Post-Doctorant F/H EV Mobility-Driven Planning and Operation of Electrical Distribution Grids
Post-Doctorant F/H EV Mobility-Driven Planning and Operation of Electrical Distribution Grids
Fonction : Post-Doctorant
Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area.
The Centre Inria de l’Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.
This project is conducted within the framework of a maturation program supporting the transfer of research outcomes toward innovation and real-world deployment. The rapid deployment of electric vehicles (EVs) is creating new challenges for electrical distribution grids. Although predictive EV mobility models can accurately forecast vehicle movements and charging demand, they are rarely exploited for grid planning and operation. As a result, distribution system operators often rely on conservative assumptions regarding charging simultaneity and peak demand, leading to unnecessary infrastructure reinforcement and underutilization of network flexibility. This project aims to bridge this gap by coupling predictive EV mobility with electrical distribution network models to develop new methodologies for congestion assessment, infrastructure planning, decentralized charging and resilient grid operation. More broadly, it will contribute to the development of coupled mobility–energy digital twins for the integrated planning and operation of future urban mobility and electrical distribution systems.
This project aims to investigate how predictive EV mobility forecasts can improve the planning and operation of electrical distribution grids. Building upon the eMob-Twin platform ( , recent advances in open-source electrical distribution network modelling, and realistic charging demand forecasts, the project will develop an integrated modelling framework coupling urban mobility and electrical distribution networks. The resulting framework will enable the prediction of network loading, electrical congestion and charging demand at both spatial and temporal scales, providing the basis for new grid-aware planning and operational strategies. Particular emphasis will be placed on decentralized charging policies, demand shifting and flexibility services capable of mitigating congestion while maximizing the utilization of existing grid infrastructure.
The proposed research will integrate predictive EV mobility models developed within eMob-Twin with realistic low- and medium-voltage electrical distribution network models generated from open geographic data and simulated using established open-source power system platforms (e.g., OpenDSS and pandapower). Publicly available benchmark and synthetic distribution networks (e.g., SimBench and IEEE test feeders) will be used to develop and validate the proposed methodologies before their application to industrial case studies. These coupled mobility-energy models will be used to analyze the impact of large-scale EV integration on distribution grids and to develop predictive methodologies for infrastructure planning, congestion assessment and decentralized charging strategies. The developed methods will be validated on realistic urban scenarios and integrated into the eMob-Twin platform to evaluate their operational benefits for future distribution system operators.
- Generate and integrate realistic electrical distribution network models using publicly available geographic information, benchmark feeders and open-source simulation platforms within the eMob-Twin framework
- Characterize the impact of large-scale EV integration on transformer loading, feeder loading, voltage profiles and electrical congestion.
- Develop predictive methodologies for distribution grid planning, congestion assessment and hosting-capacity analysis.
- Design decentralized charging and demand-shifting strategies exploiting predictive EV mobility forecasts.
- Validate the proposed methodologies using synthetic and benchmark distribution grids and, where available, industrial DSO case studies.
- Assess the benefits for distribution grid planning, operational resilience and flexibility services.