ADVANCED MACHINE LEARNING TECHNIQUES FOR REAL-TIME OPTIMIZATION OF POWER GRID STABILITY IN THE PRESENCE OF DISTRIBUTED ENERGY RESOURCES

Abstract

This study explores the application of advanced machine learning techniques for real-time optimization of power grid stability in the presence of Distributed Energy Resources (DERs). As renewable energy sources such as solar and wind become more integrated into power grids, their intermittent nature poses significant challenges for grid stability. The research employs deep learning and reinforcement learning algorithms to manage power generation fluctuations, optimize grid performance, and enhance energy efficiency. The results demonstrate that machine learning models can successfully forecast power generation from DERs and optimize grid stability in real-time. Studied data indicates that solar power operates better alongside wind electricity generation and artificial intelligence algorithms to provide continuous enhancement of precision allowing increased operational output for stabilizing power grids.  Machine learning operates efficiently by detecting particular power loss positions in gearboxes during dynamic optimization testing.  DERs controlling the power flow stability function most intensely when solar power generates maximum output.  The study explains modern management systems powered by AI which boost operation of power grids that rely on renewable energy resources.  Energy transition in modern times depends on machine learning systems to create flexible electric grids that provide reliable service according to scientific studies.