Energy Flexibility

Energy flexibility focuses on optimizing the management of energy resources to better accommodate variable renewable energy sources and fluctuating demand, primarily aiming to improve grid stability, reduce costs, and lower carbon emissions. Current research emphasizes the development and application of advanced control algorithms, including reinforcement learning (particularly multi-agent approaches like MADDPG) and machine learning models (such as LSTMs and differentiable decision trees), often within simulated environments like CityLearn to test and validate strategies. This research is significant because it enables more efficient integration of renewable energy, enhances grid resilience, and offers opportunities for cost savings and emissions reductions across various sectors, from residential buildings to industrial facilities.