Duncan Callaway, Professor

Open (1) Distributed solar and electric vehicle data mapping

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

We are working to understand how solar photovoltaics and electric vehicles impact electricity distribution networks. This Sloan Foundation project brings together engineers and economists to (1) assemble a unique data set of solar installations, EV adoption and power system impacts and (2) explore a range of questions that seek to understand how grids have historically responded (and will respond in the future) to new distributed energy resources.


The task is to generate and back-engineer a the maps of the distribution feeders for the main California Utilities. This will require a good deal of creativity and originality in the approach to construct the database. This is a project that will develop skills in geospatial analytics and the use of professional grade tools with applications to the integration of solar PV in California’s rooftops.

Day-to-day supervisor for this project: Jose Daniel Lara, Graduate Student

Qualifications: Among the tools that are relevant for the project we include Google Maps API, Python and PostGIS.

Weekly Hours: to be negotiated

Closed (2) Electric Systems modeling in multiple scales for high performance computing.

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The project is to develop modular multi-energy system simulation platform in collaboration The National Renewable Energy Laboratory (NREL) and Los Alamos National Laboratory (LANL). In this work, a new framework to model and study the effects of renewable energy at multiple time scales and in different sectors will be developed. The current practices in renewable energy integration studies don't allow analysists to perform integrated assessments and require ad-hoc mixtures of software packages developed by different vendors, making the evaluation costly and in many cases incomplete. Moreover, current tools do not reliably integrate electric power systems modeling with other sectors that in the recent years have acquired great relevance to advance the integration of renewable energy, such as natural gas and water networks.
The final objective is the development of a platform to model and analyze the interaction of renewable energy sources other sectors considering multiple timescales. The platform will be developed as an OpenSource tool available to operators and analysts to reduce the costs and technical challenges of performing a thorough assessment of renewable energy integration.

The project will be coded in the programming Language Julia with a focus in High Performance Computing Applications.

The job responsibilities include the development of code to build the models and the produce the accompanying documentation for future development.

Day-to-day supervisor for this project: Jose Daniel Lara, Graduate Student

Weekly Hours: to be negotiated

Closed (3) Cost of off-grid and decentralized solar power systems for energy access

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Over 1.3 billion people around the world still live without access to modern energy supplies. We are trying to understand the extent to which there are economies of scale in small solar-power systems for electricity access. The purpose is to characterize the relationship of costs to the size of solar power systems on the market from small lighting systems to community scale mini-grids up to 100 kW. These characterizations are necessary for efforts to accurately compare the costs of decentralized solar power systems to the grid. Given that solar panels and battery cells are modular, a large portion of the costs should scale linearly with the size of a system; however, there are additional costs that may scale differently.

A major component of this project will be analyzing usage data from solar systems in sub-Saharan Africa and modelling how peak and daily electricity consumption scales with the number of users. Additionally, the apprentice will likely survey reports and other empirical data sources to build cost models for other components of the solar system. The apprentice will have significant flexibility on their approach to the cost models. They will also have the ability to pose their own questions and explore related literature and theory.


Day-to-day supervisor for this project: Isa Ferrall, Graduate Student

Qualifications: Qualifications: Desire to learn data analysis techniques and ability to use MATLAB, Python, or R is a must, experience with data analysis is a plus. Experience with MongoDB and/or SQL, as well as experience with solar power systems also a plus. Enthusiasm is a must. Students applying for this URAP must explain 1) which aspects of the project(s) particularly engage them, 2) what skills they bring to the project(s), 3) how many hours per week they can commit.

Weekly Hours: to be negotiated