"Crystal ball for transport electrification" - DeepDive Podcast
Title: Dynamic Adaptive Charging Network Planning under Deep
Uncertainties
Abstract: Charging infrastructure is the backbone of electromobility. Due to new charging behaviors
and power distribution- and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of
investments. To this extent we propose a transport energy demand centric, dynamic adaptive
planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers
can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously
calibrate and / or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered / observed. The paper describes the components and the planning support
of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots
have shown that both transport energy demand centric and dynamic adaptive planning of charging
infrastructure planning are useful.
Keywords: charging infrastructure planning; deep uncertainties; dynamic adaptive planning; route
based network effects; spatial decisions support
