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Integration of Renewables via Demand Management

AutoGrid

Highly Dispatchable and Distributed Demand Response for the Integration of Distributed Generation

Graphic of AutoGrid's technology
Program: 
ARPA-E Award: 
$3,465,382
Location: 
Redwood Shores, CA
Project Term: 
01/11/2012 to 03/31/2014
Project Status: 
ALUMNI
Technical Categories: 
Critical Need: 

Several emerging trends, including the rapid growth in renewable generation and greater emphasis on improving grid efficiency and resiliency, are leading to a critical need to modernize the way electricity is delivered from suppliers to consumers. Modernizing the grid's hardware and software could help reduce peak power demand, increase the use of renewable energy, save consumers money on their power bills, and reduce total energy consumption--among many other notable benefits.

Project Innovation + Advantages: 

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.

Innovation Update: 
(As of May 2016) 
The team led by AutoGrid Systems has designed, developed, and deployed a scalable software-as-a-service (SaaS) demand response (DR) optimization platform used to manage unpredictable demand and supply fluctuations throughout the electric grid. This product was developed to make the power generation process more efficient and cost effective for both suppliers and consumers. AutoGrid, a start-up company, gained significant traction with utility companies while under its ARPA-E award and has since raised $21.75M in two rounds of follow-on funding from multiple investors. AutoGrid’s DR software has more than 30 implementations across the globe. In addition to success with customers, AutoGrid has received a number of awards, such as the New Energy Pioneer by Bloomberg New Energy Finance, and a Grid Edge Award by Greentech Media, among others. New utility big data analytics applications such as AutoGrid’s Demand Response Optimization and Management System – Real-Time (DROMS-RT) system promise to significantly improve the ability for utilities to optimize and control the grid, and promise to give customers 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. 
 
AutoGrid’s DROMS-RT software was developed to generate bottom up DR forecasts for individual customers on DR signals in near real-time. The team developed methods to enhance the forecasts by periodically collecting electricity usage data at individual customer locations, and to predict changes in customer load profiles using load times series of individual customers. The AutoGrid team demonstrated a machine learning engine capable of processing more than one million forecasts on a rolling basis every 10 minutes, performance significantly beyond industry expectations using existing utility DR software applications. This helps grid operators better manage unpredictable demand and supply fluctuations in short time-scales, making power system operations more efficient and cost effective for both utilities and electricity consumers.
 
For a detailed assessment of the AutoGrid team's project and impact, please click here.
 
 
Contacts
ARPA-E Program Director: 
Dr. Timothy Heidel
Project Contact: 
Amit Narayan
Partners
Columbia University
Lawrence Berkeley National Laboratory
Metabolix, Inc.
University of California, Berkeley
University of Massachusetts, Amherst
Washington State University
Release Date: 
9/29/2011