A Method for Energy Storage Dispatch and Carbon Emission Compliance Audit Based on Real-Time Carbon Factors of the Power Grid

Abstract

This invention provides a method for energy storage scheduling and carbon emission compliance auditing based on real-time grid carbon factors, belonging to the field of smart energy and carbon neutrality technology. The method uses minute-level real-time grid carbon emission factors as the core control variable and achieves this through the following steps: dynamically acquiring and calculating real-time grid carbon emission factors; assessing carbon compliance risks based on an AI prediction model; tracking the carbon content of electricity within energy storage using a virtual carbon knapsack mechanism; generating energy storage charging and discharging commands through a multi-mode collaborative strategy, integrating real-time carbon factors, risk indices, and virtual carbon knapsack status; executing scheduling and providing closed-loop feedback on the overall electricity consumption carbon emission intensity; and automatically generating a tamper-proof carbon emission audit report. This invention achieves a shift from passive accounting to proactive physical-layer carbon reduction, significantly improving the accuracy of carbon accounting and emission reduction effects, while ensuring the traceability and audit compliance of carbon emission data, providing energy-consuming units with a precise, proactive, and reliable carbon management solution.

Description

Technical Field

[0001] This invention belongs to the field of smart energy management and carbon neutrality technology, specifically involving a control method that integrates real-time sensing of grid carbon emissions, optimized scheduling of energy storage, and automated compliance auditing. Background Technology

[0002] Against the backdrop of global efforts to actively address climate change and advance towards "carbon peaking and carbon neutrality," key energy-consuming entities, such as industrial parks and commercial buildings, face increasingly stringent carbon emission regulations and audit requirements. To reduce the carbon intensity of electricity consumption, energy storage systems, as a flexible regulatory resource, are widely used for optimized scheduling. Simultaneously, accurately calculating electricity carbon emissions and generating compliant audit reports has become a necessary part of business operations. However, existing technological systems suffer from inherent flaws such as "disconnection between scheduling and accounting," "separation of accounting and control," and "passive response to audits." Specifically: 1. Energy storage dispatch strategies cannot respond to real-time changes in grid carbon emissions, which may lead to "reverse emission reduction"; 2. The carbon accounting data has low accuracy and poor timeliness, and cannot provide a reliable basis for refined carbon management; 3. Carbon emission compliance management remains at the level of ex-post accounting and passive reporting, lacking ex-ante risk warning and proactive control capabilities at the physical layer, making it difficult to meet the increasingly stringent requirements for precise auditing and real-time supervision.

[0003] Therefore, there is an urgent need for an innovative method that can deeply integrate real-time carbon signals from the power grid, dynamic energy storage scheduling, and automated audit compliance to solve the aforementioned technical problems. Summary of the Invention

[0004] In response to the technical problems pointed out in the background art, such as the existing energy storage scheduling methods ignoring carbon attributes, lagging and low accuracy of carbon accounting data, and passive response to audit compliance, this invention provides an energy storage scheduling and carbon emission compliance audit method based on real-time carbon factors of the power grid.

[0005] The technical solution of the present invention is executed cyclically according to a preset scheduling period, specifically including the following steps: Step S1: Dynamically acquire and calculate the real-time carbon emission factor of the power grid: By connecting to the power grid data platform (such as a regional power grid dispatch center or power trading platform) in real time via an encrypted API interface, data on the power grid's generation structure, including the real-time on-grid power of various power sources (such as coal-fired power, natural gas power, hydropower, wind power, and photovoltaic power), is obtained. Based on this, the real-time carbon emission factor of the power grid at time t is calculated. : ;in Let be the real-time power of the i-th power generation type at time t. This is the carbon emission coefficient for the entire life cycle of this type of power source (e.g., 820gCO2 / kWh for coal-fired power and 30gCO2 / kWh for photovoltaic power), where n is the total number of power source types. This factor reflects the carbon emission intensity corresponding to a unit of electricity purchased online in real time.

[0006] Step S2: Carbon compliance risk assessment based on predictions: Calculate the projected cumulative carbon emissions from time t to the end of the government audit period T, and assess compliance risks accordingly. Obtain load forecasts for each future time period k using an AI prediction model. And the predicted value of the power grid carbon factor Combined with the cumulative carbon emissions already consumed and the total quota allocated by the government Calculate the carbon compliance risk index : ; In the formula For the scheduling cycle, set a risk threshold (e.g., 0.95). When the threshold is exceeded, the system determines that there is a risk of exceeding the limit and will trigger the mandatory compliance mode.

[0007] Step S3: Update the virtual carbon backpack status: To accurately track the "carbon properties" of the stored energy in energy storage batteries and prevent "greenwashing," a virtual carbon backpack is established and maintained; its state variables... This represents the cumulative carbon emissions corresponding to the amount of electricity stored in the battery. The update logic is as follows: Carbon emissions accumulate during charging ; Carbon emission deduction during discharge process ; in , These are the charging and discharging efficiencies, respectively. , Each represents a historical moment The instantaneous power value of an energy storage system when it draws electrical energy from the grid for charging and discharging. At a historical moment Real-time carbon emission factors of the power grid To be at a historical moment The average carbon intensity of energy stored in the battery at the discharge moment, here It is used as an integral variable to represent any continuous historical moment from the initial running time of the system to the current moment t. In the formula, t is the current scheduling moment, that is, the specific time point at which this calculation and decision are made. This mechanism ensures that the system scheduling decision is based on the true "carbon content" of the battery's charge.

[0008] Step S4: Generate a multi-mode energy storage collaborative scheduling strategy: comprehensive , The virtual carbon backpack state is used to generate energy storage charging and discharging power commands through a multi-objective optimization algorithm of model predictive control (MPC). The specific operating modes include: ① Low carbon capture mode: When When the energy storage state of charge (SOC) is below the upper limit (e.g., 200g / kWh), charging is performed to capture green energy.

[0009] ② High-carbon substitution mode: when (e.g., 600g / kWh), SOC higher than the lower limit and marginal replacement benefit (e.g., at 50g / kWh) it performs discharge, replacing high-carbon mains power; It is a preset threshold for marginal substitution benefit.

[0010] ③ Mandatory Audit Mode: When When the carbon intensity is greater than 0.95, the energy storage is forced to discharge at maximum power and can be linked with the building automation system (BAS) to reduce flexible loads (such as air conditioning and lighting) to ensure that the real-time carbon intensity quickly returns to the compliance range.

[0011] in , It is a preset carbon strength threshold constant.

[0012] Step S5: Execute scheduling and closed-loop feedback: The generated instructions The data is sent to the power storage converter (PCS) for execution, and the comprehensive carbon emission intensity of electricity consumption at time t is calculated and monitored in real time after the intervention of energy storage. : ; in It is the instantaneous net power drawn from the public power grid at time t. It is the real-time carbon emission factor of the power grid at time t. It is the instantaneous power of the energy storage system discharging to the load at time t. It is the average carbon intensity of the energy stored inside the energy storage battery at time t. It is the instantaneous power of the total user load at time t.

[0013] This step forms a closed-loop feedback loop, used to evaluate scheduling effectiveness and optimize subsequent decisions; real-time calculations... It can be compared with preset targets or historical benchmarks to intuitively quantify the carbon intensity change brought about by the current scheduling strategy. This feedback value can be input to the optimization solver in step S4 to evaluate and adjust subsequent scheduling strategies, forming a closed loop of "prediction-decision-execution-evaluation" so that the system can continuously optimize its carbon emission reduction performance. This value is the direct basis for judging whether the real-time carbon intensity of the system is within the compliance range required by the "audit mandatory mode".

[0014] Step S6: Generate an automated, tamper-proof audit report: Record all key data such as timestamps, electricity consumption, carbon factors, and dispatch instructions to generate a carbon emission verification report that complies with GB / T 32150 and ISO 14064 standards. The core content of the report includes: ① Time-series carbon fingerprint: minute-level details of "electricity consumption - carbon factor"; ② Calculation of the contribution of energy storage to emission reduction: Quantifying the carbon emission reduction brought about by energy storage: ; ③ Anti-tampering proof: Hash the key data in the report and anchor the hash value through technologies such as blockchain to ensure the immutability and legal validity of the data.

[0015] Preferably, the load forecast value And carbon factor prediction value It is generated through a deep learning model based on the Transformer architecture (such as the DeepSeek large model). The model is trained using historical energy consumption, weather forecasts, time features, and historical data on power grid generation structure to achieve high-precision forward-looking predictions, providing reliable input for risk assessment.

[0016] Through the above steps, this invention, for the first time, deeply embeds real-time carbon signals into the energy storage scheduling closed loop and couples them with forward-looking compliance early warning and automated reliable auditing, forming a complete "carbon perception-carbon optimization-carbon compliance" methodology, which effectively overcomes the shortcomings of existing technologies.

[0017] The core of this invention lies in introducing and calculating the "real-time carbon emission factor of the power grid" in real time, using it as the primary decision variable for energy storage dispatch. This enables proactive coordination between the charging and discharging behavior of the energy storage system and the dynamic changes in the carbon intensity of the power grid, thereby directly reducing carbon emissions from electricity consumption at the physical level. Simultaneously, this method integrates high-precision carbon accounting, forward-looking risk warning, and automated audit report generation, forming a complete closed loop of "perception-decision-execution-audit," providing enterprises with proactive, accurate, and reliable carbon emission compliance management capabilities.

[0018] Compared with the prior art, the present invention has the following significant advantages: (1) The accuracy of carbon accounting has been improved by orders of magnitude, solving the core problems of data lag and large bias. Existing technologies use annual or regional average carbon emission factors for accounting, which cannot reflect the real-time changes in the power grid generation structure, resulting in high errors in the accounting results. This invention directly obtains minute-level power grid generation structure data through API and uses formulas Dynamic calculation of real-time carbon factors significantly improves the accuracy of carbon accounting; this provides enterprises with an accurate carbon asset data foundation, avoiding the problem of inflated or undervalued carbon assets due to inaccurate data.

[0019] (2) It realizes a fundamental shift from passive accounting to active physical layer carbon reduction, with direct and significant emission reduction effects; traditional carbon management only stays at the post-event accounting level, while existing energy storage scheduling only focuses on economic efficiency; this invention is the first to propose a scheduling strategy with real-time carbon factors as the first priority, through the active control logic of "charging during low carbon periods and discharging during high carbon periods", and introduces a "virtual carbon backpack" mechanism to ensure the authenticity of carbon traceability; this method can directly reduce the user's comprehensive carbon intensity of electricity consumption at the physical level.

[0020] (3) A proactive compliance assurance system of "prediction-early warning-mandated intervention" has been constructed to effectively respond to precise government audits; in response to the pain point of enterprises passively responding to audits, this invention uses the DeepSeek large model to predict future loads and carbon factors, and uses formulas Dynamically assess compliance risks. When the risk index reaches a certain level... When the value is greater than 0.95, the system can automatically trigger the "mandatory audit mode", control the maximum power discharge of energy storage and reduce flexible load in conjunction, thereby proactively pulling energy consumption behavior back to the compliance range in advance, changing "passive rectification" to "proactive supply guarantee", and ensuring smooth passage of government review.

[0021] (4) A credible audit evidence chain with legal validity is generated, solving the trust problem of data traceability and tamper-proofing; traditional audit reports rely on manual compilation, resulting in coarse data granularity and poor traceability. This invention automatically records minute-level time-series data (carbon fingerprint) of "electricity consumption - real-time carbon factor" and calculates the specific emission reduction contribution of energy storage. All key data are hashed to generate unique digital fingerprints, which can be anchored to the blockchain to form an immutable and complete chain of evidence. The generated reports fully comply with standards such as ISO 14064 and GB / T 32150, greatly enhancing the credibility and legal validity of the data submitted to regulatory agencies.

[0022] Other features and advantages of the invention will be set forth in the following description or may be learned by practicing the invention. Attached Figure Description

[0023] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0024] Figure 1 This is a flowchart of the method steps of the present invention.

[0025] Figure 2 This is a diagram of the "end-edge-cloud" three-layer physical logic architecture in this embodiment of the invention.

[0026] Figure 2 The following components are listed: ① Data Fusion Unit, ② Collaborative Optimization Solver, ③ Proactive Compliance Guardian, ④ Dynamic Accounting Engine, ⑤ AI Prediction Module, and ⑥ Digital MRV Audit Node. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. The specific embodiments described herein are only for explaining this invention and are not intended to limit this invention.

[0028] This invention provides a method for energy storage scheduling and carbon emission compliance auditing based on real-time carbon factors of the power grid, such as... Figure 1 , Figure 2 As shown, in practice, this method relies on a hardware and software architecture that works collaboratively with the perception layer, the platform layer (edge ​​side), and the cloud. The following describes each step of the method of the present invention in detail with reference to the architecture and embodiments.

[0029] I. Overview of the system environment for method execution: To efficiently execute the method described in this invention, a typical implementation environment can be built based on a three-layer logical architecture that integrates "end-edge-cloud": 1. Sensing Layer (Edge Side): Deployed in user parks or building sites; mainly includes: IoT sensors (AILO network) for collecting electrical parameters of each branch; Battery Management System (BMS) for monitoring the status of energy storage batteries; Energy Storage Converter (PCS) for executing charge and discharge commands; and bidirectional smart meters for trade settlement and carbon accounting anchors; This layer provides real-time data to the upper layer and receives control commands through protocols such as Modbus, CAN bus or API.

[0030] 2. Platform Layer (Edge-side / Edge Intelligent Control Layer): Deployed on a local server or edge computing gateway; core functional modules include: ① Data fusion unit: performs time alignment, quality verification and fusion of electrical data and BMS data sent from the perception layer and carbon factor data sent from the cloud; ② Collaborative optimization solver: Runs algorithms such as Model Predictive Control (MPC) to solve for the optimal charge and discharge commands by integrating real-time carbon factor, load forecast, energy storage status and compliance risks; ③ Proactive Compliance Guardian: Monitors the distance between carbon emission intensity and audit red lines in real time, and triggers mandatory intervention mode when exceeding the standard is predicted.

[0031] 3. Cloud Layer (Cloud Side / AIHUB Platform): Deployed on a cloud server; core functional modules include: ① Dynamic Calculation Engine: Through encrypted APIs such as HTTPS, it connects in real time to the data interface of the power grid data platform (such as regional power grid dispatch centers, power trading platforms, etc.) to obtain power generation structure data and calculates it according to the formula. Calculate minute-level real-time carbon emission factors of the power grid ; ② AI Prediction Module: Based on large model frameworks such as DeepSeek, it uses historical data to train a load prediction model. and carbon factor prediction model and provides forecasting services; ③ Digital MRV (Monitoring, Reporting, Verification) Audit Node: Aggregates data from all branches, automatically generates standard audit reports, and uses blockchain technology to store the hash values ​​of key data to ensure immutability.

[0032] This architecture ensures the reliability of the method: the cloud provides global data and intelligence, the edge layer is responsible for local real-time decision-making and control (it can operate independently even when the network is temporarily interrupted), and the perception layer faithfully executes and collects data. The method steps described below each perform their respective functions in this architecture and work together to complete the task.

[0033] II. Specific implementation details of the method and steps: Step S1: Dynamically acquire and calculate the real-time carbon emission factor of the power grid: In the dynamic accounting engine at the cloud layer, programs are written to periodically obtain snapshot data of the power generation structure of the target area's power grid through encrypted APIs (e.g., calling the real-time power generation data interfaces open to the public or authorized units by the State Grid or China Southern Power Grid). The obtained data should at least include: real-time on-grid power of major power types such as thermal power (coal power, gas power), hydropower, wind power, and photovoltaic power. .

[0034] Establish a local carbon emission coefficient database to store the carbon emission coefficients of various power sources throughout their entire life cycle. (For example: coal-fired power: 820gCO2 / kWh, natural gas power: 450gCO2 / kWh, photovoltaic power: 30gCO2 / kWh, hydropower: 10gCO2 / kWh). Then, according to the formula... Calculated Data is distributed in real time to the data fusion units of each edge platform layer via encrypted channels (such as MQTT / HTTP2).

[0035] Step S2: Carbon compliance risk assessment based on predictions: This step is completed collaboratively with the cloud at the edge platform layer: ① Predictive data acquisition: The edge platform requests load prediction values ​​for a future period of time (e.g., the next 24 hours) from the cloud AI prediction module. And the predicted value of the power grid carbon factor The cloud-based model is generated based on historical electricity consumption data, detailed weather forecasts, date types, and historical power grid data.

[0036] ② Local risk calculation: The edge platform's collaborative optimization solver or dedicated risk calculation module reads the local cumulative carbon emission measurement value. (Available from local databases) and receive carbon emission allowances set by the government. Calculate the carbon compliance risk index at the current moment. : ; in T represents the scheduling period, and T represents the end time of the audit period.

[0037] ③ Risk assessment: The calculated risk level will be determined by the risk assessment results. Compared with a preset threshold (e.g., 0.95), if If the value is greater than 0.95, a high-risk alert will be sent to the "Proactive Compliance Guardian" module, preparing to trigger the mandatory mode.

[0038] Step S3: Update the virtual carbon backpack status: The virtual carbon backpack model is maintained at the edge platform layer, and its state... It is a cumulative amount: ① Initialization: When the system is put into operation, settings are configured. ; ② Periodic update: At the end of each scheduling cycle, the system is updated based on the charging and discharging behavior during that cycle.

[0039] If charging occurs this period: Obtain the average charging power for this period. and the average real-time carbon factor of the power grid within the period Then the carbon inflow is: ; If the discharge occurs in this period: Obtain the average discharge power for this period. and the average carbon intensity of battery energy storage at the start of discharge Then the carbon outflow is: ; ③ State quantity update and intensity calculation: ; At the same time, based on the current state of charge of the battery and rated capacity Calculate the average carbon intensity of current battery energy storage: ;in To account for the average efficiency coefficient of charge-discharge cycles, This will be used in steps S4 and S5.

[0040] Step S4: Generate a multi-mode energy storage collaborative scheduling strategy: This step is completed in the collaborative optimization solver at the edge platform layer. The solver uses... , , The algorithm takes the energy storage state of charge (SOC) and load forecast as inputs and runs an MPC algorithm. In the actual code implementation, it can be simplified to a rule-based priority strategy, with the core logic as follows: ① Pattern judgment: Mandatory Audit Mode (Highest Priority): If If the value is greater than 0.95, then this mode will be entered directly; Low carbon capture mode: If (e.g., 200g / kWh) and If so, then enter this mode; High-carbon alternative models: If (e.g., 600g / kWh) and and (e.g., 50g / kWh), then enter this mode; Economic optimization mode (optional): When none of the above modes are triggered, economic optimization can be carried out by combining time-of-use pricing, but carbon constraints must be set.

[0041] ②Instruction generation: In the mandatory audit mode, the following instructions are generated: (Maximum power discharge), while simultaneously sending a flexible load reduction command to the building automation system (BAS) via API; In low-carbon capture mode, the following instructions are generated: ; Under the high-carbon substitution mode, the following instructions are generated: .

[0042] Step S5: Execute scheduling and closed-loop feedback: ① Instruction execution: The edge platform will generate Commands are sent to the energy storage converter (PCS) in the sensing layer via industrial communication protocols (such as Modbus TCP), and the PCS controls the battery to complete charging and discharging.

[0043] ② Feedback on Results: Real-time feedback is obtained from the smart meters and sensors in the perception layer after execution. , , Calculate the actual combined carbon emission intensity of electricity consumption. : ; ③ Closed-loop optimization: The deviation from the target value (or predicted value) can be used as part of the optimization objective function in the MPC algorithm to continuously optimize the scheduling instructions for the next cycle, thereby achieving continuous performance improvement.

[0044] Step S6: Generate an automated, tamper-proof audit report: This step is primarily completed by the cloud-based MRV audit node: ① Data aggregation: The cloud periodically synchronizes key data, which has undergone time alignment and verification, from each edge node, including: timestamps, , , , , , , wait.

[0045] ②Report generation: Carbon fingerprint: Generate minute-level "electricity consumption - real-time carbon factor" comparison tables.

[0046] Emission reduction calculation: based on the formula Calculate the net emission reduction contribution of the energy storage system during the audit period.

[0047] Report formatting: The system automatically fills in data and generates a complete draft carbon emission verification report according to the standard templates of GB / T 32150 or ISO 14064-1.

[0048] ③ Anti-tampering processing: Calculate the hash value (such as SHA-256) of the key original data fields in the report (such as each carbon fingerprint record), and upload these hash values ​​to a permissioned blockchain network (such as Chang'an Chain) for notarization, generating a notarization certificate containing the blockchain transaction ID and timestamp.

[0049] ④ Report Output: Finally, two materials are provided to users and government audit platforms: one is a readable PDF / Excel format verification report, and the other is a corresponding data hash certificate for third parties to verify the authenticity of the report data. III. Specific Implementation Examples: Example 1: A science and technology park's response to the audit sprint: Background: A certain industrial park is equipped with 2MWh / 1MW energy storage. In November, the system predicted that the annual carbon quota would exceed the limit by 0.5%.

[0051] The operation process on a certain day: 10:00-14:00: Cloud monitoring detected a surge in solar power generation, and calculations were performed. The edge layer triggers a low-carbon capture mode, controlling the energy storage to charge at 800kW and storing approximately 3200kWh of low-carbon energy. The virtual carbon backpack records the carbon intensity of this portion of energy as 150g / kWh.

[0052] 19:00-21:00: Evening peak, coal-fired power plants dominate peak shaving. Meanwhile, the risk index The value rose to 0.97. The system triggered mandatory audit mode.

[0053] The energy storage system discharged at full power (1MW), releasing the low-carbon electricity stored in the morning.

[0054] Simultaneously send instructions to BAS to raise the central air conditioning set temperature by 2°C and reduce lighting power by 10%.

[0055] Result: Overall carbon intensity of the park during this period The carbon emissions per kilowatt-hour decreased from 850 g / kWh when relying solely on grid electricity to approximately 320 g / kWh. Through several days of proactive intervention, the park successfully kept its cumulative carbon emissions within the allowance before the year-end audit.

[0056] Example 2: Dynamic adjustment of multi-objective weights: In the MPC solver configuration of the edge platform layer, the objective function is set as follows: Cost represents the cost of electricity, and Carbon represents carbon emissions.

[0057] During normal times ( <0.8), settings =0.7, =0.3, which ensures a certain carbon reduction effect while taking into account economic efficiency.

[0058] During the audit warning period (0.8 < <0.95), adjust =0.3, =0.7, prioritizing carbon reduction.

[0059] In the mandatory audit model ( (>0.95), MPC is bypassed, and maximum power discharge and load reduction commands are executed directly, which is equivalent to =0, =1.

[0060] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or improvements made by those skilled in the art within the scope of the technical principles disclosed in the present invention, combined with common knowledge or existing technical means, such as using other AI models for prediction or using other consensus mechanisms for data notarization, should be covered within the scope of protection of the present invention.

Claims

1. A method for energy storage dispatch and carbon emission compliance auditing based on real-time carbon factors of the power grid, characterized in that, It is executed cyclically according to a preset scheduling period, specifically including the following steps: Step S1: Dynamically acquire and calculate the real-time carbon emission factor of the power grid; Step S2: Carbon compliance risk assessment based on predictions; Step S3: Update the virtual carbon backpack status; Step S4: Generate a multi-mode energy storage collaborative scheduling strategy; Step S5: Perform scheduling and closed-loop feedback; Step S6: Generate an automated audit report that is tamper-proof.

2. The method for energy storage dispatch and carbon emission compliance auditing based on real-time carbon factors of the power grid according to claim 1, characterized in that: Step S1: The specific implementation method for dynamically acquiring and calculating the real-time carbon emission factor of the power grid is as follows: By connecting to the power grid data platform in real time via an encrypted API interface, data on the power grid's power generation structure, including the real-time grid-connected power of various power sources, is obtained. Based on this, the real-time carbon emission factor of the power grid at time t is calculated. : ;in Let be the real-time power of the i-th power generation type at time t. denoted as the carbon emission coefficient for the entire life cycle of this type of power source, where n is the total number of power source types.

3. The method for energy storage dispatch and carbon emission compliance auditing based on real-time carbon factors of the power grid according to claim 2, characterized in that: Step S2: The specific implementation method of prediction-based carbon compliance risk assessment is as follows: Calculate the projected cumulative carbon emissions from time t to the end of the government audit period T, and assess compliance risks accordingly; obtain load forecasts for each future time period k using an AI prediction model. And the predicted value of the power grid carbon factor Combined with the cumulative carbon emissions already consumed and the total quota allocated by the government Calculate the carbon compliance risk index : ； In the formula For the scheduling cycle, a risk threshold is set when... When the threshold is exceeded, the system determines that there is a risk of exceeding the limit and will trigger the mandatory compliance mode.

4. The method for energy storage dispatch and carbon emission compliance audit based on real-time carbon factor of power grid as described in claim 3, characterized in that: Step S3: The specific implementation method for updating the virtual carbon backpack state is as follows: To accurately track the "carbon properties" of the stored energy in energy storage batteries and prevent "greenwashing," a virtual carbon backpack is established and maintained; its state variables... This represents the cumulative carbon emissions corresponding to the amount of electricity stored in the battery. The update logic is as follows: Carbon emissions accumulate during charging ; Carbon emission deduction during discharge process ; in , These are the charging and discharging efficiencies, respectively. , Each represents a historical moment The instantaneous power value of an energy storage system when it draws electrical energy from the grid for charging and discharging. At a historical moment Real-time carbon emission factors of the power grid To be at a historical moment The average carbon intensity of energy stored in the battery at the discharge moment, here It is used as an integral variable to represent any continuous historical moment from the initial running time of the system to the current moment t. In the formula, t is the current scheduling moment, that is, the specific time point at which this calculation and decision are made. This mechanism ensures that the system scheduling decision is based on the true "carbon content" of the battery's charge.

5. The method for energy storage dispatch and carbon emission compliance auditing based on real-time carbon factors of the power grid according to claim 4, characterized in that: Step S4: The specific implementation method for generating the multi-mode energy storage collaborative scheduling strategy is as follows: comprehensive , The virtual carbon backpack state is used to generate energy storage charging and discharging power commands through a model predictive control multi-objective optimization algorithm. The specific operating modes include: ① Low carbon capture mode: When Furthermore, when the energy storage state of charge has not reached its upper limit, it performs charging to capture green energy; ② High-carbon substitution mode: when SOC is higher than the lower limit and marginal substitution benefit At that time, it performs discharge to replace high-carbon mains power; ③ Mandatory Audit Mode: When When the carbon intensity is greater than 0.95, the forced energy storage will discharge at maximum power and can be linked with the building automation system to reduce flexible loads, ensuring that the real-time carbon intensity quickly returns to the compliant range; in , It is a preset carbon intensity threshold constant, and SOC is the state of charge of energy storage. It is the preset marginal replacement benefit threshold. It is the average carbon emission intensity of the energy stored inside the energy storage battery at time t.

6. The method for energy storage dispatch and carbon emission compliance auditing based on real-time carbon factors of the power grid according to claim 5, characterized in that: Step S5: The specific implementation method of execution scheduling and closed-loop feedback is as follows: The generated instructions The data is sent to the energy storage converter for execution, and the comprehensive carbon emission intensity of electricity consumption at time t is calculated and monitored in real time after the energy storage is connected. : ； in It is the instantaneous net power drawn from the public power grid at time t. It is the real-time carbon emission factor of the power grid at time t. It is the instantaneous power of the energy storage system discharging to the load at time t. It is the average carbon intensity of the energy stored inside the energy storage battery at time t. It is the instantaneous power of the total user load at time t.

7. The method for energy storage dispatch and carbon emission compliance auditing based on real-time carbon factors of the power grid according to claim 6, characterized in that: Step S6: The specific implementation method for generating tamper-proof automated audit reports is as follows: Record all key data and generate a standard-compliant carbon emission verification report. The core content of the report includes: ① Time-series carbon fingerprint: minute-level details of "electricity consumption - carbon factor"; ② Calculation of the contribution of energy storage to emission reduction: Quantifying the carbon emission reduction brought about by energy storage: ； ③ Anti-tampering proof: Hash the key data in the report and anchor the hash value through blockchain technology to ensure the immutability and legal validity of the data.

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