A two-stage probabilistic flexibility management model for aggregated residential buildings

The increasing integration of renewable energy resources into power systems introduces variability and uncertainty, challenging the availability of flexible resources required to maintain grid stability. Traditionally, flexible ramping relies on conventional generation with fixed capacities, highlighting the need for alternative flexible resources. This study focuses on demand-side resources, such as aggregated residential buildings forming collaborative energy ecosystems with dispatchable flexible assets, as a promising solution to address these challenges. This paper proposes a two-stage probabilistic model for managing the flexibility of aggregated buildings, focusing on maximizing ramping capacity from energy storage systems, thermal loads, and shiftable appliances during intra-day periods. In the first stage, buildings operate normally, optimizing energy exchange based on electricity prices. In the second stage, buildings coordinate in response to aggregator signals by imposing a strategy of maximum anticipation or delay to manage energy exchange. The aggregator then assesses the total potential ramping capacities for market participation. Numerical results and sensitivity analyses demonstrate the model's effectiveness in accurately assessing aggregated ramp capacity. The findings reveal that the proposed approach significantly enhances residential building flexibility, providing accurate assessments of their contribution to grid stability and enabling efficient participation in flexibility markets.