BN
B.T. Nolte
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Heavy-duty trucks contribute significantly to greenhouse gas emissions in the transport sector, despite making up only a small portion of the total vehicle fleet. As the EU targets a 90% reduction in truck emissions by 2040, Electric Road Systems (ERS) have emerged as a potential solution by enabling trucks to charge while driving, removing the need for large, heavy batteries. However, little is known about the temporal and spatial energy demand of such an infrastructure and the impact it would have on the electricity grid—particularly in the Netherlands. This study investigates the expected temporal distribution of energy demand resulting from an ERS corridor between Rotterdam and Venlo. A quantitative approach is used to model freight flow, ERS adoption potential, and estimate energy consumption. BasGoed origin-destination data is used to map freight volumes, and a set of spatial criteria determines which trips are eligible for ERS usage. By combining the data and demand characteristics, a temporal and spatial demand pattern can be identified, that can be used by the industry and policy makers to understand ERS and what is needed to support future implementation.
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Heavy-duty trucks contribute significantly to greenhouse gas emissions in the transport sector, despite making up only a small portion of the total vehicle fleet. As the EU targets a 90% reduction in truck emissions by 2040, Electric Road Systems (ERS) have emerged as a potential solution by enabling trucks to charge while driving, removing the need for large, heavy batteries. However, little is known about the temporal and spatial energy demand of such an infrastructure and the impact it would have on the electricity grid—particularly in the Netherlands. This study investigates the expected temporal distribution of energy demand resulting from an ERS corridor between Rotterdam and Venlo. A quantitative approach is used to model freight flow, ERS adoption potential, and estimate energy consumption. BasGoed origin-destination data is used to map freight volumes, and a set of spatial criteria determines which trips are eligible for ERS usage. By combining the data and demand characteristics, a temporal and spatial demand pattern can be identified, that can be used by the industry and policy makers to understand ERS and what is needed to support future implementation.