Optimal Control Applied to Distributed Solar Collector Fields

[EN] This work describes the optimal control of a parabolic trough solar plant when the solar radiation is subject to variations due to the passage of clouds. The objective of the control strategies developed is to optimize the generated power, unlike other strategies that pursue the maintenance of the outlet oil temperature of the solar field. The solar plant model developed includes the solar field with all its loops, as well as the power generation system and the storage system. It also models the passage of clouds with dfferent sizes of coverage of the solar field. Dierent control strategies are developed to maximize the power generated and at the same time to try to produce that power as long as possible and with the smallest variations.[ES] En este trabajo se describe el control de una planta solar de colectores cilíndrico parabólicos cuando la radiación solar está sometida a variaciones debidas al paso de nubes. El objetivo de las estrategias de control desarrolladas es optimizar la potencia generada, a diferencia de otras estrategias que persiguen el mantenimiento de la temperatura de salida del campo solar. El modelo desarrollado de la planta solar incluye tanto el campo solar con todos sus lazos, como el sistema de generación de potencia y el sistema de almacenamiento. Así mismo se modela el paso de las nubes con diferentes tamaños de cobertura del campo solar. Se desarrollan diferentes estrategias de control para maximizar la potencia generada y al mismo tiempo intentar producir dicha potencia el máximo de tiempo posible y con las menores variaciones.Este trabajo ha sido soportado por los proyectos DPI2013-44135-R y DPI2015-70973-R del Ministerio Español de Ciencia e Innovación.R. Rubio, F.; Navas, SJ.; Ollero, P.; Lemos, JM.; Ortega, MG. (2018). Control Óptimo Aplicado a Campos de Colectores Solares Distribuidos. Revista Iberoamericana de Automática e Informática industrial. 15(3):327-338. https://doi.org/10.4995/riai.2018.8944OJS327338153Abutayeh, M., Alazzam, A. and El-Khasawneh, B. 2014. Balancing heat transfer fluid flow in solar fields. Solar Energy, 105, 381-389. https://doi.org/10.1016/j.solener.2014.03.025Barão, M., Lemos, J. and Silva, R. 2002. Reduced complexity adaptive nonlinear control of a distributed collector solar field. Journal of Process Control, 12-1, 131-141.Camacho, E.F., Rubio, F.R. and Gutierrez, J.A. 1988. Modelling and Simulation of a Solar Power Plant with a Distributed Collectors System. Power Systems, Modelling and Control Applications. pp 11.3.1-11.3.5, Federation IBRA-BIRA, Bruselas.Camacho, E.F., Berenguel, M. and Rubio, F.R. 1997. Advanced control of solar plants. Springer-Verlag, London. https://doi.org/10.1007/978-1-4471-0981-5Camacho, E.F., Rubio, F.R., Berenguel, M. and Valenzuela, L. 2007. A survey on control schemes for distributed solar collector fields. Part I: Modeling and basic control approaches. Solar Energy, 81-10, 1240-1251.Camacho, E.F., Rubio, F.R., Berenguel, M. and Valenzuela, L. 2007. A survey on control schemes for distributed solar collector fields. Part II: Advanced control approaches. Solar Energy, 81-10, 1252-1272.Camacho, E.F., Berenguel, M., Rubio, F.R. and Martínez, D. 2012. Control of solar energy systems. Springer-Verlag, London. https://doi.org/10.1007/978-0-85729-916-1Camacho, E.F. and Gallego, A. 2013. Optimal operation in solar trough plants :A case study. Solar Energy, 95, 106-117. https://doi.org/10.1016/j.solener.2013.05.029Carmona, R., 1985. Análisis, modelado y control de un campo de colectores solares distribuidos con sistema de seguimiento en eje. Ph.D. Thesis.Cirre, C., Berenguel, M., Valenzuela, L. and Camacho, E.F. 2007. Feedback linearization control for a distributed solar collector field. Control Engineering Practice, 15-12, 1533-1544.Cirre, C., Berenguel, M., Valenzuela, L. and Klempous, R. 2009. Reference governor optimization and control of a distributed solar collector field. European Journal of Operational Research, 193, 709-717. https://doi.org/10.1016/j.ejor.2007.05.056Colmenar-Santos, A., Munuera-Perez, F., Tawfik, M. and Castro-Gil, M. 2014.A simple method for studying the effect of scattering of the performance parameters of parabolic trough collectors on the control of a solar field. Solar Energy, 99, 215-230.G https://doi.org/10.1016/j.solener.2013.11.004Gallego, A. and Camacho, E.F. 2012. Estimation of effective solar irradiation using an unscented kalman filter in a parabolic-trough field. Solar Energy,86-12, 3512-3518.García, S. 2012. Guía técnica de la energía solar termoeléctrica Fenercom, Capítulo 1.Lemos, J.M. 2006. Adaptive control of distributed collector solar fields. International journal of systems science, Vol. 37-8, 523-533. https://doi.org/10.1080/00207720600783686Lemos, J.M., Neves-Silva, R. and Igreja, J.M., 2014. Adaptive control of solar energy collector systems. Springer-Verlag, London. https://doi.org/10.1007/978-3-319-06853-4Lima, D., Normey-Rico, J. and Santos, T. 2016. Temperature control in a solar collector field using filtered dynamic matrix control. ISA Transactions, 62, 39-49. https://doi.org/10.1016/j.isatra.2015.09.016Lippke, F. 1995. Simulation of the part-load behavior of a 30 MWe SEGS plant. Report No. SAND95-1293, SNL, Albuquerque, NM, USA.Manzolini, G., Giostri, A., Saccilotto, C., Silva, P. and Macchi, E. 2012. A numerical model for off-design performance prediction of parabolic trough based solar power plants. Journal of Solar Energy Engineering, Vol.134. https://doi.org/10.1115/1.4005105Meaburn, A. and Hughes, F.M. 1993. Resonance Characteristics of a Distributed Solar Collector Fields. Solar Energy, 51, 3, 215-221. https://doi.org/10.1016/0038-092X(93)90099-AMontes, M., Abánades, A., Martínez-Val, J. and Valdés, M. 2009. Solar multiple optimization for a solar-only thermal power plant, using oil as heat transfer fluid in the parabolic trough collectors. Solar Energy, 83-12, 2165-2176. https://doi.org/10.1016/j.solener.2009.08.010Navas, S.J., Rubio, F.R., Ollero, P. and Ortega, M.G. 2016. Modeling and simulation of parabolic trough solar fields with partial radiation. XV European Control Conference, 31-36. https://doi.org/10.1109/ECC.2016.7810259Navas, S.J., Ollero, P. and Rubio, F.R. 2017. Optimum operating temperature of parabolic trough solar fields. Solar energy, 158, 295-302. https://doi.org/10.1016/j.solener.2017.09.022Price, H., Lupfert, E., Kearney, D., Zarza, E., Cohen, G., Gee, R. and Mahoney, R. 2002. Advances in parabolic trough solar power technology. Solar Energy, 124-2, 109-125.Romera, J.A. y Santos, M. 2017. ParaTrough v1.0: Librería en Modelica paraSimulación de Plantas Termosolares. Revista Iberoamericana de Automática e Informática Industrial (RIAI), Vol 14, 412-423. https://doi.org/10.1016/j.riai.2017.06.005Rubio, F.R., Camacho, E.F. and Berenguel, M. 2006. Control de campos de colectores solares. Revista Iberoamericana de Automática e Informática Industrial (RIAI), 3-4, 26-45.Shinskey, F. 1978. Energy conservation through control. Academic Press.Smith, R. 2005. Chemical process design and integration. Wiley.Stodola, A. 1945. Steam and gas turbines. Vol. 1, Peter Smith, New York

Control of Solar Power Systems: a survey

9th International Symposium on Dynamics and Controlof Process Systems (DYCOPS 2010)Leuven, Belgium, July 5-7, 20109This paper deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, its development status and then describes the main challenges encountered when controlling solar power systems.Ministerio de Ciencia y Tecnología DPI2008-05818Ministerio de Ciencia y Tecnología DPI2007-66718-C04-04Junta de Andalucía P07-TEP-0272

A Robust Adaptive Dead-Time Compensator with Application to A Solar Collector Field

This paper describes an easy-to-use PI controller with dead-time compensation that presents robust behaviour and can be applied to plants with variable dead-time. The formulation is based on an adaptive Smith predictor structure plus the addition of a filter acting on the error between the output and its prediction in order to improve robustness. The implementation of the control law is straightforward, and the filter needs no adjustment, since it is directly related to the plant dead-time. An application to an experimentally validated nonlinear model of a solar plant shows that this controller can improve the performance of classical PID controllers without the need of complex calculations.Ministerio de Ciencia y Tecnología TAP95-37

Adaptive control of a solar furnace for material testing

IFAC Adaptive Systems in Control and Signal Processing. Glasgow. Scotland. UK. 26/08/1998This paper presents an adaptive control system for controlling the temperature of a solar furnace, which is a high solar concentrating facility made up of heliostats tracking the sun and reflecting solar radiation onto a static parabolic concentrating system at the focal spot of which a high percentage of the solar energy collected by the collector system is concentrated in a small area. A large attenuator (shutter) placed between the collector system and the concentrator serves to control the amount of solar energy used for heating the samples placed at the focal spot. The paper shows the results obtained in the application of adaptive PI controllers to a solar furnace, incorporating feedforward action, anti-windup and slew rate constraint handling mechanisms

Mathematical Modeling of the Mojave Solar Plants

Competitiveness of solar energy is one of current main research topics. Overall efficiency of solar plants can be improved by using advanced control strategies. To design and tuning properly advanced control strategies, a mathematical model of the plant is needed. The model has to fulfill two important points: (1) It has to reproduce accurately the dynamics of the real system; and (2) since the model is used to test advanced control strategies, its computational burden has to be as low as possible. This trade-off is essential to optimize the tuning process of the controller and minimize the commissioning time. In this paper, the modeling of the large-scale commercial solar trough plants Mojave Beta and Mojave Alpha is presented. These two models were used to test advanced control strategies to operate the plants.Comisión Europea OCONTSOLAR 78905

Model Predictive Control Based on Deep Learning for Solar Parabolic-Trough Plants

En la actualidad, cada vez es mayor el interés por utilizar energías renovables, entre las que se encuentra la energía solar. Las plantas de colectores cilindro-parabólicos son un tipo de planta termosolar donde se hace incidir la radiación del Sol en unos tubos mediante el uso de unos espejos con forma de parábola. En el interior de estos tubos circula un fluido, generalmente aceite o agua, que se calienta para generar vapor y hacer girar una turbina, produciendo energía eléctrica. Uno de los métodos más utilizados para manejar estas plantas es el control predictivo basado en modelo (model predictive control, MPC), cuyo funcionamiento consiste en obtener las señales de control óptimas que se enviarán a la planta basándose en el uso de un modelo de la misma. Este método permite predecir el estado que adoptará el sistema según la estrategia de control escogida a lo largo de un horizonte de tiempo. El MPC tiene como desventaja un gran coste computacional asociado a la resolución de un problema de optimización en cada instante de muestreo. Esto dificulta su implementación en plantas comerciales y de gran tamaño, por lo que, actualmente, uno de los principales retos es la disminución de estos tiempos de cálculo, ya sea tecnológicamente o mediante el uso de técnicas subóptimas que simplifiquen el problema. En este proyecto, se propone el uso de redes neuronales que aprendan offline de la salida proporcionada por un controlador predictivo para luego poder aproximarla. Se han entrenado diferentes redes neuronales utilizando un conjunto de datos de 30 días de simulación y modificando el número de entradas. Los resultados muestran que las redes neuronales son capaces de proporcionar prácticamente la misma potencia que el MPC con variaciones más suaves de la salida y muy bajas violaciones de las restricciones, incluso disminuyendo el número de entradas. El trabajo desarrollado se ha publicado en Renewable Energy, una revista del primer cuartil en Green & sustainable science & technology y Energy and fuels.Nowadays, there is an increasing interest in using renewable energy sources, including solar energy. Parabolic trough plants are a type of solar thermal power plant in which solar radiation is reflected onto tubes with parabolic mirrors. Inside these tubes circulates a fluid, usually oil or water, which is heated to generate steam and turn a turbine to produce electricity. One of the most widely used methods to control these plants is model predictive control (MPC), which obtains the optimal control signals to send to the plant based on the use of a model. This method makes it possible to predict its future state according to the chosen control strategy over a time horizon. The MPC has the disadvantage of a significant computational cost associated with resolving an optimization problem at each sampling time. This makes it challenging to implement in commercial and large plants, so currently, one of the main challenges is to reduce these computational times, either technologically or by using suboptimal techniques that simplify the problem. This project proposes the use of neural networks that learn offline from the output provided by a predictive controller to then approximate it. Different neural networks have been trained using a 30-day simulation dataset and modifying the number of irradiance and temperature inputs. The results show that the neural networks can provide practically the same power as the MPC with smoother variations of the output and very low violations of the constraints, even when decreasing the number of inputs. The work has been published in Renewable Energy, a first quartile journal in Green & sustainable science & technology and Energy and fuels.Universidad de Sevilla. Máster en Ingeniería Industria

Experience of a predictive adaptive controller on pilot and industrial plants with transport phenomena

The existing experience on using MUSMAR, a predictive adaptive controller, on industrial and large scale pilot plants with transport phenomena is discussed. The processes to control have been selected because their dynamics depends not only on time, but also on space, being therefore described by partial differential equations, and implying increase difficulties for the controller. Case studies on an industrial boiler, an arc-welding machine, a distributed collector solar field and a water distribution canal are used to illustrate the main difficulties and the corresponding solutions when using MUSMAR. These include plant model uncertainty and start-up adaptation transients, large and uncertain plant i/o transport delay, existence of un-modelled dynamics, closed-loop response shaping and constraints. The emphasis of the presentation is on the practical impact of the theoretical properties of the MUSMAR algorithm and on their illustration by means of actual experiments on the real processes mentioned above

A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

This article shows how coalitional model predictive control (MPC) can be used to maximize thermal power of large-scale solar parabolic-trough plants. This strategy dynamically generates clusters of loops of collectors according to a given criterion, thus dividing the plant into loosely coupled subsystems that are locally controlled by their corresponding loop valves to gain performance and speed up the computation of control inputs. The proposed strategy is assessed with decentralized and centralized MPC in two simulated solar parabolic-trough fields. Finally, results regarding scalability are also given using these case studies