Robust strategic planning of dynamic wireless charging infrastructure for electric buses

•A new MILP is developed to optimize charging planning and bus fleet size.•To address the uncertainty of energy demand and charging time, the RCM is derived.•Dependent and independent budget uncertainty sets are developed to control robustness.•A real bus network study is conducted to demonstrate ef...

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Vydáno v:Applied energy Ročník 307; s. 118243
Hlavní autoři: Alwesabi, Yaseen, Avishan, Farzad, Yanıkoğlu, İhsan, Liu, Zhaocai, Wang, Yong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2022
Témata:
batteries
Battery electric bus
deterministic models
Dynamic wireless charging
electricity costs
Electromobility
energy
infrastructure
Robust optimization
uncertainty
Vehicle scheduling
Battery electric bus
Vehicle scheduling
Robust optimization
Electromobility
Dynamic wireless charging
ISSN:0306-2619, 1872-9118
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Shrnutí:•A new MILP is developed to optimize charging planning and bus fleet size.•To address the uncertainty of energy demand and charging time, the RCM is derived.•Dependent and independent budget uncertainty sets are developed to control robustness.•A real bus network study is conducted to demonstrate effectiveness of the models.•Joint charging and scheduling planning can save 19.2% of the total cost. Electromobility in public bus systems is growing rapidly and experiencing a fundamental transformation in their infrastructure and operations. The dilemma of limited driving range and charging time of battery electric buses (BEBs) hinders their adoption. A novel approach to address BEB limitations is to utilize dynamic wireless charging (DWC) technology that allows buses to charge while in motion. This paper aims to analyze robust strategic planning of DWC and BEB fleet scheduling based on a real bus network at Binghamton University. The problem is first formulated as a new deterministic mixed-integer linear programming model to simultaneously optimize both the charging planning problem and fleet scheduling problem in an integrated fashion. To address the uncertainty of energy demand and charging time, a robust counterpart model (RCM) has been derived. To increase RCM flexibility, the battery status variable is formulated in a cumulative form. Dependent and independent budget uncertainty sets have been developed to control the robustness. A sensitivity analysis has been conducted to study the system behavior in response to different charging types, auxiliary energy demand, depth of discharge, charging options at terminals, battery degradation, and electricity cost. The deterministic model shows that eight homogeneous BEBs are required to operate on the selected routes with a battery capacity of 54.01 kWh and a total cost of $3,636,347. The results show that joint planning of charging infrastructure and fleet scheduling can save 19.2% of total cost compared to disjoint planning. The RCM results in 10 BEBs to ensure feasiblility against uncertainty.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2021.118243