Data-driven distributionally robust optimization for real-time economic dispatch considering secondary frequency regulation cost
With the large-scale integration of renewable power generation, frequency regulation
resources (FRRs) are required to have larger capacities and faster ramp rates, which …
resources (FRRs) are required to have larger capacities and faster ramp rates, which …
On robustness and local differential privacy
On robustness and local differential privacy Page 1 The Annals of Statistics 2023, Vol. 51, No.
2, 717–737 https://doi.org/10.1214/23-AOS2267 © Institute of Mathematical Statistics, 2023 …
2, 717–737 https://doi.org/10.1214/23-AOS2267 © Institute of Mathematical Statistics, 2023 …
A general framework for learning-based distributionally robust MPC of Markov jump systems
In this article, we present a data-driven learning model predictive control (MPC) scheme for
chance-constrained Markov jump systems with unknown switching probabilities. Using …
chance-constrained Markov jump systems with unknown switching probabilities. Using …
Distributionally Robust Policy and Lyapunov-Certificate Learning
This article presents novel methods for synthesizing distributionally robust stabilizing neural
controllers and certificates for control systems under model uncertainty. A key challenge in …
controllers and certificates for control systems under model uncertainty. A key challenge in …
Maximum mean discrepancy distributionally robust nonlinear chance-constrained optimization with finite-sample guarantee
This paper is motivated by addressing open questions in distributionally robust chance-
constrained programs (DRCCP) using the popular Wasserstein ambiguity sets. Specifically …
constrained programs (DRCCP) using the popular Wasserstein ambiguity sets. Specifically …
Iterative risk-constrained model predictive control: A data-driven distributionally robust approach
This paper proposes an iterative distributionally robust model predictive control (MPC)
scheme to solve a risk-constrained infinite-horizon optimal control problem. In each iteration …
scheme to solve a risk-constrained infinite-horizon optimal control problem. In each iteration …
Wasserstein distributionally robust risk-constrained iterative MPC for motion planning: computationally efficient approximations
This paper considers a risk-constrained motion planning problem and aims to find the
solution combining the concepts of iterative model predictive control (MPC) and data-driven …
solution combining the concepts of iterative model predictive control (MPC) and data-driven …
Data-driven distributionally robust optimization over a network via distributed semi-infinite programming
This paper focuses on solving a data-driven distributionally robust optimization problem over
a network of agents. The agents aim to minimize the worst-case expected cost computed …
a network of agents. The agents aim to minimize the worst-case expected cost computed …
Improved microgrid resiliency through distributionally robust optimization under a policy-mode framework
Critical energy infrastructure are constantly under stress due to the ever increasing
disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber …
disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber …
Advanced computational techniques for improving resilience of critical energy infrastructure under cyber-physical attacks
Critical energy infrastructure has undergone significant changes in the past decades, which
has made these systems more vulnerable to breakdowns due to the uncertainty and …
has made these systems more vulnerable to breakdowns due to the uncertainty and …