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Alessandra Parisio
Researcher at University of Manchester
Publications - 61
Citations - 4367
Alessandra Parisio is an academic researcher from University of Manchester. The author has contributed to research in topics: Model predictive control & Microgrid. The author has an hindex of 23, co-authored 52 publications receiving 3706 citations. Previous affiliations of Alessandra Parisio include Royal Institute of Technology & University of Sannio.
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Journal ArticleDOI
Multi-location virtual smart grid laboratory with testbed for analysis of secure communication and remote co-simulation: concept and application to integration of Berlin, Stockholm, Helsinki
Christian Wiezorek,Alessandra Parisio,Timo Kyntaja,Joonas Elo,Markus Gronau,Karl Henrik Johannson,Kai Strunz +6 more
TL;DR: In this article, the virtual smart grid laboratory (VSGL) is developed as described in this study, which is a communication platform for seamlessly connecting geographically distributed laboratories with distinct competences.
Journal ArticleDOI
Optimal Virtual Power Plant Management for Multiple Grid Support Services
TL;DR: A hierarchical control architecture is proposed for the optimal day-ahead commitment of multiple grid support services within a virtual power plant (VPP) and the results show the superiority of the multiple-service operation compared to providing a single grid support service.
Journal ArticleDOI
Distributed control of battery energy storage systems in distribution networks for voltage regulation at transmission–distribution network interconnection points
TL;DR: In this article , a control framework that enables distributed battery energy storage systems (BESS) connected to distribution networks (DNs) to track voltage setpoints requested by the transmission system operator (TSO) at specific interconnection points in an optimal and coordinated manner is described.
Journal ArticleDOI
Brief paper: Robust invariant sets for constrained storage systems
TL;DR: This paper provides the explicit expression of the invariant sets for any arbitrary n and considers the classical problem of determining control laws and smallest buffer levels guaranteeing that an unknown bounded demand is always satisfied.
Proceedings ArticleDOI
Stochastic model predictive control for optimal energy management of district heating power plants
TL;DR: The main goal of the control strategy is to reduce the running costs by optimally managing the boilers, the thermal energy storage and the flexible loads while satisfying a time-varying request and operation constraints.