Two-Stage Electricity Market Optimization Considering Wind Power Uncertainty and Coordinated Flexibility Resources

With the growing integration of wind energy into modern power systems, its inherent uncertainty and intermittency present significant challenges to electricity market operations. This paper proposes a two-stage electricity market optimization model that coordinates multiple flexibility resources, including energy storage systems (ESS), demand response (DR), and fast-ramping thermal units, to address these challenges. The model introduces a day-ahead and real-time coordinated decision framework. In the day-ahead stage, unit commitment, ESS baseline operation, and DR baseline load are optimized. In the real-time stage, actual wind power deviations are addressed through dynamic adjustments of flexibility resources. A distributionally robust optimization (DRO) approach is employed, using fuzzy sets to model wind power forecast errors, balancing robustness and economic efficiency. The model features finegrained constraints on ESS operation, DR response characteristics, and unit ramping rates, and includes a coordinated clearing mechanism to leverage resource complementarity. Simulation results on a modified IEEE 30-bus system demonstrate the model's effectiveness: total system cost is reduced by up to 18.6%, wind curtailment by 41.3%, and price volatility by 36.5%. The proposed model offers a robust and efficient decision-support tool for electricity markets with high renewable penetration.