Dynamic Real-Time Pricing Mechanism for Electric Vehicles Charging Considering Optimal Microgrids Energy Management System
Tawfiq M. Aljohani, Ahmed F. Ebrahim, Osama A. Mohammed
Abstract
Managing electric vehicles (EVs) charging activities when the power grid is congested is a challenging task. This article proposes a dynamic, real-time, demand-influenced energy pricing mechanism to accurately allocate fairness to the billing strategy of the EVs charging process. The proposed mechanism is based on the concept of the inverse-demand function to reflect instantaneous energy prices following the microgrids’ real-time energy supply. It comprises two pricing fractions: retail energy price representing the energy cost for non-EVs demand during peak hours, and congested energy price allocated for billing EVs charging events during congested timeslots. The proposed methodology is implemented in a hierarchal multiagent architecture with an optimal energy management system to provide a cost-efficient microgrid operation. The input to the optimization problem is stochastic models that represent day-ahead photovoltaic (PV) production forecast, EVs energy levels, and connectivity times’ prediction models based on a discrete-time Markov chain. Additionally, a predictive model of daily load is also proposed based on adaptive artificial neural networks. The aforementioned models utilized historical data for Miami-Dade County, South Florida. Through numerical simulations, we attest that the proposed pricing mechanism achieves significant energy price reduction for non-EVs consumers when compared with results from previously published pricing policies.