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.

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Global growth of distributed generation, especially photovoltaic, has given rise to new markets and new problems: large injection of power at daily intervals without increasing in the level of consumption. This scenario has already created, in certain places, overvoltage problems, harmonic distortion, etc. In Brazil, the standards regulate the connection of photovoltaic inverters, however, the control is not yet subject to standardization. For this reason, this paper presents a proposal of local and central algorithms for this power injection control in accordance with the solutions of problems such as the lack of standardization in the communication protocol of the inverters. The results of the pilot plant application are also presented.

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