Multi-Objective Particle Swarm Optimization-Based Optimization Model for Heliostat Field Design

A quantitative examination of the key factors based on a model is necessary for the design of the heliostat field. First, using geometric relations, the pitch angle, azimuth angle, and light vector of each heliostat under various operating conditions are found. From there, the normal vector of the mirror surface and the cosine efficiency are found. The average effectiveness of the heliostat field is then determined by quantifying the receiver truncation efficiency and the shadow occlusion efficiency using a ray tracing technique based on the Monte Carlo methodology. Constraint equations and objective functions are constructed based on realistic conditions in order to determine the ideal settings for the heliostat position, size, installation height, etc. A multi-objective particle swarm approach is used to identify the ideal parameter combination in order to circumvent the shortcomings of the conventional particle swarm algorithm, which include poor convergence for high-dimensional problems and falling into local optima. According to the test results, 1300 heliostats have the maximum heat transfer efficiency and the average heat output power per unit area of the mirror surface reaches 1.103 (kw / m
²
) when operating below the design size.