Scenario modelling and optimisation of renewable energy integration for the energy transition

A large number of countries have engaged themselves in an energy transition towards more re- newable energy in their energy systems. Motivations stem mainly from the need to reduce CO2 emissions, and from a desire of their population to phase out technologies such as nuclear. Most of these countries promote biomass, wind and solar energy sources, among other possibilities. How- ever the current rate of deployment of renewable energy systems globally is not sufficient to reach the CO2 emissions reduction that would allow to maintain the global average temperature increase below the 2°C threshold. The main barriers to a wider integration of renewable energy systems are i) their limited realisable potential, ii) their still limited competitiveness, iii) their intermittence; iv) public acceptance often related to poor level of energy literacy amongst citizens. Citizens are key decision-makers. They must decide on energy policies and on the energy technologies they use, hence they have the power to foster or halt the energy transition. This thesis presents two different strategies for addressing the problem of the integration of re- newable energy sources for energy transitions. The first one (Chapter 1) consists in developing an energy modelling tool to help decision-makers understand the energy system and find their own answers. The modelling approach also includes a new methodology for the calculation of the total cost of a national energy system. A model of the Swiss energy system has been created following this approach, which serves as basis to develop the Swiss-Energyscope online calculator. This calculator and its model present an optimal trade-off between scientific rigour and user-friendliness, which allows the reproduction of the energy transition scenarios conceived by the Swiss Government, and consequently its use for energy policy making. The second strategy (chapters 2 and 3) profits from the possibilities offered by mathematical mod- elling and optimisation to analyse national energy systems, and derive insights for policy and decision-makers. First, a methodology using a mix-integer linear programming (MILP) model analyses biomass usage pathways to determine its optimal use in Switzeland in 2035. Second, in order to study the role of biomass, non-linear optimisation is applied to create future scenarios. (Chapter 3) focuses on the solutions to deal with the variability of renewable electricity. To this end, a MILP model with hourly time resolution is conceived to study the use of flexible electricity supply and demand options for the integration of renewable electricity. The optimisation methodologies are validated on case studies for the Swiss energy system. Regarding biomass, the results reveal that woody biomass chemical conversion technologies can allow for an overall better performance in terms of CO2 avoided emissions compared to direct combustion, as long as the produced biofuels are used in efficient technologies. Results also show that the combination of the gasification-methanation process of woody biomass with the production of H2 produced from excess electricity would allow to reduce the Swiss natural gas imports to zero by 2050. Concerning the integration of variable renewable electricity, the cost difference between using flexible electricity supply- and demand-options or electricity imports to deal with variable renewable electricity is below 2.5% of the total cost of the energy system

Sistema de recomendación personalizada de contenido vídeo

Con este proyecto se quiere mejorar uno de los productos principales de la empresa, que consiste en un sistema de televisión interactiva que permite a los usuarios poder disfrutar de varios servicios tales como una aplicación ofimática, una guía local y la adquisición de contenidos de distintos tipos: películas, series, clips, música, información cultural y videojuegos entre otros. Actualmente existen varios sistemas de recomendación de contenido de vídeo que funcionan relativamente bien, pero únicamente en entornos con un gran número de usuarios que continuamente interactúan con el sistema. Esto implica, que los algoritmos de recomendación de contenido de vídeo actuales sólo tengan cabida en el entorno Web, dónde el número de usuarios puede llegar a ser considerable y se dispone de una alta densidad de información. Se pretende diseñar un sistema de recomendación en el que la precisión de las recomendaciones no dependa tanto de la densidad de información disponible, sino más bien en la calidad de la información, dado que estas son las características del entorno donde se implantará el sistema

Exploiting distributional semantics for content-based and context-aware recommendation

During the last decade, the use of recommender systems has been increasingly growing to the point that, nowadays, the success of many well-known services depends on these technologies. Recommenders Systems help people to tackle the choice overload problem by effectively presenting new content adapted to the user¿s preferences. However, current recommendation algorithms commonly suffer from data sparsity, which refers to the incapability of producing acceptable recommendations until a minimum amount of users¿ ratings are available for training the prediction models. This thesis investigates how the distributional semantics of concepts describing the entities of the recommendation space can be exploited to mitigate the data-sparsity problem and improve the prediction accuracy with respect to state-of-the-art recommendation techniques. The fundamental idea behind distributional semantics is that concepts repeatedly co-occurring in the same context or usage tend to be related. In this thesis, we propose and evaluate two novel semantically-enhanced prediction models that address the sparsity-related limitations: (1) a content-based approach, which exploits the distributional semantics of item¿s attributes during item and user-profile matching, and (2) a context-aware recommendation approach that exploits the distributional semantics of contextual conditions during context modeling. We demonstrate in an exhaustive experimental evaluation that the proposed algorithms outperform state-of-the-art ones, especially when data are sparse. Finally, this thesis presents a recommendation framework, which extends the widespread machine learning library Apache Mahout, including all the proposed and evaluated recommendation algorithms as well as a tool for offline evaluation and meta-parameter optimization. The framework has been developed to allow other researchers to reproduce the described evaluation experiments and make new progress on the Recommender Systems field easierDurant l'última dècada, l'ús dels sistemes de recomanació s'ha vist incrementat fins al punt que, actualment, l'èxit de molts dels serveis web més coneguts depèn en aquesta tecnologia. Els Sistemes de Recomanació ajuden als usuaris a trobar els productes o serveis que més s¿adeqüen als seus interessos i preferències. Una gran limitació dels algoritmes de recomanació actuals és el problema de "data-sparsity", que es refereix a la incapacitat d'aquests sistemes de generar recomanacions precises fins que un cert nombre de votacions d'usuari és disponible per entrenar els models de predicció. Per mitigar aquest problema i millorar així la precisió de predicció de les tècniques de recomanació que conformen l'estat de l'art, en aquesta tesi hem investigat diferents maneres d'aprofitar la semàntica distribucional dels conceptes que descriuen les entitats que conformen l'espai del problema de la recomanació, principalment, els objectes a recomanar i la informació contextual. En la semàntica distribucional s'assumeix la següent hipotesi: conceptes que coincideixen repetidament en el mateix context o ús tendeixen a estar semànticament relacionats. Concretament, en aquesta tesi hem proposat i avaluat dos algoritmes de recomanació que fan ús de la semàntica distribucional per mitigar el problem de "data-sparsity": (1) un model basat en contingut que explota les similituds distribucionals dels atributs que representen els objectes a recomanar durant el càlcul de la correspondència entre els perfils d'usuari i dels objectes; (2) un model de recomanació contextual que fa ús de les similituds distribucionals entre condicions contextuals durant la representació del context. Mitjançant una avaluació experimental exhaustiva dels models de recomanació proposats hem demostrat la seva efectivitat en situacions de falta de dades, confirmant que poden millorar la precisió d'algoritmes que conformen l'estat de l'art. Finalment, aquesta tesi presenta una llibreria pel desenvolupament i avaluació d'algoritmes de recomanació com una extensió de la llibreria de "Machine Learning" Apache Mahout, àmpliament utilitzada en el camp del Machine Learning. La nostra extensió inclou tots els algoritmes de recomanació avaluats en aquesta tesi, així com una eina per facilitar l'avaluació experimental dels algoritmes. Hem desenvolupat aquesta llibreria per facilitar a altres investigadors la reproducció dels experiments realitzats i, per tant, el progrés en el camp dels Sistemes de Recomanació

Sistema de recomendación personalizada de contenido vídeo

Con este proyecto se quiere mejorar uno de los productos principales de la empresa, que consiste en un sistema de televisión interactiva que permite a los usuarios poder disfrutar de varios servicios tales como una aplicación ofimática, una guía local y la adquisición de contenidos de distintos tipos: películas, series, clips, música, información cultural y videojuegos entre otros. Actualmente existen varios sistemas de recomendación de contenido de vídeo que funcionan relativamente bien, pero únicamente en entornos con un gran número de usuarios que continuamente interactúan con el sistema. Esto implica, que los algoritmos de recomendación de contenido de vídeo actuales sólo tengan cabida en el entorno Web, dónde el número de usuarios puede llegar a ser considerable y se dispone de una alta densidad de información. Se pretende diseñar un sistema de recomendación en el que la precisión de las recomendaciones no dependa tanto de la densidad de información disponible, sino más bien en la calidad de la información, dado que estas son las características del entorno donde se implantará el sistema

Design, development and deployment of an intelligent, personalized recommendation system

Personalization and recommendation systems are a solution to the problem of content overload, especially in large information systems. In this thesis, a personalized recommendation system enhanced with semantic knowledge has been developed in order to overcome the most common limitations of traditional approaches: the cold-start and the sparsity problems. The recommender consists of the following two main components. A user-profile learning algorithm combines user’s feedback from different channels and employs domain inferences to construct accurate user profiles. A recommendation algorithm, using content-based filtering, exploits the semantic structure of the domain to obtain accurate predictions and generate the corresponding recommendations. The system’s design proposed is flexible enough to be potentially applied to applications of any domain that can be properly described using ontologies. In addition to the development of the recommendation system, an existing Web-application in the tourism domain has been extended and adapted in order to be able to integrate the recommender into it. The overall recommendation system has been evaluated and the results obtained indicate that it satisfies the requirements established

The Impact of Uncertainty in National Energy Planning

Concerns related to climate change and security of energy supply are pushing various countries to define strategic energy plans. Strategic energy planning for national energy systems involves investment decisions (selection and sizing) for energy conversion technologies over a time horizon of 20-50 years. This long time horizon requires uncertainty to be accounted for. Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favor of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on strategic energy planning decisions. A classification of uncertainty in national energy systems decision-making is performed. A Global Sensitivity Analysis (GSA) is performed in order to highlight the influence of the model uncertain parameters onto the energy strategy. Optimization under uncertainty is then applied to a general Mixed-Integer Linear Programming (MILP) problem having as objective the total annual cost and assessing as well the IPCC Global Warming Potential LCIA indicator (CO2-equivalent emissions). The application focuses on the case study of Switzerland. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal

The information platform energyscope.ch on the energy transition scenarios

Switzerland like many countries plans to undertake an energy transition and the government proposes different paths for 2035 and 2050 to achieve it. However the authors of this paper felt the need for a better information of the public on the energy matters. A special web-based information platform was introduced in 2015. The platform includes a calculator, a book providing 100 questions and answers on energy and a MOOC for citizens with more than 20 short lectures on the various aspects of the Swiss energy transition. Unlike other interactive energy scenario calculators our calculator offers the possibility to show monthly averages of demand and supply, highlighting the strong seasonal patterns occurring when considering most countries from the central to the northern parts of Europe. The calculator indicates the effects of the user’s choices on 6 main indicators (final energy, electricity balance, % of renewables, CO2 emissions, long term wastes and costs) While the underlying model has already been published, this paper intends to discuss the reactions following the introduction of such a platform. Reactions from minority, but very active groups, including climate change deniers, ultra-pronuclear and anti-wind power opponents have been noticed and highlight the very emotional nature of the topic. Scenarios for 2050 are presented and discussed as well as examples of a new scenario that can be made using the calculator. All main parameters such a socioeconomic, heating and cogeneration technologies, transportation, electricity generation can be adapted

Exergy assessment of future energy transition scenarios with application to Switzerland

Using an exergy based indicator is highly desirable to compare future national energy strategies. A new web- based information platform called energyscope.ch, informing the general public on the Swiss energy transition was presented at ECOS2016. This paper presents a new extension of the approach that we plan to call exergyscope.ch, clearly stating exergy and distinguishing between primary exergy, final exergy and useful exergy. This allows for a graphical interpretation of the exergy efficiency of each conversion step from primary exergy to final exergy, all the way to useful exergy. Different future energy scenarios for Switzerland are compared to illustrate the gain in exergy efficiency between different strategy choices. Monthly variations in exergy supply are considered by using an average reference temperature for each month. The analysis assesses the useful exergy requirement for all energy services including building and transportation. For heating and cooling services, the proposed framework is coherent with the introduction, reported earlier, of an exergy efficiency indicator in a Law on energy. Accordingly the global exergy efficiency for providing a given useful exergy service can be calculated by multiplying the individual exergy efficiency of each conversion steps. The useful industrial thermal exergy is introduced in a simplified manner with an average service temperature

Thermo-Economic Optimization of Integrated First and Second Generation Sugarcane Ethanol Plant

The sugarcane industry has been responsible in some countries for the production of most of the sugar and ethanol available in the world for internal and external markets. In this sector, ethanol can be produced by fermentation of sugars obtained directly from sugarcane biomass, commonly called 1st generation ethanol. New processes using the enzymatic hydrolysis technology of lignocellulosic residues like bagasse and sugarcane leaves as feedstock can increase the ethanol production in these plants, reducing the land requirements and the environmental of impact biofuels production in large scale. The lignocellulosic ethanol production using enzymatic hydrolysis technology is one of the most promising alternatives of 2nd generation biofuels, due to its high conversion efficiencies and low environment impact. Some problems like high water consumption and enzymes costs must be overcome in order to reach commercial scale. The process integration and thermo-economic optimization of the process can be important for the design of this process in a sugarcane autonomous distillery aiming at the cost and environmental impact reduction. In this paper a process integration of the sugarcane ethanol distillery model is carried out taking into account 1st and 2nd generation processes in the same site using sugars and bagasse as feedstock respectively. Conflictive objectives such as maximization of the electricity or ethanol production are adopted in a multi-objective optimization technique using evolutionary algorithms, in order to provide a set of candidate solutions considering different configurations of the ethanol production process design

Admixture in Latin America: Geographic Structure, Phenotypic Diversity and Self-Perception of Ancestry Based on 7,342 Individuals

The current genetic makeup of Latin America has been shaped by a history of extensive admixture between Africans, Europeans and Native Americans, a process taking place within the context of extensive geographic and social stratification. We estimated individual ancestry proportions in a sample of 7,342 subjects ascertained in five countries (Brazil, Chile, Colombia, México and Perú). These individuals were also characterized for a range of physical appearance traits and for self-perception of ancestry. The geographic distribution of admixture proportions in this sample reveals extensive population structure, illustrating the continuing impact of demographic history on the genetic diversity of Latin America. Significant ancestry effects were detected for most phenotypes studied. However, ancestry generally explains only a modest proportion of total phenotypic variation. Genetically estimated and self-perceived ancestry correlate significantly, but certain physical attributes have a strong impact on self-perception and bias self-perception of ancestry relative to genetically estimated ancestry