1 / 26

Distributed Intelligent Automated Demand Response (DIADR) In Sutardja Dai Hall

Distributed Intelligent Automated Demand Response (DIADR) In Sutardja Dai Hall. Jason Trager. Introduction. Demand response: Turning things off instead of turning power plants on Need for Automation Need for Distributed Intelligence Ancillary Service

zeke
Download Presentation

Distributed Intelligent Automated Demand Response (DIADR) In Sutardja Dai Hall

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Distributed Intelligent Automated Demand Response (DIADR)In Sutardja Dai Hall Jason Trager

  2. Introduction • Demand response: Turning things off instead of turning power plants on • Need for Automation • Need for Distributed Intelligence • Ancillary Service • Can provide substitute for spinning reserves • … and possibly Frequency regulation

  3. Sutardja Dai Hall • 141,000 Sq. foot mixed use office space • Half of building is Marvell Nano-lab • Staff, faculty, students • Houses the Center for Information Technology In the Interest of Society (CITRIS)

  4. Initial Findings • Building is over-cooled and over-ventilated • Chillers are vastly oversized • Lighting often left on much of the night; occupants did not use the dimming functions. • Initial monitoring identified problems (e.g., stuck economizer damper) • Audit found many areas for conservation (e.g., ancillary LED monitors, electric adjustable desks)

  5. RAPID AUDIT PROTOCOL (RAP) Smart phone app: scan QR code, place on appliance, enter parameters for appliance. Uses StreamFS to automatically enter database.

  6. Slide: Andrew Krioukov Personalized Lighting Control Application sMAP/ REST API HTML/HTTP MySQL Python Control Process Python Django sMAP Gateway BACnet

  7. Credit: Linda Lee, Andrew Krioukov

  8. Baseline

  9. 90-150 kW But from where?

  10. Receptacles 125kW 90 kW Peak 90kW 44 kW 12kW

  11. Lighting 125kW 32Kw 90kW 44 kW 12kW

  12. HVAC / Elevators 125kW 155-380 kW 90kW 44 kW 12kW

  13. Experiments • HVAC – preparing for hot days • Design pre cooling algorithms • 2 degrees • 4 degrees • 6 degrees • Adjust outflow water temperature • This is tricky • Affects nano-lab

  14. Experiments • 4th floor, 7th floor DR test. Testing – light testing occurs at night • 4:15 AM, set all lights FULL • 4:30 AM, set all lights MED • 4:45 AM set all lights LOW • 5:00 AM set all lights OFF

  15. Experiments Experiments Experiments Experiments Experiments Experiments Experiments Experiments Solid, Discrete, Time series data

  16. DR Controller DR Controller N1 N2 N3 Nk

  17. Load –agnostic optimization

  18. DR Controller • Should be extensible to energy efficiency • “Continuous DR” • Should pick strategies that humans would pick • Maybe better • Should learn • Critical feature of intelligence

  19. Future work • Summer DR tests • Ongoing • Supply following load • Instead of “Demand Response” • At least one automated DR event this summer • Can we reduce more plug loads? • Computer labs shut down during DR event?

  20. Questions?

More Related