Översikt
- Datum:Startar 8 september 2025, 09:30Slutar 8 september 2025, 12:30
- Plats:SB-S393, Sven Hultins Gata 6, Vån 3, Chalmers University of Technology, 412 58 Göteborg
- Opponent:Pei Huang, Division of Sustainable Energy Systems, Mälardalen University, Sverige
- AvhandlingLäs avhandlingen (Öppnas i ny flik)
Transportation electrification driven by net-zero emission targets in the transport sector requires accurate prediction of charging demand and cost effective deployment of charging and energy infrastructure.
This thesis begins with a comprehensive review of infrastructure and energy supply for transport electrification, with emphasis on near-term charging demand prediction, the integration of renewable energy with charging infrastructure, and system-level impacts. Addressing identified research gaps, the first study develops an integrated agent-based modeling framework to generate spatiotemporal charging demand profiles. The framework jointly accounts for cost-aware charging behavior, daily activity chains, and route and mode choice, while incorporating multiple charger types, dynamic time-of-use tariffs, and probabilistic adaptive smart-charging behavior that allows users to shift charging to reduce costs while mitigating range anxiety.
Building on the near-future charging demand outputs, the second study develops a large-scale optimization framework for the deployment of multi-class public chargers, co-located photovoltaic systems, and battery energy storage (BESS). The framework jointly optimizes charger placement, PV sizing, BESS scheduling, and user incentives for short-distance spatial demand redirection, while accounting for land-use constraints, seasonal PV capacity factors, and time-of-use tariffs.
The developed approaches are demonstrated in a real-world case study of Gothenburg using multisource data. System benefits are assessed across economic, operational, and environmental dimensions. The results provide quantitative evidence on how user charging behavior and smart charging influence spatiotemporal demand, and how the integration of renewable energies with BESS and incentive-based demand management can jointly enable cost-effective and sustainable charging and energy supply for electric passenger transport.
This thesis begins with a comprehensive review of infrastructure and energy supply for transport electrification, with emphasis on near-term charging demand prediction, the integration of renewable energy with charging infrastructure, and system-level impacts. Addressing identified research gaps, the first study develops an integrated agent-based modeling framework to generate spatiotemporal charging demand profiles. The framework jointly accounts for cost-aware charging behavior, daily activity chains, and route and mode choice, while incorporating multiple charger types, dynamic time-of-use tariffs, and probabilistic adaptive smart-charging behavior that allows users to shift charging to reduce costs while mitigating range anxiety.
Building on the near-future charging demand outputs, the second study develops a large-scale optimization framework for the deployment of multi-class public chargers, co-located photovoltaic systems, and battery energy storage (BESS). The framework jointly optimizes charger placement, PV sizing, BESS scheduling, and user incentives for short-distance spatial demand redirection, while accounting for land-use constraints, seasonal PV capacity factors, and time-of-use tariffs.
The developed approaches are demonstrated in a real-world case study of Gothenburg using multisource data. System benefits are assessed across economic, operational, and environmental dimensions. The results provide quantitative evidence on how user charging behavior and smart charging influence spatiotemporal demand, and how the integration of renewable energies with BESS and incentive-based demand management can jointly enable cost-effective and sustainable charging and energy supply for electric passenger transport.
