Nuevas estrategias de planificación de la producción en plantas termosolares con almacenamiento térmico

En respuesta a los problemas energéticos actuales, la generación eléctrica basada en energía renovable intermitente, como la energía solar y eólica, ha crecido significativamente durante los últimos años gracias a la disminución de costes. Este tipo de generación presenta un carácter intermitente, variable y de difícil predicción, lo que dificulta su integración en la red eléctrica. Sin embargo, la energía termosolar posee ciertas características que pueden compensar en parte las desventajas anteriores. Esta tecnología captura la radiación solar en forma de energía térmica por medio del calentamiento de un fluido, para más tarde convertirla en electricidad.Por el hecho de emplear energía térmica como forma de energía intermedia, se complementa muy bien con sistemas de almacenamientotérmico. Gracias a este almacenamientoenergético, este tipo de plantas presenta cierto grado de gestionabilidad, existiendo la posibilidad de regular la producción. Esta propiedad favorece su participación en el mercado eléctrico, donde el objetivo del productor de electricidad es maximizar los beneficios económicos derivados de la venta de electricidad. Este objetivo puede lograrse cuando se planifica la producción en función del perfil de precios de venta de la electricidad. Puede plantear se, por tanto, un problema de planificación óptima de la producción. Considerando sólo el mercado diario de la electricidad, la resolución de este problema de optimización permite obtener el plan diario de generación, que debe enviarse al mercado normalmente el día anterior. Se deduce de lo anterior que en el caso de plantas renovables, además de la necesidad de disponer de una predicción de los precios de la electricidad, se requiere una prediccióndel recurso natural para poder abordar el problema. Los objetivos principales de esta Tesis son el diseño de nuevas estrategias de planificaciónóptima de la producción para una planta termosolar con almacenamiento térmico, y el estudio mediante simulación del rendimiento económico de cada estrategia cuando se considera la participación de la planta en el mercado diario de la electricidad. La planificación óptima se obtiene empleando programación lineal entera mixta, que es la herramienta matemática más usada para resolver este tipo de problemas. La primera estrategia realiza una replanificación horaria de la producción, considerando los ingresos derivados de la venta de electricidad en un determinado horizonte temporal y las posibles penalizaciones por desvíos respecto al plan de generación ya comprometido. Esta estrategia permite introducir en el problema la nueva información disponible cada hora, abordando de esta manera la incertidumbre presente en las predicciones y en el propio modelado del problema. La segunda estrategia incluye un mecanismo que penaliza los cambios en la producción. Además, este mecanismo penaliza de manera diferente las variaciones según el estado del bloque de potencia: operación normal, arranque y parada. De esta manera se consigue aumentar el número de grados de libertad del problema en busca de mejores soluciones. Esta reducción de la variabilidad en la generación tiene como ventajas una extensión en la vida útil de los elementos del bloque de potencia, una reducción de sus costes de mantenimiento y una simplificación de la operación. Se propon e una metodoloqía para estimar el máximo nivel de penalización de las variaciones que no perjudique el rendimiento económico. Finalmente, se desarrolló otra estrategia que combina la replanificación horaria con la penalización de las variaciones. El impacto económico de las estrategias anteriores se ha evaluado mediante simulaciones sobre una planta de 50 MW de tipo cilindro parabólico. Se han empleado datos realistas para elrecurso solar, los precios de la electricidad, los costes de penalización y las predicciones de todos estos datos. Los resultados confirman las mejoras esperadas en cada una de las estrategias.In response to current energy problems, electricity generatiOn based on intermittent renewable energy, such as solar and wind energy, has grown significantly in recent years thanks to the decrease in costs. This type of generation has an intermittent, variable and difficult prediction character, which makes it difficult to integrate into the electricity grid. However, solar thermal energy has certain characteristics that can partially compensate for the above disadvantages. This technology captures solar radiation in the form of thermal energy by heating a fluid, to later convert it into electricity. By using thermal energy as a form of intermediate energy, this technology is complemented very well with thermal energy storage systems. Thanks to this energy storage system, this type of plants has a certain degree of dispatchability, with the possibility of regulating production. This property favors its participation in the electricity market, where the objective of the electricity producer is to maximize the economic benefits derived from the sale of electricity. This objective can be achieved when production is scheduled based on the electricity sales price profile. Therefore, an optimal generation scheduling problem may arise. Considering only theday-ahead energy market, the resolution of this optimization problem allows obtaining the daily generation schedule, which must be sent to the market normally the day befare. It follows from the above that in the case of renewable plants, in addition to the need to have a prediction of electricity prices, a forecast of the natural resource is required to address the problem. The main objectíves of this Thesis are the design of new strategies for optímal generation scheduling applied to a concentrating solar power plant, and the study by simulation of the economic performance of each strategy when considering the participation of the plant in the day-ahead energy market. The optimal generation schedule is obtained using mixed integer linear programming, which is the most used mathematical tool to solve these types of problems. The first strategy performs an hourly rescheduling of the generation, considering the revenues derived from the sale of electricity in a certain time horizon and the possible penalties for deviatíons from the generation schedule already committed. This strategy allows to include into the problem the new information available every hour, thus addressing the uncertainty present in the predictions and in the modeling of the problem itself. The second strategy includes a mechanism that penalizes changes in generation. In addition, this mechanism penalizes variations differently according to the state of the power block: normal operation, startup and shutdown. In this way, it is possible to increase the number of degrees of freedom of the problem in seárch of better solutions. This reduction of the variability in the generation has as advantages an extension in the lifetime of the elements of the power block, a reduction of its maintenance costs andan easier plant operability. A methodology is proposed to estimate the maximum level of penalty for variations that does not affect economic performance. Finally, another strategy was developed that combines hourly rescheduling with the penalization of variations. The economic impact of the above strategies has been evaluated through simulations on a 50 MW parabolic trough collector plant. Realistic data for the solar resource, electricity prices, penalty costs and predictions of all these data have been used. The results confirm the expected improvements in each of the strategies

Experimental operation of a solar-driven climate system with thermal energy storages using mixed-integer nonlinear model predictive control

This work presents the results of experimental operation of a solar-driven climate system using mixed-integer nonlinear model predictive control (MPC). The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as well as a cold water storage. The system and the applied modeling approach is described and a parallelized algorithm for mixed-integer nonlinear MPC and a corresponding implementation for the system are presented. Finally, we show and discuss the results of experimental operation of the system and highlight the advantages of the mixed-integer nonlinear MPC application

Advances in the Optimization of Energy Systems and Machine Learning Hyperparameters

Intensifying public concern about climate change risks has accelerated the push for more tangible action in the transition toward low-carbon or carbon-neutral energy. Concurrently, the energy industry is also undergoing a digital transformation with the explosion in available data and computational power. To address these challenges, systematic decision-making strategies are necessary to analyze the vast array of technology options and information sources while navigating this energy transition. In this work, mathematical optimization is utilized to answer some of the outstanding issues around designing cleaner processes from resources such as natural gas and renewables, operating the logistics of these energy systems, and statistical modeling from data. First, exploiting natural gas to produce lower emission liquid transportation fuels is investigated through an optimization-based process synthesis. This extends previous studies by incorporating chemical looping as an alternative syngas production method for the first time. Second, a similar process synthesis approach is implemented for the optimal design of a novel biomass-based process that coproduces ammonia and methanol, improving their production flexibility and profit margins. Next, operational difficulties with solar and wind energies due to their temporal intermittency and uneven geographical distribution are tackled with a supply chain optimization model and a clustering decomposition algorithm. The former describes power generation through energy carriers (hydrogen-rich chemicals) connecting resource-dense rural areas to resource-deficient urban centers. Results show the potential of energy carriers for long-term storage. The latter is developed to identify the appropriate number of representative time periods for approximating an optimization problem with time series data, instead of using a full time horizon. This algorithm is applied to the simultaneous design and scheduling of a renewable power system with battery storage. Finally, building machine learning models from data is commonly performed through k-fold cross-validation. From recasting this as a bilevel optimization, the exact solution to hyperparameter optimization is obtainable through parametric programming for machine learning models that are LP/QP. This extends previous results in statistics to a broader class of machine learning models

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Topology optimization for energy problems

The optimal design of energy systems is a challenge due to the large design space and the complexity of the tightly-coupled multi-physics phenomena involved. Standard design methods consider a reduced design space, which heavily constrains the final geometry, suppressing the emergence of design trends. On the other hand, advanced design methods are often applied to academic examples with reduced physics complexity that seldom provide guidelines for real-world applications. This dissertation offers a systematic framework for the optimal design of energy systems by coupling detailed physical analysis and topology optimization. Contributions entail both method-related and application-oriented innovations. The method-related advances stem from the modification of topology optimization approaches in order to make practical improvements to selected energy systems. We develop optimization models that respond to realistic design needs, analysis models that consider full physics complexity and design models that allow dramatic design changes, avoiding convergence to unsatisfactory local minima and retaining analysis stability. The application-oriented advances comprise the identification of novel optimized geometries that largely outperform industrial solutions. A thorough analysis of these configurations gives insights into the relationship between design and physics, revealing unexplored design trends and suggesting useful guidelines for practitioners. Three different problems along the energy chain are tackled. The first one concerns thermal storage with latent heat units. The topology of mono-scale and multi-scale conducting structures is optimized using both density-based and level-set descriptions. The system response is predicted through a transient conjugate heat transfer model that accounts for phase change and natural convection. The optimization results yield a large acceleration of charge and discharge dynamics through three-dimensional geometries, specific convective features and optimized assemblies of periodic cellular materials. The second problem regards energy distribution with district heating networks. A fully deterministic robust design model and an adjoint-based control model are proposed, both coupled to a thermal and fluid-dynamic analysis framework constructed using a graph representation of the network. The numerical results demonstrate an increased resilience of the infrastructure thanks to particular connectivity layouts and its rapidity in handling mechanical failures. Finally, energy conversion with proton exchange membrane fuel cells is considered. An analysis model is developed that considers fluid flow, chemical species transport and electrochemistry and accounts for geometry modifications through a density-based description. The optimization results consist of intricate flow field layouts that promote both the efficiency and durability of the cell

Feature Papers of Forecasting

Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented