Research on Algorithm Driven Intelligent Management and Control Technology for Future Power Grid

Abstract

An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network.