Webinar: Reasoning About Energy Reliability and Renewable Energy
The share of renewable energy sources appears to be growing around the world. Headlines suggest that some places can already meet a large portion of their electricity needs by wind and solar power alone.
The accounting seems straightforward: If wind and solar sources jointly produce more electricity than the amount of electricity consumed, fossil fuel independence seems to be at hand, right?
For obvious reasons, wind and solar energy sources do not provide electricity at a steady rate. If you will, their power delivery is at the whim of nature. Even if their average energy production over a period of time, e.g., a day or a week, equals the average demand over that timeframe, that may not keep the lights on as electricity needs to be produced the very instant it is consumed.
Would installing more solar panels and more wind turbines help provide reliable energy? Could large storage batteries perhaps compensate for the volatility of wind and sun? Could efficiency improvements make a difference in terms of stability?
Future technical innovations are indeed unknowable, but physical laws are firmly established. In general, the efficiency of any system cannot exceed 100%. Specifically for wind turbines, Betz's Law states that the theoretical limit of energy extraction is 59%. With that, firm upper boundaries limit our considerations. Yet, at the same time, they highlight the potential for substantial improvement when compared to the technical realities of today.
With so many unknowns, how can we estimate the long-term potential of renewable energy sources? This is a case for reasoning under uncertainty with Bayesian networks.
As a case study, we explore El Paso, Texas, a city that — at first glance — seems to be a plausible place for exploiting renewable energy sources. It's very sunny and fairly windy, with lots of open space for wind and solar farms.
We also happen to have access to a multi-year dataset of El Paso's hourly electricity usage plus an hourly history of solar radiation and wind speed. Thus, we have the actual conditions in which alternative energy sources would need to perform.
To start the webinar, we demonstrate how to machine-learn probabilistic relationships from historical energy and climate data. Then, we augment this machine-learned Bayesian network with additional nodes that represent the physics of solar panels and wind turbines. Our objective is to explore the boundaries of what might be feasible in the future, given physical and natural constraints.
With such a framework in place, subject matter experts can then provide their beliefs regarding the future efficiency of energy technologies and their costs. Our Bayesian network can take into account these estimated future characteristics of solar panels and wind turbines, plus the anticipated efficiency and cost of traditional power sources or storage batteries, which may still have to supplement the renewables.
Based on learned probabilistic relationships, known physical constants, plus a range of expert assumptions, we can then calculate future scenarios and derive optimal energy mixes for each case. The Bayesian network model of this domain can also help us identify the areas of opportunity by examining the sensitivities of the estimated parameters.
Although this problem domain is undoubtedly important and interesting, we focus on the Bayesian network methodology instead of developing policy recommendations. No part of this webinar should be considered an endorsement or criticism of current renewable energy initiatives.
Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan, which included assignments in North America, Europe, and Asia.
Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning.