Dynamic adaptation in power transmission: integrating robust optimization with online learning for renewable uncertainties

Abstract

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IntroductionThe increasing integration of renewable energy sources, such as wind and solar, into power grids introduces significant challenges due to their inherent variability and unpredictability. Traditional fossil-fuel-based power systems are ill-equipped to maintain stability and cost-effectiveness in this evolving energy landscape.MethodsThis study presents a novel framework that integrates robust optimization with online learning to dynamically manage uncertainties in renewable energy generation. Robust optimization ensures system resilience under worst-case scenarios, while the online learning component continuously updates operational strategies based on real-time data. The framework was tested using an IEEE 30-bus test system under varying levels of renewable energy integration.ResultsSimulation results show that the proposed framework reduces operational costs by up to 12% and enhances system reliability by 1.4% as renewable energy integration increases from 10% to 50%. Additionally, the need for reserve power is significantly reduced, particularly under conditions of high variability in renewable energy outputs.DiscussionThe integration of robust optimization with online learning provides a dynamic and adaptive solution for the sustainable management of power transmission systems. This approach not only improves economic and environmental outcomes but also enhances grid stability, making it a promising strategy for addressing the challenges posed by the increasing reliance on renewable energy.

Keywords

• energy management
• transmission systems
• online learning
• renewable energy
• robust optimization