Enhancing grid stability: A weather-adaptive robust optimization to mitigating renewables curtailment

This study presents a novel framework to maximize renewable energy penetration and enhance grid reliability within interconnected energy networks. The primary objective is to address the challenges posed by the inherent variability of renewable energy generation and the complexities of managing energy storage and power-to-x (P2X) conversion technologies. A dynamic two-stage optimization model is developed to achieve this objective, enabling power grids to operate with foresight and adaptability by making strategic day-ahead decisions and real-time adjustments based on unfolding uncertainties. This study combines dynamic thermal rating with an energy degradation model, offering an integrated approach to managing thermal capacity and storage decay under real-time conditions. A hybrid Benders decomposition algorithm is integrated with robust optimization techniques to efficiently manage the computational complexity arising from the stochastic nature of renewable generation and demand fluctuations. Additionally, a weather-adaptive deteriorating inventory model is introduced to realistically manage storage units by accounting for the decay or deterioration of stored energy over time. This study also investigates the impact of ambient weather conditions on thermal capacity to improve the utilization of transmission infrastructure, reduce congestion, and facilitate the integration of renewable energy sources. The proposed model demonstrated a reduction in renewable energy curtailment by 1.37–1.58 % and a 12.4 % decrease in operational costs, with increased revenues from P2X synthesis by 8.7 %, using real data. The framework's novelty lies in its combination of adaptive thermal rating, dynamic energy deterioration modeling, and an efficient optimization structure, ensuring practical and scalable results for real-world applications.