Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Abstract

This paper aims to investigate the electric vehicle (EV) charging network design and utilization management considering user-centric decisions. A hierarchical formulation is developed with the EV charging network design and demanddriven pricing scheme in the upper level and users' charging decisions to minimize their own travel costs and charging expenses in the lower level. The model aims to minimize the facility deployment cost and maximize the charging income of the network operator while minimizing the user-centric costs. We have converted the proposed bi-level formulation into an equivalent single-level model using the lower-level objective function as complementary equations. Then, we have developed an iterative active-set based solution technique to determine the strategic decisions on charging network design. To partially overcome the computational burden, the arc travel times are estimated using a macroscopic fundamental diagram concept. The proposed integrated methodology is applied to a hypothetical and an empirical case study to evaluate its performance and solution quality. The numerical results indicate that the proposed algorithm can solve the problem efficiently and outperform a system-level bi-level optimization benchmark. Our experiments show a CPU time of 2.3 hr for the proposed approach compared to 173.1 hr of the benchmark. Finally, a series of sensitivity analyses has been conducted to study the impact of input parameters on the solutions and draw managerial insights.

Joint column generation and Lagrangian relaxation technique for incident respondent location and allocation

“…It would be very interesting to account for the impact of traffic congestion on the results in future efforts. It will also be interesting to apply the proposed framework to time‐variant demand cases, for example, using dynamic resource assignment (e.g., Hajibabai & Mirheli, 2022; Hajibabai & Ouyang, 2016; Mehrabipour et al., 2019; Ramadurai & Ukkusuri, 2011) and also account for user‐equilibrium decisions (e.g., Hajibabai et al., 2023; Mirheli & Hajibabai, 2023, 2022; Niroumand et al., 2018).…”