A distributed photovoltaic power generation efficiency evaluation method

Abstract

This invention discloses a method for evaluating the efficiency of distributed photovoltaic (PV) power generation, relating to the field of PV power generation technology. The method includes dividing the evaluation area into resource sub-regions based on regional representative points, and aggregating the business scenario elements of each PV power station to obtain a set of scenario fingerprint cards. At the end of each settlement cycle, daily deviation logs are used in conjunction with the corresponding scenario fingerprint cards and scenario performance track boards to convert the operational records of each power station within the corresponding settlement cycle into multi-dimensional efficiency indicators, which are then broken down according to responsibility roles to form efficiency evaluation results. This invention uses multi-dimensional efficiency indicators to calculate and compare resource utilization efficiency, equipment health efficiency, revenue realization efficiency, and carbon emission reduction achievement efficiency under the same scenario coordinates. This enables the location, quantification, and accountability of operational problems in distributed PV clusters, significantly improving refined operation and maintenance management and comprehensive performance analysis capabilities.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a method for evaluating the efficiency of distributed photovoltaic power generation. Background Technology

[0002] Distributed photovoltaic (PV) power generation, as an important means of increasing the proportion of renewable energy consumption, has been rapidly deployed in industrial parks, public buildings, and residential rooftops in recent years. Current operation and maintenance management typically relies on time-series records of power, irradiance, and grid connection status collected by metering and monitoring devices, combined with statistical indicators such as capacity utilization hours, equivalent utilization hours, and performance ratio, to evaluate the annual or monthly operating level of the power station. Meanwhile, some power companies have begun to utilize geographic information and historical meteorological data to manage PV resource endowments by region, and in their operational analysis, they focus on the correlation between power generation revenue performance and electricity price structure and grid connection conditions, providing data support for investment decisions and operation and maintenance scheduling.

[0003] However, existing distributed photovoltaic (PV) assessments primarily rely on overall power plant statistics, making it difficult to characterize the discrepancies between expected trajectories and actual performance under different resource sub-regions and business scenarios within a unified spatiotemporal benchmark. Furthermore, they lack evaluation mechanisms that refine operational deviations to responsibility dimensions such as resource conditions, equipment status, grid regulation and control, and electricity price contract execution. During the settlement period, operational records are often disconnected from specific irradiance scenarios, electricity price periods, and carbon emission factors. This makes it difficult to quantitatively compare and finely attribute resource utilization efficiency, equipment health efficiency, revenue realization efficiency, and carbon emission reduction achievement efficiency within the same contextual coordinate system, hindering the provision of traceable evaluation criteria for the refined management of distributed PV clusters. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for evaluating the efficiency of distributed photovoltaic power generation, which solves the problem of difficulty in uniformly quantifying and finely attributing operational deviations of distributed photovoltaics under multiple scenarios to the responsible party.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for evaluating the efficiency of distributed photovoltaic power generation, comprising:

[0008] Based on regional representative points, the evaluation area is divided into resource sub-regions, and the business scenario elements of each photovoltaic power station are collected to obtain a set of scenario fingerprint cards;

[0009] Based on the context fingerprint card set, according to the typical daily resource pattern of the representative points in the region to which each power station belongs, the settlement cycle is subdivided into multiple intraday context segments. A corresponding context performance trajectory board is generated for each intraday context segment and integrated to obtain a context performance trajectory board set.

[0010] In actual operation, the operation records of each power station are mapped to the corresponding time grid of the scenario performance trajectory board according to a unified time granularity. The scenario expected trajectory in the scenario performance trajectory board is compared with the mapped operation records to form deviation items. All deviation items are summarized into the daily deviation log of the corresponding power station.

[0011] After each settlement cycle ends, the daily deviation logs are combined with the corresponding scenario fingerprint cards and scenario performance trajectory boards to convert the operational business records of each power station in the corresponding settlement cycle into multi-dimensional efficiency indicators, which are then broken down according to the responsibility roles to form efficiency evaluation results.

[0012] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the specific steps for dividing the evaluation area into resource sub-regions based on regional representative points are as follows:

[0013] The boundary range and historical irradiance distribution of the evaluation area are obtained from geographic information. Within the boundary range, several regional representative points are selected according to the spatial differences in historical irradiance distribution, and a coverage range is set for each regional representative point to form an initial resource coverage area.

[0014] In the initial resource coverage area, boundary adjustment is performed on areas with overlapping boundaries and obvious resource differences, and areas with the same resource characteristics are merged. Areas with obvious resource differences but connected boundaries are split, resulting in resource sub-areas with relatively consistent resource characteristics and complete coverage.

[0015] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the business context elements include resource location, investment entity information, operation and maintenance entity information, grid connection point, electricity price structure, carbon emission factor and data quality rules.

[0016] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the specific steps for aggregating the business context elements of each photovoltaic power station to obtain a context fingerprint card set are as follows.

[0017] The business context elements collected for each photovoltaic power station are grouped and organized, and a hierarchical field set is established according to the resource layer, business layer, carbon asset layer and data quality layer to form a hierarchical business context record for a single station.

[0018] In the hierarchical business scenario record, a unique identifier is assigned to each photovoltaic power station, and the four hierarchical field sets are associated to generate the scenario fingerprint entry for the corresponding power station.

[0019] All context fingerprint entries for photovoltaic power plants are indexed and aggregated according to resource sub-regions, regional representative points, and operation and maintenance responsible entities to form a set of context fingerprint cards.

[0020] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the settlement period is subdivided into multiple intraday scenario segments according to the typical daily resource patterns of representative points in the region to which each power station belongs. The specific steps are as follows.

[0021] Read the typical daily sunshine change patterns corresponding to representative points in each region from the context fingerprint card set, and divide the time axis by natural days within the settlement period. Establish a time period boundary that matches the typical daily sunshine change pattern for each natural day to form an intraday context boundary.

[0022] The resource location information of each photovoltaic power station is matched with the intraday scenario boundary of the corresponding regional representative point. The time periods of different resource states are marked on the settlement cycle time axis, and the continuous time period of each natural day is defined as an intraday scenario segment.

[0023] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the step of generating corresponding scenario performance trajectory boards for each day's scenario segments and integrating them to obtain a scenario performance trajectory board set is as follows.

[0024] Establish time coordinates for time cells within each intraday scenario segment, and read resource location information, electricity price structure, carbon emission factor, and data quality rules from the corresponding scenario fingerprint card;

[0025] Align resource location information with typical daily resource patterns of representative regional points to generate expected power generation curves;

[0026] The electricity price structure is superimposed with the power generation expectation curve to generate the revenue expectation curve;

[0027] The carbon emission factor is superimposed on the power generation expectation curve to generate the carbon emission reduction expectation curve.

[0028] Based on data quality rules, available status, questionable status, and missing status are marked in each time cell to form a scenario performance trajectory board. Scenario performance trajectory boards belonging to the same photovoltaic power station are then integrated in chronological order to form a scenario performance trajectory board set.

[0029] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the specific steps for mapping the operation records of each power station to the corresponding time grid of the scenario performance trajectory board at a uniform time granularity are as follows.

[0030] During the operation of photovoltaic power generation, the operation records of each photovoltaic power station are obtained at a uniform time granularity;

[0031] Each operational record is registered in the corresponding context performance trajectory board time cell based on the power station identifier and timestamp, and then linked together in chronological order to form an operational mapping record set.

[0032] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the specific steps for comparing the expected trajectory of the scenario performance trajectory board with the mapped operational business records are as follows:

[0033] Read the corresponding expected values ​​in the power generation expectation curve, revenue expectation curve and carbon emission reduction expectation curve in each time cell of each scenario performance trajectory board, and compare them with the operation business records in the operation mapping record set item by item to generate deviation entries including deviation direction and deviation magnitude.

[0034] Based on the time grid location associated with the deviation entry and the index of the context fingerprint entry to label the deviation source category, deviation entries belonging to the same photovoltaic power station and occurring within the same natural day are summarized in chronological order to construct a daily deviation log.

[0035] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the multidimensional efficiency indicators include resource utilization efficiency, equipment health efficiency, revenue realization efficiency, and carbon emission reduction achievement efficiency.

[0036] As a preferred embodiment of the distributed photovoltaic power generation efficiency evaluation method of the present invention, the specific steps for forming the efficiency evaluation result are as follows:

[0037] Based on the deviation direction, deviation magnitude and deviation source category recorded in the daily deviation log, and combined with the power generation expectation curve, revenue expectation curve and carbon emission reduction expectation curve in the scenario performance trajectory board, all deviation items obtained by comparing the operation mapping record set and the scenario expectation trajectory of the same photovoltaic power station within the settlement period are classified and summarized to obtain multi-dimensional efficiency indicators.

[0038] Based on the categories of deviation sources and the information on investment entities, operation and maintenance entities, and grid connection points recorded in the contextual fingerprint card, the deviation quantities in the multidimensional efficiency indicators are broken down according to the investment entity, operation and maintenance entity, and grid connection point to obtain the efficiency evaluation results.

[0039] The beneficial effects of this invention are as follows: By constructing a set of contextual fingerprint cards, the resource location, investment entity information, operation and maintenance entity information, grid connection point, electricity price structure, carbon emission factor, and data quality rules of photovoltaic power plants are uniformly encoded into hierarchical business context records, enabling the operation contexts of multiple sites within the evaluation area to have comparable standardized descriptions; by using typical daily resource patterns to divide intraday context segments within the settlement cycle, and generating a contextual performance trajectory board for each intraday context segment, each time cell simultaneously carries the expected power generation curve, expected revenue curve, expected carbon emission reduction curve, and data quality status marker, providing an accurate spatiotemporal contextual reference for deviation calculation; by mapping the operation business records to the time cells of the contextual performance trajectory board at a unified time granularity and forming a daily deviation log, multidimensional efficiency indicators can be converted and compared under the same contextual coordinates for resource utilization efficiency, equipment health efficiency, revenue realization efficiency, and carbon emission reduction achievement efficiency. Combined with the investment entity information, operation and maintenance entity information, and grid connection point information in the contextual fingerprint cards, the deviation amount is split according to the responsibility role, thereby realizing the location, quantification, and accountability of distributed photovoltaic cluster operation problems, significantly improving refined operation and maintenance management and comprehensive performance analysis capabilities. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart of a method for evaluating the efficiency of distributed photovoltaic power generation.

[0042] Figure 2 Flowchart for dividing resource sub-regions.

[0043] Figure 3 To create a flowchart for the daily deviation log.

[0044] Figure 4 To generate a flowchart of efficiency evaluation results. Detailed Implementation

[0045] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0046] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0047] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0048] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for evaluating the efficiency of distributed photovoltaic power generation, comprising the following steps:

[0049] S1. Based on regional representative points, the evaluation area is divided into resource sub-regions, and the business scenario elements of each photovoltaic power station are collected to obtain a set of scenario fingerprint cards.

[0050] S1.1. In the power company's operation and management platform, the boundary range of the evaluation area is obtained based on geographic information (the physical range determined by the spatial distribution of photovoltaic power stations and related power grid access points; first, the coordinates of all photovoltaic power stations and substation access points participating in the evaluation are listed, and the smallest outer polygon enclosing all these points is found in the two-dimensional coordinate system). Historical irradiance distribution records are retrieved within the evaluation area, and the historical irradiance distribution records are statistically analyzed according to the spatial grid. In the statistical results, spatially stable measurement points with representative irradiance levels are selected as the candidate set of regional representative points according to the spatial differences in irradiance distribution. An initial coverage radius (e.g., 5 kilometers) is set for each regional representative point candidate to form the initial resource coverage area covering the evaluation area.

[0051] It should be noted that a representative measuring point refers to a grid whose annual average daily total irradiance in multi-year irradiance records is close to the average of several surrounding grids, for example, with a deviation of no more than 10% (i.e., the deviation of the annual average daily total irradiance ≤ 10%), and whose irradiance curve shape is relatively stable in different years and the same season, with an interannual fluctuation coefficient of no more than 5% and small interannual fluctuations. The irradiance statistical characteristics of the grid where the measuring point is located (e.g., seasonal highs and lows, and the proportion of sunny and cloudy days) are similar to those of most grids in the resource sub-region.

[0052] The boundaries of resource coverage areas with significant resource differences and overlapping boundaries in the initial resource coverage area are recalculated so that only the side with the irradiance characteristics closest to the representative point of the region is retained in the overlapping boundary area. At the same time, resource coverage areas with the same resource characteristics and spatial adjacency are merged, and resource coverage areas with significant resource differences but connected boundaries are split to obtain resource sub-regions with relatively consistent resource characteristics and complete coverage. The corresponding regional representative point is retained in each resource sub-region.

[0053] It should be noted that boundary recalculation refers to checking each grid in the overlapping area of ​​the boundaries of two initial resource coverage areas, calculating the difference between the historical irradiance statistics of the current grid and the historical irradiance statistics of the two regional representative points, and assigning it to the resource coverage area corresponding to the regional representative point with the smaller difference; after all overlapping grids are assigned, the resource coverage area boundary is re-recorded according to the new grid affiliation.

[0054] Splitting refers to clustering or grouping the historical irradiance statistics of all grids within a resource coverage area, finding regions where the mean difference exceeds a preset difference threshold, and cutting them into two or more resource sub-regions.

[0055] The preset difference threshold is a boundary value that can clearly distinguish between high-irradiance blocks and low-irradiance blocks, selected based on the distribution of differences in the average irradiance between grids after statistical analysis of the historical irradiance statistics of each grid in the resource coverage area. For example, it is set to about 15% of the average daily total irradiance of the coverage area over many years.

[0056] S1.2. Read the geographical coordinates of each photovoltaic power station. Based on the regional representative points in the resource sub-region, calculate the spatial distance between each photovoltaic power station and each regional representative point. Determine the nearest regional representative point for each photovoltaic power station based on the spatial distance. The expression for calculating the spatial distance is: ;

[0057] in, Indicates the first The photovoltaic power station and the first The spatial distance between representative points in each region is used to determine which representative point a photovoltaic power station is closest to. and They represent the first The longitude and latitude coordinates of each photovoltaic power station in a unified coordinate system are used to describe the location of the photovoltaic power station within the evaluation area; and They represent the first The longitude and latitude coordinates of each regional representative point in a unified coordinate system are used to describe the location of the regional representative point within the evaluation area.

[0058] After calculating the spatial distance for all regional representative points, the regional representative point with the smallest spatial distance among all photovoltaic power stations is selected as the nearest regional representative point, and the photovoltaic power station is assigned to the resource sub-region where the nearest regional representative point is located, forming a resource matching result that includes the relationship between the resource location of the photovoltaic power station and the regional representative point.

[0059] It should be noted that if a photovoltaic power station is located at the boundary of two resource sub-regions and is close to the representative points of the two regions, and the difference in irradiance characteristics between the two representative points is small, simply classifying by distance may lead to a mismatch between the power station's affiliation and its actual irradiance characteristics. In this case, classification should be based primarily on the similarity of irradiance characteristics. Specifically, if the difference in irradiance characteristics between the two representative points is small (e.g., the annual average daily total irradiance and seasonal fluctuations are similar), classification can be based on spatial distance. If the difference in irradiance characteristics between the two representative points is large (e.g., the annual average daily total irradiance deviation exceeds 10% or the interannual fluctuation coefficients differ significantly), then a representative point in the region that matches the irradiance characteristics of the photovoltaic power station should be selected first to ensure that the power station's irradiance characteristics are consistent with the irradiance characteristics of its resource sub-region.

[0060] S1.3. Collect business context elements related to the current operation and management of each photovoltaic power station. Business context elements include resource location, investment entity information, operation and maintenance entity information, grid connection point, electricity price structure, carbon emission factor and data quality rules.

[0061] Among them, the resource location is used to record the resource sub-area identifier and the corresponding regional representative point identifier of the photovoltaic power station; the investment entity information is used to record the name of the investor of the photovoltaic power station and the corresponding identification code; the operation and maintenance entity information is used to record the name of the daily maintenance entity of the photovoltaic power station and the corresponding identification code; the grid access point is used to record the line number, voltage level and access capacity of the photovoltaic power station connected to the grid; the electricity price structure is used to record the time-of-use electricity price, peak and valley time division, self-consumption and grid connection ratio rules and revenue guarantee arrangements corresponding to the photovoltaic power station; the carbon emission factor is used to record the carbon emission reduction conversion factor (emission reduction corresponding to unit grid connection electricity) corresponding to the unit grid connection electricity in the region where the photovoltaic power station is located; and the data quality rules are used to record the frequency of checks and missing marking methods for power, meteorology and metering in terms of integrity, consistency and time alignment.

[0062] After the business context elements are collected, all business context elements corresponding to each photovoltaic power station are grouped and organized according to the photovoltaic power station identifier to form a business context element group set for a single station. In the business context element group set, a hierarchical field set is established according to the resource layer, business layer, carbon asset layer and data quality layer.

[0063] Among them, the resource layer field set carries information on resource location and corresponding regional representative points; the business layer field set carries information on investment entities, operation and maintenance entities, grid access points and electricity price structures; the carbon asset layer field set carries carbon emission factors; and the data quality layer field set carries data quality rules. Together, these four layered field sets constitute a layered business scenario record for a single station.

[0064] S1.4. Assign a unique identifier to each photovoltaic power station in the hierarchical business scenario record, and associate the resource layer field set, business layer field set, carbon asset layer field set and data quality layer field set under the unique identifier to generate a scenario fingerprint entry for the corresponding photovoltaic power station. The scenario fingerprint entry shall record at least the unique identifier of the photovoltaic power station, the identifier of the resource sub-region to which it belongs, the identifier of the representative point of the region to which it belongs, the investment entity information, the operation and maintenance entity information, the grid access point, the electricity price structure, the carbon emission factor, and the data quality mark fields corresponding to power, meteorology and metering.

[0065] It should be noted that, to ensure seamless integration between historical and current data, the unique identifier rules must remain consistent. The data management entity is responsible for maintaining the unique identifiers and identifier mapping tables for each photovoltaic power station. When the unique identifier rules change, the data management entity follows the change procedure, adding records to the identifier mapping table for the old identifier, new identifier, effective date, and reason for the change. The updated identifier mapping table is then synchronized to the historical data storage medium and online computing environment. This is achieved through batch correction or adding new mapping fields to update the correspondence between the old and new identifiers in the historical data, ensuring smooth integration between the old and new data. The unique identifier for each photovoltaic power station should be unified across the entire system and used as a key in cross-cycle data loading, deviation tracing, and efficiency evaluation calculations to facilitate cross-cycle data association and matching.

[0066] All scenario fingerprint entries are indexed and aggregated according to resource sub-regions, regional representative points, and operation and maintenance entity information to form a scenario fingerprint card set covering all photovoltaic power stations in the evaluation area.

[0067] S2. Based on the context fingerprint card set, according to the typical daily resource pattern of the representative points in the region to which each power station belongs, the settlement cycle is subdivided into multiple intraday context segments. A corresponding context performance trajectory board is generated for each intraday context segment and integrated to obtain a context performance trajectory board set.

[0068] S2.1. Read the regional representative point identifiers recorded in the context fingerprint card set, and select multiple complete natural days from the historical irradiance distribution records for each regional representative point (the full-day irradiance data of the selected natural days must not be missing, and the irradiance data of each time period should be aligned with the standard time). Calculate the total daily irradiance and the average total daily irradiance of the candidate natural days in the same season on a daily basis. On this basis, remove natural days whose total daily irradiance deviates from the average total daily irradiance of the same season by more than a preset proportion threshold. For example, if the preset proportion threshold is set to 30%, natural days whose total daily irradiance deviates from the average by more than ±30% are considered natural days with extreme abnormal weather and are excluded. After removing natural days with extreme abnormal weather, calculate the average irradiance value for each time point on the remaining natural days' irradiance curves according to time alignment, forming a typical daily irradiance variation pattern describing the change of full-day irradiance over time, and use the typical daily irradiance variation pattern as the typical daily resource pattern for the corresponding regional representative point.

[0069] To further explain, when selecting natural days, if there is a settlement cycle that spans seasons (e.g., including winter and spring), it is necessary to calculate the typical daily sunshine variation pattern for each season separately, and then calculate the average irradiance value at each time point after aligning the typical sunshine curves for each season, thus forming typical daily resource patterns for different seasons.

[0070] The settlement period is divided into calendar days on the time axis. Each calendar day on the time axis is associated with a typical daily resource pattern of the representative point in the region. The time period boundary is determined according to the inflection point of irradiance change in the typical daily resource pattern. The intervals of rapid irradiance change and relatively stable intervals are distinguished on the time axis, forming an intraday scenario boundary that covers all calendar days of the settlement period.

[0071] S2.2. Match the resource location of each photovoltaic power station in the context fingerprint card set with the regional representative point identifier in the corresponding resource sub-region, so that each photovoltaic power station corresponds to a unique regional representative point and a set of intra-day context boundaries for each natural day in the settlement period; on the time axis of the settlement period, divide the natural day for each photovoltaic power station according to the intra-day context boundaries, mark the continuous time period between adjacent boundaries in each natural day as an intra-day context segment, and number the intra-day context segments in chronological order to form intra-day context segments.

[0072] S2.3. Select a time interval consistent with the frequency of operation business record collection for the intraday scenario segment of each photovoltaic power station, divide each intraday scenario segment into several time grids (e.g., 12), and establish a time coordinate for each time grid; in each time grid, read the resource location information, electricity price structure, carbon emission factor and data quality rules from the corresponding scenario fingerprint card, align the typical daily resource pattern of the regional representative point pointed to by the resource location information to the time grid time coordinate on the time axis, record the expected changes in irradiance in the order of time grids, and form a power generation expectation curve corresponding to each time grid.

[0073] It should be noted that the number of time cells should be determined based on the specific operational record collection frequency and the time span of intraday scenario segments. The specific determination logic is as follows:

[0074] The number of time slots is determined based on the collection frequency of the photovoltaic power plant's operation records (e.g., every hour, every 15 minutes, or every 5 minutes). If data is collected once per hour, each intraday scenario segment is typically divided into 24 time slots (representing 24 hours in a day); if the collection frequency is every 15 minutes, each intraday scenario segment is divided into 96 time slots (representing 24 hours in a day, with 4 time slots per hour).

[0075] If the scenario segment within the settlement period includes multiple seasons or different time periods (such as day and night), the number of time frames can be flexibly adjusted according to the characteristics of light changes in different time periods. For example, for daytime periods with drastic light changes, the number of time frames can be appropriately increased (e.g., set to 15 or 30 minutes) to more accurately capture light changes; for nighttime periods with relatively stable light changes, fewer time frames can be set (e.g., one time frame per hour).

[0076] By combining the time-of-use pricing and the ratio of self-consumption to grid connection in the electricity price structure with the time coordinates of each time slot and the expected power generation curve, a revenue expectation curve describing the expected settlement revenue for each time slot is obtained.

[0077] When carbon emission factors are combined with the expected power generation curve on a time-by-time basis and converted into an expected carbon emission reduction sequence, the expected carbon emission reduction value is the product of the expected power generation value and the carbon emission factor, forming the expected carbon emission reduction curve. The completeness, consistency and time alignment of the power, meteorology and metering corresponding to the time grid are checked according to the data quality rules. Based on the check results, the available state, suspicious state and missing state are marked in the time grid respectively.

[0078] Meanwhile, considering the potential changes in the grid baseline emission factor, if a dynamic adjustment mechanism exists for the grid baseline emission factor, the value of the grid emission factor should be updated regularly and applied to carbon emission reduction calculations. Specifically, the carbon emission factor should be adjusted synchronously with the grid baseline emission factor. At least on an annual cycle, the latest version of the grid baseline emission factor value should be loaded before or at the beginning of each calendar year. When a new baseline emission factor is released within the year, the carbon emission factor should be updated midway within a preset update time window (e.g., within one month from the date of release). When the relative deviation between the old and new grid baseline emission factors is detected to exceed a preset deviation threshold (e.g., 5%), the carbon emission reduction expectation curve for subsequent settlement cycles should be recalculated to reflect changes in the grid power generation structure and ensure that the carbon emission reduction expectation curve accurately reflects the latest grid emission levels.

[0079] It should be noted that if the data quality is marked as "questionable" or "missing," corresponding correction and supplementation rules must be implemented:

[0080] Suspicious data correction: Data marked as "suspicious" needs to be corrected using interpolation methods (such as weighted average interpolation) in adjacent time cells or historical data. Specifically, linear interpolation methods can be used to fill in missing power, meteorological, or metrological data, or weighted average correction can be performed based on seasonal patterns. The corrected data needs to be verified a second time to ensure its reasonableness.

[0081] Missing data imputation: Data marked as "missing" can be imputed in the following ways:

[0082] Use historical averages (e.g., the average of the past month or quarter) to fill in the missing data.

[0083] If the missing data involves carbon emission reduction factors, it can be estimated using the carbon emission factors of similar power plants in the region, or supplemented by the annual average carbon emission factor.

[0084] During cross-seasonal settlement cycles, missing data can be filled in based on seasonal trends and irradiance patterns.

[0085] A scenario performance trajectory board for a single intraday scenario segment is composed of time coordinates, expected power generation curves, expected revenue curves, expected carbon emission reduction curves, and data quality status markers. All scenario performance trajectory boards belonging to the same photovoltaic power station are integrated in chronological order within the settlement period to form a set of scenario performance trajectory boards covering the entire settlement period.

[0086] It should be noted that the generation of each intraday scenario segment in the Scenario Performance Trackboard needs to ensure that it is associated with the unique identifier of the photovoltaic power station, so as to accurately record the irradiance characteristics and power generation expectations of each photovoltaic power station during the settlement period. This ensures that the resource location of the photovoltaic power station can be closely linked to its corresponding regional representative point identifier, thereby ensuring the consistency and accuracy of the data.

[0087] S3. In actual operation, the operation records of each power station are mapped to the corresponding time grid of the scenario performance trajectory board according to a unified time granularity. The scenario expected trajectory in the scenario performance trajectory board is compared with the mapped operation records to form deviation items. All deviation items are summarized into the daily deviation log of the corresponding power station.

[0088] S3.1. During the operation of photovoltaic power generation, with a time granularity consistent with the time grid of the scenario performance trajectory board, extract the operation business records of each photovoltaic power station from the settlement metering records, inverter operation status records, grid regulation and control instruction records, and operation and maintenance work order status records. Add a unique identifier and timestamp field to each operation business record, and merge the settlement power, grid connection status, grid regulation and control execution status, and operation and maintenance work status into the same operation business record within the same time granularity to form operation business records arranged in chronological order.

[0089] It should be noted that the unique identifier of the photovoltaic power station in the operation business record must correspond one-to-one with the time grid of the scenario performance trajectory board, so as to ensure that each operation business record is consistent with the resource location and historical performance of the photovoltaic power station.

[0090] S3.2. Match the unique identifier of the photovoltaic power station in the operation business record with the unique identifier of the photovoltaic power station in the scenario performance trajectory board set, and compare the timestamp in the operation business record sequence with the time coordinate of the corresponding photovoltaic power station in the scenario performance trajectory board set, so that each operation business record falls into a unique time grid. In the time grid, the actual power generation, actual grid connection status, grid regulation and control execution status and actual operation and maintenance status are recorded respectively, forming the mapped operation business record; when all time grids are registered, an operation mapping record set corresponding one-to-one with the time grid of the scenario performance trajectory board is obtained for each photovoltaic power station.

[0091] S3.3. Within each time frame of the scenario performance trajectory board for each photovoltaic power station, read the expected power generation, expected settlement revenue, and expected carbon emission reduction corresponding to the time coordinates of the expected power generation curve, expected revenue curve, and expected carbon emission reduction curve at the time frame. Extract the actual power generation, actual settlement revenue, and actual carbon emission reduction registered in the same time frame from the operation mapping record set, and calculate the power generation deviation, revenue deviation, and carbon emission reduction deviation one by one according to the time frame.

[0092] Taking power generation deviation as an example, the expression is: ;

[0093] in, Indicates the first The photovoltaic power station in the first The power generation deviation within each time frame is used to describe the difference between the actual power generation and the expected power generation curve; Indicates the first The photovoltaic power station in the first The actual power generation within each time frame; Indicates the first The photovoltaic power station in the first Expected power generation within a time frame.

[0094] The formulas for revenue deviation and carbon emission reduction deviation are the same as those for power generation deviation.

[0095] S3.4. Based on the positive or negative status of the power generation deviation, revenue deviation, and carbon emission reduction deviation within each time frame, the deviation direction within the time frame is divided into three categories: higher than expected, lower than expected, and close to expected. Combining the data quality status markers in the scenario performance trajectory board and the grid regulation and control execution status and operation and maintenance status in the operation mapping record set, the correspondence between power generation deviation, revenue deviation, and carbon emission reduction deviation and changes in resource conditions, grid connection constraints, abnormal equipment status, or contract execution deviation is determined, generating a structured deviation entry for each time frame. Each deviation entry records at least the unique identifier of the photovoltaic power station, the time frame time coordinate, the power generation deviation, revenue deviation, carbon emission reduction deviation, deviation direction category, deviation source category, and the corresponding scenario fingerprint entry index, forming a traceable deviation entry.

[0096] It should be noted that when breaking down and statistically analyzing deviation items, it is necessary to ensure that each deviation item can be linked to the corresponding responsible entity information through the unique identifier of the photovoltaic power station, including the investment entity, operation and maintenance entity, and grid connection point information. This is to accurately calculate the influence of each responsible role in different photovoltaic power stations. In cases where multiple factors overlap, such as insufficient resource conditions and abnormal equipment conditions occurring simultaneously, the following priority rules should be followed to determine the source of the deviation:

[0097] If there are both abnormal equipment status (such as photovoltaic module failure, inverter shutdown, etc.) and insufficient resource conditions, then the abnormal equipment status is the main source of deviation. In the deviation source allocation ratio field, the abnormal equipment status is allocated an allocation ratio of no less than 70% of the total deviation, and the remaining allocation ratio of no more than 30% is allocated to the change in resource conditions.

[0098] If all three types of factors exist simultaneously, namely abnormal equipment status, changes in resource conditions, and grid regulation and control commands, the priority remains the same: abnormal equipment status > changes in resource conditions > grid constraints. For example, approximately 60% of the allocation can be allocated to abnormal equipment status, approximately 25% to changes in resource conditions, and approximately 15% to grid constraints.

[0099] If all four types of factors exist simultaneously—abnormal equipment status, changes in resource conditions, grid constraints, and deviations from contract execution—approximately 50% of the allocation can be assigned to abnormal equipment status, approximately 25% to changes in resource conditions, approximately 15% to grid constraints, and approximately 10% to deviations from contract execution.

[0100] For situations where only two factors overlap without involving abnormal equipment status, such as when resource condition changes and grid constraints occur simultaneously, the allocation can be made according to the principle that the resource condition change allocation ratio is greater than the grid constraint allocation ratio. For example, the resource condition change allocation ratio is 60%, and the grid constraint allocation ratio is 40%. For situations where resource condition changes and contract execution deviations overlap, approximately 70% of the allocation ratio can be allocated to resource condition changes, and approximately 30% of the allocation ratio can be allocated to contract execution deviations.

[0101] In the deviation entries, the deviation source record with the highest priority and the largest allocation ratio is the primary deviation source category, and the other deviation source records that participate in the allocation are secondary deviation sources. The allocation ratio of each deviation source is recorded separately through the deviation source allocation ratio field.

[0102] All deviation entries for the same photovoltaic power station within the same natural day are sorted according to the time grid time coordinate and archived according to the natural day dimension to form the daily deviation log of the corresponding photovoltaic power station.

[0103] It should be noted that resource condition changes refer to situations where natural conditions such as sunlight and weather differ significantly from the typical daily resource pattern in the scenario performance trajectory board. For example, the actual irradiance level may be lower than the expected level corresponding to the typical daily sunshine variation pattern for a long period of time, or there may be continuous cloudy and rainy weather that causes the power generation deviation to be negative, but the grid connection status and equipment status are normal.

[0104] Grid connection constraints refer to constraints such as power generation limitation, peak shifting, and power reduction caused by grid regulation and control commands issued by the grid side. For example, the power generation deviation is negative within a time slot, and the operation record is marked as grid regulation and control command being executed or the actual grid connection status is restricted grid connection, while the equipment operation and resource conditions are not abnormal.

[0105] Abnormal equipment status refers to situations where photovoltaic modules, inverters, or related primary equipment and secondary monitoring devices malfunction, shut down, or perform protection actions, resulting in abnormal output. For example, the inverter's operating status in the operation business record, the operation and maintenance work order status showing a fault, shutdown, or maintenance, the power generation deviation is usually negative, and there is no accompanying grid regulation and control command, and the resource conditions do not change significantly.

[0106] Contract execution deviation refers to inconsistencies between the actual self-use and grid connection ratios, peak and off-peak electricity pricing periods, and the agreed rules recorded in the context fingerprint card and electricity pricing structure. For example, the electricity pricing structure stipulates that self-use should be prioritized during weekdays, but the operation mapping record set shows that the grid connection ratio is low or the self-use ratio deviates from the set range during high electricity price periods, resulting in abnormal revenue deviation.

[0107] S4. After the end of each settlement cycle, use the daily deviation logs in conjunction with the corresponding scenario fingerprint cards and scenario performance trajectory boards to convert the operational business records of each power station in the corresponding settlement cycle into multi-dimensional efficiency indicators, and break them down according to the responsibility roles to form efficiency evaluation results.

[0108] S4.1. Filter the deviation entries in the daily deviation log according to the unique identifier of the photovoltaic power station and the settlement cycle. Summarize all deviation entries belonging to the same photovoltaic power station and whose time falls within the settlement cycle to form a set of deviation entries for the settlement cycle.

[0109] In the settlement cycle deviation item set, based on the one-to-one correspondence between the time grid time coordinates and the time grids in the scenario performance trajectory board, the expected power generation, expected settlement revenue, and expected carbon emission reduction of each time grid are read and matched with the power generation deviation, revenue deviation, and carbon emission reduction deviation recorded in the daily deviation log. The deviation direction, deviation magnitude, and deviation source category of each time grid within the settlement cycle are statistically analyzed.

[0110] S4.2. For each photovoltaic power station, filter the time cells marked as having usable data quality status in the settlement cycle deviation item set. Sum the expected power generation within the filtered time cells to obtain the total expected power generation for the settlement cycle. Sum the power generation deviations that did not meet the expectations to obtain the total negative power generation deviation for the settlement cycle. Calculate the resource utilization efficiency using the total expected power generation and the total negative power generation deviation. The expression is: ;

[0111] in, Indicates the first The resource utilization efficiency of a photovoltaic power plant in the current settlement period is used to describe the degree of deviation of the actual power generation performance from the expected power generation curve. Indicates the first The set of available time cells for the data quality status of a photovoltaic power station within the current settlement period; Indicates the first The photovoltaic power station in the first Expected power generation within each time frame; This represents the negative deviation in power generation, which is only included as a loss when the actual power generation is lower than the expected power generation. It is defined as follows: ;

[0112] when hour, Excess power generation is not considered a decrease in efficiency; when hour, It equals the difference between the expected power generation and the actual power generation; Indicates the first The photovoltaic power station in the first The expected power generation within each time frame is calculated using the same approach as resource utilization efficiency, i.e., only negative deviations are counted. This ensures that excess revenue and excess emission reduction are not misjudged as inefficiency. Furthermore, the number of equipment-related downtime frames, the power generation deviation caused by equipment abnormalities, and the total number of time frames in the settlement period are combined to calculate equipment health efficiency. These four types of indicators together constitute a multi-dimensional efficiency index.

[0113] It should be noted that the expression for calculating equipment health efficiency is: ;

[0114] in, Indicates the first The equipment health efficiency of a photovoltaic power plant during the current settlement period is used to reflect the impact of equipment downtime and equipment malfunctions on the overall operation. Indicates the first The number of equipment-related downtime slots for each photovoltaic power station during the current settlement period is used to characterize the extent of equipment downtime coverage. Indicates the first The total number of available time frames for data quality status of each photovoltaic power station within the current settlement period; Indicates the first The set of available time cells for the data quality status of a photovoltaic power station within the current settlement period; Indicates the first The set of time intervals during which the equipment status of a photovoltaic power station is abnormal within the current settlement period.

[0115] It should be noted that in calculating equipment health efficiency, both planned maintenance downtime and unplanned failure downtime should be clearly distinguished in the operation mapping record. However, when specifically calculating equipment health efficiency, the impact of the two types of downtime on efficiency should be handled using different rules. Specifically, downtime cells are divided into a set of planned maintenance downtime cells and a set of unplanned failure downtime cells using fields such as maintenance work order type, downtime reason code, and maintenance plan marker. When counting the number of equipment-related downtime cells, only unplanned failure downtime cells are included in the number of equipment-related downtime cells, while planned maintenance downtime cells are not included. That is, equipment health efficiency only measures the impact of unplanned failure downtime on equipment operating performance.

[0116] To avoid underestimating equipment health efficiency due to planned maintenance, this invention employs a fixed rule of "excluding planned maintenance" in the default configuration: planned maintenance downtime cells are only used for operation and maintenance management statistics and maintenance plan analysis, and are not included in the calculation of equipment health efficiency indicators; the number of equipment-related downtime cells is equal to the number of unplanned failure downtime cells in the current settlement period. Through these rules, equipment health efficiency can be ensured to more accurately reflect the true impact of failure-related downtime and equipment anomalies on operational performance, while avoiding the introduction of additional weighting parameters, allowing for direct calculation and comparison of equipment health efficiency.

[0117] When the downtime exceeds a fixed duration (e.g., continuous downtime exceeding 1 hour), it is counted as one downtime cell. In the case of unplanned failure downtime, if the photovoltaic power station fails to reach the expected power generation within a fixed time period and the power generation is lower than a set threshold (e.g., below 5%), it is considered an equipment downtime and marked. For planned maintenance downtime, if the downtime exceeds a set time (e.g., more than 4 hours) and is recorded according to the operation and maintenance plan, it can be marked with a certain discount weight (e.g., 50% weight) and included in the downtime cell statistics.

[0118] S4.3. For each deviation item in the settlement cycle deviation entries, read the deviation source category and use the investment entity information, operation and maintenance entity information and grid connection point information recorded in the context fingerprint card to map the deviation source category to the three responsibility roles of investment entity, operation and maintenance entity and grid connection point; for each responsibility role, count the number of deviation items corresponding to resource utilization efficiency, equipment health efficiency, revenue realization efficiency and carbon emission reduction achievement efficiency, as well as the absolute values ​​of power generation deviation, revenue deviation and carbon emission reduction deviation, to form the deviation statistics results split by responsibility role.

[0119] Taking power generation deviation as an example, the first Photovoltaic power plants and their responsibilities The expression for the responsibility deviation is: ;

[0120] in, Indicates the first Each photovoltaic power station plays a responsible role The generation responsibility deviation is used to describe the responsibility role. The cumulative impact on power generation deviation; This indicates that it belongs to the first category in the settlement cycle deviation item. A photovoltaic power station and its responsible role is Time frame.

[0121] The multidimensional efficiency indicators, together with the deviation statistics broken down by responsibility role, constitute the efficiency assessment results of the photovoltaic power plant during the settlement period.

[0122] It should be noted that the deviation statistics results should be associated with the responsible role through a unique identifier to ensure that after the identifier rules are changed, historical data and current data can be seamlessly connected and accurate results of the responsibility role division can be obtained by establishing an identifier mapping relationship.

[0123] This embodiment also provides a computer device applicable to the distributed photovoltaic power generation efficiency evaluation method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the distributed photovoltaic power generation efficiency evaluation method proposed in the above embodiment.

[0124] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0125] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the distributed photovoltaic power generation efficiency evaluation method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0126] In summary, this invention constructs a contextual fingerprint card set, uniformly encoding the resource location, investment entity information, operation and maintenance entity information, grid connection point, electricity price structure, carbon emission factors, and data quality rules of photovoltaic power plants into hierarchical business context records. This enables standardized and comparable descriptions of the operational scenarios of multiple sites within the evaluation area. It utilizes typical daily resource patterns to divide intraday scenario segments within the settlement cycle and generates a scenario performance trajectory board for each intraday scenario segment. Each time cell simultaneously carries the expected power generation curve, expected revenue curve, expected carbon emission reduction curve, and data quality status marker, providing accurate spatiotemporal contextual references for deviation calculation. By mapping operational business records to the time cells of the scenario performance trajectory board at a unified time granularity and forming a daily deviation log, multi-dimensional efficiency indicators can be converted and compared under the same scenario coordinates for resource utilization efficiency, equipment health efficiency, revenue realization efficiency, and carbon emission reduction achievement efficiency. Furthermore, by combining the investment entity information, operation and maintenance entity information, and grid connection point information in the contextual fingerprint card, the deviation amount is broken down according to responsibility roles. This achieves the location, quantification, and accountability of distributed photovoltaic cluster operational problems, significantly improving refined operation and maintenance management and comprehensive performance analysis capabilities.

[0127] Example 2, referring to Table 1, is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the distributed photovoltaic power generation efficiency evaluation method are given.

[0128] Taking a photovoltaic enrichment evaluation area as an example, it includes 10 photovoltaic power plants and 5 grid connection points. The boundary of the evaluation area was obtained through a geographic information system, and all coordinates were listed in the WGS84 coordinate system. The minimum bounding polygon was calculated to determine the physical range. Historical irradiance data was obtained from meteorological records, with a spatial grid resolution of 1 km. When selecting candidate representative points for the area, the annual average daily total irradiance of the grid where the measuring point is located was required to deviate from the average value of the surrounding grids by no more than 10%, and the interannual fluctuation coefficient was required to be less than 5%. The initial coverage radius was set to 5 km to form the initial resource coverage area. In the boundary recalculation, the difference in irradiance statistics was compared grid by grid for overlapping areas, and the areas were assigned to the closest representative points. Clustering algorithms were used to identify areas with a difference in irradiance mean exceeding 15% for splitting or merging, ultimately resulting in 5 resource sub-regions.

[0129] Photovoltaic power plants are assigned to the nearest sub-region based on their spatial distance to a representative point in the region, calculated using Euclidean distance. Business scenario elements collected include resource location, investment entity, operation and maintenance entity, grid connection point, electricity price structure, carbon emission factors, and data quality rules. Scenario fingerprint entries are organized by hierarchical fields, with unique identifiers ensuring historical data continuity. Scenario performance trajectory boards are generated based on typical daily resource patterns, with time cells divided into 15-minute intervals, forming expected curves for power generation, revenue, and carbon emission reduction. Operational business records are extracted from the monitoring system, mapped to time cells, and deviations are calculated. Daily deviation logs record the sources of deviations and prioritize them.

[0130] The details are shown in Table 1 below:

[0131] Table 1 Comparison of Photovoltaic Power Plant Efficiency Evaluation Data

[0132]

[0133] By comparing evaluation data from three photovoltaic power plants using existing technologies and the method of this invention, the significant advantages of this invention in photovoltaic power plant efficiency management and deviation attribution were verified. The table data shows that the average resource utilization efficiency of this invention is 92.3%, an increase of 14.3 percentage points compared to the 78.0% of existing technologies; equipment health efficiency increased from 81.9% to 94.5%, revenue realization efficiency increased from 75.4% to 89.3%, and carbon emission reduction achievement efficiency increased from 79.9% to 93.1%. Simultaneously, the average daily power generation deviation decreased from 123.1 kWh to 46.7 kWh, revenue deviation decreased from 99.1 yuan to 39.5 yuan, and deviation detection accuracy increased from 72.2% to 91.4%. These changes indicate that this invention, through the fine segmentation of contextual fingerprint cards and performance trajectory boards, achieves more accurate resource matching and deviation attribution.

[0134] The improvement in resource utilization efficiency stems from the optimization of resource sub-region division. Existing technologies use fixed geographical partitions, ignoring spatial differences in irradiance, leading to low efficiency of power plant P01 during resource fluctuations. This invention, however, dynamically adjusts regional representative points, improving the matching degree of irradiance characteristics and reducing negative bias. The improvement in equipment health efficiency is attributed to the prioritization of equipment status in the deviation entries. Existing technologies often misjudge downtime as resource changes, while this invention distinguishes between planned maintenance and unplanned failures. For example, the unplanned downtime of power plant P02 is reduced, improving efficiency. The advancements in revenue realization efficiency and carbon emission reduction achievement efficiency benefit from the alignment of time grids on the scenario performance trajectory board. Existing technologies use crude applications of electricity prices and carbon factors, while this invention combines time-of-use pricing and dynamic carbon emission factors, making the expected curve more realistic and reducing bias. The decrease in average daily power generation and revenue bias reflects the accuracy of the deviation detection mechanism in this invention. Existing technologies lack hierarchical field association, making the source of bias ambiguous. This invention, through scenario fingerprint card indexing, accurately maps to responsible roles. For example, the revenue bias of power plant P03 is mainly attributed to grid constraints, facilitating targeted improvements.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating the efficiency of distributed photovoltaic power generation, characterized in that: include, The evaluation area is divided into photovoltaic resource sub-regions based on regional representative points, and the business scenario elements of each photovoltaic power station are collected to obtain a scenario fingerprint card set. The specific steps of dividing the evaluation area into resource sub-regions based on regional representative points include: obtaining the boundary range and historical irradiance distribution of the evaluation area from geographic information; selecting several regional representative points within the boundary range according to the spatial differences in historical irradiance distribution; setting a coverage range for each regional representative point to form an initial resource coverage area; in the initial resource coverage area, performing boundary adjustment on areas with overlapping boundaries and obvious resource differences, merging areas with the same resource characteristics, and performing splitting processing on areas with obvious resource differences but connected boundaries to obtain resource sub-regions with relatively consistent resource characteristics and complete coverage. Based on the contextual fingerprint card set, and according to the typical daily resource patterns of representative points in the regions to which each power station belongs, the settlement period is subdivided into multiple intraday contextual segments. A corresponding business contextual performance trajectory board is generated for each intraday contextual segment and integrated to obtain a set of business contextual performance trajectory boards. Specific steps include: reading the typical daily sunshine change pattern corresponding to each regional representative point from the contextual fingerprint card set; using this typical daily sunshine change pattern as the typical daily resource pattern for the corresponding regional representative point; dividing the time axis by natural days within the settlement period; associating each natural day on the time axis with the typical daily resource pattern of the regional representative point; and... The time period boundaries are determined based on the inflection point of irradiance change in typical daily resource patterns. The intervals of rapid irradiance change and relatively stable intervals are distinguished on the time axis to form intraday scenario boundaries covering all natural days in the settlement period. The resource location information of each photovoltaic power station is matched with the intraday scenario boundaries of the corresponding regional representative point, so that each photovoltaic power station corresponds to a unique regional representative point and a set of intraday scenario boundaries for each natural day in the settlement period. On the time axis of the settlement period, the natural days of each photovoltaic power station are divided according to the intraday scenario boundaries, and the continuous time period of each natural day is defined as an intraday scenario segment. In actual operation, the operation records of each power station are mapped to the corresponding time grid of the business scenario performance trajectory board according to a unified time granularity. The scenario expectation trajectory in the business scenario performance trajectory board is compared with the mapped operation records to form deviation items. All deviation items are summarized into the daily deviation log of the corresponding power station. After each settlement cycle ends, the daily deviation logs are combined with the corresponding context fingerprint cards and business context performance trajectory boards to convert the operational business records of each power station in the corresponding settlement cycle into multi-dimensional efficiency indicators, which are then broken down according to the responsibility roles to form efficiency evaluation results.

2. The method for evaluating the efficiency of distributed photovoltaic power generation as described in claim 1, characterized in that: The business context elements include resource location information, investment entity information, operation and maintenance entity information, grid connection point, electricity price structure, carbon emission factor, and data quality rules.

3. The method for evaluating the efficiency of distributed photovoltaic power generation as described in claim 2, characterized in that: The specific steps for aggregating the business context elements of each photovoltaic power station to obtain a context fingerprint card set are as follows: The business context elements collected for each photovoltaic power station are grouped and organized, and a hierarchical field set is established according to the resource layer, business layer, carbon asset layer and data quality layer to form a hierarchical business context record for a single station. In the hierarchical business scenario record, a unique identifier is assigned to each photovoltaic power station, and the four hierarchical fields of resource layer, business layer, carbon asset layer and data quality layer are associated to generate the scenario fingerprint entry for the corresponding power station. All context fingerprint entries for photovoltaic power plants are indexed and aggregated according to resource sub-regions, regional representative points, and operation and maintenance responsible entities to form a set of context fingerprint cards.

4. The method for evaluating the efficiency of distributed photovoltaic power generation as described in claim 3, characterized in that: The process of generating and integrating corresponding business scenario performance trajectory boards for each day's scenario segments to obtain a set of business scenario performance trajectory boards involves the following specific steps. Establish time coordinates for time cells within each intraday scenario segment, and read resource location information, electricity price structure, carbon emission factor, and data quality rules from the corresponding scenario fingerprint card; Align resource location information with typical daily resource patterns of representative regional points to generate expected power generation curves; The electricity price structure is superimposed with the power generation expectation curve to generate the revenue expectation curve; The carbon emission factor is superimposed on the power generation expectation curve to generate the carbon emission reduction expectation curve. In each time cell, mark the available status, the questionable status, and the missing status according to the data quality rules; A business scenario performance trajectory board for a single intraday scenario segment is composed of time coordinates, expected power generation curves, expected revenue curves, expected carbon emission reduction curves, and data quality status markers. The business scenario performance trajectory boards belonging to the same photovoltaic power station are integrated in chronological order to form a set of business scenario performance trajectory boards.

5. The method for evaluating the efficiency of distributed photovoltaic power generation as described in claim 4, characterized in that: The specific steps for mapping the operational records of each power station to the corresponding time grid of the business scenario performance trajectory board at a unified time granularity are as follows: During the operation of photovoltaic power generation, the operation records of each photovoltaic power station are obtained at a uniform time granularity; Each operational record is registered in the corresponding business context performance trajectory board time grid according to the power station identifier and timestamp, and then linked together in chronological order to form an operational mapping record set.

6. The method for evaluating the efficiency of distributed photovoltaic power generation as described in claim 5, characterized in that: The specific steps for comparing the expected scenario trajectory in the business scenario performance trajectory board with the mapped operational business records are as follows: Within each time frame of the performance trajectory board for each business scenario, read the corresponding expected values ​​in the expected power generation curve, expected revenue curve, and expected carbon emission reduction curve, and compare them item by item with the operational business records in the operational mapping record set to generate deviation entries including the deviation direction and deviation magnitude. Based on the time grid location associated with the deviation entry and the index of the context fingerprint entry to label the deviation source category, deviation entries belonging to the same photovoltaic power station and occurring within the same natural day are summarized in chronological order to construct a daily deviation log.

7. The method for evaluating the efficiency of distributed photovoltaic power generation as described in claim 6, characterized in that: The specific steps for evaluating the formation efficiency results are as follows: Based on the deviation direction, deviation magnitude and deviation source category recorded in the daily deviation log, and combined with the power generation expectation curve, revenue expectation curve and carbon emission reduction expectation curve in the business scenario performance trajectory board, all deviation items obtained by comparing the operation mapping record set and the scenario expectation trajectory of the same photovoltaic power station within the settlement cycle are classified and summarized to obtain multi-dimensional efficiency indicators. Based on the categories of deviation sources and the information on investment entities, operation and maintenance entities, and grid connection points recorded in the contextual fingerprint card, the deviation quantities in the multidimensional efficiency indicators are broken down according to the investment entity, operation and maintenance entity, and grid connection point to obtain the efficiency evaluation results.

8. The method for evaluating the efficiency of distributed photovoltaic power generation as described in claim 7, characterized in that: The multidimensional efficiency indicators include resource utilization efficiency, equipment health efficiency, revenue realization efficiency, and carbon emission reduction achievement efficiency.

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