Fast evaluation of annual optical performance for heliostat field based on empirically validated acceleration strategies in the Monte Carlo ray-tracing workflow

Concentrated solar power (CSP) systems require accurate yet efficient annual performance evaluation of heliostat fields, which remains challenging for Monte Carlo ray tracing due to high computational costs. This study proposes two empirically validated acceleration strategies: fast occlusion detection via dual geometric constraints (rmax, θmax) and time-interval sampling (day-minute) combined with linear interpolation. Validated against the professional ray-tracing software SolTrace, the model demonstrates reliable performance under diverse operating scenarios. The occlusion detection algorithm reduces computation time by 70% with only 0.24% absolute error in occlusion efficiency. Optimized to 3day–15min sampling, the method cuts computational load by 97.62% versus the 1day–1min benchmark (valid for solar elevation α ≥ 15°), achieving an ultra-low annual relative energy error of 0.0052% without seasonal bias or error accumulation. A supplementary contribution is the refined modeling of physical gaps between heliostat facets, which effectively eliminates the systematic overestimation of optical efficiency in traditional models. This work balances speed and accuracy, providing a practical tool for CSP heliostat field design and optimization.