Opportunities and Threats in a Smart Grid Setting for AI-Enabled Demand Response
Authors:-Ryan Ed
Abstract-One of the most essential commodities for modern humans is electricity. The notions of smart grids with demand response were established to tackle problems and obstacles in the transfer of power via the conventional grid. Wind turbines, microgrids, and defect detectors are just a few of the power generating, transmission, and distribution components that contribute to the massive amounts of data produced everyday by these systems. Smart electric appliances and meters also play a role in load control. New developments in computers and big data have made it possible to use Deep Learning (DL) to forecast electrical consumption and peak hours by discovering patterns in the produced data. Inspired by the potential benefits of deep learning for smart grids, this article aims to provide a thorough overview of how DL is being used for intelligent smart grids with demand response. Here we lay down the groundwork for deep learning, smart grids, demand response, and the reasoning behind all of this. Second, we take a look at the most recent developments in deep learning’s use to smart grids and demand management, including topics like electric load predicting, state estimation, energy theft identification, energy sharing, and trading. Furthermore, we demonstrate the usefulness of DL via a range of applications and use scenarios. Lastly, we draw attention to pressing concerns and possible future possibilities in the application of DL to smart grids and demand response, as well as the difficulties already encountered in the literature.
