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

Testing and Modeling of the Indirect Solar Drying of Thin Film Mangos

In Haiti, 80% of rural people live in dire poverty living on less than a dollar a day. Cultivated and wild grown foods such as mangos and breadfruit could be used to reduce the economic and nutritional disparity for rural communities. Unfortunately in these areas there is a high amount of spoilage due to short harvesting seasons with high yields and lack of preservation options. 80% of breadfruit and 60% of mangoes are lost annually according to a local farmer’s co-op in Borgne, Haiti [1]. Based on a 2018 three-week collaborative design session with Rochester Institute of Technology (RIT) and the local women’s group SEE FANM (women for health, education, and economy), we identified mangos as a potential option for “transfòmayson fwi” - food transformation. Our team proposed drying mangos and then selling them as a juice powder in the off-season. Food preservation by solar drying has become a widespread practice in developing countries. Dryers use solar energy and other supplementary energy sources to heat air entering a drying chamber. Drying is a complex heat and mass transfer process that can take hours or days depending on the properties of the food such as ripeness and temperature and humidity of the drying air. Many studies have attempted to model the transport of moisture within fruits to predict drying performance while others have experimented with different styles of solar dryer designs. These systems are mostly tested outside in variable ambient conditions where the local temperature, relative humidity, and solar flux constantly fluctuate introducing a significant amount of noise . This “noise” is a product of these varying external conditions which affects the quality of the drying air and the performance of solar-thermal systems. Testing thermal systems in cold climates such as Rochester makes it impossible to predict performance in tropical regions like Haiti. This work focuses on eliminating many of these external factors in order to remove noise to provide faster and more repeatable testing. A testing system was designed and built to simulate the output of a solar collector in a tropical environment to explore the impact of the collector size and dryer volumetric flowrate on drying performance in a highly consistent and controlled manner. Testing demonstrated the importance of external conditions during the falling drying rate regime, where internal diffusion typically dominates drying performance. The results of these tests are used to empirically fit a bulk drying model for a shrinking fruit film that exemplifies the impact of external conditions. This model allows for the prediction of drying performance for the first 85% of moisture removed from a fruit film. A system model is also proposed that seeks to capture the deep layer effect associated with drying multiple stacks of fruit. By fitting the thin layer and system model to experimental data, a predictive model is proposed and used to explore design choices for the unglazed transpired solar collector and horizontal drying chamber used in this study. Personal experience with prototyping in Haiti led to the coupling of a simple preliminary economic model with the thin layer model to provide the predicted output per capital spent for this solar dryer design

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Automatic Control of a Parabolic Trough Solar Thermal Power Plant

This thesis is interested in improving the operation of a parabolic trough technology based solar thermal power plant by means of automatic control. One of the challenging issues in a solar thermal power plant, from the control point of view, is to maintain the thermal process variables close to their desired levels. In contrast to a conventional power plant where fuel is used as the manipulated variable, in a solar thermal power plant, solar radiation cannot be manipulated and in fact it ironically acts as a disturbance due to its change on a daily and seasonal basis. The research facility ACUREX is used as a test bed in this thesis. ACUREX is a typical parabolic trough technology based solar thermal power plant and belongs to the largest research centre in Europe for concentrating solar technologies, namely the Plataforma Solar de Almería (PSA) in south-east Spain. The plant exhibits nonlinearities as well as resonance characteristics that lie well within the desired control bandwidth. Failure to adequately capture the resonance characteristics of the plant results in an undesired oscillatory control performance. Moreover, measured disturbances are an integral part of the plant and while some of the measured disturbances do not have a significant impact on the operation of the plant, others do. Hence, with the aim of handling the plant nonlinearities and capturing the plant resonance characteristics, while taking explicit account of the measured disturbances, in this thesis a gain scheduling feedforward predictive control strategy is proposed. The control strategy is based upon a family of local linear time-invariant state space models that are estimated around a number of operating points. The locally estimated linear time-invariant state space models have the key novelty of being able to capture the resonance characteristics of the plant with the minimal number of states and hence, simple analysis and control design. Moreover, while simple classical, series and parallel, feedforward configurations have been proposed and used extensively in the literature to mitigate the impact of the measured disturbances of the ACUREX plant, the proposed control strategy incorporates a feedforward systematically by including the effects of the measured disturbances of the ACUREX plant into the predictions of future outputs. In addition, a target (set point) for a control strategy is normally set at the ACUREX plant by the plant operator. However, in this thesis it is argued that, in parallel, the operator must choose between potentially ambitious and perhaps unreachable targets and safer targets. Ambitious targets can lead to actuator saturation and safer targets imply electricity production losses. Hence, in this thesis a novel two-layer hierarchical control structure is proposed with the gain scheduling feedforward predictive control strategy being deployed in a lower layer and an adequate reachable reference temperature being generated from an upper layer. The generated reference temperature drives the plant near optimal operating conditions, while satisfying the plant safety constraints, without any help from the plant operator and without adding cost. The proposed two-layer hierarchical control strategy has the potential benefits of: (i) maximising electricity production; (ii) reducing the risk of actuator saturation; (iii) extending the life span of various elements of the plant (e.g. synthetic oil, pump and valves) and (iv) limiting the role of the plant operator. The efficacy of the proposed two-layer hierarchical control strategy is evaluated using a nonlinear simulation model that approximates the dynamic behaviour of the ACUREX plant. The nonlinear simulation model is constructed in this thesis and validated in the time and frequency domain

Integrated model concept for district energy management optimisation platforms

District heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.The research activities leading to the described developments and results, were funded by the European Uniońs Horizon 2020 MOEEBIUS project, under grant agreement No 680517. Authors would like to ex-press their gratitude to the operator of the Vozdovac district heating system (Beogradske elektrane) for the specifications used to develop and calibrate the models, and to Solintel M&P, SL for developing the initial versions of the EnergyPlus models (including only the geometrical and constructive definition of the buildings), in the framework of the MOEEBIUS project

Supervisory model predictive control of building integrated renewable and low carbon energy systems

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank

MPC-Bases energy mangement system for hybrid renewable energies

Los sistemas de suministro de energía están formados por un conjunto de subsistemas que pueden interconectarse a través de la disposición de actuadores. El proceso es un sistema dinámico híbrido multivariable que presenta varios modos de configuración necesarios para el funcionamiento diario. En esta tesis se propone un sistema de gestión de energía basado en teorías de control. La principal dificultad que presentan los sistemas de suministro está en su dinámica definida por un conjunto de ecuaciones diferenciales y expresiones lógicas, además del carácter variable de la energía producida por las fuentes renovables. Con el fin de satisfacer el suministro de energía, se considera el diseño de un controlador híbrido basado en las predicciones de energía estimadas a partir de modelos físicos y mediciones. El control predictivo (MPC) es elegido como la estrategia de control, ya que es capaz de manejar las variaciones en el suministro y demanda de energía.Departamento de Ingeniería de Sistemas y Automátic

Improved modelling of microgrid distributed energy resources with machine learning algorithms

Mención Internacional en el título de doctorRenewable energy technologies are being increasingly adopted in many countries around the world. However, the deployment of these power generation systems is becoming more diverse than ever, from small generation units in individual houses to massive power production plants. In that spectrum, distributed energy resources (DERs) cover systems from the low- to the middle-power ranges. The operation, control and assessment of these technologies is becoming more complex, and structures such as microgrids (MGs) may provide a suitable ecosystem to manage them. The beginning of this thesis covers the fundamentals of MG systems. A review was conducted by analysing the MG in a layer perspective, where each layer corresponded to a topic such as operation, business or standards, among others. The advancements in electronics, computer power and storage capability have created a paradigm in which massive amounts of data are generated and computed. The electrical sector has introduced many data acquisition technologies to assess the grid and its components. Classical modelling approaches have applied physical, chemical or electrical algorithms to model the behaviours of DERs. Nevertheless, with the extensive amount of information at our disposal, data-driven techniques such as machine learning (ML) may provide more individualised models to simulate the behaviour of these power generation technologies with the particularities of both their components and their location. Following the MG review, its power generation technologies were analysed. The information from 1,618 MGs around the world have been aggregated and studied. Also, two MG infrastructure model generators have been proposed (considering the infrastructure as the power generation technology and their rated power of an MG.). One of the models is based on the statistical data aggregated in tables and the other is based on ML techniques. The latter, which provides more particularised results, is able to generate the most typical MG infrastructure for a given location and segment of operation. Ideally, each of the DERs of a MG should be modelled, but, given the time constraints of a PhD, only the principal renewable generation technologies have been studied. Hence, ML models of photovoltaic (PV) systems plus a battery and wind energy conversion systems (WECS) have been proposed. Various ML models for PV systems were developed in two studies. First, an ML model for PV power estimation was performed using data from two real PV farms and validated using deterministic models from the literature. The ML algorithm was performed using neural networks and automatic strategies to clean the data. Neural network accuracy when trained and tested in the same location yields solid results which can be applied in performance ratio tools for PV power stations. In the second study, various mathematical models are proposed. This study provides several models for computing the annual optimum tilt angle for both fixed PV arrays and solar collectors. The optimum tilt algorithm proposed can be calculated in the absence of meteorological or software tools. To generate these models, data were collected from 2,551 sites across the world. A regression analysis with polynomial fitting, neural networks and decision trees was performed. Despite the better performance of the ML models, the ease of use of polynomial algorithms is recommended for those sites with no access to computational tools or meteorological data. The performances of the models were validated using previous research algorithms. Also, an ML algorithm was proposed to estimate the state of charge of a lithium-ion battery. The available capacity in a battery is an important feature when operating these kinds of systems. Given the complex behaviours of a battery, data-driven algorithms are able to capture the dynamic behaviours of a battery. Based on the data obtained in different experiments performed in a laboratory, an ensemble method, gradient boosting algorithm, was trained to model the state of charge of the battery. Even though the state of health of the storage system was below the theoretical life expectancy, the model was able to provide solid results. The model was validated with non-trained data. Finally, data-driven techniques were applied to model different elements of WECS. The first study provided two power coefficient algorithms, one based on polynomial fitting and another based on neural networks. To train the models, data from a corrected blade element momentum algorithm was used and three sets of data representing different wind turbine ranges, from 2 to 10 MW, were generated. Both models were validated with three datasets of real wind turbines and compared with the existing literature equations. Compared to previous equations, errors were reduced by at least 55% with the best numerical approximation from the literature. This type of reduction has a great impact for WECS dynamic and transient studies. The second study proposed for WECS develops three different ML models: one estimates the power of individual WECS, the second aggregates the data from all the WECS and estimates their power and the last one estimates the power of an entire wind farm. Given the stochastic and dynamic behaviours of the systems modelled, data pre-processing should be performed. Along with default cleaning techniques, a Student-t copula has been proposed so outliers can be automatically removed. Results show that the neural network algorithms’ performances for the three models can be improved without excessive manual intervention in the development process. Traditionally, electrical, physical and chemical models have been applied to mimic the behaviour of power systems. Now, with the power of computer and storage systems, a new era of more customised models has begun. It is time to review the existing models and provide better solutions by using ML techniques. In this thesis, only a few DERs have been modelled, but the results show that huge improvements can be made and future work in this subject should be done. The ML models proposed can be applied either as individual models for performance assessment of each DER or as a complementary tool to dynamic or static studies, unit commitment and planning software, among others.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Veganzones Nicolás.- Secretaria: María Ángeles Moreno López de Saa.- Vocal: Athanasios Kolio

JSC research and technology

The primary roles and missions of JSC incorporate all aspects of human presence in space. Therefore, the Center is involved in the development of technology that will allow humans to stay longer in Earth orbit, allow safe flight in space, and provide capabilities to explore the Moon and Mars. The Center's technology emphasis areas include human spacecraft development, human support systems and infrastructure, and human spacecraft operations. Safety and reliability are critical requirements for the technologies that JSC pursues for long-duration use in space. One of the objectives of technology development at the Center is to give employees the opportunity to enhance their technological expertise and project management skills by defining, designing, and developing projects that are vital to the Center's strategy for the future. This report is intended to communicate within and outside the Agency our research and technology (R&T) accomplishments, as well as inform Headquarters program managers and their constituents of the significant accomplishments that have promise for future Agency programs. While not inclusive of all R&T efforts, the report presents a comprehensive summary of JSC projects in which substantial progress was made in the 1992 fiscal year. At the beginning of each project description, names of the Principal Investigator (PI) and the Technical Monitor (TM) are given, followed by their JSC mail codes or their company or university affiliations. The funding sources and technology focal points are identified in the index