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2958. Dynamic and incremental expansion of large scale fixed and mobile charging infrastructure in stochastic environment: A Benders decomposition based approach

Invited abstract in session WA-56: Advancing mobility towards sustainable solutions III, stream Transportation.

Wednesday, 8:30-10:00
Room: S04 (building: 101)

Authors (first author is the speaker)

1. Atefeh Hemmati Golsefidi
DTU Managment, Technical University of Denmark (DTU)
2. Francisco Pereira
Management Engineering, DTU
3. Samitha Samaranayake
UC Berkeley

Abstract

As electric vehicle adoption increases worldwide, the growing charging demand necessitates a well-thought-out expansion of public charging infrastructure; insufficient or improperly deployed infrastructure poses the real risk of slowing down the adoption of electric vehicles. Public charging networks are likely to develop into very heterogeneous systems with, for example, fixed and mobile chargers. This paper proposes a multi-period mixed-integer programming formulation for optimally placing both fixed and mobile chargers in stochastic environments with the goal of meeting time-varying charging demands at a minimum cost. To discover EVs' uncertain spatial and temporal behavior, the energy demand scenarios are generated from an existing agent-based simulation of EVs in Frederiksberg and Copenhagen municipalities. Large-scale demand scenarios for Frederiksberg municipality are extracted from the simulation and then fed to a two-stage stochastic optimization to find the optimal expansion of fixed and mobile charging stations. As this formulation leads to an NP-hard problem, we develop a Benders decomposition for solving large-scale examples. In addition, a new algorithm is proposed to get exact sub-problem solutions and their corresponding dual variables without using linear programming solvers. Detailed experimental results confirm the effectiveness of the approach, which can provide exact integer solutions in a short computational time for even large problem instances.

Keywords

Status: accepted

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