Emiliano Dall'Anese
Boston University, MA·Engineering·Early-career
Power grids, transportation networks and computing platforms are becoming harder to run. Demand swings with data centers, electrified heating, artificial intelligence (AI) workloads and customers who now react in real time to prices.
- Fields
Engineering
Mathematics & Statistics
Computer Science
- Topics
power grids
power systems
online optimization
- Grants
- 3 known grants, through Aug 2029
What they work on
- Design real-time controllers that approximate optimization-based safety filters using only local or limited measurements, keeping networked systems with partially known dynamics within operating limits.
- Analyze decentralized, distributed, delayed-communication and sampled-data versions of these safe controllers, and test them on power grid frequency regulation and traffic congestion control.
- Develop online stochastic-gradient algorithms that use live measurements to set both control actions and user incentives when user behavior depends on the operator's decisions.
- Formulate constrained multi-agent optimization problems in which several service providers compete through incentives, and design adaptive methods that learn user responses to keep operations reliable.
- Create feedback-based online algorithms that coordinate distributed energy resources (DERs) such as solar and batteries in closed loop with the grid, using measurements instead of a full network model.
- Learn users' utility functions from their feedback during operation, and quantify tradeoffs between convergence rate, tracking accuracy, constraint violation and computational cost.
More about the research
Operators must make fast decisions without complete measurements or accurate models, while keeping the system within safe limits. The lab builds the mathematics and algorithms for this task. It combines feedback control, which corrects actions using live measurements, with online optimization, which updates the best decision continuously as conditions change. All three projects share this goal: fast, scalable, provably reliable control of large networks under uncertainty.
Why it matters
Grid operators and utilities could use these methods to keep electricity reliable as data centers, electrified heating and rooftop solar make demand and supply harder to predict. Customers who own solar panels or batteries may benefit from coordination that respects their preferences while supporting voltage and frequency. The same tools could also help manage congestion in transportation networks.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Miguel Lejeune
George Washington University, DC·Engineering
Advance real options analysis for infrastructure protection investment under multiple sources of uncertainty, building on optimal stopping theory and the Black-Scholes model from finance.
Jan Kleissl
University of California-San Diego, CA·Engineering
Build DERConnect, a remote-access testbed that integrates solar panels, battery storage, electric vehicles, wind and solar emulators, and controllable building lighting and HVAC (heating, ventilation and air conditioning) systems.
Javad Khazaei
Lehigh University, PA·Engineering·Early-career
Learn low-dimensional manifolds from time-series measurements of distributed energy resources and use them for real-time predictive control of voltage, frequency and power sharing in microgrids.
The school
Boston University: 26 labs on PI Pages, $784M a year on research, 630 research doctorates a year.
Reach out
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