Abstract The northeastern state of Assam possesses significant untapped solar energy potential requiring systematic optimization for effective utilization. This paper presents a novel Chaotic Lévy flight-enhanced Grey wolf optimizer (CLF-GWO) algorithm for multi-objective optimization of grid-connected solar photovoltaic power plants, validated using 36-month meteorological data from Guwahati, Assam. The proposed algorithm integrates logistic chaotic maps for population diversity, tent chaotic maps for adaptive parameter control, and Lévy flight mechanisms for improved escape from local optima. Comprehensive mathematical modeling incorporates temperature-dependent PV characteristics, non-linear inverter efficiency curves, and environmental derating factors specific to subtropical humid climate. The CLF-GWO demonstrates superior convergence, achieving optimal solutions 29. 6% faster than standard GWO, 34. 2% faster than PSO, and 27. 8% faster than differential evolution across 50 independent runs. The optimized 1 MWp solar plant achieves annual energy yield of 1, 542 MWh, performance ratio of 79. 8%, capacity utilization factor of 17. 6%, and levelized cost of energy of ₹ 3. 89/kWh, representing improvements of 14. 2%, 11. 8%, 28. 5%, and 23. 7%, respectively, compared to the conventional design. Sensitivity analysis confirms system robustness across 25\% parameter variations. The proposed methodology establishes a replicable approach for optimal solar power plant design in Assam and similar subtropical regions globally.
Devi et al. (2026) studied this question.