Optimal Combined Forecasts for Electricity Prices: Influence of Clean Energies

In this work, an electricity price forecasting model is developed. The performance of the proposed approach is improved by considering renewable energies (wind power and hydro generation) as explanatory variables. Additionally, the resulting forecasts are obtained as an optimal combination of a set of several univariate and multivariate time series models. The large computational experiment carried out using out-of-sample forecasts for every hour and day allows withdrawing statistically sound conclusions

A swarm intelligence approach based on coupled oscillators: an application in demand side management with photovoltaic distributed generation

Esta Tesis aborda los problemas de eficiencia de las redes eléctrica desde el punto de vista del consumo. En particular, dicha eficiencia es mejorada mediante el suavizado de la curva de consumo agregado. Este objetivo de suavizado de consumo implica dos grandes mejoras en el uso de las redes eléctricas: i) a corto plazo, un mejor uso de la infraestructura existente y ii) a largo plazo, la reducción de la infraestructura necesaria para suplir las mismas necesidades energéticas. Además, esta Tesis se enfrenta a un nuevo paradigma energético, donde la presencia de generación distribuida está muy extendida en las redes eléctricas, en particular, la generación fotovoltaica (FV). Este tipo de fuente energética afecta al funcionamiento de la red, incrementando su variabilidad. Esto implica que altas tasas de penetración de electricidad de origen fotovoltaico es perjudicial para la estabilidad de la red eléctrica. Esta Tesis trata de suavizar la curva de consumo agregado considerando esta fuente energética. Por lo tanto, no sólo se mejora la eficiencia de la red eléctrica, sino que también puede ser aumentada la penetración de electricidad de origen fotovoltaico en la red. Esta propuesta conlleva grandes beneficios en los campos económicos, social y ambiental. Las acciones que influyen en el modo en que los consumidores hacen uso de la electricidad con el objetivo producir un ahorro energético o un aumento de eficiencia son llamadas Gestión de la Demanda Eléctrica (GDE). Esta Tesis propone dos algoritmos de GDE diferentes para cumplir con el objetivo de suavizado de la curva de consumo agregado. La diferencia entre ambos algoritmos de GDE reside en el marco en el cual estos tienen lugar: el marco local y el marco de red. Dependiendo de este marco de GDE, el objetivo energético y la forma en la que se alcanza este objetivo son diferentes. En el marco local, el algoritmo de GDE sólo usa información local. Este no tiene en cuenta a otros consumidores o a la curva de consumo agregado de la red eléctrica. Aunque esta afirmación pueda diferir de la definición general de GDE, esta vuelve a tomar sentido en instalaciones locales equipadas con Recursos Energéticos Distribuidos (REDs). En este caso, la GDE está enfocada en la maximización del uso de la energía local, reduciéndose la dependencia con la red. El algoritmo de GDE propuesto mejora significativamente el auto-consumo del generador FV local. Experimentos simulados y reales muestran que el auto-consumo es una importante estrategia de gestión energética, reduciendo el transporte de electricidad y alentando al usuario a controlar su comportamiento energético. Sin embargo, a pesar de todas las ventajas del aumento de auto-consumo, éstas no contribuyen al suavizado del consumo agregado. Se han estudiado los efectos de las instalaciones locales en la red eléctrica cuando el algoritmo de GDE está enfocado en el aumento del auto-consumo. Este enfoque puede tener efectos no deseados, incrementando la variabilidad en el consumo agregado en vez de reducirlo. Este efecto se produce porque el algoritmo de GDE sólo considera variables locales en el marco local. Los resultados sugieren que se requiere una coordinación entre las instalaciones. A través de esta coordinación, el consumo debe ser modificado teniendo en cuenta otros elementos de la red y buscando el suavizado del consumo agregado. En el marco de la red, el algoritmo de GDE tiene en cuenta tanto información local como de la red eléctrica. En esta Tesis se ha desarrollado un algoritmo autoorganizado para controlar el consumo de la red eléctrica de manera distribuida. El objetivo de este algoritmo es el suavizado del consumo agregado, como en las implementaciones clásicas de GDE. El enfoque distribuido significa que la GDE se realiza desde el lado de los consumidores sin seguir órdenes directas emitidas por una entidad central. Por lo tanto, esta Tesis propone una estructura de gestión paralela en lugar de una jerárquica como en las redes eléctricas clásicas. Esto implica que se requiere un mecanismo de coordinación entre instalaciones. Esta Tesis pretende minimizar la cantidad de información necesaria para esta coordinación. Para lograr este objetivo, se han utilizado dos técnicas de coordinación colectiva: osciladores acoplados e inteligencia de enjambre. La combinación de estas técnicas para llevar a cabo la coordinación de un sistema con las características de la red eléctrica es en sí mismo un enfoque novedoso. Por lo tanto, este objetivo de coordinación no es sólo una contribución en el campo de la gestión energética, sino también en el campo de los sistemas colectivos. Los resultados muestran que el algoritmo de GDE propuesto reduce la diferencia entre máximos y mínimos de la red eléctrica en proporción a la cantidad de energía controlada por el algoritmo. Por lo tanto, conforme mayor es la cantidad de energía controlada por el algoritmo, mayor es la mejora de eficiencia en la red eléctrica. Además de las ventajas resultantes del suavizado del consumo agregado, otras ventajas surgen de la solución distribuida seguida en esta Tesis. Estas ventajas se resumen en las siguientes características del algoritmo de GDE propuesto: • Robustez: en un sistema centralizado, un fallo o rotura del nodo central provoca un mal funcionamiento de todo el sistema. La gestión de una red desde un punto de vista distribuido implica que no existe un nodo de control central. Un fallo en cualquier instalación no afecta el funcionamiento global de la red. • Privacidad de datos: el uso de una topología distribuida causa de que no hay un nodo central con información sensible de todos los consumidores. Esta Tesis va más allá y el algoritmo propuesto de GDE no utiliza información específica acerca de los comportamientos de los consumidores, siendo la coordinación entre las instalaciones completamente anónimos. • Escalabilidad: el algoritmo propuesto de GDE opera con cualquier número de instalaciones. Esto implica que se permite la incorporación de nuevas instalaciones sin afectar a su funcionamiento. • Bajo coste: el algoritmo de GDE propuesto se adapta a las redes actuales sin requisitos topológicos. Además, todas las instalaciones calculan su propia gestión con un bajo requerimiento computacional. Por lo tanto, no se requiere un nodo central con un alto poder de cómputo. • Rápido despliegue: las características de escalabilidad y bajo coste de los algoritmos de GDE propuestos permiten una implementación rápida. No se requiere una planificación compleja para el despliegue de este sistema. ABSTRACT This Thesis addresses the efficiency problems of the electrical grids from the consumption point of view. In particular, such efficiency is improved by means of the aggregated consumption smoothing. This objective of consumption smoothing entails two major improvements in the use of electrical grids: i) in the short term, a better use of the existing infrastructure and ii) in long term, the reduction of the required infrastructure to supply the same energy needs. In addition, this Thesis faces a new energy paradigm, where the presence of distributed generation is widespread over the electrical grids, in particular, the Photovoltaic (PV) generation. This kind of energy source affects to the operation of the grid by increasing its variability. This implies that a high penetration rate of photovoltaic electricity is pernicious for the electrical grid stability. This Thesis seeks to smooth the aggregated consumption considering this energy source. Therefore, not only the efficiency of the electrical grid is improved, but also the penetration of photovoltaic electricity into the grid can be increased. This proposal brings great benefits in the economic, social and environmental fields. The actions that influence the way that consumers use electricity in order to achieve energy savings or higher efficiency in energy use are called Demand-Side Management (DSM). This Thesis proposes two different DSM algorithms to meet the aggregated consumption smoothing objective. The difference between both DSM algorithms lie in the framework in which they take place: the local framework and the grid framework. Depending on the DSM framework, the energy goal and the procedure to reach this goal are different. In the local framework, the DSM algorithm only uses local information. It does not take into account other consumers or the aggregated consumption of the electrical grid. Although this statement may differ from the general definition of DSM, it makes sense in local facilities equipped with Distributed Energy Resources (DERs). In this case, the DSM is focused on the maximization of the local energy use, reducing the grid dependence. The proposed DSM algorithm significantly improves the self-consumption of the local PV generator. Simulated and real experiments show that self-consumption serves as an important energy management strategy, reducing the electricity transport and encouraging the user to control his energy behavior. However, despite all the advantages of the self-consumption increase, they do not contribute to the smooth of the aggregated consumption. The effects of the local facilities on the electrical grid are studied when the DSM algorithm is focused on self-consumption maximization. This approach may have undesirable effects, increasing the variability in the aggregated consumption instead of reducing it. This effect occurs because the algorithm only considers local variables in the local framework. The results suggest that coordination between these facilities is required. Through this coordination, the consumption should be modified by taking into account other elements of the grid and seeking for an aggregated consumption smoothing. In the grid framework, the DSM algorithm takes into account both local and grid information. This Thesis develops a self-organized algorithm to manage the consumption of an electrical grid in a distributed way. The goal of this algorithm is the aggregated consumption smoothing, as the classical DSM implementations. The distributed approach means that the DSM is performed from the consumers side without following direct commands issued by a central entity. Therefore, this Thesis proposes a parallel management structure rather than a hierarchical one as in the classical electrical grids. This implies that a coordination mechanism between facilities is required. This Thesis seeks for minimizing the amount of information necessary for this coordination. To achieve this objective, two collective coordination techniques have been used: coupled oscillators and swarm intelligence. The combination of these techniques to perform the coordination of a system with the characteristics of the electric grid is itself a novel approach. Therefore, this coordination objective is not only a contribution in the energy management field, but in the collective systems too. Results show that the proposed DSM algorithm reduces the difference between the maximums and minimums of the electrical grid proportionally to the amount of energy controlled by the system. Thus, the greater the amount of energy controlled by the algorithm, the greater the improvement of the efficiency of the electrical grid. In addition to the advantages resulting from the smoothing of the aggregated consumption, other advantages arise from the distributed approach followed in this Thesis. These advantages are summarized in the following features of the proposed DSM algorithm: • Robustness: in a centralized system, a failure or breakage of the central node causes a malfunction of the whole system. The management of a grid from a distributed point of view implies that there is not a central control node. A failure in any facility does not affect the overall operation of the grid. • Data privacy: the use of a distributed topology causes that there is not a central node with sensitive information of all consumers. This Thesis goes a step further and the proposed DSM algorithm does not use specific information about the consumer behaviors, being the coordination between facilities completely anonymous. • Scalability: the proposed DSM algorithm operates with any number of facilities. This implies that it allows the incorporation of new facilities without affecting its operation. • Low cost: the proposed DSM algorithm adapts to the current grids without any topological requirements. In addition, every facility calculates its own management with low computational requirements. Thus, a central computational node with a high computational power is not required. • Quick deployment: the scalability and low cost features of the proposed DSM algorithms allow a quick deployment. A complex schedule of the deployment of this system is not required.

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,

Unit Commitment and Electricity Prices Forecasting for Market Strategy Preparation in Interconnected Systems

In this paper we present a solution for building a better strategy to take part in external electricity markets. For an optimal strategy development, both the internal system costs as well as the future values of the series of electricity prices in external markets need to be known. But in practice, the real problems that must be faced are that both future electricity prices and costs are unknown. Thus, the first ones must be modeled and forecasted and the costs must be calculated. Our methodology for building an optimal strategy consists of three steps: The first step is modeling and forecasting market prices in external systems. The second step is the cost calculation on internal system taking into account the expected prices in the first step. The third step is based on the results of the previous steps, and consists of preparing the bids for external markets. The main goal is to reduce consumers' costs unlike many others that are oriented to increase GenCo's profits.

Integrated plant monitoring to improve plant operation strategy and results

The more and more demanding conditions in the power generation sector requires to apply all the available technologies to optimize processes and reduce costs. An integrated asset management strategy, combining technical analysis and operation and maintenance management can help to improve plant performance, flexibility and reliability. In order to deploy such a model it is necessary to combine plant data and specific equipment condition information, with different systems devoted to analyze performance and equipment condition, and take advantage of the results to support operation and maintenance decisions. This model that has been dealt in certain detail for electricity transmission and distribution networks, is not yet broadly extended in the power generation sector, as proposed in this study for the case of a combined power plant. Its application would turn in direct benefit for the operation and maintenance and for the interaction to the energy market

Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

Los Centros de Datos se encuentran actualmente en cualquier sector de la economía mundial. Están compuestos por miles de servidores, dando servicio a los usuarios de forma global, las 24 horas del día y los 365 días del año. Durante los últimos años, las aplicaciones del ámbito de la e-Ciencia, como la e-Salud o las Ciudades Inteligentes han experimentado un desarrollo muy significativo. La necesidad de manejar de forma eficiente las necesidades de cómputo de aplicaciones de nueva generación, junto con la creciente demanda de recursos en aplicaciones tradicionales, han facilitado el rápido crecimiento y la proliferación de los Centros de Datos. El principal inconveniente de este aumento de capacidad ha sido el rápido y dramático incremento del consumo energético de estas infraestructuras. En 2010, la factura eléctrica de los Centros de Datos representaba el 1.3% del consumo eléctrico mundial. Sólo en el año 2012, el consumo de potencia de los Centros de Datos creció un 63%, alcanzando los 38GW. En 2013 se estimó un crecimiento de otro 17%, hasta llegar a los 43GW. Además, los Centros de Datos son responsables de más del 2% del total de emisiones de dióxido de carbono a la atmósfera. Esta tesis doctoral se enfrenta al problema energético proponiendo técnicas proactivas y reactivas conscientes de la temperatura y de la energía, que contribuyen a tener Centros de Datos más eficientes. Este trabajo desarrolla modelos de energía y utiliza el conocimiento sobre la demanda energética de la carga de trabajo a ejecutar y de los recursos de computación y refrigeración del Centro de Datos para optimizar el consumo. Además, los Centros de Datos son considerados como un elemento crucial dentro del marco de la aplicación ejecutada, optimizando no sólo el consumo del Centro de Datos sino el consumo energético global de la aplicación. Los principales componentes del consumo en los Centros de Datos son la potencia de computación utilizada por los equipos de IT, y la refrigeración necesaria para mantener los servidores dentro de un rango de temperatura de trabajo que asegure su correcto funcionamiento. Debido a la relación cúbica entre la velocidad de los ventiladores y el consumo de los mismos, las soluciones basadas en el sobre-aprovisionamiento de aire frío al servidor generalmente tienen como resultado ineficiencias energéticas. Por otro lado, temperaturas más elevadas en el procesador llevan a un consumo de fugas mayor, debido a la relación exponencial del consumo de fugas con la temperatura. Además, las características de la carga de trabajo y las políticas de asignación de recursos tienen un impacto importante en los balances entre corriente de fugas y consumo de refrigeración. La primera gran contribución de este trabajo es el desarrollo de modelos de potencia y temperatura que permiten describes estos balances entre corriente de fugas y refrigeración; así como la propuesta de estrategias para minimizar el consumo del servidor por medio de la asignación conjunta de refrigeración y carga desde una perspectiva multivariable. Cuando escalamos a nivel del Centro de Datos, observamos un comportamiento similar en términos del balance entre corrientes de fugas y refrigeración. Conforme aumenta la temperatura de la sala, mejora la eficiencia de la refrigeración. Sin embargo, este incremente de la temperatura de sala provoca un aumento en la temperatura de la CPU y, por tanto, también del consumo de fugas. Además, la dinámica de la sala tiene un comportamiento muy desigual, no equilibrado, debido a la asignación de carga y a la heterogeneidad en el equipamiento de IT. La segunda contribución de esta tesis es la propuesta de técnicas de asigación conscientes de la temperatura y heterogeneidad que permiten optimizar conjuntamente la asignación de tareas y refrigeración a los servidores. Estas estrategias necesitan estar respaldadas por modelos flexibles, que puedan trabajar en tiempo real, para describir el sistema desde un nivel de abstracción alto. Dentro del ámbito de las aplicaciones de nueva generación, las decisiones tomadas en el nivel de aplicación pueden tener un impacto dramático en el consumo energético de niveles de abstracción menores, como por ejemplo, en el Centro de Datos. Es importante considerar las relaciones entre todos los agentes computacionales implicados en el problema, de forma que puedan cooperar para conseguir el objetivo común de reducir el coste energético global del sistema. La tercera contribución de esta tesis es el desarrollo de optimizaciones energéticas para la aplicación global por medio de la evaluación de los costes de ejecutar parte del procesado necesario en otros niveles de abstracción, que van desde los nodos hasta el Centro de Datos, por medio de técnicas de balanceo de carga. Como resumen, el trabajo presentado en esta tesis lleva a cabo contribuciones en el modelado y optimización consciente del consumo por fugas y la refrigeración de servidores; el modelado de los Centros de Datos y el desarrollo de políticas de asignación conscientes de la heterogeneidad; y desarrolla mecanismos para la optimización energética de aplicaciones de nueva generación desde varios niveles de abstracción. ABSTRACT Data centers are easily found in every sector of the worldwide economy. They consist of tens of thousands of servers, serving millions of users globally and 24-7. In the last years, e-Science applications such e-Health or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of data centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data center electricity represented 1.3% of all the electricity use in the world. In year 2012 alone, global data center power demand grew 63% to 38GW. A further rise of 17% to 43GW was estimated in 2013. Moreover, data centers are responsible for more than 2% of total carbon dioxide emissions. This PhD Thesis addresses the energy challenge by proposing proactive and reactive thermal and energy-aware optimization techniques that contribute to place data centers on a more scalable curve. This work develops energy models and uses the knowledge about the energy demand of the workload to be executed and the computational and cooling resources available at data center to optimize energy consumption. Moreover, data centers are considered as a crucial element within their application framework, optimizing not only the energy consumption of the facility, but the global energy consumption of the application. The main contributors to the energy consumption in a data center are the computing power drawn by IT equipment and the cooling power needed to keep the servers within a certain temperature range that ensures safe operation. Because of the cubic relation of fan power with fan speed, solutions based on over-provisioning cold air into the server usually lead to inefficiencies. On the other hand, higher chip temperatures lead to higher leakage power because of the exponential dependence of leakage on temperature. Moreover, workload characteristics as well as allocation policies also have an important impact on the leakage-cooling tradeoffs. The first key contribution of this work is the development of power and temperature models that accurately describe the leakage-cooling tradeoffs at the server level, and the proposal of strategies to minimize server energy via joint cooling and workload management from a multivariate perspective. When scaling to the data center level, a similar behavior in terms of leakage-temperature tradeoffs can be observed. As room temperature raises, the efficiency of data room cooling units improves. However, as we increase room temperature, CPU temperature raises and so does leakage power. Moreover, the thermal dynamics of a data room exhibit unbalanced patterns due to both the workload allocation and the heterogeneity of computing equipment. The second main contribution is the proposal of thermal- and heterogeneity-aware workload management techniques that jointly optimize the allocation of computation and cooling to servers. These strategies need to be backed up by flexible room level models, able to work on runtime, that describe the system from a high level perspective. Within the framework of next-generation applications, decisions taken at this scope can have a dramatical impact on the energy consumption of lower abstraction levels, i.e. the data center facility. It is important to consider the relationships between all the computational agents involved in the problem, so that they can cooperate to achieve the common goal of reducing energy in the overall system. The third main contribution is the energy optimization of the overall application by evaluating the energy costs of performing part of the processing in any of the different abstraction layers, from the node to the data center, via workload management and off-loading techniques. In summary, the work presented in this PhD Thesis, makes contributions on leakage and cooling aware server modeling and optimization, data center thermal modeling and heterogeneityaware data center resource allocation, and develops mechanisms for the energy optimization for next-generation applications from a multi-layer perspective.

Impact assessment of an integrated approach for thermal power generation plants asset management in the current energy market

El sector energético, en España en particular, y de forma similar en los principales países de Europa, cuenta con una significativa sobrecapacidad de generación, debido al rápido y significativo crecimiento de las energías renovables en los últimos diez años y la reducción de la demanda energética, como consecuencia de la crisis económica. Esta situación ha hecho que las centrales térmicas de generación de electricidad, y en concreto los ciclos combinados de gas, operen con un factor de utilización extremadamente bajo, del orden del 10%. Además de la reducción de ingresos, esto supone para las plantas trabajar continuamente fuera del punto de diseño, provocando una significativa pérdida de rendimiento y mayores costes de explotación. En este escenario, cualquier contribución que ayude a mejorar la eficiencia y la condición de los equipos, es positivamente considerada. La gestión de activos está ganando relevancia como un proceso multidisciplinar e integrado, tal y como refleja la reciente publicación de las normas ISO 55000:2014. Como proceso global e integrado, la gestión de activos requiere el manejo de diversos procesos y grandes volúmenes de información, incluso en tiempo real. Para ello es necesario utilizar tecnologías de la información y aplicaciones de software. Esta tesis desarrolla un concepto integrado de gestión de activos (Integrated Plant Management – IPM) aplicado a centrales de ciclo combinado y una metodología para estimar el beneficio aportado por el mismo. Debido a las incertidumbres asociadas a la estimación del beneficio, se ha optado por un análisis probabilístico coste-beneficio. Así mismo, el análisis cuantitativo se ha completado con una validación cualitativa del beneficio aportado por las tecnologías incorporadas al concepto de gestión integrada de activos, mediante una entrevista realizada a expertos del sector de generación de energía. Los resultados del análisis coste-beneficio son positivos, incluso en el desfavorable escenario con un factor de utilización de sólo el 10% y muy prometedores para factores de utilización por encima del 30%. ABSTRACT The energy sector particularly in Spain, and in a similar way in Europe, has a significant overcapacity due to the big growth of the renewable energies in the last ten years, and it is seriously affected by the demand decrease due to the economic crisis. That situation has forced the thermal plants and in particular, the combined cycles to operate with extremely low annual average capacity factors, very close to 10%. Apart from the incomes reduction, working in out-of-design conditions, means getting a worse performance and higher costs than expected. In this scenario, anything that can be done to improve the efficiency and the equipment condition is positively received. Asset Management, as a multidisciplinary and integrated process, is gaining prominence, reflected in the recent publication of the ISO 55000 series in 2014. Dealing Asset Management as a global, integrated process needs to manage several processes and significant volumes of information, also in real time, that requires information technologies and software applications to support it. This thesis proposes an integrated asset management concept (Integrated Plant Management-IPM) applied to combined cycle power plants and develops a methodology to assess the benefit that it can provide. Due to the difficulties in getting deterministic benefit estimation, a statistical approach has been adopted for the cot-benefit analysis. As well, the quantitative analysis has been completed with a qualitative validation of the technologies included in the IPM and their contribution to key power plant challenges by power generation sector experts. The cost- benefit analysis provides positive results even in the negative scenario of annual average capacity factor close to 10% and is promising for capacity factors over 30%.

Integración técnica y económica de energías renovables, en un sistema eléctrico liberalizado, a partir de vectores energéticos

El objetivo de la tesis es estudiar la bondad del almacenamiento de energía en hidrógeno para minorar los desvíos de energía respecto a su previsión de parques eólicos y huertas solares. Para ello se ha partido de datos de energías horarias previstas con 24 h de antelación y la energía real generada. Se ha procedido a dimensionar la planta de hidrógeno, a partir de una modelización de la operación de la misma, teniendo siempre como objetivo la limitación de los desvíos. Posteriormente, se ha procedido a simular la operación de la planta con dos objetivos en mente, uno limitar los desvíos y por otro lado operar la planta como una central de bombeo, generando hidrógeno en horas valle y generando electricidad en horas punta. Las dos simulaciones se han aplicado a tres parques eólicos de diferentes potencias, y a una huerta solar fotovoltaica. Se ha realizado un estudio económico para determinar la viabilidad de las plantas dimensionadas, obteniendo como resultado que no son viables a día de hoy y con la estimación de precios considerada, necesitando disminuir considerablemente los costes, dependiendo fuertemente de la bondad de los métodos de previsión de viento. Por último se ha estudiado la influencia de la disminución de los desvíos generados sobre una red tipo de 30 nudos, obteniendo como resultado, que si bien no disminuyen sensiblemente los extra costes generados en regulación, sí que mejora la penetración de las energías renovables no despachables en la red. Se observa disminuyen los vertidos eólicos cuando se usa la planta de hidrógeno. ABSTRACT The aim of this thesis is to study the benefit of hydrogen energy storage to minimize energy deviations of Wind Power and Solar Photovoltaic (PV) Power Plants compared to its forecast. To achieve this goal, first of all we have started with hourly energy data provided 24 h in advance (scheduled energy), and real generation (measured energy). Secondly, It has been sized the hydrogen plant, from a modeling of its working mode, always keeping the goal in mind of limiting energy imbalances. Subsequently, It have been simulated the plant working mode following two goals, one, to limit energy imbalances and secondly to operate the plant as a pumping power plant, generating hydrogen-in valley hours and generating electricity at peak hours. The two simulations have been applied to three wind power plants with different installed power capacities, and a photovoltaic solar power plant. It has been done an economic analysis in order to determine the viability of this sized plants, turning out not viable plants today with the estimated prices considered, requiring significantly lower costs, depending heavily on the reliability of the Wind Power forecast methods. Finally, It has been studied the influence of decreasing measured imbalances (of energy) in a 30 grid node, resulting that, while it not reduces significantly the extra costs generated by reserve power, it does improve the penetration of non-manageable renewable energy on the grid, by reducing the curtailments of power of these plants.

Renewable Energies and Community Associations: Providing Homeowners with Renewable Energy Options

As energy costs increase in Colorado more homeowners will need renewable energies to provide electricity, heating and cooling for their homes. Renewable energy technology and energy efficient measures have been available for decades but Homeowner Associations (HOA) has not permitted this technology into communities primarily because of aesthetics. In April 2008, House Bill 1270 was signed into law that gives homeowners the right to make their homes more energy efficient and install renewable energy generation devices. The purpose of this capstone is to enable HOAs with information on available technology and design guideline options that can be integrated into communities and thus encourage, instead of hinder, the use of renewable energy and energy efficient measures.

The steel industry in the European Union: Composition and drivers of energy prices and costs. CEPS Special Report No. 80, 17 December 2013

This report assesses the energy costs borne by the steel industry in the EU between 2010 and 2012, and compares the energy costs, including both the energy components and other regulatory costs, to production costs, turnover and margins of steel-makers. The estimates of energy costs are based on primary sources, i.e. is on information provided by steel-makers through a written questionnaire. This information was validated by the research team by checking annual energy bills, when available, and other public sources. In this respect, this exercise represents a unique fact-based investigation into the costs of energy for steel-makers in Europe, whereas most of the information currently available in the public domain is based on secondary or statistical information. In 2012, the median EU steel plant pays about €33/MWh for gas, up from €26/MWh in 2010. As for electricity, in 2012 the EU median plant pays €62/MWh, up from €59/MWh in 2010. The report also includes a comparison with the prices of energy carriers paid by producers based in the US.

Will the construction of a nuclear power plant in Belarus exacerbate the country's energy dependence on Russia? OSW Commentary No. 87, 2012-07-23

During Russian PM Dmitry Medvedev’s working visit to Minsk on 18 July, Russia and Belarus signed a general contract for the construction of a nuclear power plant in Belarus. The signature brought to an end the complex negotiations which had been underway since January 2009 involving the leadership in Minsk, the Russian government and Atomstroyexport, the Russian company that will be the main contractor of the investment. However, the power plant’s future ownership structure, management arrangements and terms and conditions of profit sharing remain unclear. The Belarusian leadership hopes that with the launch of the nuclear power plant, it will be able to reduce gas imports from Russia, gas being the main resource used in producing heat and electricity in Belarus. This should in turn reduce the costs of energy generation. In addition, Minsk expects that the new investment will allow it to export electricity surpluses to the European Union, including Poland. Agreements concerning the power plant have been concluded over the last year or so and, according to these, Russia has acquired partial control of the Belarusian electricity grid, especially with regard to the transmission of energy to foreign markets. Russia is also the sole creditor and contractor for the investment, and the sole future provider of nuclear fuel. Therefore, implementation of the project will exacerbate Minsk’s already significant dependence on Moscow in energy and political terms.

Outlook for Electricity and Renewable Energy in Southern and Eastern Mediterranean Countries. MEDPRO Technical Report No. 16/October 2012

The aim of this report is to elaborate the MEDPRO Energy Reference Scenario for electricity demand and power generation (by energy source) in the southern and eastern part of the Mediterranean (MED- 11 countries) up to 2030. The report assesses  the prospects for the implementation of renewable energy in the MED-11 countries over the next decades. The development of renewable energy is a cornerstone of the MED-11 countries’ efforts to improve security of supply and reduce CO2 emissions;  the prospects for regional renewable-energy plans (the Mediterranean Solar Plan, DESERTEC and Medgrid); and  the development of electricity interconnections in MED-11 countries and the possible integration of Mediterranean electricity and renewable markets (both south–south and south–north).

Gas demand for power generation peaked as early as 2010. CEPS Commentary, 12 February 2016

The outlook for natural gas demand is often considered bright, especially for gas used to generate electricity. This is because gas is the cleanest of all fossil fuels. The carbon intensity of modern gas-fired power stations is less than 50% that of modern coal plants. Moreover, gas-fired units are well-suited to follow rapid swings in supply and demand due to their flexibility. In the future, these balancing tasks will become more and more important given the intermittent character of the supply of wind and solar power. Gas seems to hold out the promise of being a key pillar of the energy transition and the perfect partner of renewables. Given the EU’s long-term climate policy goals, however, there is strong evidence that demand for gas for purposes of power generation peaked as early as 2010.

The shadow of Chornobyl. Ukraine thirty years after the disaster. OSW COMMENTARY NUMBER 210/22.04.2016

Thirty years after the Chornobyl nuclear power plant disaster, its aftermath and consequences are still a permanent element of the economic, environmental and social situation of Ukraine, Belarus and some regions of Russia. Ukraine, to which the scope of this text is limited, experienced the most severe shock because, among other factors, the plant where the accident took place was located just 100 km away from Kyiv. Its consequences have affected the course of political developments in the country, and have become part of the newly-shaped national identity of independent Ukraine. The country bore the huge cost of the clean-up effort but did not give up on nuclear energy, and today nuclear power plants generate more than half of its electricity. The system of social benefits for people recognised as disaster survivors, which was put in place by the Soviet government, has become a huge burden on the country’s budget; if implemented fully, it would account for more than 10% of total public spending, and is therefore being implemented to only a partial extent. This system has reinforced the Ukrainian people’s sense of helplessness and dependence on the state. The disaster has also become part of the ‘victim nation’ blueprint of the Ukrainian national myth, which it has further solidified. The technological and environmental consequences of the disaster, and hence also its economic costs, will persist for centuries, while the social consequences will dissipate as the affected generation passes away. In any case, Chornobyl will remain an important part of the life of the Ukrainian state and society.

Combined desalination and power generation using coal energy.

Phase I report. Site selection and estimated water costs based on current technology. Phase II report. Investigation of emerging technology, by-product recovery, and very large capacities. Period of performance: June 1965-Oct. 1966.