Power supply scheduling method and system of railway traction station based on photovoltaic grid-connected power generation

Abstract

The invention discloses a power supply scheduling method and system of a railway traction station based on photovoltaic grid-connected power generation, and relates to the field of photovoltaic power generation. The method comprises the following steps: acquiring load information after traction of a railway in a target area for a preset duration and detection information corresponding to a photovoltaic grid-connected power generation system; according to the load information and the detection information, a power supply mode of each traction station is determined, and the power supply mode comprises that any one of a photovoltaic grid-connected power generation system, an energy storage system and a power grid continuously supplies power to the corresponding traction station within a preset duration; and within the preset duration, supplying power to each traction station according to the power supply mode. By implementing the technical scheme provided by the invention, the fluctuation of photovoltaic power generation can be effectively stabilized, and the stability of overall power supply is improved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic power generation, and specifically to a power supply dispatching method and system for a railway traction depot based on photovoltaic grid-connected power generation. Background Art

[0002] With the increasing awareness of environmental protection and the development of renewable energy technology, the railway industry is actively exploring the application of green energy. As an important part of the railway system, the power supply method of the traction depot directly affects the efficiency and environmental protection of railway operations. In recent years, the application of photovoltaic power generation technology in the railway system has become increasingly widespread, providing new possibilities for the power supply of railway traction depots.

[0003] At present, some railway systems have begun to try to apply photovoltaic power generation technology to traction power supply. These systems usually combine photovoltaic power generation with traditional power grids, which not only meets the load demand of traction, but also improves the utilization rate of renewable energy.

[0004] However, the simple grid-connected mode of traditional traction power supply often cannot effectively deal with the intermittent and fluctuating characteristics of photovoltaic power generation, making it difficult to maximize the utilization of photovoltaic power generation. Summary of the invention

[0005] The present application provides a power supply scheduling method and system for a railway traction depot based on photovoltaic grid-connected power generation, which can effectively smooth out the fluctuations of photovoltaic power generation and improve the stability of the overall power supply.

[0006] In a first aspect of the present application, the present application provides a power supply dispatching method for a railway traction depot based on photovoltaic grid-connected power generation, comprising:

[0007] Obtain the load information of the railway traction in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system;

[0008] Determine a power supply mode of each traction station according to the load information and the detection information, wherein the power supply mode includes any one of a photovoltaic grid-connected power generation system, an energy storage system, and a power grid continuously supplying power to the corresponding traction station within a preset time period;

[0009] During the preset time period, each traction station is powered according to the power supply mode.

[0010] In a second aspect of the present application, a power supply dispatching system for a railway traction depot based on photovoltaic grid-connected power generation is provided, comprising:

[0011] A prediction information acquisition module, used to obtain the load information of the railway traction in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system;

[0012] A power supply mode determination module, used to determine the power supply mode of each traction station according to the load information and the detection information, wherein the power supply mode includes any one of the photovoltaic grid-connected power generation system, the energy storage system and the power grid continuously supplying power to the corresponding traction station within a preset time period;

[0013] The power supply mode application module is used to supply power to each of the traction stations according to the power supply mode within the preset time period.

[0014] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0015] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0017] By obtaining the load information of the traction station in the target area and the detection information of the photovoltaic grid-connected power generation system, the real-time status of both supply and demand sides can be fully grasped. Based on the above information, this method can dynamically determine the optimal power supply mode and flexibly adjust the power supply ratio of the photovoltaic power generation system, energy storage system and power grid. This intelligent scheduling strategy based on real-time data significantly improves the system's adaptability to the volatility of photovoltaic power generation, and effectively overcomes the defect that the traditional simple grid-connected method is difficult to cope with the intermittent nature of photovoltaic power generation.

[0018] By pre-planning the power supply mode within a preset time, this method can coordinate the output of each power supply source in advance to maximize the use of photovoltaic power generation. This not only improves the efficiency of renewable energy use, but also optimizes the power supply cost while ensuring power supply reliability. In particular, the introduction of energy storage systems as a means of regulation further enhances the flexibility of the system, can effectively smooth out the fluctuations of photovoltaic power generation, and improve the stability of the overall power supply.

[0019] Compared with the traditional simple grid-connected method, this method realizes dynamic matching of supply and demand and multi-source coordinated power supply by comprehensively considering load demand, photovoltaic power generation status and energy storage system capacity. The intelligent scheduling strategy not only maximizes the utilization of photovoltaic power generation, but also improves the reliability and economy of the entire power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a power supply dispatching method for a railway traction depot based on photovoltaic grid-connected power generation provided in an embodiment of the present application;

[0021] Figure 2 This is a main wiring layout diagram of a photovoltaic power generation switch station provided in an embodiment of the present application;

[0022] Figure 3 It is a structural schematic diagram of a photovoltaic power generation switch station provided in an embodiment of the present application;

[0023] Figure 4 It is a main wiring layout diagram of a railway traction station provided in an embodiment of the present application;

[0024] Figure 5 It is a structural schematic diagram of a power supply dispatching system for a railway traction depot based on photovoltaic grid-connected power generation provided in an embodiment of the present application;

[0025] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] The present application provides a method for dispatching power supply for a railway traction station based on photovoltaic grid-connected power generation, referring to Figure 1 , Figure 1 This is a flow chart of a method for power supply dispatching of a railway traction station based on photovoltaic grid-connected power generation disclosed in an embodiment of the present application. The steps are as follows:

[0030] Step 101: Obtaining the load information of the railway traction in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system.

[0031] Among them, the target area refers to a specific railway network range, which can be understood as the smallest unit of centralized power supply dispatching management. This unit usually contains multiple interrelated traction stations and corresponding photovoltaic grid-connected power generation systems, such as an operating section of a high-speed railway line, a subway network in a city, or a regional railway network under the responsibility of a railway bureau. Specifically, the target area may be a 100-kilometer high-speed railway line with 5 traction stations along the line, each equipped with a corresponding photovoltaic grid-connected power generation system. These traction stations and photovoltaic grid-connected power generation systems are uniformly managed and controlled by the same dispatching center.

[0032] The load information refers to the power demand of railway traction in a specific period of time. This power demand mainly comes from the traction power required for train operation and the power demand of various auxiliary equipment along the line.

[0033] Correspondingly, detection information refers to the operating status and performance parameters of the photovoltaic grid-connected power generation system. This information covers the working conditions of each component of the system, the impact of environmental factors on system performance, and the overall power generation efficiency and grid connection of the system. Detection information is collected in real time by sensors and monitoring equipment distributed at various key nodes of the photovoltaic grid-connected power generation system.

[0034] Among them, the photovoltaic grid-connected power generation system refers to a power generation system that connects the electricity generated by solar photovoltaic power generation devices to the public power grid to achieve a two-way flow of electricity. The system usually includes solar panels, inverters, grid-connected equipment, and related control and protection devices. The photovoltaic grid-connected power generation system can convert solar energy into electrical energy and transmit excess electrical energy to the public power grid to achieve efficient utilization of renewable energy.

[0035] In the embodiment of the present application, it can be understood as a photovoltaic power generation system specially designed for power supply of railway traction. The system boosts the electricity generated by photovoltaic power generation to 27.5kV through a step-up transformer to match the power supply voltage of the railway traction station, and realizes coordinated power supply with the traditional power grid through an intelligent control system. The system also includes advanced technologies such as high-precision fault monitoring, comprehensive intelligent protection functions, and microgrid dispatching control to ensure the safety, reliability and efficiency of the system.

[0036] Optional, see Figure 2 , Figure 2 FIG. 1 shows a main wiring layout diagram of a photovoltaic power generation switch station provided by an embodiment of the present application. Figure 2As shown, the photovoltaic grid-connected power generation system includes photovoltaic panels, a junction box, an inverter, a step-up transformer, and a grid-connected outlet cabinet, wherein the grid-connected outlet cabinet is provided with a photovoltaic power generation switch station, and the photovoltaic power generation switch station includes at least two switchable collector line incoming cabinets.

[0037] Specifically, photovoltaic panels, as the core power generation unit of the system, can be installed on the roof of the station or on the slope of the railway. The selection of the above installation location not only makes full use of the idle space along the railway, but also maximizes the capture of solar energy. Photovoltaic panels can convert solar energy into 24V or 48V DC power.

[0038] In order to improve the management efficiency and energy utilization of the system, the DC power generated by multiple photovoltaic panels is collected through a combiner box. The combiner box not only simplifies the line connection, but also realizes the initial current convergence and power management, providing a stable DC input for the subsequent inverter process. The inverter converts the DC power output by the combiner box into AC power with the same frequency and phase as the power grid. The AC voltage output by the inverter is usually around 800V.

[0039] Considering the special power supply requirements of the railway traction depot, the system is equipped with a step-up transformer. This component boosts the 800V AC output of the inverter to 27.5kV, achieving voltage matching between the photovoltaic power generation system and the railway traction power supply system.

[0040] Furthermore, a photovoltaic power generation switch station can be set up in the grid-connected outlet cabinet, and the photovoltaic power generation switch station includes at least two switchable collector line incoming cabinets. The setting of multiple collector lines allows the system to flexibly switch between different photovoltaic power generation units or areas. For example, when the photovoltaic panels in a certain area are shaded or require maintenance, the system can automatically switch to other normally working collector lines to ensure the stability of the overall power generation. The switchable characteristics of the photovoltaic power generation switch station are realized through an intelligent control system. The control system monitors the power generation status, load demand and possible fault conditions of each collector line in real time, and automatically switches the line according to the preset logic. Finally, the boosted 27.5kV power is connected to the 27.5kV bus of the traction station through the grid-connected access cabinet. The connection between the photovoltaic power generation system and the railway traction power supply system is realized.

[0041] Based on the above embodiment, as an optional embodiment, please refer to Figure 3 , Figure 3 A schematic diagram of the structure of a photovoltaic power generation switch station provided in an embodiment of the present application.

[0042] Specifically, the main connection layout of the photovoltaic power generation switch station includes core equipment such as 27.5kV station transformer cabinet, 27.5kV collector line incoming cabinet, busbar PT cabinet, metering cabinet and 27.5kV photovoltaic grid-connected outgoing cabinet. These equipment work together to ensure that the power of the photovoltaic power generation system can be stably and safely integrated into the 27.5kV busbar system of the traction station.

[0043] The 27.5kV station transformer cabinet reduces the 27.5kV high voltage electricity to 380V or 220V low voltage electricity through the internal transformer, providing the necessary power for the control system, monitoring equipment, protection devices and other auxiliary facilities in the switch station. It ensures the normal operation of the equipment inside the switch station, and improves the safety of the system through the equipped protection device, which can be disconnected in time when the power supply is abnormal to protect the equipment from damage.

[0044] Among them, the 27.5kV collector line incoming cabinet is responsible for receiving the 27.5kV electricity from the photovoltaic panels after being processed by the inverter and the step-up transformer. In the embodiment of the present application, the number of collector line incoming cabinets is designed to be adjustable. This flexibility enables the system to adapt to photovoltaic power generation needs of different scales. For example, for a small system, only 1-2 incoming cabinets may be needed; for a large system, multiple incoming cabinets may be required. This scalable design allows the system to be adjusted according to the actual power generation needs and the number of photovoltaic panels installed, improving the adaptability of the system and the possibility of future expansion.

[0045] The busbar PT cabinet converts the high voltage of 27.5kV into a low voltage signal of about 110V through a voltage transformer, providing real-time voltage data for monitoring equipment and protection devices. This design enables the system to detect abnormal conditions of the busbar voltage, such as overvoltage or undervoltage, in a timely manner, thereby triggering the corresponding protection mechanism. In addition, the busbar PT cabinet is also equipped with overvoltage protection and lightning arresters, which further improves the system's anti-interference ability when facing external power grid fluctuations or lightning strikes.

[0046] The meter cabinet is equipped with high-precision current transformers and voltage transformers, which can accurately record the total power generation and grid-connected power of the photovoltaic power generation system. This design not only meets the needs of operation and management, but also provides a reliable data basis for possible surplus power grid settlement.

[0047] The 27.5kV photovoltaic grid-connected outlet cabinet is equipped with circuit breakers and protection devices, which can deliver electricity to the 27.5kV busbar of the traction station under normal circumstances, and cut off the circuit in time when the system is abnormal to prevent the expansion of the fault. This design greatly improves the safety and reliability of the system.

[0048] Based on the above embodiment, as an optional embodiment, please refer to Figure 4 , Figure 4A main wiring layout diagram of a railway traction depot provided in an embodiment of the present application.

[0049] Specifically, the main wiring layout of the traction station includes two main power sources (incoming line 1 and incoming line 2), two traction transformers, multiple 27.5kV incoming lines and feeders, and photovoltaic power station grid-connected circuit breakers. The core goal of this design is to convert 110kV or 220kV high-voltage electricity into 27.5kV single-phase AC electricity while ensuring the reliability and flexibility of power supply.

[0050] Among them, Incoming Line 1 and Incoming Line 2 serve as the dual main power sources of the traction station, drawing power from the 110kV or 220kV high-voltage power grid. This dual-power redundant design greatly improves the reliability of power supply. When one of the power sources fails or needs maintenance, the other can immediately take over the power supply task to ensure uninterrupted power supply to the traction station. To achieve this function, the system is equipped with automatic control logic that can quickly switch when power supply anomalies are detected to minimize the risk of power outages.

[0051] Among them, traction transformer 1 and traction transformer 2 are the core equipment of the system, responsible for converting high voltage electricity into 27.5kV single-phase AC power. Each transformer is connected to one incoming line, further enhancing the redundancy of the system. This design ensures that even if one transformer fails or is under maintenance, the other transformer can still maintain normal power supply to the traction station, greatly improving the reliability and maintenance flexibility of the system.

[0052] Among them, the design of 27.5kV incoming lines (incoming line 1, incoming line 2, incoming line 3, incoming line 4) reflects the flexibility and scalability of the system. Multiple incoming lines not only increase the redundancy of the system, but also provide greater power supply capacity and higher power supply flexibility. Each incoming line is equipped with a circuit breaker and protection device, which can quickly isolate the problem area when a fault occurs, prevent the fault from expanding, and protect the safety of the entire system.

[0053] Among them, the multi-channel design of 27.5kV feeders (feeder 1, feeder 2, feeder 3, feeder 4) meets the needs of different catenary power supply sections. Each feeder is responsible for supplying power to a specific catenary section. This partitioned power supply method not only improves the flexibility of power supply, but also enhances the reliability of the system. When a section fails, the power supply of other sections will not be affected. Each feeder is equipped with a circuit breaker and relay protection device, which can cut off the circuit in time when a short circuit or overload occurs to protect the catenary and other parts of the traction station.

[0054] Based on the above embodiment, as an optional embodiment, in step 101: the step of obtaining the load information of the traction of the railway in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system can also specifically include the following steps:

[0055] Step 201: Obtain a train operation table of a traction depot of a railway in a target area and detection signals collected by sensors installed on the traction depot.

[0056] Among them, the train operation table refers to a timetable that records the railway operation plan in detail, which includes the operation arrangements of all trains in a specific time period. This table includes not only the departure and arrival times of the trains, but also key information such as the train type, stopovers, and operation routes. In the embodiment of the present application, it can be understood as a dynamically updated database, which contains the complete operation information of all trains within the power supply range of all traction stations in the target area. The train operation table is the basis for predicting the load of the traction station. By analyzing the train operation table, the system can estimate how many trains are running at the same time within the power supply range of a traction station at any given time, thereby predicting the power demand of the traction station. For example, when multiple high-speed trains accelerate at the same time in the power supply range of a traction station, the system can foresee the upcoming high load demand.

[0057] Correspondingly, the detection signal refers to a set of electrical parameters and operating status data collected in real time by a variety of high-precision sensors installed on the traction station. It includes the electrical parameters of the 27.5kV bus, such as voltage, current, power of each feeder, power factor, harmonic content and other electrical parameters, as well as the electrical parameters of the traction transformer, such as temperature, oil level, air pressure, and the equipment status of the circuit breaker, such as switch status and other information. At the same time, it also includes the environmental parameters inside the traction station, such as temperature, humidity, smoke concentration, and the electrical parameters of the photovoltaic power generation system, such as output voltage, current, inverter working status, etc. These signals are collected in real time at a high sampling rate through high-precision voltage transformers, current transformers, power analyzers, temperature sensors, liquid level sensors and other equipment.

[0058] During the acquisition of detection signals, the data first undergoes preliminary preprocessing, including filtering and outlier detection, to ensure the quality and reliability of the data. Subsequently, these processed signals are transmitted to the control system in real time through industrial-grade communication networks, such as industrial Ethernet or fiber optic networks. This high-speed, real-time data transmission ensures that the system can obtain the latest operating status information in a timely manner, providing a basis for rapid decision-making and response.

[0059] Step 202: Input the train operation schedule and the detection signal into the prediction model, and obtain the load information and detection information of each traction after a preset time period output by the prediction model.

[0060] A prediction model refers to a mathematical or statistical tool that uses historical data and current status to estimate future results. In the embodiments of the present application, it can be understood as a complex neural network system based on a deep learning algorithm, which is specially designed to process and analyze large-scale time series data of the railway traction power supply system. The model is mainly composed of a long short-term memory network (LSTM). The prediction model is used to make high-precision predictions of the power load demand and the output of the photovoltaic power generation system within a preset time of the railway traction station. Specifically, the prediction model is able to process multi-source heterogeneous data, including but not limited to train schedules, historical load data, real-time detection signals, weather forecast information, etc. Through in-depth analysis and learning of these complex data, the model can identify the key factors and their interactions that affect the traction station load and photovoltaic power generation, thereby generating accurate short-term and medium-term prediction results.

[0061] Specifically, comprehensive data collection is first carried out. The train timetable contains key information such as train arrival and departure times, train types, loads and speeds. At the same time, sensors in the traction station and photovoltaic power generation system are used to collect real-time electrical signals (such as voltage and current), environmental signals (such as light intensity, temperature, humidity, wind speed) and equipment signals (such as photovoltaic power generation equipment status and energy storage system power).

[0062] Then, detailed data preprocessing is performed. The first step is time synchronization, where the train schedule and sensor signal data are processed based on the same timestamp to ensure data consistency. Then, data cleaning is performed to handle missing values ​​and outliers. For missing values, the method of deletion, interpolation filling, or default value filling is selected according to the importance of the data.

[0063] For train timetables, we extract arrival and departure times, convert train types into numerical features through One-Hot encoding, and directly use load and speed as numerical features. For sensor signals, we calculate the rate of change of electrical signals, extract statistical features of environmental signals, and encode equipment status information as features that can be used by the model. At the same time, we consider time features and historical load features.

[0064] The prediction model uses a deep learning architecture based on long short-term memory networks. Model inputs include processed train features, current load, environmental data, and equipment status. In the training phase, the model is trained using historical data. The mean square error can be used as the loss function, and the model parameters are optimized through the back propagation algorithm. To prevent overfitting, the hyperparameters can be adjusted using the validation set.

[0065] Data segmentation follows the characteristics of time series and divides the data into training set, validation set and test set in chronological order. For cases with insufficient data, you can consider using data enhancement techniques such as data smoothing or generating synthetic data with similar time patterns.

[0066] After the prediction model is trained, the current train schedule and detection signals are input in real time, and the prediction model can output the predicted load value of the traction station and the expected output of the photovoltaic system at each time point (such as 15-minute intervals) within the preset time period (such as 24 hours) in the future. For example, the model can predict that the load demand of the traction station will be 5.2MW in 1 hour and 4.8MW in 2 hours, and at the same time predict that the power generation capacity of the photovoltaic power generation system in the next 1 hour will be 2.3MW and 1.9MW in 2 hours.

[0067] Step 102: Determine the power supply mode of each traction station based on the load information and the detection information. The power supply mode includes any one of the photovoltaic grid-connected power generation system, the energy storage system and the power grid continuously supplying power to the corresponding traction station within a preset time.

[0068] Among them, the power supply mode refers to the specific plan and strategy for providing electricity to the railway traction depot within a specific time period. In the embodiment of the present application, it can be understood as a dynamic scheduling mechanism, which is used to determine that in each time period within a preset duration, one or more combinations of photovoltaic grid-connected power generation systems, energy storage systems, and power grids will continuously supply power to the corresponding traction depot. This power supply mode includes not only the selection of power sources, but also the output power distribution ratio of each power source. Specifically, the power supply mode may cover a variety of situations ranging from pure photovoltaic power supply to a hybrid power supply of the three. The system will dynamically adjust according to the real-time power generation situation, load demand, and energy storage status.

[0069] For example, when photovoltaic power generation is sufficient, the system may adopt a pure photovoltaic power supply mode; when photovoltaic power generation is insufficient, it may switch to a mode that combines photovoltaics with energy storage or introduces grid supplementation.

[0070] Specifically, the system will first analyze the load information within a preset time period, which includes the power demand curve of each traction station predicted based on the train schedule and historical data. At the same time, the system will also combine the detection information of the photovoltaic grid-connected power generation system, such as current power generation, predicted power generation, equipment status, etc., as well as the capacity, charging and discharging status of the energy storage system. Based on these comprehensive data, the system will use optimization algorithms, such as mixed integer linear programming algorithms, to determine the optimal power supply mode in each time period.

[0071] When determining the power supply mode, the system will give priority to utilizing the output of the photovoltaic grid-connected power generation system. When the photovoltaic power generation is sufficient, the system will use it as the main power supply source, and the excess power can be used to charge the energy storage system or send it back to the grid. When photovoltaic power generation is not enough to meet the load demand, the system will decide whether to enable energy storage power supply based on the status of the energy storage system. Only when both photovoltaic power generation and energy storage systems cannot meet the demand will the system obtain power from the grid. This strategy not only maximizes the use of renewable energy, but also effectively reduces dependence on the grid and reduces electricity bills.

[0072] In actual operation, the system needs to consider factors such as equipment startup time, switching loss, power quality fluctuations, etc. In order to avoid system instability caused by frequent switching, certain switching thresholds and delay times are set in the algorithm. For example, the switching of power supply mode will only be triggered when the change in photovoltaic power generation exceeds the preset threshold and lasts for a certain period of time.

[0073] Based on the above embodiment, as an optional embodiment, in step 102: determining the power supply mode of each traction station according to the load information and the detection information may further include the following steps:

[0074] Step 301: Determine a first output power of the photovoltaic grid-connected power generation system within a preset time period according to electrical detection information, environmental detection information, and equipment detection information of the photovoltaic grid-connected power generation system.

[0075] Specifically, the system will comprehensively analyze the electrical detection information, environmental detection information, and equipment detection information of the photovoltaic grid-connected power generation system. Electrical detection information includes parameters such as the current output voltage, current, and power factor, which reflect the real-time operating status of the system. Environmental detection information mainly involves meteorological factors such as light intensity, temperature, humidity, and wind speed, which directly affect the power generation efficiency of photovoltaic panels. Equipment detection information includes the aging degree of photovoltaic panels, dust coverage, and the working status of inverters, which reflect the health status of the equipment and potential performance limitations.

[0076] In a feasible implementation manner, the system may determine the stability of the first collector line currently used by each traction station through detection information.

[0077] Specifically, when determining the first output power of the photovoltaic grid-connected power generation system, the system first needs to evaluate the stability of the first collector line currently used by each traction station, because the stability of the collector line directly affects the output quality and efficiency of the entire photovoltaic grid-connected power generation system.

[0078] Specifically, in order to accurately evaluate the stability of the first collector line, the system monitors and analyzes a series of key parameters in the detection information in real time. These parameters include but are not limited to voltage fluctuation range, current change rate, power factor, harmonic content, and line impedance. Specifically, the system collects real-time electrical data of the collector line through high-precision voltage transformers and current transformers, with a sampling frequency of thousands of times per second to capture instantaneous electrical fluctuations. After preliminary filtering, these raw data are input into a specially designed stability assessment algorithm.

[0079] The algorithm takes into account multiple factors to calculate the stability index. For example, it analyzes the amplitude and frequency of voltage fluctuations, which should normally be kept within ±5% of the rated value. At the same time, the algorithm also evaluates the rate of change of current. Sudden and large current changes may mean a sudden change in line load or a short circuit fault. The fluctuation of power factor is also an important indicator, which reflects the energy transmission efficiency of the system. In addition, the analysis of harmonic content can help identify potential power quality problems, while the monitoring of line impedance can promptly detect hidden dangers such as line aging or loose connections. The system compares the calculated stability index with the preset threshold. If the stability index is lower than the threshold, such as the voltage fluctuation exceeds ±7%, or the current mutation rate exceeds 20% / second, the system will determine that the current first collector line is unstable.

[0080] When it is determined that the stability is less than a threshold, the first collector line is switched to an idle second collector line.

[0081] When it is determined that the stability is greater than or equal to the threshold, the output power of the photovoltaic grid-connected power generation system is directly obtained as the first outputtable power.

[0082] When the system detects that the stability of the first collector line is less than the preset threshold, the switching mechanism will be triggered immediately to switch the power supply line from the first collector line to the pre-prepared idle second collector line. The necessity of this switching operation is to prevent equipment damage or power outages that may be caused by unstable power transmission. The switching process is performed by the intelligent control system of the photovoltaic power generation switch station and is implemented through high-speed switching equipment. Specifically, the control system first sends a disconnection command to the circuit breaker of the first collector line, and prepares for the access of the second collector line. After confirming that the first collector line is completely disconnected, the system immediately closes the circuit breaker of the second collector line to complete the switching. The entire process can usually be completed within tens of milliseconds. This fast response can minimize power losses and fluctuations during the switching process.

[0083] After the switch is completed, the system will immediately evaluate the stability of the second collector line to ensure that it can safely and stably undertake the power supply task. On the contrary, when the system determines that the stability of the first collector line is greater than or equal to the preset threshold, it indicates that the current line is in good operating condition and can safely and stably transmit electric energy. In this case, the system will directly obtain the real-time output power of the photovoltaic grid-connected power generation system as the first output power.

[0084] Step 302: Determine a second output power of the energy storage system in the traction station for a preset time period according to the first output power and load information.

[0085] Specifically, after determining the first output power of the photovoltaic grid-connected power generation system, the system needs to further evaluate and optimize the use strategy of the energy storage system to ensure the stability and efficiency of the entire power supply system. The above process first considers the priority use of photovoltaic power generation, and then supplements the output of the energy storage system as needed, which reflects the principle of renewable energy priority and diversified power supply.

[0086] Specifically, the system first calculates the actual available output power P of the photovoltaic power generation system pv,used This value depends on the current output power P of the photovoltaic system. pv and load demand P load The smaller value of P pv,used =min(P pv ,P load ). The above calculation method ensures the maximum utilization of photovoltaic power generation while avoiding energy waste caused by excessive power generation. For example, if the photovoltaic system can output 5MW at a certain moment and the load demand is 4MW, the actual photovoltaic power used will be 4MW, and the remaining 1MW can be used to charge the energy storage system or feed back to the grid.

[0087] When photovoltaic power generation cannot fully meet the load demand, the system will calculate the supplementary output power P of the energy storage system. bat,used This value is limited by two main factors: the maximum output capacity of the energy storage system P bat,max And the actual power required to be supplemented, that is (P load -P pv,used ). Therefore, P bat,used The preliminary calculation formula is min(P bat,max ,P load -P pv,used ). This ensures that the energy storage system does not exceed its designed maximum output capacity, while also avoiding unnecessary over-discharge.

[0088] However, the actual available output of the energy storage system is also affected by its current remaining capacity E bat In order to prevent the energy storage system from over-discharging, the system will further bat,used Restricted to E bat / τ, where τ represents the time period considered. This means that even if the load demand is high, the output of the energy storage system will not exceed the level that its current power can support. For example, if the energy storage system has 10MWh remaining and a time period of 1 hour is considered, its maximum output will not exceed 10MW, even though it is theoretically capable of outputting higher power.

[0089] Step 303: Determine the preset power supply power that can be allocated by the power grid of each traction station according to the load information of each traction station in the target area.

[0090] After determining the preset power supply power of each traction station, the system needs to consider a key issue: when multiple traction stations need power supply from the grid at the same time, how to ensure that the grid will not be overloaded while allocating power resources fairly and reasonably. Solving this problem is crucial to the stability and efficiency of the entire power supply system.

[0091] In order to deal with the above problems, the embodiment of the present application adopts a dynamic allocation strategy based on the load demand ratio. Specifically, assuming that there are N traction stations in the target area, the total power supply capacity of the power grid is P grid,max The system first collects the real-time load demand of each traction station. Then, the system calculates the total load demand of all traction stations. Based on the above data, the system can use the following formula to calculate the preset power supply that can be allocated by the grid to each traction station:

[0092]

[0093] Where P grid,used(i) P represents the power supply obtained by traction station i from the power grid, load(i) represents the load demand of traction station i, represents the total load demand of N traction stations in all target areas, P grid，max Indicates the maximum power supply capacity of the power grid.

[0094] The above formula ensures the fairness of power supply in the power grid, and each traction station can obtain a corresponding proportion of power supply according to its actual needs. Secondly, this method effectively prevents the risk of power grid overload. Since the total power distributed will not exceed the maximum power supply capacity of the power grid P grid,max , so the system as a whole will not be overloaded. Thirdly, this dynamic allocation method can flexibly respond to load changes. When the load demand of a traction station increases, it will automatically obtain a larger proportion of grid power supply, while the power supply proportion of other traction stations will be reduced accordingly, thus achieving optimal utilization of resources.

[0095] In actual application, the system monitors the load demand of each traction station in real time and updates the power distribution at a high frequency. This fast response mechanism ensures that the power distribution can keep up with the load changes in time, further improving the stability and efficiency of the system. In addition, in order to cope with sudden and large load changes, the system also sets up a buffer mechanism. When the load demand of a traction station suddenly increases significantly, the system will allow it to exceed the proportional power for a short period of time, but at the same time, it will quickly adjust the power supply of other traction stations to ensure that the overall power supply does not exceed P grid,max .

[0096] Step 304: According to the first output power and the second output power of each traction station, the preset power supply power of each traction station is adjusted to obtain the third output power of the power grid of each traction station.

[0097] Specifically, after determining the first output power and the second output power of each traction station, the system needs to further adjust the pre-allocated power supply power of the power grid to obtain a more accurate and efficient power supply plan, thereby optimizing the allocation of power grid resources and ensuring the stability and economy of power supply.

[0098] Specifically, the system first calculates the difference between the actual power demand of each traction station and the power that can be provided by the photovoltaic and energy storage systems, that is, P deficit =P load -P pv,used -P bat,used This difference represents the amount of electricity that each traction station still needs to obtain from the grid.

[0099] The system will then redistribute the power supply of the grid according to the actual power demand of each traction station. This process uses a dynamic adjustment mechanism, the core of which is to recalculate the distribution ratio of grid power supply based on the actual power gap of each traction station. First, the system calculates the power gap of each traction station, that is, its load demand minus the actual output power of photovoltaic power generation and energy storage system. Then, the system calculates the total power gap of all traction stations. Next, the system redistributes the power supply of the grid according to the proportion of the power gap of each traction station to the total gap.

[0100] The above method can ensure that the power supply capacity of the power grid is most effectively allocated to the traction stations that really need it. For example, if the photovoltaic power generation of a traction station exceeds expectations, resulting in a smaller power gap, the power supply power allocated to it will also be reduced accordingly. On the contrary, if the load of a traction station suddenly increases or its photovoltaic output decreases, resulting in a larger power gap, it will obtain more power supply from the grid. This dynamic adjustment mechanism can not only more accurately meet the real-time power supply needs of each traction station, but also improve the power supply efficiency of the entire system, reduce energy waste, and balance the power supply pressure between traction stations to a certain extent.

[0101] Step 305: Determine the power supply mode of each traction station according to the first output power, the second output power and the third output power of each traction station.

[0102] Specifically, after determining the first output power, the second output power, and the third output power of each traction station, the embodiment of the present application adopts an optimization method based on a mixed integer linear programming algorithm to determine the optimal power supply mode. The core goal of this step is to maximize the use of renewable energy while reducing grid consumption and power supply switching frequency while ensuring power supply stability and meeting load requirements.

[0103] Based on the above embodiment, as an optional embodiment, the process may further include the following steps:

[0104] The first output power, the second output power and the third output power of each traction station are input into the power supply optimization model to obtain the power supply mode output by the power supply optimization model, and the power supply mode is used to characterize the way in which each traction station uses the photovoltaic grid-connected power generation system, the energy storage system and the power grid to supply power to the traction station in each period within the preset time length. The objective function of the power supply optimization model includes reducing the consumption of the power grid and reducing the switching of the power supply source of the traction station.

[0105] Among them, the power supply optimization model can be understood as an optimization algorithm that allows the processing of continuous variables and discrete variables at the same time. In the power supply scheduling problem of the railway traction depot, the power supply optimization model can be used to optimize the power supply switching decision between the photovoltaic power generation system, the energy storage system and the power grid. The goal is to minimize the power cost and the number of power supply switching times while meeting the load demand and various system constraints.

[0106] Furthermore, the system needs to reasonably allocate the power supply ratio of the photovoltaic power generation system (PV), energy storage system (ES), and grid (Grid) under the premise of ensuring the load demand of the traction, and minimize the number of switching when switching these power sources to avoid system instability. Specifically, the power supply mode selection for each period can be based on the following conditions: when using the photovoltaic power generation system, give priority to its available power; when photovoltaic power generation is insufficient, use the energy storage system to supplement; when both photovoltaic power generation and energy storage system are insufficient, use the grid to meet the load demand; avoid frequent switching of power supply modes.

[0107] The objective function of the power supply optimization model mainly involves two main goals: (1) Minimizing the power cost: minimizing the power consumption of the power grid; (2) Minimizing the number of power supply switching: avoiding frequent switching of power supply sources and reducing instability. It can be expressed as:

[0108]

[0109] In the formula, T represents the preset duration, t represents the time period within the preset duration, and P Grid (t) represents the output power of the photovoltaic power generation system during time period t, C Grid(t) represents the cost of power supply from the grid in period t, λ represents the weight of the switching penalty, which is used to balance the power cost and the switching cost. PV (t) indicates whether photovoltaic power generation switches to power supply during time period t. ES (t) indicates whether the power supply is switched from the energy storage system during time period t. Grid (t) indicates whether the power supply is switched from the grid during time period t.

[0110] Among them, Minimize() represents the objective function, which is mainly used to find a balance between the two main goals of minimizing the electricity cost and minimizing the number of switching times of the power supply source. By adjusting λ, the minimum point of the objective function can be found, that is, the optimal power supply scheduling strategy.

[0111] Among them, the goal of minimizing electricity costs is to reduce the consumption of grid electricity as much as possible. This can not only reduce operating costs, but also improve the utilization rate of renewable energy, in line with the requirements of energy conservation and emission reduction. Specifically, the objective function contains an item for calculating the total cost of grid power supply, which is expressed by the sum of the product of the grid output power in each period and the unit cost of grid power supply in the corresponding period. By minimizing this item, the system will give priority to the power supply method with lower cost, that is, give priority to the use of photovoltaic power generation and energy storage systems.

[0112] Among them, the goal of minimizing the number of power supply switching times is to avoid system instability caused by frequent switching of power supply. Frequent switching may cause voltage fluctuations, increased equipment losses and other problems, affecting the reliability and life of the entire power supply system. The objective function contains an item for representing the number of power supply switching times, which is represented by the sum of the switching states of photovoltaic power generation, energy storage system and grid power supply in each time period. Among them, the switching state of each time period is represented by a binary variable, which takes the value of 1 when switching occurs and 0 otherwise.

[0113] Furthermore, in order to balance the two potentially conflicting goals of power cost and switching cost, the present embodiment introduces a switching penalty weight λ, which penalizes the number of power mode switchings. By adjusting the value of λ, frequent power switching can be effectively avoided:

[0114] Each time the power supply mode is switched (photovoltaic power generation, energy storage or grid), the switching variable Switch(t) becomes 1, thereby increasing the penalty term in the objective function. This penalty term drives the model to minimize the number of switches during the solution process to minimize the total cost.

[0115] Furthermore, the switching frequency can be controlled by adjusting λ: the larger the λ value, the stronger the penalty for switching, and the model will be more inclined to keep the current power supply mode unchanged during optimization. Even if the power supply capacity of photovoltaic power generation decreases, the system will give priority to energy storage systems or grid power supply, and will not switch frequently. When λ is large, the switching cost is high, and it will tend to reduce the number of switching to avoid frequent switching of power sources. This means that switching between photovoltaic power generation, energy storage systems and the grid will be restricted, and the system will be more inclined to keep the current power supply unchanged, even if this may mean slightly higher electricity costs.

[0116] If the λ value is small, the system will pay more attention to minimizing the total power cost by frequently switching the power supply source. When λ is small, the switching cost is low, and the algorithm will pay more attention to minimizing the total power cost and pay less attention to the number of switching. This may cause the system to frequently switch the power supply source in pursuit of the lowest power cost. By setting λ reasonably, the best balance can be found between the stability and economy of the power supply system.

[0117] In practical applications, the system first needs to obtain key information such as load demand in each time period, predicted output power of the photovoltaic power generation system, available capacity of the energy storage system, and cost of power supply from the grid. This information is obtained through data collection and analysis in the previous steps. Then, this information is input into the mixed integer linear programming algorithm, while considering various constraints, such as load balancing constraints, power upper limit constraints of each power supply source, and charge and discharge state constraints of the energy storage system. Based on these inputs and constraints, the algorithm will find the power supply mode combination that can minimize the objective function through iterative calculation.

[0118] The above-mentioned optimization method based on the objective function has many advantages. First, it can maximize economic benefits while ensuring the stability of power supply, achieving a balance between economy and reliability. Secondly, by adjusting the weight coefficient, the system can flexibly adapt to different operating strategies and environmental conditions, improving the applicability and scalability of the method. Thirdly, this method considers the overall optimization of multiple time periods, rather than simply making separate decisions for each time period, so a more globally optimal solution can be obtained. Finally, due to the use of the power supply optimization model, this method has good computational efficiency and convergence, and can obtain high-quality optimization results in a relatively short time to meet the needs of real-time scheduling.

[0119] Furthermore, when determining the power supply mode, in order to ensure the safety, reliability and efficiency of the system, the embodiment of the present application sets a series of constraints. The above constraints not only ensure the feasibility of the power supply scheme, but also ensure the stability and economy of the system during operation.

[0120] Optionally, defining constraints may include:

[0121] The total power supply in each time period t must be equal to the load demand, expressed as:

[0122]

[0123] Where P PV (t) represents the output power of the photovoltaic power generation system during time period t, P ES (t) represents the output power of the energy storage system during time period t, P Grid (t) represents the output power of the power grid during period t, P L (t) represents the load demand of the traction station.

[0124] First, the embodiment of the present application introduces a power balance constraint, which requires that the total power supply power in each time period must be equal to the load demand. This constraint ensures that at any time, the total output power of the photovoltaic power generation system, the energy storage system, and the power grid can accurately match the load demand of the traction station. This precise matching can not only ensure the normal power supply of the traction station, but also avoid energy waste and excessive load on the system. In actual operation, the system will monitor the load demand of the traction station in real time, and dynamically adjust the output power of each power supply according to this demand to maintain power balance.

[0125] The output power of a photovoltaic power generation system cannot exceed its maximum available power, which is expressed as:

[0126]

[0127] Where P PV,max (t) represents the first output power of the photovoltaic power generation system in time period t.

[0128] Secondly, the embodiment of the present application sets an upper limit constraint on the output power of each power supply. For a photovoltaic power generation system, its output power cannot exceed the first output power available in the current period. This constraint takes into account the real-time and volatility of photovoltaic power generation, ensuring that the system does not rely too much on unstable photovoltaic output. The system will calculate the maximum available power of the photovoltaic system in real time based on the current lighting conditions, environmental factors such as temperature, and device status, and use it as an upper limit constraint.

[0129] The output power of the energy storage system cannot exceed its maximum available power, and the charging and discharging state of the energy storage system must be considered. The expression is:

[0130]

[0131] Where P ES,max (t) represents the second output power of the energy storage system in time period t.

[0132] For the energy storage system, its output power is also limited by the second available output power. This constraint not only takes into account the maximum discharge capacity of the energy storage system, but also its current state of charge to ensure the sustainable use of the energy storage system. The system dynamically calculates the maximum available power for each period based on the characteristics of the energy storage device, the current state of charge, and the predicted future demand.

[0133] In addition, the available power of the energy storage system should be within its capacity range, expressed as:

[0134] E ES (t+1)=E ES (t)-P ES (t)·Δt;

[0135]

[0136] In addition, the embodiments of the present application also pay special attention to the management of the energy storage system. The available power of the energy storage system must be kept within its capacity range, and the system needs to dynamically track the charge and discharge status of the energy storage system. By setting upper and lower limit constraints on the power of the energy storage system, overcharging or over-discharging of the energy storage system can be avoided, extending its service life, and ensuring that there is enough power available when needed. The system monitors the power status of the energy storage system in real time, and takes into account the expected charge and discharge conditions in future time periods during the scheduling process to ensure that the energy storage system always operates within a safe operating range.

[0137] The output power of the power grid cannot exceed its maximum available power:

[0138]

[0139] Where P Grid,max (t) represents the third output power of the power grid in time period t.

[0140] For power supply from the grid, its output power cannot exceed the predetermined third output power. This constraint takes into account the power supply capacity and other power demand of the grid to avoid excessive pressure on the grid. The system will determine the maximum power that the grid can provide in each period based on the agreement with the grid company and the current grid load situation, and use it as a constraint.

[0141] The above constraints together constitute a complete power supply system operation framework. In each scheduling cycle, the system will input these constraints into the power supply optimization model. The algorithm will strictly abide by these constraints when solving the optimal power supply mode. For example, when allocating the output power of each power supply source, the power supply optimization model will ensure that the total is equal to the load demand, and the output of each power supply source does not exceed its maximum available power. For the energy storage system, the algorithm will also predict its charging and discharging status in the future period to ensure that it always remains within a safe operating range.

[0142] (5) When the power supply switches from one source to another, the switching variable is 1, otherwise it is 0. This can be achieved through the following logical constraints:

[0143]

[0144] Similarly, for the switching between the energy storage system and the power grid:

[0145]

[0146] In the formula, ∈ PV ,∈ PV and∈ Grid Indicates the switching tolerance (threshold) to prevent frequent switching due to slight power changes.

[0147] Specifically, in the process of determining the power supply mode, the embodiment of the present application pays special attention to the switching problem of the power supply source. Frequent switching of the power supply source may lead to problems such as system instability, increased equipment loss, and reduced power supply quality. Therefore, in order to optimize the power supply strategy, this embodiment introduces switching variables and corresponding logical constraints to effectively control the switching frequency of the power supply source.

[0148] Specifically, this embodiment defines switching variables for the photovoltaic power generation system, the energy storage system, and the power grid. These switching variables are binary, and when the corresponding power supply source switches from one state to another, its value is 1, otherwise it is 0. In this way, the system can accurately track the switching of the power supply source in each time period.

[0149] To achieve this goal, this embodiment adopts a logic constraint based on power change. For the photovoltaic power generation system, the system compares the absolute value of the output power difference between the current period and the previous period. If this difference exceeds the preset switching tolerance, it is considered that a switch has occurred, and the corresponding switching variable is set to 1. The same logic also applies to the switching judgment of the energy storage system and the power grid.

[0150] The switching tolerance is actually a threshold value used to prevent unnecessary switching operations from being triggered by small power fluctuations. The setting of this tolerance value needs to be adjusted according to the characteristics and operation requirements of the system. For example, for a photovoltaic power generation system, a larger tolerance value may need to be set to accommodate its inherent volatility; while for a relatively stable grid power supply, a smaller tolerance value may be set.

[0151] The implementation process of the above switching judgment mechanism is as follows: In each scheduling cycle, the system first obtains the current output power of each power supply source and the output power of the previous period. Then, the power difference between the two periods is calculated, and its absolute value is compared with the preset switching tolerance. If the difference exceeds the tolerance, the corresponding switching variable is set to 1, indicating that the switching has occurred; otherwise, the switching variable remains at 0, indicating that no switching has occurred.

[0152] The above-mentioned switching judgment mechanism can effectively reduce unnecessary power supply switching and improve the stability of the system. For example, when there is a short and small fluctuation in photovoltaic power generation, the system will not switch to other power supplies immediately, but will switch only when the fluctuation exceeds the tolerance, which greatly reduces the volatility of the system. Secondly, by controlling the switching frequency, the service life of the equipment is extended and the maintenance cost is reduced. Frequent switching will accelerate the wear of the switching device, and the method of this embodiment can effectively slow down this process. Thirdly, this mechanism improves the power supply quality. Reducing unnecessary switching can avoid voltage fluctuations caused by switching operations, thereby ensuring the stability of power supply.

[0153] In the embodiment of the present application, in order to efficiently solve the complex power supply scheduling problem, a mixed integer linear programming (MILP) solver, such as Gurobi or CPLEX, is used. These advanced mathematical optimization tools can quickly handle large-scale optimization problems involving continuous and discrete variables.

[0154] The main reason for using MILP solvers is the complexity and multi-dimensionality of the power scheduling problem. The system needs to consider multiple time periods, multiple power sources, multiple constraints, as well as continuous power allocation and discrete switching decisions. Traditional heuristic algorithms or simple rule-based methods are difficult to find the global optimal solution in a reasonable time, while MILP solvers can effectively handle this complexity and obtain high-quality solutions in a shorter time.

[0155] Specifically, the objective function, decision variables, and constraints defined above can be transformed into a mathematical model that can be understood by the MILP solver. This includes defining the output power of the photovoltaic power generation system, energy storage system, and power grid in each time period as a continuous variable, and defining the switching decision as a binary variable. Then, we input all constraints such as power balance constraints, power upper limit constraints of each power supply source, charge and discharge state constraints of the energy storage system, and switching logic constraints into the solver.

[0156] Next, you need to provide the input data required by the solver. This data includes the predicted load demand, the predicted output of the photovoltaic power generation system, the initial state of the energy storage system, the grid power supply cost, etc. This data can be obtained through the prediction model and real-time monitoring system in the previous steps. At the same time, you also need to set the parameters of the solver, such as the solution time limit, optimization accuracy requirements, etc., to balance the calculation time and solution quality.

[0157] After completing the model settings and data input, start the MILP solver. The solver will use efficient algorithms, such as branch and bound method, cutting plane method, etc., to search for the optimal solution in the solution space. During the solution process, the solver will continuously update the upper and lower bounds, prune infeasible or suboptimal solutions, and finally converge to the optimal solution or find the best feasible solution within the specified time.

[0158] After the solution is completed, the MILP solver will return the optimization results. These results include the optimal power supply plan for each period. The solver will output: the photovoltaic power generation power P for each period PV (t), the energy storage system power P in each period t ES (t), the power grid power P in each period t Grid (t) and whether a switch occurs in each period PV (t), Switch ES (t), Switch Grid (t).

[0159] In summary, the use of MILP solver can obtain high-quality optimization results in a very short time to meet the needs of real-time scheduling. Secondly, since all constraints and objective functions are taken into account, the solution obtained is the global optimal or approximately the global optimal, which ensures the economy and feasibility of the power supply solution. Thirdly, the results of MILP solver are very interpretable, and the power supply decisions for each period can be clearly seen, which is conducive to the operation and maintenance personnel to understand and implement these decisions.

[0160] In addition, the use of the MILP solver also improves the flexibility and adaptability of the system. When system parameters change, the solution can be quickly re-solved to obtain a new optimal solution. This dynamic optimization capability enables the system to better cope with various changes and uncertainties in actual operation.

[0161] Step 103: Within a preset time period, power is supplied to each traction station according to the power supply mode.

[0162] Specifically, the system will first convert the optimized power supply mode into a series of specific control instructions. These instructions include the output power setting values ​​of the photovoltaic power generation system, energy storage system and power grid in each time period, as well as the corresponding switching operation instructions. According to these instructions, the control system will adjust the output power of each power supply in real time and perform the power supply switching operation when necessary.

[0163] In order to ensure a smooth transition of power supply and system stability, the switching operation usually adopts a soft switching strategy. For example, when it is necessary to switch from photovoltaic power generation to energy storage system power supply, the system will first gradually reduce the output power of photovoltaic power generation, and gradually increase the output power of the energy storage system until the switching is completed. This smooth switching can effectively reduce voltage fluctuations and current shocks and improve power supply quality.

[0164] During the power supply execution process, the system will also continuously monitor the actual operating conditions, including the actual output power of each power supply source, the actual load demand of the traction station, the charging and discharging status of the energy storage system, etc. These real-time data will be compared with the results predicted by the optimization model. If there is a significant deviation between the actual situation and the prediction, such as a sharp drop in photovoltaic power generation output due to weather changes, or a sudden increase in load demand due to changes in train scheduling, the system will trigger a real-time adjustment mechanism.

[0165] The real-time adjustment mechanism allows the system to quickly adjust the power supply strategy to adapt to new situations while maintaining the original optimization goals. This may include increasing the proportion of power supply from the grid, starting the energy storage system to discharge in advance, and other operations. In some cases, if the deviation is too large, the system may even trigger a re-optimization process to recalculate the optimal power supply mode using the latest real-time data.

[0166] Reference Figure 5 The present application also provides a power supply dispatching system for a railway traction station based on photovoltaic grid-connected power generation, comprising:

[0167] A prediction information acquisition module, used to obtain the load information of the railway traction in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system;

[0168] A power supply mode determination module, used to determine the power supply mode of each traction station according to the load information and the detection information, wherein the power supply mode includes any one of the photovoltaic grid-connected power generation system, the energy storage system and the power grid continuously supplying power to the corresponding traction station within a preset time period;

[0169] The power supply mode application module is used to supply power to each of the traction stations according to the power supply mode within the preset time period.

[0170] On the basis of the above embodiment, as an optional embodiment, the prediction information acquisition module is also used to obtain the train schedule of the traction depot of the railway in the target area and the detection signals collected by the sensors installed on the traction depot; the train schedule and the detection signals are input into the prediction model to obtain the load information and detection information of each traction depot after a preset time period output by the prediction model.

[0171] On the basis of the above embodiments, as an optional embodiment, the power supply mode determination module is further used to determine the first output power of the photovoltaic grid-connected power generation system within a preset time period according to the electrical detection information, environmental detection information and equipment detection information of the photovoltaic grid-connected power generation system; determine the second output power of the energy storage system in the traction station within a preset time period according to the first output power and the load information; determine the preset power supply power that can be allocated to the power grid of each traction station according to the load information of each traction station in the target area; adjust the preset power supply power of each traction station according to the first output power and the second output power of each traction station to obtain the third output power of the power grid of each traction station; determine the power supply mode of each traction station according to the first output power, the second output power and the third output power of each traction station.

[0172] Based on the above embodiment, as an optional embodiment, the power supply mode determination module is further used to input the first output power, the second output power and the third output power of each traction station into the power supply optimization model to obtain the power supply mode output by the power supply optimization model, and the power supply mode is used to characterize the way in which each traction station uses the photovoltaic grid-connected power generation system, the energy storage system and the power grid to supply power to the traction station in each period within the preset time length. Among them, the objective function of the power supply optimization model includes reducing the consumption of the power grid and reducing the switching of the power supply source of the traction station.

[0173] On the basis of the above embodiment, as an optional embodiment, the power supply dispatching system of the railway traction depot based on photovoltaic grid-connected power generation also includes a collector line switching module, which is used to determine the stability of the first collector line currently used by each traction depot according to the detection information; when it is determined that the stability is less than a threshold, the first collector line is switched to an idle second collector line.

[0174] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0175] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executed as in the above-mentioned embodiment of the power supply scheduling method for a railway traction depot based on photovoltaic grid-connected power generation. The specific execution process can refer to the specific description of the embodiment shown, which will not be repeated here.

[0176] The present application also discloses an electronic device. Figure 6 , Figure 6 The electronic device 600 may include: at least one processor 601 , at least one network interface 604 , a user interface 603 , a memory 605 , and at least one communication bus 602 .

[0177] The communication bus 602 is used to realize the connection and communication between these components.

[0178] The user interface 603 may include a display interface and a camera interface. The optional user interface 603 may also include a standard wired interface and a wireless interface.

[0179] The network interface 604 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0180] Among them, the processor 601 may include one or more processing cores. The processor 601 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 605, and calling data stored in the memory 605. Optionally, the processor 601 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 601 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 601, and it can be implemented separately through a chip.

[0181] Among them, the memory 605 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 605 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 605 may optionally be at least one storage device located away from the aforementioned processor 601. Refer to Figure 6 , the memory 605 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a power supply scheduling method for a railway traction station based on photovoltaic grid-connected power generation.

[0182] exist Figure 6In the electronic device 600 shown, the user interface 603 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 601 can be used to call the application program stored in the memory 605 for a power supply dispatching method for a railway traction station based on photovoltaic grid-connected power generation. When executed by one or more processors 601, the electronic device 600 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0183] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0188] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0189] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A power supply dispatching method for a railway traction station based on photovoltaic grid-connected power generation, characterized in that: include: Obtain the load information of the railway traction in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system; Determine a power supply mode of each traction station according to the load information and the detection information, wherein the power supply mode includes any one of a photovoltaic grid-connected power generation system, an energy storage system, and a power grid continuously supplying power to the corresponding traction station within a preset time period; During the preset time period, each traction station is powered according to the power supply mode.

2. The power supply dispatching method for railway traction depot based on photovoltaic grid-connected power generation according to claim 1 is characterized in that: The method of obtaining the load information of the railway traction in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system includes: Obtaining a train operation table of a traction depot of a railway in a target area and a detection signal collected by a sensor disposed on the traction depot; The train operation schedule and the detection signal are input into a prediction model to obtain the load information and detection information of each traction after a preset time period output by the prediction model.

3. The power supply dispatching method for railway traction depot based on photovoltaic grid-connected power generation according to claim 1, characterized in that: The detection information includes electrical detection information, environmental detection information and equipment detection information of the photovoltaic grid-connected power generation system, wherein the electrical detection information includes the electrical parameters of the busbar, the electrical parameters of the traction transformer and the equipment status of the circuit breaker, the environmental detection information includes the environmental parameters inside the traction station and the electrical parameters of the photovoltaic power generation system, and the equipment detection information includes the working status of the inverter. The determining, according to the load information and the detection information, a power supply mode of each traction station includes: Determining a first output power of the photovoltaic grid-connected power generation system within a preset time period according to electrical detection information, environmental detection information, and equipment detection information of the photovoltaic grid-connected power generation system; Determine a second output power of the energy storage system in the traction station for a preset time period according to the first output power and the load information; Determining the preset power supply power that can be allocated by the power grid of each traction station according to the load information of each traction station in the target area; According to the first outputtable power and the second outputtable power of each traction station, the preset power supply power of each traction station is adjusted to obtain the third outputtable power of the power grid of each traction station; The power supply mode of each traction station is determined according to the first output power, the second output power and the third output power of each traction station.

4. The power supply dispatching method for railway traction depot based on photovoltaic grid-connected power generation according to claim 3 is characterized in that: The step of determining the power supply mode of each traction station according to the first outputtable power, the second outputtable power and the third outputtable power of each traction station comprises: Input the first output power, the second output power and the third output power of each traction station into a power supply optimization model, and obtain a power supply mode output by the power supply optimization model, wherein the power supply mode is used to characterize the way in which each traction station uses a photovoltaic grid-connected power generation system, an energy storage system and a power grid to supply power to the traction station in each period within a preset time period; The objective function of the power supply optimization model includes reducing the consumption of the power grid and reducing the switching of the power supply source of the traction station.

5. The power supply dispatching method for railway traction depot based on photovoltaic grid-connected power generation according to claim 4 is characterized in that: The power supply optimization model is expressed as: In the formula, T represents the preset duration, t represents the time period within the preset duration, and P Grid (t(represents the output power of the photovoltaic power generation system during time period t, C Grid (t) represents the cost of power supply from the grid in period t, λ represents the weight of the switching penalty, which is used to balance the power cost and the switching cost. PV (t) indicates whether photovoltaic power generation switches to power supply during time period t. ES (t) indicates whether the power supply is switched from the energy storage system during time period t. Grid (t( indicates whether the power supply is switched from the grid during period t.

6. The power supply dispatching method for railway traction depot based on photovoltaic grid-connected power generation according to claim 1 is characterized in that: The photovoltaic grid-connected power generation system includes photovoltaic panels, a junction box, an inverter, a step-up transformer, and a grid-connected outlet cabinet, wherein the grid-connected outlet cabinet is provided with a photovoltaic power generation switch station, and the photovoltaic power generation switch station includes at least two switchable collector line incoming cabinets.

7. The power supply dispatching method for railway traction depot based on photovoltaic grid-connected power generation according to claim 6, characterized in that: The method further comprises: Determine the stability of the first current collecting line currently used by each traction station according to the detection information; When it is determined that the stability is less than a threshold, the first power collection line is switched to an idle second power collection line.

8. A power supply dispatching system for a railway traction depot based on photovoltaic grid-connected power generation, characterized in that: include: A prediction information acquisition module, used to obtain the load information of the railway traction in the target area after a preset time and the detection information of the corresponding photovoltaic grid-connected power generation system; A power supply mode determination module, used to determine the power supply mode of each traction station according to the load information and the detection information, wherein the power supply mode includes any one of the photovoltaic grid-connected power generation system, the energy storage system and the power grid continuously supplying power to the corresponding traction station within a preset time period; The power supply mode application module is used to supply power to each of the traction stations according to the power supply mode within the preset time period.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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