On the selection of charging facility locations for EV-based ride-hailing services: a computational case study
| Autor: | Panagiotis Angeloudis, Qiming Ye, Marc E. J. Stettler, Jose Javier Escribano Macias, Daniel Ainalis, Renos Karamanis, Eleftherios Anastasiadis, Pei-Yuan Hsu |
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| Přispěvatelé: | Innovate UK |
| Rok vydání: | 2020 |
| Předmět: |
Operations research
Computer science Geography Planning and Development Environmental Studies TJ807-830 Environmental Sciences & Ecology 010501 environmental sciences Management Monitoring Policy and Law TD194-195 01 natural sciences Renewable energy sources Set (abstract data type) EV charging infrastructure 0502 economics and business Transportation Network Companies GE1-350 Green & Sustainable Science & Technology Selection (genetic algorithm) 0105 earth and related environmental sciences 050210 logistics & transportation Science & Technology Environmental effects of industries and plants Renewable Energy Sustainability and the Environment facility location Scale (chemistry) 05 social sciences Flow network Science & Technology - Other Topics Life Sciences & Biomedicine Environmental Sciences 12 Built Environment and Design |
| Zdroj: | Sustainability, Vol 13, Iss 168, p 168 (2021) Sustainability Volume 13 Issue 1 |
| Popis: | The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Our results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles. |
| Databáze: | OpenAIRE |
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