This study proposes a practical solution to deal with challenges of integrating renewable energy sources and electric vehicles into the electric grid, considering generation source intermittency and energy usage inconsistency, via a new adaptive intelligent controller. The present research describes a smart grid consisting of power plants and distributed generation, fueled via photovoltaic panels and wind turbines, and augmented with electric vehicles as power storage devices. Employing a parking lot to deal with challenges such as low penetration of the electric vehicles embedded with Vehicle-to-Grid functionalities encounters two difficulties: where they should be installed, and modeling of bi-directional power flow between electric vehicles, the grid, and the distributed generation system. In this regard, a nonlinear multi-objective problem is designed and solved via employing the Non-dominated Sorting Genetic Algorithm-II, and the forward and backward substitution method. In addition, Newton-Raphson Power Flow is adopted and modified to calculate the power flow of the distribution network. The results related to optimal placement and sizing of hybrid renewable energy systems show that bus 16 of the studied grid is the best place to integrate a parking lot – equipped with 117 photovoltaic and 10 wind turbine units - to the tested IEEE-26 buses. Furthermore, this study suggests that the aforementioned grid could employ a complex versatile control unit able to optimize the operating point, scheduling charging and discharging for a large number of electric vehicles while considering the technical aspects (total active power loss and voltage deviation). In this regard, a new hybrid control approach based on Particle Swarm Optimization-Adaptive Neuro-Fuzzy Inference System tuned via utilizing the optimal power flow problem is proposed. The controller's superiority to handle grid-to-vehicle and vehicle-to-grid services is discussed and compared to other studies.
Original languageEnglish
Pages (from-to)1053-1067
Number of pages14
StatePublished - 1 Sep 2017

    Research areas

  • Smart Grid, PSO-ANFIS controller, Electric Vehicle, Renewable energy sources, Distributed Generation

ID: 32035058