Economic and reliability determination of sustainable renewable energy mix based on social spider prey optimization algorithm

Abstract

Resolving the power crises requires the combination of different individual renewable energy sources so that one source can compensate for another. Unfortunately, renewable energy sources are not always available at certain times making their use problematic. To solve this uncertainty, it is important to combine independent renewable energy sources and determine the right set of the renewable energy mix that is economical and reliable. The sources of renewable energy data are solar PV, wind, battery, and biomass. Different scenarios of renewable energy mix or combination considered are wind–biomass–battery, solar PV–wind–biomass, PV–biomass–battery, and solar PV–wind–biomass–battery. Knowing the economic and reliable impact of these combinations helps to make the best investment decision. The nature-inspired optimization is utilized as the methodology to determine the feasible dimension, economic, and reliability of the energy mix. Historical energy-related data for one year were obtained from the National Renewable Energy Laboratory and was used to evaluate the hybrid renewable energy systems. The result shows that SSP guaranteed optimal economic costs and satisfied the reliability constraints for wind–biomass–battery system, solar PV–wind–biomass system, PV–biomass–battery, and PV–wind–biomass–battery. The outcomes suggests that SSP can provide optimal result and therefore calls for researchers to further explore the potential of integrating this algorithm in their optimization approach for solar PV–wind–biomass–battery hybrid system.