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 and Zeno Iensen Nadal
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Héricles Eduardo Oliveira Farias: Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil
Camilo Alberto Sepulveda Rangel: Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil
Leonardo Weber Stringini: Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil
Luciane Neves Canha: Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil
Daniel Pegoraro Bertineti: Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil
Wagner da Silva Brignol: Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil
Zeno Iensen Nadal: Electric Energy Paranaense Company—COPEL-DIS, Curitiba 81200-240, Brazil

Energies, 2021, vol. 14, issue 5, 1-27

Abstract: 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.

Keywords: EVCS; EPSO; rule-based; EV scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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