Being Smart about Uncertainty

This panel will showcase the applications of uncertainty-based research at all levels of the power system – from understanding the role of consumers when making control decisions in uncertain energy systems through to incorporating uncertainty models into large-scale power system security analysis.

Overview

“Uncertainties in Power Systems: Finding the ones that matter”
Dr Robin Preece (University of Manchester)

“Managing uncertainty in transmission system operation”
Line Roald (ETH Zürich)

”The role and challenges of coordinating energy endpoints under uncertainty”
Dr Alessandra Parisio (University of Manchester)

“Quantifying (un)certainty in the consumer response to dynamic pricing”
Dr Simon Tindemans (Imperial College London)

Panel

Dr Robin Preece (University of Manchester)

Robin is a Lecturer in the School of Electrical and Electronic Engineering at the University of Manchester (UK). His research focuses on the understanding of system uncertainties and their impact on the dynamic stability and control of power transmission networks, particularly mixed AC/DC systems.

Line Roald (ETH Zürich)

Line Roald is a PhD student at the Power Systems Laboratory at ETH Zürich, Switzerland. Her research involves risk modelling and methods to handle uncertainty in transmission grid operation, with a particular focus on congestion management and optimal power flow applications.

Dr Alessandra Parisio (University of Manchester)

Alessandra Parisio is a Lecturer in the School of Electrical and Electronic Engineering, University of Manchester, UK. Her research interests include the areas of manufacturing system simulation and scheduling, optimization of energy systems and stochastic constrained control.

Dr Simon Tindemans (Imperial College London)

Simon Tindemans is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London, UK. His main research interests are the quantification and control of responsive demand and the development of computational methods for reliability assessment.