A Solution to the Techno-Economic Generation Expansion Planning using Enhanced Dwarf Mongoose Optimization Algorithm

Abstract

This paper proposes a hybrid metaheuristic algorithm to solve the decade Generation Expansion Planning (GEP)problem. In this proposed hybrid approach, the mutualism phase of Symbiotic Organism Search (SOS) is implemented in the Dwarf Mongoose Optimization Algorithm (DMOA) to improve the local search capability of the DMOA. In this hybrid algorithm, global search is taken care by the DMOA, and the local search is taken care by the mutualism phase SOS algorithm, which will help in solving nonlinear and nonconvex optimization problems. In recent decade every country aims to decarbonize its economy by implementing policies that increase the penetration of Renewable Energy Sources (RES) in its power generation capacity. This paper also presents a multidimensional framework of GEP based on the increasing penetration level of RES with the help of Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA). The simulation results are discussed in the result section and compared with many previously published algorithms. The statistical study confirms the hybrid algorithm's effectiveness and resilience.