Publications (7)
Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach
Daniel Tabas, Baosen Zhang
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization pro…
Optimal L-Infinity Frequency Control in Microgrids Considering Actuator Saturation
Daniel Tabas, Baosen Zhang
Inverter-connected resources can improve transient stability in low-inertia grids by injecting active power to minimize system frequency deviations following disturbances. In pract…
Computationally Efficient Safe Reinforcement Learning for Power Systems
Daniel Tabas, Baosen Zhang
We propose a computationally efficient approach to safe reinforcement learning (RL) for frequency regulation in power systems with high levels of variable renewable energy resource…
Convex Restriction of Feasible Sets for AC Radial Networks
Ling Zhang, Daniel Tabas, Baosen Zhang
Many problems in power systems involve optimizing a certain objective function subject to power flow equations and engineering constraints. A long-standing challenge in solving the…
An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees
Ling Zhang, Daniel Tabas, Baosen Zhang
In this paper, we consider the scenario-based two-stage stochastic DC optimal power flow (OPF) problem for optimal and reliable dispatch when the load is facing uncertainty. Althou…
Consensus-Based Set-Theoretic Control in Power Systems
Daniel Tabas, Baosen Zhang
Set-theoretic control is a useful technique for dealing with the uncertainty introduced into power systems by renewable energy resources. Although set operations are computationall…