ABSTRACT
Despite the significant slowdown of economic activity in South Africa by virtue of the COVID-19 outbreak, load shedding or scheduled power outages remained at a high level. The trend of rising load-shedding hours has persisted throughout most of the year 2022. Operational issues within the South African power utility inflamed the unpredictable nature of generation capacity, resulting in unscheduled outages at several generating units, mostly due to multiple breakdowns. To forestall substantial spikes in energy costs, an increasing number of enterprises and homeowners have started to gradually adopt renewable energy technologies to sustain their operational demand. Therefore, there is an increase in the exploration and investment of battery energy storage systems (BESS) to exploit South Africa's high solar photovoltaic (PV) energy and help alleviate production losses related to load-shedding-induced downtime. As a result, the current work presents a comprehensive and consequential review conducted on the BESS specifically for solar PV application and in the South African context. The research investigations carried out on BESS for PV application are crucially examined, drawing attention to their capacities, shortcomings, constraints, and prospects for advancement. This investigation probed several areas of interest where the BESS-PV scheme is adopted, viz., choice of battery technology, mitigating miscellaneous power quality problems, optimal power system control, peak load shaving, South African BESS market and status of some Real BESS-PV projects. The techno-economic case scenario has been proposed in the current research and results yield that lithium-ion batteries are more viable than Lead–acid batteries.
ABSTRACT
Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, ABV-CoViD (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a [Formula: see text]- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the [Formula: see text] model, and [Formula: see text] model for ANN modelling. We considered the [Formula: see text](12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of [Formula: see text]-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme's efficacy in ABV measurement over conventional and manual resource allocation schemes.