A Two-Stage Joint Clearing Model for Virtual Power Plant Participating in the Flexible Ramping Product Market Based on Chance-Constrained Programming

With the continuous growth of high-penetration renewable energy integration, the volatility and uncertainty of wind and photovoltaic power generation pose significant challenges to the flexible operation of the power system. Existing market clearing models often struggle to efficiently handle these uncertainties and ensure sufficient ramping capacity. Therefore, the rational utilization of flexible resources and the improvement of the power system's flexible ramping capacity become essential measures to address these challenges. This paper presents a two-stage joint clearing model for the electricity energy market and the flexible ramping market using a sampling-based chance-constrained deterministic transformation method. The chance-constrained conditions are converted into mixed-integer linear constraints for solving. The diverse control capabilities of virtual power plant (VPP) are utilized to mitigate real-time market uncertainties and provide flexible ramping products, thereby enhancing the system's flexible regulation capacity and reducing operational costs. Numerical results from case studies show that the proposed model significantly reduces the total cost by approximately 0.17%, wind and photovoltaic power curtailment by 48%, and load shedding by 34% compared to existing works. Additionally, case studies at different penetration rates demonstrate the model's effectiveness and superiority in high-penetration renewable energy scenarios.