Abstract
Managing electric vehicles charging activities during times when the electrical grid is congested is a challenging task. In this work, we propose a fair, real-time, demand-influenced dynamic pricing structure to accurately allocate more fairness to the billing strategy to reflect updated energy prices during real-time operation of the microgrids. This pricing structure is composed of two pricing fractions; retail energy price that follows time-of-use (ToU) rates, and congested energy price that is allocated solely for billing EVs charging events during congested timeslots. The proposed methodology is implemented in a hierarchal multi-agent architecture with a stochastic energy management system that aims to provide a cost-efficient microgrid operation. The inputs to the optimization problem are day-ahead PV forecast as well as stochastic EVs energy levels and connectivity times prediction models based on a discrete-time Markov chain. Moreover, a predictive model of daily load demand is also presented based on adaptive Artificial Neural Network (ANN). We develop these models based on historical data for Miami Dade County, South Florida. Through numerical simulations, we attest that the proposed pricing structure achieves significant energy prices reduction when compared with results from previous well-established pricing policies.