The continuously growing penetration of intermittent electricity sources will increase the future demand for dispatchable power plants, which balance out fluctuations within the electrical grids. Parabolic trough power plants with thermal energy storages could be one renewable solution for regions with a high yearly direct normal irradiance (DNI) sum, but in order to compete against other renewable as well as non-renewable technologies, the economic competitiveness must be ensured. Price reductions can be achieved for example by optimizing the plant operation. One shortcoming of state of the art plant controllers is that they only use the DNI measured at one or a few positions in the solar field. Due to the spatial variability of the DNI throughout the solar field this DNI information can be misleading. In this paper, we investigate the optimization potential of solar field control strategies with access to spatially resolved DNI information from all sky imagers (ASI). Uncertainties of the ASI system are considered by introducing additional independent spatial DNI information from a shadow camera system. The spatial and temporal DNI variability of the DNI seen by the controller is classified in distinct DNI variability classes. Two new control strategies are developed, with optimized control parameters for distinct combinations of the spatial and temporal DNI variability classes. These new variability class dependent control strategies are benchmarked in a simulation environment. A relative increase in revenue in excess of 1.9% is observed over a test period with 22 days, compared to a state of the art reference controller.