Daniel Kammen, Professor

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

Applications for Fall 2017 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 Systems Data Analysis to Achieve Universal Electricity Access

Applications for Fall 2017 are now closed for this project.

Over one billion in the world do not have access to electricity, mostly in sub-Saharan Africa and parts of Asia. This research focuses on the centralized and decentralized planning efforts to achieve universal electricity access. It consists of two parts: Capacity Expansion Modeling and Data Visualization. Weekly hours to be negotiated for both projects.
(1) Capacity Expansion Modeling (one student)

Achieving universal energy access requires addressing both the supply-demand mismatch in grid-connected regions and the lack of access in off-grid regions. Capacity expansion modeling is used to explore the scale and type of energy resources required to achieve 100% access, and the role of on-grid renewables in closing the supply-demand mismatch. This project entails converting an existing capacity expansion model to Python. This conversion is ongoing. The apprentice will be expected to:

•Assist in completing the model conversion to Python.
•Particularly, the apprentice will work on improving the usability of the model such as creating a user interface for the model and/or transferring the model to a webpage.

Qualifications: Proficiency in Python (Pyomo) and web design. Basic understanding of linear programs and power systems.
Learning outcomes: Gain skills and experience in power systems modeling.

(2) Self-Generation and Energy Justice (one student)

This project involves gathering and analyzing data related to self-generation and electricity access globally. This will be used to explore the energy justice implications of achieving energy access. The apprentice will be expected to:

•Gather and analyze data on the proliferation of electricity self-generation in the United States, Latin America and sub-Saharan Africa.
•Create visualizations and graphics using the data gathered.

Learning outcomes: The apprentice will gain experience in exploratory data analysis and visualization, and will have the opportunity to research energy issues from an interdisciplinary social science perspective.

Qualifications: Proficiency in R, Python or any data visualization methods is required.

Day-to-day supervisor for this project: Nikky Avila, Ph.D. candidate

Weekly Hours: to be negotiated

Closed (3) Energy Systems Modeling: Mexico

Applications for Fall 2017 are now closed for this project.

(1) 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.

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.

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.
(2) United Nations' Data Challenge for Climate Action: "Transportations systems in Mexico: A true potential for electro-mobility development"

Our lab has partnered with an international Climate Change agency in Mexico to study the impact of traffic congestion on air quality, and vehicle electrification potential in different cities in Mexico.

This is a big data problem that will require a strong time commitment from you from now until October 1st (competition deadline), and then a reduced/reasonable time commitment afterwards. Your tasks will involve: (a) literature review, (b) database scripting, (c) statistical analysis, (d) poster design!, and (e) proposed future work.

Really unique experience to get a hands-on experience in an international competition (Go Bears!!).

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 competition.

Day-to-day supervisor for this project: Sergio Castellanos, Post-Doc

Weekly Hours: to be negotiated

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

Closed (4) Energy Systems Modeling: USA

Closed. This professor is continuing with Spring 2017 apprentices on this project; no new apprentices needed for Fall 2017.

There is much criticism that renewables are damaging the grid through high variability and that their appropriate deployment is limited to a few solar- or wind- rich regions. These studies are a way to show that widespread adoption of renewables with energy storage offer a clean grid stabilizing alternative, and thus can address some public misconceptions regarding renewable energy. With increasing amounts of renewable power being incorporated onto the electrical grid, the SWITCH (a loose acronym for “solar, wind, conventional, and hydroelectric generation and transmission”) model was created as an investment-planning tool. It is a multi-period linear programming model to determine the most cost-effective decisions regarding new energy generation projects and electrical grid improvements to both meet electricity demand and reduce carbon dioxide emissions. More information on the SWITCH model can be found at https://rael.berkeley.edu/project/switch/.

The main objective of this project is to expand the SWITCH-WECC (western USA) model to the entire continental United States. Policy makers across the country can use the updated SWITCH-USA model to make more educated recommendation to encourage appropriate deployment of both renewable energy. In order to extend the SWITCH model to the continental United States, large datasets from various sources need to be cleaned and appropriately formatted. Data will be scraped from the websites of NREL and DOE. We are looking for a *very limited number* of highly motivated students that would like to further develop and apply their data management skills.

Day-to-day supervisor for this project: Deborah Sunter, Post-Doc

Weekly Hours: to be negotiated

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