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Issue DateTitleAuthor(s)
1-Mar-2015Valuation of Flexible Transmission Investment Options Under UncertaintyKonstantelos, I; Strbac, G; Engineering & Physical Science Research Council (EPSRC); Engineering & Physical Science Research Council (E; Engineering & Physical Science Research Council (EPSRC)
29-Mar-2017Coordination and uncertainty in strategic network investment: case on the North Seas gridKonstantelos, I; Moreno, R; Strbac, G; Engineering & Physical Science Research Council (E; Engineering & Physical Science Research Council (EPSRC)
2-Jul-2015Option value of Soft Open Points in distribution networksGiannelos, S; Konstantelos, I; Strbac, G; Engineering & Physical Science Research Council (E
8-Apr-2016Stochastic optimisation-based valuation of smart grid options under firm DG contractsGiannelos, S; Konstantelos, I; Strbac, G; Engineering & Physical Science Research Council (E
9-Jul-2016Strategic valuation of smart grid technology options in distribution networksKonstantelos, I; Giannelos, S; Strbac, G; Engineering & Physical Science Research Council (E; Engineering & Physical Science Research Council (EPSRC)
1-Jul-2018Planning with multiple transmission and storage investment options under uncertainty: a nested decomposition approachFalugi, P; Konstantelos, I; Strbac, G; Engineering & Physical Science Research Council (EPSRC); Engineering & Physical Science Research Council (EPSRC), et al
1-Jan-2019Data-driven power system operation: Exploring the balance between cost and riskCremer, J; Konstantelos, I; Tindemans, S; Strbac, G; Engineering & Physical Science Research Council (E
1-Jun-2017A Five-Level MILP Model for Flexible Transmission Network Planning under Uncertainty: A Min-Max Regret ApproachMoreira, A; Strbac, G; Moreno, R; Street, A; Konstantelos, I, et al
31-Dec-2019From optimization-based machine learning to interpretable security rules for operationCremer, J; Konstantelos, I; Strbac, G; Engineering & Physical Science Research Council (E; Engineering & Physical Science Research Council (E
Jan-2019Using vine copulas to generate representative system states for machine learningKonstantelos, I; Sun, M; Tindemans, S; Issad, S; Panciatici, P, et al