Year: 2025
Type: article
Abstract: The sharp increase in residential and industrial electricity demand has overwhelmed legacy grids, leading to frequent energy waste or supply shortfalls. Acting as a “digital brain,” the smart grid continuously monitors demand and generation, then reallocates power in real time through deep data analytics. This review highlights AI-driven forecasting technology that not only anticipates the impact of weather variability on renewable energy generation but also precisely identifies next-day demand peaks, thereby enabling optimized dispatch. When coupled with a dynamic tariff scheme, this technology incentivizes consumers to shift their usage away from peak periods. Additionally, it proactively notifies consumers of low-price windows, helping them reduce energy costs. However, future work must address several key challenges, including high equipment costs, limited rural coverage, and the need to cultivate user awareness. Continuous refinement of grid operations is also essential to ensure power quality. Despite these hurdles, the smart grid remains a pivotal route to a green-energy transition.
Source: Theoretical and Natural Science
Author: Miner Huang
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413363652