High-Dimensional EV Charging Optimization: Leveraging APSO for Peak Load Management
In this study, we present an electric vehicle (EV) scheduling algorithm based on the Adaptive Particle Swarm Optimization (APSO) technique. The objective is to effectively manage a large-scale charging scenario. The scheduling problem involves numerous variables, including binary variables, represen...
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| Veröffentlicht in: | 2025 International Conference on Intelligent Computing and Control Systems (ICICCS) S. 185 - 191 |
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19.03.2025
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| Abstract | In this study, we present an electric vehicle (EV) scheduling algorithm based on the Adaptive Particle Swarm Optimization (APSO) technique. The objective is to effectively manage a large-scale charging scenario. The scheduling problem involves numerous variables, including binary variables, representing the charging states and schedules of a fleet of EVs. Uncoordinated charging leads to simultaneous charging of all vehicles would result in a significant peak power demand, posing a strain on the power grid. To address this, the APSO algorithm effectively redistributes the charging times, achieving a notable reduction in peak demand in the optimal scenario. This reduction is facilitated by the algorithm's adaptive capabilities, which allow it to dynamically adjust to changing conditions and constraints, thereby optimizing the load distribution and enhancing grid stability. The simulation results demonstrate APSO's efficacy in addressing complex EV scheduling problems, contributing to more sustainable and efficient energy use in electric mobility. |
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| AbstractList | In this study, we present an electric vehicle (EV) scheduling algorithm based on the Adaptive Particle Swarm Optimization (APSO) technique. The objective is to effectively manage a large-scale charging scenario. The scheduling problem involves numerous variables, including binary variables, representing the charging states and schedules of a fleet of EVs. Uncoordinated charging leads to simultaneous charging of all vehicles would result in a significant peak power demand, posing a strain on the power grid. To address this, the APSO algorithm effectively redistributes the charging times, achieving a notable reduction in peak demand in the optimal scenario. This reduction is facilitated by the algorithm's adaptive capabilities, which allow it to dynamically adjust to changing conditions and constraints, thereby optimizing the load distribution and enhancing grid stability. The simulation results demonstrate APSO's efficacy in addressing complex EV scheduling problems, contributing to more sustainable and efficient energy use in electric mobility. |
| Author | Aravindkumar, J. Sriabisha, R. |
| Author_xml | – sequence: 1 givenname: R. surname: Sriabisha fullname: Sriabisha, R. email: sriabisha3003.sse@saveetha.com organization: Electrical and Electronics Engineering Saveetha Institiuion of Medical and Technical Sciences,Chennai,India – sequence: 2 givenname: J. surname: Aravindkumar fullname: Aravindkumar, J. email: aravindkumarj.sse@saveetha.com organization: Electrical and Electronics Engineering Saveetha Institiuion of Medical and Technical Sciences,Chennai,India |
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| Snippet | In this study, we present an electric vehicle (EV) scheduling algorithm based on the Adaptive Particle Swarm Optimization (APSO) technique. The objective is to... |
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| SubjectTerms | Data collection Distribution Network Electric Vehicle Energy consumption Heuristic algorithms Microgrid Optimization Particle swarm optimization Pricing Scalability Stability analysis Strain Sustainable development |
| Title | High-Dimensional EV Charging Optimization: Leveraging APSO for Peak Load Management |
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