Research on a Two-Layer Demand Response Framework for Electric Vehicle Users and Aggregators Based on LLMs

TL;DR

This paper introduces a two-layer demand response framework for EVs and aggregators that uses large language models to optimize energy use, improve charging efficiency, and stabilize smart grid operations.

Contribution

It presents a novel two-layer framework leveraging LLMs and PSO optimization to enhance EV demand response and grid stability.

Findings

Improves EV charging efficiency

Alleviates peak power loads

Stabilizes smart grid operations

Abstract

The widespread adoption of electric vehicles (EVs) has increased the importance of demand response in smart grids. This paper proposes a two-layer demand response optimization framework for EV users and aggregators, leveraging large language models (LLMs) to balance electricity supply and demand and optimize energy utilization during EV charging. The upper-layer model, focusing on the aggregator, aims to maximize profits by adjusting retail electricity prices. The lower-layer model targets EV users, using LLMs to simulate charging demands under varying electricity prices and optimize both costs and user comfort. The study employs a multi-threaded LLM decision generator to dynamically analyze user behavior, charging preferences, and psychological factors. The framework utilizes the PSO method to optimize electricity prices, ensuring user needs are met while increasing aggregator profits. Simulation results show that the proposed model improves EV charging efficiency, alleviates peak power loads, and stabilizes smart grid operations.