Energy scheduling method and device for traffic hub

Abstract

The application relates to the technical field of energy scheduling, and particularly discloses an energy scheduling method and device for a traffic hub, which comprises the following steps: acquiring multi-source sensing data of the traffic hub, wherein the multi-source sensing data comprises one or more of clean energy generation sensing data, energy consumption sensing data, environment sensing data and operation state sensing data; determining total power generation of the traffic hub according to the multi-source sensing data by using a clean energy output model; determining a dynamic threshold of electricity consumption corresponding to a current time of the traffic hub according to the multi-source sensing data by using a dynamic energy consumption early warning threshold model; determining an early warning level of the current time of the traffic hub based on the dynamic threshold of electricity consumption; and performing energy scheduling on at least one target object according to the early warning level, so that the clean energy utilization efficiency and the energy consumption early warning accuracy can be significantly improved, the comprehensive energy consumption and carbon emission of the traffic hub can be reduced, and the method is suitable for various large traffic hubs.

Description

Technical Field

[0001] This disclosure generally relates to the field of energy dispatching technology, and specifically to an energy dispatching method and apparatus for transportation hubs. Background Technology

[0002] my country's large-scale transportation hubs have entered a stage of rapid development characterized by networking, intensification, and intelligence. As complex public buildings and transportation facilities with high population density, complex electromechanical systems, and concentrated energy loads, transportation hubs primarily consume electricity. The energy consumption of core buildings such as station buildings, terminals, waiting halls, and distribution halls accounts for over 70% of the total energy consumption of the hub. Significant differences in energy consumption levels exist between transportation hubs in different climate zones, with varying building sizes, operational saturation levels, and business volumes. This is particularly evident in frigid and cold regions. The energy consumption per unit area of ​​the district's transportation hub is much higher than that of hot-summer and cold-winter, hot-summer and warm-winter, and temperate regions. The energy consumption mainly consists of air conditioning and cooling systems, lighting systems, baggage and ticketing systems, escalators and walkways, low-voltage information and flight / train information systems, fire protection and security systems, apron and platform auxiliary power supply systems, and new energy vehicle charging systems. The energy consumption curve exhibits strong nonlinearity, strong time-varying characteristics, and strong fluctuations with seasonal changes, day and night times, passenger flow, train schedules, and ambient temperature and humidity. The afternoon to evening is the typical peak electricity consumption period. The existing energy operation and computing power management systems of transportation hubs generally suffer from prominent problems such as mismatch between clean energy installation planning and actual load demand, energy consumption monitoring only achieving data collection and visualization without continuous dynamic early warning capabilities, fixed quota allocation of computing power resources leading to both insufficient computing power during peak periods and idle and wasted computing power during off-peak periods, and independent and fragmented clean energy output forecasting, energy consumption load forecasting, anomaly early warning judgment, and computing power scheduling and control without forming a closed-loop collaborative mechanism. These problems result in low overall energy utilization efficiency of transportation hubs, delayed response to energy consumption anomalies, unbalanced allocation of computing power resources, and high operating costs and carbon emissions, making it difficult to meet the development requirements of green, low-carbon, safe, reliable, smart and efficient new transportation hubs. Summary of the Invention

[0003] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide an energy dispatching method and device for transportation hubs, which can significantly improve the efficiency of clean energy utilization, the accuracy of energy consumption early warning, and reduce the overall energy consumption and carbon emissions of transportation hubs, and is applicable to various large-scale transportation hubs.

[0004] In a first aspect, embodiments of this application provide an energy dispatching method for transportation hubs, comprising:

[0005] Acquire multi-source sensing data of transportation hubs, wherein the multi-source sensing data includes one or more of the following: clean energy generation sensing data, energy consumption sensing data, environmental sensing data, and operational status sensing data;

[0006] Using a clean energy output model, the total power generation of the transportation hub is determined based on the multi-source sensing data.

[0007] Using a dynamic energy consumption early warning threshold model, the dynamic threshold for electricity consumption at the current moment of the transportation hub is determined based on the multi-source sensing data.

[0008] Based on the dynamic threshold of electricity consumption, the warning level of the transportation hub at the current moment is determined;

[0009] Based on the warning level, energy scheduling is performed on at least one target object.

[0010] In some embodiments, when the target object is clean energy corresponding to the transportation hub, the step of energy dispatching for at least one target object according to the warning level includes:

[0011] If the warning level is a low warning level, the real-time output adjustment coefficient corresponding to the current moment of the transportation hub is determined based on the total power generation of the transportation hub and the multi-source sensing data.

[0012] Based on the real-time output adjustment coefficient and the current maximum theoretical output of the transportation hub, the target output of clean energy corresponding to the transportation hub is determined;

[0013] The clean energy is scheduled based on the target output of clean energy corresponding to the transportation hub and the real-time output of clean energy corresponding to the transportation hub.

[0014] In some embodiments, when the target object is the load equipment corresponding to the transportation hub, the step of energy dispatching for at least one target object according to the warning level includes:

[0015] If the warning level is a high warning level, the load reduction needs to be determined based on the multi-source sensing data;

[0016] Based on the required load reduction and the available discharge power of energy storage, multi-level scheduling instructions are generated.

[0017] In some embodiments, determining the total power generation of the transportation hub using a clean energy output model based on the multi-source sensing data includes:

[0018] By invoking the clean energy output model and continuously integrating the spatial and temporal data based on the multi-source sensing data, the total power generation of the transportation hub is obtained.

[0019] Secondly, embodiments of this application provide an energy dispatching device for transportation hubs, comprising:

[0020] The acquisition module is used to acquire multi-source sensing data of the transportation hub, wherein the multi-source sensing data includes one or more of the following: clean energy generation sensing data, energy consumption sensing data, environmental sensing data, and operational status sensing data.

[0021] The first determining module is used to determine the total power generation of the transportation hub based on the multi-source sensing data using a clean energy output model.

[0022] The second determining module is used to determine the dynamic threshold of electricity consumption at the current moment of the transportation hub based on the multi-source sensing data using a dynamic energy consumption early warning threshold model.

[0023] The third determining module is used to determine the warning level of the transportation hub at the current moment based on the dynamic threshold of electricity consumption.

[0024] The adjustment module is used to perform energy scheduling on at least one target object according to the warning level.

[0025] In some embodiments, when the target object is clean energy corresponding to the transportation hub, the adjustment module is specifically used for:

[0026] If the warning level is a low warning level, the real-time output adjustment coefficient corresponding to the current moment of the transportation hub is determined based on the total power generation of the transportation hub and the clean energy generation sensing data.

[0027] Based on the real-time output adjustment coefficient and the current maximum theoretical output of the transportation hub, the target output of clean energy corresponding to the transportation hub is determined;

[0028] The clean energy is scheduled based on the target output of clean energy corresponding to the transportation hub and the real-time output of clean energy corresponding to the transportation hub.

[0029] In some embodiments, when the target object is the load equipment corresponding to the transportation hub, the adjustment module is specifically used for:

[0030] If the warning level is high, the load reduction is determined based on the multi-source sensing data.

[0031] Based on the required load reduction and the available discharge power of energy storage, multi-level scheduling instructions are generated.

[0032] In some embodiments, the first determining module is specifically used for:

[0033] By invoking the clean energy output model and continuously integrating the spatial and temporal data based on the multi-source sensing data, the total power generation of the transportation hub is obtained.

[0034] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in embodiments of this application.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in embodiments of this application.

[0036] Fifthly, embodiments of this application provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the method described in embodiments of this application.

[0037] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0039] Figure 1 A flowchart illustrating an embodiment of the energy dispatching method for a transportation hub provided in this application is shown.

[0040] Figure 2 A schematic diagram of the structure of an energy dispatching device for a transportation hub according to an embodiment of this application is shown;

[0041] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device or server according to embodiments of this application is shown. Detailed Implementation

[0042] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0043] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation instruction steps as shown in the following embodiments or drawings, the method may include more or fewer operation instruction steps based on conventional or non-creative effort. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0045] Please refer to Figure 1 , Figure 1 A schematic flowchart of an energy dispatching method for a transportation hub according to an embodiment of this application is shown. Figure 1 As shown, the method includes:

[0046] Step 101: Obtain multi-source sensing data of the transportation hub. The multi-source sensing data includes one or more of the following: clean energy generation sensing data, energy consumption sensing data, environmental sensing data, and operational status sensing data.

[0047] It should be noted that the multi-source sensing data includes clean energy power generation equipment (photovoltaic, wind power), energy consumption monitoring points (various functional zones, major loads), environmental sensors (temperature, humidity, irradiance), and operation status acquisition units (passenger flow, schedule information, equipment start-up and shutdown status) within the entire transportation hub area, corresponding to clean energy generation sensing data, energy consumption sensing data, clean energy sensing data, and operation status sensing data.

[0048] It should be understood that, in order to collect the aforementioned multi-source sensing data, sensors with corresponding functions can be deployed in transportation hubs to collect data at the second-level sampling rate and through a spatially gridded distribution. Dual-link redundant transmission using 5G and industrial Ethernet ensures low latency and high reliability. This effectively solves the problem of traditional transportation hub energy dispatching, which suffers from a single data source and low sampling frequency, making it unable to support continuous time-domain modeling and millisecond-level early warning.

[0049] In one feasible embodiment, after acquiring the multi-source sensing data of the transportation hub, the multi-source sensing data can be further processed to obtain the indirect data required for subsequent analysis and calculation, including but not limited to real-time total energy consumption of the hub, real-time irradiance field, real-time temperature, indoor and outdoor temperature and humidity, passenger flow, train frequency, equipment load rate, etc. This application does not make specific limitations on this.

[0050] Step 102: Using a clean energy output model, determine the total power generation of the transportation hub based on multi-source sensing data.

[0051] In one feasible embodiment, a clean energy processing model is invoked, and based on multi-source sensing data, continuous integration is performed over space and time to obtain the total power generation of the transportation hub. Specifically, data such as spatiotemporal irradiance, temperature, installation parameters, and shading distribution of the transportation hub can be determined based on the multi-source sensing data.

[0052] For example, the following expression can be used:

[0053]

[0054] in, The total power generation of the transportation hub. , To calculate the start and end times, For continuous time variables, Clean energy for transportation hubs can be used to construct regional two-dimensional spatial coordinates. For the effective area of ​​clean energy in transportation hubs, Let be the spatiotemporal distribution photoelectric conversion efficiency function. For the installation method space-time correction function, This is a temperature loss correction function. This is a function for correcting the solar irradiance angle. For occlusion attenuation function, Let be the spatiotemporal distribution function of solar radiation illuminance.

[0055] Therefore, this application constructs a clean energy output model by coupling spatial double integral and time definite integral, which effectively improves the total clean energy power generation of transportation hubs throughout the entire time cycle and the entire available space range, and provides high-precision data support for long-term planning and real-time allocation.

[0056] Step 103: Using the dynamic energy consumption early warning threshold model, determine the dynamic threshold of electricity consumption at the current moment of the transportation hub based on multi-source sensing data.

[0057] Specifically, the dynamic energy consumption early warning threshold model is invoked, and based on data such as real-time energy consumption, historical energy consumption sequence, climate zone, building scale, operation saturation, peak and valley times, and indoor and outdoor temperature and humidity obtained from multi-source sensing data analysis, the dynamic threshold of electricity consumption corresponding to the current moment of the transportation hub is calculated.

[0058] For example, it can be expressed using the following formula:

[0059]

[0060] in, The dynamic energy consumption early warning threshold for transportation hubs at time t. It is a function of the historical average energy consumption for the same period. This is a climate region correction function. For building scale correction function, To run the saturation correction function, This is a peak-valley correction function for the time period. It is a bivariate response function for indoor and outdoor temperature and humidity. The first derivative of energy consumption with respect to time, This is the weighting coefficient for the rate of change.

[0061] Therefore, this application, through a dynamic threshold for electricity consumption, can effectively achieve real-time adaptive adjustment of the early warning threshold according to operating status, environmental conditions, and load change trends. Simultaneously, it introduces the energy consumption change rate to achieve proactive early warning, completely overcoming the limitations of fixed thresholds. This effectively solves the problems of fixed thresholds failing to adapt to energy consumption fluctuations in different seasons, time periods, and saturation levels, leading to false alarms or missed alarms; and the lack of perception of the rate of energy consumption change, making it impossible to identify sudden increases in risk in advance.

[0062] Step 104: Determine the warning level of the transportation hub at the current moment based on the dynamic threshold of electricity consumption.

[0063] Specifically, based on the real-time energy consumption and dynamic energy consumption warning values ​​of the transportation hub at time t, the warning level for the current time is determined according to the four-level warning rule.

[0064] In one specific embodiment, the excess amount at time t of the transportation hub is determined by the real-time energy consumption and dynamic energy consumption warning value of the transportation hub at time t. For example, the following expression is used:

[0065]

[0066] in, This represents the excess quantity at time t of the transportation hub. This represents the real-time energy consumption of the transportation hub at time t. This represents the dynamic energy consumption warning value for the transportation hub at time t.

[0067] Furthermore, in the case of overscalar quantity When the value is ≤0, the transportation hub at time t is determined to be at a low warning level, and the value exceeds the standard. When the value is greater than 0, the transportation hub at time t is designated as a high-alert level.

[0068] Step 105: Based on the warning level, perform energy dispatch on at least one target object.

[0069] It should be noted that this application determines the scheduling strategy for the target object and performs energy scheduling based on the object to be scheduled and the early warning level.

[0070] In a feasible embodiment, when the target object is clean energy corresponding to a transportation hub, if the warning level is a low warning level, the real-time output adjustment coefficient corresponding to the transportation hub at the current moment is determined based on the total power generation of the transportation hub and multi-source sensing data; the target output of the clean energy corresponding to the transportation hub is determined based on the real-time output adjustment coefficient and the current maximum theoretical output of the transportation hub; and the clean energy is dispatched based on the target output of the clean energy corresponding to the transportation hub and the real-time output of the clean energy corresponding to the transportation hub.

[0071] Specifically, the real-time output adjustment coefficient of the transportation hub at the current time t can be determined using the following expression:

[0072] in, This refers to the real-time output adjustment coefficient of the transportation hub at the current time t. The total power generation of the transportation hub. The annual average irradiance. This represents the current real-time irradiance.

[0073] Furthermore, the target clean energy output for the transportation hub is determined using the following expression:

[0074]

[0075] in, Contribute to the clean energy goals corresponding to transportation hubs. This refers to the real-time output adjustment coefficient of the transportation hub at the current time t. To contribute to the current maximum theoretical level of transportation hubs.

[0076] in, This can be the value of the basis function in the formula for the total power generation of a transportation hub at the current moment.

[0077] Specifically, if the clean energy corresponding to the transportation hub is output in real time > The clean energy output target corresponding to the transportation hub is then controlled by the clean energy storage and charging.

[0078] If the clean energy corresponding to the transportation hub is output in real time < For transportation hubs, the clean energy output target is maintained by controlling the clean energy standby mode and adjusting the inverter to track the clean energy output target. .

[0079] Therefore, this application combines long-term total assessment with real-time dynamic adjustment to solve the problem of timing adaptation between clean energy and actual load demand. It effectively solves the problem in traditional systems where clean energy output only operates according to maximum power point tracking (MPPT) without considering long-term total balance and energy storage synergy, leading to curtailment of solar and wind power or excessive reliance on grid power.

[0080] In another feasible embodiment, when the target object is the load equipment corresponding to the transportation hub, if the warning level is a high warning level, the amount of load to be reduced is determined based on multi-source sensing data; and multi-level scheduling instructions are generated based on the amount of load to be reduced and the available discharge power of energy storage.

[0081] For example, the following expression can be used:

[0082]

[0083] in, The required load reduction is as follows: This is an excessive amount. The first derivative of energy consumption with respect to time, To ensure the safe rate of increase in energy consumption, This represents the total power of non-core loads. This is the penalty coefficient for the rate of change.

[0084] Specifically, after obtaining the amount of load to be reduced, it is determined whether the energy storage discharge is sufficient. If it is sufficient, only the energy storage discharges, without cutting off the load. If it is insufficient, low-priority loads (such as landscape lighting and some escalators) are cut off first, followed by medium-priority loads (such as some air conditioning terminals), until the reduction amount is met. The load equipment includes air conditioning, lighting, etc.

[0085] Therefore, while ensuring the uninterrupted operation of core equipment, the peak energy consumption can be quickly mitigated, thus reducing the real-time energy consumption of the transportation hub at time t. Dynamic energy consumption warning value at time t of the transportation hub the following.

[0086] In summary, the energy dispatching method for transportation hubs provided in this application constructs a high-order collaborative dispatching system integrating clean energy supply and energy consumption early warning monitoring. The clean energy output calculation employs a coupled model of spatial double integral and time definite integral, significantly improving calculation accuracy and controlling the calculation error within 2%. The energy self-consistency rate is increased from below 40% in the traditional model to over 85%. The energy consumption early warning uses a dynamic threshold model of multiple composite functions containing first-order differential change rates, integrating continuous factors of climate, scale, saturation, time period, temperature, and humidity. The early warning accuracy reaches over 99%, and the response time is controlled within 0.5 seconds, enabling early identification of potential risks such as sudden load increases, equipment anomalies, and power supply fluctuations. The system as a whole can reduce the comprehensive energy consumption of transportation hubs by 15% to 28% and reduce annual carbon emissions by 1%. With a capacity of over 10,000 tons, it boasts significant economic, social, and environmental benefits. The system adopts a standardized architecture, modular design, and model-based algorithms, making it widely adaptable to various large-scale transportation hubs such as airports, high-speed rail stations, passenger centers, ports, and rail transit hubs. It possesses strong engineering application value and market promotion prospects.

[0087] It should be noted that although the operation of the method of the present invention is described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed in order to achieve the desired result.

[0088] Figure 2 A schematic diagram of the structure of an energy dispatching device for a transportation hub provided in an embodiment of this application is shown.

[0089] like Figure 2 As shown, the energy dispatching device 10 for transportation hubs includes:

[0090] The acquisition module 11 is used to acquire multi-source sensing data of the transportation hub, wherein the multi-source sensing data includes one or more of the following: clean energy generation sensing data, energy consumption sensing data, environmental sensing data, and operational status sensing data.

[0091] The first determining module 12 is used to determine the total power generation of the transportation hub based on the multi-source sensing data using a clean energy output model.

[0092] The second determining module 13 is used to determine the dynamic threshold of electricity consumption at the current moment of the transportation hub based on the multi-source sensing data using a dynamic energy consumption early warning threshold model.

[0093] The third determining module 14 is used to determine the warning level of the transportation hub at the current moment based on the dynamic threshold of electricity consumption.

[0094] The adjustment module 15 is used to perform energy scheduling on at least one target object according to the warning level.

[0095] In some embodiments, when the target object is clean energy corresponding to the transportation hub, the adjustment module 15 is specifically used for:

[0096] If the warning level is a low warning level, the real-time output adjustment coefficient corresponding to the current moment of the transportation hub is determined based on the total power generation of the transportation hub and the clean energy generation sensing data.

[0097] Based on the real-time output adjustment coefficient and the current maximum theoretical output of the transportation hub, the target output of clean energy corresponding to the transportation hub is determined;

[0098] The clean energy is scheduled based on the target output of clean energy corresponding to the transportation hub and the real-time output of clean energy corresponding to the transportation hub.

[0099] In some embodiments, when the target object is the load equipment corresponding to the transportation hub, the adjustment module 15 is specifically used for:

[0100] If the warning level is high, the load reduction is determined based on the multi-source sensing data.

[0101] Based on the required load reduction and the available discharge power of energy storage, multi-level scheduling instructions are generated.

[0102] In some embodiments, the first determining module 12 is specifically used for:

[0103] By invoking the clean energy output model and continuously integrating the spatial and temporal data based on the multi-source sensing data, the total power generation of the transportation hub is obtained.

[0104] It should be understood that the modules or modules described in the energy dispatching device 10 for transportation hubs are similar to those in the reference. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations and features described above for the method are also applicable to the energy dispatching device 10 for transportation hubs and the modules contained therein, and will not be repeated here. The energy dispatching device 10 for transportation hubs can be pre-implemented in the browser or other secure applications of electronic devices, or it can be loaded into the browser or other secure applications of electronic devices through downloading or other means. The corresponding modules in the energy dispatching device 10 for transportation hubs can cooperate with the modules in the electronic device to implement the solutions of the embodiments of this application.

[0105] The division of modules or units mentioned in the detailed description above is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0106] The following is for reference. Figure 3 , Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown.

[0107] like Figure 3 As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the system's operating instructions. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0108] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0109] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 2 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0110] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions.

[0112] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be housed in a processor; for example, a processor can be described as including an acquisition module, a first determination module, a second determination module, a third determination module, and an adjustment module. The names of these units or modules do not necessarily limit the specific unit or module itself. For example, the acquisition module can also be described as "acquiring multi-source sensing data of a transportation hub, wherein the multi-source sensing data includes one or more of clean energy generation sensing data, energy consumption sensing data, environmental sensing data, and operational status sensing data."

[0113] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the energy dispatching method for transportation hubs described in this application.

[0114] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. An energy dispatching method for transportation hubs, characterized in that, include: Acquire multi-source sensing data of transportation hubs, wherein the multi-source sensing data includes one or more of the following: clean energy generation sensing data, energy consumption sensing data, environmental sensing data, and operational status sensing data; Using a clean energy output model, the total power generation of the transportation hub is determined based on the multi-source sensing data. Using a dynamic energy consumption early warning threshold model, the dynamic threshold for electricity consumption at the current moment of the transportation hub is determined based on the multi-source sensing data. Based on the dynamic threshold of electricity consumption, the warning level of the transportation hub at the current moment is determined; Based on the warning level, energy scheduling is performed on at least one target object.

2. The energy dispatching method for transportation hubs according to claim 1, characterized in that, When the target object is clean energy corresponding to the transportation hub, the step of energy dispatching for at least one target object according to the warning level includes: If the warning level is a low warning level, the real-time output adjustment coefficient corresponding to the current moment of the transportation hub is determined based on the total power generation of the transportation hub and the multi-source sensing data. Based on the real-time output adjustment coefficient and the current maximum theoretical output of the transportation hub, the target output of clean energy corresponding to the transportation hub is determined; The clean energy is scheduled based on the target output of clean energy corresponding to the transportation hub and the real-time output of clean energy corresponding to the transportation hub.

3. The energy dispatching method for transportation hubs according to claim 1, characterized in that, When the target object is the load equipment corresponding to the transportation hub, the step of energy scheduling for at least one target object according to the warning level includes: If the warning level is a high warning level, the load reduction needs to be determined based on the multi-source sensing data; Based on the required load reduction and the available discharge power of energy storage, multi-level scheduling instructions are generated.

4. The energy dispatching method for transportation hubs according to claim 1, characterized in that, The method of using a clean energy output model to determine the total power generation of the transportation hub based on the multi-source sensing data includes: By invoking the clean energy output model and continuously integrating the spatial and temporal data based on the multi-source sensing data, the total power generation of the transportation hub is obtained.

5. An energy dispatching device for a transportation hub, characterized in that, include: The acquisition module is used to acquire multi-source sensing data of the transportation hub, wherein the multi-source sensing data includes one or more of the following: clean energy generation sensing data, energy consumption sensing data, environmental sensing data, and operational status sensing data. The first determining module is used to determine the total power generation of the transportation hub based on the multi-source sensing data using a clean energy output model. The second determining module is used to determine the dynamic threshold of electricity consumption at the current moment of the transportation hub based on the multi-source sensing data using a dynamic energy consumption early warning threshold model. The third determining module is used to determine the warning level of the transportation hub at the current moment based on the dynamic threshold of electricity consumption. The adjustment module is used to perform energy scheduling on at least one target object according to the warning level.

6. The energy dispatching device for a transportation hub according to claim 5, characterized in that, When the target object is clean energy corresponding to the transportation hub, the adjustment module is specifically used for: If the warning level is a low warning level, the real-time output adjustment coefficient corresponding to the current moment of the transportation hub is determined based on the total power generation of the transportation hub and the clean energy generation sensing data. Based on the real-time output adjustment coefficient and the current maximum theoretical output of the transportation hub, the target output of clean energy corresponding to the transportation hub is determined; The clean energy is scheduled based on the target output of clean energy corresponding to the transportation hub and the real-time output of clean energy corresponding to the transportation hub.

7. The energy dispatching device for transportation hubs according to claim 5, characterized in that, When the target object is the load equipment corresponding to the transportation hub, the adjustment module is specifically used for: If the warning level is high, the load reduction is determined based on the multi-source sensing data. Based on the required load reduction and the available discharge power of energy storage, multi-level scheduling instructions are generated.

8. The energy dispatching device for a transportation hub according to claim 5, characterized in that, The first determining module is specifically used for: By invoking the clean energy output model and continuously integrating the spatial and temporal data based on the multi-source sensing data, the total power generation of the transportation hub is obtained.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the energy dispatching method for transportation hubs as described in any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the energy dispatching method for transportation hubs as described in any one of claims 1-4.

Images