Efficient and accurate solution of wind-integrated optimal power flow based on enhanced second-order cone relaxation with rolling cutting plane technique

• Enhanced SOCR method for wind-integrated OPF with rolling cutting plane technique. • GMM based data-driven approach is proposed to model the wind generation cost. • Case studies show substantial improvements in computational accuracy and efficiency. The increasing integration of large-scale wind power introduces significant uncertainties into power system operations, posing challenges to the efficiency and reliability of optimal power flow (OPF). This paper proposes a novel solution framework for wind-integrated OPF that combines an enhanced second-order cone relaxation (SOCR) with a rolling cutting plane technique. To accurately capture the economic impacts of wind uncertainty, a Gaussian mixture model is constructed from historical wind power data and embedded into the OPF objective function to quantitatively characterize wind generation costs. The AC power flow equations are convexified through the SOCR complemented by a second-order Taylor series expansion, while the rolling cutting plane technique provides a warm-start mechanism that effectively reduces relaxation gaps and accelerates convergence. Extensive numerical experiments on standard IEEE test systems and large-scale practical networks demonstrate that the proposed solution framework yields more accurate power flow solutions compared to existing relaxation approaches. Additionally, the impact of wind generation cost modeling on system dispatch is systematically analyzed, revealing that appropriate uncertainty-aware cost representations significantly enhance operational decision-making. These findings highlight the practical significance of the proposed framework, offering a computationally efficient and scalable tool to enhance the reliability and economic performance of wind-integrated power systems.