To extend the proposed C-DR framework to incorporate uncertainty in user behavior and renewable generation, probabilistic modeling techniques can be employed. By introducing stochastic elements into the optimization models, such as probabilistic load forecasts and renewable generation forecasts, the C-DR mechanism can adapt to varying conditions. Monte Carlo simulations can be utilized to generate multiple scenarios considering different levels of uncertainty in user behavior and renewable generation. This approach allows for the development of robust scheduling strategies that account for the inherent variability in user preferences and renewable energy availability. Additionally, machine learning algorithms can be leveraged to learn patterns from historical data and adjust the C-DR responses dynamically based on real-time information.