Energy-carbon collaborative dynamic scheduling method and system for distributed energy storage and medium

Abstract

The application discloses a carbon-energy collaborative dynamic scheduling method and system for distributed energy storage, and a medium, and belongs to the field of data processing. The method comprises the following steps: data access based on distributed energy storage, obtaining power distribution network operation data and a plurality of power generation carbon emission factors; power flow calculation based on the operation data combined with the plurality of power generation carbon emission factors, determining dynamic carbon potential spatiotemporal distribution parameters, performing carbon-energy collaborative dynamic scheduling, generating carbon potential scheduling signals for distributed optimization; iterative exchange of boundary information for the distributed energy storage, when the iteration reaches a convergence condition, generating a distributed energy storage charging and discharging instruction sequence; dynamic scheduling update for the distributed energy storage, and rolling correction of the distributed energy storage charging and discharging instruction sequence in reverse. The application solves the technical problems that the existing distributed energy storage scheduling technology cannot realize node-level spatiotemporal fine coupling of electric energy and carbon emission, and the potential of energy storage carbon emission reduction is not released, and the landing and privacy protection of the scheduling scheme are insufficient.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically to a dynamic scheduling method, system, and medium for energy and carbon coordination in distributed energy storage. Background Technology

[0002] The installed capacity of distributed energy storage on the distribution network side continues to grow, and its carbon emission reduction value, in addition to grid peak shaving and renewable energy consumption, is gradually gaining attention. However, existing dispatching technologies for distributed energy storage still have significant limitations: traditional dispatching models mostly focus on grid power balance and peak-valley price arbitrage as core optimization objectives, incorporating carbon emissions only as a coarse constraint indicator into the dispatching framework, failing to achieve deep coupling between power flow and carbon emission flow; existing carbon accounting generally uses a regionally unified average carbon emission factor, which cannot accurately characterize the spatiotemporal differences in carbon potential at different nodes and time periods of the distribution network, making it difficult to support Support for energy storage to conduct refined energy-carbon synergistic optimization based on carbon intensity gradients; however, mainstream energy-carbon scheduling mostly adopts a centralized solution architecture, which requires the full collection of operating data and parameters of each energy storage node. This not only places a heavy burden on communication and computing, but also makes it difficult to adapt to the industry characteristics of distributed energy storage, which involves multiple independent operations and high data privacy requirements. Moreover, the scheduling plans are mostly generated statically in an open-loop manner, which cannot adaptively match the real-time dynamic fluctuations of distribution network operating conditions and carbon emission intensity. As a result, the carbon emission reduction potential of distributed energy storage cannot be fully released, and the actual implementation effect of the scheduling scheme deviates significantly from the expectations. Summary of the Invention

[0003] This application provides a method, system, and medium for dynamic scheduling of energy and carbon emissions in a coordinated manner for distributed energy storage. It aims to solve the technical problems that existing distributed energy storage scheduling technologies fail to achieve node-level spatiotemporal fine-grained coupling of electricity and carbon emissions, resulting in insufficient release of energy storage carbon emission reduction potential, and inadequate implementation and privacy protection of scheduling schemes.

[0004] In view of the above problems, this application provides a method, system and medium for dynamic scheduling of energy and carbon coordination for distributed energy storage.

[0005] The first aspect disclosed in this application provides a dynamic scheduling method for energy and carbon coordination in distributed energy storage, the method comprising: Data access is performed based on distributed energy storage to obtain distribution network operation data and multiple power generation carbon emission factors. Power flow calculations are then performed based on the operation data and the multiple power generation carbon emission factors to determine the dynamic carbon potential spatiotemporal distribution parameters of the distribution network nodes. Energy-carbon coordinated dynamic scheduling is then performed according to the dynamic carbon potential spatiotemporal distribution parameters to generate carbon potential scheduling signals for distributed optimization, constructing multiple autonomous sub-problems. Iterative exchange of boundary information is performed on the distributed energy storage according to the multiple autonomous sub-problems. When the iteration reaches convergence, a distributed energy storage charging and discharging command sequence is generated. The distributed energy storage is dynamically scheduled and updated according to the distributed energy storage charging and discharging command sequence, and the carbon potential data of multiple nodes of the distributed energy storage is used to reverse-engineer the distributed energy storage charging and discharging command sequence for rolling correction.

[0006] The second aspect disclosed in this application provides an energy-carbon coordinated dynamic scheduling system for distributed energy storage, the system comprising: The system comprises the following modules: a data acquisition module for data access based on distributed energy storage, obtaining distribution network operation data and multiple power generation carbon emission factors; a calculation module for power flow calculation based on the operation data and the multiple power generation carbon emission factors, determining the dynamic carbon potential spatiotemporal distribution parameters of distribution network nodes; a scheduling module for dynamic energy-carbon coordinated scheduling based on the dynamic carbon potential spatiotemporal distribution parameters, generating carbon potential scheduling signals for distributed optimization, and constructing multiple autonomous sub-problems; an iteration module for iteratively exchanging boundary information of distributed energy storage according to the multiple autonomous sub-problems, generating a distributed energy storage charging and discharging command sequence when the iteration reaches the convergence condition; and a correction module for dynamically updating the distributed energy storage according to the distributed energy storage charging and discharging command sequence, obtaining multiple node carbon potential data of the distributed energy storage to perform rolling correction of the distributed energy storage charging and discharging command sequence.

[0007] The third aspect disclosed in this application provides a computer-readable storage medium storing a computer program for executing the energy-carbon coordinated dynamic scheduling method for distributed energy storage provided in this application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: A full-link energy-carbon collaborative dynamic scheduling system was constructed, encompassing data access, carbon potential quantification, scheduling decoupling, iterative solution, and rolling correction. On one hand, it deeply integrates power flow calculation with carbon emission flow tracking to accurately solve the dynamic spatiotemporal distribution parameters of carbon potential at each node and time period of the distribution network, establishing a node-level energy-carbon coupling quantification scale. This provides a refined basis for carbon cost accounting and optimization decisions for energy storage charging and discharging, fully releasing the carbon emission reduction potential of energy storage cross-temporal air conditioning. On the other hand, based on the alternating direction multiplier method, the global scheduling problem is decoupled into multiple independently solvable autonomous subproblems. The global optimization objective can be equivalently achieved through iterative interaction of boundary information between adjacent nodes. Simultaneously, a closed-loop scheduling mechanism with real-time carbon potential monitoring and anchor-point rolling correction is established. This mechanism can adaptively match the dynamic fluctuations of distribution network operating conditions and carbon emission intensity, effectively correct execution deviations, improve the actual implementation effect of scheduling schemes, and enhance carbon emission reduction benefits, scheduling solution efficiency, multi-subject data privacy, and operational robustness.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0010] Figure 1 A flowchart illustrating the energy-carbon coordinated dynamic scheduling method for distributed energy storage is provided for the embodiments of this application; Figure 2 This application provides a schematic diagram of the structure of an energy-carbon coordinated dynamic scheduling system for distributed energy storage. Figure 3 A comparison chart of scheduling method verification data for the energy-carbon coordinated dynamic scheduling method for distributed energy storage is provided for the embodiments of this application.

[0011] Figure labeling: Acquisition module 11, Calculation module 12, Scheduling module 13, Iteration module 14, Correction module 15. Detailed Implementation

[0012] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0013] The overall concept of the technical solution provided in this application is as follows: The embodiments of the present application provide an energy-carbon collaborative dynamic scheduling method, system and medium for distributed energy storage. The spatiotemporal distribution parameters of node dynamic carbon potential are obtained through carbon emission flow tracking based on power flow calculation. Guided by the carbon potential scheduling signal, the global optimization problem is decomposed into multiple independently solvable autonomous subproblems based on the alternating direction method of multipliers. The globally optimal charging and discharging instruction sequence can be obtained by convergence only through iterative exchange of boundary information between adjacent nodes, and the scheduling instruction is rolled and corrected relying on the joint identification mechanism of real-time carbon potential monitoring and deviation. The present invention realizes accurate matching of spatiotemporal differences of carbon potential to optimize the energy storage charging and discharging strategy, improves the comprehensive carbon emission reduction benefit of distributed energy storage, and effectively enhances the robustness and carbon control accuracy of the scheduling scheme in actual complex operation scenarios through the closed-loop rolling correction mechanism.

[0014] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be specifically described below with reference to the accompanying drawings of the specification.

[0015] Embodiment 1, as shown in Figure 1 illustrated, the embodiments of the present application provide an energy-carbon collaborative dynamic scheduling method for distributed energy storage, and the method includes: S100: Perform data access based on distributed energy storage to obtain distribution network operation data and a plurality of power generation carbon emission factors.

[0016] Specifically, with the edge measurement and control node of the distributed energy storage cluster as the core access hub, the communication links of the distribution network energy management system, the distributed power metering terminal, the superior power grid carbon emission accounting platform and each energy storage converter control system are opened up, and the unified aggregation of multi-source heterogeneous data is completed relying on the standardized industrial communication protocol. Data access is a full-link data standardization processing process including protocol adaptation conversion, multi-source time scale alignment, outlier verification and elimination, missing value completion, and data format normalization. In the implementation process, the local edge gateways deployed at each energy storage site are first used to simultaneously connect the full-node real-time operation measurement points of the distribution network SCADA / EMS system, the output metering devices of distributed power sources such as distributed photovoltaics and wind power, the time-sharing carbon emission factor data interface officially released on the grid side, and the operation parameter interfaces of the battery management system and the energy storage converter supporting each distributed energy storage unit, and finally collect and output two types of core basic data that support the whole-process calculation of the entire scheme.

[0017] The first category is distribution network operation data, which is a set of real-time electrical operation parameters covering all topological nodes and branches of the distribution network. Specifically, it includes the voltage amplitude and phase angle of each bus node, the active and reactive power of each feeder branch, the line impedance and transformer turns ratio parameters, the real-time power consumption of the user-side load, the real-time output value of each distributed power source, and the current state of charge, upper and lower limits of charging and discharging power, and charging and discharging conversion efficiency of each distributed energy storage node. It fully depicts the current power transmission topology and real-time power distribution status of the distribution network.

[0018] The second category consists of multiple power generation carbon emission factors. These factors refer to the carbon dioxide emissions per unit of active electrical energy output from different types of power generation sources. The commonly used unit of measurement is kgCO2 / kWh. The multiple factors are differentiated parameters based on the type of power generation entity, the location of the power access node, and the time granularity. They include real-time operating carbon emission factors of distributed coal-fired and gas-fired units within the distribution network, as well as time-sharing marginal carbon emission factors of the upstream main grid and near-zero carbon emission factors of distributed new energy power generation. Different factors precisely correspond to different power injection nodes in the distribution network, thereby distinguishing the differences in carbon emission intensity of different sources of electricity.

[0019] After collecting the two types of core data, data cleaning and time-scale calibration were carried out simultaneously. Jumping data caused by abnormal acquisition links were removed, and missing measurement point values ​​caused by short-term communication interruptions were supplemented. The operating data with different sampling frequencies and carbon emission factors were unified under the same scheduling time step to ensure that the two types of data correspond one-to-one in the time dimension and spatial node dimension, forming a standardized dataset that can be directly used for subsequent power flow calculation and carbon potential solution.

[0020] S200: Based on the operational data and the multiple power generation carbon emission factors, power flow calculation is performed to determine the dynamic carbon potential spatiotemporal distribution parameters of the distribution network nodes.

[0021] Specifically, the validity of multiple power generation carbon emission factors is first verified by checking the reasonable range of factor values, the continuity of timestamps, and the compliance of data sources. Abnormal factor data that exceeds the reasonable range of carbon emission intensity for power generation type, has time sequence breaks, or has unreliable sources are removed. Then, the verified factors are matched and encapsulated one by one according to each power injection node of the distribution network to generate a real-time carbon emission factor data package. This data package is a standardized set of carbon emission intensity classified by node dimension and sorted by time sequence dimension. Each data entry corresponds to the unit active power carbon emission value of a specific power generation node and a specific time.

[0022] Subsequently, the distribution network operation data and real-time carbon emission factor data packets are time-aligned to generate spatiotemporal data slices. This means that electrical operation parameters and carbon emission factors at different sampling frequencies are segmented according to a preset scheduling time step, so that each time slice contains the complete distribution network topology operation status and the carbon emission factors of all generation nodes at the corresponding time. This achieves a precise correspondence between the physical state of the power grid and the carbon emission attributes at the same time segment, avoiding time misalignment that could cause deviations in subsequent carbon flow calculations.

[0023] Based on this, carbon emission flow tracing is performed along the power flow distribution direction using spatiotemporal data slices, and multiple active power flow paths are constructed. This process first relies on parameters such as node voltage, branch power, and line impedance in the distribution network operation data to complete power flow calculation through forward and backward substitution methods, solving for the active power flow direction and transmission magnitude of each branch, clarifying the complete transmission relationship of electrical energy from generation nodes through each branch to load nodes and energy storage nodes. Among them, power flow calculation is the core calculation method to characterize the physical laws of power transmission in the distribution network, restoring the power distribution state inside the grid at each moment. Carbon emission flow tracing follows the coupling principle of electricity following carbon and carbon following current, using the active power flow direction as the carrier to trace and distribute carbon emissions on the generation side along the power transmission path. The active power flow path constructed in this way is the power transmission link that starts from each generation injection node and connects all the branches and downstream nodes along the power flow direction. Each path corresponds to an active power transmission channel from the source to the end, and multiple paths together cover the power transmission network of the entire distribution network.

[0024] Next, multiple power generation carbon emission factors are used as the source carbon intensity attributes of each path. Carbon emissions are allocated to the upstream branches of multiple active power flow paths, generating multiple branch carbon emission flow parameters. That is, according to the active power ratio of each path, the carbon emissions from different power sources in each branch are calculated and superimposed to obtain the total carbon emissions of the branch. This parameter represents the total amount of carbon dioxide carried by the electrical energy flowing through the corresponding branch per unit time. It is an intermediate quantitative carrier that integrates the magnitude of active power transmission and the corresponding electrical energy carbon emission intensity, becoming a key bridge connecting the carbon source on the power generation side with the load and the carbon potential of the energy storage side nodes.

[0025] Finally, based on the spatiotemporal distribution of carbon emission flow parameters from multiple branches, the spatiotemporal distribution parameters of dynamic carbon potential of distribution network nodes are calculated. Dynamic carbon potential refers to the implicit carbon emissions corresponding to a unit of electrical energy acquired by a node in the distribution network at a certain moment. Essentially, it is the comprehensive carbon emission intensity of the electrical energy supplied by the node at that time. The spatiotemporal distribution parameters of dynamic carbon potential are a set of carbon potential values ​​covering all nodes in the distribution network and covering each time slice of the entire scheduling cycle. During the calculation, the carbon emission flows from all branches flowing into each node are summarized and divided by the total active power flowing into the node to obtain the dynamic carbon potential of the node in the current time slice. After traversing and calculating node by node and time slice by time, a complete carbon potential distribution parameter that combines spatial node differences and temporal fluctuations is formed.

[0026] S300: Based on the aforementioned dynamic carbon potential spatiotemporal distribution parameters, perform energy-carbon coordinated dynamic scheduling, generate carbon potential scheduling signals for distributed optimization, and construct multiple autonomous sub-problems.

[0027] Specifically, the dynamic carbon potential spatiotemporal distribution parameters are used as inputs. Carbon emissions are matched according to the spatial location of each energy storage node in the distribution network and the time dimension of the scheduling period. That is, the node carbon potential of each energy storage node in each scheduling period is matched with the electrical energy interaction behavior of that node. The carbon emission level corresponding to the absorption or output of a unit of electrical energy by each node in different time periods is determined, and the unit carbon emission of multiple time periods is determined. This parameter is the basic quantitative benchmark for converting carbon potential differences into the basis for energy storage charging and discharging decisions.

[0028] Next, the charging and discharging characteristics of all distributed energy storage are analyzed according to the unit carbon emission of multiple time periods. Under the premise of meeting the physical constraints such as the operating range of the energy storage battery's state of charge, the rated upper limit of charging and discharging power, and the power carrying capacity of the distribution network nodes, the charging and discharging power decision variables of each energy storage in each time period of the whole scheduling cycle are sorted out, and the charging and discharging power parameters are constructed. The charging and discharging power parameters here are the set of power to be decided covering all distributed energy storage nodes and the whole scheduling period. It is not only the core optimization object of energy and carbon coordinated scheduling, but also the constraint carrier for maintaining the power balance of the distribution network and the safe operation of energy storage.

[0029] Subsequently, dynamic carbon potential coupling analysis is performed on distributed energy storage based on charging and discharging power parameters to calculate carbon emission cost parameters. The calculation process first divides the charging and discharging periods according to the power interaction direction of the energy storage, distinguishing between charging and discharging periods. During the charging period, the charging power and duration are combined to extract the energy storage charging volume, which is then multiplied by the node carbon potential of the corresponding period to obtain the charging carbon emissions, i.e., the total implicit carbon emissions corresponding to the energy storage absorbing electricity from the grid. During the discharging period, the discharging power and duration are combined to extract the energy storage discharging volume, which is then multiplied by the node carbon potential of the corresponding period to obtain the discharging carbon emission reduction, i.e., the carbon emissions reduced by the energy storage releasing electricity to the grid and replacing the grid's high-carbon power supply. Then, carbon trading unit price information is introduced. Here, the carbon trading unit price refers to the trading price of a unit of carbon dioxide emission rights in the carbon trading market, which can convert the physical value of carbon emissions into quantifiable economic costs and benefits. The costs corresponding to charging carbon emissions and the benefits corresponding to discharging carbon reduction are offset to calculate the final carbon emission cost parameters characterizing the net carbon cost of energy storage charging and discharging behavior.

[0030] Based on this, the node power balance relationship corresponding to the charging and discharging power parameters is used as the energy storage exchange constraint, which is a global coupling constraint to ensure the conservation of power interaction between adjacent nodes and the balance of power supply and demand of distribution network nodes. Decision-sensitivity analysis is performed on carbon emission cost parameters, that is, the magnitude of carbon emission cost change caused by unit charging and discharging power change is calculated to obtain the carbon marginal cost gradient of each node. Based on this, a carbon potential scheduling signal is constructed. This signal is a coordinated guidance signal that integrates node dynamic carbon potential, carbon marginal cost, and power constraint boundary. It can guide each energy storage node to autonomously optimize charging and discharging behavior in a market-oriented signal form, replacing the global mandatory command of traditional centralized scheduling and adapting to the characteristics of multi-entity independent operation of distributed energy storage.

[0031] After generating the carbon potential scheduling signal, further distributed optimization decomposition is carried out to construct multiple autonomous subproblems. In this process, Lagrange multipliers are first introduced to relax the energy storage exchange constraints, transforming the globally coupled power balance constraints into dual penalty terms in the objective function. Simultaneously, carbon potential scheduling signal is updated to generate carbon potential scheduling update information. Then, the optimization problem is transformed based on the alternating direction multiplier method. The alternating direction multiplier method is used here as an optimization solution algorithm for distributed scenarios. Through dual decomposition, the globally coupled problem is decomposed into multiple local subproblems. The global optimal solution can be achieved with only a small amount of boundary information interaction. During the transformation process, augmented Lagrange parameters are constructed. That is, on the basis of the original carbon emission cost objective function, the Lagrange dual terms and quadratic penalty terms corresponding to the constraints are added. This preserves the coupling relationship of the global constraints and supports decoupling and decomposition by node.

[0032] Finally, the augmented Lagrange parameters of each energy storage node are decoupled, and the local objectives are sorted out by combining the carbon marginal cost gradient of each node, forming multiple autonomous sub-problems. Each autonomous sub-problem corresponds to an independent distributed energy storage entity, with its local objective being the optimal combination of its own carbon emission cost and operating cost. It only needs to consider the physical operation constraints of its own energy storage and local boundary coordination information, without needing to obtain global distribution network operation data, and can achieve independent parallel solution.

[0033] S400: Iteratively exchange boundary information for distributed energy storage according to the multiple autonomous sub-problems. When the iteration reaches the convergence condition, a distributed energy storage charging and discharging command sequence is generated.

[0034] Specifically, the constructed autonomous sub-problems are first matched one-to-one with the independent control entities of each distributed energy storage. Each energy storage entity independently solves the autonomous sub-problems in parallel based solely on local operating parameters, node carbon potential data, and its own optimization objectives, thereby obtaining a preliminary plan for its own charging and discharging power during the entire scheduling period. Then, the expected value of the boundary exchange power between adjacent energy storage is generated. Here, the expected value of the boundary exchange power refers to the active power value that a single energy storage node calculates from its own local optimal objective and expects to interact with adjacent nodes. It is the core interaction variable for maintaining the global constraint of cross-node power balance.

[0035] Subsequently, the expected value of the boundary exchange power obtained from the local solution and the Lagrange multiplier corresponding to the current iteration are encapsulated to construct boundary coordination information. The Lagrange multiplier is the dual variable used in dual optimization theory to characterize the tightness of global constraints. Here, it corresponds to the marginal carbon cost of the boundary power balance constraint, which can guide each energy storage entity to adjust its own power plan to gradually approach the global optimal solution. The boundary coordination information is the minimum necessary dataset for interaction between nodes. It only contains the expected value of the boundary power and the value of the dual multiplier, and does not involve privacy data such as the state of charge and operating costs inside the energy storage. It is naturally adapted to the scenario characteristics of distributed energy storage with multiple entities operating independently.

[0036] Next, each distributed energy storage node sends its own boundary coordination information to neighboring energy storage nodes through a peer-to-peer communication network, and simultaneously receives peer boundary coordination information returned by the peer energy storage, completing a single round of boundary information interaction. Based on the difference between the received peer boundary power expectation and the local power expectation, each node synchronously updates its local Lagrange multipliers according to the standard update rule of the alternating direction multiplier method, generating a Lagrange update multiplier. At the same time, it conducts boundary exchange power consistency analysis, calculates the deviation magnitude between the local and peer boundary power expectations, and obtains the original residual value. This original residual value is the core indicator for measuring the degree to which each node's power plan deviates from the global power balance constraint. The larger the residual value, the lower the power consensus of each node and the worse the satisfaction of the original problem constraint.

[0037] Based on this, we further compare the changes in the Lagrange multipliers before and after the update, calculate the fluctuation of the dual variable iteration to obtain the dual residual value. This dual residual value is used to measure the stability of the dual solution process and can effectively identify iterative oscillation problems. Together with the original residual value, it constitutes a two-dimensional convergence criterion, avoiding pseudo-convergence or iterative divergence problems caused by a single criterion.

[0038] After the residual calculation is completed, the original residual value and the dual residual value are compared with the preset convergence accuracy threshold. If any residual value fails to meet the threshold requirement, the current iteration is deemed not to have converged. Each energy storage node uses the updated Lagrange multiplier as input to resolve its own autonomous subproblem, generate a new local power plan and boundary coordination information, and enter the next round of information interaction and iterative update cycle. When the original residual value and the dual residual value are both lower than the preset convergence threshold, the iteration is deemed to have converged globally. At this time, the charging and discharging power plans of all energy storage nodes not only satisfy their own local operation constraints and cost optimization objectives, but also jointly satisfy the global power balance constraints of the distribution network. The target energy storage charging and discharging power sequence for the current all-time period can be locked as the final executable distributed energy storage charging and discharging command sequence.

[0039] S500: Dynamically schedule and update the distributed energy storage according to the distributed energy storage charging and discharging command sequence, and obtain the carbon potential data of multiple nodes of the distributed energy storage to perform rolling correction on the distributed energy storage charging and discharging command sequence.

[0040] Specifically, the distributed energy storage charging and discharging command sequence obtained from the solution is first sent to the local control system of each distributed energy storage through the edge communication network, driving the energy storage device to perform charging and discharging actions according to the plan. At the same time, the carbon potential monitoring thread is started to continuously collect data. The carbon potential monitoring thread here is a resident real-time acquisition process deployed in the measurement and control device of each energy storage node. Unlike periodic polling acquisition, it continuously and synchronously collects the electrical operation data of the node and the corresponding real-time carbon potential calculation value at a high frequency of sampling, so as to obtain real-time carbon potential data covering all energy storage nodes.

[0041] Meanwhile, carbon potential prediction is carried out based on distributed energy storage nodes to generate predicted carbon potential trajectory parameters. The process involves first extracting the carbon potential values ​​of each energy storage node within the historical scheduling cycle according to the time dimension, forming a carbon potential historical sequence arranged in chronological order. Then, relying on the short-term prediction capability of the distribution network energy management system, the short-term predicted values ​​of the output of distributed power sources and loads are obtained. Using these as input, the carbon potential values ​​of each node within the future scheduling window are calculated through an edge time series prediction algorithm, resulting in predicted carbon potential values ​​for multiple nodes. Finally, the predicted values ​​of all nodes are stitched together according to the time dimension to form a continuous time series change curve, i.e., the predicted carbon potential trajectory parameters. These parameters are a prediction result of the carbon potential fluctuation trend of nodes in the future period, rather than a static value at a single moment, and are used to provide a benchmark reference for deviation comparison.

[0042] Subsequently, the collected real-time carbon potential data and the predicted carbon potential trajectory parameters are compared node by node and time period by time deviation. The difference between the measured value and the predicted value is calculated and integrated according to the node dimension to generate a node carbon potential deviation vector. This vector is an ordered set composed of the carbon potential deviation values ​​of each energy storage node, which can accurately locate the node position and deviation range of abnormal fluctuations in carbon potential, and intuitively reflect the degree of difference between the actual carbon emission intensity and the previous prediction.

[0043] Simultaneously, the charge and discharge execution deviation value is calculated based on the actual operation data of distributed energy storage. This is the difference between the actual output charge and discharge power of the energy storage and the planned power in the instruction sequence. It covers multiple dimensions of deviation, such as power amplitude deviation and time period response delay, and is used to quantify the actual execution accuracy of the energy storage device for the scheduling instructions. The deviation usually comes from actual operating factors such as battery performance aging, device response delay, and communication transmission delay.

[0044] Based on this, the node carbon potential deviation vector and the charge / discharge execution deviation value are jointly coupled and analyzed. From the two types of deviations, the core variables with the highest weight influencing the scheduling target are selected, and the key parameters of carbon potential deviation are identified. These parameters are the core driving factors that cause the scheduling effect to deviate from the expected result, such as a sudden and significant increase in the carbon potential of a node or the failure of a certain energy storage charging power to meet the command requirements. These parameters serve as precise anchor points for subsequent command corrections, avoiding computational redundancy caused by full and indiscriminate corrections.

[0045] Finally, the carbon potential deviation key parameter is used as the correction anchor point and substituted into the original distributed optimization framework to perform distributed iterative optimization of the distributed energy storage charging and discharging command sequence. This involves updating the corresponding carbon potential boundary and power constraints in each autonomous subproblem, re-performing local solutions and boundary information interaction to obtain the optimized power sequence. Simultaneously, optimization coverage identifiers are generated based on the optimization results, marking the time periods and node commands that need to be replaced or updated. Finally, the original command sequence is rolled over based on the optimization coverage identifier results, i.e., the optimized command fragments cover the original commands at the corresponding positions, driving the energy storage to operate according to the corrected plan. The verification data for this scheduling method is shown in the figure below. Figure 3 As shown, the dashed left axis represents the nodal carbon potential deviation of the traditional scheduling method, the solid left axis represents the nodal carbon potential deviation of this scheme, the dotted right axis represents the carbon emission reduction benefit release rate of the traditional scheduling method, and the dashed right axis represents the carbon emission reduction benefit release rate of this scheme. The data shows that the nodal carbon potential deviation of the traditional scheduling method is 0.15~0.27 kgCO2 / kWh, while the nodal carbon potential deviation of this scheme is 0.01~0.06 kgCO2 / kWh, representing an improvement of approximately 72%. The carbon emission reduction benefit release rate of the traditional scheduling method is 35%~60%, while the carbon emission reduction benefit release rate of this scheme is 75%~95%, representing an improvement of approximately 85%.

[0046] Furthermore, in the method provided in this application embodiment, power flow calculation is performed based on the operating data and the multiple power generation carbon emission factors to determine the dynamic carbon potential spatiotemporal distribution parameters of the distribution network nodes. The method includes: validating the validity of the multiple power generation carbon emission factors and generating a real-time carbon emission factor data package; performing time alignment processing on the operating data and the real-time carbon emission factor data package to generate spatiotemporal data slices; performing carbon emission flow tracing along the power flow distribution direction based on the spatiotemporal data slices to construct multiple active power flow paths; using the multiple power generation carbon emission factors as source attributes, extracting the carbon emission allocated to the upstream branches of the multiple active power flow paths to generate multiple branch carbon emission flow parameters; and performing spatiotemporal distribution identification based on the multiple branch carbon emission flow parameters to calculate the dynamic carbon potential spatiotemporal distribution parameters of the distribution network.

[0047] Specifically, the validity of multiple power generation carbon emission factors is first verified. This validity verification refers to verifying the carbon emission factor values ​​of each power generation node one by one according to the triple verification rules of the physical reasonable range of carbon emission intensity of different power generation types, data time series continuity, and source authority. Abnormal factor data that exceeds the reasonable range of carbon emission intensity of coal-fired, gas-fired and other power sources, has time series gaps, or has untraceable sources are eliminated. For factors that are missing for a short time, linear interpolation between adjacent time periods is used to fill in the missing factors. Finally, the verified factors are classified by the dimension of power generation access node, sorted by the dimension of time, and packaged to generate a real-time carbon emission factor data package. This data package is a standardized carbon intensity dataset with unified caliber and one-to-one correspondence between nodes and time series. Each data entry is accurately matched with a power injection node in the distribution network and the unit active power carbon emission value at the corresponding time.

[0048] Subsequently, time alignment processing is performed on the distribution network operation data and the real-time carbon emission factor data package. Since the distribution network operation data is usually high-frequency sampling data at the second level, while the carbon emission factor is mostly updated data at the minute or hour level, there is an inherent difference in the sampling granularity between the two. Therefore, it is necessary to perform cross-sectional segmentation and time-scale matching on the two types of data according to the preset scheduling time step, so that each time segment contains the complete distribution network topology parameters, node voltage, branch power and other operating parameters at the corresponding time, as well as the carbon emission factors of all generating nodes. The resulting spatiotemporal data slice is a standardized calculation unit that realizes the time synchronization of electrical physical quantities and carbon emission attributes, thereby avoiding carbon flow calculation deviations caused by time misalignment from the root.

[0049] Based on this, carbon emission flow tracing is performed along the power flow distribution direction using spatiotemporal data slices, and multiple active power flow paths are constructed. This process first relies on the operating parameters such as node voltage, branch impedance, and load power in the spatiotemporal data slices, and then uses the forward-backward substitution method of the distribution network to complete the power flow calculation. This solves for the active power flow direction and transmission amplitude of each branch, as well as the voltage amplitude and phase angle of each node, thus fully characterizing the power transmission distribution state within the distribution network. The power flow calculation is based on the physical calculation method of Kirchhoff's laws for solving the steady-state power distribution of the power grid, restoring the electrical... The physical basis for real transmission paths is carbon emission flow tracing, which follows the coupling principle of carbon following electricity and electricity carrying carbon. It traces and transmits carbon emissions from the power generation side as an accessory attribute of electrical energy along the active power transmission direction. The active power flow path constructed in this way is a complete power transmission link that starts from each power generation injection node and sequentially connects the branches and downstream nodes along the forward power flow direction. Each path corresponds to an active power transmission channel from the carbon source to the terminal. All paths together cover all branches and nodes of the distribution network, forming a complete power transmission topology network.

[0050] Next, multiple power generation carbon emission factors are used as source carbon intensity attributes for corresponding power generation nodes. A proportional allocation method is employed to extract carbon emission allocation from upstream branches of multiple active power flow paths, generating multiple branch carbon emission flow parameters. Specifically, for power generation nodes... Its active power generation is The corresponding carbon emission factor is Then the initial carbon emission flow of the output branch of this node is: ; This represents the total carbon dioxide carried by the electrical energy output from the power generation node per unit time. When the carbon emission flow is transmitted to the intermediate node along with the active power, the total carbon emission flow from all upstream branches of that node is proportionally distributed according to the active power ratio of each downstream outflow branch, and then transmitted sequentially to all downstream branches. This yields the carbon emission flow values ​​for each branch in the entire network, i.e., the branch carbon emission flow parameters. This parameter is a composite parameter integrating the active power transmission of the branch and the corresponding electrical energy carbon emission intensity, typically expressed in tCO2 / h. It is a key intermediate calculation carrier connecting the carbon source on the generation side and the carbon potential on the node side. Finally, based on the spatiotemporal distribution identification of multiple branch carbon emission flow parameters, the spatiotemporal distribution parameters of the dynamic carbon potential of the distribution network are calculated. The dynamic carbon potential is the core indicator characterizing the electrical energy carbon emission intensity of a node in the distribution network at a certain moment, defined as the implicit carbon dioxide emission corresponding to a unit of active electrical energy at that node, expressed in kgCO2 / kWh. Its calculation formula is: ; In the formula, for The dynamic carbon potential at time node j for At any given moment, all upstream branches flow into the node. Total carbon emissions flow for At any given moment, all upstream branches flow into the node. The total active power is calculated by substituting all nodes in the distribution network into the formula one by one. Then, the calculation results of each time slice are spliced ​​together according to the time sequence dimension to obtain the dynamic carbon potential spatiotemporal distribution parameters covering all nodes and the entire scheduling cycle. These parameters include both the spatial carbon potential differences caused by the differences in power supply sources of different nodes and the temporal carbon potential changes caused by the fluctuation of power supply structure in different time periods, thus fully presenting the spatiotemporal distribution characteristics of carbon intensity in the distribution network.

[0051] Furthermore, in the method provided in this application embodiment, energy-carbon coordinated dynamic scheduling is performed based on the dynamic carbon potential spatiotemporal distribution parameters to generate a carbon potential scheduling signal. The method includes: performing carbon emission matching according to distribution network order acceptance based on the dynamic carbon potential spatiotemporal distribution parameters to determine the unit carbon emission amount for multiple time periods; performing charge-discharge analysis on distributed energy storage according to the unit carbon emission amount for multiple time periods to construct charge-discharge power parameters; performing dynamic carbon potential analysis on distributed energy storage based on the charge-discharge power parameters to calculate carbon emission cost parameters; and using the charge-discharge power parameters as energy storage exchange constraints to perform decision sensitivity analysis on the carbon emission cost parameters to construct a carbon potential scheduling signal.

[0052] Specifically, carbon emission matching is first performed based on the spatiotemporal distribution parameters of dynamic carbon potential according to the distribution network node dimension. Here, carbon emission matching refers to mapping and binding the spatial access location of each distributed energy storage node in the distribution network to the corresponding electrical node in the dynamic carbon potential distribution one by one. At the same time, the time sequence is matched one by one according to the time granularity of the preset scheduling period, so that each energy storage node is matched with an accurate node carbon potential value in each scheduling period. The carbon emission per unit in multiple time periods determined in this way, that is, the implicit carbon dioxide emission corresponding to each unit of active power acquired by each energy storage node in the corresponding time period, is essentially a concrete and quantitative value of dynamic carbon potential at the energy storage access node. This provides a precise measurement scale for carbon emission accounting of subsequent energy storage charging and discharging behavior on a time-by-time and node-by-node basis.

[0053] Next, the charging and discharging operation characteristics of all energy storage units were analyzed according to the unit carbon emissions in multiple time periods. Under the premise of fully considering the physical operation constraints such as the upper and lower limits of the state of charge of distributed energy storage batteries, the rated charging and discharging power amplitude, the charging and discharging conversion efficiency, and the energy conservation of the whole scheduling cycle, the charging and discharging power of each energy storage node in each time period was set as the optimization variable to be decided. The charging and discharging power parameters covering all energy storage nodes and the whole scheduling cycle were sorted out. This parameter is the core decision carrier of energy and carbon coordinated scheduling. It is not only the core input variable for subsequent carbon emission cost accounting, but also the constraint object for maintaining the power balance of the distribution network and the safe operation of energy storage. Its value boundary directly determines the operational feasibility of the scheduling scheme and the upper limit of carbon emission reduction benefits.

[0054] Subsequently, dynamic carbon potential coupling analysis was performed on distributed energy storage based on charging and discharging power parameters to calculate carbon emission cost parameters. The calculation process first divides the entire dispatch period into charging and discharging periods according to power flow direction, clarifying the charging period when energy storage absorbs power from the grid and the discharging period when it releases power to the grid. During the charging period, the energy storage charging capacity is extracted by combining the charging power amplitude and the single-period dispatch step size, and multiplied by the unit carbon emission of the corresponding period to obtain the charging carbon emission, representing the total carbon emissions carried by the energy storage during charging. During the discharging period, the discharge power amplitude and the single-period dispatch step size are combined... Extracting the discharged energy from the energy storage system and multiplying it by the unit carbon emission rate for the corresponding time period yields the carbon emission reduction from the discharge. This represents the carbon emission reduction resulting from the energy storage system releasing electricity to the grid and replacing the grid's native high-carbon power supply. Then, carbon trading price information is introduced. Here, the carbon trading price refers to the trading price of a unit of carbon dioxide emission rights in the carbon trading market. This price is used to convert the physical value of carbon emissions into a uniformly calculable economic value, achieving cost-based measurement of carbon emissions. Finally, the carbon emission cost parameter is calculated, and its quantitative formula can be expressed as: For the i-th distributed energy storage node, the carbon emission cost parameter over the entire scheduling period T is: ; In the formula, The unit price for carbon trading. , The first Time period nodes The charging power and discharging power, For single-period scheduling step size, For the first Time period nodes The corresponding unit carbon emission is the node dynamic carbon potential. This parameter characterizes the net carbon emission economic cost corresponding to the full cycle of energy storage charging and discharging behavior. The calculation result is positive, which represents the net carbon expenditure generated by energy storage charging and discharging, and negative, which represents the net benefit brought by carbon emission reduction.

[0055] Based on this, the node power balance relationship corresponding to the charging and discharging power parameters is used as the energy storage exchange constraint, which is a global coupling constraint that ensures the conservation of interactive power between adjacent energy storage nodes and the power supply and demand balance of distribution network nodes. Decision sensitivity analysis is carried out on the carbon emission cost parameter. That is, by solving the first-order partial derivative of carbon emission cost with respect to charging and discharging power, the change in carbon emission cost corresponding to unit power adjustment is obtained, i.e., the carbon marginal cost gradient. Its value is equal to the product of the carbon trading unit price and the node carbon potential in the corresponding time period. It can intuitively reflect the sensitivity of the impact of power decision adjustment on carbon cost. Finally, with this carbon marginal cost gradient as the core and integrating the dual coordination information corresponding to the power constraint, a carbon potential scheduling signal is constructed. This signal is a price-based guidance signal rather than a centralized mandatory command, which can guide each energy storage entity to autonomously adjust its charging and discharging strategy with market-based incentive logic.

[0056] Furthermore, in the method provided in this application embodiment, dynamic carbon potential analysis is performed on distributed energy storage based on the charge and discharge power parameters to calculate carbon emission cost parameters. The method includes: dividing the charge and discharge power parameters into charge and discharge periods to determine charging and discharging periods; performing energy storage charging analysis according to the charging periods and charge and discharge power parameters to extract the energy storage charging power; performing dynamic carbon potential calculation based on the energy storage charging power according to the charging periods to obtain charging carbon emissions; performing energy storage discharging analysis according to the discharging periods and charge and discharge power parameters to extract energy storage discharging power; performing dynamic carbon potential calculation based on the energy storage discharging power according to the discharging periods to obtain discharging carbon emission reduction; introducing carbon trading unit price information, analyzing the charging carbon emissions and the discharging carbon emission reduction in conjunction with the carbon trading unit price information to calculate the carbon emission cost parameters.

[0057] Specifically, firstly, the charging and discharging power parameters covering all distributed energy storage nodes and the entire scheduling cycle are divided into charging and discharging time periods. These charging and discharging power parameters are a time-series decision variable set composed of the charging and discharging power decision values ​​of each energy storage node for each time period. The division process uses the direction of power interaction as the core criterion, marking the time periods when energy storage absorbs active power from the distribution network as charging time periods and the time periods when energy storage outputs active power to the distribution network as discharging time periods. This bidirectional split enables independent accounting of carbon increment on the charging side and carbon reduction on the discharging side, fundamentally avoiding carbon emission accounting bias caused by confusion in power direction. Subsequently, for all charging time periods, energy storage charging characteristics are analyzed based on the corresponding charging and discharging power parameters, extracting the energy storage charging capacity for each time period. The charging capacity for a single time period is obtained by multiplying the charging active power of that time period by the scheduling time step. The calculation formula is as follows: ; in, For the first One energy storage node in The active power of charging during a certain period of time. The total charging power of energy storage can be obtained by summing the power of a single time period, which is the length of a single scheduling period. This power represents the total active power obtained by energy storage from the distribution network during the charging process and is the physical carrier for calculating carbon emissions on the charging side.

[0058] Based on this, the carbon emissions on the charging side are calculated by combining the energy storage charging capacity with the dynamic carbon potential corresponding to the charging period, thus obtaining the carbon emissions from charging. Here, the dynamic carbon potential is the implicit carbon emission intensity per unit of electricity of the energy storage access node in the corresponding period, obtained by tracking carbon emission flows at the front end. The unit is kgCO2 / kWh. It varies with the power grid structure and power flow distribution, changing period by period and node by node, and is the core quantitative benchmark for achieving energy-carbon coupling accounting. During the calculation, the charging capacity of each charging period is multiplied by the dynamic carbon potential of the corresponding node in that period to obtain the carbon emissions from charging in a single period. The total carbon emissions from charging are then summed up across all charging periods. The calculation formula is as follows: ; in, For charging time periods, for Time period The dynamic carbon potential of an energy storage node represents the total amount of carbon dioxide emissions transferred from the grid side during the energy storage charging process.

[0059] Next, for all discharge periods, an energy storage discharge characteristic analysis is conducted based on the charging and discharging power parameters of the corresponding periods. The energy storage discharge capacity for each period is extracted. The discharge capacity for a single period is obtained by multiplying the discharge active power of that period by the scheduling time step, i.e.: ; The total discharge capacity of the energy storage is obtained by summing them up, which represents the total active power released by the energy storage to the distribution network and is the physical basis for calculating the carbon emission reduction benefits on the discharge side.

[0060] Subsequently, based on the energy storage discharge capacity and the dynamic carbon potential corresponding to the discharge period, the carbon emission reduction on the discharge side is calculated to obtain the carbon emission reduction amount. The core logic is that the energy storage discharge supplies electricity to the grid, which can replace the same amount of high-carbon electricity originally provided by fossil fuel units, thereby reducing the total carbon emissions on the grid side. In the calculation, the discharge capacity of each discharge period is multiplied by the dynamic carbon potential of the corresponding node in that period to obtain the carbon emission reduction amount for a single period. The total carbon emission reduction amount is obtained by accumulating all discharge periods. The calculation formula is as follows: ; in, This is the set of discharge periods, and this parameter represents the total grid-side carbon dioxide emissions avoided by the energy storage discharge behavior.

[0061] Finally, carbon trading unit price information is introduced. Here, carbon trading unit price refers to the trading price of a unit of carbon dioxide emission rights in the national or regional carbon trading market, with the unit being yuan / kgCO2. Its core function is to transform the quantified value of carbon emissions at the physical level into a uniformly calculable economic value, realizing the cost-based and revenue-based measurement of carbon emissions. This allows carbon emission reduction benefits to be jointly optimized with energy storage operating costs under the same dimension. The carbon cost corresponding to charging carbon emissions and the carbon revenue corresponding to discharging carbon emission reduction are calculated as a difference to obtain the carbon emission cost parameter. The calculation formula is as follows: ; in The carbon trading unit price represents the net carbon emission economic cost brought about by the charging and discharging behavior of a single energy storage node throughout the entire scheduling cycle. A positive value indicates the net carbon cost expenditure generated by energy storage charging and discharging, while a negative value indicates the net economic benefit brought about by carbon emission reduction.

[0062] Furthermore, in the method provided in this application embodiment, before calculating the carbon emission cost parameters, the method includes: preset an average carbon potential value; comparing the energy storage nodes of distributed energy storage according to the average carbon potential value based on the charging carbon emission amount and the discharging emission reduction amount; determining whether a first energy storage node below the average carbon potential value absorbs electrical energy and releases electrical energy at a second energy storage node above the average carbon potential value; when an energy storage node below the average carbon potential value absorbs electrical energy and releases electrical energy at an energy storage node above the average carbon potential value, extracting the transferred power parameter and calculating the carbon potential difference between the first energy storage node and the second energy storage node; calculating the carbon emission reduction contribution compensation based on the transferred power parameter and the carbon potential difference to obtain the carbon emission reduction sharing compensation benefit value.

[0063] Specifically, the first step is to preset an average carbon potential. This average carbon potential serves as a benchmark reference threshold for assessing the carbon emission intensity of nodes in the regional power grid. It is typically calculated based on the dynamic carbon potential of all nodes across the entire grid during the full dispatch cycle, weighted by the total active power supply of each node. The calculation formula is as follows: ; in, The carbon potential is the average value, and N is the total number of nodes in the distribution network. for Time Node Total active power inflow, The average value represents the dynamic carbon potential of the corresponding node at the corresponding time. This average value can objectively reflect the overall average carbon emission intensity level of the distribution network and provide a unified and fair benchmark for judging the level of carbon potential at the node.

[0064] Subsequently, based on the calculated carbon emissions from charging and the emission reduction from discharging, all distributed energy storage nodes were classified and compared according to their average carbon potential. Energy storage nodes with dynamic carbon potential lower than the average carbon potential were defined as first-class energy storage nodes, i.e., low-carbon-potential energy storage nodes. The carbon emission intensity of the electricity of these nodes is lower than the average level of the entire grid, and the carbon emissions carried by absorbing electricity from these nodes for charging are less. Energy storage nodes with dynamic carbon potential higher than the average carbon potential were defined as second-class energy storage nodes, i.e., high-carbon-potential energy storage nodes. The carbon emission intensity of the electricity of these nodes is higher than the average level of the entire grid, and the high-carbon grid power supply replaced by the discharge of electricity to these nodes is more significant, and the emission reduction benefits per unit of electricity are more significant.

[0065] Based on this, collaborative behavior is determined. It is judged whether the first energy storage node, below the average carbon potential, is absorbing electrical energy during charging, and simultaneously, whether the second energy storage node, above the average carbon potential, is releasing electrical energy during discharging. This cross-node operation mode, where energy is charged and stored on the low-carbon side and discharged and supplied on the high-carbon side, essentially achieves cross-carbon potential gradient transport of electrical energy through spatial power transfer of distributed energy storage. This generates additional carbon emission reduction benefits that cannot be achieved by single-node independent operation, and is the core value source of multi-energy storage energy carbon spatial synergy. When the above-mentioned cross-node collaborative operation behavior is determined, the transferred energy parameter is first extracted and the carbon potential difference between the two types of nodes is calculated. The transferred energy parameter refers to the total effective electrical energy actually transported from the low-carbon node to the high-carbon node through cross-node energy storage collaboration. To ensure the rigor of the calculation, the smaller value between the total charging energy of the first energy storage node and the total discharging energy of the second energy storage node is usually taken as the effective transferred energy, denoted as . The calculation formula is: ; In the formula, The amount of electricity charged during the period for the first energy storage node. Set up its charging periods. The discharge capacity of the second energy storage node during the specified time period. This is the set of discharge periods for the second energy storage node, and the carbon potential difference is the difference between the average dynamic carbon potential of the second energy storage node during the discharge period and the average dynamic carbon potential of the first energy storage node during the charging period, denoted as . The calculation formula is: ; in, The average carbon potential during the charging period of the first energy storage node. The difference represents the average carbon potential during the discharge period of the second energy storage node. This difference directly determines the additional carbon emission reduction that can be generated per unit of transferred electricity. The larger the difference, the higher the emission reduction value of transferring electricity across nodes.

[0066] Subsequently, carbon emission reduction contribution compensation calculations are performed based on the transferred electricity parameters and carbon potential difference. First, the total additional carbon emission reduction generated by cross-node collaboration is calculated, which is the product of the effective transferred electricity and the carbon potential difference. The formula is as follows: ; This portion of emission reduction is the incremental emission reduction value generated by multi-node collaboration, which cannot be achieved through independent scheduling of a single node. By introducing a carbon trading price and a preset compensation allocation coefficient, the additional carbon emission reduction is converted into a calculable economic value and split according to preset allocation rules, ultimately yielding the shared carbon emission reduction compensation benefit value. The calculation formula is as follows: ; in, The unit price for carbon trading. The compensation allocation coefficient, ranging from 0 to 1, is used to divide the distribution ratio of emission reduction benefits between low-carbon nodes on the charging side and high-carbon nodes on the discharging side. The final output carbon emission reduction sharing compensation benefit value is the collaborative emission reduction economic compensation that the corresponding energy storage participants can obtain.

[0067] Furthermore, in the method provided in this application embodiment, a carbon potential scheduling signal is generated for distributed optimization to construct multiple autonomous sub-problems. The method includes: introducing Lagrange multipliers to relax the energy storage exchange constraints, updating the carbon potential scheduling signal, and generating carbon potential scheduling update information; performing alternating direction multiplier transformation based on the carbon potential scheduling update information to construct augmented Lagrange parameters; decoupling the energy storage nodes according to the augmented Lagrange parameters, calculating the carbon marginal cost gradient, and solving for the multiple autonomous sub-problems.

[0068] Specifically, Lagrange multipliers are first introduced to relax the energy storage exchange constraints. These constraints are global coupling constraints that maintain the active power balance of distribution network nodes and ensure consistent boundary interaction power between adjacent energy storage nodes. Specifically, the charging and discharging power, load power, and branch interaction power of each energy storage node must satisfy the node power conservation law. Lagrange multipliers are the dual variables of the corresponding constraints in dual optimization theory, transforming equality constraints into linear penalty terms in the objective function, thus relaxing the constraints. During the introduction process, a corresponding Lagrange multiplier vector is matched for each set of energy storage exchange constraints. The original global carbon emission cost minimization objective function and constraints are integrated into a standard Lagrange function. At the same time, the Lagrange multiplier is incorporated into the original carbon potential scheduling signal as a dual coordination quantity. The guidance strength of the signal is updated dually to generate carbon potential scheduling update information. This information not only retains the carbon cost guidance of the original carbon potential signal, but also adds the dual adjustment effect of the global power constraint.

[0069] Based on this, an alternating direction multiplier method is used to transform the carbon potential scheduling update information to construct augmented Lagrangian parameters. The alternating direction multiplier method is a distributed optimization algorithm that balances decomposability and convergence stability. Its core employs an iterative framework of decomposition and boundary coordination, achieving a globally optimal solution with only a small amount of boundary information exchanged. The augmented Lagrangian parameters are derived by adding a quadratic penalty term for constraint violations to the standard Lagrangian function, resulting in the global augmented Lagrangian function expression: ; In the formula, This represents the total number of distributed energy storage nodes. For the first The carbon emission cost function of an energy storage node This represents the residual vector corresponding to the energy storage exchange constraint. Let Lagrange multiplier vectors be used. This is the secondary penalty coefficient. The quadratic penalty term is the square of the L2 norm. This parameter significantly improves the numerical stability and convergence speed of the iterative solution by introducing the quadratic penalty term, while preserving the structural characteristics of node decoupling.

[0070] Subsequently, the augmented Lagrangian parameters are decoupled and decomposed according to the energy storage nodes, and the carbon marginal cost gradient is calculated simultaneously to support the solution of the sub-problem. During the decoupling process, since the carbon emission cost term and the local operation constraint term of the augmented Lagrangian function are naturally independent of each node, and only the constraint term corresponding to the boundary interaction power involves two adjacent nodes, the global augmented Lagrangian function can be decomposed into N local objective functions that are only related to a single energy storage node. Each local objective function only contains the local energy storage cost term, the corresponding local constraint dual term, and the quadratic penalty term, without relying on the complete operation data of the global distribution network. The carbon marginal cost gradient is calculated simultaneously during the decoupling process. Here, the carbon marginal cost gradient is the first-order partial derivative of the carbon emission cost with respect to the charging and discharging power, which is used to characterize the magnitude of the change in carbon emission cost caused by the adjustment of unit charging and discharging power. It is the core optimization direction guide in the sub-problem solving process. For the th The first energy storage node The formula for calculating the carbon marginal cost gradient corresponding to the charging power over a given period is as follows: ; The formula for calculating the carbon marginal cost gradient corresponding to the discharge power is: ; In the formula, The unit price for carbon trading. for Time period nodes The dynamic carbon potential has a positive gradient, which indicates that power increases will increase carbon costs, while a negative gradient indicates that power increases will reduce carbon costs and bring carbon benefits. By superimposing this gradient with the gradient of the constraint term in the augmented Lagrange multiplier, the complete optimality condition of the subproblem can be obtained. Finally, by combining the local physical operation constraints of each energy storage node, including the upper and lower limits of state of charge, rated charge and discharge power, charge and discharge efficiency, and periodic energy conservation, multiple autonomous subproblems are constructed. Each autonomous subproblem corresponds to an independent distributed energy storage operation entity, which can complete the solution independently locally. It only needs to interact with neighboring nodes to exchange two types of coordination information: boundary power and Lagrange multipliers, without disclosing its own operating parameters and privacy data.

[0071] Furthermore, in the method provided in this application embodiment, the distributed energy storage boundary information is iteratively exchanged according to the multiple autonomous sub-problems. When the iteration reaches the convergence condition, a distributed energy storage charging and discharging command sequence is generated. The method includes: performing subject matching on the distributed energy storage according to the multiple autonomous sub-problems to generate the expected boundary exchange power value of adjacent energy storage; encapsulating the expected boundary exchange power value with Lagrange multipliers to construct boundary coordination information; sending the boundary coordination information to adjacent distributed energy storage and receiving the peer boundary coordination information; updating the Lagrange multipliers according to the peer boundary coordination information to generate the updated Lagrange multipliers for boundary exchange power consistency analysis and calculating the original residual value; performing change amplitude analysis based on the updated Lagrange multipliers to calculate the dual residual value; performing convergence determination based on the original residual value and the dual residual value; when the original residual value and the dual residual value have not converged, entering the next iteration; when the original residual value and the dual residual value have converged, locking the target energy storage charging and discharging power sequence as the distributed energy storage charging and discharging command sequence.

[0072] Specifically, the decomposed autonomous sub-problems are first matched one-to-one with the independent operating entities of each distributed energy storage system. Here, the autonomous sub-problems are independent optimization units containing only local operating constraints and local carbon emission cost optimization objectives for a single node. After matching, each energy storage entity does not need to obtain full network operation data; it can independently solve its own sub-problem's local optimum solution based solely on local dynamic carbon potential parameters, energy storage physical parameters, and previous round boundary coordination information. This generates the expected boundary exchange power value between adjacent energy storage systems. This expected value is the active power time series sequence calculated by a single energy storage node from its local optimum objective of minimizing its own carbon emission cost, representing the expected interaction with adjacent nodes. Let $i$ be the first $i$. Nodes in round iteration For neighboring nodes The expected value of the boundary exchange power obtained by the boundary solution is Its essence is the power mapping of the local optimal solution at the node boundary, which directly reflects the power interaction requirements of a single node in the current iteration.

[0073] Subsequently, the expected value of the boundary exchange power obtained from the local solution is encapsulated with the Lagrange multipliers corresponding to the current iteration round to construct boundary coordination information, wherein the Lagrange multipliers... It is the dual variable corresponding to the boundary power balance constraint, used to characterize the marginal carbon cost of the boundary constraint. It can guide nodes to adjust their power plans during the iteration process to gradually approach the global consensus. The boundary coordination information is the minimum necessary dataset for interaction between nodes. It only contains two types of parameters: the expected sequence of boundary power and the dual multiplier. It does not involve privacy data such as the internal state of charge and operating costs of energy storage. It is naturally adapted to the privacy protection needs of distributed energy storage with multiple independent operators.

[0074] Next, each distributed energy storage node sends its own boundary coordination information to neighboring energy storage nodes through a peer-to-peer communication network, while simultaneously receiving peer boundary coordination information from the peer nodes, completing a single round of bidirectional information exchange. Each node then determines the target power based on the received peer boundary information. The local Lagrange multipliers are updated according to the standard dual update rule of the alternating direction multiplier method, based on the local power expectation, to generate the Lagrange updated multipliers. Its update formula is: ; In the formula The preset quadratic penalty coefficient is used to control the step size and convergence speed of the dual update. The original residual value for the current round is used as the basis for calculating the original residual value during a simultaneous boundary exchange power consistency analysis. This original residual value is a core indicator measuring the power expectation deviation on both sides of the boundary and reflecting the degree to which the original global power balance constraint is satisfied. The power sign is positive with the outflow node as the positive sign. The boundary power balance constraint requires that the power on both sides be equal in magnitude and opposite in direction. Therefore, the original residual value is defined as the algebraic sum of the power expectation values ​​on both sides of the boundary, calculated using the following formula: ; The larger the residual value, the lower the power consensus of each node, and the higher the degree of violation of the original problem constraints.

[0075] Based on this, an analysis of the change magnitude of the Lagrange update multipliers is conducted to calculate the dual residual value. The dual residual value is an indicator that measures the degree of iterative fluctuation of the dual variable and reflects the dual stability of the solution process. It is defined as the change in the Lagrange multipliers between two adjacent iterations, and the calculation formula is as follows: ; This indicator can effectively identify iterative oscillation problems and together with the original residual value, it forms a two-dimensional convergence judgment standard, avoiding the false convergence or iterative divergence problems caused by a single criterion.

[0076] After completing the calculation of the two types of residuals, the original residual value and the dual residual value are respectively compared with the original residual value and the preset convergence accuracy threshold. Convergence accuracy threshold of dual residuals If any residual fails to meet the threshold requirement, the iteration is deemed not to have converged. Each energy storage node then uses the updated Lagrange multipliers as input to resolve its own autonomous subproblem, generate a new local power plan and boundary coordination information, and enter the next round of information interaction and iterative update. When the original residual value and the dual residual value are both lower than the preset convergence threshold, the iteration is deemed to have converged globally. At this point, the charging and discharging power plans of all energy storage nodes not only satisfy their own local operating constraints and the optimal carbon emission cost, but also jointly satisfy the global power balance constraints of the distribution network. This allows the target energy storage charging and discharging power sequence for the current all-time period to be locked as the final executable distributed energy storage charging and discharging command sequence. Furthermore, in the method provided in this application embodiment, the distributed energy storage is dynamically scheduled and updated according to the distributed energy storage charging and discharging command sequence, and the carbon potential data of multiple nodes of the distributed energy storage is used to reversely correct the distributed energy storage charging and discharging command sequence. The method includes: sending the distributed energy storage charging and discharging command sequence to the distributed energy storage for command execution, starting a carbon potential monitoring thread for continuous acquisition, and obtaining real-time carbon potential data; performing carbon potential prediction based on the distributed energy storage, generating predicted carbon potential trajectory parameters, comparing the deviation between the real-time carbon potential data and the predicted carbon potential trajectory parameters, and generating a node carbon potential deviation vector; calculating the execution deviation based on the distributed energy storage to obtain a charging and discharging execution deviation value; jointly analyzing the node carbon potential deviation vector and the charging and discharging execution deviation value to identify key parameters of carbon potential deviation; using the key parameters of carbon potential deviation as correction anchors to perform distributed iterative optimization of the distributed energy storage charging and discharging command sequence, performing optimization coverage identification based on the optimization results, and performing rolling correction based on the optimization coverage identification results.

[0077] Specifically, the calculated distributed energy storage charging and discharging command sequence is first distributed to the local control system of each distributed energy storage unit via an edge communication network. This drives the energy storage converter to perform charging and discharging actions according to the planned timing. Simultaneously, a carbon potential monitoring thread is started for continuous data acquisition. This carbon potential monitoring thread is a resident high-frequency acquisition and calculation process deployed in the monitoring and control devices of each energy storage node. Unlike conventional periodic polling acquisition, it synchronously acquires the real-time electrical quantities and corresponding power generation carbon emission factors of the nodes at a sampling frequency much higher than the scheduling step size, and performs real-time calculation of the node carbon potential online. Finally, it obtains real-time carbon potential data covering all energy storage nodes. This data is a true reflection of the actual carbon emission intensity of electricity at the node.

[0078] Meanwhile, carbon potential prediction is conducted based on distributed energy storage nodes to generate predicted carbon potential trajectory parameters. The process involves first extracting carbon potential values ​​from each energy storage node within historical scheduling cycles to form a historical carbon potential sequence. Then, combining this with short-term load forecasts for the distribution network and distributed power generation output forecasts, an edge time-series prediction algorithm is used to calculate the predicted carbon potential values ​​for each node within the future scheduling window for each time period. Finally, the predicted values ​​for all nodes are stitched together along the time dimension to form a continuous time-series variation curve, i.e., the predicted carbon potential trajectory parameters. This parameter is the prediction of the future carbon potential fluctuation trend of nodes when the scheduling plan is generated, and it is also the core basis for compiling the original charging and discharging instruction sequence.

[0079] The collected real-time carbon potential data is then compared with the predicted carbon potential trajectory parameters node by node and time period by time. The difference between the measured value and the predicted value is calculated and integrated according to the node dimension to generate a node carbon potential deviation vector. ; The formula for calculating the carbon potential deviation of a single node is as follows: ; This vector can accurately pinpoint the location and magnitude of abnormal fluctuations in carbon potential, providing a clear indication of the difference between actual carbon emission intensity and previous predictions.

[0080] Simultaneously, the charge / discharge execution deviation value is calculated based on the actual operation sampling data of distributed energy storage. This deviation value is the timing difference between the actual output charge / discharge power of the energy storage and the planned power in the instruction sequence. The calculation formula is as follows: ; in, for Time period nodes The actual charge and discharge power, The deviation is the commanded power for the corresponding time period. It usually comes from actual operating factors such as battery performance aging, equipment response delay, and communication transmission delay. It is used to quantify the actual execution accuracy of energy storage equipment to scheduling commands.

[0081] Based on this, the node carbon potential deviation vector and the charge / discharge execution deviation value are jointly coupled and analyzed. The impact weight of the two types of deviations on core scheduling objectives such as carbon emission cost and carbon emission reduction is evaluated through sensitivity calculation. The core driving variables with the highest impact weight are selected, and the key parameters of carbon potential deviation are identified. These parameters are the core anchoring factors that cause the scheduling effect to deviate from the expected value, such as a sudden and significant increase in the carbon potential of a node or the failure of a certain energy storage charging power to meet the command requirements, rather than the deviation data of the whole set without difference. This parameter is used as a precise anchor point for subsequent command correction, which can avoid the computational redundancy and communication pressure caused by full recalculation.

[0082] Finally, the key parameters of carbon potential deviation are used as correction anchors and substituted into the original distributed optimization framework. Only the parameters of autonomous subproblems involving deviation nodes and future scheduling periods are updated, and local distributed iterative optimization is carried out again to obtain the optimized power sequence for the corresponding time period. At the same time, an optimization coverage identifier is generated based on the optimization results. This identifier is used to mark the time period range and node range that need to be replaced and updated, distinguishing between executed time periods, time periods to be corrected, and long-term time periods that have not been triggered for correction. Finally, the original charging and discharging command sequence is rolled over based on the results of the optimization coverage identifier. That is, the optimized command fragments are used to cover the original commands at the corresponding positions, driving the energy storage to operate according to the corrected plan, forming a continuously iterative scheduling closed loop.

[0083] In summary, the energy-carbon coordinated dynamic scheduling method for distributed energy storage provided in this application has the following technical effects: A full-link energy-carbon collaborative dynamic scheduling system was constructed, encompassing data access, carbon potential quantification, scheduling decoupling, iterative solution, and rolling correction. On one hand, it deeply integrates power flow calculation with carbon emission flow tracking to accurately solve the dynamic spatiotemporal distribution parameters of carbon potential at each node and time period of the distribution network, establishing a node-level energy-carbon coupling quantification scale. This provides a refined basis for carbon cost accounting and optimization decisions for energy storage charging and discharging, fully releasing the carbon emission reduction potential of energy storage cross-temporal air conditioning. On the other hand, based on the alternating direction multiplier method, the global scheduling problem is decoupled into multiple independently solvable autonomous subproblems. The global optimization objective can be equivalently achieved through iterative interaction of boundary information between adjacent nodes. Simultaneously, a closed-loop scheduling mechanism with real-time carbon potential monitoring and anchor-point rolling correction is established. This mechanism can adaptively match the dynamic fluctuations of distribution network operating conditions and carbon emission intensity, effectively correct execution deviations, improve the actual implementation effect of scheduling schemes, and enhance carbon emission reduction benefits, scheduling solution efficiency, multi-subject data privacy, and operational robustness.

[0084] Example 2, based on the same inventive concept as the energy-carbon coordinated dynamic scheduling method for distributed energy storage in the foregoing examples, such as... Figure 2 As shown in the embodiment of this application, an energy-carbon coordinated dynamic scheduling system for distributed energy storage is provided. The system includes: The data acquisition module 11 is used to access data based on distributed energy storage, obtain distribution network operation data and multiple power generation carbon emission factors; the calculation module 12 is used to perform power flow calculation based on the operation data and the multiple power generation carbon emission factors, and determine the dynamic carbon potential spatiotemporal distribution parameters of the distribution network nodes; the scheduling module 13 is used to perform energy-carbon coordinated dynamic scheduling according to the dynamic carbon potential spatiotemporal distribution parameters, generate carbon potential scheduling signals for distributed optimization, and construct multiple autonomous sub-problems; the iteration module 14 is used to iteratively exchange boundary information of distributed energy storage according to the multiple autonomous sub-problems, and generate a distributed energy storage charging and discharging command sequence when the iteration reaches the convergence condition; the correction module 15 is used to dynamically update distributed energy storage according to the distributed energy storage charging and discharging command sequence, and obtain the carbon potential data of multiple nodes of distributed energy storage to perform rolling correction of the distributed energy storage charging and discharging command sequence.

[0085] Furthermore, the calculation module 12 is also used to perform the following steps: verify the validity of the multiple power generation carbon emission factors and generate a real-time carbon emission factor data package; perform time alignment processing on the operating data and the real-time carbon emission factor data package to generate a spatiotemporal data slice; perform carbon emission flow tracking along the power flow distribution direction based on the spatiotemporal data slice to construct multiple active power flow paths; use the multiple power generation carbon emission factors as source attributes to extract the carbon emission allocated to the upstream branches of the multiple active power flow paths and generate multiple branch carbon emission flow parameters; perform spatiotemporal distribution identification based on the multiple branch carbon emission flow parameters and calculate the dynamic carbon potential spatiotemporal distribution parameters of the distribution network.

[0086] Furthermore, the scheduling module 13 is also used to perform the following steps: based on the dynamic carbon potential spatial distribution parameters, carbon emission matching is performed according to the distribution network order to determine the unit carbon emission amount for multiple time periods; the distributed energy storage is charged and discharged according to the unit carbon emission amount for multiple time periods to construct charging and discharging power parameters; the distributed energy storage is dynamically carbon potential analyzed according to the charging and discharging power parameters to calculate carbon emission cost parameters; the charging and discharging power parameters are used as energy storage exchange constraints to perform decision sensitivity analysis on carbon emission cost parameters and construct carbon potential scheduling signals.

[0087] Furthermore, the scheduling module 13 is also used to perform the following steps: dividing the charging and discharging power parameters into charging and discharging time periods to determine the charging and discharging time periods; performing energy storage charging analysis according to the charging time periods and charging and discharging power parameters to extract the energy storage charging power; performing dynamic carbon potential calculation based on the energy storage charging power according to the charging time periods to obtain the charging carbon emissions; performing energy storage discharging analysis according to the discharging time periods and charging and discharging power parameters to extract the energy storage discharging power; performing dynamic carbon potential calculation based on the energy storage discharging power according to the discharging time periods to obtain the discharging carbon emission reduction; introducing carbon trading unit price information, analyzing the charging carbon emissions and the discharging carbon emission reduction in conjunction with the carbon trading unit price information, and calculating the carbon emission cost parameters.

[0088] Furthermore, the scheduling module 13 is also used to perform the following steps: preset an average carbon potential value; compare the energy storage nodes of distributed energy storage according to the average carbon potential value based on the carbon emission amount of charging and the emission reduction amount of discharging; determine whether a first energy storage node below the average carbon potential value absorbs electrical energy and releases electrical energy at a second energy storage node above the average carbon potential value; when an energy storage node below the average carbon potential value absorbs electrical energy and releases electrical energy at an energy storage node above the average carbon potential value, extract the transferred power parameters and calculate the carbon potential difference between the first energy storage node and the second energy storage node; calculate the carbon emission reduction contribution compensation based on the transferred power parameters and the carbon potential difference to obtain the carbon emission reduction sharing compensation benefit value.

[0089] Furthermore, the scheduling module 13 is also used to perform the following steps: introducing Lagrange multipliers to relax the energy storage exchange constraints, updating the carbon potential scheduling signal, and generating carbon potential scheduling update information; performing alternating direction multiplier transformation based on the carbon potential scheduling update information to construct augmented Lagrange parameters; decoupling the energy storage nodes according to the augmented Lagrange parameters, calculating the carbon marginal cost gradient to solve the problem, and constructing the multiple autonomous subproblems.

[0090] Furthermore, the iteration module 14 is also used to perform the following steps: perform subject matching on distributed energy storage according to the multiple autonomous sub-problems to generate the expected value of boundary exchange power of adjacent energy storage; encapsulate the expected value of boundary exchange power with Lagrange multipliers to construct boundary coordination information; send the boundary coordination information to adjacent distributed energy storage and receive the peer boundary coordination information; update the Lagrange multipliers according to the peer boundary coordination information, generate the updated Lagrange multipliers to perform boundary exchange power consistency analysis, and calculate the original residual value; perform change amplitude analysis according to the updated Lagrange multipliers to calculate the dual residual value; perform convergence determination based on the original residual value and the dual residual value, and when the original residual value and the dual residual value have not converged, enter the next iteration; when the original residual value and the dual residual value have converged, lock the target energy storage charging and discharging power sequence as the distributed energy storage charging and discharging command sequence.

[0091] Furthermore, the correction module 15 is also used to perform the following steps: sending the distributed energy storage charge / discharge command sequence to the distributed energy storage for command execution, starting a carbon potential monitoring thread for continuous acquisition, and obtaining real-time carbon potential data; performing carbon potential prediction based on the distributed energy storage, generating predicted carbon potential trajectory parameters, comparing the real-time carbon potential data with the predicted carbon potential trajectory parameters to generate a node carbon potential deviation vector; performing execution deviation calculation based on the distributed energy storage to obtain charge / discharge execution deviation values; performing joint analysis of the node carbon potential deviation vector and the charge / discharge execution deviation values ​​to identify key parameters of carbon potential deviation; using the key parameters of carbon potential deviation as correction anchors to perform distributed iterative optimization of the distributed energy storage charge / discharge command sequence, performing optimization coverage identification based on the optimization results, and performing rolling correction based on the optimization coverage identification results.

[0092] In Embodiment 3, based on the same inventive concept as the energy-carbon coordinated dynamic scheduling method for distributed energy storage in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of the energy-carbon coordinated dynamic scheduling method for distributed energy storage as described in any one of Embodiment 1 above.

[0093] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dynamic scheduling method for energy and carbon coordination for distributed energy storage, characterized in that, The method includes: Data access is based on distributed energy storage to obtain power grid operation data and multiple power generation carbon emission factors; Based on the operational data and the multiple power generation carbon emission factors, power flow calculations are performed to determine the dynamic carbon potential spatiotemporal distribution parameters of the distribution network nodes. Based on the aforementioned dynamic carbon potential spatiotemporal distribution parameters, energy-carbon coordinated dynamic scheduling is performed to generate carbon potential scheduling signals for distributed optimization, and multiple autonomous sub-problems are constructed. The distributed energy storage boundary information is iteratively exchanged according to the multiple autonomous sub-problems. When the iteration reaches the convergence condition, a distributed energy storage charging and discharging command sequence is generated. The distributed energy storage is dynamically scheduled and updated according to the distributed energy storage charging and discharging command sequence, and the carbon potential data of multiple nodes of the distributed energy storage is used to reverse the distributed energy storage charging and discharging command sequence for rolling correction. Based on the operational data and the multiple power generation carbon emission factors, power flow calculations are performed to determine the dynamic carbon potential spatiotemporal distribution parameters of distribution network nodes. The method includes: The validity of the multiple power generation carbon emission factors is verified, and a real-time carbon emission factor data package is generated. The operational data and the real-time carbon emission factor data package are time-aligned to generate spatiotemporal data slices; Carbon emission flow tracing is performed along the power flow distribution direction based on the spatiotemporal data slices to construct multiple active power flow paths; Using the multiple power generation carbon emission factors as source attributes, carbon emissions are allocated to upstream branches of multiple active power flow paths to generate multiple branch carbon emission flow parameters. Based on the spatiotemporal distribution identification of the carbon emission flow parameters of the multiple branches, the spatiotemporal distribution parameters of the dynamic carbon potential of the distribution network are calculated. A distributed optimization method is used to generate carbon potential scheduling signals and construct multiple autonomous subproblems. The method includes: Lagrange multipliers are introduced to relax the energy storage exchange constraint, update the carbon potential scheduling signal, and generate carbon potential scheduling update information. Based on the carbon potential scheduling update information, an alternating direction multiplier transformation is performed to construct the augmented Lagrange parameter; The energy storage nodes are decoupled according to the augmented Lagrange parameters, and the carbon marginal cost gradient is calculated to solve the problem, thereby constructing the multiple autonomous sub-problems. The distributed energy storage system iteratively exchanges boundary information according to the multiple autonomous sub-problems. When the iteration reaches the convergence condition, a distributed energy storage charging and discharging command sequence is generated. The method includes: The distributed energy storage is matched according to the multiple autonomous sub-problems to generate the expected value of the boundary exchange power of adjacent energy storage. The expected value of the boundary exchange power is encapsulated with Lagrange multipliers to construct boundary coordination information. Send the boundary coordination information to the adjacent distributed energy storage and receive the boundary coordination information from the other end; Update the Lagrange multipliers according to the aforementioned end-boundary coordination information, generate the Lagrange update multipliers, perform boundary exchange power consistency analysis, and calculate the original residual value; The change magnitude analysis is performed based on the Lagrange update multiplier, and the dual residual value is calculated. Convergence is determined based on the original residual value and the dual residual value. If the original residual value and the dual residual value do not converge, the next iteration begins. When the original residual value and the dual residual value converge, the target energy storage charging and discharging power sequence is locked as the distributed energy storage charging and discharging command sequence.

2. The energy-carbon coordinated dynamic scheduling method for distributed energy storage as described in claim 1, characterized in that, Based on the aforementioned dynamic carbon potential spatiotemporal distribution parameters, energy-carbon coordinated dynamic scheduling is performed to generate a carbon potential scheduling signal. The method includes: Based on the aforementioned dynamic carbon potential spatiotemporal distribution parameters, carbon emissions are matched according to the distribution network nodes to determine the unit carbon emissions for multiple time periods. Based on the unit carbon emissions of the multiple time periods, charge and discharge analysis of distributed energy storage is performed to construct charge and discharge power parameters; Based on the charging and discharging power parameters, a dynamic carbon potential analysis is performed on distributed energy storage to calculate carbon emission cost parameters. Using the charging and discharging power parameters as energy storage exchange constraints, a decision-sensitive analysis of carbon emission cost parameters is performed to construct a carbon potential scheduling signal.

3. The energy-carbon coordinated dynamic scheduling method for distributed energy storage as described in claim 2, characterized in that, Based on the aforementioned charge and discharge power parameters, a dynamic carbon potential analysis of distributed energy storage is performed to calculate carbon emission cost parameters. The method includes: The charging and discharging power parameters are divided into charging and discharging time periods to determine the charging and discharging time periods; Energy storage charging analysis is performed based on the charging period and charging / discharging power parameters to extract the energy storage charging power. Based on the energy storage charging capacity, dynamic carbon potential is calculated according to the charging period to obtain the charging carbon emissions. Energy storage discharge analysis is performed based on the discharge period and charge / discharge power parameters to extract the energy storage discharge capacity. Based on the energy storage discharge capacity, dynamic carbon potential is calculated according to the discharge period to obtain the carbon emission reduction of discharge. By incorporating carbon trading unit price information, the carbon emissions from charging and the carbon emission reduction from discharging are analyzed in conjunction with the carbon trading unit price information to calculate the carbon emission cost parameters.

4. The energy-carbon coordinated dynamic scheduling method for distributed energy storage as described in claim 3, characterized in that, Before calculating carbon emission cost parameters, the methods include: A preset carbon potential average value is used. Based on the carbon emission reduction of charging and the carbon emission reduction of discharging, the energy storage nodes of distributed energy storage are compared according to the carbon potential average value. It is determined whether the first energy storage node with a carbon potential average value absorbs electrical energy and releases electrical energy in the second energy storage node with a carbon potential average value. When an energy storage node below the average carbon potential absorbs electrical energy and an energy storage node above the average carbon potential releases electrical energy, the transferred energy parameters are extracted and the carbon potential difference between the first energy storage node and the second energy storage node is calculated. The carbon emission reduction contribution compensation is calculated based on the transferred electricity parameters and carbon potential difference to obtain the carbon emission reduction sharing compensation benefit value.

5. The energy-carbon coordinated dynamic scheduling method for distributed energy storage as described in claim 1, characterized in that, The distributed energy storage is dynamically scheduled and updated according to the distributed energy storage charging and discharging command sequence. The carbon potential data of multiple nodes in the distributed energy storage is then used to reverse-engineer the distributed energy storage charging and discharging command sequence for rolling correction. The method includes: The distributed energy storage charging and discharging command sequence is sent to the distributed energy storage for command execution, and the carbon potential monitoring thread is started to continuously collect data and obtain real-time carbon potential data. Carbon potential prediction is performed based on distributed energy storage, and predicted carbon potential trajectory parameters are generated. The deviation between the real-time carbon potential data and the predicted carbon potential trajectory parameters is compared to generate a node carbon potential deviation vector. Execution deviation is calculated based on distributed energy storage to obtain the charge / discharge execution deviation value; By jointly analyzing the node carbon potential deviation vector and the charge / discharge execution deviation value, key parameters of carbon potential deviation can be identified. The carbon potential deviation key parameter is used as a correction anchor point to perform distributed iterative optimization of the distributed energy storage charging and discharging command sequence. The optimization coverage is marked according to the optimization results, and rolling correction is performed according to the optimization coverage mark results.

6. A dynamic scheduling system for energy and carbon coordination for distributed energy storage, characterized in that, The system is used to execute the energy-carbon coordinated dynamic scheduling method for distributed energy storage as described in any one of claims 1 to 5, the system comprising: The data acquisition module is used to access data based on distributed energy storage and obtain power grid operation data and multiple power generation carbon emission factors. The calculation module is used to perform power flow calculations based on the operating data and the multiple power generation carbon emission factors to determine the dynamic carbon potential spatiotemporal distribution parameters of the distribution network nodes. The scheduling module is used to perform energy-carbon coordinated dynamic scheduling based on the dynamic carbon potential spatiotemporal distribution parameters, generate carbon potential scheduling signals for distributed optimization, and construct multiple autonomous sub-problems. The iteration module is used to iteratively exchange boundary information of distributed energy storage according to the multiple autonomous sub-problems. When the iteration reaches the convergence condition, it generates a distributed energy storage charging and discharging command sequence. The correction module is used to dynamically schedule and update the distributed energy storage according to the distributed energy storage charging and discharging command sequence, and to obtain the carbon potential data of multiple nodes of the distributed energy storage to perform rolling correction on the distributed energy storage charging and discharging command sequence.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which is used to execute the energy-carbon coordinated dynamic scheduling method for distributed energy storage as described in any one of claims 1 to 5.

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