Artelys amongst the top ten finalists of the ARPA-E Grid Optimization Competition

20 February 2020 | EN | News

Artelys would like to congratulate the teams that ranked in the top ten of Challenge 1 of the ARPA-E Grid Optimization competition. We would especially like to applaud the NU_Columbia_Artelys team composed of Richard Waltz, Daniel Bienstock and Jorge Nocedal for placing seventh overall and qualifying for the maximum prize money of $400,000.

 

In order to accelerate transformational energy innovations that will create a more secure, affordable and sustainable American grid, ARPA-E has established the Grid Optimization (GO) Competition. The GO Competition challenges participating teams to develop and test power system optimization and control algorithms on a range of different synthetic and real network models.

Challenge 1 focused on security-constrained optimal power flow (SCOPF). The optimal power flow (OPF) problem aims to find the optimal dispatch and control settings for power generation, flexible customer demand, energy storage, and grid control that maximize one or more grid objectives, while the SCOPF problem additionally considers security constraints. Challenge 1 allowed the entrants to submit strategies and algorithms for solving such problems and the generated solutions were scored using four different metrics. The NU_Columbia_Artelys team, led by Artelys’ senior scientist Richard Waltz, finished in the top 10 teams across the four-scoring divisions using a software solution built around the industry leading Artelys Knitro nonlinear optimization solver.   

Improved SCOPF algorithms can yield significant monetary savings and more powerful nonlinear optimization algorithms help ensure reliable system operations as power flows become more dynamic. As the number of controllable resources connected to both transmission and distribution grow substantially, along with the reliance on stochastic resources, it is important to examine approaches that will handle the increasing complexities driven by the size, non-convexities, and uncertainties associated with power grid management problems. Participating in the design of improved algorithms was no small feat and thereby we would like to applaud again the NU_Columbia_Artelys team for their input in advancing optimization methods for the power grid.

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