Design and identification of an optimal approach for modelling a hybrid renewable energy system

Abstract Most power generation relies on fossil fuels, which are both finite resources and major contributors to greenhouse gas emissions. In recent years, renewable energy sources such as solar, wind, and biomass have played an important role in power generation to mitigate these concerns. However, the successful modelling, operation, and integration of these sources into the grid system poses significant challenges due to their inherent variability and dependency on environmental conditions. Due to these challenges, determining the optimal capacity of renewables in a hybrid system is complex. Thus, a robust methodology is required to address this design challenge effectively. To achieve this, development of advanced modelling techniques is suggested that consider the probabilistic nature of renewable energy sources and load patterns. This study analyses different approaches, including the deterministic and probabilistic methods, and proposes an optimal approach and design for a hybrid renewable energy system, which is more reliable with a reduced loss of power supply probability and produces energy with 26.3% lower levelised cost of electricity (LCOE) than fossil fuel–based alternatives such as the utility grid. A detailed analysis of the compatibility of the proposed method with the actual real-time data is carried out, and the effect of the grid purchase and sale capacities on the LCOE of the produced energy is examined.