Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations

Héricles Eduardo Oliveira Farias, Camilo Alberto Sepulveda Rangel, Leonardo Weber Stringini, Luciane Neves Canha, Daniel Pegoraro Bertineti, Wagner da Silva Brignol, Zeno Iensen Nadal
2021 Energies  
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
more » ... Ch) 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.
doi:10.3390/en14051370 doaj:bc7d61b32e3d4fd4933108d831e5ee8f fatcat:7l23wq2mpbgvxms2i6hijrh46a