Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles

TL;DR

This paper investigates how large language models can be integrated into demand side management for IoT-enabled microgrids, focusing on electric vehicle charging optimization, and introduces a retrieval-augmented approach to improve energy efficiency and customization.

Contribution

It proposes a novel retrieval-augmented LLM framework for energy management, automating problem formulation and optimization in IoT microgrids with electric vehicles.

Findings

Enhanced charging scheduling accuracy

Improved energy efficiency in microgrids

Better user customization capabilities

Abstract

The energy optimization and demand side management (DSM) of Internet of Things (IoT)-enabled microgrids are being transformed by generative artificial intelligence, such as large language models (LLMs). This paper explores the integration of LLMs into energy management, and emphasizes their roles in automating the optimization of DSM strategies with Internet of Electric Vehicles (IoEV) as a representative example of the Internet of Vehicles (IoV). We investigate challenges and solutions associated with DSM and explore the new opportunities presented by leveraging LLMs. Then, we propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. The results demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles, and highlight our solution's significant advancements in energy efficiency and user adaptability. This work shows LLMs' potential in energy optimization of the IoT-enabled microgrids and promotes intelligent DSM solutions.