Photovoltaic power generation carbon footprint modeling method and system fusing supply chain and energy flow analysis

Abstract

The application discloses a photovoltaic power generation carbon footprint modeling method and system fusing a supply chain and energy flow analysis, relates to the technical field of new energy power generation and carbon emission evaluation, and comprises the following steps: obtaining component output value, irradiation change amplitude and supply batch corresponding carbon factor value in a photovoltaic power generation scene, constructing an output fluctuation curve that progresses with time, quantitatively describing the difference value between adjacent time points in the output fluctuation curve, obtaining an instantaneous collapse amplitude threshold interval, and marking an extreme weather trigger segment based on the instantaneous collapse amplitude threshold interval. The application realizes the differentiation of a sudden change driving segment and a performance decay segment in output fluctuation by identifying the extreme weather trigger segment and constructing a meteorological disturbance intensity distribution trajectory, thereby avoiding the influence of short-time fluctuation on carbon emission evaluation. Meanwhile, carbon emission calculation and scheduling decision are more in line with actual operation state by means of carbon factor dynamic compression mapping and output curve reconstruction and in combination with change gradient constraint emission reduction scheduling direction.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation and carbon emission assessment technology, specifically to a method and system for modeling the carbon footprint of photovoltaic power generation that integrates supply chain and energy flow analysis. Background Technology

[0002] Photovoltaic power generation carbon footprint modeling, which integrates supply chain and energy flow analysis, takes the entire life cycle of photovoltaic power generation as the object and coordinates the supply chain links such as equipment raw material acquisition, component manufacturing, transportation deployment, and operation and maintenance with the energy flow processes such as irradiation reception, energy conversion, transmission and distribution, and losses during power generation. By establishing a unified data mapping relationship and emission factor correlation mechanism, it systematically characterizes the sources, transmission paths, and cumulative effects of carbon emissions at different stages, thereby achieving refined quantification, dynamic tracking, and structured decomposition of carbon emissions from photovoltaic power generation systems. This method can reflect the transmission characteristics of carbon implicit in the supply chain and reveal the impact of energy conversion efficiency on carbon emission intensity, providing a quantitative basis for low-carbon optimization scheduling and emission reduction decisions.

[0003] The existing technology has the following shortcomings: In the process of photovoltaic power generation carbon footprint modeling that integrates supply chain analysis and energy flow analysis, when extreme weather changes occur in the operating scenario, the power generation of the photovoltaic array will collapse significantly in a short period of time, and the energy flow data will show an abnormal contraction. At this time, if the carbon footprint modeling process lacks an effective identification mechanism for meteorological disturbance factors when performing trend analysis on energy flow changes, it will misjudge the above-mentioned short-term abnormal fluctuations as a continuous degradation trend of equipment performance, and accordingly make an upward estimate of the carbon emission level corresponding to the unit power generation, thus forming an overestimated assessment result of carbon emission intensity. Furthermore, the emission reduction strategy generated based on the assessment result will be directionally deviated, causing operation and maintenance scheduling, equipment replacement and supply decisions to deviate from the actual operating state, thereby affecting the accuracy and economy of the overall carbon management decision.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for modeling the carbon footprint of photovoltaic power generation that integrates supply chain and energy flow analysis, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis, comprising the following steps: The system obtains the component output value, irradiance variation amplitude, and carbon factor value corresponding to the supply batch in the photovoltaic power generation scenario, constructs the output fluctuation curve over time, quantifies the difference between adjacent moments in the output fluctuation curve, obtains the instantaneous collapse amplitude threshold range, and marks extreme weather triggering segments based on the instantaneous collapse amplitude threshold range. Synchronous mapping of irradiance variation amplitude within extreme weather trigger segments is performed to obtain meteorological disturbance intensity distribution trajectory. Then, the output fluctuation curve is re-arranged according to the meteorological disturbance intensity distribution trajectory, and the results of distinguishing between sudden change-driven segments and performance degradation segments are output. Using the mutation-driven fragments in the differentiation results, dynamic compression mapping is performed on the carbon factor values ​​corresponding to the supply batches. The contribution ratio of the carbon factor is reduced and adjusted according to the duration of the mutation-driven fragments to obtain the carbon emission inclusion interval after interference removal. Based on the carbon emission inclusion interval after interference removal, the output fluctuation curve is reconstructed by segmented translation, and the output decrease corresponding to the mutation-driven segment is transferred to the independent interference interval, while maintaining the original change trend of the non-mutation segment, so as to obtain a stable evolution curve. For the non-abrupt segments in the stable evolution curve, the emission intensity change gradient corresponding to unit power generation is calculated. The change gradient is then processed by segmentation and direction discrimination. Gradient constraint coefficients are constructed based on the magnitude and direction of the change gradient, and the emission reduction scheduling direction is weighted and corrected.

[0007] Preferably, the collaborative processing of component output values, irradiance variation amplitude, and corresponding carbon factor values ​​for each supply batch, through time alignment, difference characterization, and interval extraction, completes the identification and marking of extreme weather triggering segments, resulting in an output fluctuation curve containing the marking information of extreme weather triggering segments. The steps are as follows: The component output values ​​are continuously recorded at preset time intervals, and the irradiance change amplitude at corresponding time nodes is recorded synchronously. The carbon factor values ​​corresponding to the supply batch are mapped to the corresponding time nodes, and the component output values ​​are continuously connected to obtain the output fluctuation curve. Record the changes in component output values ​​at adjacent time points along the time direction of the output fluctuation curve, divide the changes into multiple amplitude ranges, and bind them to the irradiance change amplitude at the corresponding time points. By combining the range of changes in the amplitude range, the continuous output decrease time period is selected, the corresponding irradiance change amplitude is compared and processed, and the difference range is extracted to obtain the instantaneous collapse amplitude threshold range. By comparing each time node in the power output fluctuation curve with the instantaneous collapse amplitude threshold range, candidate trigger points are continuously combined and time segments are filtered to obtain extreme weather trigger segments and add marking information.

[0008] Preferably, the amplitude of irradiance change and the output fluctuation curve within the extreme weather-triggered segment are processed point-by-point to achieve correspondence. Synchronous mapping and segment arrangement are used to distinguish between abrupt change-driven segments and performance degradation segments. The steps are as follows: The irradiance variation amplitude is extracted along the time nodes of the extreme weather trigger segment and arranged in the time order of the output fluctuation curve. The component output value and irradiance variation amplitude at the corresponding time nodes are recorded synchronously. The irradiance variation amplitude is mapped according to the time node, and the irradiance variation amplitude is converted into a meteorological disturbance intensity characterization value, which is then bound to the corresponding time node to obtain the meteorological disturbance intensity distribution trajectory. By scanning time nodes one by one according to the distribution trajectory of meteorological disturbance intensity, continuous time nodes with the same direction of change of meteorological disturbance intensity are divided into time segments, and the direction of change of meteorological disturbance intensity is compared with the direction of change of component output value to obtain candidate segments for sudden change drive and candidate segments for performance degradation. Based on the segmentation results, the output fluctuation curve is rearranged, and the candidate segments driven by mutations are arranged in a group, while the candidate segments of performance degradation are arranged separately, so as to obtain the distinction between mutation-driven segments and performance degradation segments.

[0009] Preferably, the meteorological disturbance intensity characterization value is obtained by classifying the irradiance change amplitude. Different irradiance change amplitudes correspond to different levels of meteorological disturbance intensity characterization values, and the meteorological disturbance intensity distribution trajectory is obtained by connecting them sequentially along the time nodes. The time segment in which the direction of meteorological disturbance intensity change is consistent with the direction of component output value change is divided into abrupt change-driven candidate segment.

[0010] Preferably, the carbon factor values ​​corresponding to the mutation-driven fragment and the supply batch are time-correlated, and the carbon emission contribution is redistributed through dynamic compression mapping and duration adjustment to obtain the carbon emission inclusion interval after interference removal. The steps are as follows: Determine the start and end time nodes around the mutation-driven fragment, extract the carbon factor values ​​corresponding to each time node in chronological order and record the time position order, and group and record the carbon factor values ​​of the same supply batch. The carbon factor values ​​are dynamically compressed and mapped according to the time sequence, keeping the original value at the beginning, gradually decreasing the value at the middle, and further decreasing the value at the end, while maintaining the time sequence. The duration is calculated by combining the start and end time nodes, and a correspondence is established between the duration and the carbon factor value. The carbon factor contribution ratio is reduced and adjusted for different durations. Based on the compression mapping processing and reduction adjustment results, the carbon factor values ​​are remapped to the corresponding time nodes and arranged in a unified manner according to time order to obtain the carbon emission inclusion interval after interference removal.

[0011] Preferably, the carbon factor values ​​at consecutive time points within the same mutation-driven fragment are progressively reduced according to their temporal position, and the trend of carbon factor value changes at consecutive time points is kept consistent. At the same time, the carbon factor values ​​in different mutation-driven fragments are subject to graded reduction adjustment according to the corresponding duration.

[0012] Preferably, the carbon emission inclusion interval after interference removal is correlated with the power output fluctuation curve. The stable evolution curve is constructed by transferring the power output decrease amplitude and segmenting the shift. The steps are as follows: By matching the time nodes of the power output fluctuation curve with the carbon emission inclusion interval after interference removal, the component power output value is bound to the carbon emission inclusion status, and continuous time nodes are grouped into the time segment of the mutation-driven segment and the time segment of the non-mutation segment. The output value changes of components at adjacent time nodes are extracted around the time segment driven by the mutation. The output decrease is arranged in chronological order and corresponding to the time node position to obtain the output decrease amplitude distribution. The output fluctuation curve is reconstructed by segmenting and shifting based on the output decrease distribution. The output decrease is stripped off point by point and transferred to an independent interference interval. At the same time, the output values ​​of the components in the original time segment are shifted and adjusted while maintaining the time sequence. Based on the translation reconstruction results, the output values ​​of non-mutation fragment components are kept in their original time order and connected with the translation processing time segment to obtain a stable evolution curve.

[0013] Preferably, in the segmented translation reconstruction process, the original time marker is retained during the transfer of output decrease, and the output values ​​of the components in the original time segment are arranged in a non-decreasing direction. At the same time, the time segments of the mutation-driven segment and the time segments of the non-mutation segment are sequentially connected.

[0014] Preferably, the emission intensity change gradient is extracted and constrained for the non-abrupt segments in the stable evolution curve, and the emission reduction scheduling direction is weighted and corrected through piecewise discretization and direction discrimination. The steps are as follows: For each time node of the non-abrupt segment of the stable evolution curve, the component output value and carbon emission calculation results are read one by one. The carbon emission is recorded in correspondence with the component output value, and the changes of adjacent time nodes are extracted to obtain the change gradient data and bind it to the time node. Based on the gradient change data, all time points are divided into intervals, and the gradient change is divided into multiple intervals with an additional interval number, so that each time point has both gradient change value and interval category information. The direction of time nodes is determined by combining the change gradient value and the interval number. The change gradient relationship is recorded as a direction identifier, and consecutive time nodes with the same direction identifier are merged. Gradient constraint coefficients are constructed based on the changing gradient values, interval numbers, and direction identifiers. These coefficients are then bound to time nodes, and the gradient constraint coefficients corresponding to each time node are incorporated into the emission reduction scheduling direction processing to obtain a weighted correction result.

[0015] A photovoltaic power generation carbon footprint modeling system integrating supply chain and energy flow analysis includes a power collapse identification module, a meteorological disturbance analysis module, a carbon factor correction module, a curve reconstruction module, and a scheduling constraint module. The output collapse identification module obtains the component output value, irradiance change amplitude, and carbon factor value corresponding to the supply batch in the photovoltaic power generation scenario, constructs an output fluctuation curve that progresses over time, quantifies the difference between adjacent moments in the output fluctuation curve, obtains the instantaneous collapse amplitude threshold range, and marks extreme weather trigger segments based on the instantaneous collapse amplitude threshold range. The meteorological disturbance analysis module synchronously maps the irradiance change amplitude within the extreme weather trigger segment to obtain the meteorological disturbance intensity distribution trajectory. It then performs segment rearrangement processing on the output fluctuation curve according to the meteorological disturbance intensity distribution trajectory, and outputs the distinction results between the sudden change driving segment and the performance degradation segment. The carbon factor correction module uses the mutation-driven fragment in the differentiation results to perform dynamic compression mapping on the carbon factor value corresponding to the supply batch, and adjusts the carbon factor contribution ratio according to the duration of the mutation-driven fragment to obtain the carbon emission inclusion interval after interference removal. The curve reconstruction module performs segmented translation reconstruction on the power output fluctuation curve based on the carbon emission inclusion interval after interference removal. It transfers the power output decrease corresponding to the mutation-driven segment to the independent interference interval, while maintaining the original change trend of the non-mutation segment, thus obtaining a stable evolution curve. The scheduling constraint module calculates the emission intensity change gradient corresponding to a unit of power generation for non-abrupt segments in the stable evolution curve, performs piecewise discretization and direction discrimination processing on the change gradient, and constructs gradient constraint coefficients based on the magnitude and direction of the change gradient to perform weighted correction processing on the emission reduction scheduling direction.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention identifies extreme weather-triggered segments and introduces meteorological disturbance intensity distribution trajectories to distinguish between abrupt change-driven segments and performance degradation segments in the power output fluctuation curve. This allows short-term power output fluctuations caused by meteorological factors to be separated from continuous change trends. In the carbon footprint modeling process, it avoids incorporating short-term disturbances into long-term performance changes, thereby making the calculated carbon emission level per unit of power generation more consistent with the actual operating conditions, improving the reliability of carbon emission intensity assessment, and enabling carbon emission results to reflect the true characteristics of power generation process changes.

[0017] This invention achieves a correspondence between the carbon factor values ​​in the mutation-driven segment and the output fluctuation curve by performing dynamic compression mapping and piecewise translation reconstruction. Simultaneously, it introduces a changing gradient constraint coefficient in the non-mutation segment to weightedly correct the emission reduction scheduling direction, enabling the scheduling process to be adjusted according to the stable evolution trend. This ensures that the emission reduction strategy is consistent with the actual power generation state, improves the rationality of carbon management decisions, and reduces the impact of deviations introduced by abnormal fluctuations. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the photovoltaic power generation carbon footprint modeling method that integrates supply chain and energy flow analysis according to the present invention.

[0020] Figure 2 This is a schematic diagram of the modules of the photovoltaic power generation carbon footprint modeling system that integrates supply chain and energy flow analysis according to the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] This invention provides, for example Figure 1 The photovoltaic power generation carbon footprint modeling method shown, which integrates supply chain and energy flow analysis, includes the following steps: The system obtains the component output value, irradiance variation amplitude, and carbon factor value corresponding to the supply batch in the photovoltaic power generation scenario, constructs the output fluctuation curve over time, quantifies the difference between adjacent moments in the output fluctuation curve, obtains the instantaneous collapse amplitude threshold range, and marks extreme weather triggering segments based on the instantaneous collapse amplitude threshold range. This paper details the joint processing of component output values, irradiance variation amplitude, and carbon factor values ​​corresponding to supply batches in photovoltaic power generation scenarios. Through time alignment of multi-source data, curve construction, difference characterization, and collapse interval extraction, it achieves precise labeling of extreme weather triggering segments, providing a directly usable data foundation for subsequent analysis. The specific steps are as follows: The output values ​​of the photovoltaic power generation modules are continuously recorded at preset time intervals, with each time node corresponding to a specific output value. At the same time node, the irradiance variation amplitude is recorded synchronously, so that the output value of the modules and the irradiance variation amplitude correspond one-to-one in the time dimension. For the carbon factor value corresponding to the supply batch, the carbon factor values ​​of different batches are mapped to the corresponding time nodes according to the batch information of the modules, so that each time node not only includes the output value of the modules and the irradiance variation amplitude, but also includes the carbon factor value of the supply batch corresponding to that time node.

[0023] After aligning the three types of data in time, the component output values ​​are continuously connected in order of time nodes to construct an output fluctuation curve. At the same time, the irradiance change amplitude and the corresponding carbon factor value of each supply batch are retained in the output fluctuation curve. This allows the output fluctuation curve to maintain a continuous change shape while having the ability to carry multi-dimensional information, thus providing a unified data expression basis for the subsequent quantitative characterization of the difference between adjacent time points.

[0024] Along the time direction of the output fluctuation curve, the component output values ​​of two adjacent time nodes are selected sequentially. The change between the output value of the current time node and the output value of the previous time node is calculated, and the change is recorded as the difference data at that time position. After completing the difference calculation for all adjacent time nodes, all difference data are arranged in chronological order to obtain a complete difference distribution result. The difference distribution result is then divided according to a preset amplitude range, for example, the change is divided into multiple continuous intervals so that difference data with different amplitudes are assigned to the corresponding intervals.

[0025] While completing the interval division, each difference data point is bound to the irradiance change amplitude at the corresponding time node. This allows the difference data to reflect both the force change and the specific irradiance change. In this way, the difference distribution not only reflects the continuity of force change but also the synchronous relationship between force change and irradiance change, thus providing a clear data division basis for the subsequent extraction of the instantaneous collapse amplitude threshold interval.

[0026] Based on the obtained difference distribution results and interval division, the rapid decline behavior in the power output fluctuation curve is screened. By selecting difference data in the difference distribution that are in a specific amplitude range and show a decline in power output, and judging the continuity of these difference data according to the time sequence, difference data with consecutive power output declines in adjacent time nodes are merged into the same time period. After merging these time periods, the corresponding irradiance change amplitude in each time period is extracted one by one, and the power output decline and irradiance change are compared to see if they change synchronously at the same time node. Only the time period in which power output decline and irradiance change occur simultaneously at the same time node is retained, and the time period that does not meet the synchronous change condition is removed.

[0027] After the filtering is completed, the difference ranges corresponding to the retained time periods are summarized, the upper and lower boundaries covering these difference ranges are extracted, and the boundaries are used as the threshold range of instantaneous collapse amplitude. This threshold range can simultaneously express the amplitude range of force reduction and the change characteristics corresponding to irradiance changes, thereby providing a clear interval determination basis for the identification of extreme weather triggering segments.

[0028] Based on the instantaneous collapse amplitude threshold range, each time node in the output fluctuation curve is compared one by one. Time nodes whose difference data falls within the instantaneous collapse amplitude threshold range are marked as candidate trigger points. These candidate trigger points are then continuously combined in chronological order, and the time ranges in which candidate trigger points appear consecutively in adjacent time nodes are merged into complete time segments.

[0029] After obtaining multiple time intervals, the irradiance variation amplitude within each time interval is checked again. Only the intervals where the irradiance variation amplitude changes continuously throughout the entire time interval are retained, while time intervals that do not meet the continuous variation condition are excluded. After the screening is completed, the finally retained time intervals are marked as extreme weather trigger segments, and the corresponding marking information is added to the corresponding position in the power output fluctuation curve. This allows these time intervals to be directly identified in subsequent processing, while maintaining the original temporal continuity and data integrity of the power output fluctuation curve. This results in a power output fluctuation curve containing the marking information of extreme weather trigger segments, providing a clear data input range for subsequent meteorological disturbance intensity analysis and carbon emission processing.

[0030] Synchronous mapping of irradiance variation amplitude within extreme weather trigger segments is performed to obtain meteorological disturbance intensity distribution trajectory. Then, the output fluctuation curve is re-arranged according to the meteorological disturbance intensity distribution trajectory, and the results of distinguishing between sudden change-driven segments and performance degradation segments are output. By processing the point-by-point correspondence between the irradiance variation amplitude and the power output fluctuation curve, a continuous trajectory of the meteorological disturbance intensity distribution is constructed. Based on this, the power output fluctuation curve is segmented and organized, thus clearly distinguishing between the abrupt change-driven segment and the performance degradation segment. The specific process is as follows: The irradiance variation amplitude within the extreme weather trigger segment is extracted one by one according to time nodes, and arranged in a time sequence completely consistent with the power output fluctuation curve, so that each time node corresponds to a unique irradiance variation amplitude record. During the extraction process, the irradiance variation amplitude of each time node is registered point by point, and the corresponding component power output value of that time node is recorded synchronously, so that the same time position contains both irradiance variation amplitude and component power output value. Subsequently, the irradiance variation amplitudes of adjacent time nodes are continuously connected, and the irradiance variation amplitudes of each time node are sequentially connected to form a continuous variation trajectory unfolding along the time direction. At the same time, the original numerical information is retained at each connection position, so that the variation trajectory not only reflects the continuous trend of irradiance variation, but also retains the specific changes at each time node.

[0031] In this way, the amplitude of irradiance variation is aligned point-by-point with the power output fluctuation curve in the time dimension, thereby providing a unified time coordinate and data correspondence for subsequent synchronous mapping processing.

[0032] Based on the time alignment, a mapping process is performed on the irradiance variation amplitude at each time node within the extreme weather trigger segment, converting the irradiance variation amplitude into the corresponding meteorological disturbance intensity characterization value. During the conversion process, irradiance variations of different amplitudes are graded according to a unified mapping rule, so that different variation amplitudes are mapped to the corresponding level of meteorological disturbance intensity characterization value. After the mapping is completed at each time node, the obtained meteorological disturbance intensity characterization value is bound to the original time node, so that the meteorological disturbance intensity characterization value maintains a one-to-one correspondence with the component output value in the time dimension. Subsequently, the meteorological disturbance intensity characterization values ​​at all time nodes are connected in chronological order to form a continuously unfolding meteorological disturbance intensity distribution trajectory. This distribution trajectory extends point by point on the time axis, and the corresponding meteorological disturbance intensity characterization value is recorded at each time node, so that the meteorological disturbance change process can be presented in the form of a continuous trajectory and compared with the output fluctuation curve in the same time frame.

[0033] Based on the distribution trajectory of meteorological disturbance intensity, the power output fluctuation curve is segmented. By scanning the distribution trajectory of meteorological disturbance intensity point by point along the time direction, the direction of change of the meteorological disturbance intensity characterization value between adjacent time nodes is observed. When the meteorological disturbance intensity characterization value of multiple consecutive time nodes changes in the same direction, these time nodes are divided into the same time segment. When the direction of change of the meteorological disturbance intensity characterization value changes, the segment boundary is set at that time node, thereby dividing the entire extreme weather triggering segment into multiple consecutive time segments.

[0034] After the segmentation is completed, the changes in component output values ​​for each time segment are extracted, so that each segment has both meteorological disturbance change information and output change information. Furthermore, the direction of change in meteorological disturbance intensity and the direction of change in component output values ​​within each time segment are compared point by point. When the two show a synchronous change relationship at the same time node, the time segment is marked as a candidate segment for mutation drive. When the two change directions are different, the time segment is marked as a candidate segment for performance degradation, thus providing a classification basis for subsequent segment rearrangement.

[0035] Based on the segment division and classification results, the output fluctuation curve is processed by segment rearrangement. The time segments marked as candidate segments for mutation are arranged in the original time order, so that these segments form a continuous distribution in the data structure, while keeping the order of each time node within the segment unchanged. For the time segments marked as candidate segments for performance degradation, they are arranged separately in time order, so that they are separated from the candidate segments for mutation in the data structure.

[0036] After the segment arrangement is completed, each segment is uniformly identified. The segment in which the direction of change of meteorological disturbance intensity is consistent with the direction of change of component output value is identified as the mutation-driven segment, and the segment in which the direction of change of meteorological disturbance intensity is different from the direction of change of component output value is identified as the performance degradation segment. This results in the distinction between mutation-driven segment and performance degradation segment, and provides a clear data division basis for subsequent carbon factor adjustment and output reconstruction.

[0037] Using the mutation-driven fragments in the differentiation results, dynamic compression mapping is performed on the carbon factor values ​​corresponding to the supply batches. The contribution ratio of the carbon factor is reduced and adjusted according to the duration of the mutation-driven fragments to obtain the carbon emission inclusion interval after interference removal. By linking the carbon factor values ​​corresponding to each supply batch with the temporal distribution of the mutation-driven fragments point by point, the contribution of carbon emissions is redistributed over time, thereby obtaining the carbon emission inclusion interval after interference removal. The specific steps are as follows: For each mutation-driven segment in the differentiation results, the start and end time nodes of each mutation-driven segment are determined one by one. Each time node within the segment is traversed in chronological order, and the corresponding carbon factor value of the supply batch is extracted at each time node. At the same time, the position order of the time node in the mutation-driven segment is recorded, so that each time node has a clear time position identifier. Within the same mutation-driven segment, the carbon factor values ​​belonging to the same supply batch are grouped and recorded, and their distribution position on the time axis is kept unchanged, so that each group of carbon factor values ​​retains the original batch information and has continuous time distribution characteristics.

[0038] By repeating the above process on all mutation-driven fragments, the carbon factor values ​​in all mutation-driven fragments are arranged in a continuous distribution according to time nodes, thereby providing complete and orderly data input for subsequent compression mapping processing.

[0039] On the already compiled distribution of carbon factor values ​​in the mutation-driven fragment, dynamic compression mapping is performed point by point according to the time node sequence. During the processing, the carbon factor value of each time node is adjusted according to its relative position in the mutation-driven fragment. The carbon factor values ​​of time nodes near the beginning of the mutation-driven fragment are kept unchanged, the carbon factor values ​​of time nodes in the middle of the fragment are gradually reduced by a fixed increment, and the carbon factor values ​​of time nodes near the end of the fragment are further reduced, so that the carbon factor values ​​in the entire mutation-driven fragment show a gradual contraction change pattern along the time direction.

[0040] When performing this process, it is ensured that the carbon factor value adjustment process at each time node is based on its specific time position, so that the compression mapping process presents a continuous change state throughout the entire segment, while maintaining the order relationship between time nodes, thereby achieving an ordered compression distribution of carbon factor values ​​in the time dimension.

[0041] For each mutation-driven segment, its duration is calculated segment by segment. By recording the time interval between the start and end times of the segment, the duration of the segment is obtained, and a correspondence is established between this duration and the carbon factor values ​​at each time point within the segment. Based on this, the carbon factor contribution ratio is reduced and adjusted according to different durations. For mutation-driven segments whose duration spans multiple consecutive time points, their carbon factor contribution ratio is reduced in stages according to duration, so that segments with longer durations occupy a smaller proportion in the overall carbon emission calculation. For mutation-driven segments whose duration spans fewer time points, only their carbon factor contribution ratio is adjusted within a limited range, resulting in a differentiated distribution of mutation-driven segments with different durations in the carbon emission calculation. Through this reduction and adjustment method based on duration, the carbon factor contribution ratio can be redistributed while taking into account the influence of the time dimension.

[0042] The carbon factor values, after dynamic compression mapping and duration reduction adjustment, are remapped to the corresponding time nodes. All time nodes within the mutation-driven segments are uniformly identified so that these time nodes use the adjusted carbon factor values ​​in the carbon emission accounting process. Meanwhile, the original carbon factor values ​​are kept unchanged for time nodes not included in the mutation-driven segments.

[0043] Based on this, all time points are reorganized in chronological order. Time points using the adjusted carbon factor values ​​and those maintaining the original carbon factor values ​​are arranged in a unified manner. Carbon emission inclusion intervals are then divided according to their distribution on the time axis, so that different time segments participate in carbon emission calculations according to their corresponding carbon factor values. This yields carbon emission inclusion intervals after interference removal, providing clear data division results for subsequent power output fluctuation curve processing.

[0044] Based on the carbon emission inclusion interval after interference removal, the output fluctuation curve is reconstructed by segmented translation, and the output decrease corresponding to the mutation-driven segment is transferred to the independent interference interval, while maintaining the original change trend of the non-mutation segment, so as to obtain a stable evolution curve. The decrease in output power of mutation-driven fragments is stripped and transferred, while the original trajectory of non-mutated fragments is maintained, thus achieving the continuous construction of a stable evolution curve and making the output power changes appear as a processed, stable expression over time. The specific process is as follows: For the carbon emission inclusion interval after interference removal, a point-by-point matching process is performed on each time node in the output fluctuation curve. The carbon emission inclusion status corresponding to each time node is recorded, so that each time node is clearly distinguished as a time node within a mutation-driven segment or a time node within a non-mutation segment. During the matching process, the output fluctuation curve is scanned point by point in chronological order, and the component output value of each time node is bound to its corresponding carbon emission inclusion status. Continuous time nodes belonging to the same mutation-driven segment are merged into independent time segments, so that each mutation-driven segment has a clear start time node and end time node.

[0045] Meanwhile, the time nodes in the non-mutated segments are arranged in their original time order, so that the mutation-driven segments and non-mutated segments form a clear distribution relationship on the time axis, thereby providing a clear segmentation basis for the subsequent extraction and transfer of output reduction magnitude.

[0046] For each mutation-driven segment, the changes in component output values ​​at consecutive time points within that segment are extracted point by point. By reading the output difference between each time point and its previous time point, the changes in all output decrease directions are recorded and arranged in chronological order, so that all output decrease processes within the segment form a continuous change record. During the recording process, the output decrease corresponding to each time point is mapped to the position of that time point in the mutation-driven segment, so that the output decrease amplitude has complete distribution information in the time dimension.

[0047] Furthermore, the output decreases within the mutation-driven segment are accumulated and organized to ensure that the output decreases of the entire segment are presented in a complete time sequence and remain consistent with the time range of the segment. This provides clear numerical basis and time reference for subsequent segmented translation and reconstruction processing.

[0048] Based on the extracted output decrease magnitude, a segmented translation and reconstruction process is performed on the output fluctuation curve. The output decrease within each mutation-driven segment is extracted point by point from the original time segment and transferred to the independent interference interval in chronological order. During the transfer process, the original time identifier of each transferred time node is retained so that the output decrease in the independent interference interval can still reflect its occurrence time position. After the transfer operation is completed, the time segment corresponding to the mutation-driven segment in the original output fluctuation curve is translated to remove the decreasing part of the original output value, so that the component output values ​​in the time segment are rearranged in the non-decreasing direction, while keeping the time interval between each time node unchanged to maintain time continuity. This completes the reconstruction of the output fluctuation curve within the mutation-driven segment range.

[0049] Around the output fluctuation curve that has been translated and reconstructed, the component output values ​​of the non-mutation segment are preserved, so that each time node in the non-mutation segment continues to be arranged in the original time order and sequentially connected with the time segment that has been translated. During the connection process, the time interval between each time node is kept consistent, so that the entire output fluctuation curve forms a continuous change trajectory in the time dimension.

[0050] By integrating the original trend of the non-mutation segment with the time segment after translation processing, a new power output change curve is obtained. This curve is then output as a stable evolution curve. This stable evolution curve retains the continuous power output change characteristics during photovoltaic power generation while eliminating the influence of the power output decrease of the mutation-driven segment, thus providing a stable data expression basis for subsequent emission intensity analysis.

[0051] For the non-abrupt segments in the stable evolution curve, the emission intensity change gradient corresponding to unit power generation is calculated. The change gradient is then processed by segmented discretization and direction discrimination. Gradient constraint coefficients are constructed based on the magnitude and direction of the change gradient, and the emission reduction scheduling direction is weighted and corrected. By extracting, dividing, and identifying the direction of emission intensity variation gradient corresponding to unit power generation point by point, and constructing constraint coefficients, continuous weighted correction of emission reduction scheduling direction is achieved, ensuring consistency and adjustability of scheduling results in the time dimension. The specific steps are as follows: For the non-abrupt segments in the stable evolution curve, the component output value and the corresponding carbon emission calculation result of each time node are read sequentially along the time. The carbon emission and component output value are recorded at the same time node, so that each time node forms a set of emission intensity data. After reading all time nodes, these emission intensity data are arranged in chronological order to form a continuous trajectory of emission intensity change corresponding to unit power generation.

[0052] Along this trajectory, two adjacent time points are selected sequentially, and the changes in emission intensity between the next time point and the previous time point are recorded. The direction and magnitude of the change are mapped to the current time point, so that each time point has corresponding change gradient data. The change gradient data is then bound to the time point, thereby forming a gradient distribution of emission intensity change per unit of power generation over time.

[0053] After obtaining the gradient distribution, the gradients of all time points are segmented according to their numerical range. The gradients are divided into multiple continuous intervals from the minimum to the maximum value, and a fixed interval number is assigned to each interval so that each gradient data falls within a certain interval. During the division process, the order of the time points is kept unchanged so that the gradient of each time point retains its original numerical information and has a corresponding interval identifier.

[0054] Subsequently, the interval number is appended to the corresponding time node, so that each time node has both the changing gradient value and interval category information in the time dimension, thereby completing the segmented discretization of the changing gradient and forming a segmented expression structure of the continuously changing gradient data in the time dimension.

[0055] Based on the segmented and discretized gradient changes, a direction discrimination process is performed for each time node. By comparing the gradient change value of the current time node with that of the previous time node, it is determined whether the emission intensity corresponding to that time node is in an increasing or decreasing state, and the discrimination result is recorded as a direction identifier. During the discrimination process, the gradient change value and its corresponding interval number are recorded together, so that each time node simultaneously possesses three types of information: gradient change value, interval number, and direction identifier. Further, continuous time nodes are scanned, and adjacent time nodes with the same direction identifier are merged into the same time segment, so that time nodes with the same direction of change form a continuous distribution on the time axis, while retaining the boundary positions between changes in different directions. This forms a gradient change distribution structure containing direction information, providing a clear direction division basis for the subsequent construction of constraint coefficients.

[0056] Based on the gradient value, interval number, and direction identifier of each time node, gradient constraint coefficients are constructed for the corresponding time nodes. During the construction process, gradients in different interval numbers are assigned different weight levels, and the weight levels are adjusted according to the direction identifier, so that gradients in the upward and downward directions have different effects in the constraint coefficients. After the gradient constraint coefficients for all time nodes are constructed, the gradient constraint coefficients are bound to the corresponding time nodes, and all time nodes are arranged in chronological order. The gradient constraint coefficients corresponding to each time node are introduced into the emission reduction scheduling direction processing, so that each time node participates in the weight allocation according to its corresponding gradient constraint coefficient during the scheduling process. This performs weighted correction processing on the emission reduction scheduling direction in the time dimension, resulting in the scheduling direction adjusted based on the gradient change.

[0057] This invention identifies extreme weather-triggered segments and introduces meteorological disturbance intensity distribution trajectories to distinguish between abrupt change-driven segments and performance degradation segments in the power output fluctuation curve. This allows short-term power output fluctuations caused by meteorological factors to be separated from continuous change trends. In the carbon footprint modeling process, it avoids incorporating short-term disturbances into long-term performance changes, thereby making the calculated carbon emission level per unit of power generation more consistent with the actual operating conditions, improving the reliability of carbon emission intensity assessment, and enabling carbon emission results to reflect the true characteristics of power generation process changes.

[0058] This invention achieves a correspondence between the carbon factor values ​​in the mutation-driven segment and the output fluctuation curve by performing dynamic compression mapping and piecewise translation reconstruction. Simultaneously, it introduces a changing gradient constraint coefficient in the non-mutation segment to weightedly correct the emission reduction scheduling direction, enabling the scheduling process to be adjusted according to the stable evolution trend. This ensures that the emission reduction strategy is consistent with the actual power generation state, improves the rationality of carbon management decisions, and reduces the impact of deviations introduced by abnormal fluctuations.

[0059] This invention provides, for example Figure 2 The photovoltaic power generation carbon footprint modeling system shown includes a power collapse identification module, a meteorological disturbance analysis module, a carbon factor correction module, a curve reconstruction module, and a scheduling constraint module. The output collapse identification module obtains the component output value, irradiance change amplitude, and carbon factor value corresponding to the supply batch in the photovoltaic power generation scenario, constructs an output fluctuation curve that progresses over time, quantifies the difference between adjacent moments in the output fluctuation curve, obtains the instantaneous collapse amplitude threshold range, and marks extreme weather trigger segments based on the instantaneous collapse amplitude threshold range. The meteorological disturbance analysis module synchronously maps the irradiance change amplitude within the extreme weather trigger segment to obtain the meteorological disturbance intensity distribution trajectory. It then performs segment rearrangement processing on the output fluctuation curve according to the meteorological disturbance intensity distribution trajectory, and outputs the distinction results between the sudden change driving segment and the performance degradation segment. The carbon factor correction module uses the mutation-driven fragment in the differentiation results to perform dynamic compression mapping on the carbon factor value corresponding to the supply batch, and adjusts the carbon factor contribution ratio according to the duration of the mutation-driven fragment to obtain the carbon emission inclusion interval after interference removal. The curve reconstruction module performs segmented translation reconstruction on the power output fluctuation curve based on the carbon emission inclusion interval after interference removal. It transfers the power output decrease corresponding to the mutation-driven segment to the independent interference interval, while maintaining the original change trend of the non-mutation segment, thus obtaining a stable evolution curve. The scheduling constraint module calculates the emission intensity change gradient corresponding to a unit of power generation for non-abrupt segments in the stable evolution curve, performs piecewise discretization and direction discrimination processing on the change gradient, and constructs gradient constraint coefficients based on the magnitude and direction of the change gradient to perform weighted correction processing on the emission reduction scheduling direction.

[0060] The photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis provided in this embodiment of the invention is implemented through the photovoltaic power generation carbon footprint modeling system integrating supply chain and energy flow analysis described above. For details of the specific methods and processes of the photovoltaic power generation carbon footprint modeling system integrating supply chain and energy flow analysis, please refer to the embodiments of the photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis described above, which will not be repeated here.

[0061] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis, characterized in that, Includes the following steps: The system obtains the component output value, irradiance variation amplitude, and carbon factor value corresponding to the supply batch in the photovoltaic power generation scenario, constructs the output fluctuation curve over time, quantifies the difference between adjacent moments in the output fluctuation curve, obtains the instantaneous collapse amplitude threshold range, and marks extreme weather triggering segments based on the instantaneous collapse amplitude threshold range. Synchronous mapping of irradiance variation amplitude within extreme weather trigger segments is performed to obtain meteorological disturbance intensity distribution trajectory. Then, the output fluctuation curve is re-arranged according to the meteorological disturbance intensity distribution trajectory, and the results of distinguishing between sudden change-driven segments and performance degradation segments are output. Using the mutation-driven fragments in the differentiation results, dynamic compression mapping is performed on the carbon factor values ​​corresponding to the supply batches. The contribution ratio of the carbon factor is reduced and adjusted according to the duration of the mutation-driven fragments to obtain the carbon emission inclusion interval after interference removal. Based on the carbon emission inclusion interval after interference removal, the output fluctuation curve is reconstructed by segmented translation, and the output decrease corresponding to the mutation-driven segment is transferred to the independent interference interval, while maintaining the original change trend of the non-mutation segment, so as to obtain a stable evolution curve. For the non-abrupt segments in the stable evolution curve, the emission intensity change gradient corresponding to unit power generation is calculated. The change gradient is then processed by segmented discretization and direction discrimination. Gradient constraint coefficients are constructed based on the magnitude and direction of the change gradient, and the emission reduction scheduling direction is weighted and corrected. By coordinating the processing of component output values, irradiance variation amplitudes, and corresponding carbon factor values ​​for each supply batch, and through time alignment, difference characterization, and interval extraction, the identification and labeling of extreme weather trigger segments are completed, resulting in output fluctuation curves. The steps are as follows: The component output values ​​are continuously recorded at preset time intervals, and the irradiance change amplitude at corresponding time nodes is recorded synchronously. The carbon factor values ​​corresponding to the supply batch are mapped to the corresponding time nodes, and the component output values ​​are continuously connected to obtain the output fluctuation curve. Record the changes in component output values ​​at adjacent time points along the time direction of the output fluctuation curve, divide the changes into multiple amplitude ranges, and bind them to the irradiance change amplitude at the corresponding time points. By combining the range of changes in the amplitude range, the continuous output decrease time period is selected, the corresponding irradiance change amplitude is compared and processed, and the difference range is extracted to obtain the instantaneous collapse amplitude threshold range. By comparing each time node in the power fluctuation curve with the instantaneous collapse amplitude threshold range, the candidate trigger points are continuously combined and the time segments are screened to obtain the extreme weather trigger segments and add marking information. The amplitude of irradiance change and the output fluctuation curve within the extreme weather-triggered segment are processed point-by-point to achieve correspondence. Through synchronous mapping and segment arrangement, the difference between the sudden change-driven segment and the performance degradation segment is realized. The steps are as follows: The irradiance variation amplitude is extracted along the time nodes of the extreme weather trigger segment and arranged in the time order of the output fluctuation curve. The component output value and irradiance variation amplitude at the corresponding time nodes are recorded synchronously. The irradiance variation amplitude is mapped according to the time node, and the irradiance variation amplitude is converted into a meteorological disturbance intensity characterization value, which is then bound to the corresponding time node to obtain the meteorological disturbance intensity distribution trajectory. By scanning time nodes one by one according to the distribution trajectory of meteorological disturbance intensity, continuous time nodes with the same direction of change of meteorological disturbance intensity are divided into time segments, and the direction of change of meteorological disturbance intensity is compared with the direction of change of component output value to obtain candidate segments for sudden change drive and candidate segments for performance degradation. Based on the segment division results, the output fluctuation curve is rearranged into segments, the candidate segments driven by mutations are arranged in a group, and the candidate segments of performance degradation are arranged separately, so as to obtain the distinction between mutation-driven segments and performance degradation segments. The carbon factor values ​​corresponding to the mutation-driven fragments and supply batches are time-correlated. Carbon emission contributions are redistributed through dynamic compression mapping and duration adjustment to obtain the carbon emission inclusion interval after interference removal. The steps are as follows: Determine the start and end time nodes around the mutation-driven fragment, extract the carbon factor values ​​corresponding to each time node in chronological order and record the time position order, and group and record the carbon factor values ​​of the same supply batch. The carbon factor values ​​are dynamically compressed and mapped according to the time sequence, keeping the original value at the beginning, gradually decreasing the value at the middle, and further decreasing the value at the end, while maintaining the time sequence. The duration is calculated by combining the start and end time nodes, and a correspondence is established between the duration and the carbon factor value. The carbon factor contribution ratio is reduced and adjusted for different durations. Based on the compression mapping processing and reduction adjustment results, the carbon factor values ​​are remapped to the corresponding time nodes and arranged in a unified manner according to time order to obtain the carbon emission inclusion interval after interference removal.

2. The photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis according to claim 1, characterized in that, The intensity characterization value of meteorological disturbance is obtained by classifying the irradiance change amplitude. Different irradiance change amplitudes correspond to different levels of meteorological disturbance intensity characterization values. The meteorological disturbance intensity distribution trajectory is obtained by connecting them sequentially along the time nodes. The time segment in which the direction of meteorological disturbance intensity change is consistent with the direction of component output value change is divided into abrupt change-driven candidate segment.

3. The photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis according to claim 1, characterized in that, The carbon factor values ​​at consecutive time points within the same mutation-driven fragment are progressively reduced according to their temporal position, and the trend of carbon factor value changes at consecutive time points is kept consistent. At the same time, the carbon factor values ​​in different mutation-driven fragments are subjected to graded reduction adjustment according to the corresponding duration.

4. The photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis according to claim 1, characterized in that, The carbon emission inclusion interval after interference removal is correlated with the power output fluctuation curve. The stable evolution curve is constructed by transferring the power output decrease amplitude and segmenting the shift. The steps are as follows: By matching the time nodes of the power output fluctuation curve with the carbon emission inclusion interval after interference removal, the component power output value is bound to the carbon emission inclusion status, and continuous time nodes are grouped into the time segment of the mutation-driven segment and the time segment of the non-mutation segment. The output value changes of components at adjacent time nodes are extracted around the time segment driven by the mutation. The output decrease is arranged in chronological order and corresponding to the time node position to obtain the output decrease amplitude distribution. The output fluctuation curve is reconstructed by segmenting and shifting based on the output decrease distribution. The output decrease is stripped off point by point and transferred to an independent interference interval. At the same time, the output values ​​of the components in the original time segment are shifted and adjusted while maintaining the time sequence. Based on the translation reconstruction results, the output values ​​of non-mutation fragment components are kept in their original time order and connected with the translation processing time segment to obtain a stable evolution curve.

5. The photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis according to claim 4, characterized in that, In the segmented translation and reconstruction process, the original time marker is retained during the transfer of output decrease, and the output values ​​of the components in the original time segment are arranged in a non-decreasing direction. At the same time, the time segments of the mutation-driven segment and the time segments of the non-mutation segment are sequentially connected.

6. The photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis according to claim 4, characterized in that, The emission intensity change gradient is extracted and constrained for non-abrupt segments in the stable evolution curve. Weighted correction of emission reduction scheduling direction is then achieved through piecewise discretization and direction discrimination. The steps are as follows: For each time node of the non-abrupt segment of the stable evolution curve, the component output value and carbon emission calculation results are read one by one. The carbon emission is recorded in correspondence with the component output value, and the changes of adjacent time nodes are extracted to obtain the change gradient data and bind it to the time node. Based on the gradient change data, all time points are divided into intervals, and the gradient change is divided into multiple intervals with an additional interval number, so that each time point has both gradient change value and interval category information. The direction of time nodes is determined by combining the change gradient value and the interval number. The change gradient relationship is recorded as a direction identifier, and consecutive time nodes with the same direction identifier are merged. Gradient constraint coefficients are constructed based on the changing gradient values, interval numbers, and direction identifiers. These coefficients are then bound to time nodes, and the gradient constraint coefficients corresponding to each time node are incorporated into the emission reduction scheduling direction processing to obtain a weighted correction result.

7. A photovoltaic power generation carbon footprint modeling system integrating supply chain and energy flow analysis, used to implement the photovoltaic power generation carbon footprint modeling method integrating supply chain and energy flow analysis as described in any one of claims 1-6, characterized in that, It includes a power collapse identification module, a meteorological disturbance analysis module, a carbon factor correction module, a curve reconstruction module, and a scheduling constraint module: The output collapse identification module obtains the component output value, irradiance change amplitude, and carbon factor value corresponding to the supply batch in the photovoltaic power generation scenario, constructs an output fluctuation curve that progresses over time, quantifies the difference between adjacent moments in the output fluctuation curve, obtains the instantaneous collapse amplitude threshold range, and marks extreme weather trigger segments based on the instantaneous collapse amplitude threshold range. The meteorological disturbance analysis module synchronously maps the irradiance change amplitude within the extreme weather trigger segment to obtain the meteorological disturbance intensity distribution trajectory. It then performs segment rearrangement processing on the output fluctuation curve according to the meteorological disturbance intensity distribution trajectory, and outputs the distinction results between the sudden change driving segment and the performance degradation segment. The carbon factor correction module uses the mutation-driven fragment in the differentiation results to perform dynamic compression mapping on the carbon factor value corresponding to the supply batch, and adjusts the carbon factor contribution ratio according to the duration of the mutation-driven fragment to obtain the carbon emission inclusion interval after interference removal. The curve reconstruction module performs segmented translation reconstruction on the power output fluctuation curve based on the carbon emission inclusion interval after interference removal. It transfers the power output decrease corresponding to the mutation-driven segment to the independent interference interval, while maintaining the original change trend of the non-mutation segment, thus obtaining a stable evolution curve. The scheduling constraint module calculates the emission intensity change gradient corresponding to a unit of power generation for non-abrupt segments in the stable evolution curve, performs piecewise discretization and direction discrimination processing on the change gradient, and constructs gradient constraint coefficients based on the magnitude and direction of the change gradient to perform weighted correction processing on the emission reduction scheduling direction.

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