Integrating Reinforcement Learning for Behavioral Analysis in Interconnected Electricity and Carbon Markets

The integration of electricity and carbon markets plays a crucial role in promoting renewable energy adoption and achieving global carbon reduction targets. However, the interconnection of these markets introduces significant challenges due to their inherent complexity and dynamic nature. This paper proposes a hybrid experimental learning framework, combining realworld experimentation and reinforcement learning-based generative models, to analyze participant behavior and market dynamics in coupled electricity and carbon markets.The framework simulates trading processes by modeling strategic bidding behaviors using a reinforcement learning approach, formulated as a Markov Decision Process (MDP). Leveraging both historical and experimental data, the model generates bidding strategies that adapt to market conditions while balancing economic and environmental objectives. A case study based on the Guangdong electricity market, incorporating modified IEEE 6-bus systems and realworld carbon trading scenarios, demonstrates the effectiveness of this approach in capturing market complexities and uncovering distinct behavioral patterns among participants. Results highlight the ability of reinforcement learning-based generative models to replicate human decision-making and optimize bidding strategies in interconnected markets. The findings provide insights into market behaviors, regulatory impacts, and potential strategies for improving efficiency and sustainability in the energy sector. This study underscores the value of hybrid experimental learning as a tool for exploring coupled market dynamics and guiding the transition to low-carbon energy systems.