A Storm‐Trajectory–Inspired Oppositional Metaheuristic for Solving Nonconvex Dynamic Optimal Power Flow With Wind Uncertainty, Multi‐Mode CAES Operation, and STATCOM

ABSTRACT

The increasing penetration of renewable energy resources, energy storage technologies, and flexible AC transmission system (FACTS) devices has significantly increased the complexity of day‐ahead dynamic optimal power flow (DOPF) problems. The simultaneous consideration of wind power uncertainty, multi‐mode compressed air energy storage (CAES), STATCOM control, and valve‐point loading effects results in a highly nonlinear, nonconvex, and time‐coupled optimization problem. To address this challenge, this paper proposes an oppositional supercell thunderstorm algorithm (OSTA), in which opposition‐based learning is incorporated into the supercell thunderstorm algorithm (STA) to enhance population diversity, improve exploration capability, and mitigate premature convergence. A stochastic multi‐period DOPF framework is developed by integrating wind power uncertainty modeled through the Weibull probability distribution, detailed CAES operational characteristics including charging, discharging, and gas turbine modes, and STATCOM control within a unified optimization model. The objective is to minimize the total operating cost while satisfying power balance, network security, and operational constraints over a 24‐h scheduling horizon. The effectiveness of the proposed methodology is validated through multiple case studies on the IEEE 30‐bus system, while its scalability is further assessed on the IEEE 118‐bus system. Comparative evaluations against STA, marine predators algorithm (MPA), particle swarm optimization (PSO), and grey wolf optimizer (GWO) demonstrate that OSTA consistently provides superior solution quality, improved convergence characteristics, enhanced voltage profile performance, and better voltage stability margins. The obtained results confirm the effectiveness, robustness, and scalability of the proposed OSTA for solving large‐scale stochastic DOPF problems in renewable‐rich power systems.