Consumer perspective based placement of electric vehicle charging stations by clustering techniques

“…Different methods can be used to develop the initial fuzzy model. In this article the initial fuzzy model has been modelled using grid portioning [26, 27], subtractive clustering [28] and fuzzy c means clustering [29].…”

Section: Model Descriptionmentioning

confidence: 99%

“…In this algorithm, the relation between data points to each cluster is established using a membership function which characterises the fuzzy behaviour of the algorithm. The more details of the fuzzy c mean clustering can be found in [29].…”

Section: Model Descriptionmentioning

confidence: 99%

FCM Clustering‐ANFIS‐based PV and wind generation forecasting agent for energy management in a smart microgrid

Sujil

Kumar

Bansal

2019

J. eng.

Self Cite

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“…Moreover, these methods have not been evaluated either quantitatively or qualitatively for a single planning area. In addition to this, the current CS needs estimators are based on weird adjustment factors and these estimations are also unexplainable [14,15]. Furthermore, these estimators have not considered future estimates of CS needs for the planning areas and they have not involved increasing EV penetration in these estimates.…”

Section: Introductionmentioning

confidence: 99%

Charging Station Planning for Electric Vehicles

Kalakanti

Rao

2022

Systems

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Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need estimation, ML solutions based on quadratic regression and simulations were evaluated. Using our CS planning methods urban planners can make better CS placement decisions and can estimate CS needs for the present and the future.

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Consumer perspective based placement of electric vehicle charging stations by clustering techniques

Cited by 17 publications

References 10 publications

Impact of EV fast charging station on distribution system embedded with wind generation

Impact of EV fast charging station on distribution system embedded with wind generation

FCM Clustering‐ANFIS‐based PV and wind generation forecasting agent for energy management in a smart microgrid

Charging Station Planning for Electric Vehicles

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