Predictive control of a solar air conditioning plant with simultaneous identification

This paper presents the application of a predictive controller with simultaneous identification to a solar air conditioning plant. The time varying nature of the process makes necessary an adjustment of the controller parameters to the varying operational conditions. The main novelty with respect to classic adaptive MPC scheme is to penalize the identification error in the cost function used for control. The behaviour of the controller is illustrated by simulations and experimental results. The integration of identification and control avoids the tedious identification procedure that is necessary before the start-up of any predictive controller. This new adaptive MPC scheme shows its effectiveness in controlling the outlet temperature in the solar thermal plant.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0

Time scaling internal state predictive control of a solar plant

The control of a distributed collector solar field is addressed in this work, exploiting the plant's transport characteristic. The plant is modeled by a hyperbolic type partial differential equation (PDE) where the transport speed is the manipulated flow, i.e. the controller output. The model has an external distributed source, which is the solar radiation captured along the collector, approximated to depend only of time. From the solution of the PDE, a linear discrete state space model is obtained by using time-scaling and the redefinition of the control input. This method allows overcoming the dependency of the time constants with the operating point. A model-based predictive adaptive controller is derived with the internal temperature distribution estimated with a state observer. Experimental results at the solar power plant are presented, illustrating the advantages of the approach under consideration

Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Predictive analytics play an important role in the management of decentralised energy systems. Prediction models of uncontrolled variables (e.g., renewable energy sources generation, building energy consumption) are required to optimally manage electrical and thermal grids, making informed decisions and for fault detection and diagnosis. The paper presents a comprehensive study to compare tree-based ensemble machine learning models (random forest – RF and extra trees – ET), decision trees (DT) and support vector regression (SVR) to predict the useful hourly energy from a solar thermal collector system. The developed models were compared based on their generalisation ability (stability), accuracy and computational cost. It was found that RF and ET have comparable predictive power and are equally applicable for predicting useful solar thermal energy (USTE), with root mean square error (RMSE) values of 6.86 and 7.12 on the testing dataset, respectively. Amongst the studied algorithms, DT is the most computationally efficient method as it requires significantly less training time. However, it is less accurate (RMSE = 8.76) than RF and ET. The training time of SVR was 1287.80 ms, which was approximately three times higher than the ET training time

Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand

Advanced control strategies for optimal operation of a combined solar and heat pump system

The UK domestic sector accounts for more than a quarter of total energy use. This energy use can be reduced through more efficient building operations. The energy efficiency can be improved through better control of heating in houses, which account for a large portion of total energy consumption. The energy consumption can be lowered by using renewable energy systems, which will also help the UK government to meet its targets towards reduction in carbon emissions and generation of clean energy. Building control has gained considerable interest from researchers and much improved ways of control strategies for heating and hot water systems have been investigated. This intensified research is because heating systems represent a significant share of our primary energy consumption to meet thermal comfort and indoor air quality criteria. Advances in computing control and research in advanced control theory have made it possible to implement advanced controllers in building control applications. Heating control system is a difficult problem because of the non-linearities in the system and the wide range of operating conditions under which the system must function. A model of a two zone building was developed in this research to assess the performance of different control strategies. Two conventional (On-Off and proportional integral controllers) and one advanced control strategies (model predictive controller) were applied to a solar heating system combined with a heat pump. The building was modelled by using a lumped approach and different methods were deployed to obtain a suitable model for an air source heat pump. The control objectives were to reduce electricity costs by optimizing the operation of the heat pump, integrating the available solar energy, shifting electricity consumption to the cheaper night-time tariff and providing better thermal comfort to the occupants. Different climatic conditions were simulated to test the mentioned controllers. Both on-off and PI controllers were able to maintain the tank and room temperatures to the desired set-point temperatures however they did not make use of night-time electricity. PI controller and Model Predictive Controller (MPC) based on thermal comfort are developed in this thesis. Predicted mean vote (PMV) was used for controlling purposes and it was modelled by using room air and radiant temperatures as the varying parameters while assuming other parameters as constants. The MPC dealt well with the disturbances and occupancy patterns. Heat energy was also stored into the fabric by using lower night-time electricity tariffs. This research also investigated the issue of model mismatch and its effect on the prediction results of MPC. MPC performed well when there was no mismatch in the MPC model and simulation model but it struggled when there was a mismatch. A genetic algorithm (GA) known as a non-dominated sorting genetic algorithm (NSGA II) was used to solve two different objective functions, and the mixed objective from the application domain led to slightly superior results. Overall results showed that the MPC performed best by providing better thermal comfort, consuming less electric energy and making better use of cheap night-time electricity by load shifting and storing heat energy in the heating tank. The energy cost was reduced after using the model predictive controller

Control of Solar Thermal Linear Fresnel Collector Plants in Single Phase and Direct Steam Generation Modes

Ein wesentlicher Unterschied erneuerbarer Energiequellen zu fossilen Energieträgern liegt in der zeitlichen Verfügbarkeit. Im Gegensatz zu fossil angetriebenen Prozessen lässt sich die Energiezufuhr nicht regeln. Dies stellt vor allem die Solarenergie vor eine wesentliche Herausforderung. Die Sonne als Energiequelle unterliegt nicht nur tages bzw. jahreszeitlichen Schwankungen, sie ist oft auch nicht vorhersehbar. Dieser scheinbar kleine Unterschied zur fossilen Energiegewinnung, hat zu weitreichenden Forschungsak tivitäten in verschiedenen Bereichen der Wissenschaft und des Ingenieurwesens geführt, darunter die Entwicklung von Speichertechnologien und hybrider Systeme, die Abbildung des dynamischen Verhaltens, insbesondere im Teillastbetrieb und nicht zu Letzt die entsprechende Ausarbeitung verschiedener Regelsysteme. Das Hauptziel der vorliegenden Arbeit ist die Entwicklung geeigneter Regelgesetze für solarthermische Anlagen um der intermittierenden Natur der Sonne als Energieressource gerecht zu werden. Eine gute Regelung erhöht den Solarertrag, reduziert Stillstände, bietet eine optimierte Wahl des Betriebspunktes und garantiert die Stabilität und Robustheit des Systems, mit der Personen und Anlagensicherheit als oberste Priorität. Die Thesis gliedert sich in zwei Hauptteile. Der erste Teil beinhaltet die Regelungstechnik konzentrierender, solarthermischer Systeme mit einphasigen Strömungen. Als wesentliche Regelgröße dient die Kollektoraustritttemperatur. Mehrere Regelsysteme wurden unter verschiedenen Rahmenbedingungen und Prozessanforderungen experimentell getestet. Eine erweiterte PID-Regelung mit geeigneten Vorsteuerungsschleifen zeigt sehr gute Resultate, zudem lässt sich die Gestaltung und Umsetzung des Regelsystems einfach halten. Der zweite Teil der Arbeit widmet sich dem Regelsystem im solaren Direktverdampfungsbetrieb. Hierbei ist die Hauptregelgröße der benötigte Dampfdruck an der Prozessschnittstelle. Im Direktverdampfungsbetrieb wird die Anlage als Mehrgrößenregelsystem betrachtet. Zwei Regelkonzepte wurden mit der vorliegenden Arbeit entwickelt und getestet. Eines der Konzepte basiert auf einer erweiterten PID-Regelung mit Vorsteuerung. Dieses wurde erfolgreich umgesetzt und unter Realbedingungen über mehrere Jahre intensiv getestet. Das zweite Konzept stützt sich auf die Theorie der modellbasierten, Modellprädiktive Regelung (MPC). Hierbei handelt es sich um ein komplexes, entwicklungsintensives, Regelkonzept. Der wesentliche Vorteil ist die universelle Anwendbarkeit, mit reduziertem Aufwand für Implementierung, Bewirtschaftung und Inbetriebnahme. Die vorliegende Thesis zeigt, dass sich trotz der komplexen Dynamik solarthermischer Grossanlagen mit den vorgeschlagenen Regelkonzepten eine zuverlässige, robuste und wartungsarme Energieversorgung bewerkstelligen lässt