Daniel Kammen, Professor

Closed (1) Electric Systems Modeling in Multiple Scales for High Performance Computing

Applications for Spring 2018 are now closed for this project.

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: José Daniel Lara, Ph.D. candidate

Weekly Hours: to be negotiated

Closed (2) Energy and Environmental Modeling: Mexico

Applications for Spring 2018 are now closed for this project.

1A: SWITCH- Mexico

The objective of SWITCH-Mexico will be to test your research skills by expanding on (1) literature on decarbonization pathways, and their methodologies, and (2) wrangling data (python, SQL, GIS). For this, your work will consist first in summarizing some literature and then proposing some exciting scenarios (ex.: energy efficiency, EVs, etc.) that you will help build in our still-developing model. The results will contribute and potentially inform policy makers in Mexico, so you will experience first-hand the impact of your work.

We are also looking to implement a solid visualization who has been already established (first steps) in Javascript, and which we’d expect you to continue taking it to the next level.

Qualifications: We are looking for highly motivated students that would like to further develop and apply their data management skills. This project is an opportunity not only to improve leadership, originality, and research skills, but also for you to exposure to national energy policy.


Day-to-day supervisor for this project: Sergio Castellanos, Postdoctoral Researcher

Weekly Hours: to be negotiated

Related website: https://rael.berkeley.edu/project/switch/

1B: EVALUATING AIR QUALITY FROM BIG DATA:

This is a big data problem around the evaluation of air quality data from transportation and one that will require a strong time commitment. Your tasks will involve: (a) literature review, (b) database scripting, (c) statistical analysis, (d) visualization and (e) proposed future work.

Really unique experience to get a hands-on experience in cutting-edge data sources.

Qualifications: We are looking for highly motivated and passionate students proficient in python, SQL, GIS, and/or postGIS who have the time availability to immerse in this project.

Day-to-day supervisor for this project: Sergio Castellanos, Postdoctoral Researcher

Weekly Hours: to be negotiated

1C: CleanwebMX: Platform to Report Environmental Justice Issues


Mexico is a country where crimes against people and the environment go unpunished, unaccounted for, and are usually forgotten. As a response to an institutional gap that adequately empowers communities to report environmental justice issues, CleanwebMX has developed a platform where users can report issues anonymously via SMS, Twitter and Google forms. In addition, CleanwebMX has mapped a diverse layer of data including water pollution, air pollution, mining, illegal logging, power plant stations, and refineries, among many others issues in the country. Social demographic layers include vulnerable low, low-middle income populations and indigenous communities. The outcomes of participating in this project will be to contribute to the development of a platform to empower vulnerable communities, publish Mexico's first data-driven systematic review of environmental justice issues, and present the results to Mexico's Ministry of the Environment and Social Development.

Day-to-day supervisor for this project: Diego Ponce de Leon Barido, Ph.D. Candidate

Weekly Hours: to be negotiated


Qualifications: Qualifications for all SWITCH projects: Applicants must be highly-motivated with enthusiasm for renewable energy and data management. Preference will be given to applicants with experience with relational databases (particularly PostgreSQL), web-scraping, data munging, and data visualization. Some experience with GIS, AMPL, Python, data analysis tools. In addition, for the GUI development, we’re looking for a colleague who is well versed in JS (javascript).

Weekly Hours: to be negotiated

Closed (3) Quantifying the Role of Partisanship in Rooftop Solar PV Adoption in the U.S.

Applications for Spring 2018 are now closed for this project.

Support or opposition for renewable energy policies often take on a partisan character in U.S. politics. However, solar PV and supporting state and federal policies benefit consumers across the political spectrum. In turn, the benefits of rooftop solar PV policies create interest groups apart from ideological leanings. This has important implications for the politics of solar policy, going forward. Hence, this project seeks to quantify role of political voting behavior on adoption of solar PV.



We are looking for a student to compile, clean, and merge geospatial (GIS) data for each state’s:

1) Voting data
2) Solar PV installation data
3) U.S. Census Bureau income

Student will gain experience in data management with multiple geospatial datasets, merging and joining spatial data across scales, and applying Python scripting language to automate and document workflows. Student will have the potential for authorship if they show dedication to the project and substantially contribute to the different stages.


Day-to-day supervisor for this project: John Dees, Ph.D. Candidate and Deborah Sunter, Postdoctoral Researcher, Post-Doc

Qualifications: We are looking for a highly motivated student, interested in solar PV or renewable energy politics. Strong organizational skills and attention to details are required. Knowledge of python and some GIS background required. Experience with arcpy, ArcMap, and QGIS preferred.

Weekly Hours: to be negotiated

Closed (4) Low-cost Monitoring to Improve Grid Reliability

Applications for Spring 2018 are now closed for this project.

Frequent electrical power failures drastically reduce quality of life and weaken the economic opportunities enabled through access to the electric grid. High-resolution measurements of the frequency and duration of power outages are critical to understanding and improving grid reliability. These measurements are also necessary to study deeper economic and socio-economic questions about how unreliable electricity impacts economic development and growth. But, due to the lack of appropriate monitoring technologies, it is not economically feasible for many utility companies in the developing world to gather this data. This projects builds on a long partnership with the Zanzibar Electricity Corporation (ZECO), the sole electricity distribution utility in Unguja, Tanzania, to deploy the second generation of our novel, low-cost power grid monitoring system. This system monitors the location, duration, and scope of problems at low-voltage, distribution level of the grid in near real-time. Over the course of our proposed pilot, we aim to both measure and improve the reliability and accuracy of our system. Additionally, we plan to leverage our connections with ZECO to co-develop an interface that allows data from our pilot to directly help inform utility grid maintenance.



Day-to-day supervisor for this project: Veronica Jacome, Ph.D. candidate

Qualifications: We are looking for highly motivated and passionate students with knowledge of GIS, R, spatial statistics, electric power systems, and/or other relevant skills.

Weekly Hours: to be negotiated