Abstract

Solar power tower systems demand robust heliostat aiming strategies to maximize energy collection efficiency while ensuring system reliability and cost-effectiveness. This review comprehensively discusses a novel classification framework that systematically integrates heliostat field focusing systems, tracking mechanisms, and adaptive control strategies. Recent advancements in optical modeling and error detection methodologies are reported, where hybrid approaches combining analytical solutions with ray tracing have improved computational efficiency by up to 60%. The integration of machine learning with traditional control methods has enhanced tracking accuracy by 35%, while advanced multiobjective optimization frameworks have achieved 12%–15% improvement in annual energy collection under safe operating conditions. The critical challenges in computational efficiency, environmental adaptation, and system integration are identified to provide directions for future research. This analysis offers valuable insights for advancing heliostat control technologies in large-scale solar thermal applications.

原文DOI：https://doi.org/10.31635/renewables.025.202500105