Correlating Sky Clearness for Photovoltaic Fleet Output Estimation
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Solution Overview
Problem
Current methods for estimating photovoltaic fleet output are inefficient and costly, particularly under cloudy conditions, due to the need for high-speed time series power production data from dispersed systems, which requires extensive infrastructure and data transmission, and cannot accurately forecast performance without physical meters or reliable data communication.
Innovation Solution
A computer-implemented system and method that correlates overhead sky clearness using a statistical approach, generating high-speed time series data from a small sample of input sources like weather stations or satellite images, applicable to any fleet configuration and time resolution, enabling accurate power output estimation for grid operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-speed time series power production data is collected from dispersed photovoltaic systems, then accurate power output estimation is achieved, but infrastructure costs and data transmission requirements increase significantly
Solution Approach 1:
The patent introduces sky clearness index as an intermediary parameter that mediates between weather conditions and photovoltaic power output. Instead of directly measuring power from each system, the method uses sky clearness observations from weather stations as a mediator to estimate power output, significantly reducing infrastructure requirements while maintaining estimation accuracy
Solution Approach 2:
The patent creates a statistical model that copies the relationship between sky clearness and power output based on historical data. This model copy allows power output estimation without physical meters at each photovoltaic system, using the statistical relationship as a virtual replica of the actual physical system behavior
2Reliability
If physical meters are installed at each photovoltaic system for direct measurement, then reliable data is obtained, but deployment costs and feasibility decrease with fleet size
Solution Approach 1:
The patent merges multiple photovoltaic systems into a single statistical model based on their common dependency on sky clearness. Instead of treating each system independently with its own meter, the method combines them under a unified statistical framework that uses shared weather data, reducing deployment complexity while maintaining reliability
Solution Approach 2:
The patent enables photovoltaic fleet power output estimation to be self-service by using publicly available weather station data and statistical models. The system serves itself by leveraging the natural relationship between sky clearness and power output without requiring external measurement infrastructure at each site
3Productivity
If data collection rate is increased to capture high-speed time series, then resolution is improved, but bandwidth and storage requirements become insurmountable
Solution Approach 1:
The patent extracts only the essential information needed for power output estimation - the sky clearness index - from weather data. Instead of collecting and transmitting all raw weather parameters at high speed, the method extracts and uses only the relevant clearness metric, significantly reducing data volume while maintaining estimation capability
Data Source
AI summary
A computer-implemented system and method for correlating overhead sky clearness for use in photovoltaic fleet output estimation is provided. A temporal distance that includes a physical distance between two locations, which are each within a geographic region suitable for operation of a photovoltaic fleet, is determined in proportion to cloud speed within the geographic region. A set of input sky clearness indexes is generated as a ratio of each irradiance observation in a set of irradiance observations that has been regularly measured for one of the locations within the geographic region, and clear sky irradiance. A clearness index correlation coefficient between the two locations is determined as an empirically-derived function of the temporal distance. The set of input sky clearness indexes is weighted by the clearness index correlation coefficient to form a set of output sky clearness indexes, which indicates the sky clearness for the other of the locations.