Abstract
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.
Highlights
Fossil fuels cause emissions that contribute to an increased greenhouse effect, leading to overall environmental deterioration [1]
This paper presents a new power management strategy for Hybrid Electric Storage Systems (HESS) based on Artificial Neural Networks (ANN) with a reduced computational cost, able to manage the power flow of all HESS elements and increase lithium-ion batteries (Li-ion) batteries lifespan by reducing dynamic stress and peak current demand, while continuously supplying the load with the requested power
This paper proposed a novel HESS power management strategy based on an Artificial Neural
Summary
Fossil fuels cause emissions that contribute to an increased greenhouse effect, leading to overall environmental deterioration [1]. In [17], the authors proposed a new power management strategy of HESS applied to electric vehicles: the adaptive frequency strategy In this strategy, a simplified digital adaptive filter was used in order to guarantee the convergence of the solution and to decrease the computational cost associated with control in real time. The performed simulations indicated an extension of the Li-ion batteries lifespan by 64.8%, in comparison to battery-only ESSs. This paper presents a new power management strategy for HESSs based on ANN with a reduced computational cost, able to manage the power flow of all HESS elements and increase Li-ion batteries lifespan by reducing dynamic stress and peak current demand, while continuously supplying the load with the requested power.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.