j=1,…,4), the weight coefficient of each candidate solution for achieving the overall goal is calculated. First, the weight coefficients of the four types of indicators (after normalization) are obtained through matrix A, which are: w A1 、w A2 、w A3 、w A4 , each candidate solution S i (i=1,…,N best ) in indicator Id j The weight coefficients (after normalization) of (j=1,…,4) are: w Bi_1 、w Bi_2 、…、w Bi_4 According to formula 13, the candidate solution S is finally obtained. i The final weight coefficient of the line. The larger the final weight coefficient of the line, the more critical the line is.

[0130] Based on the same inventive concept, embodiments of the present application also provide a device for determining a multi-circuit back-to-back flexible DC access scheme, for implementing the aforementioned method for determining a multi-circuit back-to-back flexible DC access scheme. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of the device for determining a multi-circuit back-to-back flexible DC access scheme provided below can be found in the limitations of the method for determining a multi-circuit back-to-back flexible DC access scheme described above and are not further elaborated here.

[0131] See also Figure 3 The embodiment of the present invention further provides a device for determining a multi-circuit back-to-back flexible direct current access solution, comprising:

[0132] A data acquisition module is used to obtain the basic parameters of the receiving-end power grid under study based on a pre-built evaluation system; the evaluation system includes several evaluation indicators for evaluating the multi-circuit back-to-back flexible DC access solution;

[0133] The model solving module is configured to solve the pre-established multi-objective optimization model based on the basic parameters by using a multi-objective particle swarm algorithm to obtain a plurality of non-inferior solutions, each of which is a multi-loop back-to-back flexible DC access scheme for the receiving end power grid; the multi-objective optimization model is a mathematical model in which optimization of a plurality of evaluation indexes of the multi-loop back-to-back flexible DC access scheme is taken as an optimization target and the rated capacity of each back-to-back flexible DC in each multi-loop back-to-back flexible DC access scheme is taken as an optimization variable.

[0134] The scheme determining module is configured to select the multi-loop back-to-back flexible DC access schemes corresponding to the plurality of non-inferior solutions by using an analytic hierarchy process to obtain a final multi-loop back-to-back flexible DC access scheme; in the analytic hierarchy process, the bottom layer is the plurality of candidate multi-loop back-to-back flexible DC access schemes, the middle layer is the plurality of evaluation indexes, and the highest layer is the final DC access scheme.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0136] Reference Figure 4 The embodiment of the application also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory, and when the computer program is executed on the processor, the multi-loop back-to-back flexible DC access scheme determination method in any one of the above methods is implemented.

[0137] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that, Figure 4 The computer device is only an example and does not constitute a limitation on the computer device, and can include more or fewer components than the illustration, or combine certain components or different components, for example, can also include an input / output device, a network access device and the like.

[0138] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0139] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped with the computer device. Furthermore, the memory may include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is about to be output.

[0140] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for determining a multi-circuit back-to-back flexible direct current access solution as described in any one of the above methods is implemented.

[0141] In this embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the computer program for instructing the relevant hardware to complete all or part of the processes in the above-described embodiment methods can be stored in a computer readable storage medium. The computer program can be executed by a processor to implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium can not be an electrical carrier signal and a telecommunication signal.

[0142] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0143] Those of ordinary skill in the art can understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0144] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal equipment and methods can be implemented in other ways. For example, the apparatus / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0145] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining a multi-circuit back-to-back flexible direct current access scheme, characterized in that: The steps include: Obtaining basic parameters of the receiving-end power grid under study based on a pre-established evaluation system; the evaluation system includes several evaluation indicators for evaluating a multi-circuit back-to-back flexible direct current access solution; Based on the basic parameters, a multi-objective particle swarm algorithm is used to solve a pre-established multi-objective optimization model to obtain several non-inferior solutions, each of which is a multi-circuit back-to-back flexible direct current access scheme for the receiving power grid; the multi-objective optimization model is a mathematical model with the optimization of several evaluation indicators of the multi-circuit back-to-back flexible direct current access scheme as the optimization objective, and with the rated capacity of each back-to-back flexible direct current in each of the multi-circuit back-to-back flexible direct current access schemes as the optimization variable; A hierarchical analysis method is used to select the multi-circuit back-to-back flexible direct current access schemes corresponding to the plurality of non-inferior solutions to obtain a final multi-circuit back-to-back flexible direct current access scheme; the bottom layer of the hierarchical analysis method is the plurality of candidate multi-circuit back-to-back flexible direct current access schemes, the middle layer is the plurality of evaluation indicators, and the top layer is the final direct current access scheme; The multi-objective particle swarm optimization algorithm is used to solve the pre-established multi-objective optimization model and obtain several non-inferior solutions, including: Setting and initializing a particle swarm; the particle swarm includes a master particle swarm, several slave particle swarms, and a reserve set; the several slave particle swarms correspond one-to-one to the several evaluation indicators and are optimized as their respective goals; Iteratively updating the particle swarm; the iterative updating operation includes randomly selecting a particle from the reserve set as a global guiding particle of the master particle swarm, each of the slave particle swarms uses the particle in the reserve set that best corresponds to its respective optimization target as the global guiding particle, and the master and slave particle swarms iteratively update based on their respective global guiding particles and local guiding particles; Performing an update operation on the reserve set; the update operation includes, after each iteration, adding the non-inferior extreme value particles obtained from the slave particle swarm to the master particle swarm, adding the non-inferior particles obtained from the master and slave particle swarms to the reserve set, and updating the reserve set to remove particles in the reserve set that are not non-inferior solutions; Repeat the iterative update operation of the particle swarm and the update operation of the reserve set until the termination condition is met, and obtain a non-inferior solution set in the reserve set, each non-inferior solution in the solution set represents a back-to-back flexible DC access solution.

2. The method for determining a multi-circuit back-to-back flexible direct current access solution according to claim 1, characterized in that: In the iterative update of the master-slave particle swarm based on their respective global guide particles and local guide particles, the iterative search formula is as follows: ； ； Where, 、 are the search speeds of the d-th variable of particle i at the k+1th and kth iterations, respectively; ω is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers uniformly distributed on [0, 1]; 、 、 They are respectively the current value of the d-th variable position of particle i in the k-th iteration, the value of the d-th variable position of the individual local guiding particle, and the value of the d-th variable position of the global guiding particle.

3. The method for determining a multi-circuit back-to-back flexible direct current access solution according to claim 1, characterized in that: The evaluation index includes the risk index caused by conventional DC commutation failure. For the multi-circuit back-to-back flexible DC access scheme to be evaluated, in a given test fault set {F1,,..,F R } and operating mode data, for the fault F in the test fault set j , the calculation expression of the risk index is as follows: ； Where, is a risk indicator caused by the conventional DC commutation failure; is the number of faults in a given test fault set, is the fault number index; is the number of conventional DC circuits fed into the receiving grid, It is the conventional DC serial number index; Fault F j Next, Back to the conventional DC commutation failure situation, u i,j =1 means the Back to normal DC at fault F j Commutation failure occurs, u i,j =0 means the Back to normal DC at fault F j No commutation failure occurs; For the The transmission power back to conventional DC.

4. The method for determining a multi-circuit back-to-back flexible direct current access solution according to claim 1, characterized in that: The evaluation index also includes the maximum short-circuit current index of the system. For the multi-circuit back-to-back flexible DC access scheme to be evaluated, under given operation mode data, for the busbars {B1, B2, ..., B M }, the calculation expression of the maximum short-circuit current index of the system is as follows: ； Where, is the maximum short-circuit current index of the system; To investigate the number of buses with short-circuit current, The bus number index for investigating short-circuit current; and For the The three-phase current and single-phase short-circuit current of the bus; I max and I min is the short-circuit current threshold.

5. The method for determining a multi-circuit back-to-back flexible direct current access solution according to claim 1, characterized in that: The evaluation index also includes the construction cost of the back-to-back flexible DC access solution. For the multi-circuit back-to-back flexible DC access solution to be evaluated, the calculation expression of the construction cost of the back-to-back flexible DC access solution is as follows: ； Where, The construction cost of the back-to-back flexible DC access solution; is the number of candidate channels that can be connected to back-to-back flexible DC, is the candidate channel sequence index, For the The construction cost of the unit capacity of the back-to-back flexible DC channel is For the The rated active power of the back-to-back flexible DC power supply with 1 channel configuration is is the upper limit of construction cost, Whether the i-th channel is configured with back-to-back flexible DC, Indicates configuration, Indicates no configuration.

6. The method for determining a multi-circuit back-to-back flexible direct current access solution according to claim 1, characterized in that: The evaluation index also includes a system stability level index. For the multi-circuit back-to-back flexible DC access scheme to be evaluated, under a given fault set { , ,.., } and operating mode data, the calculation expression of the system stability level index is as follows: ； Where, is the system stability level indicator, The minimum number of failures that cause the system to lose stability, is the number of faults in a given fault set.

7. The method for determining a multi-circuit back-to-back flexible direct current access solution according to claim 6, characterized in that: The minimum number of failures that cause the system to lose stability in the system stability level indicator The particle swarm algorithm is used to solve the problem. The solution process includes the following steps: Set the objective function to , K is the number of faults that cause the system to lose stability; Set up a particle swarm; the particle swarm has a total of S particles, for particle i, , It refers to the transmission power of the jth back-to-back flexible DC link in the scheme to be evaluated. , is the rated capacity of the jth back-to-back flexible DC link, The number of channels configured with back-to-back flexible DC; For particle i, a fault time domain simulation analysis is performed based on the given operating mode data and fault set; the total number of faults that cause the system to lose stability is counted as the fitness value of the particle. The smaller the fitness, the better the particle, which is recorded as , n is the number of iterations, and the best result found after the nth iteration for the particle is recorded as ; Update each particle according to the following formula: ； ； ； ； Where, 、 、 、 are control parameters, n, N iter is the current number of iterations and the total number of iterations, rand() is a random number between (0,1); Indicates the value of the jth position of the i-th particle after the n-th iteration, Ulocal i,j (n) is the value of the jth position of the local optimal individual of the i-th particle after the n-th iteration, Uglobal j (n) is the value of the jth position of the optimal individual of the entire particle swarm after the nth iteration; V i,j (n) represents the j-th velocity value of the i-th particle after the n-th iteration, 、 Represents V i,j (n) lower and upper limits; represents the best particle of the particle swarm after the nth iteration; Repeat the above iterative process until the termination condition is met. The number of failures that cause the system to lose stability corresponding to the optimal solution at this time is recorded as .

8. The method for determining a multi-circuit back-to-back flexible direct current access solution according to claim 1, characterized in that: The analytic hierarchy process is used to select the multi-circuit back-to-back flexible direct current access schemes corresponding to several non-inferior solutions, including: A first matrix and a second matrix are used to respectively calculate first weight coefficients of the plurality of evaluation indicators and second weight coefficients of the plurality of candidate multi-circuit back-to-back flexible direct current access schemes under the plurality of evaluation indicators; the first matrix and the second matrix are judgment matrices for the highest layer and the middle layer, and for the middle layer and the lowest layer, respectively; The final weight coefficient of each of the multiple back-to-back flexible DC access schemes is calculated based on the first weight coefficient and the second weight coefficient, and the candidate scheme with the largest final weight coefficient is selected as the final DC access scheme; the final weight coefficient is calculated according to the following formula: ； Where, For the The final weight coefficient of each candidate solution is is the number of evaluation indicators, is the first weight coefficient after normalization of the j-th evaluation index, For the The second weight coefficient of a candidate solution normalized under the jth evaluation index.

9. A device for determining a multi-circuit back-to-back flexible direct current access scheme, characterized in that: include: A data acquisition module is used to obtain basic parameters of the receiving-end power grid under study based on a pre-built evaluation system; the evaluation system includes several evaluation indicators for evaluating the multi-circuit back-to-back flexible DC access solution; A model solving module is configured to solve a pre-established multi-objective optimization model using a multi-objective particle swarm algorithm based on the basic parameters to obtain a plurality of non-inferior solutions, each of which is a multi-circuit back-to-back flexible direct current access scheme for the receiving-end power grid; the multi-objective optimization model is a mathematical model that takes the optimization of the plurality of evaluation indicators of the multi-circuit back-to-back flexible direct current access scheme as an optimization objective, and takes the rated capacity of each back-to-back flexible direct current in each of the multi-circuit back-to-back flexible direct current access schemes as an optimization variable; a scheme determination module, configured to select the multi-circuit back-to-back flexible direct current access schemes corresponding to the plurality of non-inferior solutions using an analytic hierarchy process (AHP) to obtain a final multi-circuit back-to-back flexible direct current access scheme; wherein the bottom layer of the AHP comprises the plurality of candidate multi-circuit back-to-back flexible direct current access schemes, the middle layer comprises the plurality of evaluation indicators, and the top layer comprises the final direct current access scheme; The multi-objective particle swarm optimization algorithm is used to solve the pre-established multi-objective optimization model and obtain several non-inferior solutions, including: Setting and initializing a particle swarm; the particle swarm includes a master particle swarm, several slave particle swarms, and a reserve set; the several slave particle swarms correspond one-to-one to the several evaluation indicators and are optimized as their respective goals; Iteratively updating the particle swarm; the iterative updating operation includes randomly selecting a particle from the reserve set as a global guiding particle of the master particle swarm, each of the slave particle swarms uses the particle in the reserve set that best corresponds to its respective optimization target as the global guiding particle, and the master and slave particle swarms iteratively update based on their respective global guiding particles and local guiding particles; Performing an update operation on the reserve set; the update operation includes, after each iteration, adding the non-inferior extreme value particles obtained from the slave particle swarm to the master particle swarm, adding the non-inferior particles obtained from the master and slave particle swarms to the reserve set, and updating the reserve set to remove particles in the reserve set that are not non-inferior solutions; Repeat the iterative update operation of the particle swarm and the update operation of the reserve set until the termination condition is met, and obtain a non-inferior solution set in the reserve set, each non-inferior solution in the solution set represents a back-to-back flexible DC access solution.

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