For instance, according to Fisher-Vanden, the power system model optimizes at an hourly and spatial grid scale, whereas the economic model optimizes at a yearly and state-level scale. Water scarcity may cause certain power generators to go offline, leading to spikes in electricity prices and potential outages. Consumers of electricity will respond to these price spikes by reducing demand for electricity which will reduce the need for electricity generation. To capture these feedbacks, the two models must pass information to each other, re-optimize, and iterate until convergence is reached. Writing the code to automate and manage this process in an efficient way posed a challenge to the team.
Collaborating with ICDS’s RISE team helped the team address this computational challenge. External funding from the Program on Coupled Human and Earth Systems provided support for one RISE team member’s time for several months. Fisher-Vanden's team partnered with Jeff Nucciarone, a research and development engineer, whose expertise is in optimizing and parallelizing computer code. Both optimization and parallelization allow code to run faster by eliminating unnecessary steps in the code and breaking the code down into chunks that can run simultaneously.
“I wrote a parallelizer, which used an interface to manage 52 separate processes that would run at same time,” he said. “It also included logic to detect common failure modes, so if the code detected failure for any of the 52 processes, it would restart quickly. Improving this step allowed greater automation of the workflow.”
The result reliably and efficiently connects the power system model and the economic model, a first step in the team’s process. Now, Fisher-Vanden's team is working with RISE to integrate other models into this coupled system, specifically a water balance model and a crop/land-use model. They are also exploring whether machine learning techniques can help identify stress points in the coupled system. This could help inform decision-makers when and where older power plants should be retired, for example. These types of decisions are typically made on a state-by-state basis, but the impacts often extend beyond state lines. Being able to quantify these impacts could improve future decision-making, said the researchers.
Providing RISE time to agricultural sciences researchers
Fisher-Vanden and Grozinger praised the RISE team’s versatility and their ability to translate information between the worlds of data science and the researchers’ respective domains.
“If you want to use cutting-edge computational tools, you have to know what they are to make that connection with your research,” said Fisher-Vanden. “The RISE team was able to bridge that gap.”
After their positive experience of collaborating with RISE, Fisher-Vanden and Grozinger sought to expose others in the College of Agricultural Sciences to this valuable resource through a joint SAFES-RISE seed grant competition. Through this seed grant program, researchers can apply to be allocated time with RISE team members who can address data science or computational science challenges. The program mirrors a similar program, established by ICDS and funded by the National Science Foundation, which is designed to enable computational research at the University scale.
“Many faculty members are used to having everything run in our own lab, but for these types of data science challenges, we need help,” said Grozinger. “We have expertise within our own fields of genomics, organismal biology, and ecology — we do not have the training or expertise in computational data science that is needed for constructing these sophisticated systems. The RISE team provides a great system for having access to a team of skilled specialists.”
Researchers in the College of Agricultural Sciences can apply for a SAFES-RISE seed grant through May 31. Researchers in other Penn State colleges or campuses can also apply for RISE time through the ICDS RISE seed grant program, which will be offered each semester through 2023.