Energy flexibility of building systems in future scenarios: optimization of the control strategy of a dynamic shading system and definition of a new energy flexibility metric

Abstract

The growing awareness of climate change is constantly moving the attention of designers and policymakers from typical and current scenarios to future layouts. This new approach introduces a degree of uncertainty that should be accounted in the building design process. The multitude of possible scenarios suggests considering dynamic system that can adapt themselves to unpredicted operating conditions. The aim of this study is to test a new approach and a new flexibility metric analysing the behaviour of a high-performance dynamic internal curtain in current and future scenarios. The first part of the paper focuses on the optimization of the dynamic system in the current scenario; this preliminary analysis represents the traditional design approach and is considered the baseline for all the comparisons. Hence, defining a matrix of likely scenarios, this paper explores the behaviour of three different selected control strategies – always off (i.e., no curtain), fixed control, optimized control – in the different scenarios considering possible variations of i) climate, ii) urban context, iii) internal loads, and iv) building use. The main outcomes of the research are, on the one hand, the comparisons of the control strategies and the benefits of the dynamic system and, on the other hand, the definition of a new metric that can properly describe the flexibility of the building systems with reference to the future scenarios analysed. This metric is based on six classes – which describe the statistical distribution of the energy consumptions of all the scenarios simulated in the matrix – and an index called Energy Flexibility Index (EFI) that quantifies the flexibility of the technology considered. The case study analysed highlights how implementing a dynamic shading system, considering the same building use, can increase the EFI of nearly 21% for non-optimized control strategy and up to 23% when optimized. While changing the building use in residential reduces these values respectively, to 6% and 7%.