Range extending type traction system control method and device, storage medium and electronic equipment

Abstract

This application discloses a control method, device, storage medium, and electronic equipment for a range-extended traction system, applied in the field of rail vehicle technology. The method includes: real-time acquisition of traction power supply system network data and train operation data; prediction of traction load for the next scheduling period based on the network data and train operation data; generation of an optimization function and constraint boundaries matching the train operating conditions based on the prediction results; and generation of the optimal control configuration for the energy storage device and power conversion device based on the optimization function and constraint boundaries. Using this method, multi-dimensional optimization of system safety, operating costs, and regenerative energy utilization can be achieved while ensuring train safety.

Description

Technical Field

[0001] This application relates to the field of rail train technology, and in particular to a control method, device, storage medium and electronic equipment for a range-extended traction system. Background Technology

[0002] While pursuing high transport capacity and green operation, electrified railways have long been constrained by two major problems: First, the large amount of energy generated by train regenerative braking cannot be efficiently recovered. When fed back to the power grid, it is prone to power quality problems such as negative sequence, harmonics, and overvoltage, which endanger train safety and waste energy. Second, the traditional phase-separated power supply mode requires the installation of electrical phase separation between each traction substation. Trains need to coast and slow down when passing through, which not only limits the line's throughput capacity but also divides the traction network into multiple "power supply islands," resulting in low substation capacity utilization, severe voltage drops, and difficulty in extending the power supply radius.

[0003] In existing technologies, energy storage-type regenerative energy recovery, PFC through-power supply, RPC compensation and other technologies can only achieve partial improvement in energy recovery or grid connection on one side. They cannot solve the two major bottlenecks of "low recovery rate" and "electrical phase separation" at the same time. This results in high infrastructure investment, high operating energy consumption and complex scheduling, which makes it difficult to meet the development needs of the new generation of green, low-carbon, large-capacity and long-distance electrified railways. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a range-extended traction system control method, device, storage medium, and electronic equipment. This system, while ensuring train safety, simultaneously improves regenerative braking energy utilization and power supply distance, achieving multi-dimensional optimization of system safety, operating costs, and regenerative energy utilization. The technical solution is as follows:

[0005] In a first aspect, embodiments of this application provide a control method for a range-extended traction system, the method comprising:

[0006] Real-time collection of traction power supply system network data and train operation data; prediction of traction load for the next scheduling period based on network data and train operation data.

[0007] Based on the prediction results, an optimization function and constraint boundary that match the train's operating conditions are generated.

[0008] Based on the optimization function and constraint boundaries, the optimal control configuration for the energy storage device and the power conversion device is generated.

[0009] Combining the first aspect and the above implementation methods, in some possible implementation methods, an optimization function matching the train operating conditions is generated based on the prediction results, including:

[0010] Based on the prediction results, determine the optimization objective; based on the optimization objective, determine the optimization function.

[0011] Combining the first aspect and the above implementation methods, among some possible implementation methods, based on the prediction results, the optimization objective is determined, including:

[0012] When the prediction results show that both power supply arms are in traction state and the total active power does not exceed the sum of the rated capacity of the main transformer, or when one power supply arm at both ends is in traction state and the other is in braking state and the total active power is greater than or equal to 0, the uniform main transformer load rate is selected as the optimization target.

[0013] When the prediction results show that one side of the power supply arm at both ends is in traction state and the other side is in regenerative braking state and the total active power is less than 0, or when both power supply arms at both ends are in regenerative braking state, the optimization objective is to maximize the regenerative braking energy utilization rate.

[0014] When the prediction results show that both power supply arms are in traction mode and the total active power exceeds the sum of the rated power of the main transformer, limiting transformer overload is selected as the optimization target.

[0015] Combining the first aspect and the above implementation methods, in some possible implementation methods, constraint boundaries matching the train's operating conditions are generated based on the prediction results, including:

[0016] Based on the prediction results, an energy optimization model is established; based on the energy optimization model, constraint boundaries are generated.

[0017] Combining the first aspect and the above implementation methods, in some possible implementation methods, constraint boundaries are generated based on the energy optimization model, including:

[0018] Based on the energy optimization model, power flow constraints, converter output constraints, hybrid energy storage system output constraints, train voltage safety constraints, and traction substation power constraints are generated for the traction power supply system.

[0019] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, before real-time collection of traction power supply system network data and train operation data, and prediction of traction load for the next scheduling period based on the network data and train operation data, the following additional steps are included:

[0020] Train control strategies are set based on train operation conditions; train control strategies include constant speed control strategies and quasi-constant speed control strategies.

[0021] Combining the first aspect and the above implementation methods, in some possible implementation methods, after obtaining the optimal control configuration of the energy storage device and the power conversion device based on the optimization function and constraint boundaries, the following further applies:

[0022] Based on the optimal control configuration, the optimal control command is generated; the optimal control command is then sent to the corresponding device to achieve the corresponding optimal control.

[0023] Secondly, embodiments of this application provide a range-extended traction system control device, the device comprising:

[0024] The prediction module is used to collect real-time network data and train operation data of the traction power supply system, and to predict the traction load for the next scheduling period based on the network data and train operation data.

[0025] The first generation module is used to generate an optimization function and constraint boundary that matches the train's operating conditions based on the prediction results.

[0026] The second generation module is used to generate the optimal control configuration for the energy storage device and the power conversion device based on the optimization function and constraint boundaries.

[0027] Thirdly, embodiments of this application provide a storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps of the method described above.

[0028] Fourthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being adapted to be loaded by the processor and to execute the steps of the method described above.

[0029] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following: Through a dual-layer power control strategy of train condition judgment and optimized scheduling, the entire traction network is electrically connected, eliminating phase separation of power supply. This extends the power supply distance of a single traction substation to twice that of traditional solutions, significantly reducing the number of substations and construction costs. It ensures the safe and reliable operation of trains, significantly improves the utilization rate of regenerative braking energy, achieves a more balanced main transformer load rate, and effectively reduces peak-valley differences. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A system architecture diagram of a range-extended traction system control system provided in this application embodiment;

[0032] Figure 2 A network topology diagram of a traction power supply system provided in this application embodiment;

[0033] Figure 3A flowchart illustrating a control method for a range-extended traction system provided in an embodiment of this application;

[0034] Figure 4 A flowchart of short-term load forecasting based on traction calculation is provided for embodiments of this application;

[0035] Figure 5 A logic block diagram for traction network load forecasting provided in this application embodiment;

[0036] Figure 6 A structural block diagram of a range-extended traction system control device provided in this application embodiment;

[0037] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0038] To make the features and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. 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.

[0040] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0041] As mentioned earlier, existing electrified railway technology faces a dual bottleneck: limited power supply capacity and inefficient regenerative energy recovery. Firstly, the phase-splitting structure forces trains to slow down and cross phases, dividing the traction network into multiple single-power supply zones. This results in short power supply distances, insufficient substation capacity utilization, and exacerbated voltage drops. Secondly, regenerative braking energy is difficult to return to the grid due to negative sequence and harmonic pollution, posing a risk of overvoltage in train sets and causing energy waste. Current upgrade schemes cannot simultaneously address the issues of extending power supply distances and efficiently recovering regenerative energy, hindering the development of electrified railways towards a green and low-carbon direction.

[0042] In view of this, this application provides a control method, device, storage medium, and electronic equipment for a range-extended traction system. The solution replaces traditional intermediate substations with back-to-back converters containing hybrid energy storage, eliminating the electrical phase-separation structure and achieving full electrical continuity across the traction network. This extends the power supply distance of a single substation. Simultaneously, a hierarchical control strategy of "condition judgment-optimized scheduling" is designed: the upper layer dynamically divides three operating conditions based on short-term load forecasting; the lower layer uses an active-reactive power collaborative optimization model to match multiple objective functions for different operating conditions, including uniformizing main transformer utilization, maximizing regenerative energy recovery rate, or limiting transformer overload, while meeting train voltage safety and power flow constraints. Furthermore, it utilizes the complementary characteristics of supercapacitors and battery energy storage to simultaneously address the issues of extended power supply distance, efficient regenerative energy recovery, and safe and economical system operation.

[0043] Please see Figure 1 , Figure 1 An exemplary system architecture diagram of a range-extended traction system control method provided in this application embodiment.

[0044] like Figure 1 As shown, the system architecture may include a terminal 101, a network 102, and a server 103. The network 102 serves as the medium for providing a communication link between the terminal 101 and the server 103. The network 102 may include various types of wired or wireless communication links, such as wired communication links including fiber optic cables, twisted-pair cables, or coaxial cables, and wireless communication links including Bluetooth communication links, Wireless-Fidelity (Wi-Fi) communication links, or microwave communication links, etc.

[0045] Terminal 101 can interact with server 103 via network 102 to receive messages from or send messages to server 103. Alternatively, terminal 101 can interact with server 103 via network 102 to receive messages or data sent to server 103 by other users. Terminal 101 can be hardware or software. When terminal 101 is hardware, it can be various electronic devices, including but not limited to smartwatches, smartphones, tablets, laptops, and desktop computers. When terminal 101 is software, it can be installed in the aforementioned electronic devices and can be implemented as multiple software programs or software modules (e.g., to provide distributed services) or as a single software program or software module; no specific limitation is made here.

[0046] In this embodiment, terminal 101 can collect traction power supply system network data and train operation data in real time, and predict the traction load for the next scheduling period based on the network data and train operation data; based on the prediction results, generate an optimization function and constraint boundary that matches the train operation conditions; and based on the optimization function and constraint boundary, generate the optimal control configuration for the energy storage device and the power conversion device.

[0047] Server 103 can be a business server providing various services. It should be noted that server 103 can be either hardware or software. When server 103 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 103 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide distributed services), or as a single software program or software module; no specific limitations are made here.

[0048] Alternatively, the system architecture may not include server 103. In other words, server 103 may be an optional device in the embodiments of this specification. That is, the method provided in the embodiments of this specification can be applied to a system structure that only includes terminal 101. The embodiments of this application do not limit this.

[0049] It should be understood that Figure 1 The number of terminals, networks, and servers shown is only illustrative; the number can be any number of terminals, networks, and servers depending on the implementation requirements.

[0050] Please see Figure 2 , Figure 2This application provides a network topology diagram for a traction power supply system. In this embodiment, the traction network includes traction substation A and traction substation C, and a relay power control-energy storage system (RSPC-ESS). Substation A supplies power to phase α, and traction substation C supplies power to phase β. The original substation B is decommissioned and replaced by the RSPC-ESS. By replacing the original traction substation with the relay power control system, the phase separation between adjacent substations is eliminated. Through continuous operation of the entire traction network, the problem of "power supply islands" is effectively avoided, and the power supply distance of a single substation is extended to twice the original distance, significantly reducing the construction cost of electrified railways.

[0051] Please see Figure 3 , Figure 3 This is a flowchart illustrating a range-extended traction system control method provided in an embodiment of this application. The execution entity in this embodiment can be an electronic device that performs range-extended traction system control, a processor within an electronic device that performs the range-extended traction system control method, or a range-extended traction system control service within the electronic device performing the range-extended traction system control method. For ease of description, the following uses a processor within an electronic device as an example to illustrate the specific execution process of the range-extended traction system control method.

[0052] like Figure 3 As shown, the control method for a range-extended traction system may include at least:

[0053] S301. Real-time acquisition of traction power supply system network data and train operation data, and prediction of traction load for the next scheduling period based on the network data and train operation data.

[0054] Specifically, real-time data collection is performed on the traction power supply system network data and train operation data. The traction power supply system network data includes the impedance per unit length of the traction network and the rated capacity S of the substations at both ends. T1 S T2 Converter capacity S RSPC-ESS Train operation data includes the train's current speed and location. Please refer to [link / reference]. Figure 4 , Figure 4 This application provides a flowchart of a short-term load forecasting process based on traction calculation. To perform traction calculations, traction power supply system network data and train operation data must first be collected, laying the groundwork for subsequent steps.

[0055] In one possible embodiment, a train control strategy is set based on train operating conditions; the train control strategy includes a constant speed control strategy and a quasi-constant speed control strategy. To provide reliable train power calculation input for short-term traction load prediction, a train control strategy is set according to the train's operating conditions. The constant speed control strategy continuously adjusts the traction or braking force through the traction or braking system during train operation, ensuring that the actual train speed is always precisely maintained at the set target speed. This strategy is suitable for straight roads or scenarios with strict speed requirements. Through closed-loop adjustment of traction and braking force, the actual train speed strictly tracks the set speed. The quasi-constant speed control strategy allows for small fluctuations in train speed near the set speed to reduce energy consumption and equipment impact caused by frequent traction / braking switching. This strategy is suitable for scenarios with large gradient changes or complex operating conditions. This embodiment uses the constant speed control strategy as an example.

[0056] Furthermore, based on the current speed change of the train, the current operating condition is determined. Operating conditions can be categorized into three types: inertia, traction, and braking. The current operating condition is determined by judging the direction of the instantaneous acceleration, and this condition is directly classified into scenarios, laying the groundwork for subsequent steps. Based on the train's equation of motion, traction calculations are performed to obtain the force situation, speed, and position within the current step length. The equation of motion is as follows:

[0057]

[0058]

[0059]

[0060]

[0061] Where c is the unit net force on the train, in N / kN, f0, w0, w j b represents traction force, basic resistance, additional resistance, and braking force, respectively, all in N / kN; a represents acceleration, in m / s². 2 v is the train speed (km / h), s is the train distance (km), r is the slewing mass coefficient (typically 0.06), and g is the gravitational acceleration (typically 9.8, m / s²). 2 Please continue reading. Figure 4To ensure that the time length of the traction load prediction output is strictly equal to the "prediction period" required by the system scheduling, avoiding premature termination leading to insufficient information and infinite looping causing wasted computing power, the traction calculation results within the prediction period are output when the running position is greater than the prediction step size. Conversely, if the running position is less than the prediction step size, indicating insufficient information, the process returns to Step (b) to recalculate the various data of the train.

[0062] Furthermore, based on the power flow path within the train, the power consumed by the train on the traction network side is calculated according to the transmission efficiency, power factor, and wheel circumference output power of each component. Please refer to [link / reference]. Figure 5 , Figure 5 This is a logic block diagram for traction network load forecasting provided in an embodiment of this application. For example... Figure 5 The diagram shows the power flow path inside the train. The active and reactive power input by the pantograph of train n at time t are shown in the following formulas:

[0063]

[0064]

[0065] In the formula, These are the power factor angles of the gearbox, traction motor, inverter, rectifier, and traction transformer, respectively; η G η M η I η C η T These represent the output efficiencies of various structures within the train; P aux The power consumed by the auxiliary power supply windings of the high-speed train. Please continue reading. Figure 4 Once the relevant power of the train is calculated, the predicted traction load data is output.

[0066] S302. Based on the prediction results, generate an optimization function and constraint boundary that matches the train's operating conditions.

[0067] In one possible implementation, an optimization objective is determined based on the prediction results; an optimization function is then determined based on the optimization objective. The system operating condition is then assessed based on the load conditions of the left and right power supply arms within different scheduling steps.

[0068] In one possible implementation, when the prediction results show that both power supply arms are in traction mode and the total active power does not exceed the sum of the rated capacities of the main transformers, or when one power supply arm is in traction mode and the other is in braking mode and the total active power is greater than or equal to 0, the uniformized main transformer load rate is selected as the optimization target. Specifically, when both power supply arms are in traction mode and the total power is less than the sum of the rated capacities of the two main transformers, i.e., P... load,tα ≥0,P load,t β ≥0, P load,t α +P load,t β≤S T1 +S T2 ; or one end is in traction mode and the other end is in regenerative braking mode, and the total traction load in the system exhibits active absorption, i.e., P load,t α ·P load,t β <0 and P load,t α +P load,t β ≥0. At this point, in order to make full use of the transformer's rated capacity and improve the system's power supply capability, the optimization objective of RSPC-ESS is to uniformize the main transformer load rate, as shown in the following formula:

[0069]

[0070] Among them, P MT,t α P MT,t β S represents the output power of the main transformers at both ends. N α S N β This represents the rated power of the main transformers at both ends. Under the current operating conditions, the output power of both transformers is not less than 0, i.e., P. MT,t α ≥0, P MT,t β ≥0, and the sum of the output forces at both ends of RSPC-ESS is greater than or equal to 0, i.e., P RSPC-ESS,t α +P RSPC-ESS,t β ≥0.

[0071] When the prediction results show that one end of the power supply arm is in traction mode and the other end is in regenerative braking mode, and the total active power is less than 0, or when both ends of the power supply arm are in regenerative braking mode, the optimization objective is to maximize the regenerative braking energy utilization rate. Specifically, this means one end is in traction mode and the other end is in regenerative braking mode, and the total traction load in the system exhibits active power release, i.e., P... load,t α ·P load,t β <0 and P load,t α +P load,t β <0 or both ends exhibit regenerative braking condition, i.e., P load,t α <0,Pload,t β When the value is less than 0, the main control objective is to ensure the highest utilization rate of regenerative braking energy while uniformizing the load rate of the transformers at both ends. Therefore, the optimization objective is as follows:

[0072]

[0073] In the formula, λ is a fixed constant, typically taken as 10, which, together with the following term, forms a penalty term to ensure the highest regenerative braking energy recovery rate. The output of the transformers at both ends is not greater than 0, i.e., P... MT,t α ≤0, P MT,t β ≤0, and at this time the converter output is opposite to the load output, i.e., P RSPC-ESS,t α ·P RSPC-ESS,t β ≥0, P RSPC-ESS,t α ·P RSPC-ESS,t β ≥0.

[0074] When the prediction results show that both power supply arms are in traction mode and the total active power exceeds the sum of the rated power of the main transformers, limiting transformer overload is selected as the optimization objective. When both power supply arms are in traction mode and the total power exceeds the sum of the rated power of the main transformers at both ends, i.e., P... load,t α ≥0,P load,t β ≥0, P load,t α +P load,t β >S T1 +S T2 At this point, the primary control objective is peak shaving, specifically controlling the transformer overload. The optimization objective is shown in the following formula:

[0075]

[0076] Where k represents the overload transformer designation.

[0077] Based on the load conditions of the left and right power supply arms within different scheduling steps, the system operating conditions are judged to determine the optimization target.

[0078] In one possible implementation, an energy optimization model is established based on the prediction results. The RSPC-ESS is used to replace the original transformer as the energy hub in the traction system, not only achieving power exchange between the left and right power supply arms but also providing a DC grid connection interface for the hybrid energy storage system. The power variation relationship at each port is as follows:

[0079]

[0080] Among them, P RSPC-ESS,t α P RSPC-ESS,t β Q RSPC-ESS,t α Q RSPC-ESS,t β The RSPC-ESS values ​​at time t are respectively... Figure 2 The active and reactive power outputs of the α and β side power supply arms shown are in kW / kVar. B,t in P SC,t in P is the charging power for battery energy storage and supercapacitors. B,t out P SC,t out This refers to the discharge power of battery energy storage and supercapacitors, measured in kW.

[0081] The hybrid energy storage system consists of high-energy-density battery storage and high-power-density supercapacitors, aiming to compensate for the spatiotemporal mismatch between the traction loads of the two power supply arms through optimized configuration and coordinated control. The operating state of the hybrid energy storage is mainly evaluated through its state of charge, and its mathematical model is shown below:

[0082]

[0083] Among them, SOC B,t SOC SC,t Let η be the state of charge of the battery storage and supercapacitor at time t. in,B η out,B η in,SC η out,SC For the charging and discharging efficiency of battery energy storage and supercapacitors, η in,B η out,B η in,SC η out,SC For battery energy storage and supercapacitor charge / discharge efficiency, P B,t in P SC,t in P B,t out P SC,t out The charging and discharging power of battery energy storage and supercapacitors, measured in kW.

[0084] This leads to the establishment of a mathematical model for the relay power control system, laying the groundwork for solving the reactive power coordinated optimization scheduling model of the relay power control system in electrified railways.

[0085] In one possible implementation, based on an energy optimization model, power flow constraints, converter output constraints, hybrid energy storage system output constraints, train voltage safety constraints, and traction substation power constraints are generated for the traction power supply system. Since the equivalent chain network belongs to the radial network, and the optimization operation model in this embodiment considers the operating voltage level of the train sets and the coordinated scheduling of active and reactive power, the dist-flow method is used to establish the power flow constraints for the traction power supply system. Based on this, the original model is transformed into a convex programming form through second-order cone relaxation, obtaining the global optimal solution and a good solution speed, as shown in the following equation:

[0086]

[0087] In the formula, the subscripts i and j represent the node numbers in the distribution network; P gen,j,t Q gen,j,t P L,j,t Q L,j,t P represents the reactive power output of the power source and load connected at node j at time t; ij,t and Q ij,t Let v be the active and reactive power at the beginning of branch ij at time t, respectively; i,t and v j,t Let r be the voltages at nodes i and j at time t, respectively; ij x ij These are the resistance and reactance of branch ij, respectively; I ij,t P represents the current flowing through branch ij; jk,t and Q jk,t Let be the active and reactive power of the first end of branch j to k at time t; k is the set of child nodes from j to k with node j as the parent node.

[0088] The converter output constraint is specifically defined by the following formula:

[0089]

[0090] In the formula, S RSPC-ESS design The design capacity of the RSPC-ESS converter is set to ensure that the RSPC-ESS is not overloaded.

[0091] The specific output constraint of the hybrid energy storage system is as follows:

[0092]

[0093]

[0094] In the formula, P B max P SC maxQ represents the rated power of battery energy storage and supercapacitor, respectively. B,t in Q SC,t in Q B,t out Q SC,t out Reactive power absorbed or released by inverters configured for battery energy storage and supercapacitors, measured in kW (s). Soc,min B S Soc,max B S Soc,min SC S Soc,max SC These represent the upper and lower limits of the state of charge for battery energy storage and supercapacitors, respectively. B design S SC design The design capacities are for battery energy storage and supercapacitor inverters, respectively.

[0095] Furthermore, since the time step of the optimization strategy is on the order of seconds, in order to distinguish the output of battery energy storage from that of supercapacitors, the ramp power of battery energy storage operation is limited as shown in the following formula:

[0096]

[0097] In the formula, PBrp and PSCrp represent the ramp rates of battery energy storage and supercapacitor, respectively.

[0098] The specific train operating voltage constraint is as follows:

[0099]

[0100] In the formula, U train,t,n U is the voltage at the pantograph of train n at time t, in kV, which can be obtained by performing open network power flow calculations on the traction power supply system; train min and U train max These represent the minimum and maximum voltage limits required to ensure normal train operation, respectively, in kV.

[0101] The power constraint of the traction substation is specifically defined by the following formula:

[0102]

[0103] In the formula, such as Figure 2 As shown, P MT,t α P MT,t β QMT,t α Q MT,t β S represents the active and reactive power outputs of main transformer A in the α power supply arm and main transformer C in the β power supply arm at time t, respectively, in kW / kVar; max α S max β The apparent power of the main transformers A and C is the upper limit, in kVA. During intraday dispatching, the transformer is allowed to be overloaded by 1.5 times for a short period of time.

[0104] S303. Based on the optimization function and constraint boundaries, generate the optimal control configuration for the energy storage device and the power conversion device.

[0105] Specifically, for solving the model, in this embodiment, the optimized solver is: Large-scale optimizers, in In the runtime environment, call The solver solves the model to obtain the optimal control signal of RSPC-ESS and packages it into an energy optimization data package, which is then transmitted to the controlled device.

[0106] In one possible implementation, optimal control commands are generated based on the optimal control configuration; these commands are then sent to the corresponding devices to achieve optimal control. Based on the aforementioned optimization function and constraint boundaries, multiple power control commands are generated and sent to the corresponding execution devices in the traction power supply system, thereby realizing multi-objective power optimization control of the railway relay power control system.

[0107] This application provides a range-extended traction system control method. By replacing the traditional intermediate substation with a "Relay Power Control System with Energy Storage (RSPC-ESS)," the electrical continuity of the traction network is achieved, thereby eliminating the phase-separated electrical structure and significantly extending the power supply distance of a single traction substation. For the optimized control of RSPC-ESS, a hierarchical control architecture is designed: the upper-level rule management layer dynamically selects the energy dispatch mode based on short-term load forecast data and equipment operating status; the lower layer constructs and solves the reactive power coordinated dispatch model of the traction power supply system, achieving multi-dimensional optimization of system safety, operating costs, and regenerative energy utilization rate while ensuring train safety.

[0108] Please see Figure 6 , Figure 6 This is a structural block diagram of a range-extended traction system control device provided in an embodiment of this application. Figure 6 As shown: The range-extended traction system control device 600 includes: a prediction module 610, a first generation module 620, and a second generation module 630. Wherein:

[0109] The prediction module 610 is used to collect real-time traction power supply system network data and train operation data, and to predict the traction load for the next scheduling period based on the network data and train operation data.

[0110] The first generation module 620 is used to generate an optimization function and constraint boundary that matches the train operating conditions based on the prediction results.

[0111] The second generation module 630 is used to generate the optimal control configuration of the energy storage device and the power conversion device based on the optimization function and the constraint boundary.

[0112] In some possible embodiments, the first generation module 620 is specifically used to determine an optimization objective based on the prediction results; and to determine an optimization function based on the optimization objective.

[0113] In some possible embodiments, the first generation module 620 is specifically used to select the uniformized main transformer load rate as the optimization target when the prediction result shows that both power supply arms are in traction state and the total active power does not exceed the sum of the rated capacity of the main transformer, or when one side of the power supply arms at both ends is in traction state and the other side is in braking state and the total active power is greater than or equal to 0; when the prediction result shows that one side of the power supply arms at both ends is in traction state and the other side is in regenerative braking state and the total active power is less than 0, or when both power supply arms at both ends are in regenerative braking state, to select maximizing the regenerative braking energy utilization rate as the optimization target; and when the prediction result shows that both power supply arms at both ends are in traction state and the total active power exceeds the sum of the rated power of the main transformer, to select limiting transformer overload as the optimization target.

[0114] In some possible embodiments, the first generation module 620 is specifically used to establish an energy optimization model based on the prediction results; and to generate constraint boundaries based on the energy optimization model.

[0115] In some possible embodiments, the first generation module 620 is specifically used to generate power flow constraints of the traction power supply system, power output constraints of the converter, power output constraints of the hybrid energy storage system, train voltage safety constraints, and power constraints of the traction substation based on the energy optimization model.

[0116] In some possible embodiments, the range-extended traction system control device 600 is also used to set a train control strategy based on the train's operating conditions; the train control strategy includes a constant speed control strategy and a quasi-constant speed control strategy.

[0117] In some possible embodiments, the range-extended traction system control device 600 is also used to generate optimal control commands based on the optimal control configuration; and send the optimal control commands to the corresponding devices to achieve the corresponding optimal control.

[0118] It should be noted that the range-extended traction system control device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the range-extended traction system control method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the range-extended traction system control device and the range-extended traction system control method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0119] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0120] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 700 may include: at least one processor 701, at least one network interface 704, a user interface 703, a memory 705, and at least one communication bus 702.

[0121] The communication bus 702 is used to enable communication between these components.

[0122] The user interface 703 may include a display screen and a camera. Optional user interfaces 703 may include standard wired interfaces and wireless interfaces.

[0123] The network interface 704 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0124] The processor 701 may include one or more processing cores. The processor 701 connects to various parts within the electronic device 700 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 705, and by calling data stored in the memory 705. Optionally, the processor 701 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 701 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 701 and may be implemented as a separate chip.

[0125] The memory 705 may include random access memory (RAM) or read-only memory. Optionally, the memory 706 may include a non-transitory computer-readable storage medium. The memory 705 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 705 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 705 may also be at least one storage device located remotely from the aforementioned processor 701. Figure 7 As shown, the memory 705, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a range-extended traction system control application program.

[0126] exist Figure 7In the illustrated electronic device 700, the user interface 703 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 701 can be used to call the driving range-extended traction system control application stored in the memory 705 and specifically perform the following operations:

[0127] The system collects real-time network data of the traction power supply system and train operation data, and predicts the traction load for the next scheduling period based on the network data and train operation data. Based on the prediction results, it generates an optimization function and constraint boundary that matches the train operation conditions. Based on the optimization function and constraint boundary, it generates the optimal control configuration for the energy storage device and power conversion device.

[0128] In some possible embodiments, the processor 701 executes an optimization function that matches the train's operating conditions based on the prediction results, specifically for performing: determining the optimization objective based on the prediction results; and determining the optimization function based on the optimization objective.

[0129] In some possible embodiments, the processor 701 performs an optimization objective based on the prediction results, specifically: when the prediction results show that both power supply arms are in traction mode and the total active power does not exceed the sum of the rated capacity of the main transformer, or when one power supply arm is in traction mode and the other is in braking mode and the total active power is greater than or equal to 0, the uniform main transformer load rate is selected as the optimization objective; when the prediction results show that one power supply arm is in traction mode and the other is in regenerative braking mode and the total active power is less than 0, or when both power supply arms are in regenerative braking mode, the maximum regenerative braking energy utilization rate is selected as the optimization objective; when the prediction results show that both power supply arms are in traction mode and the total active power exceeds the sum of the rated power of the main transformer, limiting transformer overload is selected as the optimization objective.

[0130] In some possible embodiments, the processor 701 performs the following actions: generating constraint boundaries that match the train's operating conditions based on the prediction results; specifically, it performs the following actions: establishing an energy optimization model based on the prediction results; and generating constraint boundaries based on the energy optimization model.

[0131] In some possible embodiments, the processor 701 performs constraint boundary generation based on the energy optimization model, specifically for performing: generating power flow constraints for the traction power supply system, converter output constraints, hybrid energy storage system output constraints, train voltage safety constraints, and traction substation power constraints based on the energy optimization model.

[0132] In some possible embodiments, before the processor 701 performs real-time acquisition of traction power supply system network data and train operation data, and predicts the traction load for the next scheduling period based on the network data and train operation data, it is also used to perform: setting a train control strategy based on the train operation status; the train control strategy includes a constant speed control strategy and a quasi-constant speed control strategy.

[0133] In some possible embodiments, after the processor 701 obtains the optimal control configuration of the energy storage device and the power conversion device based on the optimization function and the constraint boundary, it is also used to: generate the optimal control instruction based on the optimal control configuration; and send the optimal control instruction to the corresponding device to achieve the corresponding optimal control.

[0134] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the above-described instructions. Figure 3 One or more steps in the illustrated embodiment. If the constituent modules of the above-described range-extended traction system control device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0135] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid state disks (SSDs)).

[0136] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium includes various media capable of storing program code, such as Read Only Memory (ROM), Random Access Memory (RAM), magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation schemes can be combined arbitrarily.

[0137] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a range-extended traction system, characterized in that, The method includes: Real-time acquisition of traction power supply system network data and train operation data; prediction of traction load for the next scheduling period based on the network data and train operation data. Based on the prediction results, an optimization function and constraint boundary that match the train's operating conditions are generated. Based on the optimization function and constraint boundaries, the optimal control configuration for the energy storage device and the power conversion device is generated.

2. The method as described in claim 1, characterized in that, The step of generating an optimization function that matches the train's operating conditions based on the prediction results includes: Based on the prediction results, determine the optimization objective; Based on the optimization objective, the optimization function is determined.

3. The method as described in claim 2, characterized in that, The process of determining the optimization objective based on the prediction results includes: When the prediction results show that both power supply arms are in traction state and the total active power does not exceed the sum of the rated capacity of the main transformer, or when one power supply arm at both ends is in traction state and the other is in braking state and the total active power is greater than or equal to 0, the uniform main transformer load rate is selected as the optimization target. When the prediction results show that one side of the power supply arm at both ends is in traction state and the other side is in regenerative braking state and the total active power is less than 0, or when both power supply arms at both ends are in regenerative braking state, the optimization objective is to maximize the regenerative braking energy utilization rate. When the prediction results show that both power supply arms are in traction mode and the total active power exceeds the sum of the rated power of the main transformer, limiting transformer overload is selected as the optimization target.

4. The method as described in claim 1, characterized in that, The step of generating constraint boundaries that match the train's operating conditions based on the prediction results includes: Based on the prediction results, an energy optimization model is established; Based on the energy optimization model, constraint boundaries are generated.

5. The method as described in claim 4, characterized in that, The generation of constraint boundaries based on the energy optimization model includes: Based on the energy optimization model, power flow constraints, converter output constraints, hybrid energy storage system output constraints, train voltage safety constraints, and traction substation power constraints are generated.

6. The method as described in claim 1, characterized in that, Before the real-time acquisition of traction power supply system network data and train operation data, and the prediction of traction load for the next scheduling period based on the network data and train operation data, the method further includes: Train control strategies are set based on the train operation conditions; the train control strategies include constant speed control strategies and quasi-constant speed control strategies.

7. The method as described in claim 1, characterized in that, After obtaining the optimal control configuration of the energy storage device and the power conversion device based on the optimization function and the constraint boundary, the process further includes: Based on the optimal control configuration, generate the optimal control command; The optimal control command is sent to the corresponding device to achieve the corresponding optimal control.

8. A control device for a range-extended traction system, characterized in that, The device includes: The prediction module is used to collect real-time network data of the traction power supply system and train operation data, and to predict the traction load for the next scheduling period based on the network data and train operation data. The first generation module is used to generate an optimization function and constraint boundary that matches the train's operating conditions based on the prediction results. The second generation module is used to generate the optimal control configuration of the energy storage device and the power conversion device based on the optimization function and constraint boundaries.

9. A storage medium, characterized in that, The storage medium stores a plurality of instructions adapted for loading by a processor and executing the steps of the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes 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 method as described in any one of claims 1 to 7.

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