A user equilibrium-based fast-charging location model considering heterogeneous vehicles in urban networks

Abstract

This is a repository copy of A user equilibrium-based fast-charging location model considering heterogeneous vehicles in urban networks.

Optimal placement and sizing of charging infrastructure for EVs under information-sharing

“…While their approach is closely related to the set covering type of models, it applies a stochastic programming approach to allow for heterogeneous demand, account for uncertainties in multiple factors, and be able to integrate with other systems without suffering from aggregation bias. Another recent contribution is that of Tran et al (2020) who use a cross-entropy based method to solve the charger location problem as a bi-level optimisation problem. First by minimising the overall system cost, and secondly by solving a lower level assignment equilibrium problem.…”

“…The cross-entropy optimisation scheme was formulated in the seminal paper from Rubinstein (1999) and represent a global search algorithm suited for large-scale combinatorial optimisation problems. The motivation for considering this approach in this particular context is due to Tran et al (2020), where the approach was applied to a largely similar problem, although with a different demand side representation. In the current paper we apply the 'CEoptim' R package implementation (Benham et al, 2015) for discrete problems.…”

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

“…The strategic charging facility design does not fully satisfy the time-variant demand, particularly in high-demand neighborhoods. Moreover, [21] have developed a bi-level program that aims to minimize the total travel time and charging facility deployment cost with a UE assignment for a mixed EV and internal combustion engine vehicle traffic. The proposed model is solved using the cross-entropy method that randomly samples the location of charging facilities and update them iteratively to push the solutions toward optimality.…”

Cost-Benefit of a State-Road Charging System: The Case of Denmark

“…Another interesting contribution is due to Yıldız et al (2019), whose approach -although closely related to the set covering type of models -applies stochastic programming to allow for heterogeneous demand, account for uncertainties in multiple factors, and be able to integrate with other systems without suffering from aggregation bias. Another recent contribution by Tran et al (2020) uses a cross-entropy based method to solve the charger location problem as a bi-level optimisation problem by minimising the overall system cost first, and then by solving a lower level assignment equilibrium problem. A common characteristic of models from the operational research community is that user charging behaviour is formulated in simplistic ways and often without allowing for systematic randomness.…”

“…While their model is used to explore optimal pricing and waiting-time distributions, the location choice is absent from the analysis. Other agent-based models with an explicit representation of supply and demand are presented in van der Kam et al (2019) and Viswanathan et al (2016). However, queuing behaviour is more simplistic, and their focus is mostly on the integration with the power grid.…”