Rail train power supply partition power determination method and device, medium and equipment

Abstract

The application provides a power determination method, device, medium and equipment for power supply partition of a rail train. In the application, the power distribution network side constraint in the peak load reduction problem is effectively decoupled from the rail train side constraint. On the one hand, the power distribution network side can generate a peak power envelope in a future preset time period according to its own operation safety requirements. On the other hand, the rail train side can complete feasibility verification and parallel computing under the premise of reserving traction network and train operation detailed data, taking into account the calculation efficiency and data privacy.

Description

Technical Field

[0001] This application relates to the field of rail transit technology, and more specifically, to a method, apparatus, medium, and equipment for determining the power of a rail train power supply zone. Background Technology

[0002] The traction load of urban rail transit trains has become one of the important loads in modern urban power systems. Compared with conventional electrical loads (such as residential and office building loads), urban rail traction loads are characterized by their suddenness and rapid changes. For example, during the transition between acceleration and braking states, the traction power changes drastically, forming short-duration but high-amplitude traction power peaks, which may cause voltage exceedances in the distribution network, transformer overloads, and other operational safety issues. Therefore, reducing the peak impact of urban rail traction loads on the distribution network is of great significance for improving the operational safety and flexibility of the urban distribution network.

[0003] Currently, since the DC-side terminal voltage of flexible DC traction substations can be actively adjusted, coordinating the terminal voltage control among multiple flexible DC traction substations to alter the traction network power flow distribution provides a feasible approach to reduce the peak traction load on distribution transformers. However, because the distribution network and the rail transit system are typically operated by different entities, data security considerations between these entities often prevent the distribution network from directly obtaining complete rail transit operation information. This results in significant data barriers in load control methods based on flexible DC traction substations. Therefore, there is an urgent need for a method that can reduce the peak load on distribution transformers without requiring the sharing of rail transit operation information between the distribution network and the rail transit system. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus, medium and equipment for determining the power supply zone of a rail train, so as to achieve peak load reduction of the distribution transformer without sharing rail train operation information between the distribution network and the rail train system.

[0005] Specifically, this application is implemented through the following technical solution: In a first aspect, embodiments of this disclosure provide a method for determining the power supply zone of a rail train, including: For any rail transit line, on the power distribution network side, based on the Benders cuts of each future time point within the future preset time period returned by the rail transit side in the previous iteration, auxiliary information for each future time point in the previous iteration is determined; the auxiliary information is the optimal cut or feasible cut in the Benders cut. The first objective is to minimize the sum of the peak load levels of the distribution transformers corresponding to each power supply zone and the sum of the auxiliary information. Based on the operation constraints of the distribution network nodes and the Benders cut under the previous iteration, the candidate peak power of each distribution transformer under the current iteration is determined. If the candidate peak power does not meet the iteration stop condition, the candidate peak power is sent to the track train side. On the rail train side, the second objective is to minimize the value of the indicator function at each future time point and ensure that the peak power of each distribution transformer is less than the candidate peak power at the current iteration. Based on the rail train operation data during the preset future time period, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the terminal voltage of each flexible DC traction substation. The value of the indicator function is determined based on the Benders cut at the current iteration. Returning to the step of determining auxiliary information for each future time point in the previous iteration based on the Benders cut of each future time point within the future preset time period returned by the rail train side in the previous iteration, on the distribution network side, until the iteration stop condition is met, the candidate peak power in the latest iteration is taken as the target peak power of the distribution transformer in the future preset time period.

[0006] In one possible implementation, the operational constraints of the distribution network node include at least: The square of the node voltage of each distribution network node is not less than the square of the preset lower voltage limit; the square of the branch current corresponding to the branch formed by any two adjacent distribution network nodes does not exceed the square of the preset lower current limit; the peak load level of each distribution network node does not exceed the target value; and the power flow model constraints of the first branch corresponding to each distribution network node.

[0007] In one possible implementation, on the rail train side, with the second objective being to minimize the value of the indicator function at each future time point and ensure that the peak power of each distribution transformer is less than the candidate peak power in the current iteration, the Benders cut at each future time point in the current iteration is determined in parallel based on the rail train operation data during the preset future time period by coordinating the terminal voltage of each flexible DC traction substation, including: On the rail train side, the voltage constraints of each traction network node in the traction network where the rail train is located are determined, as well as the power capacity constraints of each flexible DC traction station in the traction network are determined. Based on the aforementioned rail train operation data, determine the second branch power flow model constraints for each traction network node at each future time point. According to the second objective, based on the voltage constraint, the power capacity constraint and the second branch power flow model constraint, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the control of the terminal voltage of each flexible DC traction station.

[0008] In one possible implementation, based on the rail train operation data, the second branch power flow model constraints corresponding to each distribution network node at each future time point are determined, including: For any future point in time, the positions of each traction network node at that future point in time are determined based on the aforementioned rail train operation data; Based on the location of each traction network node and the node admittance matrix corresponding to the conductance between any two adjacent traction network nodes, the injected power corresponding to each train node in the traction network is determined through traction calculation. Based on the location of each traction network node and the injected power, the second branch power flow model constraints at this future time point are constructed.

[0009] In one possible implementation, the step of determining the Benders cut at each future time point in the current iteration by coordinating the terminal voltage of each flexible DC traction substation and based on the voltage constraint, the power capacity constraint, and the second branch power flow model constraint, according to the second objective, includes: For any future time point, according to the second objective, the corresponding Benders subproblem is determined based on the voltage constraint, the power capacity constraint, and the second branch power flow model constraint. By coordinating the control of the terminal voltage of each flexible DC traction station, it can be determined whether the sub-problems corresponding to each future time point are feasible. If not, then based on the dual information of the Benders subproblem corresponding to each future time point, generate feasible cuts corresponding to each future time point; Based on each of the feasible cuts, generate Benders cuts for each future time point in the current iteration.

[0010] In one possible implementation, the method further includes: If all Benders subproblems at each future time point are feasible, then the optimal cut at each future time point is generated based on the dual information of the Benders subproblems at each future time point. Based on each of the optimal cuts, generate Benders cuts for each future time point in the current iteration.

[0011] In one possible implementation, the peak load level of the distribution transformer is determined according to the following steps: The peak load level of the distribution transformer is determined based on the fixed load of the distribution transformer in a future preset time period and the candidate peak power in the current iteration, as well as the rated power capacity and power factor of the distribution transformer.

[0012] In one possible implementation, the iteration stopping condition includes that the auxiliary information at each future time point in the previous iteration is feasible cut, and the power error between the candidate peak power in the current iteration and the candidate peak power in the previous iteration is less than a preset error.

[0013] In one possible implementation, when the current iteration is the first iteration, the Benders cut at each future time point in the previous iteration is set to a preset value.

[0014] Secondly, embodiments of this disclosure also provide a power determination device for a rail train power supply zone, comprising: The first determining module is used to determine, on the distribution network side, auxiliary information for each future time point in the previous iteration based on the Benders cuts returned by the rail train side in the previous iteration for any rail train line; the auxiliary information is the optimal cut or feasible cut in the Benders cuts. The second determining module is used to take the sum of the peak load levels of the distribution transformers corresponding to each power supply zone as the first objective, and the sum of the auxiliary information as the minimum, and to determine the candidate peak power of each distribution transformer in the current iteration based on the operation constraints of the distribution network nodes and the Benders cut under the previous iteration. If the candidate peak power does not meet the iteration stop condition, the candidate peak power is sent to the track train side. The third determining module is used on the rail train side to determine the Benders cut at each future time point in the current iteration, with the second objective being that the value of the indicator function at each future time point is minimized and the peak power of each distribution transformer is less than the candidate peak power in the current iteration. Based on the rail train operation data in the future preset time period, the module coordinates the terminal voltage of each flexible DC traction substation and determines the Benders cut at each future time point in the current iteration in parallel. The value of the indicator function is determined based on the Benders cut in the current iteration. The loop module is used to return to the step of determining the auxiliary information of each future time point in the previous iteration based on the Benders cut of each future time point in the future preset time period returned by the rail train side on the distribution network side, until the iteration stop condition is met, and the candidate peak power in the latest iteration is taken as the target peak power of the distribution transformer in the future preset time period.

[0015] Thirdly, an optional implementation of this disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the first aspect above, or any possible implementation of the first aspect.

[0016] Fourthly, an optional implementation of this disclosure also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the first aspect above, or any possible implementation of the first aspect.

[0017] The power determination method, apparatus, medium, and equipment for rail train power supply zones provided in this disclosure divide the peak power calculation of distribution transformers into an iterative solution based on Benders cut on both the distribution network side and the rail train side. This allows for iterative reduction and optimization of the peak load of each distribution transformer by exchanging only candidate peak power and Benders cut values ​​between the distribution network and rail train sides. This optimizes the target peak power of each distribution transformer after reduction, avoiding the need for sharing rail train operation information between the distribution network and rail train sides. Furthermore, by constructing a second objective that minimizes the peak power and the indicator function value, and performing parallel Benders cut solutions according to the second objective through coordinated control of the terminal voltages of each flexible DC traction substation, the power flow distribution of the traction network can be actively altered. Traction loads that would otherwise be concentrated on heavily loaded distribution transformers can be distributed across different distribution transformers, effectively reducing the peak traction load of the distribution network and mitigating the risks of distribution transformer overload and voltage exceeding limits. Furthermore, compared to the existing technology that directly sends the precise reference power of each flexible traction unit at each moment to the rail train side, this application determines the target peak power of the distribution transformer in a future preset time period and feeds it back to the rail train side. This allows the rail train side to flexibly control traction in the future preset time period according to the target peak power limit issued by the distribution network side. This avoids the problem of difficulty in tracking the reference power caused by the rapid fluctuation of the rail train traction load on a second-level scale, and makes the peak load reduction result (i.e., the target peak load) more suitable for engineering implementation.

[0018] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an exemplary embodiment of this application of a method for determining the power supply zone of a rail train; Figure 2 This is a schematic diagram illustrating the structure of a traction network topology at a certain point in time, as shown in an exemplary embodiment of this application; Figure 3 This is a simplified structural diagram of a traction network topology shown in an exemplary embodiment of this application; Figure 4 This is a schematic diagram of a power determination device for a power supply zone of a rail train, as shown in an exemplary embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device shown in an exemplary embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0022] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0023] Research has revealed that traditional DC traction power supply systems typically employ diode rectifier units. Because the diode rectifiers in these units lack active voltage control, the traction load on each DC traction substation is primarily determined by the natural distribution of the traction network power flow. When a train accelerates, DC traction substations closer to the train and their upstream distribution transformers may bear a larger instantaneous traction power, while those farther away may still be under light load, leading to uneven distribution of the traction load among different distribution transformers. Furthermore, since distribution transformers in urban centers typically already handle high levels of conventional electricity loads (such as residential and office electricity loads), the uneven distribution of traction load among distribution transformers becomes more pronounced when trains operate in urban centers, and each distribution transformer becomes more sensitive to peak traction power.

[0024] The flexible DC traction power supply system for urban rail transit based on voltage source converters provides a new technical means to solve the problem of uneven distribution of traction load among different distribution transformers. This is because the DC-side terminal voltage of the flexible DC traction substation can be actively adjusted. Through coordinated control of the terminal voltages among multiple flexible DC traction substations, the power flow distribution of the traction network can be actively changed, thereby transferring the traction load originally concentrated on heavily loaded distribution transformers to idle distribution transformers with greater margin. This is of great significance for reducing the peak traction load of key distribution transformers in urban centers. Therefore, based on the flexible DC traction system, the traction load distribution within the urban rail transit system can be transformed from a passively subjected to the impact load of power flow distribution to a flexible load with active adjustment capabilities. In other words, the traction load of rail trains can be distributed from the original method of allocating it to the nearest distribution transformer according to the power flow distribution to a method of allocating it to multiple different distribution transformers through coordinated control of the terminal voltages among different flexible DC traction substations, avoiding the problem of some distribution transformers being overloaded while others remain relatively idle.

[0025] However, using flexible DC traction power supply systems to reduce peak loads on the power grid still faces certain data barriers. This is mainly because the distribution network and the rail transit system are usually operated by different entities. The distribution network side cannot directly obtain complete train locations, train power, traction network topology (such as the topology composed of subway station locations and distribution transformer locations), and detailed train operation data under the rail transit system. This makes it difficult for the distribution network side to accurately reduce the peak load of the distribution transformers to match the actual operation of the rail trains. Therefore, a more reasonable peak load reduction method should achieve coordination between the safe operation requirements of the distribution network side and the traction operation constraints of the rail train side, without requiring centralized sharing of detailed rail train information.

[0026] Based on the above research, this disclosure provides a method, apparatus, medium, and equipment for determining the power supply zone of a rail train. By dividing the solution of the peak power of the distribution transformer into an iterative solution based on Benders cut on both the distribution network side and the rail train side, it is possible to iteratively reduce and optimize the peak load of each distribution transformer by exchanging only candidate peak power and Benders cut between the distribution network side and the rail train side. This optimizes the target peak power of each distribution transformer after reduction, avoiding the need for the distribution network side and the rail train side to share rail train operation information. Furthermore, by constructing a second objective that minimizes the peak power and the value of the indicator function, and performing parallel Benders cut solution according to the second objective by coordinating the terminal voltage of each flexible DC traction substation, it is possible to actively change the power flow distribution of the traction network, distributing the traction load that should be concentrated on heavy-load distribution transformers to different distribution transformers, thereby effectively reducing the peak traction load of the distribution network and mitigating the risks of distribution transformer overload and voltage exceeding limits. Furthermore, compared to the existing technology that directly sends the precise reference power of each flexible traction unit at each moment to the rail train side, this application determines the target peak power of the distribution transformer in a future preset time period and feeds it back to the rail train side. This allows the rail train side to flexibly control traction in the future preset time period according to the target peak power limit issued by the distribution network side. This avoids the problem of difficulty in tracking the reference power caused by the rapid fluctuation of the rail train traction load on a second-level scale, and makes the peak load reduction result (i.e., the target peak load) more suitable for engineering implementation.

[0027] The shortcomings of the above solutions are the result of the inventor's practical experience and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below are all contributions made by the inventor to this disclosure.

[0028] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0029] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0030] To facilitate understanding of this embodiment, a method for determining the power of a rail train power supply zone disclosed in this disclosure will first be described in detail. The execution subject of the method for determining the power of a rail train power supply zone provided in this disclosure is generally a terminal device or other processing device with certain computing capabilities. The terminal device can be a user equipment (UE), a mobile device, a terminal, a personal digital assistant (PDA), a handheld device, a computer device, a control system, etc. In some possible implementations, the method for determining the power of a rail train power supply zone can be implemented by a processor calling computer-readable instructions stored in memory.

[0031] The following describes the method for determining the power of a rail train power supply zone provided in this embodiment, taking the control system as the executing entity as an example.

[0032] like Figure 1 The flowchart shown is a method for determining the power of a rail train power supply zone according to an embodiment of this disclosure, which may include the following steps: S101: For any rail train line, on the distribution network side, based on the Benders cuts of each future time point within the future preset time period returned by the rail train side in the previous iteration, determine the auxiliary information of each future time point in the previous iteration; the auxiliary information is the optimal cut or feasible cut in the Benders cut.

[0033] Here, the control system in this application may include a main problem calculation device deployed on the distribution network side and a sub-problem calculation device deployed on the rail train side. The two calculation devices, on their respective sides, solve for peak power using the Benders decomposition method or the generalized Benders decomposition method. The distribution network side is used to solve for the peak power limits of each distribution transformer under the rail train line, while the rail train side is used to solve for the peak power limits on the distribution network side, verifying, based on local rail train operation data, whether the peak power limits can be achieved through coordinated control of the terminal voltages between each flexible DC traction substation. That is, this application divides the peak power reduction of distribution transformers into a main problem solved on the distribution network side and a sub-problem verified on the rail train side. The main problem is used to determine the peak load limits of each distribution transformer, and the sub-problems are used to verify whether the peak load limits are feasible during rail train operation.

[0034] The reason this application provides a solution method based on Benders decomposition or generalized Benders decomposition is that distributed solution strategies have high engineering value in problems involving data isolation between the distribution network side and the rail train side. Regarding distributed solution strategies, the alternating direction multiplier method is suitable for consensus-based problems where multiple entities jointly optimize coupled variables; Benders decomposition and generalized Benders decomposition are more suitable for problems with a clear master-sub-problem structure and a relationship between upper-level decision-making and lower-level feasibility verification. Since the peak load reduction problem can be divided into a master problem on the distribution network side and a sub-problem on the rail train side, constructing a distributed coordinated solution framework based on Benders decomposition and generalized Benders decomposition can more effectively achieve peak load solution compared to using the alternating direction multiplier method. Based on this idea, this application proposes a method to generate peak power limits for distribution transformers on the distribution network side, and then verify whether the peak power limits can be achieved through coordinated control of the flexible DC traction power supply system on the rail train side, while retaining detailed local rail train data. Specifically, the main problem on the distribution network side involves generating peak traction power limits (i.e., candidate peak power) for each distribution transformer while satisfying distribution network voltage constraints, distribution network line capacity constraints, and distribution transformer capacity constraints. The sub-problem on the rail train side, given the peak power limit, verifies whether this limit can be achieved through coordinated control of the flexible DC traction power supply system based on the traction network model topology and rail train operation data. The distribution network side and the rail train side only need to exchange peak traction power limits and Benders cut plane information to optimize the peak traction power; detailed rail train operation data from the rail train side does not need to be uploaded to the distribution network side. These constraints will be discussed in detail later.

[0035] The rail train line can be any train line within the rail transit system of any city, and the rail train can specifically be an urban subway train. The distribution network side and the rail train side achieve the solution for the final reduction of peak load through interactive Benders cut and candidate peak power (described later).

[0036] Future preset time period ( The duration of the time can be set based on experience, and this application embodiment does not impose a specific limitation. Future time points t ( The sampling interval can be a time point sampled within a preset future time period, and this application embodiment does not limit the sampling interval. For example, the preset future time period can be 15 minutes in the future or 1 hour in the future, and the sampling interval can be every 10 seconds, 15 seconds, etc.

[0037] To facilitate understanding of the embodiments of this application, some key concepts are explained below: A city power grid may include a transmission network with voltages higher than 110 kV and a distribution network with voltages lower than 110 kV. The distribution network, also commonly referred to as the power grid, is part of the city power grid and is used to distribute electricity to the city. The traction network is the power grid specifically for supplying power to rail trains. The traction network can be further divided into the contact network and the return network. The contact network is the portion of the traction network that draws power from the rail trains for operation. The return network refers to the portion of the traction network that returns the electricity generated by the rail trains to the distribution network. It should be noted that the power determination method in this application does not consider the return network.

[0038] An iteration can be understood as the distribution network side solving for the candidate peak power and sending it to the rail train side. The rail train side, based on the coordinated control of the terminal voltages of each flexible DC traction substation, verifies the feasibility of the candidate peak power and feeds back Benders cut plane information. Each iteration returns a Benders cut that corresponds to each future time point within a preset time period. Specifically, the Benders cut includes indication information for the feasibility of the subproblem (i.e., whether the candidate peak power is feasible) and auxiliary information. The auxiliary information can be the optimal cut when feasible or the feasible cut information when infeasible; it is variable information that changes during the iteration process. A future time point. t One auxiliary information corresponds to one iteration. , Used to indicate the optimal or feasible cut for the train side subproblem at a future time point t.

[0039] In practical implementation, for any rail transit line, when it is possible to calculate the peak traction power (i.e., peak traction load) of each distribution transformer under that rail transit line within a future preset time period, the distribution network side can first obtain the Benders cut plane information for each future time point within the future preset time period returned by the rail transit side after the previous iteration. Then, the distribution network side can determine the auxiliary information required for each future time point in the current iteration based on the Benders cut plane information for each future time point. .

[0040] In one embodiment, when the current iteration is the first iteration, the Benders values ​​for each future time point in the previous iteration are set to preset values, and the auxiliary information for each future time point in the previous iteration is also not set to preset values. Here, the preset values ​​can be set based on experience, for example, they can be 0.

[0041] For example, in the case of the current iteration being the first iteration, meaning that the distribution network side is solving for the candidate peak power of each flexible DC traction substation for the first time in a future preset time period, since there has been no interaction with the rail train side, there is no Benders cut plane information returned by the rail train side. Therefore, the preset value can be used as the Benders cut and auxiliary information for each future time point in the previous iteration. Based on the preset value and the steps described below, the candidate peak power corresponding to the first iteration is solved and interaction with the rail train side is performed.

[0042] Understandably, in the current iteration, which is the th k The next iteration ( k In the case of a value greater than 1, that is, in the case that the current iteration is not the first iteration, the distribution network side can obtain the data returned by the rail train side. k -1 iterations of Benders cuts at each future time point, and determine auxiliary information at each future time point based on the Benders cuts. .

[0043] S102: Taking the sum of the peak load levels of the distribution transformers corresponding to each power supply zone as the first objective, and the sum of each auxiliary information as the minimum, the candidate peak power of each distribution transformer in the current iteration is determined according to the operation constraints of the distribution network nodes and the Benders cut under the previous iteration. If the candidate peak power does not meet the iteration stop condition, the candidate peak power is sent to the track train side.

[0044] Here, each power supply zone can refer to a power supply area divided under a rail train line. A power supply zone may include at least one flexible DC traction substation, and each power supply zone may correspond to the deployment of one distribution transformer. The number of flexible DC traction substations is typically consistent with the number of stations on the rail train line. For example, for any given rail train line, a flexible DC traction substation can be deployed at each station on that line, dividing one or more flexible DC traction substations into a power supply zone, and setting up one distribution transformer for each power supply zone. The various power supply zones constitute the flexible DC traction power supply system under the rail train transportation system.

[0045] Peak load level is used to characterize the proportion of the total output power of a distribution transformer to its rated power. The primary objective is to minimize the peak load level of each distribution transformer, thereby reducing the peak traction power of each distribution transformer as much as possible.

[0046] Operational constraints on distribution network nodes indicate the conditions that must be met for the safe operation of the distribution network. Specifically, these constraints may include distribution network voltage constraints (for constraining the voltage of each distribution network node), distribution network line capacity constraints (for constraining the branch currents of branches formed by different distribution network nodes), distribution transformer capacity constraints (for constraining the peak load level of distribution transformers), and power flow constraints (for constraining the branches formed by different distribution network nodes). A distribution network node can include any node in the distribution network topology; for example, a distribution network node can be a bus node in the distribution network.

[0047] The candidate peak power can be the peak traction power limit determined by the distribution network side for the distribution transformer in the current iteration. The iteration stopping condition can be a pre-defined condition, used to indicate the cutoff condition for the iterative calculation between the distribution network side and the rail train side. Specifically, the iteration stopping condition can include that the auxiliary information at each future time point under the previous iteration is a feasible cut, and the power error between the candidate peak power in the current iteration and the candidate peak power in the previous iteration is less than a preset error. That is, the iteration stopping condition can be that the auxiliary information under the previous iteration is a feasible cut (meaning that each rail train side subproblem in the previous iteration is feasible) and the power error between the candidate peak power of each distribution transformer in the current iteration and the candidate peak power of that distribution transformer in the previous iteration is less than a preset error (meaning that the candidate peak power obtained for each distribution transformer in the two iterations converges). The preset error can be set empirically, and this application embodiment does not impose specific limitations.

[0048] In practical implementation, the first objective can be expressed as the following formula: (Formula 1) in, This represents the first objective under the main problem on the distribution network side, which is to minimize the sum of the peak load levels of each distribution transformer plus the sum of all auxiliary information, where m is the identifier of the main problem on the distribution network side. This refers to the set of distribution transformers T that comprise all the distribution transformers T along a railway line. a Represents the first in the set of distribution transformers a Taiwan distribution transformer. Indicates the first a Peak load level of the distribution transformer; Indicates a future preset time period. t This indicates a future point in time within a pre-defined future time period; Indicates a future point in time. t The following is supplementary information.

[0049] Furthermore, operational constraints corresponding to distribution network nodes can be constructed based on the distribution network topology for a future preset time period. Then, according to the first objective corresponding to Formula 1 above, based on the operational constraints and the Benders cuts obtained in the previous iteration, the candidate peak power (i.e., the peak traction power limit of each distribution transformer in the current iteration) can be optimized to meet the operational constraints and the first objective in the current iteration. Then, based on the Benders cuts obtained in the previous iteration, the candidate peak power of each distribution transformer obtained in the previous iteration, and the candidate peak power corresponding to the current iteration, it can be determined whether the iteration stopping condition is met. If so, the candidate peak power of each distribution transformer in the current iteration can be used as the peak traction power limit of each distribution transformer in the future preset time period and sent to the rail train side. The rail train side can control the terminal voltage of each flexible DC traction substation according to the peak traction power limit in the future preset time period. If not, the candidate peak power of each distribution transformer in the current iteration can be sent to the rail train side.

[0050] Understandably, if the current iteration is the first iteration, it can be determined that the candidate peak power of each distribution transformer in the current iteration does not meet the iteration stopping condition, and the candidate peak power of each distribution transformer in the current iteration is sent to the track train side.

[0051] In one embodiment, the operational constraints of a distribution network node include at least: The square of the node voltage of each distribution network node is not less than the square of the preset lower voltage limit; the square of the branch current corresponding to the branch formed by any two adjacent distribution network nodes does not exceed the square of the preset lower current limit; the peak load level of each distribution network node does not exceed the target value; and the power flow model constraints of the first branch corresponding to each distribution network node.

[0052] Here, the preset lower voltage limit can be the minimum node voltage of the distribution network node, which can be set according to the operational safety requirements of the distribution network. The preset lower current limit can be the minimum value of the branch circuit corresponding to any two adjacent distribution network nodes, which can also be set according to the operational safety requirements of the distribution network. The target value can be 1, which means that the peak traction load level of the distribution network node cannot exceed the power capacity of the distribution network node. The first branch power flow model constraint can be the DistFlow model constraint corresponding to the distribution network branch.

[0053] In practical implementation, to achieve peak load reduction for each distribution transformer, a peak load reduction optimization model can be established first. Specifically, considering that the flexible DC traction power supply system under the rail transit system consists of various power supply zones under the rail train line, and multiple flexible DC traction substations within the same power supply zone are powered by the same upstream distribution transformer, from the perspective of the distribution network, each power supply zone can be regarded as an equivalent traction load aggregation. Since the distance between the flexible DC traction substation and the corresponding upstream distribution transformer is usually short, the line transmission loss between the flexible DC traction substation and the corresponding upstream distribution transformer is relatively small. Therefore, the traction load borne by the distribution transformer can be approximated as the sum of the active power output by each flexible DC traction substation in its downstream power supply zone, which is expressed by the following formula: (Formula 2) in, Indicates the first a The traction power (i.e., traction load) of the distribution transformer. Indicates the first a The set of flexible DC traction units, which comprises all flexible DC traction units within a power supply zone, is defined as follows: since one power supply zone corresponds to one distribution transformer, the first... a The power supply zone corresponds to the first a Taiwan distribution transformer. express The Middle c The traction power (i.e., active power) of a flexible direct current traction unit.

[0054] Furthermore, because the position and traction power of the railcar change over time, the traction load borne by each distribution transformer also changes over time. This changes over a given future preset time period (which can also be called the scheduling cycle). within, no. a The peak traction load of a distribution transformer can be defined by the following formula: (Formula 3) in, Indicates the first a Taiwan distribution transformer Peak traction load within, Indicates the first a Taiwan distribution transformer Future time points within t The traction power below; Indicates the first a Taiwan distribution transformer Various future time points within t Traction power below The maximum value in.

[0055] Furthermore, unlike the prior art which directly provides the reference power command for each flexible DC traction substation in the future preset time period, this application uses the peak power envelope method to characterize the control demand on the distribution network side. That is, the distribution network side can only determine the peak traction load limit that each distribution transformer can withstand within the dispatch cycle, instead of directly specifying the precise reference power value of each flexible traction substation at each future time point.

[0056] To reduce the peak load impact of urban rail train traction load on the distribution network, this application constructs a peak load reduction optimization model. The goal of this model is to reduce the peak load level of each distribution transformer while simultaneously satisfying the operational constraints of the distribution network nodes and the feasibility constraints of the coordinated control of the flexible DC traction power supply system. This peak load reduction optimization model can be expressed as Equations 4-9 below: (Formula 4) (Formula 5) (Formula Six) (Formula 7) (Formula 8) (Formula Nine) In Formula 4, This represents minimizing the peak traction load level of each distribution transformer. sum.

[0057] Formula 5 characterizes the voltage constraints of the distribution network, ensuring that the node voltage at each distribution network node meets operational limits and preventing voltage exceedances due to peak traction loads. Formula 5 ensures that the square of the node voltage at each distribution network node is not less than the square of a preset lower voltage limit; where, Indicates being bound by ; Indicates the preset lower voltage limit; That is, the square of the preset lower voltage limit. This represents the set of distribution network nodes, which is a combination of various distribution network nodes in a distribution network. Represents the set of nodes in the distribution network. j The square of the node voltage of each distribution network node. Formula 6 is used to characterize the power flow relationship constraints and line capacity constraints of the distribution network, and is used to prevent the branch current of the distribution network branch formed by each distribution network node from exceeding the limit. Formula 6 can be used to ensure that the square of the branch current corresponding to any two adjacent distribution network nodes does not exceed the square of the preset lower current limit; Represents the set of nodes in the distribution network. j The first distribution network node and the first kThe square of the branch current of a distribution network branch consisting of distribution network nodes. This indicates the preset lower current limit, which is also the allowable current of the transmission line; This represents the square of the preset lower current limit. Formula 7 is used to characterize the constraint on the peak load level of distribution network nodes, ensuring that the peak load level of each distribution network node does not exceed the target value; Represents the set of nodes in the distribution network. j Peak load levels of each distribution network node.

[0058] Formula 8 is used to characterize the power flow constraints in the form of the DistFlow model of the distribution network, which is the same as the first branch power flow model constraint mentioned above; where, Indicates the first j The set of all downstream distribution network branches of a distribution network node; a distribution network node k It can be the first j One adjacent node of a distribution network node; Indicates the first j The first distribution network node and the first k The active power at the beginning of a distribution network branch consisting of distribution network nodes; Indicates the first i The first distribution network node and the first j The active power at the beginning of a distribution network branch consisting of distribution network nodes; Indicates the first i The first distribution network node and the first j Branch resistance of a distribution network branch consisting of a distribution network node; Indicates the first j Active power of each distribution network node; Indicates the first i The first distribution network node and the first j The square of the branch current of a distribution network branch consisting of distribution network nodes; Indicates the first j The first distribution network node and the first k The reactive power at the beginning of a distribution network branch consisting of a distribution network node; Indicates the first i The first distribution network node and the first j The reactive power at the beginning of a distribution network branch consisting of a distribution network node; Indicates the first i The first distribution network node and the first j Branch reactance of a distribution network branch consisting of a distribution network node; Indicates the first j Reactive power of each distribution network node; Indicates the first i The square of the node voltage of the distribution network node; typically, the first... iThe first distribution network node, the first j The first distribution network node and the first k The positional relationship between the nth distribution network nodes is the nth i The distribution network node is located at the first j To the left of the distribution network node, the first k The distribution network node is located at the first j The right side of each distribution network node; This represents the L2 norm.

[0059] Formula 9 represents the capacity constraint of the distribution transformer, which is used to ensure that the peak power envelope determined on the distribution network side can be achieved by the flexible DC traction power supply system through coordinated control.

[0060] In addition to formulas four through nine mentioned above, the peak load reduction optimization model may also include formulas thirteen through fifteen, which will be discussed later.

[0061] Furthermore, the peak load level of any distribution transformer can be determined according to the following steps: The peak load level of the distribution transformer is determined based on the fixed load of the distribution transformer in a future preset time period, the candidate peak power in the current iteration, the rated power capacity and power factor of the distribution transformer.

[0062] Here, fixed loads can refer to various loads other than rail train traction loads that the distribution transformer will experience within a predetermined time period in the future. Specifically, this includes distribution network-side loads and rail train-side lighting loads. Distribution network-side loads may include, for example, residential electricity loads, shopping mall electricity loads, and office building electricity loads within the predetermined time period. Lighting loads may include lighting loads and air conditioning loads at rail train stations within the predetermined time period. Since fixed loads change slowly over a long dispatching cycle, they can be approximated as remaining constant within each predetermined time period and can be directly obtained.

[0063] Rated power capacity is used to indicate the rated power of a distribution transformer. The power factor can be set based on experience, and this application does not impose specific limitations on the embodiments.

[0064] For example, the purpose of Formula 4 above is to reduce the peak load level of the distribution transformer, therefore, the first formula can be used to reduce the peak load level of the distribution transformer. a The peak load level of a distribution transformer is defined by the following formula I: (Formula I) in, Indicates the first a The fixed load of the distribution transformer within a preset time period. Indicates the first a Taiwan distribution transformer The peak traction load within the time frame, in each iteration, this load can be the first... a The candidate peak power determined by the distribution transformer. Indicates the first a The rated power capacity of the distribution transformer; Indicates the first a The power factor of the distribution transformer.

[0065] Understandably, the first node in the distribution network node set j Peak load level of each distribution network node The calculation process is similar to that of Formula I above, and will not be repeated here.

[0066] Through the aforementioned peak load reduction optimization model, the distribution network side does not need to directly control the instantaneous power of each flexible DC traction unit at each future time point, but can directly generate the hourly peak power envelope for the distribution transformer. Furthermore, since the aforementioned peak load reduction optimization model involves both the operational constraints of the distribution network and the collaborative control constraints of the flexible DC traction power supply system, a centralized solution would require simultaneous acquisition of power flow data, distribution equipment parameters, train location, train power, and traction network parameters from the distribution network side. This could be limited by data security, operational boundaries, and communication conditions in practical engineering. Therefore, this application proposes a distributed solution method using the generalized Benders decomposition method for the peak load reduction optimization model. Specifically, this application decomposes the solution problem of the aforementioned peak load reduction optimization model into a main problem on the distribution network side and a sub-problem on the rail train side. The main problem on the distribution network side needs to generate the peak traction power limit for each distribution transformer in each iteration, under the conditions of satisfying the node voltage constraints, line capacity constraints, and distribution transformer capacity constraints of the distribution network nodes (i.e., satisfying the constraints corresponding to formulas five to eight above). The train-side subproblem is used to verify, given a peak traction power limit, whether the limit can be achieved through coordinated control of a flexible DC traction power supply system, based on the traction network topology and train operation data. Only the peak traction power limit and Benders cut plane information need to be exchanged between the two sides; detailed train operation data from the train side does not need to be uploaded to the distribution network side.

[0067] Specifically, the main problem on the distribution network side is described by the following formula ten: (Formula 10) Among them, in the above formula ten This indicates that the conditions are imposed by Equations 5 to 8 above. In Equation 10, the Benders cut represents the Benders cut plane information returned from the previous iteration by the train side. Representing auxiliary information at various future points in time. The constraint of being greater than or equal to 0 must be satisfied.

[0068] During each iteration, the distribution network side can calculate the candidate peak power of each distribution transformer in the current iteration based on the Benders cuts at each future time point within the preset future time period returned by the rail train side in the previous iteration, as well as the auxiliary information at each future time point in the previous iteration, according to Formula 10 above. Then, it is determined whether the candidate peak power meets the iteration stopping condition. If not, the candidate peak power of each distribution transformer in the current iteration can be sent to the rail train side.

[0069] S103: On the rail train side, with the minimum value of the indicator function at each future time point and the peak power of each distribution transformer being less than the candidate peak power at the current iteration as the second objective, based on the rail train operation data in the future preset time period, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the control of the terminal voltage of each flexible DC traction substation; the value of the indicator function is determined based on the Benders cut at the current iteration.

[0070] Here, the indicator function The value can be determined based on future time points. t The Benders cut in the current iteration is determined. If the Benders cut in the current iteration is the optimal cut, its value is 0; if the Benders cut in the current iteration is the feasible cut, its value is infinity. In other words, the indicator function... The value of is used to represent a future time point in each iteration. t The following subproblem is determined by whether the train on the track is feasible. If it is feasible, the indicator function takes the value of 0; if it is not feasible, the indicator function takes the value of infinity.

[0071] The second objective is used to represent the solution objective under the rail train side problem. Rail train operation data may include, but is not limited to, rail train timetables, train positions, train power, and traction network parameters.

[0072] In practical implementation, after the distribution network side provides the candidate peak power of each distribution transformer in the current iteration, the rail train side can solve sub-problems based on the candidate peak power. Since the traction network coordinated control constraints at each future time point within a preset future time period are independent, the rail train side sub-problems can be decomposed into multiple single future time point sub-problems, which can be solved in parallel. t The second objective corresponding to the sub-problem at each future time point can be expressed as the following formula eleven: (Formula Eleven) in, Indicates a future time point on the side of the rail train. t The second objective of the subproblem is to minimize future time points. t The value of the indicator function in the current iteration, where s is the identifier of the track train side subproblem; This indicates the peak power of each distribution transformer. Less than the candidate peak power in the current iteration Furthermore, after determining the second objective, each future time point can be determined based on the rail train operation data over a predetermined future time period. t The following traction network topology and future time points t Information such as train power is available. This is based on the traction network topology and future time points. t The train power is adjusted accordingly, and the terminal voltages of each flexible DC traction substation are coordinated and controlled according to the second objective, while the future time points are determined in parallel. t The feasibility of the track train subproblem is determined, thus obtaining the Benders cut for each future time point in the current iteration. Furthermore, the track train side can return the Benders cut for each future time point in the current iteration to the distribution network side for the next iteration calculation.

[0073] In one embodiment, the above-described S103 can be implemented according to the following steps: S103-1: On the side of the rail train, determine the voltage constraints of each traction network node in the traction network where the rail train is located, and determine the power capacity constraints of each flexible DC traction substation in the traction network.

[0074] Here, voltage constraints at traction network nodes are used to prevent voltage exceedances at the traction network nodes, while power capacity constraints at flexible DC traction substations are used to prevent excessive traction power output from the flexible DC traction substations. The traction network can include a DC network composed of flexible DC traction substation nodes and train nodes. A traction network node can be either a flexible DC traction substation node or a train node. A train node is the node corresponding to the location of a rail train. Since the rail train will be running for a predetermined period of time, the train nodes in the traction network are constantly changing. However, since flexible DC traction substations are usually deployed at rail train stations, the location of the flexible DC traction substation node is usually fixed within the traction network.

[0075] In practice, on the rail train side, the voltage constraints of the traction network nodes can be determined based on the upper and lower voltage limits set for the node voltages of the traction network nodes. Simultaneously, the power capacity constraints of each flexible DC traction substation can be determined based on its rated power capacity.

[0076] S103-2: Based on the rail train operation data, determine the second branch power flow model constraints corresponding to each traction network node at each future time point.

[0077] Here, the second branch flow model constraint can be the DistFlow model constraint corresponding to the traction network branch.

[0078] In practice, for each future time point, the train position at each future time point can be determined based on the rail train operation data. This train position can be the position of each rail train running under the rail train line. Based on the position of each rail train at the future time point and the position of each flexible and direct current traction substation, the position of each traction network node at the future time point can be determined, and the second branch power flow model constraint at the future time point can be constructed based on this position.

[0079] In one embodiment, S103-2 above can be implemented according to the following steps: S103-2-1: For any future point in time, determine the location of each traction network node at that future point in time based on the rail train operation data.

[0080] In practical implementation, from the perspective of the rail train side, during the current iteration of calculation, for each future time point, the real-time position of each rail train operating at that future time point can be determined based on detailed rail train operation data along the rail train line. This real-time position is used as the train node position in the traction network at that future time point, and the positions of each train station along the rail train line are used as the flexible-DC traction substation node positions in the traction network at that future time point. Furthermore, based on the positions of each train node and each flexible-DC traction substation node, the traction network topology and the position of each traction network node at that future time point can be determined. A traction network node can be either a train node or a flexible-DC traction substation node.

[0081] like Figure 2 The diagram shown is a schematic representation of a traction network topology at a specific point in time, according to an embodiment of this application. Figure 2 In this context, node T represents a train node. Figure 2 In the diagram, an "S" represents a flexible straight traction node, and two parallel lines represent the traction network. Indicates the first in the traction net i The branch resistance of each contact wire branch, Indicates the first in the traction net i +1 branch resistance of the contact wire branch, Indicates the first in the traction net i +2 branch resistances of the contact wire branches; , , They represent the first in the traction network. i The branch resistance of the first return network branch, the firsti +1 branch resistance of the return network branch, the first i +2 branch resistances of the return network branches; Indicates the first in the traction net i The electrical conductivity of the rails to ground at each traction network node. , and They represent the first in the traction network. i+ The ground conductivity of the rail at a traction network node, the first i+ The ground conductivity of the rails at the two traction network nodes, the first i+ The ground conductivity of the rails at the three traction network nodes. Furthermore, to reduce the complexity of the traction network topology and the complexity of coordinated control between flexible DC traction substations, this application simplifies the structure of the traction network topology. For example... Figure 3 The diagram shown is a simplified structural schematic of a traction network topology provided in an embodiment of this application. In this simplified topology, the ground conductivity is ignored, and the branch resistances corresponding to the same branch in the contact network and return network are uniformly merged as the branch resistances of the traction network branches. Figure 3 middle, Indicates the first in the traction net i The branch resistance of a traction network branch, Indicates the first in the traction net i +1 branch resistance of the traction network branch, Indicates the first in the traction net i +2 branch resistances of the traction network branches. This application can adopt the following when determining the traction network topology at each future time point: Figure 3 The simplified traction network topology is shown.

[0082] S103-2-2: Based on the location of each traction network node and the node admittance matrix corresponding to the conductance between any two adjacent traction network nodes, the injected power corresponding to each train node in the traction network is determined through traction calculation.

[0083] In practice, for any future time point, the traction network topology at that future time point can be determined based on the positions of each traction network node in the traction network topology determined for that future time point. Based on the traction network topology at that future time point, every two adjacent traction network nodes are identified. Then, based on the conductance between the traction network branches formed by every two adjacent traction network nodes, the node admittance matrix of the traction network at that future time point is determined. Based on the node admittance matrix at that future time point, the injected power corresponding to each train node in the traction network is determined through traction calculations. The injected power is the train power (traction power) required by the track train at the train node. Since the positions of the train nodes are different at each future time point, the adjacent traction network nodes change at different future time points, and consequently, the node admittance matrix also changes continuously.

[0084] S103-2-3: Based on the location of each traction network node and the injected power, construct the constraints of the second branch power flow model at this future time point.

[0085] In practice, for each future time point, the DistFlow model constraint at that future time point can be constructed based on the location of each traction network node and the injected power of each train node at that future time point, and this constraint can be used as the second branch power flow model constraint at that future time point.

[0086] For example, to achieve coordinated control of the terminal voltages of each flexible DC traction substation on the train side, a coordinated control model for the urban rail flexible DC traction power supply system can be established first. Since the flexible DC traction substation has DC-side terminal voltage regulation capability, it can be equivalent to a controllable voltage source; the injected power of the train can be obtained from train operation calculations, and can be equivalent to a given power source. Therefore, the traction network power flow can be jointly determined by the terminal voltage of the flexible DC traction substation, the position of the train, the injected power of the train, and traction network line parameters (such as the distance between two traction network nodes, guide resistance, etc.). To reduce the complexity of the coordinated control model, this application can equate the traction network to a DC network composed of flexible DC traction substation nodes and train nodes when calculating the traction network power flow, as described above. Figure 3 For each time point, the traction network topology and the corresponding node admittance matrix can be updated based on the real-time track train position. The real-time track train injection power can then be calculated using traction based on the node admittance matrix, thus forming the traction network power flow model for that time point. The general form of the traction network power flow model can be expressed as the following formula (xv): ; (Formula 12) in, I This represents the injected current vector at the traction network nodes (including train nodes and flexible DC traction substation nodes). PThis represents the injected power vector of the traction network nodes (including train nodes and flexible-CRT traction substation nodes). U This represents the node voltage vector of the traction network node (including train nodes and flexible DC traction substation nodes). Y This represents the nodal admittance matrix corresponding to a node in the traction network. "" represents the Hadamard product. Based on Formula Twelve above, each traction network node (flexible DC traction substation node or train node) can have injected current, injected power, and node voltage. For a train node, the injected power can be the train power obtained through traction calculations. The injected current and node voltage of the train node can be calculated using Formula Twelve and the injected power. The node voltage of the flexible DC traction substation can be actively regulated through coordinated control. The injected current and injected power of the flexible DC traction substation can be calculated based on the regulated node voltage and Formula Twelve.

[0087] Furthermore, the DistFlow model can be modified to address the nonlinear power flow constraint corresponding to Formula Twelve above. Also, since urban rail traction networks typically have a chain topology along the direction of the rail train lines, with adjacent traction network nodes connected by a single line, the traction network model does not involve complex multi-branch structures. Therefore, in the DistFlow model, the relationship between line power, node voltage, and line current in the traction network can be expressed as Formula Thirteen below: (Formula Thirteen) in, This represents the set of traction network nodes, which consists of all the individual traction network nodes in the traction network. Indicates the first w The first traction network node and the first z The active power at the beginning of a traction network branch consisting of traction network nodes. Indicates the first x The first traction network node and the first w The active power at the beginning of a traction network branch consisting of traction network nodes. Indicates the first x The first traction network node and the first w Branch resistance of a traction network branch consisting of traction network nodes; Indicates the first x The first traction network node and the first w The square of the branch current of a traction network branch consisting of traction network nodes; Indicates the first w The active power of the traction network node, in the th w When a traction network node is a rail train node, the active power is the injected power of the rail train. Indicates the first w The square of the node voltage of each traction network node; Indicates the first x The square of the node voltage of each traction network node; Indicates the first x The first traction network node and the first w The square of the branch resistance of a traction network branch consisting of traction network nodes. Indicates the first w The first traction network node and the first z The square of the branch current of a traction network branch consisting of traction network nodes. Formula thirteen above is the constraint of the second branch power flow model at any future time point.

[0088] The flexible DC traction power supply system can change the power flow distribution of the traction network by coordinating the terminal voltages of each flexible DC traction substation, enabling multiple flexible DC traction substations to actively share the traction load of railcars. To ensure the safe operation of the flexible DC traction power supply system, the cooperative control model must satisfy the voltage constraints and power capacity constraints shown in Equations 14 and 15, respectively: (Formula Fourteen) in, This indicates the pre-set lower limit of the node voltage for the traction network nodes. This represents the pre-set upper limit of the node voltage for the traction network node. Formula 14 above is the voltage constraint for the traction network node.

[0089] (Formula Fifteen) in, This represents the collection of flexible and straight traction elements within the traction network. for A subset of, which is composed of It consists of various flexible straight traction nodes. Indicates the first c The output power (i.e., traction power) of a flexible straight traction unit. Indicates the first c The rated power capacity of the first flexible direct current traction substation, that is, the first... c The upper limit of the power capacity of a flexible direct current traction unit. Indicates the first c The lower limit of the power capacity of each flexible-CH traction substation. Formula 15 above is the power capacity constraint of each flexible-CH traction substation in the traction network, and also the power capacity constraint of each flexible-CH traction substation node in the traction network.

[0090] Formulas 13 to 15 above constitute the feasible region for coordinated control of the flexible DC traction power supply system at a single point in time. This feasible region describes the adjustable range of the terminal voltage and output power of each flexible DC traction substation under given train position (i.e., the real-time position of the train at a given point in time) and train power (i.e., the injected power of the train at a given point in time). Typically, within this feasible region for coordinated control, the train side can determine whether the peak traction power limit of the distribution transformer can be met within the terminal voltage and output power adjustment ranges at each iteration, thereby determining the feasibility of the train side sub-problem.

[0091] S103-3: In accordance with the second objective, based on voltage constraints, power capacity constraints, and the second branch power flow model constraints, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the terminal voltage of each flexible DC traction station.

[0092] For example, for each future point in time, the second objective described above can be followed, based on the future point in time. t The injected power of the train on the track is determined in parallel by coordinating the terminal voltages of each flexible DC traction substation and determining the future time points. t Benders cut in the current iteration.

[0093] In one embodiment, when solving the Benders cut for a subproblem based on the Benders partitioning method, the feasibility of the subproblem can be determined. Specifically, for S103-3 above, when solving the Benders cut for each future time point in parallel, the following steps can be followed: S103-3-1: For any future time point, according to the second objective, and based on the voltage constraint, power capacity constraint, and second branch power flow model constraint, determine the corresponding Benders subproblem for that future time point.

[0094] In practice, the rail train side, during each iteration, considers any future time point... t Based on the second objective, voltage constraints, power capacity constraints, and second branch power flow model constraints, any future time point can be... t The following equation represents the train sidewall problem: (Formula Sixteen) Among them, the content of Formula Sixteen above " "" indicates that it is subject to Formulas 13 to 15 and Formula 9 above. That is, a future point in time t The following indicator function, whose value is determined based on a future time point. tThe feasibility of the subproblem of the train track is determined by taking 0 if the subproblem is feasible and infinity if the subproblem is not feasible.

[0095] On the side of the rail train, for each future point in time. t It can be based on future time points t The injection power of the under-rail train and future time points t The topology of the traction network corresponding to the position of the train under the given conditions is used to determine the Benders subproblem of form 16 above for the future time point t. Here, a Benders subproblem is equivalent to a train side subproblem at a future time point.

[0096] S103-3-2: By coordinating the control of the terminal voltages of each flexible DC traction station, determine whether the sub-problems corresponding to each future time point are feasible.

[0097] In practice, for the Benders subproblems at each future time point, the rail train side can determine whether the subproblems at each future time point are feasible under the given candidate peak power on the distribution network side by coordinating the control of the terminal voltage of each flexible DC traction substation.

[0098] S103-3-3: If not, then generate feasible cuts corresponding to each future time point based on the dual information of the Benders subproblems corresponding to each future time point.

[0099] In practical implementation, if at least one future time point corresponds to an infeasible Benders subproblem, it indicates that the candidate peak power limit given by the distribution network side is too strict, and the rail train side cannot achieve it through the coordinated control of the flexible DC traction power supply system. In this case, for each future time point, the rail train side can generate a feasible cut corresponding to each future time point based on the dual information of the Benders subproblem corresponding to that future time point.

[0100] Among them, any future time point t The feasible cut of the corresponding Benders subproblem can be expressed by the following formula: (Formula 17) in, Indicates the first k In the next iteration, the dual problem of the Benders subproblem is the polar ray. Indicates the first k In the next iteration, at a future time point t The feasible cuts for the corresponding Benders subproblem.

[0101] S103-3-4: Based on each feasible cut, generate Benders cuts for each future time point in the current iteration.

[0102] In practical implementation, for each future time point, the indication information indicating the infeasibility of the subproblem and the feasible cut at that future time point can be used as the Benders cut for that future time point in the current iteration. Furthermore, the rail train side can feed back the Benders cuts for each future time point in the current iteration to the distribution network side for the next iteration. In the next iteration, the distribution network side can appropriately amplify the candidate peak power of the distribution transformer based on the feasible cuts in the Benders cuts.

[0103] In another embodiment, if all Benders subproblems corresponding to each future time point are feasible, then the optimal cut corresponding to each future time point is generated based on the dual information of the Benders subproblems corresponding to each future time point; and based on each optimal cut, the Benders cut for each future time point in the current iteration is generated.

[0104] For example, if all Benders subproblems at each future time point are feasible, the train can generate the optimal cut for each future time point based on the dual information of the Benders subproblem at that time point. The optimal cut can be represented by the following formula: (Formula 18) in, Indicates a future point in time. t The feasible cuts for the corresponding Benders subproblem. Indicates the first k The optimal point of the dual problem of the Benders subproblem at the next iteration. Indicates the first k In the next iteration, at a future time point t The optimal cut for the corresponding Benders subproblem.

[0105] Furthermore, for each future time point, the indication information used to indicate the feasibility of the subproblem and the optimal cut at that future time point can be used as the Benders cut for that future time point in the current iteration. Further, the railcar side can feed back the Benders cuts for each future time point in the current iteration to the distribution network side for the next iteration. In the next iteration, the distribution network side can adjust the candidate peak power of the distribution transformers based on the optimal cut in the Benders cut. Thus, by continuously iterating and solving the main problem on the distribution network side, and continuously iterating and solving the subproblems in parallel on the railcar side, while adding Benders cut plane information, until all subproblems meet the feasibility requirements and achieve convergence accuracy, the final peak power envelope of each distribution transformer in the future preset time period can be obtained.

[0106] S104: Return to the distribution network side, based on the Benders cut of each future time point within the future preset time period returned by the rail train side in the previous iteration, determine the auxiliary information of each future time point under the previous iteration, until the iteration stop condition is met, and take the candidate peak power under the latest iteration as the target peak power of the distribution transformer under the future preset time period.

[0107] Here, the target peak power refers to the peak traction power limit determined for the distribution transformer in the future preset time period. This power can be used by the rail train side to provide traction power for the rail train when the future preset time period is reached.

[0108] In specific implementation, after the rail train side provides feedback on the Benders cuts at each future time point in the current iteration, the distribution network side can use these Benders cuts returned by the rail train side as the new Benders cuts in the previous iteration, and return to execute S101 as described above, until the candidate peak power determined by the distribution network side for each distribution transformer in the latest iteration meets the iteration stop condition. The candidate peak power determined for each distribution transformer in the latest iteration is then used as the target peak power for each distribution transformer in the future preset time period and sent to the rail train side. When the future preset time period arrives, the rail train side can control the terminal voltage of the flexible DC traction substation according to the target peak power, thereby providing traction power for the operation of the rail train in the future preset time period.

[0109] In summary, the power determination method for rail transit power supply zones provided in this application establishes a peak load reduction optimization model oriented towards the safe operation of the distribution network. While considering distribution network voltage constraints, line capacity constraints, and transformer capacity constraints, it determines the peak traction power limit that the distribution transformers corresponding to each power supply zone can withstand. Specifically, this application first actively changes the traction network power flow distribution through coordinated control of the flexible DC traction power supply system, transferring the traction load originally concentrated on heavily loaded distribution transformers to distribution transformers with greater margins, thereby reducing the peak traction load of each distribution transformer and mitigating the risks of distribution transformer overload and voltage exceeding limits. Second, this application adopts a method of issuing peak traction power envelopes at the level of a preset control cycle (i.e., a preset future time period) instead of issuing reference power commands for each flexible DC traction system. This avoids the difficulty in command tracking caused by the second-level rapid fluctuations in urban rail traction load, making the peak load reduction results more suitable for engineering implementation. Third, this application decomposes the peak load reduction optimization problem into a main problem on the distribution network side and a sub-problem on the rail train side through generalized Benders decomposition. It only requires the exchange of peak power limits (i.e., candidate peak power) and cut plane information (i.e., Benders cuts) between the distribution network side and the rail train side, without requiring centralized sharing of detailed train operation data and traction network data on both sides, thus improving data privacy protection and engineering deployability. Fourth, since the rail train side sub-problem can be solved in parallel according to future time points, the overall computation time is significantly reduced, improving the solution efficiency of the peak load reduction optimization model in long scheduling cycles and large-scale systems.

[0110] To facilitate understanding of the embodiments of this application, the specific process of a method for determining the power of a rail train power supply zone provided by the embodiments of this application will be described below: In the power determination method of this application, the input information may include distribution network operation data and distribution transformer rated capacity constraints input to the distribution network side, as well as flexible DC traction power supply system operation data and rail train operation data input to the rail train side. The distribution network side and the rail train side can use the generalized Benders decomposition method to determine the peak power of the distribution transformer based on the aforementioned peak load reduction model. The final output can be the target peak power of each distribution transformer in a future preset time period. Specifically, the distribution network operation data and distribution transformer rated capacity constraints can be various data related to formulas four to nine above, and the flexible DC traction power supply system operation data and rail train operation data can be various data related to formulas thirteen to fifteen and formula nine above. The process of solving for the peak power of the distribution transformer can be as follows: Initialize the number of iterations k And initialize the Benders values ​​for each future time point to preset values.

[0111] Distribution network side based on initialization iteration number k Using preset values ​​at various future time points, solve the main problem on the distribution network side, obtain the candidate peak power of each distribution transformer in the current iteration, and send it to the rail train side.

[0112] The train side solves the sub-problems at each future time point in parallel to verify whether the candidate peak power can be achieved through coordinated control of the flexible DC traction power supply system.

[0113] If it can be achieved through coordinated control, that is, if all sub-problems at each future time point are feasible, then the track train side can generate the optimal cut at each future time point, and generate the Benders cut at each future time point based on the optimal cut, and feed it back to the distribution network side.

[0114] If it cannot be achieved through coordinated control, that is, if there is at least one infeasible subproblem at a future time point, then the rail train side can generate feasible cuts at each future time point, and generate Benders cuts for each future time point based on the feasible cuts, and feed them back to the distribution network side.

[0115] The distribution network side can update the main problem based on the Benders cut feedback from the rail train side and enter the next iteration until all subproblems are feasible and the convergence error meets the requirements, and output the final peak power envelope (that is, until the iteration stop condition is met, the candidate peak power under the latest iteration is used as the target peak power of the distribution transformer in the future preset time period).

[0116] Thus, the power determination method for rail train power supply zones provided in this application, as a peak load reduction method for urban rail flexible DC traction power supply systems based on cooperative control, can effectively decouple the distribution network-side constraints and rail train-side constraints in the peak load reduction problem. On the one hand, the distribution network side can generate a peak power envelope according to its own operational safety requirements; on the other hand, the rail train side can complete feasibility verification and parallel computation while retaining detailed data on the traction network and train operation, thereby balancing computational efficiency and data privacy. Experimental results in actual regional distribution networks and rail transit systems show that the power determination method for rail train power supply zones provided in this application can significantly reduce the peak traction load of distribution transformers compared to conventional constant voltage control and droop control, and provide additional operational margin for the distribution network (i.e., free control according to the target peak power), so that the system operation constraints can still be met when the traction load increases within a certain range.

[0117] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0118] Based on the same inventive concept, this disclosure also provides a power determination device for a rail train power supply zone corresponding to the power determination method for a rail train power supply zone. Since the principle of the device in this disclosure for solving the problem is similar to the power determination method for a rail train power supply zone described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0119] like Figure 4 The diagram shown is a schematic of a power determination device for a rail train power supply zone according to an embodiment of this disclosure, including: The first determining module 401 is used to determine, on the distribution network side, auxiliary information for each future time point in the previous iteration based on the Benders cuts returned by the rail train side in the previous iteration for any rail train line; the auxiliary information is the optimal cut or feasible cut in the Benders cuts. The second determining module 402 is used to determine the candidate peak power of each distribution transformer in the current iteration based on the operation constraints of the distribution network node and the Benders cut under the previous iteration, with the first objective being to minimize the sum of the peak load levels of the distribution transformers corresponding to each power supply zone and the sum of the auxiliary information. If the candidate peak power does not meet the iteration stop condition, the candidate peak power is sent to the track train side. The third determining module 403 is used, on the rail train side, with the second objective being to minimize the value of the indicator function at each future time point and to ensure that the peak power of each distribution transformer is less than the candidate peak power at the current iteration, and based on the rail train operation data during the future preset time period, to determine the Benders cut at each future time point in the current iteration by coordinating the control of the terminal voltage of each flexible DC traction substation; the value of the indicator function is determined based on the Benders cut at the current iteration. The loop module 404 is used to return to the step of determining the auxiliary information of each future time point in the previous iteration based on the Benders cut of each future time point in the future preset time period returned by the rail train side on the distribution network side, until the iteration stop condition is met, and the candidate peak power in the latest iteration is taken as the target peak power of the distribution transformer in the future preset time period.

[0120] In one possible implementation, the operational constraints of the distribution network node include at least: The square of the node voltage of each distribution network node is not less than the square of the preset lower voltage limit; the square of the branch current corresponding to the branch formed by any two adjacent distribution network nodes does not exceed the square of the preset lower current limit; the peak load level of each distribution network node does not exceed the target value; and the power flow model constraints of the first branch corresponding to each distribution network node.

[0121] In one possible implementation, the third determining module 403, on the rail train side, takes as its second objective the minimum value of the indicator function at each future time point and the peak power of each distribution transformer being less than the candidate peak power at the current iteration. Based on the rail train operation data during the future preset time period, it determines the Benders cut time at each future time point in the current iteration by coordinating the terminal voltage of each flexible DC traction substation. This is used for: On the rail train side, the voltage constraints of each traction network node in the traction network where the rail train is located are determined, as well as the power capacity constraints of each flexible DC traction station in the traction network are determined. Based on the aforementioned rail train operation data, determine the second branch power flow model constraints for each traction network node at each future time point. According to the second objective, based on the voltage constraint, the power capacity constraint and the second branch power flow model constraint, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the control of the terminal voltage of each flexible DC traction station.

[0122] In one possible implementation, the third determining module 403, when determining the second branch power flow model constraints corresponding to each distribution network node at each future time point based on the rail train operation data, is used to: For any future point in time, the positions of each traction network node at that future point in time are determined based on the aforementioned rail train operation data; Based on the location of each traction network node and the node admittance matrix corresponding to the conductance between any two adjacent traction network nodes, the injected power corresponding to each train node in the traction network is determined through traction calculation. Based on the location of each traction network node and the injected power, the second branch power flow model constraints at this future time point are constructed.

[0123] In one possible implementation, the third determining module 403, when determining the Benders cut at each future time point in the current iteration according to the second objective, based on the voltage constraint, the power capacity constraint, and the second branch power flow model constraint, through coordinated control of the terminal voltage of each flexible DC traction substation, is used for: For any future time point, according to the second objective, the corresponding Benders subproblem is determined based on the voltage constraint, the power capacity constraint, and the second branch power flow model constraint. By coordinating the control of the terminal voltage of each flexible DC traction station, it can be determined whether the sub-problems corresponding to each future time point are feasible. If not, then based on the dual information of the Benders subproblem corresponding to each future time point, generate feasible cuts corresponding to each future time point; Based on each of the feasible cuts, generate Benders cuts for each future time point in the current iteration.

[0124] In one possible implementation, the third determining module 403 is further configured to: If all Benders subproblems at each future time point are feasible, then the optimal cut at each future time point is generated based on the dual information of the Benders subproblems at each future time point. Based on each of the optimal cuts, generate Benders cuts for each future time point in the current iteration.

[0125] In one possible implementation, the second determining module 402 is configured to determine the peak load level of the distribution transformer according to the following steps: The peak load level of the distribution transformer is determined based on the fixed load of the distribution transformer in a future preset time period and the candidate peak power in the current iteration, as well as the rated power capacity and power factor of the distribution transformer.

[0126] In one possible implementation, the iteration stopping condition includes that the auxiliary information at each future time point in the previous iteration is feasible cut, and the power error between the candidate peak power in the current iteration and the candidate peak power in the previous iteration is less than a preset error.

[0127] In one possible implementation, when the current iteration is the first iteration, the Benders cut at each future time point in the previous iteration is set to a preset value.

[0128] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0129] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0130] Based on the same technical concept, embodiments of this application also provide a computer device. (Refer to...) Figure 5 The diagram shown is a structural schematic of a computer device provided in an embodiment of this application, comprising: The processor 501, memory 502, and bus 503 are included. Memory 502 stores machine-readable instructions executable by the processor 501. The processor 501 executes the machine-readable instructions stored in memory 502. When the machine-readable instructions are executed by the processor 501, the processor 501 performs the steps S101 to S104 described above.

[0131] The aforementioned memory 502 includes a main memory 5021 and an external memory 5022. The main memory 5021, also known as internal memory, is used to temporarily store the computational data in the processor 501, as well as the data exchanged with external memory such as a hard disk 5022. The processor 501 exchanges data with the external memory 5022 through the main memory 5021. When the computer device is running, the processor 501 and the memory 502 communicate through the bus 503, so that the processor 501 executes the execution instructions mentioned in the above method embodiments.

[0132] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the power determination method for the power supply zone of a rail train as described in the above-described method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0133] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the power determination method for the power supply zone of the rail train described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0134] The computer program product can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0135] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it can be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the user through pop-up information or by asking the user to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0140] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining the power supply zone of a rail train, characterized in that, The method includes: For any rail transit line, on the power distribution network side, based on the Benders cuts of each future time point within the future preset time period returned by the rail transit side in the previous iteration, auxiliary information for each future time point in the previous iteration is determined; the auxiliary information is the optimal cut or feasible cut in the Benders cut. The first objective is to minimize the sum of the peak load levels of the distribution transformers corresponding to each power supply zone and the sum of the auxiliary information. Based on the operation constraints of the distribution network nodes and the Benders cut under the previous iteration, the candidate peak power of each distribution transformer under the current iteration is determined. If the candidate peak power does not meet the iteration stop condition, the candidate peak power is sent to the track train side. On the rail train side, the second objective is to minimize the value of the indicator function at each future time point and ensure that the peak power of each distribution transformer is less than the candidate peak power at the current iteration. Based on the rail train operation data during the preset future time period, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the terminal voltage of each flexible DC traction substation. The value of the indicator function is determined based on the Benders cut at the current iteration. Returning to the step of determining auxiliary information for each future time point in the previous iteration based on the Benders cut of each future time point within the future preset time period returned by the rail train side in the previous iteration, on the distribution network side, until the iteration stop condition is met, the candidate peak power in the latest iteration is taken as the target peak power of the distribution transformer in the future preset time period.

2. The method according to claim 1, characterized in that, The operational constraints of the distribution network nodes include at least the following: The square of the node voltage of each distribution network node is not less than the square of the preset lower voltage limit; the square of the branch current corresponding to the branch formed by any two adjacent distribution network nodes does not exceed the square of the preset lower current limit; the peak load level of each distribution network node does not exceed the target value; and the power flow model constraints of the first branch corresponding to each distribution network node.

3. The method according to claim 1, characterized in that, On the rail train side, the second objective is to minimize the value of the indicator function at each future time point and ensure that the peak power of each distribution transformer is less than the candidate peak power in the current iteration. Based on the rail train operation data during the preset future time period, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the terminal voltage of each flexible DC traction substation, including: On the rail train side, the voltage constraints of each traction network node in the traction network where the rail train is located are determined, as well as the power capacity constraints of each flexible DC traction station in the traction network are determined. Based on the aforementioned rail train operation data, determine the second branch power flow model constraints for each traction network node at each future time point. According to the second objective, based on the voltage constraint, the power capacity constraint and the second branch power flow model constraint, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the control of the terminal voltage of each flexible DC traction station.

4. The method according to claim 3, characterized in that, Based on the aforementioned rail train operation data, the constraints of the second branch power flow model corresponding to each distribution network node at each future time point are determined, including: For any future point in time, the positions of each traction network node at that future point in time are determined based on the aforementioned rail train operation data; Based on the location of each traction network node and the node admittance matrix corresponding to the conductance between any two adjacent traction network nodes, the injected power corresponding to each train node in the traction network is determined through traction calculation. Based on the location of each traction network node and the injected power, the second branch power flow model constraints at this future time point are constructed.

5. The method according to claim 3 or 4, characterized in that, According to the second objective, based on the voltage constraint, the power capacity constraint, and the second branch power flow model constraint, the Benders cut at each future time point in the current iteration is determined in parallel by coordinating the terminal voltage of each flexible DC traction substation and controlling the terminal voltage of each substation. This includes: For any future time point, according to the second objective, the corresponding Benders subproblem is determined based on the voltage constraint, the power capacity constraint, and the second branch power flow model constraint. By coordinating the control of the terminal voltage of each flexible DC traction station, it can be determined whether the sub-problems corresponding to each future time point are feasible. If not, then based on the dual information of the Benders subproblem corresponding to each future time point, generate feasible cuts corresponding to each future time point; Based on each of the feasible cuts, generate Benders cuts for each future time point in the current iteration.

6. The method according to claim 5, characterized in that, The method further includes: If all Benders subproblems at each future time point are feasible, then the optimal cut at each future time point is generated based on the dual information of the Benders subproblems at each future time point. Based on each of the optimal cuts, generate Benders cuts for each future time point in the current iteration.

7. The method according to claim 1, characterized in that, The peak load level of the distribution transformer is determined according to the following steps: The peak load level of the distribution transformer is determined based on the fixed load of the distribution transformer in a future preset time period and the candidate peak power in the current iteration, as well as the rated power capacity and power factor of the distribution transformer.

8. The method according to claim 1, characterized in that, The iteration stopping conditions include that the auxiliary information at each future time point in the previous iteration is feasible, and the power error between the candidate peak power in the current iteration and the candidate peak power in the previous iteration is less than a preset error.

9. The method according to claim 1, characterized in that, If the current iteration is the first iteration, the Benders cuts at each future time point in the previous iteration are set to preset values.

10. A power determination device for power supply zones of rail trains, characterized in that, The device includes: The first determining module is used to determine, on the distribution network side, auxiliary information for each future time point in the previous iteration based on the Benders cuts returned by the rail train side in the previous iteration for any rail train line; the auxiliary information is the optimal cut or feasible cut in the Benders cuts. The second determining module is used to take the sum of the peak load levels of the distribution transformers corresponding to each power supply zone as the first objective, and the sum of the auxiliary information as the minimum, and to determine the candidate peak power of each distribution transformer in the current iteration based on the operation constraints of the distribution network nodes and the Benders cut under the previous iteration. If the candidate peak power does not meet the iteration stop condition, the candidate peak power is sent to the track train side. The third determining module is used on the rail train side to determine the Benders cut at each future time point in the current iteration, with the second objective being that the value of the indicator function at each future time point is minimized and the peak power of each distribution transformer is less than the candidate peak power in the current iteration. Based on the rail train operation data in the future preset time period, the module coordinates the terminal voltage of each flexible DC traction substation and determines the Benders cut at each future time point in the current iteration in parallel. The value of the indicator function is determined based on the Benders cut in the current iteration. The loop module is used to return to the step of determining the auxiliary information of each future time point in the previous iteration based on the Benders cut of each future time point in the future preset time period returned by the rail train side on the distribution network side, until the iteration stop condition is met, and the candidate peak power in the latest iteration is taken as the target peak power of the distribution transformer in the future preset time period.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 9.

12. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 9.

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