The U.S. Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) is challenging the international research and industrial communities to develop innovative power system optimization and control algorithms for the future power grid.
Entrants will develop transformational ways to solve power flow optimization problems across a wide range of operating conditions. This is the central challenge underlying all grid planning and operations tools.
The goal of the Grid Optimization (GO) Competition is to increase power grid flexibility, reliability, safety, security, and efficiency, while lowering the costs of integrating renewable generation technologies into the grid system.
The first challenge of the GO Competition is an algorithm competition to develop solutions to the electric power sector's security-constrained optimal power flow (SCOPF) problem. Optimal power flow requires determining generator settings that best enable power to be routed to customers across a complex grid in a reliable and cost-effective manner.
Algorithms will be tested on complex, realistic power system models, and participants will be scored on how well their algorithms perform relative to other competitors' algorithms. Winning teams will efficiently find a minimum-cost solution to the SCOPF problem.
"Challenge 1 is focused on security-constrained AC optimal power flow (SCOPF). Challenge 1 will cover a 12-month timeline including trial events that will lead up to the final event in fall 2019," stated Dr. Kory W. Hedman, Program Director, Advanced Research Projects Agency-Energy (ARPA-E), US Department of Energy.
"Challenge 1 will include 4 scoring divisions (see scoring document). Prize Eligible Entrants that place in the top 10 will receive $100k for each top 10 division placement, up to $400k total. Challenge 1 will lead into Challenge 2, which will focus on a more advanced SCOPF, where the top 3 winners will receive even larger cash prizes than Challenge 1," continued Dr. Hedman.
Challenge 1 details:
"We are currently in the process of designing additional competitions with large cash prizes focused on other grid software related challenges.
“These topics may include: stochastic optimization, unit commitment, grid resilience, restoration, system stability and dynamics, PMU data utilization/applications, DER management, and T&D integration," Dr. Hedman added.
Additional challenges are planned beginning in 2019 in topics including DERs, intermittent resources, storage, grid resilience, grid restoration, grid dynamics, and cyber threats.
Existing grid software was designed for a power grid based on conventional generation and transmission technologies which are dominated by large, centralized power plants. The rapid development in recent years of new resources, including DERs, intermittent resources (wind and solar), and storage has created a new set of challenges for grid management.
Currently, grid management software does not allow for new forms of generation and storage to be used at full potential. Existing grid software makes several simplifying assumptions that produce suboptimal power flow solutions and result in increased electricity costs; the effects of these assumptions grow as the number of DERs grows.
Furthermore, increasing emphasis on grid resilience demands innovative management of more diverse and decentralized resources, which existing grid software is not equipped to handle. Innovation is needed regarding the underlying modeling, optimization, and control methods to increase grid flexibility, reliability, and resilience while substantially reducing system costs and barriers to fully integrated emerging technologies.
If successful, GO Competition participants will further the development of advanced software to enable a responsive, resilient, and efficient power grid
Increased resource diversity and decentralization combined with smarter grid management software (which can direct power where it is needed in the event of a generator failure) can improve grid resilience.
Enabling the integration of much higher levels of intermittent renewable resources can greatly reduce power sector emissions.
Improved optimization techniques will reduce reliance on costly modeling assumptions currently used by today’s grid software while enabling the full utilization of distributed and intermittent resources.