Comparative Analysis of MILP and RL Algorithms for EV Charging Scheduling at Airport Parking

This study presents a comprehensive analysis of electric vehicle (EV) charging scheduling at Harry Reid International Airport parking, employing a Mixed-Integer Linear Programming (MILP) based charge allocation algorithm alongside a Reinforcement Learning (RL) approach. The primary focus is to condu...

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Veröffentlicht in:IEEE Kansas Power and Energy Conference (Online) S. 1 - 6
Hauptverfasser: Mehadi, Abdullah Al, Watson, Orion, Kamineni, Abhilash
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 25.04.2024
Schlagworte:
Airport Parking EV Charging Management
Airports
Customer satisfaction
EV Scheduling
Job shop scheduling
MILP
Photovoltaic systems
Reinforcement learning
Scheduling algorithms
Vehicle-to-grid
ISSN:2997-7460
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Zusammenfassung:This study presents a comprehensive analysis of electric vehicle (EV) charging scheduling at Harry Reid International Airport parking, employing a Mixed-Integer Linear Programming (MILP) based charge allocation algorithm alongside a Reinforcement Learning (RL) approach. The primary focus is to conduct a parallel assessment of these methodologies on the same dataset to determine their efficacy in three critical areas: power allocation efficiency, final state of charge (SOC) in each vehicle, and revenue generation for the charging station. The MILP algorithm, known for its precision in optimization under constraints, is comparable with the adaptive and potentially more scalable RL algorithm. This highlights the importance of understanding the intricacies involved in employing RL for EV charging scheduling, especially under grid constraints. Thus, our comparison seeks to illuminate the trade-offs and potential synergies between the MILP and RL approaches, particularly as they relate to the increasingly complex issue of EV scheduling. Moreover, in this study specific energy demands and logistical nuances of the airport such as vehicle stay duration and vehicle arrival time is considered. The result shows that, MILP had significant customer satisfaction as well as revenue.
ISSN:2997-7460
DOI:10.1109/KPEC61529.2024.10676090