Integration of Renewables via Demand Management
Technology Description:
AutoGrid, in conjunction with Lawrence Berkeley National Laboratory and Columbia University, will design and demonstrate automated control software that helps manage real-time demand for energy across the electric grid. Known as the Demand Response Optimization and Management System - Real-Time (DROMS-RT), the software will enable personalized price signals to be sent to millions of customers in extremely short timeframes—incentivizing them to alter their electricity use in response to grid conditions. This will help grid operators better manage unpredictable demand and supply fluctuations in short time-scales—making the power generation process more efficient and cost effective for both suppliers and consumers. DROMS-RT is expected to provide a 90% reduction in the cost of operating demand response and dynamic pricing programs in the U.S.
Potential Impact:
If successful, AutoGrid's demand response optimization system could allow homeowners and businesses to exert more control over their energy usage and utility bills by providing them with up-to-date information on prices and real-time, grid-wide energy demand and supply conditions.
Security:
A more efficient, reliable grid would be more resilient to potential disruptions from failure, natural disasters, or attack.
Environment:
Enabling increased use of wind and solar power would result in a substantial decrease in carbon dioxide emissions in the U.S.—40% of which are produced by electricity generation.
Economy:
A more efficient and reliable grid would help protect U.S. businesses from costly power outages and brownouts that stop automated equipment, bring down factories, and crash computers.
Contact
ARPA-E Program Director:
Dr. Timothy Heidel
Project Contact:
Amit Narayan
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
amit@auto-grid.com
Partners
University of Massachusetts, Amherst
Washington State University
Metabolix, Inc.
University of California, Berkeley
Columbia University
Lawrence Berkeley National Laboratory
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Release Date:
04/20/2011