BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230130T214752Z UID:6A343B13-EBF5-4A73-B000-121DFE7AAF2E DTSTART;TZID=US/Eastern:20230130T130000 DTEND;TZID=US/Eastern:20230130T140000 DESCRIPTION:Building demand flexibility (DF) research has recently gained a ttention. To unlock building DF as a predictable grid resource\, we must e stablish a quantitative understanding of the resource size\, performance v ariability\, and predictability based on large empirical datasets. The ele ctrical grid’s geographically diverse and changing nature presents chall enges to comparing building DF performance measured under different condit ions (i.e.\, benchmarking DF). To address this challenge\, a novel DF benc hmarking framework focused on load shedding and shifting is presented\; th e foundation is a set of simple\, proven single-event metrics with attribu tes describing event conditions. These enable benchmarking and identifying trends that represent how these attributes influence DF. To test its feas ibility and scalability\, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand respo nse participation data. These examples provided a pathway for using both b uilding level benchmarking and aggregation to extract insights into buildi ng DF about magnitude\, consistency\, and influential factors.\n\nThis web inar can draw more attention from energy audience to focus on the grid-int eractive efficiency buildings and participate in the SBCS subcommittee and associated task forces. The presented work also provides cutting-edge res earch outcome for the community.\n\nBuilding demand flexibility (DF) resea rch has recently gained attention. To unlock building DF as a predictable grid resource\, we must establish a quantitative understanding of the reso urce size\, performance variability\, and predictability based on large em pirical datasets. The electrical grid’s geographically diverse and chang ing nature presents challenges to comparing building DF performance measur ed under different conditions (i.e.\, benchmarking DF). To address this ch allenge\, a novel DF benchmarking framework focused on load shedding and s hifting is presented\; the foundation is a set of simple\, proven single-e vent metrics with attributes describing event conditions. These enable ben chmarking and identifying trends that represent how these attributes influ ence DF. To test its feasibility and scalability\, the DF framework was ap plied to two case studies of 11 office buildings and 121 big-box retail bu ildings with demand response participation data. These examples provided a pathway for using both building level benchmarking and aggregation to ext ract insights into building DF about magnitude\, consistency\, and influen tial factors.\n\nThis webinar can draw more attention from energy audience to focus on the grid-interactive efficiency buildings and participate in the SBCS subcommittee and associated task forces. The presented work also provides cutting-edge research outcome for the community.\n\nCo-sponsored by: Richard Kolodziejczyk\n\nSpeaker(s): Jingjing Liu\, \, P.E.\, CDCP\, \ n\nVirtual: https://events.vtools.ieee.org/m/344404 LOCATION:Virtual: https://events.vtools.ieee.org/m/344404 ORGANIZER:rkolod@ieee.org SEQUENCE:7 SUMMARY:The Power Chapter WEBINAR: Benchmarking Demand Flexibility for Grid -interactive Efficient Buildings URL;VALUE=URI:https://events.vtools.ieee.org/m/344404 X-ALT-DESC:Description: <br /><p>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 empiri cal datasets. The electrical grid&rsquo\;s geographically diverse and chan ging nature presents challenges to comparing building DF performance measu red under different conditions (i.e.\, benchmarking DF). To address this c hallenge\, 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 be nchmarking and identifying trends that represent how these attributes infl uence DF. To test its feasibility and scalability\, the DF framework was a pplied to two case studies of 11 office buildings and 121 big-box retail b uildings with demand response participation data. These examples provided a pathway for using both building level benchmarking and aggregation to ex tract insights into building DF about magnitude\, consistency\, and influe ntial factors.<br /><br />This webinar can draw more attention from energy audience to focus on the grid-interactive efficiency buildings and partic ipate in the SBCS subcommittee and associated task forces. The presented w ork also provides cutting-edge research outcome for the community.</p>\n<p >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. The electrical gr id&rsquo\;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 benchmark ing framework focused on load shedding and shifting is presented\; the fou ndation is a set of simple\, proven single-event metrics with attributes d escribing event conditions. These enable benchmarking and identifying tren ds that represent how these attributes influence DF. To test its feasibili ty and scalability\, the DF framework was applied to two case studies of 1 1 office buildings and 121 big-box retail buildings with demand response p articipation data. These examples provided a pathway for using both buildi ng level benchmarking and aggregation to extract insights into building DF about magnitude\, consistency\, and influential factors.<br /><br />This webinar can draw more attention from energy audience to focus on the grid- interactive efficiency buildings and participate in the SBCS subcommittee and associated task forces. The presented work also provides cutting-edge research outcome for the community.</p> END:VEVENT END:VCALENDAR