Bits & Watts 2020 SMART Internship program.
SMART - Summer Microgrid Analysis and Research Training
Eight undergraduate, Masters and Ph.D students spent the summer working remotely with mentors from Stanford faculty and many Bits & Watts member companies on three microgrid related projects. These completed projects will provide the jumping off point for further research in these areas.
Reflections from our interns:
"I learned a lot about microgrids and after the knowledge I gained I want to further research and do an internship around energy storage and different forms of batteries. A lot of this opportunity was informed by the work this summer. I feel like I have a better grasp on what my future career will be like. I also learned how to better manage meetings, present, and work cooperatively even in a group setting." - Raymond Zhen
“I discovered a passion. I am motivated in working in energy policy and environmental sustainability in the near future. I want to continue to grow my network of individuals in the energy and environmental policy, further develop my technical competencies, and help deliver value. Throughout the summer, I really had to demonstrate grit and tenacity, and I am happy with the work I delivered on.“ - Oluwaseyi Olaleye
Project #1 - Global Microgrid Comparison Study
Microgrids are trending in our community nowadays. Resilience and reliability are the key drivers to many of the existing Microgrid deployments due to the impact of extreme weather events. The group of students will spend 8 weeks this summer working on the analysis of Microgrid cases in California, North America and other countries to understand the industry practice and challenges; identify technology gaps and future research needs. Link to final project presentation
Project #2 - Stanford Community Microgrid Design
Stanford community microgrid will encompass feeder-level microgrids with multiple points of common coupling to the surrounding distribution grid. To support the needs of local populations and critical infrastructure (such as hospital, important labs) during extended grid outages, the design must consider diverse DER options, like energy storage (battery and thermal storage), energy efficiency, flexible loads, solar PV. Link to final project presentation
Project #3 – Reinforcement Learning for Microgrid Operations and Design
Existing microgrids use control/optimizations over lumped models (no power flow) for operation. However, as the size of the microgrid grows, power flow becomes necessary. The problem immediately becomes hard to solve. In an attempt to connect the gaps between what is available today and what is needed in the future, this project leverages RL for efficiently solving a hard microgrid operation problem with the help of open source RL frameworks. Link to final project presentation