原文信息：
Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings
原文链接：
https://www.sciencedirect.com/science/article/pii/S2666792422000257
Highlights
•Introduces a novel demand flexibility (DF) benchmarking metrics framework.
•Allows benchmarking across geographic areas with different climates and grid needs.
•Enables empirical approaches to achieve understanding of key influential factors of DF.
•Framework tested with two benchmarking case studies of 132 office and retail buildings.
•Identified load shed intensity range and variability; influence of event duration and timing.
Abstract
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.
Keywords
Demand flexibility
Metrics
Benchmarking
Demand response
Field data
Global Temperature Adjustment
Graphics
Fig. 1. Metrics Used in DR Field Studies, Ranked by Frequency.
Fig. 3. DF Benchmarking Metrics Framework with Three Building Blocks: Single-event Metrics, Metrics Attributes, and Benchmarking Metrics.
Fig. 17. Two-dimensional Plot for 16 Retail Buildings in California: Singleevent DDI Metric by Shed Event Duration with colour Scale Showing Event Day Peak Outside Air Temperature (All During 12–6 pm in 2020).
Fig. 18. Potential Applications of DF Benchmarking by Stakeholder Group.
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