Energy conversion carbon metering monitoring method and system based on dynamic carbon monitoring

Abstract

The invention discloses a method and a system for monitoring energy conversion carbon measurement based on dynamic carbon monitoring. The method comprises the following steps: acquiring energy flow data, carbon emission parameters, equipment working conditions and environment data in real time; constructing a carbon emission correction model containing a dynamic energy conversion coefficient and real-time fuel components; adopting an efficiency compensation function and a working condition self-adaptive correction function related to an equipment load rate; and visually displaying the real-time numerical value of the carbon emission and triggering the early warning of the carbon emission based on the model output. The system comprises a multi-source data real-time acquisition module, a dynamic carbon measurement model and a real-time monitoring and early warning platform, the method has the advantages of accurate decision support, environmental supervision enhancement and adaptation to energy transformation.

Description

Technical Field

[0001] The present invention relates to the field of energy and environmental monitoring technology, and in particular to an innovative monitoring method and system for achieving energy-to-carbon conversion through dynamic carbon monitoring. The method aims to provide accurate carbon emission quantification methods for various energy consumption scenarios, thereby facilitating the implementation of energy conservation, emission reduction and sustainable development strategies. Background Art

[0002] In today's climate change response, accurately measuring carbon emissions from energy consumption is a key prerequisite for developing effective emission reduction strategies and promoting energy structure transformation. Traditional carbon measurement methods often rely on static parameters and fixed-cycle calculations, making them difficult to adapt to the increasingly complex and changing operational dynamics of energy systems. On the one hand, the energy supply structure continues to diversify, and the proportion of new energy access continues to rise; on the other hand, the load characteristics of energy consumption are dynamically changing with production and living patterns. Existing technologies are unable to track the impact of these dynamic factors on carbon emissions in real time, resulting in large deviations in carbon measurement results and an inability to meet the needs of refined management. A new dynamic monitoring and measurement solution is urgently needed.

[0003] Current carbon emission measurement mainly relies on static emission factors (such as IPCC default values) and periodic manual accounting, which has the following defects:

[0004] Unable to adapt to dynamic energy changes: the increasing proportion of renewable energy grid connection and load fluctuations lead to fixed factor deviations greater than 15%;

[0005] Ignoring the impact of operating conditions: Equipment efficiency varies with load rate and ambient temperature (e.g., the carbon intensity of a coal-fired unit increases by 20% at low load);

[0006] Poor data timeliness: Traditional methods calculate on a monthly / annual basis and cannot support real-time regulation.

[0007] Existing patented LCA-based carbon accounting methods still use static databases and do not solve the problem of dynamic correction. Summary of the Invention

[0008] To address the aforementioned technical issues, the present invention aims to provide a monitoring method and system for energy-to-carbon measurement based on dynamic carbon monitoring. The system aims to build a comprehensive carbon monitoring system integrating real-time data acquisition, dynamic modeling, and early warning feedback, achieving a carbon emissions measurement error of less than 5% and a response delay of ≤10 minutes. This system can accurately capture the dynamic changes in carbon emissions throughout the energy conversion and consumption process, providing timely and accurate carbon emissions data for energy management in enterprises, industrial parks, and even cities, supporting scientific decision-making and the implementation of emission reduction actions.

[0009] The purpose of the present invention is achieved through the following technical solutions:

[0010] A monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring, comprising:

[0011] Step A collects energy flow data, carbon emission parameters, equipment operating conditions and environmental data in real time;

[0012] Step B: constructing a carbon emission correction model with dynamic energy conversion coefficients and real-time fuel composition;

[0013] Step C uses an efficiency compensation function related to the equipment load rate and an adaptive correction function for the working condition;

[0014] Step D visualizes the real-time value of carbon emissions based on the model output and triggers carbon emission warnings.

[0015] A monitoring system for energy conversion carbon measurement based on dynamic carbon monitoring, including

[0016] Multi-source data real-time acquisition module, dynamic carbon measurement model and real-time monitoring and early warning platform;

[0017] The multi-source data real-time acquisition module includes the acquisition of energy flow data, carbon emission related parameters, and operating conditions and environmental data;

[0018] The dynamic carbon measurement model includes an energy conversion model and a carbon emission dynamic calculation model;

[0019] The real-time monitoring and visual early warning platform is used to display the real-time value, historical trend and forecast curve of carbon emissions through a visual interface; and

[0020] Through the carbon emission intensity / total amount threshold, multi-channel warnings are automatically triggered when the limit is exceeded.

[0021] Compared with the prior art, one or more embodiments of the present invention may have the following advantages:

[0022] Accurate decision-making support: Provides real-time, accurate carbon emissions data to energy management departments and enterprises, helping to optimize energy procurement strategies and production scheduling plans, accurately formulate emission reduction targets and paths, and improve energy utilization efficiency and economic benefits.

[0023] Strengthening environmental supervision: Environmental protection departments can rely on this monitoring method to achieve dynamic control of key emission sources, promptly detect abnormal emission behaviors, enhance the timeliness and effectiveness of environmental law enforcement, and promote improvements in regional environmental quality.

[0024] Adapt to energy transformation: Effectively respond to the challenges brought about by large-scale grid connection of new energy and diversification of energy consumption, provide reliable technical support for carbon emission accounting during the low-carbon transformation of the energy system, and promote the process of green and low-carbon development. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a monitoring flow chart for energy-to-carbon measurement based on dynamic carbon monitoring;

[0026] Figure 2 It is a flow chart of the algorithm of the dynamic carbon accounting model;

[0027] Figure 3 It is a system architecture diagram. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to embodiments and accompanying drawings.

[0029] like Figure 1-Figure 3 The following is the monitoring process of energy-to-carbon measurement based on dynamic carbon monitoring, which includes the following steps:

[0030] Step 10: Real-time collection of energy flow data, carbon emission parameters, equipment operating conditions, and environmental data;

[0031] Step 20: constructing a carbon emission correction model including a dynamic energy conversion coefficient and real-time fuel composition;

[0032] Step 30 uses an efficiency compensation function related to the equipment load rate and an operating condition adaptive correction function;

[0033] Step 40 visualizes the real-time value of carbon emissions based on the model output and triggers a carbon emission warning.

[0034] In step 10: high-precision sensors such as smart electricity meters, gas meters, and heat meters are deployed at various energy production, transmission, and consumption nodes (such as power plants, substations, industrial plant distribution rooms, and large commercial complex distribution boxes). The energy flow data sampling interval is ≤5 minutes, and energy flow data such as electricity, heat, and gas are collected in real time and transmitted to the cloud platform via 5G / LoRa. Carbon emission-related parameter collection: continuous emission parameters are installed at key carbon emission sources such as power generation facility discharge ports, industrial kiln flues, and boiler room chimneys. The concentrations of pollutants such as carbon dioxide, sulfur dioxide, and nitrogen oxides are monitored through the CEMS monitoring system. Combined with parameters such as flue gas flow rate, temperature, and pressure, the flue gas CO2 concentration, flow rate, and temperature are obtained in real time.

[0035] Operating conditions and environmental data include unit load rate, ambient temperature and humidity, and fuel composition (coal quality analysis, natural gas composition, etc.) to provide basic data for subsequent energy conversion.

[0036] Operating conditions and environmental data collection: With the help of temperature sensors, humidity sensors, pressure sensors and production equipment operating status monitoring modules, the operating conditions (load rate, start and stop status, efficiency parameters, etc.) of energy conversion equipment (such as generator sets, boilers, heat pumps, etc.) and surrounding environmental data are collected. These data are used as key variables affecting the dynamic changes of carbon emission factors to participate in the measurement model calculation.

[0037] In step 20, a dynamic energy conversion coefficient library is established based on the inherent properties of different energy types, such as low calorific value and carbon content, combined with the collected real-time fuel composition data. For example, for coal, as the coal quality fluctuates, its unit calorific value carbon content is updated in real time, and the energy conversion coefficient is adaptively updated using the formula:

[0038]

[0039] Among them, C equiv is the dynamically calculated energy carbon equivalent, i is the energy type index, E i is the real-time consumption of the i-th type of energy, k i (T, p) is the dynamic conversion coefficient of temperature / pressure adaptation, c i (M fuel ) is based on real-time medium / gas composition (M fuel )’s unit calorific value carbon content.

[0040] Taking into account the impact of energy conversion efficiency, equipment operating conditions, and environmental factors on carbon emissions, a correction factor is introduced. Taking thermal power generation as an example, when the unit load rate changes, the power generation efficiency changes accordingly, and the carbon emission intensity also fluctuates accordingly. The formula for the dynamic correction model of carbon emissions is:

[0041]

[0042] Among them, C emission is the final corrected carbon emissions, η eff is the efficiency function related to the load rate L, and f is the equipment operating temperature θ / ambient temperature T amb / Adaptive correction function of atmospheric pressure p, ε CEMS It is the calibration item of CEMS measured value.

[0043] The above working condition adaptive correction function f is fitted by a machine learning algorithm, and the input parameters include the equipment operating temperature θ, the ambient temperature T amb and atmospheric pressure p.

[0044] The massive amount of data collected in step 40, as well as the real-time carbon emission values, historical trends, and forecast curves calculated by the model, are displayed through an intuitive visualization interface, including bar charts, line charts, heat maps, and other forms, to facilitate managers to quickly understand the carbon emission situation. Specifically, the carbon emission data visualization uses a GIS map overlaid with a heat map to display regional carbon emission intensity.

[0045] The carbon emission warning is triggered by the threshold warning: When the intelligent warning: When When the cumulative value exceeds the limit, a multi-level alarm (SMS / API push) is triggered. Specifically, thresholds for key indicators such as carbon emission intensity and total amount are set, and comparative analysis is performed based on real-time monitoring data. Once an indicator exceeds the threshold range, an early warning message is immediately sent to enterprise managers, environmental protection departments and other relevant personnel through SMS, email, system pop-up windows, etc., prompting timely regulatory measures such as adjusting production load and optimizing energy scheduling. The specific design is as follows:

[0046] Instantaneous rate of change formula:

[0047]

[0048] ΔC is the change in emission rate within the time interval Δt, where Δt is the time interval;

[0049] The actual calculation uses discrete data:

[0050] Δt = sampling interval

[0051] This formula is used to approximate the instantaneous rate of change of actual discrete data by difference; where rate t is the estimated rate of change of the emission rate at time t, C t is the emission rate value at discrete time point t, C t-1 is the emission rate value at the previous discrete time point t-1, and Δt is the sampling interval;

[0052] Cumulative value calculation:

[0053]

[0054] Among them, total t is the total cumulative emissions up to time t, k is the discrete time index, C k : emission rate value at time index k;

[0055] Alarm triggering conditions:

[0056] Instantaneous rate of change: rate t >threshold rate

[0057] Cumulative value: total t >threshold total .

[0058] Carbon emissions can be traced by issuing alarms and storing key node data on the blockchain. The multi-level alarm strategy is shown in Table 1:

[0059] Table 1

[0060] Alarm level Trigger Conditions Response measures Low-level Instantaneous rate of change > threshold 1 or cumulative value > threshold 1 SMS notification to production supervisor intermediate Instantaneous rate of change > threshold 2 or cumulative value > threshold 2 SMS + API push management system advanced Instantaneous rate of change > threshold 3 or cumulative value > threshold 3 Multiple SMS messages + emergency API + system logs

[0061] This embodiment also provides a monitoring system for energy-to-carbon measurement based on dynamic carbon monitoring, including: a multi-source data real-time acquisition module, a dynamic carbon measurement model, and a real-time monitoring and early warning platform;

[0062] The multi-source data real-time acquisition module includes the acquisition of energy flow data, carbon emission related parameters, and operating conditions and environmental data;

[0063] The dynamic carbon measurement model includes an energy conversion model and a carbon emission dynamic calculation model;

[0064] The real-time monitoring and visual early warning platform is used to display the real-time value, historical trend and forecast curve of carbon emissions through a visual interface; and

[0065] Through the carbon emission intensity / total amount threshold, multi-channel warnings are automatically triggered when the limit is exceeded.

[0066] The carbon emission related parameters are collected by monitoring the carbon emission related parameters through the CEMS monitoring module;

[0067] The carbon emission dynamic calculation model is obtained by edge computing nodes through big data analysis and machine learning algorithm fitting to obtain an adaptive correction function for working conditions.

[0068] The early warning includes a graded response strategy: the first-level warning triggers energy efficiency optimization suggestions, and the second-level warning forces the activation of the backup clean energy system.

[0069] In specific implementation:

[0070] System Deployment: Based on the scale and complexity of the monitored objects, sensors and monitoring equipment are strategically deployed across the energy supply network and consumer terminals. Local communication networks are constructed to aggregate data to central servers. For large industrial parks, multiple monitoring zones can be created, each with its own data collection nodes, ensuring full coverage and stable data transmission.

[0071] Initialization and calibration: When the system is first launched, various sensors are calibrated to ensure the accuracy of the collected data. Historical energy consumption and carbon emission data are used to initialize and train the dynamic carbon measurement model, optimize the initial model parameters, and make the calculation results fit the actual situation.

[0072] Continuous operation and optimization: After the system enters the stable operation stage, the model is updated and optimized regularly (such as weekly), and the energy conversion coefficient and correction factor function relationship are adjusted based on the latest data feedback; the scope and depth of data collection are continuously expanded to adapt to new changes in the energy system and ensure the long-term effectiveness of the monitoring method.

[0073] Application deployment in thermal power plants: Data collection: CEMS (measurement range 0-20% CO2) installed in the boiler flue. Coal quality data is acquired in real time via an online analyzer using a belt scale. 0.2S-class smart meters are installed on the turbine side. Model training: 3 months of historical data are collected to train an LSTM correction network (input: load rate + main steam temperature; output: efficiency compensation factor η). eff ); when the unit load suddenly drops from 80% to 60%, the correction function automatically increases the carbon emission intensity system by 12.7%; in response to the early warning, when the carbon emission intensity exceeds 0.85Tco2 / MWh, the platform automatically pushes the gas group peak-shaving instruction; effect verification: compared with the measured CEMS data, the model calculation deviation under typical operating conditions is ≤3.7%.

[0074] Industrial park-level deployment: Data from various factory areas is integrated through the OPC UA protocol to build a real-time operation model for edge computing nodes. When the platform detects a sudden 20% increase in carbon intensity in Area A, it automatically links with the EMS system to reduce non-essential loads.

[0075] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of patent protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring, characterized in that: The following steps are involved: Step A collects energy flow data, carbon emission parameters, equipment operating conditions and environmental data in real time; Step B: constructing a carbon emission correction model with dynamic energy conversion coefficients and real-time fuel composition; Step C uses an efficiency compensation function related to the equipment load rate and an adaptive correction function for the working condition; Step D visualizes the real-time value of carbon emissions based on the model output and triggers carbon emission warnings.

2. The monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring according to claim 1, characterized in that: In the step A: Energy flow data sampling interval is ≤5 minutes and transmitted to the cloud platform via 5G / LoRa; Emission parameters are obtained in real time through the CEMS system: flue gas CO2 concentration, flow rate and temperature; Operating conditions and environmental data, including unit load rate, ambient temperature and humidity, and fuel composition.

3. The monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring according to claim 1, characterized in that: In the step B: Energy conversion adaptive update, using the formula: Among them, C equiv is the dynamically calculated energy carbon equivalent, i is the energy type index, E i is the real-time consumption of the i-th type of energy, k i (T, p) is the dynamic conversion coefficient of temperature / pressure adaptation, c i (M fuel ) is based on real-time medium / gas composition (M fuel )’s unit calorific value carbon content.

4. The monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring according to claim 1, characterized in that: In the step B: The formula for the dynamic correction model of carbon emissions is: Among them, C emission is the final corrected carbon emissions, η eff is the efficiency function related to the load rate L, and f is the equipment operating temperature θ / ambient temperature T amb / Adaptive correction function of atmospheric pressure p, ε CEMS It is the calibration item of CEMS measured value.

5. The monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring according to claim 4 is characterized in that: The working condition adaptive correction function f is fitted by a machine learning algorithm, and the input parameters include the equipment operating temperature θ, the ambient temperature T amb and atmospheric pressure p.

6. The monitoring method for energy conversion carbon measurement based on dynamic carbon monitoring according to claim 1 is characterized in that: The carbon emission data visualization in step D displays regional carbon emission intensity by superimposing a heat map on a GIS map; The carbon emission warning is triggered by the threshold warning: when the intelligent warning: when the intelligent warning: when the instantaneous change rate of the emission rate Or when the accumulated value exceeds the limit, a multi-level alarm is triggered; Carbon emissions can be traced by issuing alarms and storing key node data on the blockchain.

7. The monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring according to claim 6, characterized in that: Instantaneous rate of change formula: ΔC is the change in emission rate within the time interval Δt, where Δt is the time interval; The actual calculation uses discrete data: This formula is used to approximate the instantaneous rate of change of actual discrete data by difference; where rate t is the estimated rate of change of the emission rate at time t, C t is the emission rate value at discrete time point t, C t-1 is the emission rate value at the previous discrete time point t-1, and Δt is the sampling interval; Cumulative value calculation: Among them, total t is the total cumulative emissions up to time t, k is the discrete time index, C k : emission rate value at time index k; Alarm triggering conditions: Instantaneous rate of change: rate t >threshold rate Cumulative value: total t >threshold total .

8. The system used in the monitoring method for energy-to-carbon measurement based on dynamic carbon monitoring according to any one of claims 1 to 7, characterized in that: include: Multi-source data real-time acquisition module, dynamic carbon measurement model and real-time monitoring and early warning platform; The multi-source data real-time acquisition module includes the acquisition of energy flow data, carbon emission related parameters, and operating conditions and environmental data; The dynamic carbon measurement model includes an energy conversion model and a carbon emission dynamic calculation model; The real-time monitoring and visual early warning platform is used to display the real-time value, historical trend and forecast curve of carbon emissions through a visual interface; as well as Through the carbon emission intensity / total amount threshold, multi-channel warnings are automatically triggered when the limit is exceeded.

9. The monitoring system for energy-to-carbon measurement based on dynamic carbon monitoring according to claim 8, characterized in that: The carbon emission related parameters are collected by monitoring the carbon emission related parameters through the CEMS monitoring module; The carbon emission dynamic calculation model is obtained by edge computing nodes through big data analysis and machine learning algorithm fitting to obtain an adaptive correction function for working conditions.

10. The monitoring system for energy-to-carbon measurement based on dynamic carbon monitoring according to claim 8, characterized in that: The early warning includes a graded response strategy: the first-level warning triggers energy efficiency optimization suggestions, and the second-level warning forces the activation of the backup clean energy system.

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