Category Control of Solar Energy Systems
Ambient temperature affects the thermal losses of the solar field, heat storage tanks and turbines. It also affects the electrical demand and it may affect the prices of electrical energy. Low ambient temperature increases the need for heating and, therefore, demand. Similarly, high ambient temperature increases the need for air cooling and, therefore, electrical energy demand and energy prices. Ambient temperature predictions can be obtained from weather forecasting institutions and corrected for by using current measured variables for short term predictions, as done in the case of solar irradiance in Chap. 2.Read More
Direct solar irradiance is the most important variable in thermal solar power plants, as the electrical energy generated depends basically on it. Different models for solar irradiance prediction and forecasting have been explained and developed in Chap. 2, for both short and long term forecasting.Read More
Planning and control of solar plants calls for dynamical models of their main parts. This requires modeling the following elements: the solar field (Chap. 4), the thermal storage system and the power conversion system (PCS). A prediction model of the solar radiation (Chap. 2), electrical tariffs and electrical demands are also needed for optimal operation planning. Important variables to be considered are the temperature of the inlet HTF (TinTur) and of the outlet HTF (ToutTur) of the power block, as well as the HTF flow (qT) going from the storage system to the PCS. Other variables with an economic profile in the objective function appear, such as operational cost Cop, or electrical selling price tariff e1, or net benefits, B.
Solar radiation, ambient temperature and electricity selling p...Read More
The price of electrical energy is needed to determine the best operating modes and hourly electrical production. This changes with demand. Prediction models for electrical demand are useful for decision making and for the design of hierarchical control schemes capable of optimizing electrical production, being necessary to model all plant subsystems; that is, the solar collector field, the thermal storage system and the power conversion system (PCS). Electrical demand may change substantially depending on the date and environmental conditions.
Since optimal scheduling of solar power plants depends greatly on predictions about solar radiation and other weather conditions, optimal scheduling windows for this type of plant are limited by the realistic predicting windows of weather condit...Read More
This chapter deals with the upper control level of solar power plants. Models for predicting solar irradiance and electrical loads, as well as models of the energy storage systems and power conversion systems, are needed to generate optimal operating modes and the corresponding set points, which are then sent to the lower level controllers.Read More
Using the hybrid model descriptions of the plant, different MPC control approaches can be applied, as those commented on in Chap. 6, Sect. 6.8.4. As an example, some of the results obtained using a hierarchical scheme  can be seen in Figs. 7.34 and 7.35. Figure 7.34 shows the operating modes throughout the day and the solar irradiance. It can be seen that the Hybrid MPC system decides the appropriate operating mode. Figure 7.35 shows the inlet and outlet temperatures at the generator. Notice that the oscillations observed are due to the on-off gas heater, the behavior is smooth when the solar field produces enough energy.
Different control strategies have been applied to the air conditioning plant and as an example, a robust controller based on the Иж mixed sensitivity...Read More
Every model developed in this work has been individually tested and validated with data from the real system, but only the full hybrid model will be presented here to save space. Two experiments of 400 and 150 min are presented. The latter experiment is so short because it has been prepared to force changes in the operation modes of the plant.
The first validation test uses real data collected on 28/08/08. Figures 7.32(a) and 7.32(b) show the environment conditions for the validation test and the system volumetric flows, respectively. Figures 7.32(c) and 7.32(d) show the real system and simulated output temperature and the operation mode, respectively.
The experiment started when the system output temperature was close to the operating point...Read More
A Discrete Hybrid Automata (DHA) is a connection of a finite state machine (FSM) and a switched affine system (SAS) through a mode selector (MS) and an event generator (EG) .
A set of linear dynamic systems defining the cooling system has been integrated into a state-space model. Their interconnection has been defined as a set of states, defined as an automaton (see Fig. 7.31) and the transition among these states has been defined according to a set of logical rules according to Table 7.5. This allows a DHA for the system to be defined. From this representation, an abstract representation in a set of constrained linear difference equations involving mixed-integer and continuous variables may be found that yield the equivalent Mixed Logical Dynamical (MLD) model...Read More