Researchers at Lehigh University's Optimization and Machine Learning research group and IBM Research have made progress in solving optimal power flow problems.
Electric power should flow through the grid in a streamlined fashion, which is made possible by machines that process large quantities of data in real time and make optimal decisions.
The physics of the alternating-current model of power flows corresponds to polynomial optimization problems (POPs) but, until recently, researchers could apply the so-called Newton method to POPs to obtain a solution quickly.
Using recent developments in numerical algebraic geometry, the group has developed conditions and methods to also test surrogate problems more efficiently and, when it is safe, switch to the Newton method.
"This revolutionizes the field of polynomial optimization," says IBM Research's Jakob Marecek.
From Lehigh University
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