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Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks
Stavros Orfanoudakis, Peter Palensky, Pedro P. Vergara
TL;DR
EV charging methods face challenges with the dimensionality and dynamics of real-time charging, a concern for grid stability amid EV adoption. The paper combines graph representations and an LLM-based offline RL approach; it reports better trade-offs than heuristic and RL baselines in its evaluated scenario.
Problem
Traditional optimization and RL methods struggle with the high dimensionality and dynamic nature of real-time EV charging, while efficient infrastructure use matters for grid stability.
Source: §I · p. 1Method
The approach converts EV charging state-action data into graphs and trains a Decision Transformer, an LLM-based sequence-prediction model, using offline RL data.
Source: B. GNN Embeddings · p. 2, C. Learning with LLMs · p. 2, A. Data Gathering for Offline RL · p. 2Results
The GNN+LLM achieved a better trade-off among user satisfaction, costs, and grid violations than heuristic baselines; PPO had 85.6% average user satisfaction and 168.9 kW in power violations.
Source: §IV · p. 3Takeaways & Limitations
In the evaluated complex, large-scale EV charging scenarios, the authors conclude that the hybrid approach addresses scalability and dynamic challenges that conventional methods struggle to overcome.
Source: §V · p. 3
Abstract
from arXiv · showhide
Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions.
I. INTRODUCTION
EV charging must scale without worsening grid congestion, supporting grid stability and EV adoption. The paper introduces graph-based representations and LLM decision-making for charging, reporting improved accuracy and reliability.
- Efficient use of charging infrastructure is presented as important for preventing grid congestion and supporting grid stability and EV adoption.Source: §I · p. 1
- The paper introduces LLMs for EV-charging decision-making and presents the approach as a new application in this domain.Source: §I · p. 1
- The graph embedding recasts EV charging as a graph so GNNs can capture relational dynamics in charging infrastructure.Source: §I · p. 1
- The authors report that combining LLMs and dynamic GNNs enhances the accuracy and reliability of smart charging.Source: §I · p. 1
II. EV SMART CHARGING
The section frames V2G smart charging as a CPO problem: schedule EV charging and discharging to maximize profit while meeting grid and user constraints.
- CPOs seek to maximize profit through price arbitrage, charging when electricity prices are low and discharging when they are high.Source: §II · p. 1
- The schedule must keep aggregate power within distribution-system limits and provide each EV enough energy by departure.Source: §II · p. 1
- Dynamic prices, grid limits, and user requirements make V2G charging a complex real-time decision problem.Source: §II · p. 1
III. LLMS FOR SMART CHARGING
The proposed LLM-based charging approach has three stages: collect trajectories, convert state-action vectors into graphs, and train with offline RL.
- Trajectories are collected by interacting with the environment under optimal and random policies.Source: §III · p. 1
- State-action representations are converted from vectors into graphs.Source: §III · p. 1
- Offline RL training uses an LLM as its core.Source: §III · p. 1
A. Data Gathering for Offline RL
Offline RL learns charging policies from a fixed dataset of past interactions, supplemented with random-policy trajectories to broaden the training data.
- The fixed dataset contains past charging sessions optimally managed by CPOs, with random-policy trajectories added to broaden the data.Source: A. Data Gathering for Offline RL · p. 1, Figure 1 · p. 2
- Offline RL trains a policy to maximize expected discounted return without further environment exploration.Source: A. Data Gathering for Offline RL · pp. 1–2
- The added trajectories cover diverse conditions, including changing prices, EV arrival and departure times, and grid constraints.Source: A. Data Gathering for Offline RL · p. 2
- Training on the static dataset can uncover charging and discharging strategies that minimize costs and maintain grid stability.Source: A. Data Gathering for Offline RL · p. 2
B. GNN Embeddings
The paper encodes the charging network as a graph so GNN embeddings can represent relationships and constraints for EV charging decisions.
- Nodes represent EVs, chargers, transformers, or the CPO, while edges encode power or information exchange.Source: B. GNN Embeddings · p. 2
- Dedicated MLPs and successive graph-convolution layers turn node features into embeddings enriched by messages from neighboring nodes.Source: B. GNN Embeddings · p. 2
- The resulting embeddings capture local node context and overall network structure, incorporating local and global constraints more efficiently than a long state vector.Source: B. GNN Embeddings · p. 2
- These graph-based representations feed the RL framework to inform charging and discharging actions.Source: B. GNN Embeddings · p. 2
C. Learning with LLMs
The method trains a Decision Transformer on charging trajectories to predict actions as sequences, with graph embeddings providing network context.
- The Decision Transformer [4] reframes RL as sequence prediction and uses causal attention to capture dependencies across time steps.Source: C. Learning with LLMs · p. 2
- Training predicts each sequence’s next action and adjusts model parameters against the action actually taken.The model learns from trajectories generated under optimal and random policies.Source: C. Learning with LLMs · p. 2
- GNN embeddings of the charging-network graph are fed into the Decision Transformer to provide context for charging and discharging decisions.Source: C. Learning with LLMs · p. 2
IV. RESULTS
In a 50-station scenario with dynamic grid limits, GNN+LLM outperforms heuristic and online RL strategies on the reported balance of user satisfaction, costs, and grid violations.
- The evaluation uses EV2Gym to compare heuristic, online RL, and offline Optimal baselines in a 50-station scenario with unexpected EV arrivals and dynamic aggregated power limits.Source: §IV · p. 2
- In the 50-station dynamic-limit scenario, GNN+LLM charges EVs to desired capacity, exploits V2G opportunities, and keeps aggregate power within grid limits.Source: §IV · pp. 2–3, Figure 2 · p. 3
- GNN+LLM achieves a strong balance across user satisfaction, costs, and grid violations, outperforming heuristic and online RL strategies.Source: Table I · p. 3, §IV · p. 3
- The combined Optimal + Random training dataset produces the highest maximum reward among the three datasets, despite having a lower average reward than Optimal-only data.Source: §IV · p. 3, Table II · p. 3
V. CONCLUSIONS
The conclusion is that combining LLMs and GNNs improves charging-schedule optimization in complex, large-scale EV scenarios.
- Integrating LLMs with GNNs enhances charging-schedule optimization for complex, large-scale EV charging scenarios.Source: §V · p. 3
- The hybrid approach outperforms traditional RL algorithms by capturing long-term dependencies and intricate network relationships.Source: §V · p. 3
- The authors conclude that this approach addresses scalability and dynamic challenges faced by conventional methods.Source: §V · p. 3