Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Relating Electric Vehicle Charging to Speed Scaling with Job-Specific Speed Limits. |
| Συγγραφείς: |
Winschermann, Leoni1 (AUTHOR) l.winschermann@utwente.nl, Antoniadis, Antonios1 (AUTHOR) a.antoniadis@utwente.nl, Gerards, Marco E. T.1 (AUTHOR) m.e.t.gerards@utwente.nl, Hoogsteen, Gerwin1 (AUTHOR) g.hoogsteen@utwente.nl, Hurink, Johann1 (AUTHOR) j.l.hurink@utwente.nl |
| Πηγή: |
Operations Research. May/Jun2026, Vol. 74 Issue 3, p1165-1186. 22p. |
| Θεματικοί όροι: |
*Scheduling, *Resource allocation, *Electric power distribution grids, Optimization algorithms, Electric vehicle charging stations, Predictive control systems, Energy demand management |
| Περίληψη: |
With the adoption of electric vehicles emerges a need for coordinated charging strategies to cater to the increased and often synchronized energy demand without violating physical infrastructure limits. Common charging strategies repeatedly solve a corresponding offline problem. In "Relating Electric Vehicle Charging to Speed Scaling with Job-Specific Speed Limits," Winschermann, Antoniadis, Gerards, Hoogsteen, and Hurink thoroughly analyze that offline problem and present the flow-based offline charging scheduler (FOCS), an algorithm that solves the problem to optimality. The authors continue to quantify the approximation ratio of repeatedly solving the offline problem throughout the day as opposed to solving the offline problem with a priori knowledge of future arrivals. Numerical experiments confirmed that the found worst-case approximation ratio of 4 is far from the achieved empirical ratio of 1.3 when evaluating the ratio for real-world electric vehicle charging instances. Experiments based on that same real-world data show that FOCS only takes seconds to schedule 400 EVs in 15-minute granularity, which is competitive with commercial solvers. Because of the ongoing electrification of transport in combination with limited power grid capacities, efficient ways to schedule the charging of electric vehicles (EVs) are needed for the operation of, for example, large parking lots. Common approaches such as model predictive control repeatedly solve a corresponding offline problem. In this work, we first present and analyze the flow-based offline charging scheduler (FOCS), an offline algorithm to derive an optimal EV charging schedule for a fleet of EVs that minimizes an increasing and strictly convex function of the corresponding aggregated power profile. To this end, we relate EV charging to processor speed scaling models with job-specific speed limits. Experiments based on real-world EV charging data show that FOCS takes only 2.5 seconds to schedule 400 EVs in 15-minute granularity. Furthermore, we analyze the online algorithms Average Rate and Optimal Available and show that they are, respectively, 2α−1αα and αα competitive, where α is typically two in energy applications. Further numerical experiments show that, for the real-world EV charging use case, both algorithms achieve approximation ratios of less than 1.3. Furthermore, they significantly improve on the uncontrolled default often applied in practice. Funding: This work was supported by the Ministerie van Economische Zaken en Klimaat [Grant MOOI32005] and the Ministerie van Binnenlandse Zaken en Koninkrijksrelaties [Grant MOOI32005]. Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2024.1044. [ABSTRACT FROM AUTHOR] |
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