This paper proposes a flexible framework for scheduling and real time operation of electric vehicle charging stations (EVCS). The methodology applies a multi-objective evolutionary particle swarm optimization algorithm (EPSO) for electric vehicles (EVs) scheduling based on a day-ahead scenario. Then, real time operation is managed based on a rule-based (RB) approach. Two types of consumer were considered: EV owners with a day-ahead request for charging (scheduled consumers, SCh) and non-scheduling users (NSCh). EPSO has two main objectives: cost reduction and reduce overloading for high demand in grid. The EVCS has support by photovoltaic generation (PV), battery energy storage systems (BESS), and the distribution grid. The method allows the selection between three types of charging, distributing it according to EV demand. The model estimates SC remaining state of charge (SoC) for arriving to EVCS and then adjusts the actual difference by the RB. The results showed a profit for EVCS by the proposed technique. The proposed EPSO and RB have a fast solution to the problem that allows practical implementation.

The current reality of the energy market requires generation, control, distribution and consumption to become more efficient. Recent arrangements with electric energy stored in accumulators appear as strategic tools in the environment where the cost of energy supplied by the concessionaires and permission holders has accumulated successive increases, indirectly enabling the control and management of applications of micro or local minigeneration, from renewable sources in general. Systems with energy storage (e.g. batteries) and local demand management (many consumer units) achieve great efficiency by allowing the optimized consumption of the available energy, both by the local power grid and by the accumulated grid. Other advantages are presented for the distributors, allowing the relief of the electricity network, remunerating the investment in reduced intervals of time. Consideration should be given to the possibility of local autonomy, even if partially, by providing energy from the storage to the local loads in eventual failures in the supply of energy by the distribution network or at times where supply has a higher cost.