Feasibility Study and Optimal Placement of Solar Power Plants Using Binary Genetic Algorithm

ABSTRACT This paper presents a comprehensive techno‐economic assessment framework for grid‐connected photovoltaic systems, integrating a binary genetic algorithm (BGA) optimization with detailed simulation tools. The study addresses optimal siting and sizing of two solar plants (865 and 739 kWp) in Karaj, Iran, using MATLAB for BGA implementation, PVsyst for system performance analysis, and RETScreen for economic evaluation. Compared to conventional hybrid genetic‐analytical algorithm (GA‐IA), the proposed BGA achieved 9.9% lower power losses (0.146 vs. 0.162 MW) and superior voltage profile enhancement. Technical simulations demonstrated robust system performance with annual average PRs of 84.4% and 83.4%, yielding energy productions of 1725.5 and 1458.6 MWh for the Site1 and Site2, respectively. Economic analysis under a feed‐in tariff of $0.0361/kWh revealed attractive financial indicators, including internal rates of return of 23.8%–25.9% and payback periods (PBPs) of 4.9–5 years. Environmental assessment showed significant emission reduction potential of 16,563–13,943 tons CO₂ over 20 years, while grid integration analysis confirmed peak load reduction of 1.46 MW during critical demand periods. This research provides valuable insights for renewable energy planning, offering a validated methodology for sustainable power system development in regions with high solar potential.