Assessment of the Potential of Renewable Energy with Bias Correction Due to Climate Change Over South Asia Using Global Atlas Dataset

Abstract

In order to meet the rising energy needs in South Asia as a result of population growth and societal development, the development of renewable energy is of paramount importance. With the hindcast data from the global atlas (GWA3 and Solargis) dataset, the aim of this study is to determine how climate change impacts wind power density (WPD) at 50 m above the ground level (AGL) through the power law method and photovoltaic (PV) potential over the subcontinent by using the climate change historical evaluation. The developing countries require the use of renewable energy, because of this, knowing the ideal location is important, and implementing wind and solar energy initiatives is crucial in this regard. The validation of GWA3 is based on the wind atlas methodology and the wind atlas analysis and application program (WAsP). Specifically, when analyzing threshold wind speed factors that influence wind energy production, according to the novelty of this research that applies the bias correction techniques quantile mapping based on Weibull distribution. Plus, global solar atlas uses Solargis PV modeling software and a high-resolution database of solar resources. The duration of data for wind energy and long-term reference data (ERA 5) is 2 and 12 years, respectively. In addition, overall subcontinent the average daily/yearly totals of solar PV plant that produces 1 kW-peak electricity, calculated for the period of 20 recent years. For WAsP modeling, the mean wind speeds of the predicted wind climates have an estimated ~ 6.5% standard uncertainty in the region of Pakistan and Maldives, and Bangladesh, respectively. For GWA3 modeling, the mean wind speeds of the predicted wind climates have an estimated mean absolute bias of 17% ± 8% and ± 2% standard deviation (assuming no bias or uncertainty on the WAsP modeling) in the region of Pakistan and Maldives, respectively. Moreover, for Bangladesh 1% ± 0 percent standard deviation is. Furthermore, in the simulation, losses due to dirt and soiling were estimated at 3.5%, while the total loss from all conversion losses was estimated at 7.5%. Another point worth mentioning was to estimate the energy density based on the data results on wind speed (m/s) at the hub height of 50 m AGL over the subcontinent. For instance, maximum and minimum energy density regions are Afghanistan and Maldives, respectively. Meanwhile, the PV system consists of crystalline silicon PV modules mounted at a fixed position, tilted to maximize yearly energy production. Consequently, understanding long-term WPD and PV power potential for micro-scale energy yields is crucial for understanding subcontinental variations. Additionally, the RE system reduces GHGs and limits global climate change. The CO₂ can be switched to low with this method.