Photovoltaic Forecast Tuning via Irradiance and Power Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current photovoltaic power generation forecasting methods are inaccurate due to errors in weather data and simulation models, which are not integrated effectively, leading to suboptimal forecasting of solar irradiance and power production.
Innovation Solution
A two-step tuning process is implemented, integrating weather and photovoltaic plant performance tuning to correct irradiance data and power conversion inaccuracies, and operational plant status tuning to address unpredictable performance and maintenance events, using a digital computer system with sensors and processors to adjust and refine forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate unconnected processes are used for weather data production and photovoltaic simulation model development, then each process can be optimized independently, but the overall forecast accuracy deteriorates due to integration errors and assumptions of perfect weather data inputs
Solution Approach 1:
The patent merges the previously separate weather data production process and photovoltaic simulation model development into a single integrated tuning process. The system simultaneously adjusts weather data inputs and simulation model parameters together, eliminating the assumption of perfect weather data and reducing integration errors, thereby improving overall forecast accuracy while maintaining ease of model development.
2Device complexity
If perfect weather data inputs are assumed in simulation models, then model development becomes simpler, but forecast reliability deteriorates because perfect weather data is unavailable in reality
Solution Approach 1:
The patent changes the parameter assumptions in the simulation model by removing the assumption of perfect weather data inputs. Instead, the system uses tunable weather data inputs with adjustable parameters that reflect real-world data quality variations. This allows the model to account for data imperfections while maintaining reasonable complexity through systematic parameter adjustment rather than complex data validation routines.
3Measurement precision
If detailed tuning of irradiance data and power conversion is performed, then forecast accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the detailed tuning process into distinct modular components: irradiance data tuning, power conversion tuning, and operational status tuning. Each segment handles specific aspects of the forecast adjustment independently, allowing for systematic parameter optimization without overwhelming computational complexity. This modular segmentation enables detailed tuning while maintaining manageable process complexity.
4Reliability
If operational plant status tuning is added to correct unpredictable performance and maintenance events, then forecast reliability improves, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by incorporating operational plant status tuning that proactively accounts for unpredictable performance variations and maintenance events before they significantly impact forecasts. The system pre-adjusts for typical operational deviations and schedules maintenance impacts in advance, improving reliability by addressing issues before they cause large forecast errors, rather than reacting to them after occurrence.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy of photovoltaic power generation forecasts, reducing errors from 16% to under 9% for individual plants and achieving a 1% error for the overall fleet, enhancing reliability and confidence in photovoltaic power as a grid source.
Implementation Method 1
at least one irradiance sensor configured to regularly measure over an observation period a time series of solar irradiance values
Implementation Method 2
photovoltaic power generation plant
Data Source
AI summary
A system for tuning a photovoltaic power generation plant forecast with the aid of a digital computer is provided. Global horizontal irradiance (GHI), ambient temperature and wind speed for a photovoltaic power generation plant over a forecast period are obtained. Simulated plane-of-array (POA) irradiance is generated from the GHI and the plant's photovoltaic array configuration as a series of simulated observations. Inaccuracies in GHI conversion are identified and the simulated POA irradiance at each simulated observation is corrected based on the conversion inaccuracies. Simulated module temperature is generated based on the simulated POA irradiance, ambient temperature and wind speed. Simulated power generation over the forecast period is generated based on the simulated POA irradiance, simulated module temperature and the plant's specifications and status. Inaccuracies in photovoltaic power conversion are identified and the simulated power generation at each simulated input power level is corrected based on the power conversion inaccuracies.