%0 Journal Article %K Demand response %K Benchmarking %K Metrics %K Demand flexibility %K Field data %K Global Temperature Adjustment (GTA) %A Jingjing Liu %A Rongxin Yin %A Lili Yu %A Mary Ann Piette %A Marco Pritoni %A Armando Casillas %A Jiarong Xie %A Tianzhen Hong %A Monica Neukomm %A Peter Schwartz %B Advances in Applied Energy %D 2022 %G eng %P 100107 %R 10.1016/j.adapen.2022.100107 %T Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings %U https://linkinghub.elsevier.com/retrieve/pii/S2666792422000257 %V 8 %8 12/2022 %! Advances in Applied Energy %X

Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfil the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. These examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders.