An Optimization Scheduling Method and System for a Full-line Bidirectional Converter

Abstract

The present invention relates to the technical field of subway power flow simulation, and more specifically, to an optimized scheduling method and system for full-line bidirectional converters. The present invention provides an optimized scheduling method for full-line bidirectional converters, including the following steps: Step S1, input the information of bidirectional converters, train information, and network parameters; Step S2, calculate the node conductance matrix using the train position, bidirectional converter position, and network parameters; Step S3, perform power flow iterative calculation using a double-layer iterative algorithm model; Step S4, optimize and calculate the output voltage of the bidirectional converter with the minimum overall network energy consumption as the objective function; Step S5, send the obtained output voltage of the bidirectional converter to the corresponding bidirectional converters. The optimized scheduling method and system for full-line bidirectional converters based on minimum energy consumption proposed by the present invention effectively implement power flow simulation calculation based on bidirectional converters, improve power supply efficiency, achieve energy conservation and consumption reduction, and extend the service life of bidirectional converters.

Description

Technical Field

[0001] The present invention relates to the technical field of subway power flow simulation, and more specifically, to an optimized scheduling method and system for a full-line bidirectional converter. Background Art

[0002] With the rapid development of China's economy, the scale of the road network has been continuously expanding and the passenger volume has increased sharply. The total energy consumption of urban rail transit has also increased significantly, and energy conservation and consumption reduction in rail transit have been increasingly concerned.

[0003] In addition, with the rise of equipment devices, the resistor energy-consuming absorption device has been gradually replaced by an inverter feedback device. The inverter feedback device feeds back the redundant regenerative braking energy to the AC power grid, avoiding a large amount of energy consumption on the ground energy-consuming resistors.

[0004] At present, domestic research on the technology of utilizing regenerative braking energy mostly focuses on the control strategy, topological structure, transmission characteristics, power quality control, etc. of the device itself.

[0005] Some domestic institutions have replaced the inverter feedback device + diode power supply system with a developed bidirectional current conversion system, reducing equipment investment and strengthening the control of the DC network voltage, improving the power supply efficiency, and greatly reducing the civil engineering cost.

[0006] Currently, in subway power flow calculation and simulation, most of them construct a power flow model and perform calculations for a combined device of a 24-pulse uncontrolled rectifier + inverter feedback device.

[0007] Due to the characteristics of the bidirectional converter such as positive and negative energy control and voltage controllability, power flow control can be more effectively implemented from a system perspective.

[0008] Therefore, using a bidirectional converter to perform optimized scheduling of the power flow of a DC traction power supply system is an efficient and feasible technical means.

[0009] With the emergence of new power supply equipment and power supply methods, it is necessary to construct a mathematical model of the bidirectional converter and perform power flow calculations based on it in order to correspond to the actual situation of the network.

[0010] In addition, in the existing subway power flow simulation calculation field, only the power flow calculation is completed, and the global power flow optimized scheduling is not implemented from an optimized perspective. Summary of the Invention

[0011] The purpose of the present invention is to provide an optimized scheduling method and system for a full-line bidirectional converter, and solve the problem that it is difficult to perform optimized scheduling for the power flow simulation of the bidirectional converter in the prior art.

[0012] To achieve the above purpose, the present invention provides an optimized scheduling method for a full-line bidirectional converter, including the following steps:

[0013] Step S1: Input the information of the bidirectional converter, train information, and network parameters;

[0014] Step S2: Calculate the nodal conductance matrix using the train position, bidirectional converter position, and network parameters;

[0015] Step S3: Perform power flow iteration calculation using the double-layer iterative algorithm model;

[0016] Step S4: Optimize and calculate the output voltage of the bidirectional converter with the minimum total network energy consumption as the objective function;

[0017] Step S5: Send the obtained output voltage of the bidirectional converter to the corresponding bidirectional converters.

[0018] In one embodiment, in step S1:

[0019] The information of the bidirectional converter includes the distance of all bidirectional converters relative to the reference starting point, the total number of bidirectional converters, the rated power of the bidirectional converter, and the voltage stabilization target value of the bidirectional converter;

[0020] The train information includes the distance of the up-train relative to the reference starting point and the number of up-train vehicles, the distance of the down-train relative to the reference starting point and the number of down-train vehicles;

[0021] The network parameters include the unit resistance of the catenary, the unit resistance of the running rail, and the leakage unit resistance of the track to the ground.

[0022] In one embodiment, in step S2, the corresponding expression of the nodal conductance matrix Y is:

[0023]

[0024] In the formula, Y is the nodal conductance matrix; the subscript S represents the bidirectional converter, the subscript U represents the up-train, the subscript D represents the down-train, and 0 represents the zero matrix.

[0025] In one embodiment, step S3 further includes the following steps:

[0026] Step S31: Inner-layer Picard voltage iteration;

[0027] Step S32: Outer-layer bidirectional converter state iteration;

[0028] Step S33: Calculate the power of the bidirectional converter according to the bidirectional converter state, voltage, and current.

[0029] In one embodiment, step S31 further includes:

[0030] The inner layer performs iterative calculation of the node voltage of the DC power supply network according to the state of the bidirectional converter, the node conductance matrix Y, and the train power.

[0031] In one embodiment, step S31 further includes the following steps:

[0032] All initial states of the bidirectional converters are in the constant voltage mode, and the node power equation is

[0033] I = YU

[0034] where U is the node voltage vector, I is the node injection current vector, and Y is the node conductance matrix;

[0035] The result of the k-th iteration of the Picard voltage iteration is:

[0036] U (k) = (Y) -1 I (k-1)

[0037] where k is the number of iterations, U is the node voltage vector, and I is the node injection current vector;

[0038] When the voltage difference between U (k) and U (k+1) is less than the specified threshold, it is determined that the inner layer iteration has converged.

[0039] In one embodiment, step S31 further includes the following steps:

[0040] When the bidirectional converter is in the full power mode, the value of the node injection current I is the rated power value of the bidirectional converter divided by the port voltage;

[0041] When the bidirectional converter is in the constant voltage mode, the value of the node injection current I is the constant voltage value of the bidirectional converter.

[0042] In one embodiment, step S32 further includes:

[0043] The outer layer performs multi-state switching of the bidirectional converter according to the power of the bidirectional converter calculated from the node voltage of the inner layer.

[0044] In one embodiment, step S32 further includes:

[0045] When the power of the bidirectional converter calculated using the result of the node voltage of the inner layer is greater than its rated power, the state of the bidirectional converter changes to the full power mode; otherwise, it changes to the constant voltage mode;

[0046] When the states of two consecutive iterations of the outer layer are the same, the outer layer loop ends, and the iterative calculation of the power flow of both the inner and outer layers ends.

[0047] In one embodiment, step S4 further includes the following steps:

[0048] Step S41: Determine whether the bidirectional converter is in the full-power mode. If so, proceed to step S42; otherwise, proceed to step S45.

[0049] Step S42: Determine whether the full-power station is in the traction state or the feedback state. If it is in the traction state, proceed to step S43; if it is in the feedback state, proceed to step S44.

[0050] Step S43: Raise the voltage of the adjacent station, and the current flows from the adjacent station to this station, so that the adjacent station supports the traction power for this station.

[0051] Step S44: Lower the voltage of the adjacent station, and the current flows from this station to the adjacent station, so that the adjacent station shares the feedback power for this station.

[0052] Step S45: According to the bidirectional converter state calculated in step S3, construct an optimization model for traction energy consumption, and solve it using a linear programming solution algorithm.

[0053] In one embodiment, step S45 further includes a linear programming model that optimizes the output voltage of the bidirectional converter with the minimum output power of the bidirectional converter as the objective function. The corresponding expression is as follows:

[0054]

[0055] s.t.U oL ≤U o ≤U oH

[0056] U gL ≤U g ≤U gH

[0057] P C <P n1

[0058] P D <P n2

[0059] YU=I

[0060] In the formula, i represents the bidirectional converter number; S represents the set of bidirectional converter numbers in the traction state;

[0061] and are respectively the output voltage and current of the bidirectional converter;

[0062] U o and U g are respectively the traction network voltage and the rail voltage;

[0063] U oL ,U oH ,U gL and U gH are the lower and upper limits of the traction network voltage and rail voltage respectively;

[0064] P C and P D are the output powers of the bidirectional converter in feedback and traction states respectively;

[0065] P n1 and P n2 They are the upper limits of the bidirectional converter power when in feedback and traction states respectively;

[0066] Y is the node conductance matrix, U and I are the node voltage vector and node injection current vector of the whole network respectively.

[0067] In order to achieve the above object, the present invention provides an optimization scheduling system for full-line bidirectional converters, comprising:

[0068] a memory for storing instructions executable by a processor;

[0069] A processor is used to execute the instructions to implement any of the methods described above.

[0070] In order to achieve the above object, the present invention provides a computer-readable medium on which computer instructions are stored, wherein when the computer instructions are executed by a processor, any of the above methods is executed.

[0071] The present invention proposes an optimization scheduling method and system for full-line bidirectional converters based on minimum energy consumption, which effectively implements power flow simulation calculations based on bidirectional converters, improves power supply efficiency, achieves energy saving and consumption reduction, and prolongs the service life of bidirectional converters. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The above and other features, properties and advantages of the present invention will become more apparent through the following description in conjunction with the accompanying drawings and embodiments, in which the same reference numerals always represent the same features, wherein:

[0073] Figure 1 A flow chart of an optimization scheduling method for a full-line bidirectional converter according to an embodiment of the present invention is disclosed;

[0074] Figure 2 A switching principle diagram of a full power mode and a constant voltage mode according to an embodiment of the present invention is disclosed;

[0075] Figure 3 A principle block diagram of an optimization scheduling system for a full-line bidirectional converter according to an embodiment of the present invention is disclosed. Specific implementation mode

[0076] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the invention and are not used to limit the invention.

[0077] An optimization scheduling method and system for a full-line bidirectional converter based on minimum energy consumption proposed by the present invention is based on two modes of a bidirectional converter with a constant voltage source and full power, and constructs a double-layer iterative power flow calculation model. The outer layer performs state iterative calculation of the bidirectional converter, and the inner layer performs node voltage iterative calculation through Picard.

[0078] Based on the double-layer iterative power flow calculation results, a linear programming model with equality and inequality constraints is constructed with the minimum network energy consumption as the objective function, and finally the output voltage of the bidirectional converter is obtained by solving with the simplex method.

[0079] Figure 1 Reveals a flowchart of an optimization scheduling method for a full-line bidirectional converter according to an embodiment of the present invention, as Figure 1 shown, an optimization scheduling method for a full-line bidirectional converter based on minimum energy consumption proposed by the present invention includes the following steps:

[0080] Step S1: Input bidirectional converter information, train information and network parameters;

[0081] Step S2: Calculate the node conductance matrix using the train position, bidirectional converter position and network parameters;

[0082] Step S3: Perform power flow iterative calculation using the double-layer iterative algorithm model;

[0083] Step S4: Optimize and calculate the output voltage of the bidirectional converter with the minimum network energy consumption as the objective function;

[0084] Step S5: Send the obtained output voltage of the bidirectional converter to the corresponding bidirectional converters.

[0085] The following is a detailed description of each step.

[0086] Step S1: Input bidirectional converter information, train information and network parameters.

[0087] The bidirectional converter information includes the distance of all bidirectional converters relative to the reference starting point, the total number of bidirectional converters, the rated power of the bidirectional converters and the regulated voltage target value of the bidirectional converters;

[0088] The train information includes the distance of the up-train relative to the reference starting point and the number of up-train cars, the distance of the down-train relative to the reference starting point and the number of down-train cars;

[0089] The network parameters include the unit resistance of the catenary, the unit resistance of the running rail, and the leakage unit resistance of the track to the ground.

[0090] Step S2: Calculate the nodal conductance matrix using the train position, the position of the bidirectional converter, and the network parameters.

[0091] The corresponding expression of the nodal conductance matrix Y is:

[0092]

[0093] In the formula, Y is the nodal conductance matrix; the subscript S represents the bidirectional converter, the subscript U represents the up-train, and the subscript D represents the down-train.

[0094] Step S3: Perform power flow iterative calculation using the double-layer iterative algorithm.

[0095] The double-layer iteration refers to an iterative process including two inner and outer links. The outer layer performs the state iterative calculation of the bidirectional converter, and the inner layer performs the nodal voltage iterative calculation through Picard.

[0096] The said step S3 further includes the following steps:

[0097] Step S31: Inner-layer Picard voltage iteration.

[0098] The inner layer completes the calculation of the nodal voltage of the DC power supply network according to the state of the bidirectional converter, the nodal conductance matrix Y, and the train power.

[0099] All initial states of the bidirectional converters are in the constant voltage mode, and the nodal power equation is:

[0100] I = YU (2)

[0101] In the formula, U is the nodal voltage vector, including the nodal voltage of the traction network and the nodal voltage of the rail;

[0102] I is the nodal injection current vector, including the injection current of the bidirectional converter and the injection current of the train;

[0103] When the bidirectional converter is in the full power mode, the value of the nodal injection current I is the rated power value of the bidirectional converter divided by the port voltage;

[0104] When the bidirectional converter is in the constant voltage mode, the value of the nodal injection current I is the constant voltage value of the bidirectional converter.

[0105] Picard iteration is a mathematical method suitable for solving non-linear equations.

[0106] The result of the k-th iteration of the Picard voltage iteration is as follows:

[0107] U (k) =(Y) -1 I (k-1) (3)

[0108] Where k is the number of iterations;

[0109] U is the node voltage vector, including the traction network node voltage and the rail node voltage;

[0110] I is the node injection current vector, including the bi-directional converter injection current and the train injection current.

[0111] When the voltage difference between U (k) and U (k+1) is less than the specified threshold, it is determined that the inner iteration has converged.

[0112] Step S32, outer bi-directional converter state iteration.

[0113] The outer layer completes the multi-state switching of the bi-directional converter according to the power of the bi-directional converter calculated based on the inner node voltage.

[0114] Figure 2 Reveals the switching schematic diagram of the full-power mode and the constant-voltage mode according to an embodiment of the present invention. The states of the bi-directional converter include the full-power mode and the constant-voltage mode, and the switching mode between the two is as Figure 2 shown:

[0115] P C and P D are respectively the output powers of the bi-directional converter in the feedback and traction states;

[0116] P n1 and P n2 are respectively the upper limits of the rated power of the bi-directional converter in the feedback and traction states.

[0117] When the output power of the bi-directional converter calculated using the inner voltage result is greater than its rated power upper limit, the state of the bi-directional converter changes to the full-power mode, otherwise, it changes to the constant-voltage mode.

[0118] When the states of the outer layer in two consecutive iterations are the same, the outer loop ends, and the power flow iteration calculations of both the inner and outer layers end, and the results are output.

[0119] The above steps simulate the operating states of the constant-voltage mode and the full-power mode of the bi-directional converter and establish its equivalent model, construct a power flow model and algorithm based on the bi-directional converter, and effectively implement the power flow simulation calculation based on the bi-directional converter.

[0120] Step S33: Calculate the power of the bidirectional converter based on the status, voltage, and current of the bidirectional converter.

[0121] Step S4: Optimize and calculate the output voltage of the bidirectional converter with the minimum total network energy consumption as the objective function.

[0122] Step S41: Determine whether the bidirectional converter is in the full-power mode. If so, proceed to Step S42; otherwise, proceed to Step S45.

[0123] Step S42: Determine whether the full-power station is in the traction state or the feedback state. If it is in the traction state, proceed to Step S43; if it is in the feedback state, proceed to Step S44.

[0124] Step S43: When the full-power station is in the traction state, it is necessary to raise the voltage of the adjacent station so that the general trend of the current is from the adjacent station to the local station, enabling the adjacent station to support the traction power for the local station.

[0125] The lifting value is in steps of 10V, and the specific value depends on the line parameters.

[0126] For example, in steps of 10V, the traction network resistance is 5%Ω / km, the distance between substation stations is 2 - 3km, the resistance is approximately 0.1 - 0.15Ω, and the adjustment of the primary voltage can allow at least 0.6kW - 1kW to be shared, and at least 1.2kW - 2kW to be shared together on both sides.

[0127] Step S44: When the full-power station is in the feedback state, it is necessary to lower the voltage of the adjacent station so that the general trend of the current is from the local station to the adjacent station, enabling the adjacent station to share the feedback power for the local station.

[0128] The above steps propose a full-power sharing strategy for the bidirectional converter, reducing the continuous operation time of the bidirectional converter at full power, that is, reducing the excessive operation of the bidirectional converter under abnormal conditions and extending the service life of the bidirectional converter.

[0129] Step S45: Based on the status of the bidirectional converter calculated in Step S3, construct an optimization model for traction energy consumption and solve it using a linear programming solution algorithm.

[0130] Taking the minimum output power of the bidirectional converter as the objective function, the linear programming model for optimizing the output voltage of the bidirectional converter has the following corresponding expression:

[0131]

[0132] In the formula, i represents the bidirectional converter number; S represents the set of bidirectional converter numbers in the traction state.

[0133] and are respectively the output voltage and current of the bidirectional converter.

[0134] U o and U g are the traction network and rail voltages, respectively;

[0135] U oL ,U oH ,U gL and U gH are the lower and upper limits of the traction network voltage and rail voltage respectively;

[0136] P C and P D are the output powers of the bidirectional converter in feedback and traction states respectively;

[0137] P n1 and P n2 They are the upper limits of the bidirectional converter power when in feedback and traction states respectively;

[0138] Y is the node conductance matrix, U and I are the node voltage vector and node injection current vector of the whole network respectively.

[0139] Formula (4) is a linear programming model for optimizing the output voltage of a bidirectional converter with the minimum output power of the bidirectional converter as the objective function. The solution can be completed using an existing linear programming algorithm, such as a simplex algorithm or its improved algorithm.

[0140] The above steps construct an optimization scheduling model based on bidirectional converter with traction energy consumption as the target, and control the output voltage of bidirectional converter in real time to reduce the energy consumption of DC traction power supply system, improve power supply efficiency and achieve energy saving and consumption reduction.

[0141] Step S5: sending the adjusted or calculated bidirectional converter output voltage to each bidirectional converter.

[0142] Figure 3 The block diagram of the optimized scheduling system of the full-line bidirectional converter based on the minimum energy consumption according to one embodiment of the present invention is disclosed. The optimized scheduling system of the full-line bidirectional converter based on the minimum energy consumption may include an internal communication bus 301, a processor (processor) 302, a read-only memory (ROM) 303, a random access memory (RAM) 304, a communication port 305, and a hard disk 307. The internal communication bus 301 can realize data communication between components of the optimized scheduling system of the full-line bidirectional converter based on the minimum energy consumption. The processor 302 can make judgments and issue prompts. In some embodiments, the processor 302 can be composed of one or more processors.

[0143] The communication port 305 can enable data transmission and communication between the optimization scheduling system of the full-line bidirectional converter based on minimum energy consumption and external input / output devices. In some embodiments, the optimization scheduling system of the full-line bidirectional converter based on minimum energy consumption can send and receive information and data from the network through the communication port 305. In some embodiments, the optimization scheduling system of the full-line bidirectional converter based on minimum energy consumption can perform data transmission and communication with external input / output devices in a wired form through the input / output terminal 306.

[0144] The optimization scheduling system of the full-line bidirectional converter based on minimum energy consumption may further include program storage units and data storage units in different forms, such as a hard disk 307, a read-only memory (ROM) 303, and a random access memory (RAM) 304, which can store various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 302. The processor 302 executes these instructions to implement the main part of the method. The results processed by the processor 302 are transmitted to an external output device through the communication port 305 and displayed on the user interface of the output device.

[0145] For example, the implementation process file of the above-mentioned optimization scheduling method of the full-line bidirectional converter based on minimum energy consumption can be a computer program, stored in the hard disk 307 and can be recorded in the processor 302 for execution to implement the method of the present application.

[0146] When the implementation process file of the optimization scheduling method of the full-line bidirectional converter based on minimum energy consumption is a computer program, it can also be stored in a computer-readable storage medium as an article of manufacture. For example, the computer-readable storage medium may include, but is not limited to, magnetic storage devices (such as hard disks, floppy disks, magnetic strips), optical discs (such as compact discs (CDs), digital versatile discs (DVDs)), smart cards, and flash memory devices (such as electrically erasable programmable read-only memories (EPROMs), cards, sticks, key drives). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain, and / or carry code and / or instructions and / or data.

[0147] An optimization scheduling method and system of a full-line bidirectional converter based on minimum energy consumption proposed by the present invention specifically have the following beneficial effects:

[0148] 1) Simulated the operating states of the constant voltage mode and the full power mode of the bidirectional converter and established its equivalent model, constructed a power flow model and algorithm based on the bidirectional converter, and effectively implemented the power flow simulation calculation based on the bidirectional converter;

[0149] 2) An optimization scheduling model based on a bidirectional converter is constructed with the traction energy consumption as the goal, and the output voltage of the bidirectional converter is controlled in real time to reduce the energy consumption of the DC traction power supply system, improve the power supply efficiency, and achieve energy conservation and consumption reduction;

[0150] 3) A full-power sharing strategy for the bidirectional converter is proposed to reduce the time of continuous full-power operation of the bidirectional converter, that is, to reduce the excessive operation of the bidirectional converter under abnormal conditions and extend the service life of the bidirectional converter.

[0151] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in a different order and / or concurrently with other actions that are illustrated and described herein or not illustrated and described herein but are understandable to those skilled in the art.

[0152] As shown in this application and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0153] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as causing a departure from the scope of the present invention.

[0154] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the inventive concept of the present invention. Therefore, the protection scope of the present invention is not limited by the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. An optimized scheduling method for a full-line bidirectional converter, characterized in that, it includes the following steps: Step S1, input the bidirectional converter information, train information, and network parameters; Step S2, calculate the nodal conductance matrix using the train position, bidirectional converter position, and network parameters; Step S3, perform power flow iteration calculation using a double-layer iterative algorithm model; Step S4, optimize and calculate the output voltage of the bidirectional converter with the minimum total network energy consumption as the objective function; Step S5, send the obtained output voltage of the bidirectional converter to the corresponding bidirectional converter; Among them, the said Step S3 further includes the following steps: Step S31, inner-layer Picard voltage iteration; Step S32, outer-layer bidirectional converter state iteration; Step S33, calculate the power of the bidirectional converter according to the bidirectional converter state, voltage, and current; The said Step S32 further includes: The outer layer performs multi-state switching of the bidirectional converter according to the power of the bidirectional converter calculated from the inner-layer nodal voltage; When the power of the bidirectional converter calculated using the inner-layer nodal voltage result is greater than its rated power, the bidirectional converter state switches to the full-power mode, otherwise, it switches to the constant-voltage mode; When the states of the outer layer in two consecutive iterations are the same, end the outer loop, and the power flow iteration calculations of both the inner and outer layers are completed; The said Step S4 further includes the following steps: Step S41, determine whether the bidirectional converter is in the full-power mode. If so, enter Step S42, otherwise enter Step S45; Step S42, determine whether the full-power station is in the traction state or the feedback state. If it is in the traction state, enter Step S43, if it is in the feedback state, enter Step S44; Step S43, raise the voltage of the adjacent station, and the current flows from the adjacent station to this station, so that the adjacent station supports the traction power to this station; Step S44, lower the voltage of the adjacent station, and the current flows from this station to the adjacent station, so that the adjacent station shares the feedback power for this station; Step S45, construct an optimization model for traction energy consumption according to the bidirectional converter state calculated in Step S3, and solve it using a linear programming solution algorithm.

2. The optimized scheduling method for a full-line bidirectional converter according to claim 1, characterized in that, in the said Step S1: The said bidirectional converter information includes the distances of all bidirectional converters relative to the reference starting point, the total number of bidirectional converters, the rated power of the bidirectional converters, and the voltage stabilization target value of the bidirectional converters; The said train information includes the distance of the up-train relative to the reference starting point and the number of up-train vehicles, the distance of the down-train relative to the reference starting point and the number of down-train vehicles; The said network parameters include the unit resistance of the catenary, the unit resistance of the running rail, and the leakage unit resistance of the track to the ground.

3. The optimized scheduling method for a full-line bidirectional converter according to claim 1, characterized in that, in the said Step S2, the corresponding expression of the nodal conductance matrix Y is: In the formula, Y is the nodal conductance matrix; The subscript S represents the bidirectional converter, the subscript U represents the up-train, the subscript D represents the down-train, and 0 represents the zero matrix.

4. The optimized scheduling method for a full-line bidirectional converter according to claim 1, characterized in that, the said Step S31 further includes: The inner layer iteratively calculates the node voltage of the DC power supply network according to the state of the bidirectional converter, the node conductance matrix Y and the train power.

5. The optimization scheduling method for full-line bidirectional converters according to claim 4, It is characterized in that The step S31 further comprises the following steps: All bidirectional converters are initially in constant voltage mode, and the node power equation is: I=YU Where U is the node voltage vector, I is the node injection current vector, and Y is the node conductance matrix; The result of the kth iteration of Picard voltage iteration is: U (k) =(Y) -1 I (k-1) Where k is the number of iterations, U is the node voltage vector, and I is the node injection current vector; When U (k) and U (k+1) the voltage difference is less than the specified threshold, it is determined that the inner iteration has converged.

6. The method for optimizing the scheduling of bidirectional converters of the entire line according to claim 4, It is characterized in that The step S31 further comprises the following steps: When the bidirectional converter is in full power mode, the node injection current I value is the rated power value of the bidirectional converter divided by the port voltage; When the bidirectional converter is in constant voltage mode, the node injection current I value is the constant voltage value of the bidirectional converter.

7. The method for optimizing the scheduling of bidirectional converters of the entire line according to claim 1, It is characterized in that The step S45 further includes optimizing the linear programming model of the output voltage of the bidirectional converter with the minimum output power of the bidirectional converter as the objective function, and the corresponding expression is as follows: s.t.U oL ≤U o ≤U oH U gL ≤U g ≤U gH P C <P n1 P D <P n2 YU=I Where i represents the number of the bidirectional converter; S represents the set of bidirectional converter numbers in the traction state; and are the output voltage and current of the bi-directional converter, respectively; U o and U g are the catenary voltage and the rail voltage respectively; U oL , U oH , U gL and U gH are respectively the lower and upper limit values of the catenary voltage and the rail voltage; P C and P D are the output powers of the bidirectional converters in the feedback and traction states, respectively; P n1 and P n2 are the upper limits of the power of the bidirectional converter when in the feedback and traction states, respectively; Y is the node conductance matrix, U and I are the node voltage vector and node injection current vector of the whole network respectively.

8. An optimized dispatching system for bidirectional converters of the entire line, include: a memory for storing instructions executable by a processor; A processor, configured to execute the instructions to implement the method according to any one of claims 1 to 7.

9. A computer-readable medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is executed.

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