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The Precourt Institute for Energy is part of the Stanford Doerr School of Sustainability.

AI for Decarbonized, Climate-Resilient, and Equitable Energy Infrastructure and Services

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Precourt Pioneering Project

Awarded in the area of Artificial Intelligence (AI) to solve critical challenges in energy, climate, and integrated energy systems.
Award start date: July 1, 2021.

Background

Graphic of energy services supply and demand

The electricity grid is undergoing a transformation with the addition of renewable power, natural gas generation, heat pumps for residential heating, electric vehicles, and adoption of information technology to understand when and how we use this power. These changes are highlighting the interrelation of the power grid, natural gas, transportation, information, and the inequality of access by communities to these infrastructures. This inequality is highlighted by the issues faced when the grid and power infrastructure lack resilience against extreme weather events.


Project Goals

This project is aimed at answering three major questions to achieve the goals of decarbonization, equity and resilience, from the neighborhood to the national level.

1. How do we build a decarbonized energy system that serves all people and is resilient to climate change and extreme events?
2. How do we assess climate risks and population vulnerability?
3. How do we adapt?


Approach and Timeline

Machine learning will be used to optimize four criteria simultaneously: decarbonization, equity, affordability, and resilience to the impacts of climate change, including extreme weather events. Using a spatially aware approach the researchers will consider contextual features such as hotspots where extreme weather events are likely to occur, the impacts to infrastructure, and to people and services. The tools created will be made publicly available, and the information will be provided to grid operators, planners, policymakers, and others across the U.S. and world.

In year 1, the team will build the extreme event scenario analysis and forecasting modules; the key metrics of the Access Atlas, including algorithms for vulnerability detection; complete mapping work in Energy Atlas; and begin work on the management module by investigating optimal reserve management strategies.

In year 2, the team will build and make publicly available: Access Atlas, Grid Risk Atlas; Energy Atlas; and Pre-post grid adaptation management.


Publications

1. Bergman, D., Sun, T., Buechler, E., Zanocco, C., Rajagopal, R. 2022. “The Grid under Extremes: Pandemic Impacts on California Electricity Consumption.” IEEE Power and Energy Magazine. 

2. Zuin, G., Buechler, R., Sun, T., Zanocco, C., Castro, D., Veloso, A., Rajagopal, R. 2022. “Revealing the Impact of Extreme Events on Electricity Consumption in Brazil: A Data-Driven Counterfactual Approach”. Proceedings of the 2022 IEEE PES General Meeting

3. Zanocco, C., Sun, T., Stelmach, G., Flora, J., Rajagopal, R., Boudet, H. 2022. “Assessing Californians’ awareness of their electricity use patterns.” Nature Energy. 

4. Wang, Z., Arlt, M.L., Zanocco, C., Majumdar, A., Rajagopal, R. 2023. “DeepSolar++: Understanding Residential Solar Adoption Trajectories with Computer Vision and Technology Diffusion Models”. Joule

5. Zuin, G., Buechler, R., Sun, T., Zanocco, C., Galuppo, F., Veloso, A., Rajagopal, R. 2023. “Extreme event counterfactual analysis of electricity consumption in Brazil: Historical impacts and future outlook under climate change.” Energy.

6. Sun, T., Zanocco, C., Flora, J., Johnson, S., Soto, H., Rajagopal, R. 2023. “Cooling-Related Electricity Consumption Patterns for Small and Medium Businesses in California: Current Impacts and Future Projections under Climate Change.” Energy and Buildings. 

7. Wang, Z., Wara, M., Majumdar, A., & Rajagopal, R. 2023. “Local and Utility-Wide Cost Allocations for a More Equitable Wildfire-Resilient Distribution Grid.Nature Energy.

8. Wang, Z., Majumdar, A., & Rajagopal, R. 2023. “Geospatial Mapping of Distribution Grid with Machine Learning and Publicly-Accessible Multi-Modal Data.Nature Communications. 

9. Sun, T., Zanocco, C., Flora, J., Rajagopal. 2024. “Mapping the Depths: A Stocktake of Underground Power Distribution in United States.” IEEE Power & Energy Society General Meeting (PESGEM).

10. Wussow, M., Zanocco, C., Wang, Z., Prabha, R., Flora, J., Neumann, D., Majumdar, A., Rajagopal, R. 2024. “Exploring the potential of non-residential solar to tackle energy justiceNature Energy.


Team Members

Ram Rajagopal

Ram Rajagopal
Ram Rajagopal is an expert in power systems and machine learning. He has published >300 papers in data-driven analytics of power systems and smart grid. Directs the Stanford Global Projects Center and the Sustainable Systems Lab. Has led large DOE projects, including Powernet, an ARPA-e NODES program.
 

Dr. Arun Majumdar

Arun Majumdar
Prof. Arun Majumdar is an expert in power systems and energy policy. He was the founding director of the Advanced Research Projects Agency - Energy (ARPA-E) under the Department of Energy.

 

Andrew Ng

Andrew Ng
Andrew Ng is VP & Chief Scientist of Baidu; Co-Chairman and Co-Founder of Coursera; and an Adjunct Professor at Stanford University.  In 2011 he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to >100,000 students, leading to the founding of Coursera.
 

Inês Azevedo

Ines Azevedo
Prof. Ines Azevedo research focuses on sustainable, realistically feasible and equitable energy systems. Her work uses quantitative models to understand the effects of policies and decisions on environmental, climate, economic and air pollution related outcomes, with an emphasis on incorporating distributional and environmental justice issues.