Predictions, pitfalls and some solutions
Today’s expert blogger is Nikhil Kumar, Program Lead – Utility Economics for Intertek. Nikhil is based in Sunnyvale, CA and is leading the Smart - Asset Integrity Management (AIM) services business for the company.
08 October 2012
Two news reports that ran in the Wall Street Journal on September 11, 2012 summed up the uncertainty of the energy sector in North America. While one report was clear to point out the impact of the increased natural gas resources resulting in large old coal plants to idle (see Coal-Fired Plants Mothballed by Gas Glut), the other was discussing the recent surge in the resource’s prices (see Natural Gas Rallies 6.4%). This question – the uncertainty of the energy sector in North America – not only plagues CEO’s at larger utility companies who have to determine their budgets and investments, but individual investors like you and me. Intertek, along with several other companies around the world, use complex economic models to perform this type of analysis. With advances in both hardware and software, we can now perform fairly detailed analysis of what the impacts of fuel prices will be on power generation. However, no such analysis can accurately predict the near future – let alone determine a scenario 10 years out.
Most studies that analyze this problem either take clues from historical trends or from publicly available sources such as the Energy Information Administration (EIA). To counter the uncertainty in the future, these studies perform what is called a “Sensitivity Analysis”. Simply put: This analysis changes one input at a time to see the corresponding change in the output. Come to think of it, when your analysis includes thousands of power plants, with tens of hundreds of input parameters, then changing a few inputs at a time makes the most sense if you want to capture the “sensitivity” of this input. Moreover, before we change inputs, it is very important to study existing data thoroughly. Imagine entering a value a hundred times its normal! While it may be a good experiment, in reality this may result in a prediction way off target.
Typical analysis includes several such sensitivity predictions. After completing the analysis, we look at three key results. First, is the best estimate future prediction and the other two are the lower and upper bounds of this prediction. In the next blog, we will go through some recent predictions and follow their accuracy over time. We also will share some of the advanced analytics of power plant operating data that we have performed over the years.
Do you have a question about predictions and this type of analysis for the energy sector? Or would you like to know more about this topic? If so, please leave a comment below.
Nikhil Kumar, Program Lead – Utility Economics for Intertek