Least Cost Generation Expansion Planning considering Renewable Energy Resources Using Sine Cosine Algorithm

Ever-growing development in technology and rising energy demand playing a vital role in seeking the attention of power planners to design such systems which fulfill the electrical demand by minimizing the generation cost through the optimum strategies. Nowadays, Generation Expansion Planning (GEP) is very important to make an adequate energy management system, and it is a highly nonlinear, constrained optimization problem and has been solved in a mathematical programming environment. However, due to the complexity of the GEP problem and the limitations of mathematical programming techniques, the meta-heuristic method has the capability to handle the GEP problem. The Sine Cosine Algorithm (SCA) is a novel population-based algorithm that can handle complex constrained optimization problems. In this research, the GEP problem is mapped into SCA in the presence of Renewable Energy Resources (RER) subject to technical and environmental constraints. The developed SCA-PFA is tested for validation, and case studies are solved using standard test systems. The obtained results are better in terms of cost and computational time when compared with results available in the literature. This shows the promise of the approach.