A carbon asset dynamic accounting method and system for a light storage and charging integrated power station

Abstract

The application belongs to the technical field of carbon asset accounting, and relates to a kind of light storage integration power station carbon asset dynamic accounting method and system, comprising: collecting target power station integrated power flow data for integration and attribution operation, constructing weighted energy data frame containing source attribute;Obtain external market data and equipment state parameters, calculate discharge cost index;With index as characteristic weight, the data frame is reorganized and stored in order, and the energy account data table is updated;Respond to terminal power request, combined with physical loss rate, execute addressing traversal and on-demand segmentation, extract the subset of data frame to be adjusted out;Check the source attribute of the subset, start physical loss calculation, and generate carbon asset deduction record;Change the subset of data frame and deduction record to invalid state and settle net emission reduction, and then issue the fixed-point release operation message.The application solves the problems of difficult dynamic weighting and ordered storage of heterogeneous energy flow and difficult closed-loop accounting and cancellation of physical loss.

Description

Technical Field

[0001] This invention belongs to the technical field of carbon asset accounting, and relates to a dynamic accounting method and system for carbon assets of integrated photovoltaic, energy storage and charging power plants. Background Technology

[0002] With the development of distributed renewable energy, integrated photovoltaic-storage-charging power stations have played a positive role in improving the consumption of clean energy and supporting grid operation. Dynamically calculating and managing the energy flow and carbon emission reduction attributes within these systems is a crucial issue in the industry's development. The energy within integrated photovoltaic-storage-charging power stations is characterized by multiple sources, multiple flow directions, and multiple time-varying value dimensions. The physical ownership and economic value of their carbon assets continuously change with the system's operating status and the external market environment. Traditional static or periodic accounting methods struggle to fully capture these dynamic characteristics, thus affecting the quantification and activation of carbon asset value.

[0003] Currently, the carbon asset accounting methods commonly used in the industry are typically based on long-term net electricity consumption statistics, combined with fixed grid emission factors to calculate carbon emission reductions. Chinese invention patent CN116596329A discloses a method and system for calculating the transmission of carbon emission factors in multi-energy complementary enterprises. This scheme mainly uses topological analysis of metering data to transmit and calculate carbon emission factors at different nodes to handle the attribution of multi-energy flows. Meanwhile, Chinese invention patent CN117217497A discloses an integrated energy management platform and integrated energy management method. This scheme focuses on platform architecture construction, realizing the collection, monitoring, and statistics of multi-source data.

[0004] The aforementioned existing technologies have certain limitations when addressing integrated photovoltaic-storage-charging power station scenarios that require dynamic binding of physical energy flows with virtual carbon asset flows. On one hand, the calculation process based on metering data topology analysis tends to statically backtrack historical data, making it difficult to dynamically assign weights and implement ledger-style chain management at real-time nodes where energy transfer occurs. This makes it difficult to provide flexible energy release strategies in response to real-time market price fluctuations, and it rarely involves the dynamic selection and write-off logic of energy blocks when charging service requests are made. On the other hand, solutions that focus on data collection and monitoring do not easily internalize real-time market signals and physical states into the dynamic attributes of the energy data itself. In cases of computational lag, it is difficult to link and attribute micro-processes such as AC / DC conversion losses during cross-media storage of physical energy with changes in macro-level carbon asset records in real time, easily leading to calculation discrepancies between carbon asset accounts and physical energy losses. Summary of the Invention

[0005] In a first aspect, the present invention provides a method for dynamic accounting of carbon assets in an integrated photovoltaic, energy storage, and charging power station, comprising the following steps: S1. Collect the comprehensive power flow data of the target power plant, perform integration and attribution calculations on the data, and construct a weighted energy data frame containing source attributes and time characteristics; S2. Obtain external market data and operating status parameters of energy storage equipment to construct a multi-dimensional state vector matrix and calculate the discharge cost index of the weighted energy data frame. S3. Using the discharge cost index as a feature weight, reorganize the weighted energy data frame, generate a complete data frame, and execute the storage sorting mechanism to update the energy ledger data table of the target energy storage device. S4. Respond to the power request action command activated by the external charging terminal, and perform address traversal and on-demand segmentation operations based on the energy ledger data table and combined with the physical loss rate to extract the subset of data frames to be retrieved that can match the power demand. S5. Verify the energy source attribute attribution characteristics within the subset of data frames to be retrieved, start the physical loss calculation task, and generate the corresponding associated carbon asset deduction record. S6. Change the subset of data frames to be transferred out and the carbon asset deduction record to an invalid state, perform settlement processing, calculate the net carbon emission reduction, and issue a fixed-point release operation message to the underlying power conversion execution agency based on the total dispatched electricity of the subset of data frames to be transferred out.

[0006] A further aspect of the present invention, step S1, includes the following steps: Constructing a weighted energy data frame that includes source attributes and temporal characteristics involves the following steps: Acquire the local monitoring and control terminal within the target power station, and record the continuous photovoltaic power generation signal and the continuous energy storage charging and discharging power signal according to the preset sampling time granularity, and simultaneously extract the continuous interactive power signal at the location where the target power station interacts with the external power grid. Based on this sampling time granularity, each power signal is integrated to obtain its associated discrete power value; By combining the actual flow direction of physical power, source identification data is generated.

[0007] In a further embodiment of the present invention, step S1 further includes the following steps: Based on the environmental baseline information, which includes the grid-based carbon emission factor, in the initial configuration of the equipment, the inherent initial carbon emission reduction of this discrete electricity value is calculated; The source identification data, time characteristics, discrete electricity values, and initial carbon emission reductions are encapsulated to generate independent weighted energy data frames.

[0008] A further aspect of the present invention, step S2, includes the following steps: Load real-time electricity price data stream and real-time carbon price data stream; Retrieve the real-time state of charge parameters of the energy storage device and trigger the local power consumption side load calculation service to obtain the power demand forecast curve; The predicted total electricity demand is extracted from the electricity demand forecast curve and reconstructed and spliced ​​into a multi-dimensional state vector matrix by combining it with real-time electricity price data stream, real-time carbon price data stream and real-time state of charge parameters. A pre-trained nonlinear index mapping function is introduced to process the multidimensional state vector matrix, and the discharge cost index, which characterizes the cost of energy release hysteresis, is solved and output.

[0009] A further aspect of the present invention, step S3, includes the following steps: The discharge cost index is used as a key weight field and appended to the data structure of the weighted energy data frame to synthesize a complete data frame. Confirm the location of all currently stored data nodes in the energy ledger data table; Based on the preset queuing rules mechanism, the discharge cost index is compared, and the complete data frame is inserted into the specified vertical linked list position according to the ascending order.

[0010] A further aspect of the present invention, step S4, includes the following steps: Analyze the target electricity demand contained within the electricity request action command; Starting from the head of the ascending linked list of the energy ledger data table, and combining the current AC / DC conversion loss rate, the effective delivered power corresponding to each complete data frame is retrieved and calculated one by one, and then accumulated to obtain the total value of the effective delivered power. Monitor the fluctuations in the cumulative total of effective delivered electricity. When the threshold boundary condition of being greater than or equal to the target demand electricity is met, terminate the retrieval process, perform on-demand segmentation and stripping operation on the last data frame that caused the threshold to be exceeded, and determine the currently extracted data frame set as the subset of data frames to be retrieved.

[0011] A further aspect of the present invention, step S5, includes the following steps: Read the source identifier data of each complete data frame contained in the subset of data frames to be retrieved one by one; By using feature character matching logic, subsequences with photovoltaic power generation characteristics are selected and aggregated to generate a photovoltaic data subset.

[0012] A further aspect of the present invention generates corresponding associated carbon asset deduction records, including the following steps: For the target data frame within the photovoltaic data subset, the current AC / DC conversion loss rate is extracted, and then the power loss is calculated. By combining the lost electricity with its original paired grid-based carbon emission factor, a carbon asset deduction record representing a negative value is generated, and this carbon asset deduction record is written back to the energy ledger data table.

[0013] A further aspect of the present invention, step S6, includes the following steps: Call the status change interface to centrally change the internal settlement tags of the subset of data frames to be retrieved and carbon asset deduction records to an invalid status, thereby blocking the permission for repeated calls; Extract and calculate the sum of the positive values ​​of the initial carbon emission reductions, and perform algebraic summation with the negative conversion values ​​specifically reflected in the carbon asset offset records to generate the net carbon emission reductions, and perform a synchronous refresh of the global ledger in the cloud.

[0014] Secondly, this invention provides a dynamic carbon asset accounting system for an integrated photovoltaic, energy storage, and charging power station, comprising the following modules: The power acquisition and encapsulation module is used to acquire the comprehensive power flow data of the target power plant, perform integration and attribution calculations on the data, and construct a weighted energy data frame containing source attributes and time characteristics. The dynamic index calculation module is used to acquire external market data and the operating status parameters of energy storage equipment, thereby constructing a multi-dimensional state vector matrix and calculating the discharge cost index of the weighted energy data frame. The energy ledger storage module is used to reorganize the weighted energy data frame using the discharge cost index as a feature weight, generate a complete data frame, and execute the storage sorting mechanism to update the energy ledger data table of the target energy storage device. The demand response recall module is used to respond to the power request action command activated by the external charging terminal. Based on the energy ledger data table, combined with the physical loss rate, it performs address traversal and on-demand segmentation operations to extract a subset of data frames to be recalled that can match the power demand. The loss attribution processing module is used to verify the energy source attribute attribution characteristics within the subset of data frames to be retrieved, initiate the physical loss calculation task, and generate the corresponding associated carbon asset deduction records. The status settlement execution module is used to change the subset of data frames to be transferred out and the carbon asset deduction record to an invalid state, perform settlement processing, calculate the net carbon emission reduction, and issue a fixed-point release operation message to the underlying power conversion execution agency based on the total dispatched electricity of the subset of data frames to be transferred out.

[0015] In summary, the present invention has the following beneficial technical effects: 1. By constructing a weighted energy data frame and introducing a discharge cost index calculated based on external market data and the operating status of energy storage devices, the energy blocks reflecting the amount of electricity and their source are reconstructed into a data structure with scheduling evaluation parameters. When responding to charging demands, the system can perform ascending-order retrieval and data aggregation based on this index, and combine this with physical loss rates to perform effective electricity calculation and on-demand partitioning. This mechanism allows the system to prioritize energy data with lower current discharge cost indices, enabling the energy storage power station's discharge strategy to dynamically respond to real-time electricity market prices, carbon market prices, and its own physical state, reducing opportunity cost losses during discharge. Simultaneously, the on-demand partitioning mechanism helps reduce the risk of physical energy spillover, narrowing the calculation discrepancy between ledger records and physical delivery, thereby improving the overall resource utilization efficiency and scheduling rationality of the power station.

[0016] 2. When calculating carbon asset losses during charging and discharging, a physical loss calculation task is introduced. Real-time AC / DC conversion loss rates are extracted from the subset of photovoltaic data to be retrieved, and the electricity loss due to physical medium conversion is calculated. This, combined with the grid benchmark carbon emission factor, generates a negative carbon asset deduction record and writes it back to the ledger. This mechanism establishes a linkage between physical energy loss and virtual carbon asset reduction, enabling macroscopic carbon accounting results to deduct energy losses from microscopic physical processes at the data level. This helps improve the accounting bias caused by traditional ex-post, extensive conversion and enhances the consistency between carbon asset data and physical reality throughout the energy conversion process.

[0017] 3. By calling the status change interface after generating carbon asset deduction records, the cleared subset of data frames and the deduction record set are changed to an invalid state, restricting their subsequent repeated retrieval and retrieval. This processing logic internally cuts off the path for the same batch of energy or carbon asset quotas to be recalculated, reducing the risk of over-calculation or duplicate reporting due to repeated system reads. Furthermore, by synchronizing the calculated net carbon emission reductions with the global ledger in the cloud, the correspondence between edge-side physical execution data and cloud ledger summary data is achieved, improving the data traceability and reliability of distributed carbon asset certificates flowing between different levels. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings are used to provide a further understanding of the present invention.

[0019] Figure 1 A flowchart illustrating an embodiment of this application is disclosed.

[0020] Figure 2 Structural schematic diagrams of embodiments of this application are disclosed. Detailed Implementation

[0021] The following is in conjunction with the appendix Figures 1-2 A preferred description of the present invention is provided below.

[0022] See attached document Figure 1 This invention proposes a dynamic accounting method for carbon assets of integrated photovoltaic, energy storage, and charging power plants, comprising the following steps: S1. Collect the comprehensive power flow data of the target power plant, perform integration and attribution calculations on the data, and construct a weighted energy data frame containing source attributes and time characteristics; S2. Obtain external market data and operating status parameters of energy storage equipment to construct a multi-dimensional state vector matrix and calculate the discharge cost index of the weighted energy data frame. S3. Using the discharge cost index as a feature weight, reorganize the weighted energy data frame, generate a complete data frame, and execute the storage sorting mechanism to update the energy ledger data table of the target energy storage device. S4. Respond to the power request action command activated by the external charging terminal, and perform address traversal and on-demand segmentation operations based on the energy ledger data table and combined with the physical loss rate to extract the subset of data frames to be retrieved that can match the power demand. S5. Verify the energy source attribute attribution characteristics within the subset of data frames to be retrieved, start the physical loss calculation task, and generate the corresponding associated carbon asset deduction record. S6. Change the subset of data frames to be transferred out and the carbon asset deduction record to an invalid state, perform settlement processing, calculate the net carbon emission reduction, and issue a fixed-point release operation message to the underlying power conversion execution agency based on the total dispatched electricity of the subset of data frames to be transferred out.

[0023] In one embodiment of the present invention, step S1 includes the following steps: Constructing a weighted energy data frame that includes source attributes and temporal characteristics involves the following steps: Acquire the local monitoring and control terminal within the target power station, and record the continuous photovoltaic power generation signal and the continuous energy storage charging and discharging power signal according to the preset sampling time granularity, and simultaneously extract the continuous interactive power signal at the location where the target power station interacts with the external power grid. Based on this sampling time granularity, each power signal is integrated to obtain its associated discrete power value; By combining the actual flow direction of physical power, source identification data is generated; Based on the environmental baseline information, which includes the grid-based carbon emission factor, in the initial configuration of the equipment, the inherent initial carbon emission reduction of this discrete electricity value is calculated; The source identification data, time characteristics, discrete electricity values, and initial carbon emission reductions are encapsulated to generate independent weighted energy data frames.

[0024] The central processing unit (CPU) uses its integrated communication interface and industry-standard protocols such as Modbus RTU or Modbus TCP to send data acquisition commands to multiple measurement units within the power plant. These measurement units include, but are not limited to, photovoltaic inverters connected to the DC or AC side of the photovoltaic array, energy storage converters (PCS) connected to the energy storage battery pack, and bidirectional energy meters installed at the connection point between the power plant and the external power grid.

[0025] The central processing unit (CPU) operates according to a preset sampling time granularity. The aforementioned measurement units are periodically polled to simultaneously extract three key power signal sequences. Specifically, a continuous photovoltaic power generation signal is extracted. A continuous signal representing the charging or discharging power of an energy storage system. And the interactive power continuous signal characterizing energy exchange with the power grid. .in, A positive value indicates that the energy storage system is discharging, while a negative value indicates that it is charging. A positive value indicates that electricity is supplied to the power grid, while a negative value indicates that electricity is purchased from the power grid.

[0026] Next, the central processing unit (CPU) performs sampling at each time granularity. The signal segment within the time granularity is integrated to convert the continuous power signal into discrete electrical quantity values. For scenarios with small internal power fluctuations, this integration operation can be simplified to dividing the average power value over the time period by the time granularity. Multiply them. From this, the discrete electricity values ​​of photovoltaic power generation can be calculated separately. Discrete energy storage charging and discharging values and discrete electricity values ​​of grid interaction At the same time, the central processing unit initiates a logic judgment program to determine the actual physical direction of power flow based on the positive or negative sign of the power signal, and generates source identification data accordingly. For example, if within a certain time granularity, detection Greater than zero and A value less than zero indicates that the electricity generated by the photovoltaic system is charging the energy storage system; the source identification data corresponding to the energy generated at this time is... It is labeled as a source of photovoltaic power.

[0027] Finally, the central processing unit retrieves preset environmental baseline information from its internal non-volatile memory. This information mainly includes grid baseline carbon emission factors published by the regional grid management agency. For discrete electricity values ​​originating from photovoltaic power generation. The central processing unit calculates its inherent initial carbon emission reduction using the following formula. : For electricity sourced from the power grid, the initial carbon emission reduction is zero. After completing the calculation, the central processing unit will process the source identification data generated in the preceding steps. Discrete electricity values ​​and their corresponding initial carbon emission reductions These data are collectively encapsulated into structured data units. These data units constitute the independent weighted energy data frames of this invention, whose data structures include clearly defined energy, source attributes, and timestamp fields, providing the basic data objects for subsequent dynamic calculation steps.

[0028] For time granularity The mathematical expression for integral operations within a given range is as follows:

[0029] In the formula, For the first Discrete electrical quantities of the branch within the time granularity; This is the start time of the integration process; For the first The power of the branch is a continuous signal; This represents the average power within that time granularity.

[0030] Discrete power values The calculation formula is:

[0031] In the formula, This represents the discrete electricity values ​​generated by photovoltaic power generation. This serves as the benchmark carbon emission factor for the power grid.

[0032] Among them, sampling time granularity This is a pre-set time constant, preferably ranging from 60 to 900 seconds. This value represents a balance between ensuring data real-time performance and reducing data processing load; for example, it could be set to 900 seconds based on the electricity market transaction settlement cycle. (Source identification data) It is an enumerated variable used to uniquely identify the source attribute of energy. Typical values ​​may include PV_SOURCE (photovoltaic source), GRID_SOURCE (grid source), etc.

[0033] The core of environmental benchmark information is the grid benchmark carbon emission factor. This parameter represents the amount of carbon dioxide emissions generated per unit of electricity consumed from the grid. Its value is set based on the grid structure of the geographical area where the power plant is located. For example, in a grid area where coal-fired power generation is dominant, this value can be set to [value missing]. This value is derived from the annual average emission factor of the power grid published by national authoritative agencies or obtained in real time through the communication interface with the regional power dispatch center.

[0034] A weighted energy data frame is a data structure in computer programming that can be implemented as an object containing multiple fields, such as a discrete energy value field of type float, a source identifier data field of type enum, and an initial carbon emission reduction field of type float.

[0035] In one specific embodiment, it is assumed that the starting point of the current execution of this step is... The sampling time granularity set by the local measurement and control terminal The duration is 900 seconds. During this period, the central processing unit collects a stable power signal: a continuous photovoltaic power generation signal. The average value is 50 kW, and the energy storage charging and discharging power is a continuous signal. The mean value is -50 kW, indicating that all photovoltaic power generation is used for energy storage charging, and the interactive power is continuous. The average value is 0 kW. The system's preset grid benchmark carbon emission factor... for First, the central processing unit performs integration calculations to determine the discrete electrical quantity being charged into the energy storage system. According to the formula, this value is 12.5 kWh. Since this electrical energy originates from photovoltaic power generation, the central processing unit generates source identification data. The value is then assigned to PV_SOURCE. Subsequently, the initial carbon emission reduction for this portion of electricity is calculated. Its value is the product of the discrete electricity value and the grid baseline carbon emission factor, i.e. Finally, the central processing unit packages these three data points into a weighted energy data frame, whose internal data can be represented as {discrete energy value: 12.5, source identification data: PV_SOURCE, initial carbon emission reduction: 7.5}. This data frame will then be passed to subsequent steps for processing.

[0036] In one embodiment of the present invention, step S2 includes the following steps: Load real-time electricity price data stream and real-time carbon price data stream; Retrieve the real-time state of charge parameters of the energy storage device and trigger the local power consumption side load calculation service to obtain the power demand forecast curve; The predicted total electricity demand is extracted from the electricity demand forecast curve and reconstructed and spliced ​​into a multi-dimensional state vector matrix by combining it with real-time electricity price data stream, real-time carbon price data stream and real-time state of charge parameters. A pre-trained nonlinear index mapping function is introduced to process the multidimensional state vector matrix, and the discharge cost index, which characterizes the cost of energy release hysteresis, is solved and output.

[0037] Specifically, after the central processing unit completes the construction of the weighted energy data frame, it immediately launches parallel market and environment perception tasks to calculate the key dynamic weights for the data frame.

[0038] First, the central processing unit (CPU) uses its configured wide area network (WAN) communication module and a secure application programming interface (API) based on the HTTPS protocol to send a request to the data server of the regional power dispatch center. This request is used to load and parse the real-time electricity price data stream, which contains time-of-use (TOU) price information for the next 24 hours, issued by the dispatch center. .

[0039] Through another MQTT protocol subscription channel, the central processing unit subscribes in real time to topics published by the carbon trading service network, thereby obtaining real-time regional carbon market trading prices and forming a real-time carbon price data stream. .

[0040] The central processing unit then focuses on monitoring the status of internal devices. It sends predefined query frames to the battery management system built into the energy storage system via the CAN bus or RS485 bus to read and obtain the overall real-time state-of-charge parameters of the current energy storage battery pack. Simultaneously with acquiring this parameter, the central processing unit triggers a locally deployed electricity demand-side load measurement service. This service embeds a prediction model based on a long short-term memory network, which has been trained offline using historical charging data from the power station. Once activated, this service immediately processes the most recent charging behavior sequence and outputs a predicted electricity demand curve describing the target charging terminal group within a specific future time window. .

[0041] After acquiring the four sets of heterogeneous data, the central processing unit (CPU) performs data reconstruction and computation tasks. It concatenates the current real-time electricity price, real-time carbon price, real-time energy storage state of charge, and key feature values ​​extracted from the electricity demand forecast curve—for example, the total predicted electricity consumption for the next four hours—to reconstruct a unified multi-dimensional state vector matrix. .

[0042] Multidimensional state vector matrix Input to a pre-trained nonlinear index mapping function The function is essentially a multilayer perceptron neural network model, whose network weights have undergone supervised learning using a reward optimization objective based on historical data. (Multidimensional state vector matrix) Through forward propagation calculations using this function, a dimensionless scalar value is ultimately solved and output. This value is the discharge cost index, used to characterize the opportunity cost or hysteresis cost of immediately releasing this energy data under the current market and equipment conditions. This index provides the core basis for subsequent deposit sorting.

[0043] Discharge cost index The calculation process can be expressed by the following formula:

[0044] In the formula, It is a non-linear activation function; This represents the total number of indicators contained in the state vector; For the first obtained from pre-training The weight parameters of each feature; A multidimensional state vector matrix The first in One input feature quantity; This is a normalization function that maps the input features; This is a bias term.

[0045] Real-time electricity price data stream It is the electricity price signal obtained from the electricity market operator, in units of... This reflects the direct economic value of energy. Real-time carbon price data stream. This is a price signal obtained from the carbon emissions trading market, in units of... This reflects the indirect environmental value associated with energy. Real-time state of charge parameters It is a percentage value, and the range of values ​​is... ,high This means that energy storage systems are approaching saturation, and the demand for power dissipation is quite urgent. (Electricity demand forecast curve) It is a function that describes how future power demand changes over time. The feature values ​​extracted from it, such as total future electricity consumption, characterize the intensity of future energy demand.

[0046] In this embodiment, the predicted total electricity demand is defined. Multidimensional state vector matrix It includes the four quantitative indicators mentioned above. A one-dimensional vector. Nonlinear index mapping function. It is a machine learning model that is already embedded in the measurement and control terminal, and its weights and bias It is obtained through regression training on historical data, and the training objective is to make... The ranking result can maximize the long-term economic and environmental benefits of the power plant. For example, in a scenario assuming high electricity prices, high carbon prices, and low future load, the function output... A higher output indicates a higher opportunity cost to hold that energy, suggesting a preference for conserving it; conversely, a lower output indicates a lower output. .

[0047] It should be noted that, in this embodiment, the nonlinear index mapping function weight and bias It is obtained in advance through supervised learning training in the cloud or on a local server. To address the problem in this field that it is difficult to quantify the opportunity cost of the current action in the temporal and spatial distribution of energy, this invention constructs a specialized training dataset and loss function. The specific training process is as follows: Construction of training samples and labels: Extract historical operation records of the power plant, targeting each sampling moment on the historical timeline. Obtain the historical state vector at this moment. Historical electricity prices, historical carbon prices, historical state of charge (SOC), and historical predicted load are used as input features for the model. For the labels in supervised learning, i.e., the optimal discharge cost index It is generated using a post-hoc global optimization algorithm: because in historical data, The actual electricity price, carbon price, and load curve after time point are all known quantities. A dynamic programming algorithm is used to... The objective function is to maximize the overall revenue (electricity fee revenue + carbon emission credit revenue) over the next 24 hours from the given time point, and the following is derived in reverse: The theoretically optimal discharge priority at any given time. This priority is then normalized and mapped to... Interval, serving as a supervised learning label for that historical moment. .

[0048] Loss function design: During the training phase, the multilayer perceptron model calculates the loss function based on historical inputs. Output Predicted Discharge Cost Index The mean squared error is used as the loss function for model training, and its mathematical expression is as follows: ,in, This represents the total number of samples in the training batch. The weights are updated using backpropagation via gradient descent. and bias until the loss function Converging to below the preset threshold.

[0049] Through the regression training described above, which uses the post-hoc global optimal solution as a pre-hoc guidance label, the model ultimately embedded in the measurement and control terminal can be trained solely based on the current observable state vector matrix. Accurately outputs the discharge cost index that approximates the global optimal decision. .

[0050] In one specific embodiment, the weighted energy data frame {discrete energy value: 12.5, source identification data: PV_SOURCE, initial carbon emission reduction: 7.5} generated in the aforementioned steps is simultaneously calculated by the central processing unit. Assume the acquired external and internal data are as follows: the real-time electricity price data stream shows the current electricity price as... The real-time carbon price data stream shows the current carbon price as follows: Real-time state of charge parameters retrieved The figure is 60%; the electricity demand forecast curve output by the local forecasting service indicates that the total electricity demand for the next 4 hours is 40 kWh. First, these data are constructed into a multi-dimensional state vector matrix. raw input value Assume the normalization ranges are respectively , , , Then the normalized vector for Next, the nonlinear index mapping function is called, assuming its internal pre-trained weights. for bias If the result is -0.1, then a linear summation operation is performed: Finally, the Sigmoid activation function is used to calculate: Therefore, the final calculated discharge cost index associated with this weighted energy data frame is 0.5.

[0051] In one embodiment of the present invention, step S3 includes the following steps: The discharge cost index is used as a key weight field and appended to the data structure of the weighted energy data frame to synthesize a complete data frame. Confirm the location of all currently stored data nodes in the energy ledger data table; Based on the preset queuing rules mechanism, the discharge cost index is compared, and the complete data frame is inserted into the specified vertical linked list position according to the ascending order.

[0052] Specifically, after calculating the discharge cost index, the central processing unit immediately executes the data frame reorganization and storage logic. The discharge cost index, temporarily stored in memory, is used as a new key weight field and appended to the data structure of the weighted energy data frame generated in step S1. This operation is implemented at the software level by merging two independent data variables into a new, more complete data structure to generate a complete data frame.

[0053] The system activates a pre-allocated and initialized data structure in memory, which is the energy ledger data table used to characterize the target energy storage device. This data table structure is implemented at the underlying level as a singly linked list ordered according to specific rules. The central processing unit first obtains a pointer to the head node of this linked list, and using this as a starting point, confirms the positions of all currently stored data nodes in the energy ledger data table and their respective discharge cost index values.

[0054] The central processing unit inserts newly generated complete data frames into the energy ledger data table according to a preset queuing rule mechanism. The core of this mechanism is to compare the discharge cost index contained in the complete data frame with the discharge cost index of each existing data node in the linked list.

[0055] Specifically, the processor traverses the linked list starting from the head node, comparing each node until it finds the first existing node whose discharge cost index is greater than or equal to the new data frame index. At this point, the processor performs a pointer redirection operation, inserting the new data frame node before the existing node, thereby maintaining the characteristic that the entire linked list is arranged in ascending order of discharge cost index.

[0056] If no node meeting the conditions is found after traversing to the end of the linked list, the new data frame is inserted at the end of the linked list. Through this operation, the closed loop completes the entire process of orderly storing the complete data frame representing virtualized energy into the ledger.

[0057] The complete data frame is a data structure extended from the weighted energy data frame. Its added field is the discharge cost index, a floating-point number used for subsequent sorting and retrieval. The energy ledger data table is a data structure used to persistently store and organize complete data frames in memory. Logically, it is an ordered list; physically, it is implemented through a sequence of pointer-linked nodes, ensuring efficient insertion and ordered access.

[0058] The queuing rule mechanism defines the sorting algorithm followed when inserting complete data frames into the energy ledger data table, namely, always maintaining all nodes within the data table structure according to their discharge cost index. The values ​​are arranged in ascending order.

[0059] In one specific embodiment, there is a complete data frame to be stored, whose internal data is {Discrete power value: 12.5, Source identification data: PV_SOURCE, Initial carbon emission reduction: 7.5, Discharge cost index: 0.5}. Assume that at the time of this step, the energy ledger data table already contains two data nodes, node A and node B, arranged in ascending order. Node A contains a discharge cost index of 0.3, and node B contains a discharge cost index of 0.7. The current linked list structure is: Head pointer -> Node A -> Node B -> NULL. The central processing unit (CPU) begins the insertion operation. It first accesses node A and compares the index 0.5 of the data frame to be inserted with the index 0.3 of node A. Since 0.5 is greater than 0.3, the processor continues traversing. Next, it accesses node B and compares the index 0.5 of the data frame to be inserted with the index 0.7 of node B. Since 0.5 is less than 0.7, the insertion condition is met. The processor then performs pointer modification: changing the successor pointer of node A from pointing to node B to pointing to the new data frame node, and simultaneously setting the successor pointer of the new data frame node to point to node B. After the operation is completed, the final state of the energy ledger data table is: head pointer -> node A -> new data frame node -> node B -> NULL, and the sequence of discharge cost indices for each node is 0.3, 0.5, 0.7, strictly maintaining an ascending order.

[0060] In one embodiment of the present invention, step S4 includes the following steps: Analyze the target electricity demand contained within the electricity request action command; Starting from the head of the ascending linked list of the energy ledger data table, and combining the current AC / DC conversion loss rate, the effective delivered power corresponding to each complete data frame is retrieved and calculated one by one, and then accumulated to obtain the total value of the effective delivered power. Monitor the fluctuations in the cumulative total of effective delivered electricity. When the threshold boundary condition of being greater than or equal to the target demand electricity is met, terminate the retrieval process, perform on-demand segmentation and stripping operation on the last data frame that caused the threshold to be exceeded, and determine the currently extracted data frame set as the subset of data frames to be retrieved.

[0061] The central processing unit (CPU) continuously runs a network monitoring service, which is bound to a designated TCP port to receive power request commands sent from external charging terminals via the local area network. Once a data packet conforming to a predetermined communication protocol is detected, such as a JSON-formatted message containing the user's identity and requested power amount, the CPU immediately parses the command and extracts the target power demand encoded as a floating-point number. The successful completion of this parsing action generates an internal logic signal, namely, a data retrieval trigger pulse signal sent to the system kernel of the central processing unit.

[0062] Upon receiving the data retrieval trigger pulse signal, the central processing unit immediately drives a function to execute the data retrieval. This function first extracts the current AC / DC conversion loss rate from the energy storage converter. Initialize the valid delivery power accumulator variable The value is set to zero, and a temporary set is declared to construct the output results. The address pointer is set to point to the head node of the ascending linked list maintained by the energy ledger data table. Then, a loop search process is entered, traversing the nodes one by one.

[0063] In each iteration, the processor reads the discrete energy values ​​contained in the current node. Calculate its effective delivered power after deducting losses. Add it to In the middle. When monitored The first time the target power demand is met or exceeded The retrieval process terminates when the boundary conditions are met.

[0064] To prevent physical overcharging hazards and asset waste, the system immediately executes a data frame segmentation and stripping mechanism: calculating the actual amount of electricity in the last node's ledger that only needs to be extracted. The last node is split into two independent data frames, with a ledger capacity of [missing information]. The data frames that were initially allocated to the temporary set are now allocated to the temporary set. The remaining data frames inherit the initial carbon emission reduction proportionally and retain their original discharge cost index and source attributes, remaining in their current linked list position in the energy ledger data table. At this point, the temporary set is officially designated as the subset of data frames to be retrieved.

[0065] Total effective delivered electricity The calculation and termination conditions of the retrieval process can be expressed by the following formula. Let the retrieval process be... The discrete electricity values ​​contained in a complete data frame are When the first one is retrieved When a complete data frame is:

[0066] In the formula, This represents the current AC / DC conversion loss rate of the system.

[0067] The search process is in The segmentation and stripping mechanism will be terminated and triggered for the first time when the following conditions are met:

[0068] In the formula, The target power demand.

[0069] A power request action command is a data packet initiated by an external charging terminal and following a specific application layer protocol. Its content includes at least the target power demand specified in the request. This value is in kilowatt-hours. The data retrieval trigger pulse signal is a logical software interrupt or event used to activate the data retrieval program in the storage system.

[0070] In software implementation, the addressing pointer is a pointer variable whose value is the memory address of a node in the energy ledger data table.

[0071] The subset of data frames to be retrieved is a temporarily generated collection of data, such as an array or list, which stores copies or references of all complete data frames selected to satisfy this power request.

[0072] In one specific embodiment, the energy ledger data table has three nodes arranged in ascending order of discharge cost index: Node A: index 0.3, discrete energy value 10.0 kWh; New data frame node: index 0.5, discrete energy value 12.5 kWh; Node B: index 0.7, discrete energy value 20.0 kWh.

[0073] At this time, an electricity demand action command is received, indicating the target electricity demand. The current AC / DC conversion loss rate of the energy storage system is 18.0 kWh. The percentage is 10%. This request triggers the data retrieval process, initializing... The value is 0. In the first iteration, the discrete energy value of node A is read as 10.0 kWh, and its effective delivered energy is 9.0 kWh. The new data frame node is updated to 9.0 kWh, and node A is fully included in the subset of data frames to be rescheduled. Since 9.0 kWh is less than 18.0 kWh, the cursor moves to the new data frame node. In the second iteration, the new data frame node's power is read as 12.5 kWh, and the effective delivered power is 11.25 kWh, bringing the total to 20.25 kWh. At this point, 20.25 kWh is greater than 18.0 kWh, triggering the splitting mechanism. The new data frame node only needs to contribute an additional 9.0 kWh of physical delivered power to meet the demand, which translates to 10.0 kWh of discrete ledger power to be rescheduled internally. Therefore, the new data frame node is split into two: the node containing 10.0 kWh is added to the subset of data frames to be rescheduled, while the remaining node containing 2.5 kWh remains in the original ledger list for the next scheduling.

[0074] In one embodiment of the present invention, step S5 includes the following steps: Read the source identifier data of each complete data frame contained in the subset of data frames to be retrieved one by one; By using feature character matching logic, subsequences with photovoltaic power generation characteristics are selected and aggregated to generate a photovoltaic data subset. Generating corresponding associated carbon asset deduction records includes the following steps: For the target data frame within the photovoltaic data subset, the current AC / DC conversion loss rate is extracted, and then the power loss is calculated. By combining the lost electricity with its original paired grid-based carbon emission factor, a carbon asset deduction record representing a negative value is generated, and this carbon asset deduction record is written back to the energy ledger data table.

[0075] Specifically, after the central processing unit (CPU) identifies the subset of data frames to be retrieved, it immediately initiates the physical loss attribution calculation task. First, the CPU iterates through each complete data frame in the subset and reads its encapsulated source identifier data field. By performing logical judgments based on feature character matching, such as checking if the source identifier data equals a preset PV_SOURCE enumeration value, all energy data generated by the photovoltaic power generation system is filtered out. All the filtered complete data frames are aggregated into a new temporary sequence, forming the photovoltaic data subset.

[0076] Next, the central processing unit performs loss calculations for each target data frame within the photovoltaic data subset.

[0077] For each target data frame, the central processing unit retrieves the AC / DC conversion loss rate under the current operating conditions, which was extracted in real time during the preceding demand matching step. This parameter reflects the energy conversion efficiency of the energy storage system under the current charge / discharge power and ambient temperature. Subsequently, the processor extracts the actual discrete energy values ​​retrieved from this target data frame. Using the obtained loss ratio, the power loss caused by the electrochemical conversion of the physical medium and the heat dissipation of the power electronic devices is calculated. .

[0078] The central processing unit combines the calculated power loss to generate a negative record for financial deduction. The processor then traces back from the raw data of this target data frame and extracts the grid-based carbon emission factor initially paired in step S1. .

[0079] By multiplying the lost electricity by this weighted ratio and assigning a negative sign, a carbon asset deduction record representing a negative value is generated. For energy data originating from the power grid, since it initially lacks carbon emission reduction attributes and has an initial carbon emission reduction of zero, its physical losses do not result in the reduction of virtual carbon assets. Therefore, there is no need to generate corresponding carbon asset deduction records, thereby reducing the system's ineffective calculation overhead.

[0080] Once the operation is complete, the central processing unit will trigger the writing of this newly generated carbon asset deduction record as a new entry back to the energy ledger data table within the transaction processing cycle. This ensures that while energy data is retrieved from the ledger, the carbon asset reduction corresponding to its physical conversion loss is also recorded in a timely and accurate manner, thereby maintaining data consistency at the underlying physical energy conversion loss attribute level.

[0081] Power consumption The calculation formula is:

[0082] Carbon asset deduction records The numerical calculation formula is as follows:

[0083] The photovoltaic data subset is a data set whose members are all complete data frames from the subset of data frames to be retrieved, with the photovoltaic attribute as their source. AC / DC conversion loss rate. This is a real-time changing floating-point number that characterizes the proportion of energy loss in a single complete charge-discharge cycle of the energy storage system. Its value is determined by the physical characteristics of the energy storage device and its current operating state, with a typical range between 0.08 and 0.15. In this embodiment, the grid benchmark carbon emission factor is the grid benchmark carbon emission factor defined in step S1. .

[0084] Carbon asset offset records are a special type of data entry whose structure includes a negative carbon asset value and a timestamp. They are specifically used to record the loss of carbon reduction potential corresponding to energy that could not be actually utilized due to physical depletion.

[0085] In one specific embodiment, the subset of data frames to be retrieved includes node A and the newly retrieved data frame nodes after splitting. First, the central processing unit (CPU) identifies the new data frame nodes as photovoltaic sources through feature matching. Node A is ignored, as it lacks emission reduction attributes and is therefore exempt from calculation. This is combined with the AC / DC conversion loss rate. The value is 0.1. The processor extracts the discrete power value of the photovoltaic node, 10.0 kWh, and calculates the power loss. The value is 1.0 kWh. Then, the processor uses the grid-based carbon emission factor to generate a carbon asset deduction record. The core value of this record is... Calculated as Finally, it includes numerical values. The carbon asset deduction record is created and added back to the energy ledger data table as a new entry in the same database transaction, completing the closed-loop attribution of this loss.

[0086] In one embodiment of the present invention, step S6 includes the following steps: Call the status change interface to centrally change the internal settlement tags of the subset of data frames to be retrieved and carbon asset deduction records to an invalid status, thereby blocking the permission for repeated calls; Extract and calculate the sum of the positive values ​​of the initial carbon emission reductions, and perform algebraic summation with the negative conversion values ​​specifically reflected in the carbon asset offset records to generate the net carbon emission reductions, and perform a synchronous refresh of the global ledger in the cloud.

[0087] After generating carbon asset deduction records, the CPU immediately initiates the final settlement and execution process. To prevent data from being accessed or recalculated repeatedly, the CPU invokes the state change interface provided by the system kernel. This interface performs batch state change operations on the subset of data frames to be retrieved from memory and the carbon asset deduction records generated along with them.

[0088] Specifically, the processor iterates through each data object in these two sets, uniformly changing the pre-defined settlement tag field within each object from active to inactive. These data resources are logically inaccessible, and any subsequent access and retrieval operations on the energy ledger data table will automatically block and skip these marked-inactive entries, thus ensuring that the carbon assets consumed by each electricity request are uniquely settled.

[0089] The central processing unit (CPU) performs net asset value calculations and updates the global ledger. The CPU first extracts and sums the positive values ​​of the initial carbon emission reductions carried in all complete data frames contained in the subset of data frames to be retrieved.

[0090] The positive sum is then combined with the negative discounted value from the carbon asset deduction record and added together. By performing an algebraic summation operation, the net carbon emission reduction, representing the actual net emission reduction effect of this electricity service, is obtained.

[0091] The processor reports this net carbon emission reduction to the global ledger of the cumulative carbon assets associated with the power plant, deployed in the cloud, via an encrypted API call. Upon receiving this data, the cloud server performs a synchronization update operation, which adds the net value realized this time to the power plant's carbon asset account, ensuring that the accounting results on the edge side are reflected in the global asset view in a timely and accurate manner.

[0092] To complete the energy delivery in the physical world, the central processing unit (CPU) calls the configuration interface and, based on the total scheduled power of the corresponding subset of data frames to be retrieved in the preceding step S4, sends a targeted release operation message to the underlying power conversion actuator, namely the energy storage converter. This message is encoded according to communication protocols such as CAN or IEC 61850, and its core instructions include the total released power and the specified output power. After parsing this operation message, the PCS controls its internal IGBT modules to drive the energy conversion bus lines of the energy storage system. Following the preset current injection mode specified in the message, it directly injects the physical energy stored in the batteries through cables and delivers it to the requesting external charging terminal. This process completes closed-loop control from data acquisition and carbon asset accounting to the execution of the underlying physical equipment.

[0093] Net carbon emission reduction The calculation formula is:

[0094] In the formula, This represents a subset of data frames to be retrieved. Represents the set of the first The initial positive carbon emission reduction value carried in each data frame; A set representing a subset of photovoltaic data; Represents the set of the first The carbon asset deduction record generated by each data frame contains a negative value.

[0095] The state change interface is used to modify specific flags of memory data blocks. Failure status is an internal logical flag used at the database or data structure level to mark data items as processed and unavailable. The cumulative carbon asset global ledger is a central database deployed on a cloud server, used to aggregate and manage the total carbon assets of one or more power plants. A targeted release operation message is a data frame conforming to a specific industrial communication protocol, containing all the parameters required to perform a physical discharge operation.

[0096] In one specific embodiment, following the previous steps, the subset of data frames to be retrieved includes node A and the newly split data frame nodes. The value of the carbon asset deduction record is... The ledger shows a total of 20.0 kWh of electricity transferred out. After deducting 10% losses, this physically just meets the external terminal's target demand of 18.0 kWh. First, the data object is changed to an invalid state. Then, the net carbon asset value is calculated, and the cumulative positive sum is... Calculate net carbon emission reductions by deducting carbon asset offsets: This value is sent to the cloud-based global ledger for synchronous updating. Finally, a job message is generated, instructing the PCS to discharge using the 20.0 kWh actually retrieved from the ledger as the battery-side discharge baseline. Assuming completion is required within 15 minutes, the instruction includes an internal output power of 80 kW. After the PCS executes the instruction and deducts the 2.0 kWh internal physical conversion heat loss, the external charging terminal receives its initially requested 18.0 kWh of physical energy, achieving an equivalent closed-loop conversion between the ledger flow and the energy flow.

[0097] See appendix Figure 2 This invention also proposes a dynamic carbon asset accounting system for integrated photovoltaic, energy storage, and charging power plants, comprising the following modules: The power acquisition and encapsulation module is used to acquire the comprehensive power flow data of the target power plant, perform integration and attribution calculations on the data, and construct a weighted energy data frame containing source attributes and time characteristics. The dynamic index calculation module is used to acquire external market data and the operating status parameters of energy storage equipment, thereby constructing a multi-dimensional state vector matrix and calculating the discharge cost index of the weighted energy data frame. The energy ledger storage module is used to reorganize the weighted energy data frame using the discharge cost index as a feature weight, generate a complete data frame, and execute the storage sorting mechanism to update the energy ledger data table of the target energy storage device. The demand response recall module is used to respond to the power request action command activated by the external charging terminal. Based on the energy ledger data table, combined with the physical loss rate, it performs address traversal and on-demand segmentation operations to extract a subset of data frames to be recalled that can match the power demand. The loss attribution processing module is used to verify the energy source attribute attribution characteristics within the subset of data frames to be retrieved, initiate the physical loss calculation task, and generate the corresponding associated carbon asset deduction records. The status settlement execution module is used to change the subset of data frames to be transferred out and the carbon asset deduction record to an invalid state, perform settlement processing, calculate the net carbon emission reduction, and issue a fixed-point release operation message to the underlying power conversion execution agency based on the total dispatched electricity of the subset of data frames to be transferred out.

[0098] Each of the modules can be implemented in whole or in part through software, hardware, or a combination thereof. It supports hardware embedded in or independent of the processor in the computer device, and also supports software stored in the memory of the computer device, so that the processor can call and execute the operations corresponding to each of the above modules.

[0099] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for dynamic accounting of carbon assets in an integrated photovoltaic-storage-charging power station, characterized in that, Includes the following steps: S1. Collect the comprehensive power flow data of the target power plant, perform integration and attribution calculations on the data, and construct a weighted energy data frame containing source attributes and time characteristics; S2. Obtain external market data and operating status parameters of energy storage equipment to construct a multi-dimensional state vector matrix and calculate the discharge cost index of the weighted energy data frame. S3. Using the discharge cost index as a feature weight, reorganize the weighted energy data frame, generate a complete data frame, and execute the storage sorting mechanism to update the energy ledger data table of the target energy storage device. S4. Respond to the power request action command activated by the external charging terminal, and perform address traversal and on-demand segmentation operations based on the energy ledger data table and combined with the physical loss rate to extract the subset of data frames to be retrieved that can match the power demand. S5. Verify the energy source attribute attribution characteristics within the subset of data frames to be retrieved, start the physical loss calculation task, and generate the corresponding associated carbon asset deduction record. S6. Change the subset of data frames to be transferred out and the carbon asset deduction record to an invalid state, perform settlement processing, calculate the net carbon emission reduction, and issue a fixed-point release operation message to the underlying power conversion execution agency based on the total dispatched electricity of the subset of data frames to be transferred out.

2. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 1, characterized in that, Step S1 includes the following steps: Constructing a weighted energy data frame that includes source attributes and temporal characteristics involves the following steps: Acquire the local monitoring and control terminal within the target power station, and record the continuous photovoltaic power generation signal and the continuous energy storage charging and discharging power signal according to the preset sampling time granularity, and simultaneously extract the continuous interactive power signal at the location where the target power station interacts with the external power grid. Based on this sampling time granularity, each power signal is integrated to obtain its associated discrete power value; By combining the actual flow direction of physical power, source identification data is generated.

3. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 2, characterized in that, Step S1 also includes the following steps: Based on the environmental baseline information, which includes the grid-based carbon emission factor, in the initial configuration of the equipment, the inherent initial carbon emission reduction of this discrete electricity value is calculated; The source identification data, time characteristics, discrete electricity values, and initial carbon emission reductions are encapsulated to generate independent weighted energy data frames.

4. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 1, characterized in that, Step S2 includes the following steps: Load real-time electricity price data stream and real-time carbon price data stream; Retrieve the real-time state of charge parameters of the energy storage device and trigger the local power consumption side load calculation service to obtain the power demand forecast curve; The predicted total electricity demand is extracted from the electricity demand forecast curve and reconstructed and spliced ​​into a multi-dimensional state vector matrix by combining it with real-time electricity price data stream, real-time carbon price data stream and real-time state of charge parameters. A pre-trained nonlinear index mapping function is introduced to process the multidimensional state vector matrix, and the discharge cost index, which characterizes the cost of energy release hysteresis, is solved and output.

5. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 1, characterized in that, Step S3 includes the following steps: The discharge cost index is used as a key weight field and appended to the data structure of the weighted energy data frame to synthesize a complete data frame. Confirm the location of all currently stored data nodes in the energy ledger data table; Based on the preset queuing rules mechanism, the discharge cost index is compared, and the complete data frame is inserted into the specified vertical linked list position according to the ascending order.

6. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 5, characterized in that, Step S4 includes the following steps: Analyze the target electricity demand contained within the electricity request action command; Starting from the head of the ascending linked list of the energy ledger data table, and combining the current AC / DC conversion loss rate, the effective delivered power corresponding to each complete data frame is retrieved and calculated one by one, and then accumulated to obtain the total value of the effective delivered power. Monitor the fluctuations in the cumulative total of effectively delivered electricity. When the threshold boundary condition of being greater than or equal to the target demand electricity is met, terminate the retrieval process, perform on-demand segmentation and stripping operation on the last data frame that caused the threshold to be exceeded, and determine the currently extracted data frame set as the subset of data frames to be retrieved.

7. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 1, characterized in that, Step S5 includes the following steps: Read the source identifier data of each complete data frame contained in the subset of data frames to be retrieved one by one; By using feature character matching logic, subsequences with photovoltaic power generation characteristics are selected and aggregated to generate a photovoltaic data subset.

8. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 7, characterized in that, Generating corresponding associated carbon asset deduction records includes the following steps: For the target data frame within the photovoltaic data subset, the current AC / DC conversion loss rate is extracted, and then the power loss is calculated. By combining the lost electricity with its original paired grid-based carbon emission factor, a carbon asset deduction record representing a negative value is generated, and this carbon asset deduction record is written back to the energy ledger data table.

9. The method for dynamic carbon asset accounting of an integrated photovoltaic-storage-charging power station according to claim 1, characterized in that, Step S6 includes the following steps: Call the status change interface to centrally change the internal settlement tags of the subset of data frames to be retrieved and carbon asset deduction records to an invalid status, thereby blocking the permission for repeated calls; Extract and calculate the sum of the positive values ​​of the initial carbon emission reductions, and perform algebraic summation with the negative conversion values ​​specifically reflected in the carbon asset offset records to generate the net carbon emission reductions, and perform a synchronous refresh of the global ledger in the cloud.

10. A dynamic carbon asset accounting system for an integrated photovoltaic-storage-charging power station, characterized in that, Includes the following modules: The power acquisition and encapsulation module is used to acquire the comprehensive power flow data of the target power plant, perform integration and attribution calculations on the data, and construct a weighted energy data frame containing source attributes and time characteristics. The dynamic index calculation module is used to acquire external market data and the operating status parameters of energy storage equipment, thereby constructing a multi-dimensional state vector matrix and calculating the discharge cost index of the weighted energy data frame. The energy ledger storage module is used to reorganize the weighted energy data frame using the discharge cost index as a feature weight, generate a complete data frame, and execute the storage sorting mechanism to update the energy ledger data table of the target energy storage device. The demand response recall module is used to respond to the power request action command activated by the external charging terminal. Based on the energy ledger data table, combined with the physical loss rate, it performs address traversal and on-demand segmentation operations to extract a subset of data frames to be recalled that can match the power demand. The loss attribution processing module is used to verify the energy source attribute attribution characteristics within the subset of data frames to be retrieved, initiate the physical loss calculation task, and generate the corresponding associated carbon asset deduction records. The status settlement execution module is used to change the subset of data frames to be transferred out and the carbon asset deduction record to an invalid state, perform settlement processing, calculate the net carbon emission reduction, and issue a fixed-point release operation message to the underlying power conversion execution agency based on the total dispatched electricity of the subset of data frames to be transferred out.

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