An Intelligent Day Ahead Energy Management Framework for Networked Microgrids Considering High Penetration of Electric Vehicles

Abstract

Considering the insufficient stability and complex energy management in networked microgrids, this article proposes an intelligent stochastic framework for optimal operating and managing of these systems with high penetration of renewables. The proposed model incorporates the high mobile charging demands of electric vehicles as well as the random nature of renewable energy sources. To mitigate the negative effect of vehicles on the individual microgrids, V2G scheme is deployed and got compatible with the microgrid cost function. As a supporting policy of renewable sources, a novel machine learning-based probabilistic approach based on support vector machine and point estimation method is proposed which will operate the system at a safe zone considering their randomness nature. Moreover, an intelligent optimization algorithm based on modified bat evolutionary optimization is devised that looks for the optimality in the global space. An IEEE test system inspects the appropriate applicability of the proposed intelligent model.