Energy system management and control method and system based on carbon footprint dynamic tracking

Abstract

The application discloses an energy system management and control method and system based on carbon footprint dynamic tracking, and relates to the technical field of energy management and control. The method comprises the following steps: load fluctuation determination, carbon emission control and time sequence lag control. Through the smoothness determination of the load change of the power generation equipment in the energy system, the application can pre-evaluate the load fluctuation change before the carbon emission control, thereby effectively identifying the energy efficiency fluctuation caused by the load abnormality, and avoiding misjudgment and redundant intervention in the carbon emission control. On this basis, the setting of carbon emission control and the time sequence integrated decision mechanism are introduced, the time sequence dynamic coupling analysis between carbon emission and energy efficiency is realized, and the timeliness and accuracy of carbon emission control are improved. Further, by improving the time sequence accuracy of carbon footprint and energy efficiency, the adjustment of carbon emission is more accurate, and more fine and scientific energy system management and control is realized.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to energy system management methods and systems based on dynamic carbon footprint tracking. Background Technology

[0002] Traditional static and periodic accounting methods can no longer meet the increasingly complex practical needs of rapid changes in energy structure, large-scale grid connection of renewable energy, and load fluctuations. Therefore, introducing a "dynamic carbon footprint tracking" method can promptly identify high-carbon emission or inefficient operation nodes, thereby achieving refined and dynamic management and optimization, which has important and urgent practical significance.

[0003] In energy systems, the first step is to deploy smart sensors and metering devices to monitor energy consumption in real time. These sensors include electricity meters, gas meters, and temperature and humidity sensors, capable of collecting various energy usage data. Data acquisition devices transmit the collected data in real time to a cloud platform or local data processing system via IoT technology. After initial cleaning and preprocessing, the collected energy consumption data is used to calculate the carbon emissions of various energy sources based on predetermined carbon emission factors and energy consumption. After carbon footprint calculation, big data processing and machine learning algorithms are used to analyze the energy data in real time to identify trends in carbon emissions. When carbon emission levels approach or exceed preset thresholds, a carbon emission early warning mechanism is automatically triggered. Early warning information is sent to energy system operators through various channels (such as SMS and email) to enable timely emission reduction measures. Simultaneously, carbon emission reports are generated regularly to record detailed carbon emission data for different time periods, providing managers with further analysis and decision-making support.

[0004] For example, Chinese invention patent CN118278713B discloses an intelligent management and control method and device for an energy system, which includes: predicting a second energy influence factor in a second preset time period based on a first energy influence factor in a first preset time period and the changes in historical energy influence factors in the same period; predicting energy change data of the energy system in the second preset time period based on the comprehensive energy influence coefficient of the first energy influence factor and the second energy influence factor; determining the equipment output demand of each energy device based on the energy change data and equipment information; and then determining an energy system management and control strategy for the energy system.

[0005] For example, Chinese invention patent CN118228940B discloses a smart management method and system for the entire carbon footprint of an airport, including: a data acquisition module, a data processing module, a carbon emission calculation module, a carbon emission control module, and a carbon emission display and benchmarking analysis module. The data acquisition targets are fixed and mobile sources throughout the airport, and the acquisition parameters are energy type, energy consumption, and carbon emission factors for each energy source. The data processing module cleans, standardizes, transmits, and stores the acquired data. The carbon emission calculation module calculates the carbon emissions for each area. The carbon emission control module performs partitioning, classification, and itemized analysis, and the carbon emission analysis and control module uses the total emissions across the entire airport as a limit value to decompose indicators for different areas and different emission targets and performs real-time benchmarking and control.

[0006] The above-mentioned technology has at least the following technical problems: Many energy systems (such as power grids and gas supply) and carbon emission tracking systems often operate independently, lacking effective integration. This results in carbon emission data acquisition and analysis not directly influencing energy dispatch decisions. Without real-time feedback mechanisms, energy systems struggle to optimize energy dispatch and usage based on the latest carbon emission data. This disconnect between carbon emission data acquisition and analysis and energy management prevents true intelligent dispatch and integrated management, thereby weakening overall energy efficiency and emission reduction effectiveness.

[0007] At the same time, the current technological system is prone to creating a contradiction between pursuing efficiency and emission reduction. Carbon emission tracking mainly focuses on controlling the total amount or intensity, while energy system efficiency optimization focuses on reducing energy consumption and improving equipment performance. Specifically, in practical applications, in order to reduce carbon emissions, it may be necessary to sacrifice some energy efficiency, or in order to improve energy utilization efficiency, carbon emissions may exceed the limit. Existing technologies have not yet been able to effectively achieve a balance between carbon emissions and energy efficiency, and lack a mechanism for dynamic adjustment, which further reduces the overall energy use efficiency and emission reduction effect in the process of energy system management. Summary of the Invention

[0008] To address the technical problem of low overall energy efficiency and emission reduction effectiveness in the energy system management process of existing technologies, embodiments of the present invention provide an energy system management method and system based on dynamic carbon footprint tracking, the technical solution of which is as follows: On the one hand, an energy system management method based on dynamic carbon footprint tracking is provided. This method includes: S1, determining the stability of load changes of power generation equipment in the energy system to obtain management results for load fluctuation management, thereby determining whether to set up carbon emission management for power generation equipment; S2, if so, obtaining time-series integrated decision results to characterize the time-series lag effect between carbon emissions and energy efficiency after setting up carbon emission management for power generation equipment; if not, performing time-series synchronous change detection to obtain time-series integrated decision results; S3, determining whether to perform carbon footprint-energy efficiency time-series lag management to improve the time-series alignment accuracy between carbon emissions and energy efficiency based on the obtained time-series integrated decision results.

[0009] On the other hand, an energy system management system based on dynamic carbon footprint tracking is provided, including: a load fluctuation determination module, a carbon emission control module, and a time lag control module; the load fluctuation determination module is used to determine the stability of load changes of power generation equipment in the energy system to obtain the control results for load fluctuation change control, thereby determining whether to set carbon emission control for power generation equipment; the carbon emission control module is used to obtain the time-series integrated decision results to characterize the time lag effect between carbon emissions and energy efficiency after setting carbon emission control for power generation equipment if it is set, and to perform time-series synchronization change detection to obtain the time-series integrated decision results if it is not set; the time lag control module is used to determine whether to perform carbon footprint-energy efficiency time lag control to improve the time alignment accuracy between carbon emissions and energy efficiency based on the obtained time-series integrated decision results.

[0010] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention first monitors and analyzes the load changes of power generation equipment in the energy system, assessing the degree of load fluctuation through statistical analysis of the total system load. If the load fluctuation exceeds a set threshold, subsequent carbon emission control settings are triggered. This process allows for timely measures to be taken when load fluctuations are large, preventing excessive carbon emissions or energy waste caused by abnormal load fluctuations. Compared to traditional load monitoring methods, this judgment method is more accurate and sensitive, enabling early detection of potential problems and providing warnings, thereby avoiding over-adjustment of the system or a decline in energy efficiency. After confirming the need for carbon emission control, the lag effect between carbon emissions and energy efficiency is further considered, and the time-series integrated decision-making results are used to determine whether carbon footprint-energy efficiency time-series lag control should be implemented. If carbon emission control is not required, time-series synchronous change detection is performed directly. Through time-series analysis of carbon emission and energy efficiency data, considering the lag effect between the two, more accurate data support can be provided for subsequent control decisions. Compared with existing technologies, traditional carbon emission control methods often focus on immediate emission intensity or average energy efficiency levels, while neglecting the dimension of "time lag between emission changes and energy efficiency changes." By introducing time-series integrated decision-making, this method can identify lag effects and adjust response mechanisms, thereby improving control accuracy and reducing blind responses, making it more dynamic and intelligent than traditional methods. Furthermore, many existing technologies for energy system management only focus on "energy efficiency optimization" or "carbon emission optimization," often neglecting the coupling, synchronization, and lag differences between the two in the time dimension. This invention uses "time alignment" as the control objective, ensuring that carbon emissions and energy efficiency change synchronously over time by adjusting the time alignment accuracy. This control approach improves the matching degree between carbon emissions and energy efficiency, making carbon emission adjustments more precise and maximizing energy efficiency. It fills the gap in traditional solutions that ignore time lag effects, making control more refined, real-time, and closed-loop. Compared with traditional methods, this invention not only improves the energy utilization efficiency of energy systems but also reduces carbon emissions, achieving a more refined and scientific energy management effect with stronger sustainable development potential.

[0011] 2. During the control period, the total system load data of all power generation equipment in the energy system is obtained, and the load change fluctuation response value and the average load level response value are calculated as the basis for subsequent steps. The total system load is the sum of the real-time loads of all power generation equipment and is a key data point for judging the stability of the energy system and the trend of load changes. By introducing the comprehensive consideration of the two response values, load fluctuations can be quantified more accurately, thereby providing more refined management and decision-making, and avoiding unnecessary resource waste caused by rough judgments. Compared with existing technologies that usually rely on the load data of a single power generation equipment or only focus on some equipment, which cannot comprehensively reflect the load status of the entire energy system and are prone to misjudgment or omissions, this invention can more accurately assess the overall load fluctuations, ensuring the comprehensiveness and accuracy of the control strategy. On this basis, through special correction for the volatility of renewable energy, misjudgments caused by the natural fluctuations of wind and solar power generation are avoided, thereby improving the reliability of the judgment. Secondly, through secondary judgment and adjustment mechanisms for the next control period, the control process is made more flexible, more responsive, and reduces false triggering or delayed triggering. Currently, many load fluctuation or carbon emission control strategies have not fully distinguished the different impacts between the natural fluctuations of renewable energy power generation and the operational fluctuations of traditional power generation equipment. This invention enhances reliability in scenarios with high proportions of renewable energy integration by introducing a renewable energy fluctuation factor, making load fluctuation control more refined and precise, thereby improving the responsiveness and stability of the energy system. Next, based on load fluctuation control indicators, total carbon emission constraints and carbon emission intensity constraints are dynamically adjusted to ensure that power generation equipment operates within an appropriate carbon emission range. Traditional carbon emission control models generally set fixed total carbon emission or intensity targets, lacking dynamic adaptation to load fluctuations. This invention, however, combines load fluctuation indicators with constraint settings, making carbon emission control more closely aligned with actual operating conditions, improving the real-time performance, precision, and responsiveness of control. Simultaneously, the correction mechanism ensures that carbon emission control is not a static, one-time setting, but can be dynamically adjusted according to actual load fluctuation deviations. This means that when the system fluctuation level, renewable energy ratio, or load response characteristics change, the control strategy can be corrected in real time, making carbon emission control more adaptable and continuously optimizing. Compared to traditional methods, this correction process can adjust deviations in real time, avoiding rigid control under fixed thresholds, thereby improving the accuracy and response speed of carbon emission control.

[0012] 3. By introducing two quantitative indicators—the lag distance parameter and the lag time parameter—between carbon emissions and energy efficiency, the temporal misalignment or disconnect between the two can be quantitatively identified, thus providing a more accurate basis for control decisions. If a significant lag is found, it indicates that the system may have issues such as carbon emissions not decreasing in a timely manner after efficiency improvements, or carbon emissions decreasing but efficiency improvements lagging behind. Traditional energy or carbon emission control mechanisms are mostly based on static indicators or simple time-overlay analysis, often ignoring the lag effect between two sets of data time series. This invention, based on a dynamic time warping algorithm, starts from the time series dimension and can reveal the lag relationship hidden behind the data, making control more sensitive, timely, and targeted. Based on this, according to the time-series integrated decision results, it is determined whether to implement carbon footprint-energy efficiency time-series lag control. If the decision result indicates normal integrated control, no control is implemented, and a carbon emission control report is directly generated. If the decision result indicates a lag in integrated control, the carbon footprint-energy efficiency time-series lag control stage is entered. At this point, by analyzing the lag distance and lag time parameters, it is determined whether the sampling frequency needs to be adjusted, thus optimizing the data sampling process. Adjusting the sampling frequency enables more accurate carbon emission tracking, reduces data noise, and improves monitoring accuracy. Compared to traditional single carbon emission control methods, this invention introduces time-series integrated decision analysis, which dynamically determines whether to implement control measures based on the lag effects of carbon emissions and energy efficiency. This improves the targeting and response speed of the control strategy. Simultaneously, by introducing a dynamic adjustment mechanism for lag distance and lag time, the sampling frequency is optimized, ensuring more accurate and real-time carbon emission monitoring. It is also necessary to consider that existing technologies typically use fixed-frequency data sampling and monitoring, neglecting to dynamically adjust the monitoring sampling frequency based on lag conditions. Furthermore, data integrity and time synchronization are often supplementary checks rather than being embedded in the lag control mechanism. This method integrates sampling frequency setting, data integrity checks, and time synchronization error monitoring into the lag control process, giving the system stronger real-time monitoring capabilities, better data support, and less risk of failure due to data lag or asynchrony. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating an energy system management method based on dynamic carbon footprint tracking, provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the stability determination of an energy system management method based on dynamic carbon footprint tracking, as provided in an embodiment of the present invention. Figure 3 A time-series integrated decision result acquisition logic diagram for the energy system management method based on dynamic carbon footprint tracking provided in this embodiment of the invention; Figure 4 This is a schematic diagram of the structure of an energy system management system based on dynamic carbon footprint tracking, provided in an embodiment of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0016] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0017] With the increasing severity of global climate change, greenhouse gas emissions, especially carbon dioxide emissions, have become a global focus. As a major source of carbon emissions, the energy system plays a crucial role in addressing climate change and achieving carbon neutrality. Therefore, effectively managing carbon emissions within the energy system, particularly dynamically tracking and managing carbon footprints, has become a key task for global energy management and environmental protection.

[0018] like Figure 1 The diagram shown is a flowchart of an energy system management method based on dynamic carbon footprint tracking provided in an embodiment of the present invention. (Refer to...) Figure 1 The method includes the following steps: load fluctuation determination, carbon emission control, and time lag control.

[0019] Specifically, the first step of the energy system management method based on dynamic carbon footprint tracking is load fluctuation determination, namely: S1, to determine the stability of load changes of power generation equipment in the energy system in order to obtain the management results for load fluctuation control, thereby determining whether to set up carbon emission control for power generation equipment; by determining the stability of load changes of power generation equipment, it is possible to identify whether there are drastic fluctuations, sudden changes or abnormal trends in the load, which further helps to promptly detect operating states in the system that may lead to a decrease in power generation efficiency, an increase in fuel use, and an increase in carbon emission intensity.

[0020] refer to Figure 2 ,like Figure 2The diagram shows a flowchart of the stationarity determination process for an energy system management method based on dynamic carbon footprint tracking, provided in this embodiment of the invention. The corresponding logic is as follows: Based on the total system load obtained within the management period, load change fluctuation response value and average load level response value are obtained. Fluctuation quantification processing is performed on the load change fluctuation response value and average load level response value to obtain a load fluctuation control index. The load fluctuation control index is compared with a stored load fluctuation degree setting value. If the load fluctuation control index is greater than the load fluctuation degree setting value, it indicates that the management result is non-stationary load change. Carbon emission data of power generation equipment in the energy system within the management period is obtained, and carbon emission control settings for power generation equipment are implemented. After setting, a time-series integrated decision result is obtained. Conversely, if the carbon emission data is less than the set value, it indicates that the management result is stationary load change. Carbon emission data and energy efficiency data of power generation equipment in the energy system within the management period are obtained, and time-series synchronous change detection is performed to obtain a time-series integrated decision result. Here, time-series synchronous change detection is based on a dynamic time warping algorithm to obtain the carbon emission-energy efficiency lag distance parameter and lag time parameter. Specifically, the steps for determining the stationarity of load changes of power generation equipment in the energy system are as follows: S11: During the control period, the total system load of power generation equipment in the energy system is acquired through the Supervisory Control and Data Acquisition (SCADA) system. Based on the total system load during the control period, statistical analysis is performed to obtain the load change fluctuation response value and the average load level response value. The load change fluctuation response value is represented by the standard deviation of the total system load during the control period, and the average load level response value is represented by the average value of the total system load during the control period. Compared to the traditional method of judging load stability based on experience, using standard deviation and average value to quantify load changes enhances the visualization and quantification capabilities of load changes, enabling managers to intuitively grasp the system's load fluctuation amplitude, trend, and average status during the control period. Through index-based processing, load status is transformed from "fuzzy judgment" to "detailed indicators," improving the scientific and precise level of control.

[0021] S12, the obtained load change fluctuation response value and load average level response value are subjected to fluctuation quantification processing to obtain a load fluctuation control index used to reflect the overall fluctuation degree of power generation equipment in the energy system; wherein, the load change fluctuation response value and the load average level response value are both de-unitized, and the fluctuation quantification processing is the ratio calculation.

[0022] S13. The obtained load fluctuation control index is compared with the stored load fluctuation setpoint. If the load fluctuation control index is greater than the load fluctuation setpoint, it indicates that the control result is non-stationary load change. Carbon emission data of power generation equipment in the energy system within the control period is obtained, and carbon emission control settings for power generation equipment are configured. After configuration, the time-series integrated decision result is obtained. Conversely, if the load fluctuation control index is less than the setpoint, it indicates that the control result is stationary load change. Carbon emission data and energy efficiency data of power generation equipment in the energy system within the control period are obtained, and time-series synchronous change detection is performed to obtain the time-series integrated decision result. It should be noted that the load fluctuation setpoint is stored in the energy system control database. This database is created first when designing the energy system control method based on carbon footprint dynamic tracking, specifically to store core configuration information.

[0023] By determining whether to proceed with simultaneous energy efficiency and carbon emission analysis based on whether load changes are stable, the system avoids blindly entering the carbon emission or energy efficiency analysis process when the load fluctuates wildly or the system is unstable. This saves resources and improves the relevance and reliability of the control results. Furthermore, systems with large load fluctuations are prone to frequent adjustments to power generation equipment, decreased efficiency, and accelerated wear, which may also lead to increased carbon emissions. By determining whether the load is stable in advance and initiating carbon emission control during periods of load anomalies, it helps reduce efficiency losses and equipment risks caused by sudden load changes, while also reducing unnecessary carbon emissions, thus contributing to the stability and economy of the energy system.

[0024] As a further step, the following procedures are outlined for setting up carbon emission controls for power generation equipment: S131, the carbon emission data of power generation equipment in the energy system and the corresponding carbon emission constraint values ​​of the carbon emission data are obtained through the monitoring and data acquisition system during the control period; the carbon emission constraint values ​​include the total carbon emission constraint value and the carbon emission intensity constraint value, which are used to describe the maximum value of the carbon emission data allowed by the power generation equipment in the energy system. The carbon emission data includes the total carbon emission and the carbon emission intensity. The carbon emission intensity is the result of the ratio calculation of the total carbon emission and the total power generation.

[0025] S132, the load fluctuation control index is input into the corresponding output of the carbon emission total constraint tolerance model, which is used to relax the carbon emission total constraint value. The carbon emission total constraint value is then set based on the carbon emission total tolerance value; that is, the carbon emission total tolerance value is set as the carbon emission total constraint value. The carbon emission total constraint tolerance model is pre-trained using historical load fluctuation control indicators and carbon emission total tolerance values ​​set by professionals based on empirical rules. By setting the carbon emission total constraint value, it is ensured that power generation equipment will not exceed the allowable total carbon emissions within a certain time period, avoiding excessive carbon emissions. The introduction of the load fluctuation control index further optimizes the flexibility of this constraint value, allowing for appropriate relaxation of the constraint under conditions of large load fluctuations, which helps to balance system stability and environmental protection requirements.

[0026] S133 sets carbon emission intensity constraints based on the acquired carbon emission intensity tolerance value. Specifically, the carbon emission intensity tolerance value is set as the carbon emission intensity constraint value. The carbon emission intensity tolerance value is the output result corresponding to the load fluctuation control index input into the carbon emission intensity constraint tolerance model. The carbon emission intensity constraint tolerance model is pre-trained using historical load fluctuation control indicators and carbon emission intensity tolerance values ​​set by professionals based on empirical rules. Setting carbon emission intensity constraints helps limit the carbon emission intensity per unit of energy output while ensuring power generation efficiency. This mechanism ensures that the system minimizes carbon emissions without sacrificing efficiency, promoting the development of the energy system towards a low-carbon and energy-saving direction.

[0027] S134: Obtain carbon emission data of power generation equipment in the energy system for the next control cycle at the carbon emission monitoring frequency. The carbon emission monitoring frequency is set by professionals according to the standards in the field. The obtained carbon emission data is compared with the corresponding set carbon emission constraint value. If any carbon emission data is greater than the corresponding carbon emission constraint value, a carbon emission tracking anomaly prompt is sent. Otherwise, the carbon emission data of power generation equipment in the energy system continues to be monitored.

[0028] In energy systems, especially those with a high proportion of renewable energy, load fluctuations are significantly affected by the volatility of renewable energy sources (such as wind and solar power). Due to the instability of renewable energy generation, the total system load may fluctuate considerably, impacting overall carbon emission control and energy efficiency management. Therefore, it is necessary to introduce a renewable energy volatility factor to correct the assessment of total system load volatility and adjust carbon emission control measures based on the corrected volatility. Specifically, the obtained load fluctuation control indicators are compared with stored load volatility setpoints, and the process also includes: Q1. When the proportion of renewable energy power generation equipment in the energy system, as obtained through monitoring and data acquisition systems, exceeds a set reference proportion, a renewable energy volatility factor is introduced to correct the impact of renewable energy volatility on the determination of total system load volatility. The load volatility control index is coupled with the renewable energy volatility factor to obtain a load volatility control correction index. The renewable energy volatility factor is set by professionals according to industry standards, and the coupling process involves multiplication. By introducing the renewable energy volatility factor, the impact of renewable energy volatility on system load volatility can be corrected. This correction allows the system to more accurately determine the actual degree of load volatility, rather than simply relying on the original load volatility control index, avoiding misjudgments and over-control due to excessive volatility. Simultaneously, this refined volatility handling can reduce carbon emission control errors caused by unnecessary volatility corrections, thereby allowing for more rational adjustment of carbon emission settings.

[0029] Q2. When the load fluctuation control index is greater than the load fluctuation setpoint, determine whether the load fluctuation control correction index is still greater than the load fluctuation setpoint. Specifically, if not, obtain the control correction offset, which reflects the degree of deviation between the load fluctuation control correction index and the load fluctuation setpoint. That is, the absolute value of the difference between the load fluctuation control correction index and the load fluctuation setpoint. Set the control period according to the control tracking period output by inputting the control correction offset into the carbon footprint tracking period mapping set. The carbon footprint tracking period mapping set is obtained by pre-training using historical control correction offsets and control tracking periods set by professionals based on experience rules. It is used to describe the mapping relationship between the control correction offset and the control tracking period.

[0030] If so, the adjustment for the next control cycle will be carried out first, followed by the correction of the carbon emission control settings for power generation equipment.

[0031] Q21, adjust for the next control cycle. Specifically, set the next control cycle based on the control cycle output after inputting the load fluctuation control correction index into the control adjustment cycle mapping table. The control adjustment cycle mapping table is pre-trained using historical load fluctuation control correction indices and control adjustment cycles set by professionals based on experience rules. It is used to describe the mapping relationship between the load fluctuation control correction index and the control adjustment cycle.

[0032] Q22, to carry out corrective processing for revising the carbon emission control settings of power generation equipment, specifically: First, the fluctuation correction deviation, which reflects the degree of deviation between the load fluctuation control index and the load fluctuation control correction index, is compared with the set maximum deviation reference value. The fluctuation correction deviation is the absolute value of the difference between the load fluctuation control index and the load fluctuation control correction index. Specifically, if the fluctuation correction deviation is greater than the set maximum deviation reference value, the arithmetic mean of the load fluctuation control index and the load fluctuation control correction index is input into the corresponding output of the carbon emission total constraint correction model, which is used to correct the degree of relaxation of the carbon emission total constraint value. Otherwise, the load fluctuation control correction index is input into the carbon emission total constraint correction model. The corresponding output is the carbon emission total correction tolerance value. Based on the carbon emission total correction tolerance value, the carbon emission total constraint value is set, that is, the carbon emission total correction tolerance value is set as the carbon emission total constraint value. The carbon emission total constraint correction model is obtained by pre-training using the arithmetic average of historical load fluctuation control indicators and load fluctuation control correction indicators, the load fluctuation control correction indicators, and the carbon emission total correction tolerance value set by professionals based on empirical rules. It is used to describe the mapping relationship between the load fluctuation control indicators and the load fluctuation control correction indicators, and the load fluctuation control correction indicators and the carbon emission total correction tolerance value.

[0033] Secondly, based on the obtained carbon emission intensity correction tolerance value, the carbon emission intensity constraint value is set. Specifically, the carbon emission intensity correction tolerance value is set as the carbon emission intensity constraint value. The carbon emission intensity correction tolerance value is set when the fluctuation correction deviation exceeds the set maximum deviation reference value; in this case, the arithmetic average of the load fluctuation control index and the load fluctuation control correction index is input into the output of the carbon emission intensity tolerance correction model. Alternatively, when the fluctuation correction deviation does not exceed the set maximum deviation reference value, the load fluctuation control correction index is input into the output of the carbon emission intensity tolerance correction model. The carbon emission intensity tolerance correction model is pre-trained using the arithmetic average of historical load fluctuation control indices and load fluctuation control correction indices, the load fluctuation control correction index, and the carbon emission intensity correction tolerance value set by professionals based on empirical rules. It describes the mapping relationship between the arithmetic average of the load fluctuation control indices and load fluctuation control correction indices, the load fluctuation control correction index, and the carbon emission intensity correction tolerance value. The corrected total carbon emissions and intensity constraint values ​​better reflect the actual operating status of the system. Through this dynamic adjustment, the system can effectively control carbon emissions while ensuring energy consumption efficiency, avoiding resource waste or efficiency decline caused by a single fixed constraint value. This further makes the carbon emission control strategy more flexible and adaptable, and can make timely adjustments according to actual load fluctuations, thereby taking into account the reliability of energy system operation.

[0034] Q3. When the load fluctuation control index is not greater than the load fluctuation set value, determine whether the load fluctuation control correction index is still less than the load fluctuation set value. Specifically: if yes, continue to monitor the load fluctuation control index and the load fluctuation control correction index; if no, perform correction processing to correct the carbon emission control settings of the power generation equipment.

[0035] By revising the total carbon emission constraints and carbon emission intensity constraints, the system can more accurately control carbon emissions in actual operation, reduce excessive intervention and optimize energy efficiency, improve the response speed and accuracy of carbon emission monitoring, and at the same time ensure the rationality and scientific nature of energy management decisions.

[0036] The second step of the energy system management method based on dynamic carbon footprint tracking is carbon emission control, namely: S2. If this is implemented, after setting up carbon emission control for power generation equipment, a time-series integrated decision result is obtained to characterize the time-series lag effect between carbon emissions and energy efficiency. If not implemented, time-series synchronous change detection is performed to obtain the time-series integrated decision result. Figure 3 The diagram shows the logic for obtaining the time-series integrated decision result of the energy system management method based on dynamic carbon footprint tracking provided in this embodiment of the invention. The corresponding logic is as follows: if the obtained carbon emission-energy efficiency lag distance parameter is greater than the set maximum allowable lag parameter, then the time-series integrated decision result is determined to be integrated management lag; otherwise, the time-series integrated decision result is determined to be integrated management normal and the lag time parameter is obtained; if the lag time parameter is greater than the set maximum allowable lag time, then the time-series integrated decision result is updated to integrated management lag; otherwise, the time-series integrated decision result is not updated, and the carbon emission-energy efficiency lag distance parameter and the lag time parameter are monitored.

[0037] Specifically, refer to Figure 3 The time-series integrated decision results are obtained through the following steps: S21. Based on the dynamic time warping algorithm, obtain the carbon emission-energy efficiency lag distance parameter, which is used to measure the time lag effect between the carbon emission intensity and power generation efficiency of power generation equipment in the energy system. That is, calculate the minimum distance between the time series of carbon emission intensity and power generation efficiency based on the dynamic time warping algorithm. Compare the obtained carbon emission-energy efficiency lag distance parameter with the set maximum allowable lag parameter to obtain the time series integrated decision result.

[0038] S22, if the carbon emission-energy efficiency lag distance parameter is greater than the set maximum allowable lag parameter, then the time series integrated decision result is determined to be integrated control lag.

[0039] S23, otherwise, determine the time series integration decision result as normal integration control, and obtain the lag time parameter used to measure the time step difference between carbon emission intensity and power generation efficiency based on the dynamic time warping algorithm, that is, the average value of the absolute value of the difference in time steps between all time series alignment points between carbon emission intensity and power generation efficiency.

[0040] S24. If the lag time parameter is greater than the set maximum allowable lag time, the time series integration decision result will be updated to the integrated control lag. Otherwise, the time series integration decision result will not be updated, and the carbon emission-energy efficiency lag distance parameter and lag time parameter will be continuously monitored.

[0041] The carbon emission-energy efficiency lag distance and lag time parameters obtained based on the dynamic time warping algorithm can accurately measure the lag effect between carbon emissions and power generation efficiency, providing a dynamic basis for time-series integrated decision-making. This process can improve the accuracy and flexibility of carbon emission control, optimize control cycles and adjustment strategies, reduce unnecessary interventions, and ultimately enhance the stability, efficiency, and environmental performance of the energy system.

[0042] The third step in the energy system management method based on dynamic carbon footprint tracking is time-lag management, namely: S3, which determines whether to implement carbon footprint-energy efficiency time-lag management to improve the time alignment accuracy between carbon emissions and energy efficiency based on the acquired time-series integrated decision results. The specific decision-making process for carbon footprint-energy efficiency time-lag management is as follows: S31. If the time-series integrated decision result is that the integrated control is normal, then carbon footprint-energy efficiency time-series lag control will not be carried out. The control result and the time-series integrated decision result will be fed back and a carbon emission control report will be generated.

[0043] S32, if the time-series integrated decision result indicates a lag in integrated management, then carbon footprint-energy efficiency time-series lag management will be implemented. As a further solution, the specific process for implementing carbon footprint-energy efficiency time-series lag management is as follows: S321, if the carbon emission-energy efficiency lag distance parameter is greater than the set maximum allowable lag parameter, then the carbon emission-energy efficiency lag distance parameter is input into the carbon emission tracking mapping model to output the carbon emission tracking sampling frequency; the carbon emission tracking mapping model is obtained by pre-training using historical carbon emission-energy efficiency lag distance parameters and carbon emission tracking sampling frequencies set by professionals based on empirical rules.

[0044] S322, if the carbon emission-energy efficiency lag distance parameter is not greater than the set maximum allowable lag parameter, then the result of the arithmetic average of the obtained carbon emission-energy efficiency lag distance parameter and lag time parameter is input into the carbon emission sampling mapping model to output the carbon emission tracking sampling frequency; both the carbon emission-energy efficiency lag distance parameter and the lag time parameter have been de-unitized, and the carbon emission sampling mapping model is pre-trained by using the result of the arithmetic average of the historical carbon emission-energy efficiency lag distance parameter and lag time parameter, as well as the carbon emission tracking sampling frequency set by professionals based on empirical rules.

[0045] S323 samples carbon emission data based on the output carbon emission tracking sampling frequency.

[0046] By implementing time-lag management of carbon footprint and energy efficiency, the sampling frequency of carbon emission data can be precisely adjusted, thereby improving the real-time performance and accuracy of carbon emission tracking. Dynamic adjustments based on real-time data allow for flexible and precise control of the sampling frequency under varying carbon emission and energy efficiency fluctuations, enhancing the reliability of carbon emission tracking and optimizing the system's carbon emission monitoring and energy efficiency management. Through more accurate carbon emission data sampling, the system can better reflect the dynamic relationship between carbon emissions and energy efficiency, providing a more accurate basis for subsequent energy management and decision-making, and driving the system to achieve more efficient and environmentally friendly operation.

[0047] In the process of carbon footprint and energy efficiency management in energy systems, ensuring data integrity and synchronization is crucial. Therefore, after implementing carbon footprint-energy efficiency time-series lag management, it is essential to perform data integrity checks and time synchronization error detection. This analysis process ensures that the carbon emission and energy efficiency data collected by the system meets quality requirements, thereby supporting more reliable and accurate management decisions. Specifically, carbon footprint-energy efficiency time-series lag management also includes: First, the proportion of missing values ​​used for integrity checks of carbon emission and energy efficiency data after carbon footprint-energy efficiency time-series lag control is obtained through data preprocessing tools (such as Python's Pandas library), and compared with the stored maximum threshold for missing values. If the proportion of missing values ​​is greater than the maximum threshold for missing values, a data sampling anomaly prompt is sent, and carbon emission data is resampled.

[0048] Secondly, if the proportion of missing values ​​is not greater than the maximum threshold for missing values, a time processing tool (such as Python's datetime module) is used to obtain the time synchronization error between carbon emission data and energy efficiency data. When the time synchronization error is greater than the maximum threshold for time synchronization, a data synchronization error prompt is sent, and the carbon emission data and energy efficiency data are resynchronized. When the time synchronization error is not greater than the maximum threshold for time synchronization, the proportion of missing values ​​and the time synchronization error continue to be monitored.

[0049] By performing data integrity checks and time synchronization error detection after carbon footprint-energy efficiency time-series lag management, the system can ensure the accuracy of collected data in terms of both quality and timing, thereby improving the reliability of decision-making. Checking the percentage of missing values ​​can effectively avoid calculation errors caused by missing data, ensuring that problems are detected and corrected in a timely manner during data sampling, and preventing incomplete data from affecting carbon emission and energy efficiency analysis results.

[0050] Secondly, the time synchronization error check ensures precise alignment between carbon emission data and energy efficiency data in time. This is crucial for analyzing the relationship between carbon emissions and energy efficiency. Large time synchronization errors can lead to the failure of carbon emission control measures or an inaccurate reflection of the system's energy efficiency level. Through real-time monitoring and error alerts within the system, when the time synchronization error exceeds the set maximum value, the system will promptly synchronize the data, ensuring the timeliness and accuracy of subsequent analysis results. This data quality control mechanism provides a guarantee for the refined management of the energy system, improving the accuracy and reliability of carbon footprint and energy efficiency control.

[0051] like Figure 4 The diagram shown is a structural schematic of an energy system management and control system based on dynamic carbon footprint tracking provided in an embodiment of the present invention. The energy system management and control system based on dynamic carbon footprint tracking provided in an embodiment of the present invention includes: a load fluctuation determination module, a carbon emission management and control module, and a time lag management and control module.

[0052] Specifically, the load fluctuation determination module is used to determine the stability of load changes of power generation equipment in the energy system, so as to obtain the control results for load fluctuation control, and thus determine whether to set up carbon emission control for power generation equipment.

[0053] The carbon emission control module is used to obtain the time-series integrated decision results, which characterize the time-series lag effect between carbon emissions and energy efficiency, after setting the carbon emission control of power generation equipment, if it is implemented; otherwise, it performs time-series synchronous change detection to obtain the time-series integrated decision results.

[0054] The timing lag control module is used to determine whether to implement carbon footprint-energy efficiency timing lag control to improve the timing alignment accuracy between carbon emissions and energy efficiency based on the acquired timing integration decision results.

[0055] 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.

[0056] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, 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 machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] 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.

[0058] 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.

[0059] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0060] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0061] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An energy system management method based on dynamic carbon footprint tracking, characterized in that, Includes the following steps: S1, determine the stability of load changes of power generation equipment in the energy system to obtain control results for load fluctuation control, and thus determine whether to set up carbon emission control for power generation equipment; The specific steps for determining the stability of load changes in power generation equipment within the energy system are as follows: During the control period, the total system load of power generation equipment in the energy system is obtained, and based on the total system load during the control period, the load change fluctuation response value and the load average level response value are obtained. The obtained load change fluctuation response value and load level response value are subjected to fluctuation quantification processing to obtain a load fluctuation control index that reflects the overall fluctuation degree of power generation equipment in the energy system. The obtained load fluctuation control index is compared with the stored load fluctuation setpoint: if the load fluctuation control index is greater than the load fluctuation setpoint, it indicates that the control result is that the load change is non-stationary. The carbon emission data of the power generation equipment in the energy system within the control period is obtained and the carbon emission control of the power generation equipment is set. After setting, the time-series integrated decision result is obtained. Conversely, if the load fluctuation control index is less than the setpoint, it indicates that the control result is that the load change is stationary. The carbon emission data and energy efficiency data of the power generation equipment in the energy system within the control period are obtained and the time-series synchronous change detection is performed to obtain the time-series integrated decision result. The specific steps for obtaining the time-series integrated decision result are as follows: The carbon emission-energy efficiency lag distance parameter is obtained based on the dynamic time warping algorithm to measure the time lag effect between the carbon emission intensity and power generation efficiency of power generation equipment in the energy system. The obtained carbon emission-energy efficiency lag distance parameter is compared with the set maximum allowable lag parameter to obtain the time-series integrated decision result. If the carbon emission-energy efficiency lag distance parameter is greater than the set maximum allowable lag parameter, then the time-series integrated decision result is determined to be integrated control lag. Otherwise, if the time-series integration decision result is determined to be normal, the lag time parameter used to measure the time step difference between carbon emission intensity and power generation efficiency is obtained based on the dynamic time warping algorithm. If the lag time parameter is greater than the set maximum allowable lag time, the time series integrated decision result will be updated to the integrated control lag; otherwise, the time series integrated decision result will not be updated, and the carbon emission-energy efficiency lag distance parameter and lag time parameter will be monitored. S2, if performed, then after setting, the time-series integrated decision results used to characterize the time-series lag effect between carbon emissions and energy efficiency are obtained; if not performed, then time-series synchronous change detection is performed to obtain the time-series integrated decision results. S3, based on the acquired time-series integrated decision results, determines whether to implement carbon footprint-energy efficiency time lag control to improve the time-series alignment accuracy between carbon emissions and energy efficiency.

2. The energy system management method based on dynamic carbon footprint tracking according to claim 1, characterized in that, The process of comparing the obtained load fluctuation control index with the stored load fluctuation setting value further includes: When the proportion of renewable energy power generation equipment in the power generation equipment of the energy system is greater than the set reference proportion, a renewable energy volatility factor is introduced to correct the impact of the volatility of renewable energy on the determination of the total load volatility of the system. The load volatility control index is coupled with the renewable energy volatility factor to obtain the load volatility control correction index. When the load fluctuation control index is greater than the load fluctuation set value, it is determined whether the load fluctuation control correction index is still greater than the load fluctuation set value. Specifically, if so, the adjustment for the next control cycle is carried out first, and then the correction process for correcting the carbon emission control settings of the power generation equipment is carried out. The adjustment for the next control cycle is specifically as follows: the next control cycle is set according to the control adjustment cycle output after the load fluctuation control correction index is input into the control adjustment cycle mapping table; If not, obtain the control correction offset, which reflects the degree of deviation between the load fluctuation control correction index and the load fluctuation setpoint, and set the control period according to the control tracking period output by inputting the control correction offset into the carbon footprint tracking period mapping set. When the load fluctuation control index is not greater than the load fluctuation set value, determine whether the load fluctuation control correction index is still less than the load fluctuation set value. Specifically, if yes, continue to monitor the load fluctuation control index and the load fluctuation control correction index; if no, perform correction processing to correct the carbon emission control settings of the power generation equipment.

3. The energy system management method based on dynamic carbon footprint tracking according to claim 1, characterized in that, The specific process for setting up carbon emission control for power generation equipment is as follows: Obtain carbon emission data of power generation equipment in the energy system and the corresponding carbon emission constraint values ​​for the carbon emission data during the control period; The carbon emission constraints include total carbon emission constraints and carbon emission intensity constraints, which are used to describe the maximum carbon emission data allowed by power generation equipment in the energy system. The load fluctuation control index is input into the carbon emission total constraint tolerance model and the corresponding output carbon emission total constraint value is used to relax the carbon emission total constraint value. The carbon emission total constraint value is set according to the carbon emission total tolerance value. The carbon emission intensity constraint value is set based on the obtained carbon emission intensity tolerance value, wherein the carbon emission intensity tolerance value is the output result of inputting the load fluctuation control index into the carbon emission intensity constraint tolerance model. The carbon emission data of power generation equipment in the energy system is obtained at the carbon emission monitoring frequency for the next control cycle. The obtained carbon emission data is compared with the corresponding set carbon emission constraint value. If any carbon emission data is greater than the corresponding carbon emission constraint value, a carbon emission tracking anomaly prompt is sent. Otherwise, the carbon emission data of power generation equipment in the energy system continues to be monitored.

4. The energy system management method based on dynamic carbon footprint tracking according to claim 2, characterized in that, The correction process for revising the carbon emission control settings of power generation equipment specifically includes: The fluctuation correction deviation, which reflects the degree of deviation between the load fluctuation control index and the load fluctuation control correction index, is compared with the set maximum deviation reference value. Specifically, if the fluctuation correction deviation is greater than the set maximum deviation reference value, the result of the arithmetic average of the load fluctuation control index and the load fluctuation control correction index is input into the carbon emission total constraint correction tolerance value, which is used to correct the degree of relaxation of the carbon emission total constraint value, in the corresponding output of the carbon emission total constraint correction model. Otherwise, the load fluctuation control correction index is input into the carbon emission total constraint correction tolerance value, which is the corresponding output of the carbon emission total constraint correction model, and the carbon emission total constraint value is set according to the carbon emission total constraint correction tolerance value. The carbon emission intensity constraint value is set based on the obtained carbon emission intensity correction tolerance value. The carbon emission intensity correction tolerance value is when the fluctuation correction deviation is greater than the set maximum deviation reference value, the result of the arithmetic average of the load fluctuation control index and the load fluctuation control correction index is input into the output result of the carbon emission intensity tolerance correction model, or when the fluctuation correction deviation is not greater than the set maximum deviation reference value, the load fluctuation control correction index is input into the output result of the carbon emission intensity tolerance correction model.

5. The energy system management method based on dynamic carbon footprint tracking according to claim 1, characterized in that, The specific judgment process for the carbon footprint-energy efficiency time-lag management is as follows: If the time-series integrated decision result is that the integrated management and control is normal, then carbon footprint-energy efficiency time-series lag management and control will not be carried out. The management and control results and the time-series integrated decision result will be fed back and a carbon emission management and control report will be generated. If the time-series integrated decision result is that the integrated management is lagging, then carbon footprint-energy efficiency time-series lag management will be implemented.

6. The energy system management method based on dynamic carbon footprint tracking according to claim 5, characterized in that, The specific process for implementing carbon footprint-energy efficiency time-lag management is as follows: If the carbon emission-energy efficiency lag distance parameter is greater than the set maximum allowable lag parameter, then the carbon emission-energy efficiency lag distance parameter will be input into the carbon emission tracking mapping model to output the carbon emission tracking sampling frequency; If the carbon emission-energy efficiency lag distance parameter is not greater than the set maximum allowable lag parameter, the result of the arithmetic average of the obtained carbon emission-energy efficiency lag distance parameter and lag time parameter is input into the carbon emission sampling mapping model to output the carbon emission tracking sampling frequency. Carbon emission data is sampled based on the output carbon emission tracking sampling frequency.

7. The energy system management method based on dynamic carbon footprint tracking according to claim 6, characterized in that, The aforementioned carbon footprint-energy efficiency time-lag management also includes: After obtaining the carbon footprint-energy efficiency time-lag management, the percentage of missing values ​​used to perform integrity checks on carbon emission data and energy efficiency data is compared with the maximum threshold for missing values ​​in the storage. If the percentage of missing values ​​exceeds the maximum threshold for missing values, a data sampling anomaly alert will be sent, and carbon emission data will be resampled. Otherwise, obtain the time synchronization error between carbon emission data and energy efficiency data. If the time synchronization error is greater than the maximum time synchronization error set, send a data synchronization anomaly prompt and resynchronize the carbon emission data and energy efficiency data. If the time synchronization error is not greater than the maximum time synchronization error set, continue to monitor the percentage of missing values ​​and the time synchronization error.

8. An energy system management system based on dynamic carbon footprint tracking, employing the energy system management method based on dynamic carbon footprint tracking as described in any one of claims 1-7, characterized in that, include: Load fluctuation judgment module, carbon emission control module, and time lag control module; The load fluctuation determination module is used to determine the stability of load changes of power generation equipment in the energy system, so as to obtain the control results for load fluctuation control, and thus determine whether to set up carbon emission control of power generation equipment. The carbon emission control module is used to obtain a time-series integrated decision result that characterizes the time-series lag effect between carbon emissions and energy efficiency after setting if the setting is performed; otherwise, it performs time-series synchronous change detection to obtain the time-series integrated decision result. The time lag control module is used to determine whether to perform carbon footprint-energy efficiency time lag control to improve the time alignment accuracy between carbon emissions and energy efficiency based on the obtained time integration decision results.

Images