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User Location-Specific Data for Smarter Smart Grids

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0018469
Agency Tracking Number: 0000234660
Amount: $225,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 06b
Solicitation Number: DE-FOA-0001770
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-04-09
Award End Date (Contract End Date): 2019-01-08
Small Business Information
34 Buena Vista Ave.
Fairfax, CA 94930-2224
United States
DUNS: 957765493
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Erik Page
 (415) 448-6575
 erik@erikpage.com
Business Contact
 Erik Page
Phone: (415) 448-6575
Email: erik@erikpage.com
Research Institution
 Lawrence Berkeley National Laboratory
 Peter Schwartz
 
1 Cyclotron Road
Berkeley, CA 94720-8047
United States

 (510) 486-6926
 Federally funded R&D center (FFRDC)
Abstract

Society at large and DOE in particular have an interest in the continued development and refinement of a smart grid infrastructure that more efficiently and reliably matches electrical resources to demands. While technological advances in recent years have improved the smart grid, these advances have been limited by a lack of granularity of data (e.g. environmental conditions at multiple locations in a space) and control (e.g. ability to control every light in a room independently). This proposal focuses on providing this missing granular data by collecting user-location specific (ULS) data that can be utilized by relevant building systems and ultimately by the smart grid. ULS data can allow for more sophisticated control algorithms for loads (such as lighting and air conditioning) so that total energy use is reduced while individual users receive services (e.g. light levels, temperatures) that better meet their needs. Additionally, when building control systems that rely on ULS data are tied to the smart grid, they can reduce loads when needed (e.g. during peak load events) in a manner that maximizes load shedding while still providing individual users the service levels they need.

* Information listed above is at the time of submission. *

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