Frameworks and results in distributionally robust optimization
H Rahimian, S Mehrotra - Open Journal of Mathematical Optimization, 2022 - numdam.org
The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …
have developed significantly over the last decade. The statistical learning community has …
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 …
Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a …
Data-driven chance constrained programs over Wasserstein balls
We provide an exact deterministic reformulation for data-driven, chance-constrained
programs over Wasserstein balls. For individual chance constraints as well as joint chance …
programs over Wasserstein balls. For individual chance constraints as well as joint chance …
Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness
S Küçükyavuz, R Jiang - EURO Journal on Computational Optimization, 2022 - Elsevier
Chance-constrained programming (CCP) is one of the most difficult classes of optimization
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
Distributionally robust chance constrained optimal power flow with renewables: A conic reformulation
The uncertainty associated with renewable energy sources introduces significant challenges
in optimal power flow (OPF) analysis. A variety of new approaches have been proposed that …
in optimal power flow (OPF) analysis. A variety of new approaches have been proposed that …
Distributionally robust joint chance-constrained dispatch for integrated transmission-distribution systems via distributed optimization
This paper focuses on the distributionally robust dispatch for integrated transmission-
distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually …
distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually …
Distributionally robust frequency constrained scheduling for an integrated electricity-gas system
Power systems are shifted from conventional bulk generation toward renewable generation.
This trend leads to the frequency security problem due to the decline of system inertia. On …
This trend leads to the frequency security problem due to the decline of system inertia. On …
Tractable convex approximations for distributionally robust joint chance-constrained optimal power flow under uncertainty
Uncertainty arising from renewable energy results in considerable challenges in optimal
power flow (OPF) analysis. Various chance-constrained approaches are proposed to …
power flow (OPF) analysis. Various chance-constrained approaches are proposed to …
A fast polytope-based approach for aggregating large-scale electric vehicles in the joint market under uncertainty
The aggregation of electric vehicles (EVs) has been advocated as an effective means to
manage and control large-scale plug-in EVs. However, the efficient aggregation is …
manage and control large-scale plug-in EVs. However, the efficient aggregation is …
Distributionally robust joint chance-constrained optimization for networked microgrids considering contingencies and renewable uncertainty
In light of a reliable and resilient power system under extreme weather and natural disasters,
networked microgrids integrating local renewable resources have been adopted extensively …
networked microgrids integrating local renewable resources have been adopted extensively …