Wind Power Output Forecasting via Machine Learning
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Solution Overview
Problem
The intermittent and uncontrollable nature of wind power generation poses challenges for electric utilities in integrating wind power into the grid, leading to involuntary curtailment due to transmission congestion or oversupply, resulting in revenue loss and grid instability.
Innovation Solution
A computer-implemented method using historical and real-time data to train machine learning models that predict wind power output, allowing for accurate forecasting and curtailment management by converting wind speed forecasts into power output values, enabling grid operators to balance supply and demand effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wind power generation is increased to meet energy demand, then energy supply is improved, but grid stability deteriorates due to intermittency and uncontrollability
Solution Approach 1:
The system performs preliminary actions by forecasting wind power generation output in advance using machine learning models. This allows grid operators to anticipate intermittent generation patterns and take proactive measures to maintain grid stability, such as scheduling conventional power plants or adjusting demand response programs before the intermittency issues occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual wind power generation against predicted values and using this information to adjust grid operations. The machine learning models are trained on historical data and continuously improved, creating a feedback loop that enhances prediction accuracy and enables better grid stability management over time.
2Reliability
If wind power generation is curtailed to maintain grid balance, then grid stability is improved, but energy supply deteriorates due to involuntary curtailment
Solution Approach 1:
The system enables preliminary action by predicting wind power generation output in advance, allowing grid operators to plan for potential surpluses before they occur. This proactive approach allows for optimized scheduling of conventional power plants and demand response programs, reducing the need for involuntary curtailment while maintaining grid balance.
3Ease of operation
If machine learning models are used to predict wind power output, then curtailment management is improved, but system complexity increases
Solution Approach 1:
The system applies self-service by using machine learning models that automatically learn from historical data and continuously improve prediction accuracy without requiring manual intervention. The models autonomously process weather forecasts and historical generation data to produce predictions, reducing the operational burden on grid operators while improving curtailment management.
Data Source
AI summary
Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.