Optimization and scheduling of microgrids based on improved Rüppell's fox optimizer algorithm

Microgrids face challenges, such as strong intermittency of renewable energy, output uncertainty, and coupling of multiple constraints, making their optimal scheduling a high-dimensional nonlinear complex problem. This paper first establishes a microgrid model incorporating photovoltaic, wind turbine, and various distributed energy resources, and introduces a scenario generation method combining a Frank copula and time-varying Markov copulas to capture wind-solar temporal correlations. An objective function minimizing daily operational and emission treatment costs is then formulated with full constraints. To remedy the shortcomings of the original Rüppell's fox optimizer algorithm, namely, premature convergence and entrapment at local optima, a modified thinking innovation strategy is incorporated, resulting in an improved Rüppell's fox optimizer algorithm (IRFO). Benchmark function tests show that IRFO outperforms other comparative methods in convergence speed, accuracy, and stability. Finally, simulation analysis is conducted on a certain microgrid. Case studies on the microgrid under both deterministic and uncertain conditions confirm that IRFO achieves competitive total daily cost and enhanced robustness across the evaluated scenarios.