Managing Uncertainty by Leveraging Flexibility in Smart Energy Systems: AI-Supported Distributionally Robust Chance-Constrained Optimization

The rapid decarbonization of the energy systems to meet climate targets is driving increased electrification across various energy sectors, with the electrification of the household heating sector posing significant challenges for electrical networks in many parts of Europe. Electric heat pumps (UP), paired with thermal storage (TS), have cemented themselves as the key technology in this transition, offering high efficiency and signif-icant flexibility for load shifting and energy storage. However, this flexibility is subject to uncertainty due to variable weather conditions and user behavior. In this paper, an AI -enhanced framework for optimizing the operation of a UP-dominated res-idential network under uncertainty is presented. The framework utilizes a Bayesian neural network to generate forecasts for UP demand based on real measured data, creating a data-driven, AI-enhanced ambiguity set in the distributionally robust chance-constrained (DRCC) optimization. This approach enhances the confidence level in the proposed dispatch plan by addressing forecast uncertainty in a statistically robust manner. By effectively leveraging the thermal system's flexibility, it mitigates the risk of constraint violations and ensures reliable grid operation. Val-idation on a residential network demonstrates that the proposed framework achieves higher robustness and reliability compared to traditional deterministic models, highlighting its potential to support the energy transition.