The growing deployment of distributed energy resources can result in significant environmental and economic benefits but, at the same time, in reduced total system inertia and controllability, hence in new challenges to the power grid operation. Within this context, flexibility (i.e., the ability to adjust to the time-varying grid conditions) plays a crucial role for the transition towards power systems that can efficiently accommodate high shares of renewable energy sources.

However, managing flexibility in urban districts and in distribution networks requires control and optimisation tools not yet available. Furthermore, there are several multi-energy systems within a district (i.e., systems with interconnected electricity/heating/gas networks), which currently lack coordination, and which can be regarded as excellent flexibility providers.

Novel control strategies and schemes are needed to harness their unique potential. There is still a limited understanding of how to devise effective frameworks for coordinating an arbitrarily large number of flexibility sources.

Filling this knowledge gap is essential for the transition to a more sustainable energy grid. In this talk, promising distributed control approaches for coordinating flexible resource, which leverage advanced methods, such as model predictive control and time-varying online optimisation, and data, are explored and illustrative case studies are discussed.

Speaker

Dr Alessandra Parisio is a Reader in the Department of Electrical and Electronic Engineering at The University of Manchester, UK, where she is/has been principal or co-investigator on research projects supported by EPSRC, Innovate UK, EC H2020 and industrial partners in the areas of building energy management and distributed control for flexibility service and grid support provision, totalling over £7 million as University of Manchester share.

Her main research interests include the areas of energy management systems under uncertainty, model predictive control, stochastic constrained control, and distributed optimisation for power systems.