Distributed Energy Resources and Fast Charging Stations Allocation for Enhanced System Operation and Charging Infrastructure Coverage

The growing insertion of Electric Vehicles (EVs) in the distribution system is a promising solution to mitigate the greenhouse emission issue. However, as the number of EVs significantly increases, the excessive charging demand of Fast Charging Stations (FCSs) may lead to instability problems in the distribution network. Therefore, this paper proposes a multi-objective optimization approach for the allocation and sizing of Fast Charging Stations (FCSs) with the objective of minimizing their installation and operation costs, while maximizing the coverage for EVs. It is well-known that the integration of EVs results in increased power losses. As a consequence, in this study, an optimized planning of Distributed Energy Resources (DERs) was also performed with the purpose of mitigating this issue. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the 33-bus test feeder and 25-node transportation network, considering commercial and residential areas. Based on the solution selected by the Fuzzy Decision-Making method, it was possible to minimize the voltage deviation even with the insertion of the FCSs in the system.