Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks

Alizadeh, Mahnoosh; Wai, Hoi-To; Chowdhury, Mainak; Goldsmith, Andrea; Scaglione, Anna; Javidi, Tara · 2016 · OpenAlex-citations

DOI: 10.1109/tcns.2016.2590259

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Summary

This paper addresses the coupled operational challenges arising from the large-scale adoption of Electric Vehicles (EVs), which simultaneously impact power and transportation networks. The authors argue that ignoring the interdependence between these infrastructures—specifically how EV charging decisions affect grid congestion and how traffic patterns influence charging locations—can lead to system instabilities. The research aims to develop control schemes that guide both networks toward a socially optimal operating point, either through collaboration between independent operators or via reserve capacity pricing in the absence of such cooperation. The study models the individual EV driver’s decision-making process as an Energy-aware Shortest Path Problem (ESPP). To solve this, the authors introduce an "extended transportation graph" that incorporates virtual arcs representing charging options at specific nodes. This graph allows drivers to optimize their route and charging strategy by minimizing total costs, which include travel time, tolls, electricity prices, and plug-in fees, while ensuring battery energy feasibility. At the system level, the paper analyzes the collective effects of these individual decisions on network flows. It examines two scenarios: one where the Independent Power System Operator (IPSO) and Independent Transportation System Operator (ITSO) collaborate using dual decomposition to design joint prices and tolls while keeping operational data private, and another where they operate independently, requiring the IPSO to procure generation reserves to manage demand stochasticity. The findings demonstrate that the extended graph framework effectively captures the complex interactions between traffic congestion and grid load. The authors show that collaboration between the IPSO and ITSO is necessary for correct price design to achieve social optimality, minimizing total transportation delay and electricity generation costs. Numerical simulations highlight that a lack of attention to the interdependencies between the two infrastructures results in adverse operational effects, including inefficiencies in electricity pricing, power delivery, and traffic distribution. The study also quantifies the optimal reserve capacity requirements needed when operators do not collaborate, providing a benchmark for the cost of non-cooperative management. The significance of this work lies in its analytical treatment of the coupled power-transportation system, moving beyond previous studies that treated EV plug-in locations as exogenous or ignored electricity price dynamics. By modeling EVs as mobile loads that make economic choices across both networks, the paper provides a framework for designing pricing mechanisms that account for spatial and temporal variations in congestion. The results imply that coordinated pricing strategies are essential for the reliable and efficient integration of large EV populations into existing infrastructure, offering insights for policymakers and system operators on the benefits of data-sharing collaborations versus the costs of isolated operation.

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