Parabolic trough solar power plants use a thermal fluid to transfer thermal energy from solar radiation to a water-steam Rankine cycle in order to drive a turbine that, coupled to an electrical generator, produces electricity. These plants have a heat transfer fluid (HTF) system with the necessary elements to transform solar radiation into heat and to transfer that thermal energy to the water-steam exchangers. In order to get the best possible performance in the Rankine cycle and, hence, in the thermal plant, it is necessary that the thermal fluid reach its maximum temperature when leaving the solar field (SF). Also, it is mandatory that the thermal fluid does not exceed the maximum operating temperature of the HTF, above which it degrades. It must be noted that the optimal temperature of the thermal fluid is difficult to obtain, since solar radiation can change abruptly from one moment to another. The aim of this document is to provide a model of an HTF system that can be used to optimize the control of the temperature of the fluid without interfering with the normal operation of the plant. The results obtained with this model will be contrasted with those obtained in a real plant.

Control of highly nonlinear processes such as solar collector fields is usually a challenging task. A common approach to this problem involves deploying a set of operation point range-specific controllers, whose actions are to be combined in a switching strategy. Discontinuities in control actions upon switching may lead to instabilities and, therefore, achieving bumpless transitions is always a concern. In addition, linear adaptive predictive controllers need to cope with nonlinearities by using high adaptation speeds, often leading to model vulnerability in the presence of aggressive perturbations. Finally, most of the proposed solutions rely on complex plant model developments. In this work, a multivariable nonlinear model-based adaptive predictive controller has been developed and tested against a parabolic trough solar power plant simulation. Since the model employed by this controller accounts for process nonlinearities, adaptation speed can be dramatically reduced, therefore increasing model robustness. The controller is easily initialized and is able to identify and track the process dynamics, including its nonlinearities as it evolves with time, thus requiring neither process up-front modeling nor switching. The presented controller outperforms its linear counterpart both in terms of accuracy and robustness and, due to the generality of its design, it is expected to be applicable to a wide class of linear and nonlinear processes.

Isolated microgrids are microgrids which operate autonomously. This paper presents an isolated microgrid which combines a Hydraulic Turbine Generator (HTG) with a Wind Turbine Generator (WTG) to supply consumers forming a Wind Hydro Isolated Microgrid (WHIM). The WHIM includes a Dump Load (DL) to dissipate the active power excess. The WHIM has been modeled and its operation has been simulated in two modes: Wind-Hydro (WH), where both HTG and WTG supply power, and Wind-Only (WO) mode, where the WTG is the active power supplier and the HTG keeps connected to the grid with null power to generate the grid voltage. In WO, a fast frequency regulation is achieved by means of a controller which commands the DL to consume the WTG power excess. Additionally, the simulation of the mode transition from WO to WH, which is triggered by a system active power deficit in WO mode, is shown. A kick starting system designed to speed up the HTG power production improves the transient from WO to WH mode change. Finally, the simulations in WH mode show the interaction between the HTG and WTG. The two controls proposed have been proved effective and the simulations show a good WHIM dynamic performance.

This paper presents the application of an Adaptive Predictive Expert (ADEX) Control System, entitled “Steam Temperature Optimizer” (STO), to control the temperature of high pressure steam in the combined cycle at Barranco de Tirajana in the Canary Islands. The STO was integrated in parallel with the plant's control system by means of OPC communication. The ADEX control strategy applied to the steam temperature process is discussed and the results presented and analyzed in comparison with those obtained by the existing PID system. The STO improved the control precision and stability of the steam temperature and confirmed ADEX as a reliable self-tuning methodology for this kind of process.

Hydraulic turbine generators (HTG) inherently exhibit slow dynamic responses; therefore, when operating as the sole power source in an islanded microgrid (MG), they may be unable to comply with the frequency limits imposed by existing grid codes for islanded MG under all operating conditions. By means of simulations, this article shows how an islanded MG supplied exclusively by an HTG and equipped with a classical governor results in frequency excursions beyond the 46/53 Hz limits defined in applicable grid codes for islanded MG when 0.17 per unit positive/negative load steps are applied. Settling times in this case are as long as 23/22 s. This work demonstrates that, by adding a small capacity battery energy storage (BES) of 11.9 kWh to the islanded grid along with an adaptive predictive controller (APC), which controls the sum of the powers provided by theHTGand the BES, the dynamic behavior of the islandedMGis greatly improved. Through simulations of the HTG+BES+APC grid, it is shown that the minimum/maximum frequency deviations in the upgraded MG are smaller (48.73/51.245 Hz) and the settling times are greatly reduced to 2.8/3 s for the same load steps, respectively. The simulations showthat the BES+APC combination dramatically improves the islanded MG dynamics.