Short-term scheduling strategy of renewable integrated hydrothermal system using chaotic chimp-sine cosine algorithm considering wind uncertainty and grid stability

The integration of renewable energy sources (RES) is vital for achieving long-term sustainability in power systems; however, it introduces significant operational challenges owing to the inherent variability of wind and solar generation. Existing research often neglects uncertainties or fails to address them within the realistic constraints of hydrothermal systems. This study proposes a robust and comprehensive short-term scheduling framework for renewable-integrated hydrothermal systems, accounting for the stochastic nature of RES. The proposed chaotic chimp sine cosine algorithm (C-CHOA-SC) effectively tackles the non-convex hydrothermal wind solar scheduling problem by enhancing exploration via chaotic dynamics and avoiding local optima. In this model, wind uncertainty is modelled using the Weibull distribution, and solar generation is capped at 30% of the load demand to maintain transient stability. Numerical simulations revealed that C-CHOA-SC reduced fuel costs by 9.5% compared to differential evolution (DE), 3.1% compared to particle swarm optimization (PSO), and 1.5% compared to grey wolf optimization (GWO). Emission reductions were even more pronounced, being 92% lower than DE, 21% lower than PSO, and 97% lower than GWO. Sensitivity analyses confirmed the algorithm's robustness and adaptability to real-world variations in RES availability. Overall, this framework offers an efficient and practical solution for smart grid operators and policymakers aiming to optimize economic performance, reduce emissions, and enhance operational reliability in renewable-dominated power systems.