Solar tower power plants (STP) with thermal energy storage have the ability to temporally shift power generation, regulate peak load and modulate frequency. The power production of such systems not only depends on the available solar resources, but also on the operation strategies, such as the operational thresholds used to control when the equipment starts or stops in certain conditions. Currently, most of the operational thresholds are determined by operators’ experience or the rated parameters given by the equipment manufacturers, which are usually unoptimized fixed values. Therefore, to guide the optimal operations of STP, optimization methods are developed in this work to optimize the daily operational thresholds according to the solar radiation at present and in the future. Firstly, a well-validated in-house model with easy-to-tune operation strategies for STP is developed to optimize the thresholds. Then, five operational thresholds affecting the power generation of STP are identified. Finally, an optimization algorithm is proposed to optimize the operational thresholds. The proposed optimization algorithm uses a rolling optimization based on holism to enhance regular particle swarm optimization (R-PSO). The power generation of a 50 MW STP under three different operation strategies is compared: (1) unoptimized operation strategy, (2) operation strategy optimized by regular PSO algorithm, and (3) operation strategy optimized by R-PSO algorithm. The average daily power generation under the three operation strategies is 451.16 MWh, 490.25 MWh, and 511.63 MWh, respectively. The results show that the proposed R-PSO could increase averaged daily power production by 13.4 % and 4.36 % when compared with cases with no optimization and PSO optimization, respectively. Therefore, the power production of STP can be significantly improved by optimizing the identified daily operational thresholds using the R-PSO algorithm