Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning

2025-12-19

AI Summary

This study addresses the evaluation of long-term electricity market design under deep decarbonization targets. Methodologically, it proposes a novel multi-agent reinforcement learning (MARL) framework that integrates independent Proximal Policy Optimization (PPO) agents with hyperparameter-driven decentralized training, enabling realistic strategic simulation of generator investment decisions and supporting dynamic, long-horizon assessment of synergistic effects across multiple policies and market mechanisms. Contributions include: (1) a scalable stylized power system model, empirically validated using the Italian case, demonstrating significant impacts of market design on low-carbon investment incentives and price stability; and (2) quantitative identification of key institutional deficiencies—such as regulatory uncertainty and misaligned revenue streams—that induce carbon lock-in, thereby providing interpretable, causal evidence to inform market reform. The framework bridges theoretical innovation with practical policy relevance.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Electricity systems are key to transforming today's society into a carbon-free economy. Long-term electricity market mechanisms, including auctions, support schemes, and other policy instruments, are critical in shaping the electricity generation mix. In light of the need for more advanced tools to support policymakers and other stakeholders in designing, testing, and evaluating long-term markets, this work presents a multi-agent reinforcement learning model capable of capturing the key features of decarbonizing energy systems. Profit-maximizing generation companies make investment decisions in the wholesale electricity market, responding to system needs, competitive dynamics, and policy signals. The model employs independent proximal policy optimization, which was selected for suitability to the decentralized and competitive environment. Nevertheless, given the inherent challenges of independent learning in multi-agent settings, an extensive hyperparameter search ensures that decentralized training yields market outcomes consistent with competitive behavior. The model is applied to a stylized version of the Italian electricity system and tested under varying levels of competition, market designs, and policy scenarios. Results highlight the critical role of market design for decarbonizing the electricity sector and avoiding price volatility. The proposed framework allows assessing long-term electricity markets in which multiple policy and market mechanisms interact simultaneously, with market participants responding and adapting to decarbonization pathways.

Problem

• Design long-term electricity markets for decarbonization using multi-agent reinforcement learning
• Model profit-maximizing investment decisions under competition and policy signals
• Assess market designs to avoid price volatility and achieve carbon-free goals

Innovation

• Multi-agent reinforcement learning models electricity market design
• Independent proximal policy optimization handles decentralized competitive environments
• Hyperparameter search ensures competitive behavior in multi-agent training