A real-time carbon emission monitoring method for a carbon-neutral depot

Abstract

This invention relates to the field of urban rail transit energy management and carbon emission management technology, and provides a real-time carbon emission monitoring method for carbon-neutral depots. This method is designed for intelligent rail transit depots using onboard power batteries, with the vehicle charging system as the main carbon emission metering link. Through multi-source energy topology modeling of the depot, dynamic hybrid carbon factor calculation during charging, carbon factor tracing during energy storage charging, and a power tracing algorithm based on the multi-source energy topology of the depot, it achieves second-level monitoring and analysis of carbon emission intensity at the charging event level, equipment level, and overall depot level. This invention is applicable to carbon-neutral depots or vehicle bases that simultaneously configure pantograph charging poles, DC fast charging piles, photovoltaic systems, energy storage, and station loads.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit energy management and carbon emission management technology, and more specifically, to a real-time carbon emission monitoring method for carbon-neutral depots. Background Technology

[0002] Intelligent rail transit (IRT) is an urban rail transit system that uses onboard power batteries or other new energy storage devices for power supply. Traditional overhead contact lines are generally not installed along the line. Vehicles rely on pantographs for rapid charging at depots, terminal stations, or some intermediate stations. At the same time, vehicles are usually equipped with DC fast charging interfaces, and DC fast charging guns are installed in the depot as backup or emergency power supply methods.

[0003] The energy structure of intelligent rail transit depots has the following typical characteristics:

[0004] 1. Medium-voltage incoming lines and main transformers of the power grid;

[0005] 2. Charging converter cabinet and DC busbar that supply power to pantograph charging poles and DC fast charging piles;

[0006] 3. Station loads such as maintenance depots, parking and inspection areas, and the main building;

[0007] 4. Rooftop photovoltaic power generation system;

[0008] 5. Station-level electrochemical energy storage system;

[0009] Some depots are also equipped with gas-fired boilers or diesel generators as auxiliary or emergency energy sources.

[0010] Under the "dual carbon" target requirements, rail transit operators generally build energy management systems and carbon emission management platforms. Existing systems are mostly targeted at factories, industrial parks or stations. By collecting data on various energy sources such as electricity, water, and gas, they can achieve energy consumption statistics, energy consumption visualization and energy saving analysis. Some systems introduce carbon emission factor libraries to convert annual or monthly energy consumption into carbon emissions for carbon inventory reports and energy saving assessments.

[0011] In the scenario of intelligent rail carbon neutral depots, existing solutions have the following shortcomings:

[0012] 1. Carbon emission boundaries are mostly limited to the statistical level of "annual electricity consumption multiplied by a fixed carbon factor", which makes it difficult to depict the real-time carbon emission changes of "vehicle concentrated fast charging window plus grid time-of-use carbon factor plus local photovoltaic and power station energy storage".

[0013] 2. The multi-source energy flow modeling is insufficient. It is usually impossible to describe the complete path of "the power grid, photovoltaic and energy storage flowing into the charging bus, and then entering the vehicle battery through the pantograph or DC fast charging gun" in a unified model. It is difficult to quantitatively assess the contribution of photovoltaic self-consumption and energy storage peak shaving and valley filling to the carbon emissions of the vehicle depot.

[0014] 3. There is a lack of correlation between vehicle charging behavior and specific operational tasks. The amount of electricity and carbon emissions on the charging side are difficult to accurately allocate to specific vehicles, trips, and routes, making it difficult to form operational indicators such as "carbon footprint per trip" and "carbon intensity per route".

[0015] 4. The problem of carbon factor traceability for energy storage systems charging during low-carbon periods and discharging during high-carbon periods has not been solved. Existing solutions often directly use the current bus carbon factor as the discharge carbon factor, which cannot trace the carbon emission attributes of energy storage discharge back to the energy structure at the time of charging. This can easily lead to double-counting of carbon emissions or underestimation of peak shaving and emission reduction effects.

[0016] 5. In the depot power distribution network with multiple power sources and multiple loads, a charging bus is often connected to the grid, photovoltaic and energy storage at the same time. Traditional platforms mostly make rough statistics according to the electricity meter zone, which makes it difficult to accurately separate the power and carbon emission contributions from different energy sources.

[0017] 6. Existing systems generally use sampling periods of minutes or longer and rely on centralized computing by central servers. In scenarios with high-frequency centralized fast charging in intelligent rail transit sections, this can easily mask short-term carbon emission peaks and fluctuations, and it is also difficult to provide second-level real-time signals for energy consumption scheduling and operation optimization.

[0018] 7. In terms of carbon neutrality certification and carbon emission reduction project development, current practices mostly extract data offline during annual carbon inventory, which cannot make full use of the second-level carbon emission information during operation and is not conducive to forming a real-time data foundation that is naturally connected with standards such as ISO 14064 and PAS 2060. Summary of the Invention

[0019] The present invention aims to provide a real-time carbon emission monitoring method for carbon-neutral depots to solve the problems existing in the above-mentioned intelligent rail carbon-neutral depot scenario.

[0020] This invention provides a real-time carbon emission monitoring method for carbon-neutral vehicle depots, comprising:

[0021] Construct a multi-source energy topology map for the vehicle depot;

[0022] Multi-source energy data is collected based on the multi-source energy topology map of the depot.

[0023] Set the grid time-sharing carbon factor and the photovoltaic power generation carbon factor;

[0024] The dynamic mixed carbon factor of the charging bus is calculated based on the collected multi-source energy data, the grid time-sharing carbon factor, and the photovoltaic power generation carbon factor.

[0025] By utilizing collected multi-source energy data and based on the dynamic mixing carbon factor of the charging bus, the energy storage discharge carbon factor is calculated through energy batch management and traceability.

[0026] Based on the multi-source energy topology of the depot, the time-sharing carbon factor of the power grid, the carbon factor of photovoltaic power generation, and the carbon factor of energy storage discharge, the carbon emissions at the equipment level are calculated.

[0027] By utilizing collected multi-source energy data and based on the dynamic mixing carbon factor of the charging bus, vehicle-level carbon emission measurement is calculated with charging events as the unit.

[0028] The vehicle-level carbon emission metering is allocated to each train trip to obtain the train trip-level carbon emission amount and carbon emission intensity.

[0029] Carbon emission structure analysis and peak identification are performed based on equipment-level and vehicle-level carbon emission data, and data archiving is completed.

[0030] In a preferred embodiment, constructing the multi-source energy topology map of the vehicle depot includes:

[0031] The connection relationships between source nodes, station load nodes, and charging load nodes are abstracted as directed edges, thereby forming a multi-source energy topology graph of the depot.

[0032] The source nodes include grid incoming nodes, photovoltaic inverter nodes, and energy storage converter nodes;

[0033] The station load nodes include station transformer nodes and station power distribution nodes;

[0034] The charging load nodes include the DC bus node of the charging converter cabinet, the pantograph charging column node, and the DC fast charging pile node.

[0035] In a preferred embodiment, the acquisition of multi-source energy data based on the multi-source energy topology map of the vehicle depot includes:

[0036] Multi-source energy data is collected according to the sampling period, including:

[0037] Power is collected at the grid incoming node;

[0038] Photovoltaic power is collected at photovoltaic inverter nodes;

[0039] Energy storage charging power and energy storage discharging power are collected at the energy storage converter node;

[0040] The active power during the charging process and the on / off status of each charging load node are collected at the DC bus node of the charging converter cabinet, the pantograph charging column node, and the DC fast charging pile node.

[0041] The station service load power and the on / off status of each station service load node are collected at the station service transformer node and station service distribution node.

[0042] In a preferred embodiment, the dynamic mixing carbon factor of the charging bus is represented as:

[0043]

[0044] in, Dynamic mixing of carbon factors in the charging bus For the sampling time of multi-source energy data, For grid power, For the time-sharing carbon factor of the power grid, For photovoltaic power, For carbon factors in photovoltaic power generation, For energy storage discharge power, For energy storage and discharge carbon factor, To prevent tiny positive numbers with a denominator of zero.

[0045] In a preferred embodiment, the step of utilizing collected multi-source energy data and calculating the energy storage discharge carbon factor through energy batch management traceability based on the dynamically mixed carbon factor of the charging bus includes:

[0046] Each charging process of the energy storage at the power station is considered as an energy batch. In batch Corresponding time set Inside, batch The energy input carbon factor is expressed as:

[0047]

[0048] in, For batch Energy storage carbon factor Power for energy storage charging;

[0049] When the energy storage converter node is at time During discharge, energy is extracted from each batch of energy cells, and the carbon factor of the energy storage discharge is calculated and expressed as follows:

[0050]

[0051] in, For energy storage and discharge carbon factor, To be from batch Energy withdrawn from the energy pool.

[0052] In a preferred embodiment, the calculation of equipment-level carbon emissions based on the depot multi-source energy topology map, grid time-sharing carbon factor, photovoltaic power generation carbon factor, and energy storage discharge carbon factor includes:

[0053] Based on the switching state and the collected power, the energy flow direction is from the load node. Backtracking to each source node Calculate the source node For load nodes The transmission coefficient;

[0054] For connection to load nodes equipment Using the transfer coefficient and equipment The active power, calculate the source node For equipment The power contribution;

[0055] Within the sampling interval, power contribution calculation equipment is used. From the source node The electrical energy obtained;

[0056] Based on device From the source node The electrical energy obtained and the source node Corresponding carbon factor, computing device At each source node carbon emissions;

[0057] By accumulating devices At each source node The carbon emissions of the equipment Total carbon emissions.

[0058] In a preferred embodiment, the step of calculating vehicle-level carbon emission measurement based on charging events using collected multi-source energy data and dynamic mixing carbon factors of the charging bus includes:

[0059] The continuous charging process of a vehicle connecting to a charging system via a pantograph charging pole or a DC fast charging pile is defined as a charging event; within the time set of charging events... Within this timeframe, the carbon emissions of this charging event are calculated and expressed as follows:

[0060]

[0061] in, Carbon emissions from charging events. For charging power, For dynamic mixing of carbon factors in the charging bus, For the sampling time of multi-source energy data, The sampling period for multi-source energy data;

[0062] Calculate vehicles The total carbon emissions from charging within an operating cycle are the sum of the carbon emissions from each charging event.

[0063] In a preferred embodiment, vehicle-level carbon emission metering is allocated to each vehicle trip according to the proportion of traction energy consumption or operating mileage; wherein, energy balance is used to verify vehicle charging metering and onboard energy.

[0064] In a preferred embodiment, the acquired multi-source energy data needs to be time-aligned and quality-managed.

[0065] In a preferred embodiment, multi-source energy data is collected through an edge computing gateway, and the collected multi-source energy data needs to be time-aligned and quality-managed; carbon emission structure analysis and peak identification are performed through a central platform, and data archiving is completed.

[0066] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0067] 1. This invention targets intelligent rail transit depots employing onboard power batteries. Using the vehicle charging system as the main link for carbon emission metering, it achieves second-level monitoring and analysis of carbon emission intensity at the charging event level, equipment level, and overall depot level through multi-source energy topology modeling of the depot, dynamic mixed carbon factor calculation during charging, carbon factor tracing during energy storage charging, and a power tracing algorithm based on the multi-source energy topology of the depot. This invention is applicable to carbon-neutral depots or vehicle bases that simultaneously configure pantograph charging poles, DC fast charging piles, photovoltaic systems, energy storage, and station loads.

[0068] 2. This invention constructs a multi-source energy topology map of the vehicle depot, which uniformly describes the energy flow from the power grid, photovoltaics, and station energy storage to the charging system and then to the vehicle battery, overcoming the traditional modeling assumptions centered on the overhead contact line and continuous traction power supply.

[0069] 3. This invention constructs a batch traceability mechanism for the dynamic hybrid carbon factor of the charging bus and the carbon factor of the energy storage discharge, so that the carbon emission attributes of the energy storage discharge can be strictly traced back to the energy structure at the time of its charging. This avoids the deviation caused by simply using the current bus carbon factor, accurately reflects the time-shifting effect of energy storage peak shaving and valley filling on carbon emission, and improves the accuracy of carbon emission calculation.

[0070] 4. This invention solves for the transfer coefficient on a multi-source energy topology map of a vehicle depot. When the charging bus is simultaneously connected to the power grid, photovoltaics, and energy storage, it can accurately calculate the power and carbon emission contribution obtained by each charging interface and major station equipment from different energy sources, achieving refined carbon emission monitoring.

[0071] 5. This invention uses a real-time carbon emission measurement method based on charging events as the basic unit to achieve second-level carbon emission calculation for each pantograph charging and DC fast charging process, which can reflect the differences in carbon emission intensity between different charging windows.

[0072] 6. This invention accurately allocates the carbon emissions from vehicle depot charging to specific vehicles and train trips through a vehicle operation energy balance and train trip allocation mechanism, forming a train trip-level carbon emission intensity index, which provides a quantitative basis for timetable optimization, charging strategy adjustment and carbon performance comparison between lines.

[0073] 7. This invention combines second-level sampling, time alignment, and carbon emission calculation. Through sliding window analysis, identification of major contributing devices, and peak value identification, it can achieve a carbon emission update cycle of no more than one second while ensuring system scalability, meeting the real-time energy and carbon monitoring needs of carbon-neutral vehicle depots. Furthermore, through structured archiving and hierarchical aggregation of second-level carbon emission data, annual carbon emission inventories and carbon footprint reports can be directly generated, shortening the development cycle of carbon neutrality verification and carbon reduction projects, and providing a high-resolution data foundation for carbon asset management platforms and carbon trading decisions. Attached Figure Description

[0074] Figure 1 A flowchart illustrating a real-time carbon emission monitoring method for a carbon-neutral vehicle depot, as provided in an embodiment of the present invention.

[0075] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0077] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0078] To address the problems of existing solutions in the scenario of carbon-neutral intelligent rail transit depots, the following analysis and design are conducted focusing on carbon-neutral intelligent rail transit depots:

[0079] I. How to construct a multi-source energy topology model encompassing the power grid, photovoltaics, station energy storage, charging converter cabinets, pantograph charging poles, DC fast charging piles, and station loads. This model will support unified modeling and power traceability of multi-source energy flows within the depot. Furthermore, how to establish a real-time carbon emission metering method based on single charging events, centered around the pantograph charging poles and DC fast charging guns, to achieve second-level charging carbon emission calculations by incorporating the grid's time-sharing carbon factor and local green electricity.

[0080] II. How to construct a dynamic hybrid carbon factor model for the charging bus that simultaneously considers the grid's time-of-use carbon factor, photovoltaic output, and power station energy storage output. This model should accurately reflect the actual energy structure of charging power at different times. Furthermore, it should establish a carbon factor traceability mechanism for power station energy storage systems, implement batch energy management during the energy storage charging process, and restore the carbon factor during the discharge phase to ensure the time conservation and accuracy of carbon emission calculations when energy storage participates in peak shaving and valley filling.

[0081] 3. How to combine the depot power distribution topology and real-time switch status to establish the power transfer coefficient of the source-to-station load node and realize source-specific carbon emission allocation.

[0082] IV. How to complete carbon emission updates within a time resolution of no more than one second, and identify the main carbon emission contributing devices and peak carbon emission periods within a sliding time window.

[0083] V. How to structurally archive and summarize second-level carbon emission time series data while complying with standards such as ISO 14064 and PAS 2060, to support carbon inventory, carbon neutrality certification, and carbon reduction project development for carbon-neutral vehicle depots.

[0084] Based on the above analysis and design, this invention provides a real-time carbon emission monitoring method for carbon-neutral vehicle depots. Targeting intelligent rail depots using onboard power batteries, the method uses the vehicle charging system as the main carbon emission metering link. Through multi-source energy topology modeling of the vehicle depot, dynamic hybrid carbon factor calculation during charging, carbon factor tracing during energy storage and charging, and a power tracing algorithm based on the multi-source energy topology of the vehicle depot, it achieves second-level monitoring and analysis of carbon emission intensity at the charging event level, equipment level, and overall vehicle depot level.

[0085] like Figure 1 As shown, the real-time carbon emission monitoring method for the carbon-neutral vehicle depot includes:

[0086] S101: Construct a multi-source energy topology map for the vehicle depot.

[0087] Based on the primary system diagram of the depot, source nodes, station service load nodes, and charging load nodes are abstracted into node sets, and the connection relationships between source nodes, station service load nodes, and charging load nodes are abstracted into directed edges, thus forming a multi-source energy topology diagram of the depot. The source nodes include grid incoming line nodes, photovoltaic inverter nodes, and energy storage converter nodes, etc.; the station service load nodes include station service transformer nodes and station service distribution nodes, etc.; and the charging load nodes include charging converter cabinet DC bus nodes, pantograph charging column nodes, and DC fast charging pile nodes, etc. Each node can be configured with a node type and load affiliation identifier.

[0088] This step overcomes the traditional modeling assumptions centered on the overhead contact line and continuous traction power supply by constructing a multi-source energy topology map of the vehicle depot, which uniformly describes the energy flow from the power grid, photovoltaics, and station energy storage to the charging system and then to the vehicle battery.

[0089] S102: Collect multi-source energy data based on the multi-source energy topology map of the depot.

[0090] Let the sampling period be Then the sampling time Represented as from the initial sampling time Beginning of the first The time of each sampling period, i.e. According to the sampling period Collect multi-source energy data, including:

[0091] Power is collected at the power grid incoming node. ;

[0092] Photovoltaic power is collected at the photovoltaic inverter node. ;

[0093] Energy storage charging power is collected at the energy storage converter node. and energy storage discharge power ;

[0094] Active power during the charging process is collected at the DC bus node of the charging converter cabinet, the pantograph charging column node, and the DC fast charging pile node. and the on / off status of each charging load node;

[0095] The station service load power and the on / off status of each station service load node are collected at the station service transformer node and station service distribution node.

[0096] The vehicle battery voltage, current, traction power, regenerative braking power, and state of charge (SOC) are collected on the vehicle side.

[0097] The collected multi-source energy data can be accessed through a fieldbus or industrial Ethernet to the vehicle depot edge computing gateway for subsequent processing, such as time alignment and quality management of the collected multi-source energy data, and writing it into the edge cache. Specifically:

[0098] The time alignment refers to the process by which the vehicle depot edge computing gateway unifies multi-source energy data with different sampling periods into a single sampling period. In this embodiment of the invention, time alignment of multi-source energy data (asynchronous data) with different sampling periods is performed by interpolation or preserving methods.

[0099] The quality management refers to the process by which the depot edge computing gateway performs missing value imputation, outlier removal, and short-term spike filtering on multi-source energy data. For example, when the deviation between active power and the moving average exceeds a preset multiple, it is marked as an anomaly and interpolated using data from adjacent time points in the calculation.

[0100] S103: Set the grid time-sharing carbon factor and photovoltaic power generation carbon factor.

[0101] Based on the time-of-use carbon emission coefficients published by the regional power grid operator, set the power grid time-of-use carbon factor. The unit can adopt .

[0102] Set the carbon factor of photovoltaic power generation It can be approximated as zero or a very low constant.

[0103] The grid time-of-use carbon factor and photovoltaic power generation carbon factor can be stored in a database and indexed by time and energy type for easy retrieval. In some scenarios, if the depot is equipped with a natural gas boiler and / or a diesel generator, it is necessary to set the corresponding natural gas boiler carbon factor. and / or diesel generator carbon factor .

[0104] S104: Calculate the dynamic mixed carbon factor of the charging bus based on the collected multi-source energy data, the grid time-sharing carbon factor, and the photovoltaic power generation carbon factor.

[0105] In this embodiment of the invention, it is assumed that at the sampling time A charging bus receives power input from three sources: the power grid, photovoltaic (PV) power, and energy storage power. Based on the grid power, PV power, and energy storage discharge power, as well as the corresponding grid time-of-use carbon factors, PV power generation carbon factors, and energy storage discharge carbon factors, the dynamic mixed carbon factor of the charging bus is calculated, expressed as:

[0106]

[0107] in, Dynamic mixing of carbon factors in the charging bus For the sampling time of multi-source energy data, For grid power, For the time-sharing carbon factor of the power grid, For photovoltaic power, For carbon factors in photovoltaic power generation, For energy storage discharge power, For energy storage and discharge carbon factor, To prevent tiny positive numbers with a denominator of zero, specifically, when there is no energy storage discharge, it can be set as follows: .

[0108] S105: Utilize the collected multi-source energy data and the dynamic mixing carbon factor of the charging bus to calculate the energy storage discharge carbon factor through energy batch management traceability.

[0109] Each charging process of the energy storage at the power station is considered as an energy batch. In batch Corresponding time set Inside, batch Energy storage carbon factor Represented as:

[0110]

[0111] When the energy storage converter node is at time During discharge, energy is withdrawn from each batch of energy pools according to a first-in-first-out (FIFO) or weighted average strategy. Energy storage and discharge carbon factor Represented as:

[0112]

[0113] The energy pool is a batch set, and each batch Maintenance batch remaining energy status With energy storage carbon factor During the sampling period In the middle, the energy storage converter node collects the energy storage discharge power. The total outbound energy for this sampling period was then obtained. , represented as:

[0114]

[0115] Total outbound energy The batches were allocated according to a first-in, first-out (FIFO) or weighted average strategy. ,satisfy At the same time, update the remaining energy status of each batch. To ensure the conservation of energy.

[0116] This step establishes a batch traceability mechanism for the dynamic hybrid carbon factor of the charging bus and the carbon factor of the energy storage discharge, enabling the carbon emission attributes of the energy storage discharge to be strictly traced back to the energy structure at the time of its charging. This avoids the deviation caused by simply using the current bus carbon factor, accurately reflects the time-shifting effect of energy storage peak shaving and valley filling on carbon emissions, and improves the accuracy of carbon emission calculation.

[0117] S106: Calculate equipment-level carbon emissions based on the multi-source energy topology map of the depot, the time-sharing carbon factor of the power grid, the carbon factor of photovoltaic power generation, and the carbon factor of energy storage discharge.

[0118] In the multi-source energy topology diagram of the depot, the grid incoming node, photovoltaic inverter node, and energy storage converter node are marked as the source node set. For load nodes (Including charging load nodes and station service load nodes), at time Based on the switching state and the collected power, the energy flow direction is from the load node. Backtracking to each source node Calculate the source node For load nodes Transmission coefficient The constraints are:

[0119]

[0120] The transfer coefficient can be solved using graph algorithms or recursive methods based on branch power ratios on the multi-source energy topology map of the depot. When the charging bus is simultaneously connected to the grid, photovoltaics, and energy storage, the power and carbon emission contribution obtained by each charging interface and major station equipment from different energy sources can be accurately calculated, enabling refined carbon emission monitoring.

[0121] For connection to load nodes equipment Using the transfer coefficient and equipment The active power, calculate the source node For equipment The power contribution is expressed as:

[0122]

[0123] in, For the source node For equipment power contribution, For equipment The active power.

[0124] Within the sampling interval, power contribution calculation equipment is used. From the source node The electrical energy obtained is:

[0125]

[0126] in, For equipment From the source node The electrical energy obtained The sampling period for multi-source energy data.

[0127] Based on device From the source node The electrical energy obtained and the source node Corresponding carbon factor, computing device At each source node The carbon emissions are:

[0128]

[0129] in, For equipment carbon emissions, For the source node The corresponding carbon factors include grid time-of-use carbon factor, photovoltaic power generation carbon factor, and energy storage discharge carbon factor.

[0130] By accumulating devices At each source node The carbon emissions of the equipment The total carbon emissions are expressed as:

[0131]

[0132] in, For equipment The total carbon emissions, also known as the equipment-level carbon emissions.

[0133] S107: Calculate vehicle-level carbon emission measurement based on charging events by utilizing collected multi-source energy data and dynamic mixing carbon factors of the charging bus.

[0134] The continuous charging process of a vehicle connecting to a charging system via a pantograph charging pole or a DC fast charging station is defined as a charging event. ; in the time set of charging events The active power during the internal charging process is Then the charging energy and carbon emissions of this charging event are respectively:

[0135]

[0136]

[0137] in, The charging energy for the charging event. Carbon emissions from charging events. For charging power, For dynamic mixing of carbon factors in the charging bus, For the sampling time of multi-source energy data, The sampling period for multi-source energy data.

[0138] Therefore, calculate the vehicle The total carbon emissions from charging within an operating cycle are the sum of the carbon emissions from each charging event, expressed as:

[0139]

[0140] in, For vehicles The total carbon emissions from charging within an operating cycle, also known as vehicle-level carbon emissions. Therefore, by using a real-time carbon emission measurement method with charging events as the basic unit, the carbon emissions for each pantograph charging and DC fast charging process can be calculated at the second level, reflecting the differences in carbon emission intensity between different charging windows.

[0141] S108: Allocate vehicle-level carbon emission measurements to each train trip to obtain train trip-level carbon emission amount and carbon emission intensity.

[0142] In this embodiment of the invention, vehicle-level carbon emission metering is allocated to each vehicle trip according to the proportion of traction energy consumption or operating mileage. Specifically, energy balance is used to verify vehicle charging metering and onboard energy. Assume the vehicle... At any moment The traction power is Braking regenerative power is Define positive power:

[0143]

[0144]

[0145] in, For traction positive power, Braking feedback positive power.

[0146] Then, the traction energy is calculated based on this. and feedback energy , represented as:

[0147]

[0148]

[0149] Assume the vehicle battery is in and The electrical energy at each time point is respectively and The energy balance relationship of the vehicle is then expressed as:

[0150]

[0151] in, For a moment The energy stored for charging comes from the time allocation of charging energy for the charging event in step S107.

[0152] This step accurately allocates the carbon emissions from vehicle depot charging to specific vehicles and train trips through a vehicle operation energy balance and train trip allocation mechanism, forming a train trip-level carbon emission intensity index, which provides a quantitative basis for timetable optimization, charging strategy adjustment, and carbon performance comparison between lines.

[0153] S109: Perform carbon emission structure analysis and peak identification based on equipment-level and vehicle-level carbon emissions, and complete data archiving. Specifically:

[0154] Equipment-level carbon emissions and vehicle-level carbon emissions Write to the time series database.

[0155] In containing Within a sliding time window of one sampling step, the computing device Cumulative carbon emissions:

[0156]

[0157] in, for Within a sliding time window of a sampling step, the device Cumulative carbon emissions; This is the sequence number of the sampling step.

[0158] By examining the equipment Cumulative carbon emissions The sorting algorithm identifies the main carbon emission contributing devices and their carbon emission percentages within a sliding and time window. It also compares vehicle-level carbon emissions. By comparing historical statistics, the peak carbon emission times of the depot and their corresponding energy structures are identified. The carbon emission time series at the second level is summarized by day, month and year to form a carbon emission list consistent with the depot's organizational boundaries. Through the structured archiving and hierarchical summarization of the second-level carbon emission data, an annual carbon emission list and carbon footprint report can be directly generated, shortening the development cycle of carbon neutrality verification and carbon emission reduction projects, and providing a high-resolution data foundation for carbon asset management platforms and carbon trading decisions.

[0159] Furthermore, carbon emission structure analysis and peak identification are performed through the central platform, and data archiving is completed. Thus, by combining edge computing's second-level sampling, time alignment, and carbon emission calculation, and then using the central platform's sliding window analysis, identification of major contributing devices, and peak identification, the collaborative architecture of edge computing and the central platform can achieve a carbon emission update cycle of no more than one second while ensuring system scalability, meeting the real-time energy and carbon monitoring needs of carbon-neutral vehicle depots.

[0160] Application Example 1:

[0161] The power grid incoming capacity of a certain intelligent rail transit depot is 4MVA.

[0162] The rooftop photovoltaic installation capacity is 1MW P .

[0163] The energy storage system at the site has a capacity of 2MWh.

[0164] It is equipped with four pantograph charging posts and two DC fast charging piles.

[0165] The output side of the charging converter cabinet is a DC bus.

[0166] Provides DC power to pantograph charging posts and DC fast charging piles.

[0167] Construct the multi-source energy topology diagram of the depot according to step S101. The grid incoming node, photovoltaic inverter node, and energy storage converter node are designated as source nodes. The DC bus node of the charging converter cabinet, each pantograph charging column, and DC fast charging pile are designated as charging load nodes. The station service transformer node and station service distribution nodes (such as the maintenance depot distribution cabinet and the comprehensive building distribution cabinet) are designated as station service load nodes.

[0168] Following step S102, multi-source energy data is collected based on the multi-source energy topology map of the depot. Smart meters are installed at each node of the multi-source energy topology map of the depot, with a sampling period of [missing information]. The edge computing gateway collects real-time data on grid power, photovoltaic power, energy storage charging power, energy storage discharging power, active power of charging load nodes, and station load power. For a given moment... Multi-source energy data includes:

[0169] Grid power ;

[0170] Photovoltaic power ;

[0171] Energy storage discharge power ;

[0172] The energy storage system was previously charged during low-carbon periods.

[0173] According to step S103, during a certain summer daytime period, the time-of-use carbon factor of the power grid is set. Carbon factors in photovoltaic power generation The energy storage is in a discharging state.

[0174] According to step S105, the energy storage previously completed charging and formed energy batches during low-carbon periods. Calculate the carbon factor of a certain batch of energy entering the warehouse. At the current moment Collected energy storage discharge power The sampling period is Then the total energy outflow from the storage during this sampling period is:

[0175]

[0176] In this application example, the outbound energy is generated from a single batch. Supply, then Then, the carbon factor for energy storage discharge is calculated according to step S105 as follows:

[0177]

[0178] Substituting into step S104, we obtain the dynamic mixing carbon factor of the charging bus at that moment:

[0179]

[0180] Calculate the equipment-level carbon emissions according to step S106, and use the equipment corresponding to the pantograph charging column. For example, its active power The total input power of the charging bus is The transfer coefficients of each source node are expressed as power percentages:

[0181] Power grid incoming node to equipment The transfer coefficient is:

[0182]

[0183] Photovoltaic inverter node-to-node equipment The transfer coefficient is:

[0184]

[0185] Energy storage converter node-to-node equipment The transfer coefficient is:

[0186]

[0187] Power grid incoming node to equipment The power contribution is:

[0188]

[0189] Photovoltaic inverter node-to-node equipment The power contribution is:

[0190]

[0191] Energy storage converter node-to-node equipment The power contribution is:

[0192]

[0193] equipment The total carbon emissions are:

[0194]

[0195] According to step S107, the active power of the charging process at this moment is... Then the charging energy at that moment and carbon emissions They are respectively:

[0196]

[0197]

[0198] By summing up the above calculations over time, the charging energy and carbon emissions of each charging event can be obtained. Then, the vehicle... The total carbon emissions from charging within an operating cycle are the sum of the carbon emissions from each charging event. In this example, vehicles on the same day... The total daily carbon emissions from charging after two charging events are: ,in, The carbon emissions from the first charging event of the day. This refers to the carbon emissions from the second charging event of the day.

[0199] The vehicle-level carbon emission metering is allocated to each vehicle trip according to step S108. (Vehicles on that day) It undertakes three operational missions, with a total operating mileage of 120km. If the carbon emissions are allocated based on the percentage of mileage per trip, the average carbon emission intensity of the vehicle would be... for:

[0200]

[0201] The total daily carbon emissions from vehicle charging can be further calculated based on the proportion of mileage for each trip. It is allocated to each train.

[0202] Step S109 involves analyzing the carbon emission structure and identifying peak values ​​based on equipment-level and vehicle-level carbon emissions, followed by data archiving. Specifically, a sliding time window analysis and ranking of equipment-level carbon emissions in the depot revealed that air conditioning system emissions account for nearly half of the depot's total carbon emissions during the afternoon hours. The dispatching department can adjust air conditioning operation strategies while ensuring comfort. Furthermore, the utilization of photovoltaic power and energy storage charging / discharging strategies are optimized in conjunction with the grid's time-of-use carbon factor curve.

[0203] Application Example 2:

[0204] This application example adds an emergency DC fast-charging gun and a gas boiler system to Application Example 1. It illustrates the application of the method of this invention in more complex energy structures.

[0205] During a certain winter heating season, in addition to the power grid, photovoltaic system, and energy storage, the depot also equipped itself with a natural gas hot water boiler to provide heating for the office area. Several DC fast charging stations were also reserved to provide emergency DC fast charging for vehicles in case of pantograph system failure.

[0206] During a cold morning rush hour, due to temporary capacity adjustments, some intelligent rail transit vehicles were recharged using DC fast charging guns at the depot. The DC fast charging stations were connected to the same charging busbar as the pantograph charging poles. At a certain moment:

[0207] Grid power ;

[0208] Photovoltaic power ;

[0209] Energy storage discharge power ;

[0210] Time-of-use carbon factor of power grid ;

[0211] Energy storage discharge carbon factor .

[0212] The dynamic mixing carbon factor of the charging bus is obtained according to step S104:

[0213]

[0214] A DC fast charging station is currently charging vehicles. Provides active power during the charging process Then we have:

[0215]

[0216]

[0217] By summing over the entire charging event time set, the vehicle can be obtained. The carbon emissions from this emergency DC fast charging event can be assessed. Considering the vehicle's subsequent operational tasks, the impact of this emergency charging on the carbon footprint of a single vehicle can be evaluated. Simultaneously, the natural gas boiler consumed a certain volumetric flow rate of natural gas during this period. Using the low calorific value of natural gas Carbon factors in natural gas boilers Calculate carbon emissions from natural gas boilers :

[0218]

[0219] The carbon emissions from the natural gas boilers were included as a thermal component in the total carbon emissions statistics of the vehicle depot.

[0220] By summarizing the daily carbon emission data at the second level in step S109, we can obtain the sub-curves for electricity carbon emission, heat carbon emission, and charging carbon emission, and then construct the carbon emission reduction baseline scenario and project scenario to provide quantitative basis for carbon emission reduction projects such as photovoltaic substitution, energy storage peak shaving, and boiler retrofitting.

[0221] Those skilled in the art will understand that, without altering the basic idea of ​​the present invention, the sampling period, energy storage traceability rules, transfer coefficient calculation method, and train allocation weight definition can be adjusted according to different depot sizes and wiring methods.

[0222] Based on the same technical concept, embodiments of the present invention also provide an electronic device that can implement the real-time carbon emission monitoring method for carbon-neutral vehicle sections provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic equipment. Figure 2 As shown, the electronic device may include:

[0223] At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 2 The example used is the connection between the processor and memory via a bus. The bus... Figure 2 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 2 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0224] In this embodiment of the invention, the memory stores instructions that can be executed by at least one processor. By executing the instructions stored in the memory, at least one processor can execute the real-time carbon emission monitoring method for a carbon-neutral vehicle section as described above.

[0225] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0226] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0227] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the real-time carbon emission monitoring method for a carbon-neutral vehicle section disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0228] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.

[0229] By designing and programming the processor, the code corresponding to the real-time carbon emission monitoring method for a carbon-neutral vehicle segment described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during operation. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0230] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a real-time carbon emission monitoring method for a carbon-neutral vehicle segment as described above.

[0231] In some alternative embodiments, the present invention also provides a method for real-time carbon emission monitoring of a carbon-neutral vehicle depot, which can also be implemented as a program product including program code. When the program product is run on a device, the program code is used to cause the control device to perform the steps in the method for real-time carbon emission monitoring of a carbon-neutral vehicle depot according to various exemplary embodiments of the present invention as described above.

[0232] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0233] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0235] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0236] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0237] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0238] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0239] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A real-time carbon emission monitoring method for a carbon neutral depot, characterized in that, include: Construct a multi-source energy topology map for the vehicle depot; Multi-source energy data is collected based on the multi-source energy topology map of the depot. Set the grid time-sharing carbon factor and the photovoltaic power generation carbon factor; By utilizing collected multi-source energy data and based on the dynamic mixing carbon factor of the charging bus at historical moments, the energy storage discharge carbon factor is calculated through energy batch management and traceability. The dynamic mixed carbon factor of the charging bus at the current moment is calculated based on the collected multi-source energy data, energy storage discharge carbon factor, grid time-sharing carbon factor and photovoltaic power generation carbon factor. Based on the multi-source energy topology of the depot, the time-sharing carbon factor of the power grid, the carbon factor of photovoltaic power generation, and the carbon factor of energy storage discharge, the carbon emissions at the equipment level are calculated. By utilizing the collected multi-source energy data and based on the dynamic mixing carbon factor of the charging bus at the current moment, vehicle-level carbon emission measurement is calculated with charging events as the unit. The vehicle-level carbon emission metering is allocated to each train trip to obtain the train trip-level carbon emission amount and carbon emission intensity. Carbon emission structure analysis and peak identification are performed based on equipment-level and vehicle-level carbon emission, and data archiving is completed. The construction of the multi-source energy topology map of the vehicle depot includes: The connection relationships between source nodes, station load nodes, and charging load nodes are abstracted as directed edges, thereby forming a multi-source energy topology graph of the depot. The source nodes include grid incoming nodes, photovoltaic inverter nodes, and energy storage converter nodes; The station load nodes include station transformer nodes and station power distribution nodes; The charging load nodes include the DC bus node of the charging converter cabinet, the pantograph charging column node, and the DC fast charging pile node. The collection of multi-source energy data based on the multi-source energy topology map of the vehicle depot includes: Multi-source energy data is collected according to the sampling period, including: Power is collected at the grid incoming node; Photovoltaic power is collected at photovoltaic inverter nodes; Energy storage charging power and energy storage discharging power are collected at the energy storage converter node; The active power during the charging process and the on / off status of each charging load node are collected at the DC bus node of the charging converter cabinet, the pantograph charging column node, and the DC fast charging pile node. The station service load power and the on / off status of each station service load node are collected at the station service transformer node and station service distribution node.

2. The real-time carbon emission monitoring method for carbon-neutral vehicle depots according to claim 1, characterized in that, The dynamic mixing carbon factor of the charging bus is expressed as: in, Dynamic mixing of carbon factors in the charging bus For the sampling time of multi-source energy data, For grid power, For the time-sharing carbon factor of the power grid, For photovoltaic power, For carbon factors in photovoltaic power generation, For energy storage discharge power, For energy storage and discharge carbon factor, To prevent tiny positive numbers with a denominator of zero.

3. The real-time carbon emission monitoring method for carbon-neutral vehicle depots according to claim 2, characterized in that, The process of utilizing collected multi-source energy data and dynamically mixing carbon factors at historical charging bus times to calculate energy storage discharge carbon factors through energy batch management and traceability includes: Each charging process of the energy storage at the power station is considered as an energy batch. In batch Corresponding time set Inside, batch The energy input carbon factor is expressed as: wherein, for batch energy-in carbon factor, is the energy storage charging power; When the energy storage converter node is at time At discharge, the out-of-pool energy is taken from each batch energy pool, and the energy storage discharge carbon factor is calculated and represented as: in, For energy storage and discharge carbon factor, To be from batch Energy withdrawn from the energy pool.

4. The real-time carbon emission monitoring method for carbon-neutral vehicle depots according to claim 1, characterized in that, The calculation of equipment-level carbon emissions based on the multi-source energy topology map of the depot, the time-sharing carbon factor of the power grid, the carbon factor of photovoltaic power generation, and the carbon factor of energy storage discharge includes: Based on the switching state and the collected power, the energy flow direction is from the load node. Backtracking to each source node Calculate the source node For load nodes The transmission coefficient; For connection to load nodes equipment Using the transfer coefficient and equipment The active power, calculate the source node For equipment The power contribution; Within the sampling interval, power contribution calculation equipment is used. From the source node The electrical energy obtained; Based on device From the source node The electrical energy obtained and the source node Corresponding carbon factor, computing device At each source node carbon emissions; By aggregating the devices At each source node The carbon footprint of the devices The total carbon footprint of the devices 5.The real-time carbon emission monitoring method of the carbon-neutral railyard of claim 1, wherein, The method of calculating vehicle-level carbon emission measurements based on charging events using collected multi-source energy data and dynamic carbon factor mixing at the charging bus includes: The continuous charging process of a vehicle connecting to a charging system via a pantograph charging pole or a DC fast charging pile is defined as a charging event; within the time set of charging events... Within this timeframe, the carbon emissions of this charging event are calculated and expressed as follows: in, Carbon emissions from charging events. For charging power, For dynamic mixing of carbon factors in the charging bus, For the sampling time of multi-source energy data, The sampling period for multi-source energy data; Calculate vehicles The total carbon emissions from charging within an operating cycle are the sum of the carbon emissions from each charging event. 6.The real-time carbon emission monitoring method of the carbon-neutral railyard of claim 1, wherein, Vehicle-level carbon emission metering is allocated to each vehicle trip according to the proportion of traction energy consumption or operating mileage; among which, energy balance is used to verify vehicle charging metering and on-board energy.

7. The method of claim 1, wherein the method further comprises: The collected multi-source energy data needs to be time-aligned and quality-managed. 8.The method of claim 7, wherein, Multi-source energy data is collected through edge computing gateways, and time alignment and quality management of the collected multi-source energy data are required; carbon emission structure analysis and peak identification are performed through the central platform, and data archiving is completed.

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