Ensuring Efficiency and Safety in the Power Grid with Optimization and Control
by Chloe de Leon
Associate Professor Emiliano Dall’Anese (ECE, SE incoming Associate Head) pursues research in automatic control, system theory, and optimization, with a specific focus on applications in power grids. Power systems are an increasingly topical area of research driven by rapid technological developments and rising energy demands.
While Dall’Anese has been exploring solutions to emerging challenges in power systems since 2012, he emphasized how the industry continues to change.
“Something that we just started in collaboration with one of my colleagues is look[ing] at problems related to the integration of data centers into the [power] grid and ensur[ing] that they can be served in a reliable manner to support the growth in AI,” Dall’Anese said.
His research explores how systems can be refined to effectively control distributed resources, such as photovoltaic systems, electric vehicles, and intelligent loads. By applying advances in control theory and optimization to these systems, Dall’Anese and his team create mathematical models that optimize efficiency and increase reliability. Their models must consider usage inputs across vast geographic spaces and return outputs within a second. Control over distributed resources is essential to prevent failures in systems people rely on.
Another area of interest is ensuring stability in grids powered by renewable energy. Such grids are more vulnerable to frequency instability. Implementing control systems for converters — that change current or voltage to more safely move power — can mitigate the risks associated with implementing renewable energy.
As Dall’Anese continues to explore and address challenges in the power grid, he emphasizes that his research connects mathematical foundations with feasible implementations.
“We are talking about the safety of critical infrastructure,” Dall’Anese said. “We don’t want to deploy technologies or algorithms that are not grounded on solid foundations.”
More broadly, Dall’Anese’s work in control theory has potential beyond the energy industry. New methods and algorithms can be applied to optimize systems across many fields. Dall’Anese expressed that fundamentally, the ability to describe a system using mathematics—from differential equations to stochastic optimization—is key to safe and optimal control systems. Future research on how user interaction shapes behavioral modeling will further improve this methodology.
“We’re having a lot of fun discovering new theories and methods, especially related to feedback control, large-scale optimization, and making progress in terms of fundamental knowledge,” Dall’Anese said.