TY - GEN
T1 - eFlx
T2 - 16th Annual ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2025, held as part of the CPS-IoT Week 2025
AU - Zhou, Liangkai
AU - Zhao, Yue
AU - Yuan, Yukun
AU - Xu, Ce
AU - Lin, Shan
N1 - Publisher Copyright: © 2025 ACM.
PY - 2025/5/7
Y1 - 2025/5/7
N2 - An e-taxi fleet consumes a significant amount of energy daily, making it a substantial electricity consumer. Unlike traditional consumers, such as factories and buildings, a fleet coordinates charging activities across both times and locations, offering considerable flexibility in its energy demand. This allows a fleet to achieve substantial reductions in energy consumption in response to demand response requests while maintaining transportation service quality. To better understand and control this intrinsic energy flexibility, we propose the eFlx framework for managing e-taxi fleets for demand response. In the eFlx framework, we establish a model to characterize the energy flexibility upon receiving a real-time demand response request. We then investigate the energy flexibility provisioning problem, formulated as a bi-level optimal control problem, which aims to optimize and maintain the energy flexibility of the fleet for potential demand response requests that could arise at any time. To achieve real-time flexibility provisioning, we develop an efficient iterative algorithm to solve this problem. Data-driven evaluations with NYC datasets demonstrate that eFlx achieves a 19. 98% greater reduction in energy demand compared to existing solutions, without requiring extra charging or compromising the quality of taxi service.
AB - An e-taxi fleet consumes a significant amount of energy daily, making it a substantial electricity consumer. Unlike traditional consumers, such as factories and buildings, a fleet coordinates charging activities across both times and locations, offering considerable flexibility in its energy demand. This allows a fleet to achieve substantial reductions in energy consumption in response to demand response requests while maintaining transportation service quality. To better understand and control this intrinsic energy flexibility, we propose the eFlx framework for managing e-taxi fleets for demand response. In the eFlx framework, we establish a model to characterize the energy flexibility upon receiving a real-time demand response request. We then investigate the energy flexibility provisioning problem, formulated as a bi-level optimal control problem, which aims to optimize and maintain the energy flexibility of the fleet for potential demand response requests that could arise at any time. To achieve real-time flexibility provisioning, we develop an efficient iterative algorithm to solve this problem. Data-driven evaluations with NYC datasets demonstrate that eFlx achieves a 19. 98% greater reduction in energy demand compared to existing solutions, without requiring extra charging or compromising the quality of taxi service.
KW - Energy flexibility
KW - demand response
KW - e-taxi fleet
KW - grid services
KW - real-time provisioning
UR - https://www.scopus.com/pages/publications/105007288045
U2 - 10.1145/3716550.3722026
DO - 10.1145/3716550.3722026
M3 - Conference contribution
T3 - Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems, ICCPS 2025, held as part of the CPS-IoT Week 2025
BT - Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems, ICCPS 2025, held as part of the CPS-IoT Week 2025
PB - Association for Computing Machinery, Inc
Y2 - 6 May 2025 through 9 May 2025
ER -