English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Urban regions account for 64% of global primary energy use and 70% of carbon emissions. For that reason, options to decarbonize urban environments are receiving increasing attention. In this context, public transport shall play a key role in decarbonizing urban road transport. One efficient way to achieve that is shifting towards clean fuels and modern electric buses, an option that is already under implementation in several cities around the world. In this paper, the basis for developing a dynamic optimization model for establishing charging infrastructure for electric buses is presented, using Stockholm, Sweden, as a case study. The model places constraints depending on the bus stop type (end or middle stop) which affects the time available for charging at each particular location. It also identifies the optimal technology type for the buses: conductive or inductive. In addition, the electric buses compete with buses run on biogas or biodiesel. In this paper, we present the results of a cost minimization scenario with constraints placed on the available charging time and power, differentiated between end stops and major public transport hubs. The mean charging time is 7.33 minutes, with a standard deviation of 4.78 minutes for all bus stops. The inner city bus routes require less charging time, which ranges on average at around 3 minutes. The installation of chargers at the locations proposed in the model would require scheduling adjustments and careful planning for the density of charging occasions

Developing a dynamic optimization model for electric bus charging infrastructure