The increasing integration of renewable energy sources into power systems presents new challenges for optimizing energy usage, particularly in environments where energy availability is unpredictable. Unlike traditional power sources, renewables such as solar and wind are not consistently reliable due to fluctuating weather conditions. This variability necessitates the use of adaptive strategies that can manage power-down scenarios efficiently in real-time. Online algorithms provide a powerful solution to this problem, allowing systems to make immediate decisions based on current data, without knowledge of future energy availability. In this paper, we investigate online algorithms with adaptation to minimize energy costs. We explore the trade-offs between performance and energy usage in dynamic systems, emphasizing how online algorithms can optimize energy consumption by adapting to the intermittent nature of renewable energy sources. Our approach demonstrates that online algorithms are critical for balancing energy efficiency and reliability in systems increasingly dependent on renewable power.