Paper Info
Title
Uncertainty Modeling for Participation of Electric Vehicles in Collaborative Energy Consumption
Abstract
This paper proposes an accurate and efficient probabilistic method for modeling the nonlinear and complex uncertainty effects and mainly focuses on the Electric Vehicle (EV) uncertainty in Peer-to-Peer (P2P) trading. The proposed method captures the uncertainty of the input parameters with a low computational burden and regardless of the probability density function (PDF) shape. To this end, for each uncertain parameter, multitude of random vectors with the specification of corresponding uncertain parameters are generated and a fuzzy membership function is then assigned to each vector. Since the most probable samples occur repeatedly, they are recognized by the superposition of the generated fuzzy membership functions. The simulation results on various case studies indicate the high accuracy of the proposed method in comparison with Monte-Carlo simulation (MCs), Unscented Transformation (UT), and Point Estimate Method (PEM). It also scales down the computational burden compared to MCs. Also, a real-world case study is employed to examine the ability of the method in capturing the uncertainty of EVs’ arrival and departure time. The studies on this case reveal that involving EVs in P2P trading augments the amount of energy traded within the prosumers.
Year
DOI
Venue
2022
10.1109/TVT.2022.3184514
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
EV uncertainty,P2P trading,uncertainty modeling,vehicle to home
Journal
71
Issue
ISSN
Citations 
10
0018-9545
0
PageRank 
References 
Authors
0.34
9
7
Name
Order
Citations
PageRank
Naser Hashemipour100.34
Jamshid Aghaei25419.74
Pedro Crespo Del Granado300.34
Abdollah Kavousi-Fard426831.99
Taher Niknam5201.81
Miadreza Shafie-khah602.37
João P. S. Catalão701.69