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The increase in renewable energy sources and new technologies such as electric vehicles and storage can generate uncertainties in distribution grid operations, increasing the likelihood of congestions in power lines. Distribution system operators (DSOs) face several challenges while operating their grids in such conditions. These congestions deteriorate the electrical equipment in the long term, reducing its life span. This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. The framework is tested on a modified IEEE-33 bus system and CINELDI MV Reference system with hourly synthetic data. The results showed that the framework is able to closely predict the congestions on the lines. Computational capabilities are reported and discussed. The study indicates that the framework is a suitable tool for day-to-day congestion predictions in smart distribution grids yielding low error in expected values., The authors would like to thank Rubi Rana for discussion regarding the CINELDI Reference System. This work was supported by the project consortium of the research project FINE (Flexible Integration of Local Energy Communities into the Norwegian Electricity Distribution System), financed by the Research Council of Norway [project number 308833]. This work has also been supported by the BD4OPEM H2020 project, which has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 872525. Mònica Aragüés is Associate Professor and Eduard Bullich-Massagué is a lecturer of the Serra Húnter programme., Peer Reviewed, Postprint (published version)

Recent studies have developed a power electronic device known as the power router, able to fully control the power flows over the connected power lines. The present paper aims to extend the application of power routers to an electrical grid and explore its characteristics and limitations. It defines the power router grid concept, including the conditions for interconnection and architecture; it proposes a mathematical modelling methodology, analyses its operational requirements, and explores the degree of flexibility the proposed grid architecture provides. Finally, two case studies are presented to show the application of the power router grid in a practical example., Peer Reviewed, Postprint (published version)

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution, Distribution System Operators face a challenging environment largely affected by the ever-growing integration of Distributed Energy Resources. Especially, Electric Vehicles have a rapidly growing presence in the distribution grids, being both a challenge, but also an enabler for active network management. This work analyses the operation of charging infrastructures in coordination with the DSO in the context of the FEVER project. The objective of the project is to exploit control of power flow through DC/AC converters towards demand management and voltage compensation. The paper describes the different modules required to coordinate this operation in a flexibility market context. DSO support tools have been developed to forecast possible critical events and prepare a mitigation plan leveraging flexibility. Response to this flexibility demand is covered by Vehicle-to-Grid charging stations, equipped with DC converters, capable of implementing flexibility strategies., Peer Reviewed, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Postprint (published version)