Not many engineers carry a water filter, malaria pills, and sunscreen to
work every day. However, when you work in rural energy development in East
Africa, these are just as important as that TI-86 calculator that has become an
essential to any engineer.
Prior to coming to MIT’s LGO program, my wife and I lived in Rwanda for two
years. I was a chief engineer for a hydropower developer, and she lectured at a
Rwandan university in biology and chemistry (my wife is great…you know you’ve
found a good one when she agrees to marry you and move to Africa just two weeks
after the wedding). We lived at the base of five stunning volcanoes, rode
mountain bikes to work every morning, ate more mangoes than I can count, and
hiked volcanoes on weekends.
When we came to MIT, I was worried that I would lose connection with my work
and personal research from our time in Rwanda, but I was wrong. One of the best
things about the program is the openness to learn, research, and push the
boundaries of what students can do. Before coming to LGO, I had been working on
a project looking at optimizing Rwandan energy consumption, and quickly found
support in the mechanical engineering department to continue with the research.
MISTI travel grants gave great opportunities to travel abroad and continue to
research. I felt extremely supported in
my work. Essentially, I was given the opportunity to work on this fulfilling
research while taking summer and fall courses at MIT with 48 brilliant LGO
minds from around the globe. It was paradise.
My project centered on reducing the diesel generation that occurs in Rwanda
every year. Rwanda has a lot of good resources for hydropower, but peak energy
consumption is covered with diesel generators. Without even taking into
effect environmental costs, diesel is extremely expensive
for a land-locked country like Rwanda. I wanted to look at the structure of feed-in
tariffs for hydropower developers to incentivize them to store water during the
morning and to release it during the day. This capacity would cost the
developers more capital, but would obviously pay out in less
diesel consumption. The problem essentially turns into a multivariable convex
cost function, where we change turbine sizes and storage sizes of lakes. It
requires a fairly complex model to implement multiple rivers, flows, rainfalls,
and turbine sizes.
Throughout the course of this project, I have felt very
thankful for the opportunity to attend a prestigious school like MIT while
working on concurrent, personally fulfilling research. By far, my favorite
thing about MIT is that there is an absolutely impossible number of things that
I can learn and study here, and that the only bottleneck in the learning
process is myself. I love the vast array of courses, design competitions,
prototyping workshops, research opportunities, and relationship building that
is available here. When I am the limiting factor in my learning, I know I’m at
the right place.