Clustering-driven design and predictive control of hybrid PV-battery storage systems for demand response in energy communities
Anthony Maturo, Charalampos Vallianos, Annamaria Buonomano, Andreas Athienitis, Benoit Delcroix
Abstract
The integration of renewable energy sources and participation in demand response requires advanced modelling and control strategies to enhance building-grid interaction. This study presents a comprehensive methodology for selecting typical days and evaluating how controllable building thermal loads influence the design and operation of grid-supportive technologies, specifically photovoltaic (PV) and battery storage systems. Typical days are identified through dynamic time warping (DTW) and hierarchical clustering approaches, supported by six internal validation metrics. Grey-box and regression models are employed to predict building energy consumption, while PV and battery models assess system performance. A two-level Model Predictive Control (MPC) framework is employed to optimize the buildings demand and coordinate the operation of grid-supportive technologies. At the first level, a distributed MPC algorithm manages thermal loads in individual buildings to enable demand response. At the second level, a supervisory MPC optimizes the operation of the hybrid PV-battery storage system to achieve targeted grid flexibility. The case study considers a virtual community in Varennes, Québec, consisting of institutional and residential buildings. Through efficient thermal load management, the methodology shows that community peak demand can be reduced by over 40% compared to current operational practices, and the required capacity of grid-supportive systems can be reduced by up to 26% in a worst-case scenario analysis.