Locating charging infrastructure for electric buses in Stockholm

Abstract

Highlights Presenting an optimization model for the location of electric bus charging stations.  Model application to a large-scale case study for the bus network of Stockholm, Sweden.  Results show that low fuel costs for electricity can balance annual infrastructure costs.  Emissions decrease up to 51% and energy consumption up to 34% with electrification.  The model may assist decision-making for investments in public transport.Keywords electric bus; charging infrastructure; optimization; Mixed Integer Linear Programing; public transport; Sweden AbstractCharging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.

Energy System Models as a Means of Visualising Barriers and Drivers of Forest-Based Biofuels: An Interview Study of Developers and Potential Users

“…The BeWhere Sweden model, which has been the example in this paper, could obviously help to illustrate opportunities and obstacles regarding forest-derived biofuel production in Sweden, similar to how the BeWhere model in a smaller geographical context has previously been used to analyse the design of a charging infrastructure for electric buses [35]. However, on a broader level, the study also illustrates the complexity that characterises this policy area, where a multitude of different interests interact and where an energy system model can answer some of the 'what if .…”

“…The model has been applied at local, national and supranational levels. Initially the scope of application was limited to bioenergy plants, e.g., [26][27][28][29][30][31], but this has, in recent years, been expanded to also include, for example, solar, wind, hydropower, public transport, and algae-based plants [32][33][34][35]. BeWhere is based on mixed-integer linear programming (MILP).…”

“…One example of recent policy targeted use of BeWhere is within the S2Biom project [37], where the model was used as part of an integrated toolset aimed at developing harmonised data sets, strategies and roadmaps at geographical levels ranging from the local up to the pan-European level (more information can be found at the project webpage: http://www.s2biom.eu/en/about-s2biom.html). Another example, at the local/regional level, involves the design and localisation of charging infrastructure for electric buses in Stockholm, Sweden, where the BeWhere model was used in collaboration with representatives for the local public transportation company, as well as concerned municipalities [35].…”

A model for determining the locations of electric charge stations in Istanbul

“…Authors presented MILP model. The model aimed to minimize transportation cost of bus network [14]. The authors in [15], implemented two-step model by using the data of the flow of EVs and the road network.…”

A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System

“…As the energy supply of EVs, the charging infrastructure plays a crucial role in its promotion [1][2][3]. Due to the shorter recharging duration required compared to AC charging, as well as the lower investment cost compared to battery-swap charging, DC fast charging has become the mainstream method used for electric bus, taxi, and emergency charging [4,5]. In addition, given the potential for increased battery capacity, DC fast charging is expected to be beneficial for daily use [6,7].…”