Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Ehsan Saqib and Gyozo Gidófalvi ()
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Ehsan Saqib: Department of Urban Planning and Environment-Geoinformatics, KTH Royal Institute of Technology, Teknikringen 10A, 11428 Stockholm, Sweden
Gyozo Gidófalvi: Department of Urban Planning and Environment-Geoinformatics, KTH Royal Institute of Technology, Teknikringen 10A, 11428 Stockholm, Sweden

Energies, 2024, vol. 17, issue 21, 1-27

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. This 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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