Event-triggered model predictive control for dynamic energy management of electric vehicles in microgrids

Abstract

The rapidly increasing charging demand of electric vehicles (EVs) represents a significant new load with myriad uncertainties. This paper proposes an event-triggered model predictive control (ET-MPC) method for dynamic energy management of EVs in microgrids (MGs). The study novelties lie in the forecasting models of EV status in an environment of MGs and the event-triggered mechanism for dynamic energy management of EVs. Firstly, the characteristics of EVs from different types of MGs are analysed by fitting the EV states with different probability distributions to improve the accuracy of forecasting of their status. Secondly, an ET-MPC method focusing on the energy management of EVs is first proposed to achieve coordination between the computational efficiency and optimisation impact of EVs. The event-triggered mechanism is achieved by monitoring the errors between the real EV states and their forecast values, which is only required to carry out optimisation when the forecast error of the EV states meet a set trigger level. Numerical simulations show that unlike time-triggered MPC methods—with long calculation times and fixed mechanisms—the ET-MPC method proposed achieves nearly the same energy management impact with a significantly shorter calculation overhead.