Like many students, I have been trying to see what I could do with my coursework in defining a career.  At Sacramento State, I chose a math major because I knew, combined with my minor in computer science, it would be the best combination of the two fields I loved the most. I also chose an emphasis in Statistics to go along with my math degree. I do not know what my profession will be, I just know what I like to do. So I talked with someone who was using my two passions in their own career in the Energy Sector.

Miles Butler was a physics student who now works at Energia. Surprisingly transitioning from physics to electricity, he realized that he needed to see everything as a collection of models and assumptions. He looks at the various loadshapes of energy use of houses. Taking this data, he works towards making the energy more efficient (expand on this more), and the main work he does is finding the impacts on distribution networks and performing distribution planning projects.

These mathematical models are representations of potential energy demand and supply loads in the future.  There are generally two things he looks at: Network and Non-network solutions. Network solutions, which are physical equipment that has to be bought, is typically more complicated and harder. Non-network solutions, like influencing customer use through energy efficiency plans and peak charges, are easier but are limited through regulation and elasticity of customer energy demand.  Balancing them is important, that is where Models come in, examining the tradeoffs and customer behaviors under different conditions.

One thing that surprised me were the margins of error. Margins of errors did not bother Miles because models are not designed to tell facts, but instead to explore dynamics. With models, one is exploring how someone’s actions and expectations impact the grid, but expectations and behaviors are constantly changing. These changing expectations make modeling important for exploring and preparing a Utility for the Future, especially with how much utilities want to push people to clean energy.

Models are based on statistics and it seems that my emphasis in statistics would suit that very well if I chose to go into this sector. All STEM eventually goes back to mathematics.

It was good to find out there were some immediate applications of what I had been learning in classes.  Sometimes it seems like classes and careers don’t overlap.  But clearly they do.