Justin Luke


Email: justin dot luke at stanford dot edu

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. A. Singhal, D. Gammelli, J. Luke, K. Gopalakrishnan, D. Helmreich, and M. Pavone, “Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning,” 2024. (Submitted)

    Abstract: Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available cars to ride requests, rebalancing idle cars to areas of high demand, and charging vehicles to ensure sufficient range. While this problem can be posed as a linear program that optimizes flows over a space-charge-time graph, the size of the resulting optimization problem does not allow for real-time implementation in realistic settings. In this work, we present the E-AMoD control problem through the lens of reinforcement learning and propose a graph network-based framework to achieve drastically improved scalability and superior performance over heuristics. Specifically, we adopt a bi-level formulation where we (1) leverage a graph network-based RL agent to specify a desired next state in the space-charge graph, and (2) solve more tractable linear programs to best achieve the desired state while ensuring feasibility. Experiments using real-world data from San Francisco and New York City show that our approach achieves up to 89% of the profits of the theoretically-optimal solution while achieving more than a 100x speedup in computational time. Furthermore, our approach outperforms the best domain-specific heuristics with comparable runtimes, with an increase in profits by up to 3x. Finally, we highlight promising zero-shot transfer capabilities of our learned policy on tasks such as inter-city generalization and service area expansion, thus showing the utility, scalability, and flexibility of our framework.

      author = {Singhal, A. and Gammelli, D. and Luke, J. and Gopalakrishnan, K. and Helmreich, D. and Pavone, M.},
      title = {Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning},
      year = {2024},
      note = {Submitted},
      keywords = {sub},
      owner = {gammelli},
      timestamp = {2023-11-15},
      url = {https://arxiv.org/abs/2311.05780}
  2. M. Ribeiro, J. Luke, S. Martin, E. Balogun, G. Cezar, M. Pavone, and R. Rajagopal, “Towards a 24/7 Carbon-Free Electric Fleet: A Digital Twin Framework,” in Int. Conf. on Applied Energy, Doha, Qatar, 2023. (In Press)

    Abstract: Although vehicle electrification and utilization of on-site solar PV generation can begin reducing the greenhouse gas emissions associated with bus fleet operations, a method to intelligently coordinate bus-route assignments, bus charging, and energy storage is needed to fully decarbonize fleet operations while simultaneously minimizing electricity costs. This paper proposes a 24/7 Carbon-Free Electrified Fleet digital twin framework for modeling, controlling, and analyzing an electric bus fleet, co-located solar PV arrays, and a battery energy storage system. The framework consists of forecasting modules for marginal grid emissions factors, solar generation, and bus energy consumption that are input to the optimization module, which determines bus and battery operations at minimal electricity and emissions costs. We present a digital platform based on this framework, and for a case study of Stanford University’s Marguerite Shuttle, the platform reduced peak charging demand by 99%, electric utility bill by 2778, and associated carbon emissions by 100% for one week of simulated operations for 38 buses. When accounting for operational uncertainty, the platform still reduced the utility bill by 784 and emissions by 63%.

      author = {Ribeiro, M. and Luke, J. and Martin, S. and Balogun, E. and Cezar, G. and Pavone, M. and Rajagopal, R.},
      title = {Towards a 24/7 Carbon-Free Electric Fleet: A Digital Twin Framework},
      booktitle = {{Int. Conf. on Applied Energy}},
      year = {2023},
      address = {Doha, Qatar},
      month = dec,
      note = {In press},
      keywords = {press},
      owner = {jthluke},
      timestamp = {2023-11-15}
  3. 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.

    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},
      address = {Indianapolis, IN},
      month = sep,
      doi = {10.1109/ITSC48978.2021.9565089},
      owner = {jthluke},
      timestamp = {2021-06-29},
      url = {http://arxiv.org/abs/2107.00165}
  4. 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, vol. 8, no. 3, pp. 1163–1176, 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}},
      volume = {8},
      number = {3},
      pages = {1163--1176},
      year = {2021},
      doi = {10.1109/TCNS.2021.3059225},
      url = {https://arxiv.org/abs/1905.00200},
      owner = {jthluke},
      timestamp = {2021-02-21}