The Optimization Firm Secures Funds to Strengthen the Power Grid

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The Optimization Firm is building new software solutions for large-scale power systems.

The Optimization Firm has received a $250,000 grant from The Advanced Research Projects Agency-Energy (ARPA-E) to fund a one-year project devoted to building software solutions for U.S. power grid optimization.

The company will compete for up to $4 million in awards by participating in the U.S. Department of Energy’s Grid Optimization (GO) Competition, a first-of-its-kind initiative to build new software solutions for electricity routing.

“The GO Competition brings together top researchers in optimization to tackle a challenging problem in the distribution of power flow, while ensuring that power sources are utilized in the most cost-effective and reliable way,” said Nick Sahinidis, The Optimization Firm’s CEO. “We were very pleased that we were chosen by ARPA-E to participate in this challenge.”

The goal of the competition is to incentivize the development of robust algorithms that can be used to solve the most pressing power system problems, while ensuring more Americans receive diverse energy sources across the country.

The competition will address the central optimization challenge underlying numerous grid planning and operational tools: the security-constrained optimal power flow (SCOPF) problem. This problem accounts for security constraints while identifying the most efficient, low-cost, and reliable operation settings of a power system.

Since the 1960s, researchers in engineering and optimization have addressed various challenges in solving the SCOPF problems. Yet, a major challenge persists: the nonconvex nature of SCOPF makes it difficult to find feasible solutions, let alone optimal ones.

“Building the grid of the future requires new ways of thinking about how we move power around efficiently, while still protecting our country from threats to grid stability,” Secretary of Energy Rick Perry said during his announcement of the GO Competition.

Powerful advances in optimization theory, algorithms, and software have made it possible over the past decade to solve large-scale mixed-integer nonlinear programming (MINLP) problems to global optimality.

“Our goal is to develop and implement novel global MINLP algorithms that will make it possible to solve, for the first time, large-scale SCOPF instances to global optimality,” Sahinidis said.

To support this goal, The Optimization Firm will provide transformational technologies for engineering in addition to building and testing its algorithm performance on realistic, large-scale power system models. ARPA-E will score the company’s algorithms on a collection of models of different sizes and operating scenarios for a variety of challenging operating conditions.

Challenge 1 began in November and focuses solely on transmission networks. ARPA-E will score the teams several times over the one-year time period and will evaluate their algorithm selection, performance, and solution quality.

If selected to advance to future challenges, The Optimization Firm will have the opportunity to participate in another 12-month competition involving more complicated grid operation optimization and dispatch algorithms that will handle the increasing complexities, non-convexities, and uncertainties associated with power grid management problems.

To read about the GO Competition, see:

The Optimization Firm ( develops innovative software to help companies make better decisions through the use of analytics and optimization. Fast, accurate, and with deterministic guarantee, BARON is the most advanced solver for global optimization of mixed-integer nonlinear problems. In addition to revolutionizing global optimization technology by introducing BARON in 2001, the company is known for its novel machine learning tools. ALAMO, our powerful machine learning software, helps users with datasets build the simplest surrogate models possible. ALAMO pushes the limits of technologies like data sampling and design of experiments, and has been used to develop data-driven models of refineries, chemical reactions, building efficiencies, and much more.

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