Reducing Grid Dependency and Operating Cost of Micro Grids with Effective Coordination of RES and EV Storage

The growth of smart grids has led to number of challenges in order to maintain power quality and reliability. On the other hand, the advanced technologies are helping to address the issues that arise due to the heterogeneous entities such as Intermittent Renewable Energy Sources (IRES) and Electric Vehicles (EV's). EV's storage can be utilized to support the energy management in micro grids and other flexible areas like Industries and educational organizations. In order to use EV s as virtual storage units, one has to ensure all the constraints and limitations that are involved. Above all, EV usage for grid energy management should not put EV owner in a chaotic state. There has been numerous EV control strategies proposed for V2G and G2V operations since last decade. Still, it is a big challenge in the real-time environment to address sensitive issues such as: owner flexibility, battery degradation, economic benefit and other uncertainties. This work mainly focuses on maximization of EV storage usage with consideration of battery degradation. A prioritization-based EV strategy is proposed using Adaptive Neuro-Fuzzy Inference System (ANFIS) with four decision variables. A win-win strategy for maximization of battery lifetime and EV exploitation is considered during the prioritization. The proposed EV technique is implemented for educational organization with real-time travel data. The case study presented in this article provides the comprehensive analysis on the impact of proposed control strategy in different aspects.