Optimized electric vehicles charging strategy based on differential evolution (DE) algorithm
As the urgency to combat global warming escalates, the focus on electrifying transportation to mitigate greenhouse gas emissions has intensified. This thesis delves into the topic of EVs integrating into the power grid with a focused emphasis on formulating the optimal charging strategy to reduce ch...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175911 |
| Summary: | As the urgency to combat global warming escalates, the focus on electrifying transportation to mitigate greenhouse gas emissions has intensified. This thesis delves into the topic of EVs integrating into the power grid with a focused emphasis on formulating the optimal charging strategy to reduce charging cost. This study presents a methodological framework centered around Monte Carlo Simulation (MCS) to analyze EV charging load demands. Subsequently, an innovative optimization framework utilizing the differential evolution (DE) algorithm is implemented. This framework incorporates real-time pricing (RTP) mechanisms and Vehicle-to-Grid (V2G) technology to mitigate charging cost.
Simulation results demonstrate the effectiveness of the proposed approach in reducing charging expenses for EV owners and enhancing grid flexibility simultaneously. In addition, it reveals the significant optimization potential of V2G technology in the broader context of future energy systems. |
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