A Probability-Based Algorithm for Electric Vehicle Behaviour in a Microgrid with Renewable Energy and Storage Devices

Abstract

The recent growth in the use of Electric Vehicles (EVs) in transportation systems has increased their importance in electrical power systems. In unison, new entities and ways of operating electrical power systems have emerged. An example of this is the concept of microgrid. In this paper, a probability-based algorithm is developed to simulate the behaviour of EVs. This algorithm is based on data of initial State of Charge (SOC), plugin/out times, types of vehicles and chargers. The algorithm uses the Monte Carlo method to perform its simulations. Also, a two-stage stochastic energy scheduling model for a Microgrid (MG) is proposed to make a day-ahead optimal decision in the first stage. Real-time operations, including wind/solar power, baseload demand and the EV demand variability, are minimised in the second stage. Finally, the model is tested in a case study to verify its correct behaviour and applicability. The case study is implemented using profiles obtained from historical data and the algorithm developed for that purpose.