Correlating Point-to-Point Sky Clearness for PV 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, which is challenging to collect and analyze across dispersed systems with varying configurations, leading to unpredictable performance and data loss.
Innovation Solution
A statistical approach using a digital computer to correlate point-to-point sky clearness by evaluating mean and standard deviation of irradiance measures, applying an empirically-derived exponential function to calculate a correlation coefficient, and weighting clearness indexes to estimate photovoltaic fleet output, enabling high-speed data generation even without high-resolution input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-speed time series power production data is collected from dispersed photovoltaic systems, then estimation accuracy is improved, but data collection cost and complexity increase significantly
Solution Approach 1:
The patent introduces sky clearness index as an intermediary parameter that mediates between satellite imagery data and photovoltaic power output estimation. Instead of directly collecting complex time series data from dispersed systems, the method uses sky clearness index derived from satellite images to estimate power production, significantly reducing data collection infrastructure requirements while maintaining estimation accuracy
Solution Approach 2:
The patent replaces the mechanical data collection system (physical sensors and communication infrastructure at each photovoltaic site) with a remote sensing approach using satellite imagery. The sky clearness index derived from satellite images substitutes for direct local measurements, eliminating the need for extensive ground-based data collection infrastructure
2Productivity
If point-to-point sky clearness correlation is calculated using empirical functions, then computational efficiency is improved, but precision under cloudy conditions deteriorates
Solution Approach 1:
The patent applies dynamic correction to the sky clearness index calculation by incorporating temporal and spatial variability factors. The empirical function is enhanced with dynamic adjustment parameters that adapt to changing cloud conditions, allowing the model to maintain computational efficiency while improving accuracy under cloudy conditions through real-time parameter adjustments
Solution Approach 2:
The patent changes the parameters used in the empirical function by introducing multiple sky clearness indexes at different time points and locations, along with correlation coefficients that vary with temporal distance. This parameter expansion allows the model to capture complex cloud dynamics while maintaining computational tractability through the structured use of empirical relationships
3Measurement precision
If extensive data collection is performed across dispersed photovoltaic systems, then fleet output estimation accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating sky clearness indexes from satellite imagery before the actual power estimation is needed. The temporal distance-based correlation framework allows pre-computation of clearness indexes at multiple time points, so that when fleet output estimation is required, the computationally intensive data processing has already been completed, significantly reducing real-time computation time
Solution Approach 2:
The patent uses satellite imagery as a copy or proxy for direct ground-based measurements. Instead of collecting actual power production data from each dispersed photovoltaic system, the method creates a virtual representation of sky conditions through satellite images and derives power estimation from these copies, eliminating the need for time-consuming direct data collection from numerous sites
Data Source
AI summary
Statistically representing point-to-point photovoltaic power estimation and area-to-point conversion of satellite pixel irradiance data are described. Accuracy on correlated overhead sky clearness is bounded by evaluating a mean and standard deviation between recorded irradiance measures and the forecast irradiance measures. Sky clearness over the two locations is related with a correlation coefficient by solving an empirically-derived exponential function of the temporal distance. Each forecast clearness index is weighted by the correlation coefficient to form an output set of forecast clearness indexes and the mean and standard deviation are proportioned. Additionally, accuracy on correlated satellite imagery is bounded by converting collective irradiance into point clearness indexes. A mean and standard deviation for the point clearness indexes is evaluated. The mean is set as an area clearness index for the bounded area. For each point, a variance of the point clearness index is determined and the mean and standard deviation are proportioned.