Journal ArticleDOI
Solving two-stage robust optimization problems using a column-and-constraint generation method
TLDR
A computational study on a two-stage robust location-transportation problem shows that the column-and-constraint generation algorithm performs an order of magnitude faster than existing Benders-style cutting plane methods.About:
This article is published in Operations Research Letters.The article was published on 2013-09-01. It has received 1010 citations till now. The article focuses on the topics: Robust optimization & Cutting-plane method.read more
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Co-Optimization of Supply and Demand Resources for Load Restoration of Distribution System Under Extreme Weather
TL;DR: In this article, a new methodological framework for enhancing the load restoration of DS during a hurricane is presented, which integrates mobile emergency generators (MEGs) and prosumer communities (PCs) incorporating combined heat and power (CHP) units, electric boilers (EBs), photovoltaic (PV) sources, and demand response (DR) resources.
Posted Content
A Scalable Algorithm for Two-Stage Adaptive Linear Optimization
TL;DR: This research extends the column-and-constraint generation method in a way that maintains scalability and always produces feasible first-stage decisions if they exist, and compares the method to several recently proposed methods and finds that it reaches high accuracies faster and solves significantly larger problems.
Journal ArticleDOI
Robust optimization in power systems: a tutorial overview
Antonio J. Conejo,Xuan Wu +1 more
TL;DR: This paper provides a tutorial overview of robust optimization in power systems, including robust optimization and adaptive robust optimization, and introduces distributionally robust optimization.
Journal ArticleDOI
Robust Optimization for Island Partition of Distribution System Considering Load Forecasting Error
TL;DR: A two-stage robust optimization island partition program is formulated to restore as many loads as possible, while satisfying operation and topology constraints, and uncertainty budget is set to control the degree of conservatism of the robust optimization result.
Journal ArticleDOI
A Robust Mixed-Integer Convex Model for Optimal Scheduling of Integrated Energy Storage—Soft Open Point Devices
Ilias Sarantakos,Meltem Peker,Natalia-Maria Zografou-Barredo,Matthew Deakin,Charalampos Patsios,Timur Sayfutdinov,Phil Taylor,David Greenwood +7 more
TL;DR: In this paper , a robust mixed-integer convex model for the optimal scheduling of integrated ES-SOPs to ensure a zero probability of constraint violation is presented, which enables efficient scheduling of the energization state of subsystems to reduce no-load losses.
References
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Journal ArticleDOI
The Price of Robustness
Dimitris Bertsimas,Melvyn Sim +1 more
TL;DR: In this paper, the authors propose an approach that attempts to make this trade-off more attractive by flexibly adjusting the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations.
The price of the robustness
D Bertsimas,M Sim +1 more
TL;DR: An approach is proposed that flexibly adjust the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations, and an attractive aspect of this method is that the new robust formulation is also a linear optimization problem, so it naturally extend to discrete optimization problems in a tractable way.
Journal ArticleDOI
Robust Convex Optimization
Aharon Ben-Tal,Arkadi Nemirovski +1 more
TL;DR: If U is an ellipsoidal uncertainty set, then for some of the most important generic convex optimization problems (linear programming, quadratically constrained programming, semidefinite programming and others) the corresponding robust convex program is either exactly, or approximately, a tractable problem which lends itself to efficientalgorithms such as polynomial time interior point methods.
Journal ArticleDOI
Generalized Benders decomposition
TL;DR: In this paper, the extremal value of the linear program as a function of the parameterizing vector and the set of values of the parametric vector for which the program is feasible were derived using linear programming duality theory.