Justin Luke

Justin Luke

Justin is a PhD candidate in the department of Civil and Environmental Engineering and is co-advised by Professor Marco Pavone and Professor Ram Rajagopal (Stanford Sustainable Systems Lab). His research focuses on grid integration of autonomous electric vehicle fleets, in particular identifying synergies with renewable energy resources integration. His current projects include charging station siting and sizing, grid-aware electricity pricing, and an optimization toolkit for decarbonization-focused planning and operations of electric vehicle fleets. Justin is supported by the Stanford Bits & Watts EV50 Project. He has obtained a MS in Electrical Engineering at Stanford in 2020 and a BS in Energy Engineering at the University of California, Berkeley in 2018.

Justin is passionate about environmental sustainability and enjoys hiking, hockey, and playing various musical instruments in his free time.

ASL Publications

  1. J. Luke, M. Salazar, R. Rajagopal, and M. Pavone, “Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Indianapolis, IN, 2021. (In Press)

    Abstract: Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.

      author = {Luke, J. and Salazar, M. and Rajagopal, R. and Pavone, M.},
      title = {Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2021},
      note = {In press},
      address = {Indianapolis, IN},
      month = sep,
      url = {http://arxiv.org/abs/2107.00165},
      keywords = {press},
      owner = {jthluke},
      timestamp = {2021-06-29}
  2. A. Estandia, M. Schiffer, F. Rossi, J. Luke, E. C. Kara, R. Rajagopal, and M. Pavone, “On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks – An Optimal Power Flow Approach,” IEEE Transactions on Control of Network Systems, 2021.

    Abstract: In future transportation systems, the charging behavior of electric Autonomous Mobility on Demand (AMoD) fleets, i.e., fleets of electric self-driving cars that service on-demand trip requests, will likely challenge power distribution networks (PDNs), causing overloads or voltage drops. In this paper, we show that these challenges can be significantly attenuated if the PDNs’ operational constraints and exogenous loads (e.g., from homes or businesses) are accounted for when operating an electric AMoD fleet. We focus on a system-level perspective, assuming full coordination between the AMoD and the PDN operators. From this single entity perspective, we assess potential coordination benefits. Specifically, we extend previous results on an optimization-based modeling approach for electric AMoD systems to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints. For a case study of Orange County, CA, we show that the coordination between the electric AMoD fleet and the PDNs eliminates 99% of the overloads and 50% of the voltage drops that the electric AMoD fleet would cause in an uncoordinated setting. Our results show that coordinating electric AMoD and PDNs can help maintain the reliability of PDNs under added electric AMoD charging load, thus significantly mitigating or deferring the need for PDN capacity upgrades.

      author = {Estandia, A. and Schiffer, M. and Rossi, F. and Luke, J. and Kara, E. C. and Rajagopal, R. and Pavone, M.},
      title = {On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow Approach},
      journal = {{IEEE Transactions on Control of Network Systems}},
      year = {2021},
      doi = {10.1109/TCNS.2021.3059225},
      url = {https://arxiv.org/abs/1905.00200},
      owner = {jthluke},
      timestamp = {2021-02-21}