This paper aims to investigate the impact of different Electric Vehicle (EV) penetration on quasi real-time Volt-VAR Optimization (VVO) of smart distribution networks. Recent VVO solutions enable capturing data from Advanced Metering Infrastructure (AMI) in quasi real-time to minimize distribution networks loss costs and perform Conservation Voltage Reduction (CVR) to save energy. The emergence of EVs throughout distribution feeder increases grid complexity and uncertainty levels that could affect AMI-based VVO objectives. Hence, this paper primarily introduces an AMI-based VVO engine, able to minimize grid loss and Volt-VAR control assets operating costs while maximizing CVR benefit. It then presents a real-time co-simulation platform comprised of the VVO engine, grid model in a real-time simulator and monitoring platform, communicating with each other through DNP.3 protocol, to test the precision and performance of AMI-based VVO in presence of different EV penetration levels. Accordingly, 33-node distribution feeder is studied through different EV penetration scenarios. The results show significant changes in AMI-based VVO performance especially in CVR sub-part of VVO according to EV model and type. Thus, this study could lead near future VVO solutions to gain higher levels of accuracy and efficiency considering smart microgrid components such as EV in their models.
Contributors
          Author: Farhangi, Hassan
          Author: Manbachi, Moein
          Author: Sadu, Abhinav
          Author: Monti, Antonello
          Author: Palizban, Ali
          Author: Ponci, Ferdinanda
          Author: Arzanpour, Siamak
              Copyright Date
          2016
              Abstract
              Subject (Topical)
          
      Member of
          
      Note
          Applied Energy
Received 27 November 2015, Revised 8 January 2016, Accepted 22 January 2016, Available online as of 16 February 2016
Identifier
          https://doi.org/10.1016/j.apenergy.2016.01.084
              Publisher
          Elsevier
              Type
              
          Language
          
      Rights
          Copyright © 2016 Elsevier Ltd. All rights reserved.