Sample-Efficient Hyperparameter Optimization of an Aim Point Controller for Solar Tower Power Plants by Bayesian Optimization

This work introduces a sample-efficient algorithm to optimize the control parameters of an aim point controller for solar power tower plants. Optimizing the control parameters increases the performance of the aim point controller, and thus the efficiency of the plant. However, optimizing the parameters in simulation will not yield the true optimal parameters at the real plant due to mismatches between simulation and reality. Thus, optimization must be done at the real tower to find a true optimum. As this can be time consuming and costly, the optimizer should require a minimum number of steps. Hence, a sample-efficient optimization strategy is needed. This work introduces a new algorithm based on Bayesian Optimization (BO), which leverages multiple sets of simulation data to accelerate the optimization. The algorithm is tested on a six-dimensional test function representing an arbitrary aim point controller. The proposed algorithm outperformed standard Bayesian Optimization by reaching near optimal parameter configurations of 95% accuracy within 33% less optimization steps. In a second test, the proposed algorithm is used to optimize a simulated Vant-Hull aim point controller with two hyperparameters. Here, the algorithm also needs 33% less optimization iterations than the standard BO.