A comprehensive evaluation of photovoltaic simulation software: A decision-making approach using Analytic Hierarchy Process and performance analysis

The growing adoption of renewable energy, particularly photovoltaic (PV) solar systems, has led to the development of numerous simulation software tools to simplify system design, analysis, and optimization. This study evaluates five widely used PV simulation software packages—SAM, PVsyst, HOMER, PV∗SOL, and RETScreen—by analyzing their features and performance across ten critical criteria, including cost, solar database accessibility, modeling capabilities, and ease of use. Using the Analytic Hierarchy Process (AHP), the criteria are ranked by importance, with the working platform identified as the most influential factor in software selection, followed by economic modeling capabilities and software cost. Additionally, the flexibility of simulation data requirements, reporting and analysis options, and user friendliness and ease of use are identified as important criteria, albeit ranking lower in importance. Performance analysis using simulation and real data further validates the evaluation process, providing insights into the accuracy and reliability of the simulation results generated by each software. Our findings indicate that SAM delivers superior accuracy when compared to real-world data, making it the most reliable tool for PV system analysis. PV∗SOL also ranks highly for its robust reporting and modeling capabilities. These results provide valuable insights for professionals and researchers in selecting the most suitable software for PV system design and optimization, emphasizing the balance between functionality, cost-effectiveness, and user-friendliness. • Developed AHP-based framework for PV simulation software evaluation. • Evaluated PV software using 10 key criteria for comprehensive analysis. • Integrated real-world PV data for enhanced study credibility. • Incorporated expert opinions via pairwise comparisons for assessment. • Compared simulation with real data for insights into predictive capabilities.