用大模型辅助优化充电桩投资与调度，降本三成。
Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study
- 用大语言模型自动生成优化模型公式，减少人工建模负担。
- 结合时空需求动态，使总成本降低30%。
- 适合交通规划、能源管理领域的研究人员和决策者。
电动汽车充电基础设施需求增长带来重大规划挑战，亟需高效的投资与运营策略以实现低成本服务。然而，充电分配在应对时空需求变化方面的潜力尚未充分挖掘。本文提出一种集成方法，联合优化投资决策与充电分配，同时考虑时空需求动态及其相互依赖关系。为提升建模效率，我们利用大语言模型（LLM）从结构化自然语言描述中生成并优化数学公式，显著降低建模难度。所提优化模型可实现投资与运营的最优协同决策。此外，针对高维场景下的计算复杂性，提出基于交替方向乘子法（ADMM）的分布式优化算法，可在标准计算平台运行。通过使用中国成都150万条真实出行记录的案例研究验证，相比无充电分配的基线方案，总成本降低30%。
原文摘要 · Abstract (English)
The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges, requiring efficient strategies for investment and operation to deliver cost-effective charging services. However, the potential benefits of EV charging assignment, particularly in response to varying spatial-temporal patterns of charging demand, remain under-explored in infrastructure planning. This paper proposes an integrated approach that jointly optimizes investment decisions and charging assignments while accounting for spatial-temporal demand dynamics and their interdependencies. To support efficient model development, we leverage a large language model (LLM) to assist in generating and refining the mathematical formulation from structured natural-language descriptions, significantly reducing the modeling burden. The resulting optimization model enables optimal joint decision-making for investment and operation. Additionally, we propose a distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) to address computational complexity in high-dimensional scenarios, which can be executed on standard computing platforms. We validate our approach through a case study using 1.5 million real-world travel records from Chengdu, China, demonstrating a 30% reduction in total cost compared to a baseline without EV assignment.
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