Search

This work develops digital entities of a commercial Fresnel Solar Collector (FSC) installed in an absorption cooling plant. The objective is to create and validate models that describe the FSC dynamics across its whole operation range during the day and the night. Thus, the temperatures range between operation temperature of 180° C and almost ambient temperature due to overnight heat losses. In the same sense, the flow range between zero to 13m³/h . The idea is that the digital twin will aid start-up and shut-down optimization and control design reliability. The paper employs two modeling approaches, then evaluates their twinning/adaptation time and performance validation. One model uses phenomenological modeling through Partial Differential Equations (PDE) and parameters identification, and another uses a data-driven technique with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The available measurement data sets comprise 25 days of operation with a sampling time of 20 s which, after outlier removal, filtering and treatment, resulted in 108416 samples. The validation considers six separate operating days. Results show that both models can twinning/adapt considering measured data. The models present pretty good results and are suitable for control and optimization. Besides, this is the first paper considering the FSC mirror defocus action on dynamic modeling and validation.

Concentrating Solar Power systems are widely applied as a means to utilize the sun's abundant renewable energy, but the design of these processes facilitates the occurrence of overheating. This work presents two approaches for calculating the mirror angles of a fresnel collector in order to limit the amount of solar energy collected. The proposed structures are designed to receive a desired focus value from a controller and generate references for the mirror inclination controllers of the collector. The first strategy consists of the use of an optimization problem coupled with a simplified model of the collector. Whereas, the second approach consists of an ANFIS network that is trained with data from a reliable collector model made in SolTrace®. Both approaches are compared with simulations in SolTrace® the results show that the ANFIS solution presented overall better results, taking into account error and computational time.

The thematic unit of this thesis is the control of solar thermal concentrating plants. The work is divided into three parts. Part I develops neuro-fuzzy models to integrate the digital twins of the solar absorption plant built at the University of Seville intending further applications to optimization and control. The models dynamically describe the Fresnel solar absorption machine and the Fresnel concentrating collector. Both processes are complex and present challenges for phenomenological modeling. In addition, the measured and available plant data are incomplete and noisy. This study employs Adaptive Neuro-Fuzzy Inference Systems (ANFIS) because they can describe complex dynamic systems even with incomplete and noisy training data. The data for training and validation was massive, generating generalized models with relative errors lower than 3\% with continuous operation between day and night. In addition, Fresnel collector models are the first to dynamically describe the effect of the defocus of the collector mirrors on their output temperature. Part II presents the design of controllers in the regulatory layer considering both the proportional defocus of the mirrors and the flow as actuators for normal regulation of the output temperature. In the literature, the defocus is seen as the last control effort for the safety of the solar collector because it means a waste of solar energy. However, the author noticed that the defocus is necessary for the regular operation of solar concentration plants with solar multiple greater than one. In addition, controls that consider defocusing use computationally costly hybrid MPC controls or state machines. This dissertation proposes using the solar collector's solar tracking system not only to aim the sunbeam to match the position of the absorber tube but also to vary the focal point of solar irradiation proportionally. The basic idea is to manipulate the set of mirrors of the fresnel collector as if they were a parabolic m, La unidad temática de esta tesis es el control de plantas de concentración solar térmica. La obra se divide en tres partes. Parte I desarrolla modelos neuro adaptativos difusos para integrar los gemelos digitales de la planta de absorción solar construida en la Universidad de Sevilla para futuras aplicaciones de optimización y control. Los modelos desarrollados describen dinámicamente la máquina de absorción y el colector solar de concentración Fresnel. Ambos procesos son complejos y presentan desafíos para el modelado fenomenológico. Además, los datos medidos y disponibles de planta son incompletos y ruidosos. Por lo tanto, se utilizó Adaptive Neuro-Fuzzy Inference Systems (ANFIS) porque son capaces de describir sistemas dinámicos complejos incluso con datos de entrenamiento incompletos y ruidosos. La cantidad de datos para entrenamiento y validación fue masiva, generando modelos generalizados con errores relativos inferiores al 3% con operación continua entre el día y la noche. Además, los modelos del colector fresnel son novedosos porque describen dinámicamente el efecto del desenfoque de los espejos colectores en su temperatura de salida. Parte II presenta el diseño de los controladores en la capa reguladora considerando tanto el desenfoque proporcional de los espejos como el flujo como actuadores para la regulación normal de la temperatura de salida. En la literatura, el desenfoque se ve como un último esfuerzo de control para la seguridad del colector solar porque significa desperdicio de energía solar. Sin embargo, se notó que el desenfoque es necesario para el funcionamiento normal de las plantas de concentración solar con múltiplos solares mayores que uno. Además, los controles que consideran el desenfoque suelen utilizar controles MPC híbridos o máquinas de estado computacionalmente costosas. En esta tesis se propone utilizar el sistema de seguimiento solar del colector solar no solo para apuntar los rayos del sol para que coincidan con la posición del tub

Solar absorption plants are renewable energy systems with a special advantage: the cooling demand follows the solar energy source. The problem is that this plant presents solar intermittency, phenomenological complexity, and nonlinearities. That results in a challenge for control and energy management. In this context, this paper develops a Digital Twin of an absorption chiller High Temperature Generator (HTG) seeking accuracy and low computational efort for control and management purposes. A neuro-fuzzy technique is applied to describe HTG, internal Lithium-Bromide temperature, and water outlet temperature. Two Adaptative Neuro-Fuzzy Inference Systems (ANFIS) are trained considering real data of eight days of operation. Then, the obtained model is validated considering two days of real data. The validation shows a RMSE of 1.65e−2 for the internal normalized temperature, and 2.05e−2 for the outlet normalized temperature. Therefore, the obtained Digital Twin presents a good performance capturing the dynamics of the HTG with adaptive capabilities considering that each day can update the learning step.

This paper proposes the first application of a split-range control technique on a concentrating solar collector to improve an absorption plant production. Solar absorption plants have solar power availability in phase with cooling demand under design conditions. Thus, it is a powerful cooling technology in the context of renewable energy and energy efficiency. These plants need control systems to cope with solar irradiance intermittency, reject irradiation disturbances, manage fossil fuels backup systems and dump closed-loop thermal-hydraulic oscillations. In this work, control techniques are proposed and simulated in an absorption plant in Spain. The plant consists of a concentrating Fresnel solar collector connected to an absorption chiller. The objectives are to operate with 100% renewable solar energy and avoid safety defocus events while reducing temperature oscillations and control actuators effort. Firstly, the current available plant controllers are defined, then two modifications are proposed. The first modification is a split-range controller capable of manipulating both flow and defocus of the Fresnel collector, the second modification is a PI controller to substitute the original chiller on-off controller. The results compare, through validated models, the different control systems and indicate that using both proposed controllers reduces 94% of the sum of actuators effort and 43% of the integral of absolute set-point tracking error compared to the plant's factory pre-set controllers. The suggested controllers increase 66% of energy production and 63% of exergy production. Besides, the split-range technique can be extended to any concentrating solar collector control.

In this paper, we formalise a fault-tolerant control and mitigation strategy for the energy management of renewable microgrids. The scheme is composed of a Model Predictive Control (MPC) algorithm based on a Linear Parameter Varying (LPV) model for the microgrid energy dispatch. The MPC uses fault diagnosis information in order to correctly update the LPV microgrid model, while adapting the process constraints in consonance with the level and location of faults. We show how closed-loop stability is ensured with simple quadratic terminal ingredients, provided through LMI-solvable remedies. Nonlinear simulation results of a real microgrid benchmark are included to demonstrate the effectiveness of the approach.

In this paper, we address the problem of modeling error in economically optimal control. A single layer controller is proposed that integrates the economical part of the Real Time Optimization (RTO), the dynamic part of the Model Predictive Control (MPC) and the Modifier Adaptation strategy (MA), resulting in a controller with the following characteristics: a) recursive feasibility guarantee of the controller; b) asymptotic closed-loop stability for any change in the economic cost function; c) convergence guarantee to the economic optimum of the real plant (offset-free) for any change in the cost function of the controller; and d) simple implementation of the controller. We show the behaviour of the proposal by means of a motivating example that highlights the performance of the proposed algorithm

This work presents a process supervision and advanced control structure, based on Model Predictive Control (MPC) coupled with disturbance estimation techniques and a finite-state machine decision system, responsible for setting energy productions set-points. This control scheme is applied to energy generation optimization in a sugar cane power plant, with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as biomass. The energy plant is bound to produce steam in different pressures, cold water and, imperiously, has to produce and maintain an amount of electric power throughout each month, defined by contract rules with a local distribution network operator (DNO). The proposed predictive control structure uses feedforward compensation of estimated future disturbances, obtained by the Double Exponential Smoothing (DES) method. The control algorithm has the task of performing the management of which energy system to use, maximize the use of the renewable energy sources, manage the use of energy storage units and optimize energy generation due to contract rules, while aiming to maximize economic profits. Through simulation, the proposed system is compared to a MPC structure, with standard techniques, and shows improved behavior.