Nonlinear Programming-Based Multi-Period Approach for Security Constrained Optimal Power Flow

Abstract The increasing complexity of modern power systems, driven by renewable energy integration, distributed energy resources, and evolving operational requirements, has intensified the need for advanced optimization tools capable of ensuring secure and economical system operation. Security-Constrained Optimal Power Flow (SCOPF) is a fundamental tool used to ensure system security under N-1 contingency scenarios. This paper proposes a novel nonlinear programming (NLP)-based multi-period SCOPF formulation that enhances computational efficiency and scalability while preserving solution accuracy. The proposed approach explicitly incorporates load shifting, energy storage systems (ESSs), and renewable generation with reactive power injection, addressing the operational needs of modern, renewable-dominated grids. The methodology is validated using IEEE 9-, 30-, 118-, and 300-bus systems, demonstrating consistent scalability across network sizes. Unlike conventional approaches that rely on linearization or DC approximations to reduce computational burden, the proposed NLP framework directly addresses the full nonlinear AC power flow equations. Simulation results indicate that the method effectively handles the non-convexities of the problem, ensuring superior solution accuracy and strict adherence to voltage and reactive power constraints. These results confirm that the proposed framework offers a reliable and robust tool for secure power system operation, capable of managing the complex dynamics of renewable penetration without compromising physical power systems modelling fidelity.