Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila

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ID: 286945
2022
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Abstract
The Philippines is currently facing debilitating issues regarding extreme traffic conditions and excessive greenhouse gas emissions, both of which are mainly caused by its outdated transportation sector. As a result, the country is currently enacting laws and programs that involve the modernization of this sector, most of which are involved with the advocacy and support for electric vehicle (EV) technology. However, given the integration of EVs into the country, there must exist a reliable and efficient ecosystem that could help support the successful proliferation of the technology. Therefore, this thesis aims to propose a spatiotemporal methodology for the optimal allocation of EV charging stations within the National Capital Region (NCR). The data to be used will be empirical ridesharing traces, given that they provide a clear picture of human day-to-day movement. A combination of K-means clustering and clustering by fast search and find of density peaks (CFS) will then be used on the traces in order to determine areas of interest. After which, the proposed clusters will be put through a Discrete Event Simulation (DES) in order to estimate and model the charging demand given a configuration of charging stations. Then, the charging demand will be distributed based on the number of traces in each cluster. Additionally, the resulting demand will be projected and cross-referenced between Business-As-Usual and Tax Incentivized scenarios. This will be done to ensure the optimal location and number of the stations, while taking into consideration the number of charging slots per station. The main novelty of this study is that it aims to address the research gap between EV user behavior, charging station location, and charging demand, by further solidifying their relationship with respect to the geography of the NCR in the Philippines.
Reference Key
persistent_1760660130_68f18aa2daabd Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lisaba Jr., Edwin Bernard F.
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_mecheng/9
Keywords Keywords not found

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