Optimal probabilistic charging of electric vehicles in distribution systems. Issue 3 (1st September 2017)

• Record Type: Journal Article
• Authors: Arias, Andrés; Granada, Mauricio; Castro, Carlos A.
• Is Part Of: IET electrical systems in transportation. Volume 7:Issue 3(2017)
• Page Start: 246
• Page End: 251
• Publication Date: 2017-09-01
• Subjects: electric vehicle charging -- secondary cells -- battery powered vehicles -- distribution networks -- stochastic processes -- Monte Carlo methods -- risk analysis -- decision making -- demand side management -- reactive power -- probability -- minimisation; optimal probabilistic charging -- electric vehicle charging -- distribution systems -- optimal EV charging -- demand losses -- smart charging characteristics -- electric network -- stochastic driving patterns -- battery capacity -- active power -- reactive power -- load nodes -- energy loss cost minimisation -- battery charging -- energy price variation -- demand response -- quality indices -- risk analysis -- decision making -- Monte Carlo simulation -- MCS -- secondary distribution network; Electric vehicles -- Periodicals; Electricity in transportation -- Periodicals; 621.3105
• DOI: 10.1049/iet-est.2016.0072
• Languages: English
• ISSNs: 2042-9738

Abstract

Abstract : 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.