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.

Many thermal solar power plants use thermal oil as heat transfer fluid, and molten salts as thermal energy storage. Oil absorbs energy from sun light, and transfers it to a water-steam cycle across heat exchangers, to be converted into electric energy by means of a turbogenerator, or to be stored in a thermal energy storage system so that it can be later transferred to the water-steam cycle. The complexity of these thermal solar plants is rather high, as they combine traditional engineering used in power stations (water-steam cycle) or petrochemical (oil piping), with the new solar (parabolic trough collector) and heat storage (molten salts) technologies. With the engineering of these plants being relatively new, regulation of the thermal energy storage system is currently achieved in manual or semiautomatic ways, controlling its variables with proportional-integral-derivative (PID) regulators. This makes the overall performance of these plants non optimal. This work focuses on energy storage systems based on molten salt, and defines a complete model of the process. By defining such a model, the ground for future research into optimal control methods will be established. The accuracy of the model will be determined by comparing the results it provides and those measured in the molten-salt heat storage system of an actual power plant.

Thermoelectric solar plants transform solar energy into electricity. Unlike photovoltaic plants, the sun’s energy heats a fluid (heat transfer fluid (HTF)) and this, in turn, exchanges its energy, generating steam. Finally, the steam generates electricity in a Rankine cycle. One of the main advantages of this double conversion (sun energy to heat in the HTF-Rankine cycle) is the fact that it facilitates energy storage without using batteries. It is possible to store the heat energy in melted salts in such a way that this energy will be recovered when necessary, i.e., during the night. These molten salts are stored in containers in a liquid state at high temperature. The HTF comes into the solar field at a given temperature and increases its energy thanks to the solar collectors. In order to optimize the sun to HTF energy transference, it is necessary to keep an adequate temperature control of the fluid at the output of the solar fields. This paper describes three different algorithms to control the HTF output temperature.

The energy production of concentrated solar power (CSP) plants not only depends on their design, but also of the weather conditions and the way they are operated. A performance model (PM) of a CSP plant is an essential tool to determine production costs, to optimize design and also to supervise the operation of the plant. The challenge is developing a PM that is both easy enough to be useful during the earlier stages of the project, and also useful for supervision of plant operation. This requires one to be able to describe the step between the different modes of operation and to fit the response to transient meteorological phenomena, not so relevant in terms of aggregate values, but crucial for the supervision. The quasi-dynamic performance model (QD-PM) can predict the net energy exported to the grid, as well as all the key operational variables. The QD-PM was implemented using Matlab-Simulink of Mathwoks (MA, USA) with a modular structure. Each module is developed using specific software and a state machine is used to simulate the sequence between the operation modes. The validation of the PM is made using one complete year of commercial operation of a 50 MWe CSP plant situated in Spain. The comparison between the actual data and the results of the model shows an excellent fit, being especially noteworthy as follows the transients between the different CSP operation modes. Then, QD-PM provides an accuracy better than the usual PM, and, almost, as good as that of a fully dynamic model but with a shorter simulation time. But, the main advantage of the QD-PM is that it can be use not only in the feasibility and design stages, but it can be used to supervise the operation of the plant.

Thermal oil has been used as heat transfer fluid in many solar thermal power plants, which also use molten salts as thermal energy storage system. Since the engineering of these plants is relatively new, control of the thermal energy storage system is currently achieved in manual or semiautomatic ways, controlling its variables with proportional-integral-derivative (PID) regulators. Once the plant is running, it is very difficult to obtain permission to try new control strategies. Hence, most plants keep running on these simple, sometimes inefficient control algorithms. This paper explores the results obtained with different control strategies implemented on a complete model of energy storage systems based on molten salt. The results provided by the model allow the optimum control strategy to be selected. Comparison of the results obtained by simulation of these control strategies and actual results obtained from a real plant, confirm the accuracy of the selection made.

The authors wish to make the following corrections to this paper [1]: Figure 8 on page 6730 is replaced as follows: