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Data-driven multi-objective optimization for electric vehicle charging infrastructure.

Farzaneh Farhadi1, Shixiao Wang2, Roberto Palacin1

  • 1School of Engineering, Newcastle University, Stephenson Building, Newcastle upon Tyne NE1 7RU, UK.

Iscience
|September 18, 2023
PubMed
Summary
This summary is machine-generated.

This study optimizes electric vehicle (EV) charging infrastructure by combining simulation and multi-objective optimization. The findings prioritize slower charging points for efficient EV deployment.

Keywords:
Electrical engineeringEnergy ResourcesEnergy engineering

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Area of Science:

  • Transportation Engineering
  • Computational Sustainability
  • Energy Systems Analysis

Background:

  • Growing electric vehicle (EV) adoption necessitates strategic planning for charging infrastructure.
  • Existing transportation policies require efficient implementation methods for EV charging deployment.
  • Newcastle upon Tyne serves as a case study for developing a scalable methodology.

Purpose of the Study:

  • To develop and apply a data-driven methodology for optimizing EV charging infrastructure.
  • To determine optimal types, locations, and quantities of charging points under various future energy scenarios.
  • To balance infrastructure deployment with capital and operational expenditures.

Main Methods:

  • Utilized a baseline simulation model to forecast EV demand from 2020 to 2050.
  • Employed multi-objective optimization to identify optimal charging point configurations.
  • Analyzed four distinct future energy scenarios to assess robustness.

Main Results:

  • Optimal solutions prioritize slower charging points.
  • Faster charging points constitute smaller proportions (10%-13%) of the total.
  • Variations in charging point type distribution across scenarios remained within a 3% range.

Conclusions:

  • The proposed methodology enables efficient implementation of transportation policy commitments for EV infrastructure.
  • The findings provide a strategic framework for planning cost-effective and demand-responsive charging networks.
  • Slower charging solutions are identified as the primary component for widespread EV infrastructure deployment.