Multi-Horizon Urban EV Charging Infrastructure Planning: Integrating Activity Patterns, Grid Dynamics, and Uncertainty

While huge resources have been set aside for the deployment of Electric Vehicle (EV) charging infrastructure, optimally deploying EV charging stations to encourage the decarbonization of the transportation system is a non-trivial task. Indeed, EV charging interacts with several dimensions that operate on different temporal and spatial scales: activity patterns, electric grid operation, land use—to name a few. In this project, the research team aims to develop a multi-horizon planning model that can assist policymakers in determining the timing and location of EV charging stations in an urban environment. The model incorporates several dimensions of decision-making, operation, and planning relevant to EV charging. First, at the lower level, the team incorporates activity scheduling and its interaction with charger and parking location, availability, and price. Additionally, the team accounts for the uncertainty in EV adoption, as it directly impacts the usefulness and availability of charging. Second, the model incorporates the power grid and its operational constraints, paying especially attention to its stability and dynamics. Third, at the upper level, the team considers a multi-horizon planning problem whose aim is to optimally deploy EV charging stations both in space and time. The team pays special attention to the fact that the future is uncertain and, hence, deploying stations as fast as possible might not always be optimal. The model and insights will prove valuable to several stakeholders: policymakers; federal, state, and city transportation and planning agencies; and power grid operators and regulators.


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Subject/Index Terms

Filing Info

  • Accession Number: 01897930
  • Record Type: Research project
  • Source Agency: Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)
  • Contract Numbers: 69A3551747124
  • Files: UTC, RIP
  • Created Date: Oct 30 2023 10:53PM