402. Optimal management of electric vehicle fleets: An optimistic bilevel optimization model
Invited abstract in session TC-12: Optimization for sustainable energy systems, stream Applications: AI, uncertainty management and sustainability.
Tuesday, 14:00-16:00Room: B100/8009
Authors (first author is the speaker)
| 1. | Eleni Michaelidou
|
| School of Mathematics, University of Edinburgh | |
| 2. | Miguel Anjos
|
| School of Mathematics, University of Edinburgh |
Abstract
We consider an electric vehicle (EV) charging service provider that operates a set of charging stations and uses time-based pricing to minimize the impact of a large EV fleet — as operated by transportation and delivery companies, among others — on the power grid by encouraging the fleet operator to shift its flexible load to off-peak hours and benefit from lower electricity rates. We propose an optimistic bilevel optimization problem that captures the hierarchical interaction between the provider (leader) and the EV fleet owner (follower). The latter can participate in vehicle-to-grid (V2G) schemes, where EV batteries can be used as energy storage devices to supply energy back to the grid during peak hours. At the upper level, the provider defines the charging and discharging prices to minimize peak demand. In response, at the lower level, the EV fleet owner decides when to charge and whether and when to discharge while considering several operational constraints of the fleet, mainly related to the delivery schedule of the fleet, to minimize its total charging cost. The bilevel optimization problem is reformulated into a single-level formulation using a Karush–Kuhn–Tucker (KKT)-based heuristic approach along with the Special Ordered Sets 1 (SOS1) technique, which can be solved using off-the-shelf solvers. We report preliminary computational results for the proposed model.
Keywords
- Multi-level optimization
- Linear and nonlinear optimization
Status: accepted
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