Research on Uncertainty Modeling and Optimization Trading Algorithms in the Electricity-Carbon Coupled Market

The integration of carbon trading with electricity markets has created a complex coupled market structure, where uncertainties in renewable energy generation, market prices, and carbon quotas pose significant challenges to efficient trading. Traditional market-clearing mechanisms struggle to handle the stochastic nature of renewable power generation and the volatility of carbon prices, leading to inefficiencies in trading decisions and carbon compliance strategies. To address these challenges, this paper proposes a comprehensive framework for uncertainty modeling and optimization trading in electricity-carbon coupled markets. The framework leverages robust stochastic optimization techniques to model uncertainties in renewable energy output, electricity price fluctuations, and carbon quota dynamics, thereby enhancing decision-making accuracy and improving trading strategies. A risk-aware, adaptive trading algorithm is developed to maximize profit while ensuring compliance with carbon emission constraints and system reliability. The proposed algorithm integrates data-driven forecasting models, stochastic programming, and real-time market adjustments to dynamically respond to changes in both electricity and carbon markets. Experimental results demonstrate that the framework effectively mitigates risks associated with uncertainty, enhances economic efficiency, and ensures carbon compliance, leading to improved overall market stability. This research provides valuable insights for promoting the synergy between electricity and carbon markets under dynamic and uncertain conditions, offering a scalable and practical solution for future low-carbon power market development.