Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

C Ning, F You - Computers & Chemical Engineering, 2019 - Elsevier
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

Stochastic model predictive control: An overview and perspectives for future research

A Mesbah - IEEE Control Systems Magazine, 2016 - ieeexplore.ieee.org
Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …

Resilient decentralized optimization of chance constrained electricity-gas systems over lossy communication networks

T Qian, X Chen, Y Xin, W Tang, L Wang - Energy, 2022 - Elsevier
With the gradual growth of natural gas units, the coupling between power networks and
natural gas networks has deepened, and the synergistic operation between them has …

Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey

T Martin, TB Schön, F Allgöwer - Annual Reviews in Control, 2023 - Elsevier
This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …

Distributionally robust chance-constrained optimal power flow with uncertain renewables and uncertain reserves provided by loads

Y Zhang, S Shen, JL Mathieu - IEEE Transactions on Power …, 2016 - ieeexplore.ieee.org
Aggregations of electric loads can provide reserves to power systems, but their available
reserve capacities are time-varying and not perfectly known when the system operator …

Chance-constrained AC optimal power flow: Reformulations and efficient algorithms

L Roald, G Andersson - IEEE Transactions on Power Systems, 2017 - ieeexplore.ieee.org
Higher levels of renewable electricity generation increase uncertainty in power system
operation. To ensure secure system operation, new tools that account for this uncertainty are …

On distributionally robust chance constrained programs with Wasserstein distance

W Xie - Mathematical Programming, 2021 - Springer
This paper studies a distributionally robust chance constrained program (DRCCP) with
Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a …

A data-enhanced distributionally robust optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty

B Zhao, T Qian, W Tang, Q Liang - Energy, 2022 - Elsevier
With growing penetrations of wind power in electricity systems, the coordinated dispatch of
integrated electricity and natural gas systems is becoming a popular research topic …

Flexible spacing adaptive cruise control using stochastic model predictive control

D Moser, R Schmied, H Waschl… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
This paper proposes a stochastic model predictive control (MPC) approach to optimize the
fuel consumption in a vehicle following context. The practical solution of that problem …

Monte Carlo sampling-based methods for stochastic optimization

T Homem-de-Mello, G Bayraksan - Surveys in Operations Research and …, 2014 - Elsevier
This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …