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Stochastic Optimal Power Flow

Arizona State University (ASU)

Stochastic Optimal Power Flow for Real-Time Management of Distributed Renewable Generation and Demand Response

ARPA-E Award: 
Tempe, AZ
Project Term: 
07/11/2016 to 10/10/2018
Project Status: 
Technical Categories: 
Critical Need: 
The infrastructure that defines the U.S. electric grid is based largely on pre-digital technologies developed in the first part of the 20th century. In subsequent decades, grid development has evolved through emphasis on safety, accessibility, and reliability to security and resiliency. Throughout this evolution, the grid mainly relied on centralized power plants and developed protocols to provide system reliability based on that model. However, the increasing use of renewable generation and distributed energy resources (DER), such as residential solar and home energy storage, along with customers' changing energy usage patterns are leading to greater uncertainty and variability in the electric grid. New tools are required to create a flexible and modern electric grid that can meet this increase in renewable generation and DERs, while providing the quality of service, resiliency, and reliability that customers expect.
Project Innovation + Advantages: 

Arizona State University (ASU) will develop a stochastic optimal power flow (SOPF) framework, which would integrate uncertainty from renewable resources, load, distributed storage, and demand response technologies into bulk power system management in a holistic manner. The team will develop SOPF algorithms for the security-constrained economic dispatch (SCED) problem used to manage variability in the electric grid. The algorithms will be implemented in a software tool to provide system operators with real-time guidance to help coordinate between bulk generation and large numbers of DERs and demand response. ASU's project features unique data-analytics based short-term forecast for bulk and distributed wind and solar generation utilized by the advisory tool that generates real-time recommendations for market operators based on the SOPF algorithm outputs.

Potential Impact: 

If successful, projects included in the NODES Program will develop innovative hardware and software solutions to integrate and coordinate generation, transmission, and end-use energy systems at various points on the electric grid. These control systems will enable real-time coordination between distributed generation, such as rooftop and community solar assets and bulk power generation, while proactively shaping electric load. This will alleviate periods of costly peak demand, reduce wasted energy, and increase renewables penetration on the grid.

Innovations from this program would help the U.S. grid assimilate at least 50% of renewable generation and provide system reliability and resiliency while managing emerging energy generation and consumption patterns.
The addition of flexible loads and DERs into the U.S. grid could offset 3.3 quads of thermal generation and displace 290 million tons of CO2 emissions.
Using the NODES approach to integrate flexible loads and DERs into the grid could replace 4.5 GW of spinning reserves (i.e. generation capacity on stand-by in case of outages and unforeseen intermittency), a value of $3.3 billion per year. A more efficient and reliable grid would help protect U.S. businesses from costly power outages and brownouts.
ARPA-E Program Director: 
Dr. Sonja Glavaski
Project Contact: 
Junshan Zhang
Nexant, Inc.
PJM Interconnection
Sandia National Laboratory
Release Date: