A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid Integration
Electric buses (E-buses) are gaining popularity in urban transportation due to their environmental benefits and operational efficiency. However, large-scale integration of E-buses and Vehicle-to-Grid (V2G) technology introduces scheduling complexities for charging and discharging operations arising...
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MDPI AG
2025-04-01
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| author | Mingyu Kang Bosung Lee Younsoo Lee |
| author_facet | Mingyu Kang Bosung Lee Younsoo Lee |
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| description | Electric buses (E-buses) are gaining popularity in urban transportation due to their environmental benefits and operational efficiency. However, large-scale integration of E-buses and Vehicle-to-Grid (V2G) technology introduces scheduling complexities for charging and discharging operations arising from uncertainties in energy consumption and load reduction requests. While prior studies have explored electric vehicle scheduling, few have considered robust optimization for E-bus fleets under uncertain parameters such as trip energy consumption and load reduction requests. This paper proposes a robust optimization approach for the charging and discharging scheduling problem at E-bus depots equipped with V2G. The problem is formulated as a robust mixed-integer linear program (MILP), incorporating real-world operational constraints including dual-port chargers, emergency charging, and demand response. A budgeted uncertainty set is used to model uncertainty in energy consumptions and discharging requests, providing a balance between robustness and conservatism. To ensure tractability, the robust counterpart is reformulated into a solvable MILP using duality theory. The effectiveness of the proposed model is validated through extensive computational experiments, including simulation-based performance assessments and out-of-sample tests. Experiment results demonstrate superior profitability and reliability compared to deterministic and box-uncertainty models, highlighting the practical effectiveness of the proposed approach. |
| format | Article |
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| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-6a6926a658f44f0d922370486833c2ae2025-08-20T02:24:49ZengMDPI AGMathematics2227-73902025-04-01139138010.3390/math13091380A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid IntegrationMingyu Kang0Bosung Lee1Younsoo Lee2Department of Industrial and Information Systems Engineering, Soongsil University, Seoul 06978, Republic of KoreaDepartment of Industrial and Information Systems Engineering, Soongsil University, Seoul 06978, Republic of KoreaDepartment of Industrial and Information Systems Engineering, Soongsil University, Seoul 06978, Republic of KoreaElectric buses (E-buses) are gaining popularity in urban transportation due to their environmental benefits and operational efficiency. However, large-scale integration of E-buses and Vehicle-to-Grid (V2G) technology introduces scheduling complexities for charging and discharging operations arising from uncertainties in energy consumption and load reduction requests. While prior studies have explored electric vehicle scheduling, few have considered robust optimization for E-bus fleets under uncertain parameters such as trip energy consumption and load reduction requests. This paper proposes a robust optimization approach for the charging and discharging scheduling problem at E-bus depots equipped with V2G. The problem is formulated as a robust mixed-integer linear program (MILP), incorporating real-world operational constraints including dual-port chargers, emergency charging, and demand response. A budgeted uncertainty set is used to model uncertainty in energy consumptions and discharging requests, providing a balance between robustness and conservatism. To ensure tractability, the robust counterpart is reformulated into a solvable MILP using duality theory. The effectiveness of the proposed model is validated through extensive computational experiments, including simulation-based performance assessments and out-of-sample tests. Experiment results demonstrate superior profitability and reliability compared to deterministic and box-uncertainty models, highlighting the practical effectiveness of the proposed approach.https://www.mdpi.com/2227-7390/13/9/1380robust optimizationschedulingVehicle-to-Gridelectric bus |
| spellingShingle | Mingyu Kang Bosung Lee Younsoo Lee A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid Integration Mathematics robust optimization scheduling Vehicle-to-Grid electric bus |
| title | A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid Integration |
| title_full | A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid Integration |
| title_fullStr | A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid Integration |
| title_full_unstemmed | A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid Integration |
| title_short | A Robust Optimization Approach for E-Bus Charging and Discharging Scheduling with Vehicle-to-Grid Integration |
| title_sort | robust optimization approach for e bus charging and discharging scheduling with vehicle to grid integration |
| topic | robust optimization scheduling Vehicle-to-Grid electric bus |
| url | https://www.mdpi.com/2227-7390/13/9/1380 |
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