Assessing the challenges and opportunities of electric mobility through the optimization of carsharing considering demand uncertainty

Abstract

Although electric vehicle (EV) sales have been increasing over the years, worldwide EV adoption is still low. The key factors influencing this are the EV high acquisition cost and the reduced charging infrastructure. Therefore, traditional business models may not be adequate for a region and stagnate EV diffusion. Thus, designing innovative business models can be crucial to accelerate the transition to electric mobility. However, the increase in EV charging may come with drawbacks for the DSO. A possible way to mitigate those problems is to implement dynamic tariffs. Therefore, the objective of this thesis is twofold. First, to discuss the application of business models for EV adoption and charging stations (CSs) worldwide. Then, it highlights the challenges and opportunities for some business model options. Second, to assess the profitability of an e-carsharing company based on distribution local marginal price (DLMP) and vehicle-to-grid (V2G) that cooperates with the DSO through a two-stage stochastic model. The AC optimal power flow (ACOPF) is modelled using second-order cone programming (SOCP) linearized by the global polyhedral approximation. The IEEE 33 bus test system and a real Kernel distribution for the EV rental demands are used in four planning cases. The results indicate that planning disregarding the power grid perspective is the most profitable solution, but the operation may not be possible in real applications due to the high-power flows via V2G. Finally, the e-carsharing planning considering the DSO perspective increased the charging cost by 0.24% but also reduced the DLMP peak, peak demand and cable reinforcement cost by 1.5%, 5.1%, and 66%, respectively. The technical benefits brought to the DSO by the e-carsharing company could be turned into public policies and benefit both agents, increasing profit and mitigating negative impacts.