Can agents optimize CO2 transport?
Agent-Based Modeling for Multimodal Transportation of CO2 for Carbon Capture, Utilization, and Storage: CCUS-Agent†
November 25, 2024
https://arxiv.org/pdf/2411.14438This paper introduces CCUS-Agent, a multi-agent simulation model for optimizing Carbon Capture, Utilization, and Storage (CCUS) transportation in the US. The model simulates interactions between CO2 supply agents (power plants, industrial facilities), demand agents (storage sites, utilization facilities), and transportation networks (pipeline, rail, water, truck) under various cost and policy scenarios.
Key points for LLM-based multi-agent systems:
- Agent-based modeling: The system uses decentralized agents (supply, demand, transport) interacting based on individual incentives and constraints. This aligns with the core principles of multi-agent systems.
- Complex System Dynamics: The model captures emergent behavior from the interactions of many agents, providing a more realistic view than optimization approaches that assume perfect information. This is a strength of multi-agent simulations, and LLMs can bring additional nuance to agent behavior.
- Matching Algorithms: The paper explores various algorithms for matching supply and demand agents, analogous to task allocation in multi-agent systems. LLMs could be used to develop more sophisticated dynamic matching strategies.
- Policy Impact Evaluation: Simulating 45Q tax credit and other policy incentives demonstrates the potential of multi-agent systems to assess real-world policy interventions. LLMs could further analyze policy impacts by reasoning about policy features and agent responses.
- Scenario Planning: The model supports what-if analyses by varying costs, transport options, and other parameters. This is directly applicable to multi-agent systems, where LLMs can generate and evaluate diverse scenarios.
- Scalability: The model simulates a large-scale, complex system. This demonstrates the potential for developing large-scale multi-agent systems, where LLMs could be used for coordination, negotiation, and learning amongst agents.
- Modularity & Extensibility: The modular design of the model could be replicated in LLM-based multi-agent systems, enabling flexibility and easier integration of new agents and behaviors. LLMs can be used as modular components within these agents.
The paper showcases how an agent-based model, potentially enhanced by LLMs in the future, can support complex decisions in real-world applications like CCUS.