Variance-Based Photovoltaic Fleet Power Statistics
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Solution Overview
Problem
Current methods for obtaining high-speed time series power production data from photovoltaic fleets are impractical due to high costs, data loss, and computational challenges, especially for geographically dispersed systems under cloudy conditions, where shading and varying weather conditions lead to unpredictable performance.
Innovation Solution
A variance-based approach using satellite imagery to estimate photovoltaic fleet power statistics by correlating irradiance data across multiple locations, simplifying calculations by eliminating zero correlations based on distance and converting area statistics to point statistics, allowing for efficient generation of high-speed data usable by grid operation tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct power measurements are collected from every photovoltaic system at high speed, then accurate fleet power data is obtained, but the cost and data communication bandwidth requirements become prohibitively high
Solution Approach 1:
The patent introduces satellite imagery as an intermediary data source to estimate photovoltaic fleet power output. Instead of directly measuring power from every system, the method uses satellite-based solar irradiance measurements combined with system configurations to calculate expected power output, thereby avoiding the need for extensive direct measurement infrastructure
Solution Approach 2:
The patent creates a virtual model of photovoltaic fleet behavior by copying and scaling individual system characteristics to fleet level. By measuring a small number of actual systems and using simulation models to represent the entire fleet, the method reproduces fleet-wide power production patterns without requiring direct measurement of every system
2Productivity
If high-speed data collection is implemented across dispersed photovoltaic systems, then detailed power statistics are obtained, but data loss occurs and computational burden increases
Solution Approach 1:
The patent performs preliminary calculations by pre-computing correlation matrices and system response characteristics offline. By preparing statistical models and correlation data in advance, the system can quickly process incoming satellite imagery and generate power estimates without requiring high-speed real-time data collection from every system, thereby maintaining data reliability while achieving fast results
3Measurement precision
If correlation matrices are calculated for all photovoltaic system pairs to account for geographic dispersion, then accurate variance estimation is achieved, but computational complexity becomes unmanageable
Solution Approach 1:
The patent segments the photovoltaic fleet into geographic regions or clusters based on spatial correlation. By dividing the fleet into smaller groups where systems within each group have similar weather conditions and correlation characteristics, the method calculates correlation matrices only within segments rather than for all system pairs, dramatically reducing computational complexity while maintaining accuracy
Solution Approach 2:
The patent calculates correlation matrices only for photovoltaic system pairs that are geographically close and likely to have significant correlation, rather than computing all possible pairs. By focusing computational effort on the most relevant relationships and using distance-based thresholds to eliminate negligible correlations, the method achieves sufficient variance estimation accuracy with manageable computational burden
Data Source
AI summary
The calculation of the variance of a correlation coefficient matrix for a photovoltaic fleet can be completed in linear space as a function of decreasing distance between pairs of photovoltaic plant locations. When obtaining irradiance data from a satellite imagery source, irradiance statistics must first be converted from irradiance statistics for an area into irradiance statistics for an average point within a pixel in the satellite imagery. The average point statistics are then averaged across all satellite pixels to determine the average across the whole photovoltaic fleet region. Where pairs of photovoltaic systems are located too far away from each other to be statistically correlated, the correlation coefficients in the matrix for that pair of photovoltaic systems are effectively zero. Consequently, the double summation portion of the calculation can be simplified to eliminate zero values based on distance between photovoltaic plant locations, substantially decreasing the size of the problem space.