Project 4

To improve grid balancing (SDG7), esp. in case of many renewable energy resources and fluctuating demand, AI forecasting methods (WP2) can be combined with Demand Response (DR) methods. DR motivates energy consumers in some way (e.g. pricing-based) to adjust their energy usage to the available energy resources and demand.

Smart grid technology allows DR to be more data-driven and a multitude of AI technologies have already been applied to DR (Antonopoulos et al., 2020), including ML, deep learning and agent-based approaches. However, little research has investigated the consumer side of DR apart from simple customer segmentation approaches (Antonopoulos et al., 2020). Rather than having consumers (household or industry) passively follow the DR (pricing) scheme, AI technology such as recommender algorithms could play an active role in recommending consumers how and when to distribute their energy usage and return based energy forecasts and DR information. Such tailored interventions to improve DR approaches and optimize dynamic grid balancing.

PhD promoters: Dr.Ir. Martijn Willemsen and Dr. Claudia Zucca