Dynamic real-time aiming strategy optimization of multi-horizons heliostat fields

The cloud prediction error is the dominant factor causing uncertainty and tracking errors in the heliostat aiming strategy optimization of Solar Power Tower (SPT) plants. In this work, an effective and scalable optimization method is proposed to address these cloud prediction errors. Specifically, a Multi-step Unscented Kalman filter (Mt-UKF) is developed to predict the SPT output flux trajectory under the influence of cloud prediction errors. Additionally, an improved Grey Wolf Optimization (GWO) algorithm is proposed, which integrates a reconfigured Grey Wolf social hierarchy with a Dimensionality Extension Learning (DEL) mechanism. This improvement enables the feedback correction of optimization errors in the heliostat field caused by cloud prediction errors. A simulated heliostat field is introduced as the experimental scenario to validate the proposed method. The Dimensionality Extension Learning Grey Wolf Optimization (DEL-GWO) algorithm is compared against four other state-of-the-art swarm intelligence algorithms. Experimental results and statistical tests demonstrate that the Mt-UKF combined with DEL-GWO exhibits high competitiveness and significantly outperforms the other algorithms. This combination effectively mitigates tracking errors induced by cloud prediction errors, demonstrating its robustness and applicability for heliostat field optimization.