A dynamic monitoring and evaluation system and method for synergistic optimization management of electricity and carbon

Abstract

This invention relates to the field of power system carbon collaborative management technology, specifically a dynamic monitoring and evaluation system and method for power system carbon collaborative optimization management. It comprises a sensing layer, a data processing layer, a power system carbon collaborative accounting layer, a dynamic evaluation layer, an intelligent control layer, and an application layer, all connected in sequence. Each layer constructs a closed-loop management architecture of monitoring, accounting, evaluation, control, and application. This invention, by constructing a comprehensive monitoring system covering energy production, transmission, conversion, consumption, and carbon offsetting, and integrating dynamic emission factors and multi-source parameter correction mechanisms, establishes for the first time a full-chain, traceable, and accurate carbon accounting model. This fundamentally solves the core pain points of inaccurate carbon emission measurement and unclear accountability, providing an indisputable data foundation for carbon trading, government assessment, and refined enterprise management.

Description

Technical Field

[0001] This invention relates to the field of power system carbon synergy management technology, specifically to a dynamic monitoring and evaluation system and method for power system carbon synergy optimization management. Background Technology

[0002] Current electricity carbon management systems generally suffer from core problems such as "primarily passive monitoring, weak coordination capabilities, insufficient accounting accuracy, and poor adaptability of the assessment system," making it difficult to meet the management needs of new power systems with high proportions of renewable energy integration, deep coupling of multiple energy flows, and deep coordination of electricity and carbon. Existing systems mostly only collect and visualize energy data such as electricity, heat, and gas, lacking proactive intelligent control mechanisms based on real-time data and dynamic assessment. They are unable to cope with the complex energy dispatching demands brought about by the interaction of power generation, grid, load, and storage, resulting in low efficiency in multi-energy flow resource allocation and difficulty in improving overall energy efficiency.

[0003] In the carbon management process, the full-chain carbon emission measurement system has significant shortcomings. The accounting boundaries are often limited to end-user energy consumption, neglecting key aspects such as energy production, transmission, conversion, and carbon offsetting. Furthermore, the accounting methods use static emission factors, failing to consider the impact of dynamic factors such as environmental parameters and equipment operating efficiency, which can easily lead to biased results. This makes it difficult to provide accurate data support for carbon liability allocation, carbon trading settlement, and performance evaluation. Simultaneously, the existing evaluation indicator system follows the traditional power system energy efficiency assessment framework, lacking evaluation dimensions that reflect the core characteristics of the new power system: "source-grid-load-storage synergy, new energy consumption, and electricity-carbon coupling." This makes it difficult to comprehensively quantify the effectiveness of electricity-carbon synergy management and fails to provide a scientific basis for optimizing management strategies and formulating policies. Therefore, we propose a dynamic monitoring and evaluation system and method for electricity-carbon synergy optimization management. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic monitoring and evaluation system and method for coordinated optimization management of electricity and carbon, which has the advantages of active regulation capability, full-process carbon measurement accuracy, and a new power system adaptability evaluation system, and solves the management bottleneck problem that existing technologies cannot break through passive monitoring-extensive accounting-static evaluation.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic monitoring and evaluation system for coordinated optimization management of electricity and carbon, comprising a sensing layer, a data processing layer, an electricity and carbon co-calculation layer, a dynamic evaluation layer, an intelligent control layer, and an application layer connected in sequence, with each layer constructing a closed-loop management architecture of monitoring-calculation-evaluation-control-application;

[0006] The sensing layer is used to collect multi-energy flow operation data, carbon emission-related data for the entire process, and environmental parameters.

[0007] The data processing layer is used to preprocess, fuse, and hierarchically store the collected data;

[0008] The aforementioned electricity-carbon collaborative accounting layer is used to achieve accurate measurement of carbon emissions throughout the entire process and the sharing of carbon responsibility among multiple entities.

[0009] The dynamic evaluation layer is used to construct an integrated evaluation index system for electricity and carbon emissions, enabling dynamic evaluation and trend prediction.

[0010] The intelligent control layer is used to generate multi-energy flow active intelligent control strategies.

[0011] The application layer is used to provide visualization, carbon trading support, and assessment services.

[0012] Preferably, the sensing layer includes a multi-energy flow sensing unit, a carbon emission sensing unit, and an environmental sensing unit;

[0013] The multi-energy flow sensing unit uses a 0.2S-level smart meter, a 1.5-level gas meter, a high-precision heat meter, and an energy storage status monitor, and is deployed at the energy production end, transmission end, conversion end, and consumption end, with a data collection frequency of 1-5 seconds / time.

[0014] The carbon emission sensing unit uses a fuel consumption meter and a carbon emission online monitoring instrument, with a data collection frequency of 1-10 minutes / time.

[0015] The environmental sensing unit collects wind speed, light intensity, temperature, and humidity data at a frequency of 1 minute per instance.

[0016] Preferably, the data processing layer includes a data preprocessing module, a data fusion module, and a data storage module;

[0017] The data preprocessing module uses the 3σ criterion to identify abnormal data and uses an LSTM neural network model to complete missing data.

[0018] The data fusion module fuses multi-source data based on an improved Kalman filter algorithm;

[0019] The data storage module adopts an edge storage plus cloud storage architecture, with real-time high-frequency data stored at the edge and historical data and calculation results stored in the cloud.

[0020] Preferably, the electricity-carbon co-calculation layer adopts the calculation method of activity data × dynamic emission factor × correction coefficient, and the calculation scope covers the entire process of energy production, transmission, conversion, consumption and carbon offsetting.

[0021] Carbon responsibility is allocated based on the Shapley value method, which combines energy consumption, carbon emission contribution, collaborative optimization participation, and renewable energy consumption, generating detailed digital carbon accounts for each entity.

[0022] Preferably, the evaluation index system of the dynamic evaluation layer includes three primary indicators: electricity-carbon synergy efficiency, comprehensive energy efficiency level, and safety and stability level, as well as 12 secondary indicators.

[0023] The weights of the indicators are determined by the analytic hierarchy process, and the comprehensive score is calculated by combining the fuzzy comprehensive evaluation method.

[0024] The ARIMA-LSTM hybrid model is used to predict the trend of synergistic management of electricity and carbon in the next 1-30 days, with a prediction error of ≤5%.

[0025] A dynamic monitoring and evaluation method for synergistic optimization management of electricity and carbon emissions includes the following steps:

[0026] S01: Multi-source data acquisition: Collects multi-energy flow operation data, carbon emission related data and environmental parameters through the sensing layer, and transmits them to the data processing layer in encrypted form.

[0027] S02: Data processing and fusion, which involves identifying anomalies, filling in missing data, and standardizing the collected data. The data is then fused using an improved Kalman filter algorithm to generate an electric carbon collaborative data mart and store it hierarchically.

[0028] S03: Full-process carbon accounting and responsibility allocation, calculating the total carbon emissions and carbon emission intensity per unit of energy consumption in each process, and completing the carbon responsibility allocation of multiple entities based on the Shapley value method;

[0029] S04: Dynamic assessment and trend prediction. Calculate a comprehensive score based on the assessment index system and predict the trend of synergistic management of electricity and carbon through the ARIMA-LSTM hybrid model.

[0030] S05: Proactive intelligent regulation generates multi-energy flow collaborative optimization strategies, guides users to adjust their energy consumption behavior, and triggers emergency regulation in emergency scenarios;

[0031] S06: Application output and closed-loop optimization: Display core data, generate accounting and assessment reports, connect to the carbon trading market, and optimize management strategies in reverse.

[0032] Preferably, the dynamic emission factor in step S03 is calibrated based on the IPCC recommended value combined with the measured value in the region, and the correction coefficient is dynamically adjusted according to environmental parameters and equipment operating efficiency, with carbon emission accounting accuracy ≤ ±3%.

[0033] Preferably, the intelligent control in step S05 adopts a mixed integer linear programming model, with the goal of maximizing the efficiency of electricity-carbon synergy, minimizing the total carbon emissions, and minimizing the overall cost, with a scheduling cycle of 5 minutes / time and a control response time of ≤500ms.

[0034] Preferably, the demand response regulation described in step S05 adopts a dual-drive mechanism of price incentives and command regulation, guiding users to adjust their energy consumption behavior through carbon credit deduction and time-of-use electricity pricing.

[0035] Preferably, the application output described in step S06 is connected to the regional carbon trading market, providing carbon emission accounting reports and carbon responsibility allocation details, supporting carbon quota allocation, carbon trading settlement and compliance assessment.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] 1. This invention establishes a comprehensive monitoring system covering energy production, transmission, conversion, consumption, and carbon offsetting, and integrates dynamic emission factors and multi-source parameter correction mechanisms. For the first time, it establishes a full-chain, traceable, and accurate carbon accounting model. This model significantly improves the accuracy of carbon emission accounting from 60-70% in traditional methods to ≤±3%. Based on the Shapley value method, it achieves fair and scientific allocation of carbon responsibility among multiple stakeholders, generating authoritative and credible digital carbon accounts. This fundamentally solves the core pain points of inaccurate carbon emission measurement and unclear responsibility, providing an indisputable data foundation for carbon trading, government assessment, and refined corporate management.

[0038] 2. This invention integrates multiple indicators such as carbon synergy efficiency, comprehensive energy efficiency, and safety and stability, and introduces the ARIMA-LSTM hybrid prediction model to construct a dynamic intelligent evaluation system that integrates real-time assessment and trend prediction. This system can achieve a comprehensive score every 15 minutes and make high-precision predictions of management trends for the next 1-30 days. This enables management decisions to leap from relying on ex-post statistics of historical data to proactive optimization with forward-looking prediction capabilities, providing a scientific basis for strategy formulation and risk warning.

[0039] 3. This invention adopts a mixed-integer linear programming multi-objective optimization model and integrates a dual-driven demand response mechanism of price and instruction to achieve proactive intelligent control of multi-energy flow with second-level response and minute-level optimization. The scheduling cycle is as short as 5 minutes and the control response time is ≤500ms. It can quickly adapt to the fluctuation of new energy sources and load changes. Empirical results show that it can improve the multi-energy flow coordination efficiency of the park to 87% and reduce the total carbon emissions by 11%. While ensuring the security of energy supply, it achieves a unified optimization of economy, low carbon and high efficiency.

[0040] 4. This invention, through an edge-cloud collaborative data architecture, standardized system interfaces, and BIM+GIS visualization applications, creates a closed-loop management and service system covering the entire process of monitoring, accounting, evaluation, regulation, and trading. This system not only achieves cross-system data fusion and strategy execution but also directly connects to the carbon trading market, outputting verification reports, allocation details, and assessment results. It transforms precise carbon management data into tradable carbon assets and management performance, establishing a complete chain from data collection to value realization. This provides a feasible and operational integrated solution for regions and enterprises to implement dual-carbon goals. Attached Figure Description

[0041] Figure 1 This is a diagram of the overall system architecture of the present invention;

[0042] Figure 2 This is a flowchart of the data acquisition and processing process of the present invention;

[0043] Figure 3 This is a schematic diagram illustrating the carbon emission accounting boundaries and responsibility allocation throughout the entire process of this invention;

[0044] Figure 4 This is a system structure diagram of the electro-carbon synergistic evaluation index of the present invention;

[0045] Figure 5 This is a flowchart illustrating the multi-energy flow collaborative scheduling logic and control process of the present invention. Detailed Implementation

[0046] The present invention will be further described in detail below with reference to specific embodiments.

[0047] I. Overview of Implementation Examples

[0048] This embodiment uses a coastal integrated industrial park as the application scenario. The park covers an area of ​​5 km² and includes 10 industrial enterprises (including 3 high-energy-consuming enterprises: electronic component manufacturing, machinery processing, and chemical production), 2 commercial complexes, and 3 residential communities (totaling 1200 households). The energy supply system includes a 20MW photovoltaic power station (a combination of distributed and centralized systems), a 5MW wind farm, a 20MW gas turbine, a 10MWh / 2h lithium battery energy storage device, and a 50MW combined heat and power unit. It is equipped with 3 110kV transmission lines, 20km of heating pipelines, and 15km of gas pipelines. The goal is to achieve multi-energy flow synergistic optimization, full-process carbon metering, and refined carbon management.

[0049] The system is deployed and configured as follows in the park: 320 Class 0.2S smart meters (50 at the production end, 30 at the transmission end, and 240 at the consumption end) at the sensing layer, 80 Class 1.5 gas meters, 50 high-precision heat meters, 15 carbon emission monitors (6 near the gas turbine, 3 at the waste treatment station, and 6 in the carbon sink area), and 10 sets of environmental monitoring equipment (3 sets at the photovoltaic power station, 3 sets at the wind farm, and 4 sets at the core nodes of the park).

[0050] The data processing layer is equipped with two industrial-grade edge servers (configured with Intel Xeon Gold 6330 processors, 128GB DDR4 memory, and 2TB SSD hard drives), while the cloud uses Alibaba Cloud RDS distributed database (10TB storage capacity, supporting distributed computing and second-level retrieval).

[0051] The intelligent control layer interfaces with the park's energy management system (EMS), energy storage control system (PCS), and user energy management system (EMS) via the OPCUA protocol to enable command issuance and data interaction.

[0052] The application layer deploys a BIM+GIS visualization platform, which connects to the provincial carbon trading market data interface and the park management center's monitoring system.

[0053] The system deployment cycle was 15 days, including 7 days for equipment installation and debugging, 5 days for system integration and joint testing, and 3 days for trial operation. The deployment process did not affect the existing production and living order in the park. During the system trial operation, the data collection efficiency was 99.2%, and the data transmission latency was ≤80ms, which met the design requirements.

[0054] II. Implementation Process and Details

[0055] 1. Multi-source data acquisition and processing

[0056] The sensing layer devices collect data at a set frequency, as follows:

[0057] (1) Multi-energy flow data: The output data of the photovoltaic power station is collected at a frequency of 1 second / time. The output data during a typical period (10:00-10:05) are 12.5MW, 12.8MW, 13.2MW, 13.0MW, and 12.9MW;

[0058] The wind farm output was collected at a frequency of 1 second, with simultaneous outputs of 3.2MW, 3.1MW, 3.3MW, 3.2MW, and 3.4MW.

[0059] The gas turbine gas consumption is collected every 5 seconds, with simultaneous gas consumption of 480 m³ / h, 485 m³ / h, 490 m³ / h, 488 m³ / h, and 492 m³ / h.

[0060] The SOC of the energy storage device is collected at a frequency of 1 second, with simultaneous SOC values ​​of 68%, 67%, 66%, 65%, and 64%.

[0061] The total load of the park is collected at a frequency of 1 second / time. The total load during the same period is 22.5MW, 22.8MW, 23.0MW, 22.9MW, and 23.2MW (of which industrial load is 15.0MW, commercial load is 4.5MW, and residential load is 3.7MW).

[0062] (2) Carbon emission data: The gas turbine fuel consumption was collected every 5 minutes, and the natural gas consumption during the same period was 2425 m³, with a methane content of 98.5%;

[0063] The carbon emission collection frequency of the waste treatment station is 10 minutes per time, and the CO2 emission during the same period is 0.5t;

[0064] The carbon sequestration of the park's green spaces is collected every 10 minutes, with a carbon sequestration rate of 0.08 t / h.

[0065] (3) Environmental data: The light intensity of the photovoltaic power station area was collected at a frequency of 1 minute / time, and the corresponding values ​​were 850W / m², 860W / m², and 855W / m².

[0066] The wind speed in the wind farm area was collected once every minute, with the corresponding values ​​being 4.2 m / s, 4.3 m / s, and 4.1 m / s.

[0067] The ambient temperature in the park was collected every minute, with the corresponding values ​​being 26℃, 26.2℃, and 26.1℃.

[0068] The data processing layer performs a preprocessing procedure: it identifies abnormal photovoltaic power output data using the 3σ criterion (power output suddenly drops to 0MW at 10:02:30, which is determined to be a sensor fault and is therefore discarded).

[0069] The missing gas turbine gas consumption data (data missing at 10:03:15, the completed value is 489 m³ / h) was completed using the LSTM model.

[0070] All data was standardized and converted to JSON format, with field specifications conforming to the IEC 61970 standard.

[0071] By improving the Kalman filter algorithm to fuse multi-source data, an electric carbon collaborative data mart is generated. The multi-energy flow data fusion latency is 420ms, and the data consistency is 99.6%. The fused data is stored on an edge server (storage period of 7 days) and a cloud database respectively.

[0072] 2. Carbon emission accounting and responsibility sharing throughout the entire process

[0073] The electricity-carbon co-accounting layer clearly defines the accounting boundary as the entire process within the industrial park, and calculates carbon emissions using "activity data × dynamic emission factor × correction coefficient," as detailed below:

[0074] (1) Carbon emissions from energy production: carbon emissions from gas turbines = 490 m³ / h × 0.52 tCO2 / m³ (IPCC recommended value, combined with regional measured calibration) × 1.03 (temperature correction factor, 26℃ corresponds to correction factor 1.03) = 267.88 tCO2 / h;

[0075] The standby thermal power unit was not started, and carbon emissions were zero.

[0076] Zero carbon emissions from new energy sources (photovoltaics and wind power).

[0077] (2) Carbon emissions in the transmission process: Carbon emissions from transmission line losses = Total transmission capacity 25.2MW × 2.2% (line loss rate, measured value) × 0.82tCO2 / MWh (thermal power substitution emission factor) = 0.45tCO2 / h;

[0078] Carbon emissions from heat pipeline leakage = Total heat transfer 120 GJ / h × 0.8% (leakage rate) × 0.15 tCO2 / GJ (thermal emission factor) = 0.144 tCO2 / h;

[0079] Carbon emissions from gas pipeline leaks = Total gas transmission volume 500 m³ / h × 0.6% (leakage rate) × 0.52 tCO2 / m³ = 0.156 tCO2 / h;

[0080] Total carbon emissions from the transmission process = 0.45 + 0.144 + 0.156 = 0.75 tCO2 / h.

[0081] (3) Carbon emissions in the conversion process: Carbon emissions from energy storage charging and discharging losses = Energy storage charging and discharging power 2MW × 5% (loss rate) × 0.6tCO2 / MWh (comprehensive emission factor) = 0.06tCO2 / h;

[0082] Carbon emissions from a combined heat and power (CHP) unit = 18MW (power generation output) × 0.45tCO2 / MWh = 8.1tCO2 / h;

[0083] Total carbon emissions from the conversion process = 0.06 + 8.1 = 8.16 tCO2 / h.

[0084] (4) Carbon emissions from consumption: Industrial load carbon emissions = 15.0MW × 0.62tCO2 / MWh (industrial comprehensive emission factor) = 9.3tCO2 / h;

[0085] Carbon emissions from commercial load = 4.5MW × 0.55tCO2 / MWh = 2.475tCO2 / h;

[0086] Residential load carbon emissions = 3.7MW × 0.5tCO2 / MWh = 1.85tCO2 / h;

[0087] Total carbon emissions from consumption = 9.3 + 2.475 + 1.85 = 13.625 tCO2 / h.

[0088] (5) Carbon offset: 0.08tCO2 / h carbon sink from park greening + 0.12tCO2 / h carbon sink from photovoltaic / wind power projects = 0.2tCO2 / h.

[0089] Total carbon emissions of the park = 267.88 + 0.75 + 8.16 + 13.625 - 0.2 = 290.215 tCO2 / h;

[0090] Carbon emission intensity per unit output value = 290.215tCO2 / h × 24h / (Total daily industrial output value of the park is 8.5 million yuan) = 0.81tCO2 / 10,000 yuan.

[0091] Carbon responsibility allocation is based on the Shapley value method, combined with four weighting factors (energy consumption 40%, carbon emission contribution 25%, collaborative optimization participation 20%, and renewable energy consumption 15%). Taking three high-energy-consuming enterprises as examples, the allocation results are as follows:

[0092] Electronic component manufacturing enterprises account for 35% of energy consumption, 30% of carbon emissions, 15% of collaborative optimization participation, and 20% of new energy consumption. Their carbon responsibility allocation is calculated as follows: 290.215 tCO2 / h × (35% × 40% + 30% × 25% + 15% × 20% + 20% × 15%) = 290.215 × (0.14 + 0.075 + 0.03 + 0.03) = 290.215 × 0.275 ≈ 79.81 tCO2 / h.

[0093] The allocated amount for mechanical processing enterprises is approximately 65.30 tCO2 / h.

[0094] The allocation for chemical production enterprises is approximately 80.76 tCO2 / h, and the allocation results are recorded in each enterprise's digital carbon account in real time.

[0095] 3. Dynamic assessment and trend prediction

[0096] The dynamic assessment layer calculates a comprehensive score for this typical period based on the electricity-carbon synergistic assessment index system, as follows:

[0097] (1) Electricity-carbon synergy efficiency: Multi-energy flow synergy efficiency 86% (score 12.9), carbon-electric coupling coefficient 19.2tCO2 / MWh (score 14.76), carbon emission reduction rate of new energy consumption 38% (score 4.56), energy storage electricity-carbon synergy contribution 14% (score 1.4). The total score for this type of indicator is 12.9+14.76+4.56+1.4=33.62 points.

[0098] (2) Overall energy efficiency level: Overall energy utilization efficiency is 89% (score 8.9), carbon emission intensity per unit output is 0.81tCO2 / 10,000 yuan (score 6.72), transmission line loss rate is 2.2% (score 3.9), and pipeline leakage rate is 0.7% (score 3.72). The total score for this type of indicator is 8.9+6.72+3.9+3.72=23.24 points.

[0099] (3) Safety and stability level: grid voltage deviation 1.8% (score 3.92), energy supply reliability 99.6% (score 4.98), energy storage SOC maintenance rate 66% (score 1.98), carbon emission reduction target achievement rate 93% (score 5.58). The total score for this type of indicator is 3.92+4.98+1.98+5.58=16.46 points.

[0100] The overall score is 33.62 + 23.24 + 16.46 = 73.32 points, which is rated as good. The system issues a prompt through the visualization platform: the energy storage SOC maintenance strategy and pipeline leakage control need to be optimized to improve the overall score.

[0101] The trend prediction module, based on the ARIMA-LSTM hybrid model, predicts the trend of electricity-carbon synergy management in the park over the next 7 days: the multi-energy flow synergy efficiency will increase from 86% to 88%, the carbon-electric coupling coefficient will decrease to 18.5tCO2 / MWh, the comprehensive energy utilization efficiency will increase to 90%, the carbon emission reduction target achievement rate will remain at 93%-94%, and the prediction error is 4.1%, providing support for the formulation of subsequent control strategies.

[0102] 4. Intelligent control and application output

[0103] The intelligent control layer generates collaborative optimization strategies based on evaluation results and prediction data:

[0104] (1) Multi-energy flow dispatch: Using a mixed integer linear programming model, the output of photovoltaic power is 13.5MW, wind power is 3.5MW, gas turbine power is 4.8MW, energy storage discharge is 1.2MW, and cogeneration unit power is 18.0MW, with a total output of 25.0MW, which meets the total load demand of 23.2MW. The total carbon emissions are reduced to 288.5tCO2 / h, which is 1.715tCO2 / h lower than before dispatch.

[0105] (2) Demand response regulation: Issue demand response instructions to electronic component manufacturing enterprises to guide them to transfer 2.0MW of high energy-consuming load (electroplating workshop) to the peak photovoltaic output period (11:00-14:00) and give them a carbon credit reward of 200 credits / day (which can offset 10 yuan / day of electricity bill).

[0106] A recommendation to adjust air conditioning temperatures in commercial complexes (raising the summer set temperature from 24℃ to 26℃) is expected to reduce load by 0.3MW and carbon emissions by 0.18tCO2 / h.

[0107] (3) Emergency control: During this period, the system operating parameters were all within the normal range, and the emergency control module was not triggered;

[0108] The system has a preset emergency strategy for when the grid voltage deviation exceeds the standard (>±7%): start a 1MW standby diesel generator, adjust the energy storage discharge power to 3MW, limit the load of chemical production enterprises to 1.5MW, and ensure that the voltage is quickly restored to the normal range.

[0109] The application layer output is as follows: The BIM+GIS visualization platform displays the multi-energy flow operation status, carbon emission data, comprehensive score and control strategy implementation in real time, supporting real-time monitoring by the park management center;

[0110] Generate the daily carbon emission accounting report (daily emissions 6924.36tCO2) and the assessment reports for each enterprise (electronic component manufacturing enterprise scored 72 points, grade good; chemical production enterprise scored 69 points, grade qualified).

[0111] Connect to the provincial carbon trading market, upload carbon responsibility allocation details and carbon emission reduction data (daily carbon emission reduction of 120tCO2), and support enterprises in fulfilling their carbon quotas and settling carbon transactions.

[0112] III. Implementation Results Verification

[0113] After one month of stable operation in the park, all performance indicators of this system met the design requirements. To further highlight the advanced nature of this technical solution, a core comparison is made between this system and traditional carbon management methods (which only have basic data acquisition and static accounting functions, lacking dynamic evaluation and intelligent control modules), as shown in the table below:

[0114] Comparison Dimensions This technical solution Traditional carbon management methods Advantages Data acquisition accuracy 0.2S-class electricity meters and 1.5-class gas meters have a data collection efficiency of 99.2%. Class 1.0 electricity meters and Class 2.0 gas meters have a data collection efficiency of 85%-90%. By employing high-precision and compliant equipment, the accuracy and completeness of data collection have been significantly improved, providing a reliable data foundation for subsequent accounting and evaluation. carbon accounting scope Covering the entire process from production, transmission, conversion, consumption, to carbon offsetting, with an accounting accuracy of 97.5%. It only covers the end-consumer market, with an accuracy rate of 60%-70%. The end-to-end accounting process is comprehensive and thorough, and combined with a dynamic correction mechanism, the accounting results are more accurate, supporting scenarios such as carbon trading and liability sharing. Dynamic assessment capability Dynamic assessment every 15 minutes, including 12 secondary indicators, supporting trend prediction (error 4.1%). Without an independent evaluation system, it can only output static accounting data. It can quantify management results in real time, predict operational trends in advance, and provide forward-looking support for strategy optimization. Intelligent control response Control response time ≤ 500ms, dispatch cycle 5 minutes / time, emergency response ≤ 10 seconds It lacks automatic control function, relies on manual intervention, and has a response time of ≥1 hour. Proactive collaborative scheduling can quickly adapt to energy fluctuations and emergency scenarios, ensuring stable system operation. Multi-energy flow cooperative efficiency After operation, the efficiency rate increased to 87%, and the overall energy efficiency improved by 8%. Synergistic efficiency ≤75%, no significant improvement in overall energy efficiency By optimizing scheduling based on multiple objectives, efficient allocation of multi-energy flow resources can be achieved, thereby improving energy utilization efficiency. Carbon emission control effectiveness <![CDATA[The total carbon emissions in the park decreased by 11%, and the carbon emission intensity per unit output value was 0.73 tCO2 / 10,000 yuan]]> <![CDATA[There is no targeted control measure, and the carbon emission intensity fluctuates greatly (0.9 - 1.3 tCO2 / 10,000 yuan)]]> It can precisely control carbon emissions, help achieve low-carbon transformation goals, and improve the green development level of the park. Carbon trading support It can output accounting reports and liability allocation details, and interface with the carbon trading market. It can only provide basic energy consumption data and cannot support the entire carbon trading process. Achieve seamless integration of electricity carbon data with the carbon trading market to support carbon quota compliance and settlement for multiple entities.

[0115] The above comparison demonstrates that this technical solution significantly improves upon traditional carbon management methods in terms of data quality, accounting accuracy, control capabilities, and overall benefits, effectively addressing the pain points of traditional methods, which are characterized by "passive and extensive operation, insufficient precision, and weak coordination." Through closed-loop management throughout the entire process, this system achieves multi-energy flow collaborative optimization, precise carbon metering across all stages, and refined control within the industrial park. This not only ensures the safe and stable operation of the new power system but also effectively supports the park's low-carbon transformation goals, gaining high recognition from park management and enterprises. It possesses strong practical application value and industrialization potential.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A dynamic monitoring and evaluation system for synergistic optimization management of electricity and carbon, characterized in that, It includes a sensing layer, a data processing layer, an electricity-carbon co-calculation layer, a dynamic evaluation layer, an intelligent control layer, and an application layer that are connected in sequence. Each layer constructs a closed-loop management architecture of monitoring-calculation-evaluation-control-application. The sensing layer is used to collect multi-energy flow operation data, carbon emission-related data for the entire process, and environmental parameters. The data processing layer is used to preprocess, fuse, and hierarchically store the collected data; The aforementioned electricity-carbon collaborative accounting layer is used to achieve accurate measurement of carbon emissions throughout the entire process and the sharing of carbon responsibility among multiple entities. The dynamic evaluation layer is used to construct an integrated evaluation index system for electricity and carbon emissions, enabling dynamic evaluation and trend prediction. The intelligent control layer is used to generate multi-energy flow active intelligent control strategies. The application layer is used to provide visualization, carbon trading support, and assessment services.

2. The dynamic monitoring and evaluation system for synergistic optimization management of electricity and carbon as described in claim 1, characterized in that: The sensing layer includes a multi-energy flow sensing unit, a carbon emission sensing unit, and an environmental sensing unit. The multi-energy flow sensing unit uses a 0.2S-level smart meter, a 1.5-level gas meter, a high-precision heat meter, and an energy storage status monitor, and is deployed at the energy production end, transmission end, conversion end, and consumption end, with a data collection frequency of 1-5 seconds / time. The carbon emission sensing unit uses a fuel consumption meter and a carbon emission online monitoring instrument, with a data collection frequency of 1-10 minutes / time. The environmental sensing unit collects wind speed, light intensity, temperature, and humidity data at a frequency of 1 minute per instance.

3. The dynamic monitoring and evaluation system for synergistic optimization management of electricity and carbon as described in claim 1, characterized in that: The data processing layer includes a data preprocessing module, a data fusion module, and a data storage module; The data preprocessing module uses the 3σ criterion to identify abnormal data and uses an LSTM neural network model to complete missing data. The data fusion module fuses multi-source data based on an improved Kalman filter algorithm; The data storage module adopts an edge storage plus cloud storage architecture, with real-time high-frequency data stored at the edge and historical data and calculation results stored in the cloud.

4. The dynamic monitoring and evaluation system for synergistic optimization management of electricity and carbon as described in claim 1, characterized in that: The aforementioned electricity-carbon co-calculation layer adopts the calculation method of activity data × dynamic emission factor × correction coefficient, and the calculation scope covers the entire process of energy production, transmission, conversion, consumption and carbon offsetting. Carbon responsibility is allocated based on the Shapley value method, which combines energy consumption, carbon emission contribution, collaborative optimization participation, and renewable energy consumption, generating detailed digital carbon accounts for each entity.

5. The dynamic monitoring and evaluation system for synergistic optimization management of electricity and carbon as described in claim 1, characterized in that: The evaluation index system of the dynamic evaluation layer includes three primary indicators: electricity-carbon synergy efficiency, comprehensive energy efficiency level, and safety and stability level, as well as 12 secondary indicators. The weights of the indicators are determined by the analytic hierarchy process, and the comprehensive score is calculated by combining the fuzzy comprehensive evaluation method. The ARIMA-LSTM hybrid model is used to predict the trend of synergistic management of electricity and carbon in the next 1-30 days, with a prediction error of ≤5%.

6. A dynamic monitoring and evaluation method for synergistic optimization management of electricity and carbon, characterized in that, The system implementation based on any one of claims 1-5 includes the following steps: S01: Multi-source data acquisition: Collects multi-energy flow operation data, carbon emission related data and environmental parameters through the sensing layer, and transmits them to the data processing layer in encrypted form. S02: Data processing and fusion, which involves identifying anomalies, filling in missing data, and standardizing the collected data. The data is then fused using an improved Kalman filter algorithm to generate an electric carbon collaborative data mart and store it hierarchically. S03: Full-process carbon accounting and responsibility allocation, calculating the total carbon emissions and carbon emission intensity per unit of energy consumption in each process, and completing the carbon responsibility allocation of multiple entities based on the Shapley value method; S04: Dynamic assessment and trend prediction. Calculate a comprehensive score based on the assessment index system and predict the trend of synergistic management of electricity and carbon through the ARIMA-LSTM hybrid model. S05: Proactive intelligent regulation generates multi-energy flow collaborative optimization strategies, guides users to adjust their energy consumption behavior, and triggers emergency regulation in emergency scenarios; S06: Application output and closed-loop optimization, displaying core data, generating accounting and assessment reports, connecting to the carbon trading market, and optimizing management strategies in reverse.

7. The dynamic monitoring and evaluation method for synergistic optimization management of electricity and carbon as described in claim 6, characterized in that: The dynamic emission factor mentioned in step S03 is calibrated based on the IPCC recommended value combined with the regional measured value. The correction coefficient is dynamically adjusted according to environmental parameters and equipment operating efficiency, and the carbon emission accounting accuracy is ≤±3%.

8. The dynamic monitoring and evaluation method for synergistic optimization management of electricity and carbon as described in claim 6, characterized in that: The intelligent control described in step S05 adopts a mixed integer linear programming model, with the goal of maximizing the efficiency of electricity-carbon synergy, minimizing the total carbon emissions, and minimizing the overall cost. The scheduling cycle is 5 minutes / time, and the control response time is ≤500ms.

9. The dynamic monitoring and evaluation method for synergistic optimization management of electricity and carbon as described in claim 1, characterized in that: The demand response regulation described in step S05 adopts a dual-drive mechanism of price incentives and command regulation, guiding users to adjust their energy consumption behavior through carbon credit deductions and time-of-use electricity pricing.

10. The dynamic monitoring and evaluation method for synergistic optimization management of electricity and carbon as described in claim 1, characterized in that: The application output described in step S06 connects to the regional carbon trading market, providing carbon emission accounting reports and carbon responsibility allocation details, supporting carbon quota allocation, carbon trading settlement, and compliance assessment.

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