Optimal Economic and Environmental Aspects in Different Types of Loads via Modified Capuchin Algorithm for Standalone Hybrid Renewable Generation Systems

Greenhouse gas emissions have become a significant concern for many countries due to their effect on the global economy and environment. This work discusses a standalone hybrid renewable generation system (HRGS) for use in isolated areas with different load demand profiles. Three load profiles were studied in this work: educational, residential, and demand-side management (DSM)-based residential load profiles. To investigate the economic and environmental aspects, a proposed modified capuchin search algorithm (MCapSA) was implemented, and the obtained results were compared with those of different conventional optimal procedures, such as the genetic algorithm (GA), particle swarm optimization (PSO), and HOMER. The Levy flight distribution method, which is based on random movement, enhances the capuchin algorithm's search capabilities. The cost of energy (CoE), electric source deficit (ESD), greenhouse gas (GHG) emissions, and renewable factor (RF) indicators were all optimized and estimated to emphasize the robustness of the proposed optimization technique. The results reveal that the shift in the residential load profile based on individual-household DSM-scale techniques leads to significant sharing of renewable sources and a reduction in the utilization of diesel generators, consequently diminishing GHG emissions. The proposed MCapSA achieved optimal values of economic and environmental aspects that are equal to or less than those achieved through PSO. From the overall results of the three scenarios, the modified algorithm gives the best solution in terms of GHG, COE, and ESD compared to other existing algorithms. The usage of MCapSA resulted in decreases in COE and GHG in three types of loads. The robustness and effectiveness of MCapSA are demonstrated by the fact that the DSM-based optimal configuration of the renewable energy sources produces the lowest CoE and GHG emissions of 0.106 USD/kWh and 137.2 kg, respectively.