The Duke University Energy Initiative has appointed Dr. Luana Marangon Lima as its Associate Director for Educational Programs. She will work closely with faculty, staff, and students to enhance energy education offerings at Duke, where the Energy Initiative serves as a hub for energy activity across the university.
Lima is also Visiting Assistant Professor of Energy and Environment in the Environmental Sciences and Policy Division of Duke University’s Nicholas School of the Environment.
“Dr. Lima has brought interdisciplinary expertise and pedagogical savvy to the classroom and Bass Connections teams at Duke, particularly when it comes to the application of engineering, economics and data science approaches to power systems and grid management problems,” noted Energy Initiative director Dr. Brian Murray. “We know she will make valuable contributions as the Energy Initiative seeks to cultivate transformational energy education offerings for Duke students at all levels and across numerous degree programs.”
“While I was trained as an engineer, my research and teaching regularly cross paths with economics, law, policy, data science, and more,” said Lima. “I appreciate how this opportunity with the Energy Initiative will give me the chance to interact with students and faculty across the university and help strategize about our approach to energy education.”
Prior to joining Duke, Lima was an assistant professor of the electrical and energy systems department at Universidade Federal de Itajuba (Brazil). She also worked as a consultant for several years at MC&E, a consulting company that specializes in the generation, transmission and distribution of electricity.
Her research and teaching focus on optimization methods and data analytics applications to energy systems more specifically renewable energy integration, electricity markets, power generation planning and scheduling, and transmission and distribution grid regulation.
She received her Ph.D. in operations research and industrial engineering from the Cockrell School of Engineering at University of Texas at Austin (2011). She holds a M.Sc. (2007) and B.Sc. (2005) in electrical engineering from Universidade Federal de Itajuba, Brazil.
The position of associate director for educational programs was most recently held by Dr. Lori Bennear, the Juli Plant Grainger Associate Professor of Energy Economics and Policy, who is currently on sabbatical.
This blog post's authors—Simeng Deng, Asger Hansen, Galen Hiltbrand, Santiago Sinclair Lecaros, and Sean Maddex—are pursuing master’s degrees in environmental management at Duke University’s Nicholas School of the Environment. Their team's fall 2018 journey to Puerto Rico was supported by funding from the Duke University Energy Initiative and the Nicholas School's Career & Professional Development Center.
How can Puerto Ricans increase grid reliability on their island?
That’s the question our team of five students has been tackling this year for our master’s project at Duke University’s Nicholas School of the Environment, with guidance from faculty mentors Betsy Albright and Lori Bennear.
We are working with the community of Toro Negro, which recently founded the Comunidad Solar de Toro Negro to own and manage Puerto Rico’s first fully operational community solar microgrid.
In fall 2018, three of our team members (Asger, Galen, and Santiago) visited Toro Negro, located in the mountainous province of Ciales, to learn more about the microgrid system.
Many factors fueled the creation of this unique project, which now powers the day-to-day activities of 26 households. Toro Negro has confronted the challenges of an obsolete power grid as well as the biggest power outage in US history. After Hurricane Maria hit the island in September 2017, Toro Negro went 8 months without power. But one critical factor has worked in Toro Negro’s favor: a unified and well-organized community.
By spending a week in Puerto Rico and meeting with dozens of local stakeholders, we gained insights about the island’s energy system that months of research in Durham could have never provided.
Here are 3 of our takeaways:
1. Policy cannot keep up.
While in Puerto Rico, we saw firsthand how activity on the ground by residents and communities is moving faster than policy or regulations. As can be seen in Toro Negro, communities are moving forward with innovative renewable energy projects to decrease their dependence on the unreliable grid. Policymakers are one step behind, working to set regulations for systems that are already in place. Meanwhile, since policies are still changing, it’s difficult for nongovernmental organizations (NGOs) to identify which types of projects will be the most attractive in the long-term.
2. Puerto Ricans are taking charge.
In multiple meetings, local stakeholders told us of a longstanding mentality on the island: the idea that “if there’s a problem, the government will come in and fix it.” They explained that this mindset has been reinforced by the history of colonialism on the island and the fact that it remains a territory of the U.S. As a result, they explained, many have historically depended blindly on officials and authorities to provide support and solutions for every situation.
But going without power for over half a year was a devastating lesson for much of the island. Many lives were lost during this time due to the lack of access to energy, limited medical services, and primary health centers being without power.
Stakeholders told us that more and more Puerto Ricans are realizing that the government does not have the capacity to fix some problems. Instead, the aftermath of Hurricane Maria has encouraged communities to take matters into their own hands. That’s why, with support from NGOs like Fundación Comunitaria de Puerto Rico and Somos Solar, community members in Toro Negro initiated the microgrid cooperative project to increase electricity reliability and resilience in the face of natural disasters.
3. Microgrids are outperforming the main grid.
Today Puerto Rico's power reliability is delicate. It is normal to have outages or frequency issues in communities across the island, from isolated mountain areas to urban centers. The situation is being improved by projects installed around the island that couple renewable energy generation and storage. Thanks to these projects, some rural communities experience a greater degree of power reliability than do those in cities.
Halfway through our visit, we met with Javier Rivera, the project engineer of Fundación Comunitaria de Puerto Rico, which is overseeing the Toro Negro project. He told us that every time the power goes out in San Juan (the island’s capital and largest city), he calls Tito Figueroa the community leader in Toro Negro. He asks, “Hey Tito, how’s your electricity system holding up?” And invariably, Tito replies, “It’s working great.” Javier will then inform Tito that he’s lucky because San Juan and other parts of the island are facing an outage. Then they both laugh and hang up.
While Toro Negro is the first community to successfully implement a microgrid on Puerto Rico, the community is not alone in its move toward renewable energy. Supported by local foundations and developers, more communities, such as El Coqui and Vieques, are taking action, supported by local NGOs and developers to create microgrids and cooperatives that benefit families and municipalities across the island.
However, moving forward, funding will be a formidable challenge for these projects. As time passes and Hurricane Maria is less at the forefront of people’s minds, the island cannot expect the same level of philanthropic investment.
That’s why our team of five students is collaborating with Toro Negro to establish a strong microgrid business plan—in the hopes that it can serve as a model for other communities looking to increase grid reliability by shifting to renewable energy.
Data+ is a 10-week summer research experience that welcomes Duke undergraduates interested in exploring new data-driven approaches to interdisciplinary challenges. Students join small project teams, collaborating with other teams in a communal environment. They learn how to marshal, analyze, and visualize data, while gaining broad exposure to the modern world of data science.
For Summer 2019's program, students can choose from an unprecedented number of energy projects. From oil and gas production to smart meters, wholesale energy markets, energy access, and Duke's own energy use, these projects tackle a wide range of real-life energy problems. Check out the Data+ Fair on Thursday, January 17th to talk with project leads, and learn more and apply by the Feb. 25th deadline! For more on these six energy projects, check out the descriptions below:
Producing oil and gas in the North Sea, off the coast of the United Kingdom, requires a lease to extract resources from beneath the ocean floor and companies bid for those rights. This team will consult with professionals at ExxonMobil to understand why these leases are acquired and who benefits. This requires historical data on bid history to investigate what leads to an increase in the number of (a) leases acquired and (b) companies participating in auctions. The goal of this team is to create a well-structured dataset based on company bid history from the U.K. Oil and Gas Authority; data which will come from many different file structures and formats (tabular, pdf, etc.). The team will curate these data to create a single, tabular database of U.K. bid history and work programs.
Producing oil and gas in the Gulf of Mexico requires rights to extract these resources from beneath the ocean floor and companies bid into the market for those rights. The top bids are sometimes significantly larger than the next highest bids, but it’s not always clear why this differential exists and some companies seemingly overbid by large margins. This team will consult with professionals at ExxonMobil to curate and analyze historical bid data from the Bureau of Ocean Energy Management that contains information on company bid history, infrastructure, wells, and seismic survey data as well as data from the companies themselves and geopolitical events. The stretch goal of the team will be to see if they can uncover the rationale behind historic bidding patterns. What do the highest bidders know that other bidders to not (if anything)? What characteristics might incentivize overbidding to minimize the risk of losing the right to produce (i.e. ambiguity aversion)?
A team of students led by researchers in the Energy Access Project will develop means to evaluate non-technical electricity losses (theft) in developing countries through machine learning techniques applied to smart meter electricity consumption data. Students will use data from smart meters installed at transformers and households through a randomized control trial. Students will develop algorithms that can be used to detect anomalies in the electricity consumption data and create a dataset of such indicators. This project will provide researchers with new ways of incorporating electricity consumption data and applications for electricity utilities in developing country settings.
This team will explore how to develop machine learning techniques for analyzing satellite imagery data for identifying energy infrastructure that can be trained once and applied almost anywhere in the world. Led by researchers from the Energy Data Analytics Lab and the Sustainable Energy Transitions Initiative, the team will design two datasets: the first containing satellite imagery from diverse geographies with all energy infrastructure labeled, and the second a synthetic version of the same imagery. These data will enable research into whether synthetic imagery may be used to adapt algorithms to new domains. The better these techniques adapt to new geographies, the more information can be provided to researchers and policymakers to design sustainable energy systems and understand the impact of electrification on the welfare of communities.
Duke must reduce its energy footprint as Duke strives for Carbon Neutrality by 2024. To help this cause, a team of students will review troves of utility usage data and attempt to build an attractive and practical monthly energy use report for every building and school at Duke. This report will not only show historical usage but also develop an energy benchmark for comparison and conservation tips for local administrators to take action. Duke Energy has been using a similar report to encourage conservation at the residential level for years. It is time to bring energy use transparency to the broader Duke community and inspire action.
Tether Energy finances and operates various distributed energy resources operating in wholesale energy markets, ranging from solar panels to residential smart thermostats. Tether also does financial trading when it identifies arbitrage opportunities in these markets. One of Tether Energy's main operational risks is the very high volatility in wholesale real time (or spot) energy prices. Where stock markets consider a 30% change in price large, energy markets routinely face changes in price on the order of 300%. This high volatility comes from three main "shocks": 1. power demand changes, due to unpredictable weather, industrial patterns, or human consumption; 2. fuel shortages, driven by trade, extraction/exploration, and gathering/transportation economics; 3. electrical transmission outages, driven by operational failure, extreme weather events, and human behavior.
First, this project team will identify what should be considered an "extreme" price shock from 5-10 years of historical data in PJM. Second, the team will work to automatically identify potential causes for the rare events from news articles, public filings, and Tether's own structured data. Third, the team will build reasonable priors for the occurrences of these rare events, and incorporate potential covariance between the events using copulas or similar methods. Finally, the team will create a simple classifier such as logistic regression to predict the likelihood of a price shock on a given day. The model needs to be evaluated with a walk-forward backtest, training on about 3 years of data at a time, and shifting forward the training window in approximately one-month increments, to smooth out potential bias and overfitting in the model.