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

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.

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

“…Chau et al 36 employed autoregressive moving average (ARMA) and ANNs to forecast solar energy for short periods and long periods, respectively, in a system of ESS‐PV to provide economic advantages. Faria et al 37 proposed an ANN with a single hidden layer for a PV system coupled with a hybrid ESS to obtain a power management strategy. The study by Yang et al 38 considers an air source heat pump, PV, and a battery storage system, and developed an algorithm based on improved fuzzy and double fuzzy logic in order to stabilise energy dispatch and fluctuation in a microgrid system.…”

“…Chau et al 36 employed artificial neural networks (ANNs) to forecast solar energy in a PV‐ESS system and assessed the economic advantages. Faria et al 37 proposed a power management strategy using an ANN model for a PV‐ESS system. Yang et al 38 employed fuzzy logic to stabilise energy dispatch for an air source heat pump, PV, and a battery storage system.…”

Proportional Resonant Current Control and Output-Filter Design Optimization for Grid-Tied Inverters Using Grey Wolf Optimizer

“…The over-exploitation of non-renewable natural resources has led an overall environmental degradation on the planet. A suitable alternative solution to mitigate the environmental degradation on the planet is the use of renewable energy sources [1,2]. Nowadays, solar and wind energy are some of the most attractive renewable energy sources for electrical energy production [3].…”

Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling

“…Apparently, the performance of analytical methods depends on the parameter accuracy, while the data-driven artificial neural network (ANN) algorithm abandons the predominated parameters and equivalent circuit, and is able to build the PV model from historical data with no assumption. ANN has been widely applied in the PV field [14], such as the estimation and prediction of global solar irradiance data [15,16], maximum power point tracking of PV modules [17,18,19], and performance prediction of the PV module using electrical equivalent model [20]. In this paper, ANN is used to predict the I-V curve of single crystal silicon modules under different irradiances and temperatures without any parameters, and the prediction accuracy is proved to be better than the parameter based method.…”