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Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

Faria, J. ; Pombo, J. ; Calado, M.R.A. ; Mariano, S.J.P.S.

Energies Vol. 12, Nº 5, pp. 902 - 902, March, 2019.

ISSN (print): 1996-1073
ISSN (online):

Journal Impact Factor: 2,077 (in )

Digital Object Identifier: 10.3390/en12050902

Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for
powering up off-grid communities. However, this type of application requires the use of energy
storage systems (ESS) to manage the intermittency of PV production. The most commonly used
ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the
imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy
storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great
potential to make up for the limitations of Li-ion batteries, a proper power management strategy
is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper
proposes a novel power management strategy based on an artificial neural network for a standalone
PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system
is used to demonstrate and validate the performance of the proposed power management strategy.
To demonstrate its effectiveness, computational simulations with short and long duration were
performed. The results show a minimization in Li-ion battery dynamic stress and peak current,
leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management
strategy increases the level of SC utilization in comparison with other well-established strategies in
the literature.