Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study
This work addresses cost-effective infrastructure planning for electric vehicle adoption, though it is incremental in applying LLMs to optimization modeling.
The paper tackles the problem of planning electric vehicle charging infrastructure by jointly optimizing investment and charging assignments, using a large language model to assist in model formulation and a distributed algorithm for computational efficiency, achieving a 30% cost reduction in a real-world case study.
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.