An Optimized Aiming Strategy Tracking Flux Set Point for Solar Power Tower System

Abstract

Solar receivers collect solar radiation and convert solar energy into high-temperature thermal energy by heating fluid in a solar power tower (SPT) system. Solar radiation absorbed on receiver aperture changes over time due to the variation of the solar position and Direct Normal Irradiance (DNI) caused by passing clouds. Fluctuations of solar flux influence the heat transfer fluid (HTF) temperature at the outlet of the receiver tube, which possibly disturbs the stability of the power generation systems and even leads to risks of permanent damage to the components. An appropriate and effective aiming strategy for heliostats in the field can obtain steady solar flux outputs tracking a set point according to the power system requirements. The strategy is a feedforward to coordinate with the HTF flow rate to control receiver outlet temperature in the feedback control loop. In this paper, an optimized aiming strategy based on heliostats working states is proposed to make the focused solar flux tracking the set point under the condition of solar position variation and DNI forecast. The optimization model is a constrained nonlinear 0-1 programming problem solved by Genetic Algorithm. The optical model calculating the collected solar flux is generated by SOLSTICE software using the Monte Carlo method. The simulation compared the optimized aiming strategy with conventional processing means dealing with DNI variation by randomly setting heliostat working state. The results verified that the optimized aiming strategy had a better effect on limiting the fluctuation of absorbed solar radiation and tracked the reference flux more accurately.