K
Kostas Margellos
Researcher at University of Oxford
Publications - 115
Citations - 2610
Kostas Margellos is an academic researcher from University of Oxford. The author has contributed to research in topics: Probabilistic logic & Computer science. The author has an hindex of 22, co-authored 96 publications receiving 2124 citations. Previous affiliations of Kostas Margellos include University of Milan & University of California, Berkeley.
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On the Road Between Robust Optimization and the Scenario Approach for Chance Constrained Optimization Problems
TL;DR: This work proposes a new method for solving chance constrained optimization problems that lies between robust optimization and scenario-based methods, and imposes certain assumptions on the dependency of the constraint functions with respect to the uncertainty.
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A Probabilistic Framework for Reserve Scheduling and ${\rm N}-1$ Security Assessment of Systems With High Wind Power Penetration
TL;DR: A probabilistic framework to design an N-1 secure day-ahead dispatch and determine the minimum cost reserves for power systems with wind power generation is proposed and a reserve strategy according to which the reserves are deployed in real-time operation is identified.
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Hamilton–Jacobi Formulation for Reach–Avoid Differential Games
Kostas Margellos,John Lygeros +1 more
TL;DR: The main advantage of the approach proposed is that it can be applied to a general class of target-hitting continuous dynamic games with nonlinear dynamics, and has very good properties in terms of its numerical solution, since the value function and the Hamiltonian of the system are both continuous.
Proceedings ArticleDOI
Cyber attack in a two-area power system: Impact identification using reachability
TL;DR: A new framework is developed and a systematic methodology is defined, based on reachability, for identifying the impact that an intrusion might have in the Automatic Generation Control loop, which regulates the frequency and the power exchange between the controlled areas.
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Dual decomposition for multi-agent distributed optimization with coupling constraints
TL;DR: This work proposes a novel distributed algorithm to minimize the sum of the agents’ objective functions subject to both local and coupling constraints, where dual decomposition and proximal minimization are combined in an iterative scheme.