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

• Published in: Arabian journal for science and engineering (2011) Vol. 48; no. 5; pp. 6185 - 6203
• Main Authors: Abbas, Tauseef, Ashraf, Muhammad Mansoor, Malik, Tahir Nadeem
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023; Springer Nature B.V
• Subjects: Algorithms; Complexity; Computing time; Constraints; Energy management; Energy resources; Energy sources; Engineering; Heuristic methods; Humanities and Social Sciences; Mathematical analysis; Mathematical programming; multidisciplinary; Optimization; Programming environments; Renewable energy; Renewable resources; Research Article-Electrical Engineering; Science; Trigonometric functions; Sine cosine algorithm; Generation expansion planning; Meta-heuristic optimization; Renewable energy resources; Equivalent energy function method; Power system planning
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-022-07303-5

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.