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 number of connections with distributed generation has been growing steadily in Brazil, however, there is still a problem associated with it, the intermittence. Although a consumer with photovoltaic solar system (PVSS) is able to generate energy even when the grid is unavailable, the load is still susceptible to power outages given the intermittence of the source, one possible solution for this is the use of energy storage elements to supply part of the load, or even all of it when needed. In this study is analyzed, under the economic viability point of view, the impact of using a Battery Energy Storage System (BESS) coupled with a PVSS to supply a residential consumer in the event of power outages.
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