Geoffrey Pettet, Malini Ghosal, Shant Mahserejian, Sarah Davis, Siddharth Sridhar, Abhishek Dubey, Michael Meyer
2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) 2020
Electric bus fleets face a critical challenge in coordinating charging decisions to minimize costs while maintaining grid reliability and schedule adherence. This work is innovative because it bridges transportation and energy systems by jointly optimizing for both domains, rather than treating them separately. The decision support framework enables transit authorities to make informed scheduling choices that balance multiple competing objectives in a unified manner.
This paper presents a decision support framework for electric bus charge scheduling that integrates traffic and power grid models to optimize charging decisions. The framework uses a Markov Decision Process to model the bus charging problem, considering both operational costs and power grid impacts. The system was evaluated on the Tri-Cities transit network in Washington using detailed traffic simulation and power grid models to assess real-world applicability. The approach provides both offline planning and online decision-making capabilities for fleet management.
The framework achieved $860 in optimization cost, which is $50 lower than a greedy charging policy for the test scenario. The power grid impact metric was 376, significantly better than the greedy approach's score of 362. When scaled to the full 75-bus Richland transit system, results suggest potential savings of over $100k per year without requiring grid infrastructure upgrades. The framework demonstrated that considering grid constraints during charge scheduling can substantially reduce operational costs.
@inproceedings{pettetisgt2020,
author = {Pettet, Geoffrey and Ghosal, Malini and Mahserejian, Shant and Davis, Sarah and Sridhar, Siddharth and Dubey, Abhishek and Meyer, Michael},
booktitle = {2020 IEEE Power \& Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
title = {A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling},
year = {2020},
organization = {IEEE},
abstract = {While there are many advantages to electric public transit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause an adverse impact on the grid's distribution system. We present a grid aware decision-theoretic framework for electric bus charge scheduling that accounts for energy price and grid load. The framework co-simulates models for traffic (Simulation of Urban Mobility) and the electric grid (GridLAB-D), which are used by a decision-theoretic planner to evaluate charging decisions with regard to their long-term effect on grid reliability and cost. We evaluated the framework on a simulation of Richland, WA’s bus and grid network, and found that it could save over $100k per year on operating costs for the city compared to greedy methods.},
contribution = {colab},
tag = {ai4cps,power},
keywords = {electric buses, charge scheduling, grid-aware decision support, Markov decision process, transportation-energy integration, traffic simulation}
}