A Data-Driven Approach to Manage Charging Infrastructure for Electric Vehicles in Parking Lots
The ever-increasing number of electric vehicles (EV) on the road is in line with many governments' efforts to tackle urgent environmental challenges. This inherently means that there is a growing need for charging infrastructure as well. A potential solution to address the need for charging stations is to transform traditional parking lots into EV-enabled parking lots (EVPLs), in a sense that EVPLs provide not only parking services, but also the possibility for EV owners to charge their cars for a price. Due to the inherently complex and dynamic environment, a potential obstacle, from a business perspective, to the process of transforming parking lots into EVPLs is the complexity of estimating the EVPL's profitability and, consequently, the length of time required to recover the cost of the investment in the EV charging infrastructure. We propose a novel framework based on discrete-event simulations to estimate an EVPL's profit during a certain period of time. Our framework relies on historical data from parking lots and electricity markets as well as behavioral data related to EV owners. We illustrate the use of our framework in a real-world setting involving the city of Melbourne in Australia. In particular, we show how our framework can determine the profitability of an EVPL and, consequently, the payback period for transforming traditional parking lots into parking lots with EV chargers. To obtain these results, our framework first determines the ideal number of EV chargers to be installed and the underlying charging tariff so as to maximize profitability. We also discuss how our framework allows for what-if analyses that can provide valuable managerial and policy-making insights.
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|Organisation||Erasmus Research Institute of Management|
Babic, J, Carvalho, A, Ketter, W, & Podobnik, V. (2017). A Data-Driven Approach to Manage Charging Infrastructure for Electric Vehicles in Parking Lots. Retrieved from http://hdl.handle.net/1765/105368