PV Soiling Loss Estimation Using Merged Energy and Weather Data
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Solution Overview
Problem
Current models for estimating the performance of solar photovoltaic (PV) systems are inaccurate due to factors like soiling, shading, and snow cover, leading to overestimation or underestimation of energy production, especially in uncontrolled settings, and lack the granular data needed to accurately quantify real-world losses.
Innovation Solution
A computer processor-based method that estimates energy losses due to soiling by merging modeled and measured energy time series data with clear sky and precipitation data, calculating a performance ratio, and determining soiling loss fractions to provide a more accurate assessment of PV system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If derate factors are used to estimate energy losses in PV systems, then the modeling process is simple, but the accuracy of energy production estimates deteriorates due to short-term fluctuations in losses from soiling, shading, snow cover, and hardware failure
Solution Approach 1:
The patent segments the overall energy loss into distinct categories (soiling losses, shading losses, snow cover losses, hardware failure losses) and models each separately using specific environmental and operational parameters. This allows each loss type to be estimated independently based on relevant factors, improving overall accuracy while maintaining manageable complexity through modular modeling approaches.
2Measurement precision
If granular categorized loss estimates are included in PV performance models, then the accuracy of performance description improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent applies local quality by using location-specific environmental parameters (precipitation data, wind speed, temperature, humidity) and system-specific operational data (panel orientation, cleaning schedules, soiling rates) to customize the loss estimation for each PV system. This allows the model to capture local conditions that affect soiling and performance without requiring a completely new model for each location, balancing accuracy with complexity through parameterization.
3Measurement precision
If detailed environmental data (precipitation, wind, temperature, humidity) is collected to estimate soiling losses, then the accuracy of soiling estimation improves, but the cost and complexity of data collection increases
Solution Approach 1:
The patent leverages multi-functionality by utilizing environmental data that is already collected for other PV system operations (irradiance measurement, temperature monitoring for efficiency calculations, wind data for structural loading assessments) and repurposing it for soiling loss estimation. This approach maximizes the value of existing data infrastructure without requiring dedicated sensors or measurement systems solely for soiling monitoring.
Data Source
AI summary
The present invention provides methods and systems to estimate energy losses due to soiling in photovoltaic (PV) systems from data including the measured energy and power produced over the lifetime of the system, the system size and configuration data, the weather conditions (including irradiance, precipitation, ambient and panel temperature, and wind conditions) over the lifetime of the system, and derived meteorological condition information (e.g., the history of clear-sky conditions at the location of the site).