用大模型自动优化电动车充电，提升能效与用户适应性
Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles
- 用检索增强生成技术让大模型自动设计优化方案
- 在电动车充电调度中实现更高能效和更好适应性
- 适合智能电网、能源管理方向的研究者参考
物联网赋能的微电网能量优化与需求侧管理正被生成式人工智能（如大语言模型）深刻变革。本文探讨将大语言模型融入能源管理，以车联网中的电动车辆（IoEV）为例，展示其在自动化优化需求侧管理策略中的作用。研究分析了相关挑战与解决方案，并探索了大模型带来的新机遇。为此提出一种创新方法：通过检索增强生成技术，使大模型具备自动问题建模、代码生成与优化定制能力。实验结果表明，该方案在电动汽车充电调度与优化中表现优异，显著提升了能源效率与用户适应性。本工作展示了大模型在物联网微电网能量优化中的潜力，推动了智能化需求侧管理的发展。
原文摘要 · Abstract (English)
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.
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