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.