Least-Cost Generation Expansion Planning Using Crayfish Optimization Algorithm Considering Emission Reduction

Reducing carbon emissions is a top priority for the electric power industry in an effort to lessen environmental damage and increase cost effectiveness. Increasingly incorporating renewable energy sources, such as wind and solar, into plans for generation development is a crucial tactic in this effort. However, geographical location can have a considerable impact on how successful these renewable sources are. An investigation was carried out to create a strong framework for resolving emission-constrained generation expansion planning (GEP) issues in order to satisfy the twin goals of cutting expenses and minimizing emissions. The suggested framework forms the hybrid COA-CMMI technique by combining the Crayfish Optimization Algorithm (COA) with a Correction Matrix Method with Indi-cator (CMMI). This model has been enhanced with site-specific assessment of renewable energy potential for power outcomes like solar and winds. Overall, the proposed COA-CMMI strategy provided reasonable outcomes and efficiency concerning how the GEP forecast and offered cost-effective GEP solutions while demonstrating remarkable emission reductions. The suggested approach showed a better performance as compared to the results presented in the recent literature, indicating that the approach could help advance the deployment of renewable energy in the power domain.