The researchers train the machine by feeding it a set of data that includes the solutions, as if the machine were studying previous “exams” before trying new ones. “Instead of solving a number of difficult optimization models over several hours or days, we train the model ahead of time and then get the answer right away.” “The idea is to generate a large number of scenarios and train the machine learning model to tell us the answer,” said Kibaek Kim, an assistant computational mathematician at Argonne. With resources such as the Argonne Leadership Computing Facility ( ALCF), a DOE Office of Science User Facility, researchers can simulate multiple scenarios in parallel, moving the process along more quickly. Solving such a complex model is time-consuming. Which of those possibilities will merit the most attention? In a region with 1,000 electric power assets, such as generators and transformers, an outage of just three assets can produce nearly a billion scenarios of potential failure. Now, fluctuating supplies of renewable energy, some of it flowing from consumers with rooftop solar panels outfitted with smart meters, are increasing the number of variables grid operators must consider.Īrgonne researchers are working on optimization models that use machine learning, a form of AI, to simulate the electric system and the severity of various problems. Grid operators have always dealt with challenges and some amount of uncertainty from factors such as extreme weather to equipment failures. The work combines Argonne’s long-standing grid expertise with its advanced computing facilities and experts. Department of Energy’s ( DOE) Argonne National Laboratory are developing new ways to extract insights from mountains of data on the electric grid, with the goal of ensuring greater reliability, resilience and efficiency. With the assistance of artificial intelligence ( AI), researchers at the U.S. ![]() This challenge of factoring in both the certain and the unknown to deliver electricity under all kinds of scenarios involves a series of incredibly complex math problems. This information can be used to alert the operator that they may have something they don’t expect on the grid.” - Mihai Anitescu, senior computational mathematician at Argonne National Laboratory ![]() “Argonne’s approach decides whether the current conditions of the system are expected based on past behavior, or whether something is new and different. To manage the inherent uncertainty in predicting power needs and avoid surprises, electric grid operators rely on computer models that help estimate everything from power demand to traffic patterns. How much electricity will you need tomorrow? Answering that question is a lot like looking ahead to your morning commute - somewhat predictable, but by no means ironclad. The following article is part of a series on Argonne National Laboratory’s efforts to use the predictive power of artificial intelligence, specifically machine learning, to advance discoveries in a broad range of scientific disciplines.
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