In this study, a probabilistic approach for the optimal charging of electric vehicles (EVs) in distribution systems is proposed. The costs of both demand and energy losses in the system are minimised, subjected to a set of constraints that consider EVs smart charging characteristics and operative aspects of the electric network. The stochastic driving patterns for EVs’ owners, battery capacity and active and reactive power demanded at load nodes are considered. The optimal charging of EVs connected to the system benefits the system's operation, as it does a strategy to minimise the cost of energy losses and evaluate the capability of the system to charge EVs’ batteries fully under certain penetration scenarios. Priority periods of EVs’ recharge and the variation of energy price contribute to an adequate demand response, assisting the network operator for complying with quality indices (decrement of power losses) set forward by regulatory entities and developing studies of risk analysis for decision making. On the other hand, there is a valuable participation of the EVs’ owners in improving the operation of the distribution system. Monte Carlo simulation (MCS) is used to assess the stochastic nature of the problem in a secondary (low voltage) distribution network.
Optimal probabilistic charging of electric vehicles in distribution systems
IET Electrical Systems in Transportation ; 7 , 3 ; 246-251
2017-05-24
6 pages
Article (Journal)
Electronic Resource
English
optimal probabilistic charging , distribution systems , probability , Monte Carlo simulation , electric vehicle charging , electric network , demand losses , energy loss cost minimisation , secondary distribution network , decision making , MCS , minimisation , quality indices , reactive power , secondary cells , smart charging characteristics , distribution networks , risk analysis , load nodes , battery powered vehicles , optimal EV charging , stochastic driving patterns , active power , energy price variation , stochastic processes , battery charging , battery capacity , Monte Carlo methods , demand response , demand side management
Metadata by IET is licensed under CC BY 3.0
Optimal probabilistic charging of electric vehicles in distribution systems
Wiley | 2017
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