This paper proposes an organized stochastic methodology to model the power demand of plug-in electric vehicles (PEVs) which can be embedded into probabilistic distribution system planning. Time schedules as well as traveling and refueling information of a set of commuter vehicles in Tehran are utilized as the input dataset. In order to generate the required synthetic data, the correlation structure of the aforesaid random variables is taken into account using a multivariate student’s t function. Afterwards, a Monte Carlo based stochastic simulation is provided to extract the initial state-of-charge of batteries. Further, a non-Gaussian probabilistic decision making algorithm is developed that accurately infers whether the PEVs charging should take place every day or not. Then, through presenting a state transition model to describe the charging profile of a PEV battery, hourly demand distributions of the PEVs are derived. The obtained distributions can be used to generate the random samples required in probabilistic planning problems. Eventually, the extracted distributions are employed to estimate demand profile of a fleet that can be efficiently utilized in various applications.
Non-Gaussian multivariate modeling of plug-in electric vehicles load demand
International Journal of Electrical Power and Energy Systems ; 61 ; 197-207
2014
11 Seiten, 24 Quellen
Article (Journal)
English
Agent-Based Modeling for Scale Evolution of Plug-In Electric Vehicles and Charging Demand
BASE | 2018
|Foresight Analysis of Power Demand Due to Plug-in Electric Vehicles
SAE Technical Papers | 2011
|Foresight Analysis of Power Demand Due to Plug-in Electric Vehicles
British Library Conference Proceedings | 2011
|Space-Time Modeling of Plug-In Electric Vehicles
Tema Archive | 2013
|