A predictive discrete event approach for the optimal charging of electric vehicles in microgrids

Distributed generation, renewables (RES), electric vehicles (EVs), storage systems and microgrids are increasing widespread all over the world owing to the necessity of applying policies for sustainable development. In particular, the progressive shift from traditional vehicles to EVs is considered as one of the key measures to achieve the objective of a significant reduction in the emission of pollutants, especially in urban areas. One of the major problem to be solved to make EVs a viable solution for the sustainable mobility is the development of effective facilities for vehicles. In this context, besides to technological aspects, one of the most important issues is the definition of fair and efficient policies for the sequencing and scheduling of the vehicle charging. In fact, scheduling problems are widely recognized as representing one of the most challenging class of optimization problems. Besides, the additional presence of specific features concerning vehicle charging systems (like controllable execution times, presence of intermittent energy sources, etc.) make even more difficult the vehicle charging problem. In this framework, despite the fact that optimization problems regarding energy systems are generally considered within a discrete-time setting, in this paper a discrete event approach is proposed. The reasons for this choice are essentially two. The first one is the necessity of containing the number of the decision variables, which grows beyond reasonable values when a small-time discretization step is chosen. The second is the impossibility of an accurate tracking of process and events using a discrete-time approach. The considered optimization problem regards the charging of a series of vehicles by a charging station that is integrated in a microgrid. Such a microgrid includes also renewable and traditional energy sources, storage systems and a local load. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the cost related to the use of fossil fuels, and the overall tardiness of the services provided to the customers. The effectiveness of the proposed approach is tested on a real case study.