2022
DOI: 10.1109/tii.2022.3146292
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Optimal Charging Infrastructure Portfolio for Minimizing Grid Impact of Plug-In Electric Vehicles

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Cited by 10 publications
(11 citation statements)
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“…It can be observed that initially, EV data are processed and EVs are clustered into four groups, similar to [32]. This is due to the presence of several commercially available EV models; an overview of considered EV models can be seen in [33]. The EV parameters of interest for EV load estimation are the useable battery size and energy economy of each EV.…”
Section: Ev Load Estimationmentioning
confidence: 99%
See 2 more Smart Citations
“…It can be observed that initially, EV data are processed and EVs are clustered into four groups, similar to [32]. This is due to the presence of several commercially available EV models; an overview of considered EV models can be seen in [33]. The EV parameters of interest for EV load estimation are the useable battery size and energy economy of each EV.…”
Section: Ev Load Estimationmentioning
confidence: 99%
“…Finally, in the case of commercial chargers, the need for the next recharge is determined after each trip based on the distance and mileage efficiency of the EV. Then, similar to [33], the vehicle arrival rate is estimated using the decision tree. Similar to public chargers, the SOC of each EV is updated after each interval.…”
Section: Ev Load Estimationmentioning
confidence: 99%
See 1 more Smart Citation
“…Considering the significant impact of high EV penetrations on the market electricity prices, the cost production model is used to simultaneously deploy charging infrastructure and expand the capacity of clean energy gener-ators in [22], but the studied topic is limited to planning for the FCS. Different types of charging technologies are studied in [23], where SCS and BSS planning are not considered, and the EVCI planning under renewable integration only tries to mitigate the system peak load.…”
Section: Introductionmentioning
confidence: 99%
“…2) Identify the interdependence among different EV charging loads and interactions of EVs and renewables in smart grids using the proposed predictive learning method. Instead of using given EV load profiles [23] for EVCI planning, individual EV activities and the system cost production model are incorporated in the model to optimize the EV charging load ratio, and at the same time, achieve a system identification. 3) Estimate the capacity of different EV charging stations including SCS, FCS, SFCS and BSS, to support potential large-scale EV charging demand and high renewable penetration in power grids, and coordinate EVCI expansion planning among different stakeholders costeffectively.…”
Section: Introductionmentioning
confidence: 99%