Year: 2024
Type: article
Abstract: Modern power grids face growing complexity due to the rise of distributed energy resources (DERs), electric vehicles, and fluctuating renewable sources. Traditional energy management methods often fall short in handling this dynamic environment. This paper introduces a deep reinforcement learning (DRL)–based framework for smart grid energy distribution. It uses multiple intelligent agents operating at different grid levels to make real-time decisions based on supply, demand, pricing, and grid conditions. The agents are trained within a simulated environment and tested across varied scenarios. The framework demonstrates improved energy efficiency, peak load reduction, and system resilience. Overall, this study highlights how DRL can effectively manage energy in a decentralized, adaptive grid environment
Authors: Rakhi Biswas, Sunit Jana, Mrinmoy Pal, Koushik Pal, Antara Ghosal, Suparna Biswas
Institution: Guru Nanak Institute of Technology
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411655675