Dynamic building thermal mass clustering for energy flexibility assessment: An application to demand response events.

• A methodology for clustering buildings according to their thermal mass is presented. • Characteristic time constants are defined as flexibility indicators. • The response to different types of demand response events is considered. • A labelling of buildings according to their dynamic response capacity is proposed. • The effectiveness of the methodology is tested on 28 already known real buildings. Demand response programs encompass a range of externally control strategies designed to modify consumer end-use load according to specific grid demands. In the current renewable integration context, power systems need to implement such demand strategies to provide energy flexibility during grid stress periods. Nevertheless, the extensive adoption of demand response initiatives in the building sector is confronted by notable obstacles, mainly due to the absence of standardized assessment methods and metrics, and the lack of established regulatory frameworks, all of which hinder the formation of competitive flexibility asset portfolios. Indeed, energy flexibility quantification frameworks are not unified and are usually based on the control objectives and quantification indicators. In this framework, this paper proposes a methodology to cluster residential buildings based on the analytical assessment of their dynamic thermal response, regardless the boundary conditions (i.e., weather data, occupancies, ...) and the type of demand response event. The proposed methodology provides a quick and simple quantification of how a building is expected to respond under different demand response events and durations, which is critical for both customers and demand response agents to decide and select the involvement of buildings in each event and potentially to design personalized demand response events for each building. An extensive analysis was conducted to evaluate the methodology based on 28 real residential buildings, whose data were presented in a previous study. Results provide the potential effectiveness and application for energy flexibility purposes of this methodology based on dynamic thermal building clustering. Moreover, it can be concluded that it is not possible to deduce a thermal inertia available classification exclusively based on design thermal and geometric characteristics of the building; being necessary to consider the duration of involvement, since they highly influence on the residential building thermal behavior, and thus, on the corresponding clustering. [ABSTRACT FROM AUTHOR]