A contribution to multi-criteria decision making in sustainable energy management based on fuzzy and qualitative reasoning

Energy problems are serious problems caused by limited resources and by human activity such as deforestation, water pollution and various other long-term practices that have environmental impact which produces global warming and climate change. These complex problems usually involve multiple conflicting criteria and multiple decision makers. They require the use of multi-criteria decision-making methods to evaluate different types of variables with respect to sustainability factors addressing conflicting economic, technological, social and environmental aspects. These factors, especially social ones, are not always precise, as imprecision and uncertainty are features of the real world. Therefore, in order to provide useful data from experts' assessments, in this thesis a new multi-criteria decision-making method, as a useful tool in energy planning, is presented. This method supports decision makers in all stages of the decision-making process with uncertain values. An exhaustive literature review on multi-criteria decision analysis and energy planning has been conducted in this thesis. First, the in-depth study of criteria and indicators in the energy planning area is presented. Some well-known multi-criteria decision-making methods and their applications are introduced. In these problems, it is often difficult to obtain exact numerical values for some criteria and indicators. In order to overcome this shortcoming, qualitative reasoning techniques integrated with multi-criteria decision-making methods are capable of representing uncertainty, emulating skilled humans, and handling vague situations. This study proposes a Qualitative TOPSIS (Q-TOPSIS) method, which is a new method for ranking multi-criteria alternatives in group decision making. This new method, in its first step, takes into account qualitative data provided by the decision makers' individual linguistic judgments on the performance of alternatives with respect to each criterion, without any previous aggregation or normalization. Then, in its second step, it incorporates the judgments of decision makers into the modified TOPSIS method to generate a complete ranking of alternatives. Three applications of the proposed method in energy planning are presented. In the first case, the application of the Q-TOPSIS method in a case study of renewable energy alternatives selection is presented. These alternatives are ranked and the proposed method is compared with the modified fuzzy TOPSIS method. A simulation of thirty scenarios using different weights demonstrates that the simplicity and interpretability of Q-TOPSIS provides a general improvement over classic TOPSIS in the case of ordinal assessments. Second, a real case study in a social framework to find an appropriate place for wind farm location in Catalonia is presented. In this case different alternatives were proposed based on social actors' preferences for the location of the desired wind farms in a region between the counties of Urgell and Conca de Barbera. Ranking alternatives concludes that an alternative combining two different initial projects is the best option. Using the proposed method to handle a high degree of conflict in group decision making involving multi-dimensional concepts simplified the experts' measurements. Finally, an application to energy efficiency in buildings using the SEMANCO (Semantic tools for carbon reduction in urban planning) platform is presented in order to assess the energy performance and CO2 emissions of projected urban plans at the city level in Manresa. In this case study, an application of Q-TOPSIS helps decision makers to rank different projects with respect to multi-granular quantitative and qualitative criteria and offers outputs which are very easy for decision makers to understand.Los problemas de la energía son problemas graves causados por los recursos limitados y las actividades humanas como la deforestación, contaminación del agua y otras prácticas con efectos a largo plazo. Estas prácticas tienen un gran impacto ambiental y dan lugar al efecto invernadero, que ocasiona el calentamiento global y cambio climático. Los problemas complejos implican generalmente múltiples criterios contradictorios y múltiples decisores. Requieren el uso de métodos toma de decisiones multicriterio para evaluar diferentes tipos de variables con respecto a factores de sostenibilidad, incluyendo aspectos conflictivos económicos, tecnológicos, sociales y ambientales. Estos factores, especialmente los sociales, no siempre son precisos, dado que la imprecisión y la incertidumbre son características del mundo real. Por lo tanto, con el fin de proporcionar datos útiles a partir de evaluaciones de expertos, en esta tesis se presenta un nuevo método de toma de decisiones multicriterio, como una herramienta útil en la planificación de la energía. Este método permite a los decisores utilizar valores con imprecisión en todas las etapas de la toma de decisiones. En esta tesis se ha realizado una revisión exhaustiva de la literatura sobre el análisis de la decisión multicriterio y la planificación de la energía. En primer lugar, se presenta el estudio a fondo de los criterios e indicadores en el área de planificación de la energía. Se introducen algunos de los métodos más conocidos de toma de decisiones multicriterio y sus aplicaciones. En estos problemas, a menudo es difícil obtener valores numéricos exactos para algunos criterios e indicadores. Para superar esta deficiencia, la integración de técnicas de razonamiento cualitativo en métodos de decisión multicriterio permite representar la incertidumbre, emular el trabajo de seres humanos cualificados y manejar situaciones vagas. Este estudio propone un método TOPSIS cualitativo (Q-TOPSIS), que es un nuevo método de ranking de alternativas para la toma de decisiones multicriterio en grupo. Este nuevo método, toma en cuenta los datos cualitativos proporcionados por los juicios lingüísticos individuales de los decisores sin necesidad de previa agregación o normalización. Se presentan tres aplicaciones del método propuesto en la planificación de la energía. En el primer caso, se presenta la aplicación del método Q-TOPSIS en un caso práctico de selección de alternativas de energías renovables. Una simulación de treinta escenarios utilizando diferentes pesos demuestra que la simplicidad y la interpretabilidad de Q-TOPSIS proporcionan una mejora general del TOPSIS clásico en el caso de evaluaciones ordinales. En segundo lugar, se presenta un estudio de un caso real para decidir el lugar apropiado para ubicación de parques eólicos en una zona de Cataluña. En este caso, las distintas alternativas fueron propuestas en base a las preferencias de los actores sociales sobre la ubicación de los parques eólicos deseados en una región entre los condados del Urgell y la Conca de Barberà. El ranking obtenido de las alternativas concluye que la mejor opción es una alternativa que combina dos proyectos iniciales diferentes. La utilización del método propuesto para la decisión en grupo permite manejar un alto grado de conflicto entre conceptos multidimensionales y simplifica las mediciones de los expertos. Por último, se presenta una aplicación a la eficiencia de la energía en edificios mediante la plataforma SEMANCO (Herramientas semánticas para la reducción de carbono en la planificación urbana) para evaluar la eficiencia de la energía y las emisiones de CO2 de planes urbanísticos proyectados en la ciudad de Manresa. En este caso estudio, la aplicación de Q-TOPSIS ayuda a los decisores a realizar el ranking de los diferentes proyectos con respecto a criterios cuantitativos y cualitativos multi-granulares y ofrece resultados fácilmente inteligibles para los decisores

Estudi bibliomètric any 2014. Campus del Baix Llobregat: EETAC i ESAB

En el present informe s’analitza la producció científica de les dues escoles del Campus del Baix Llobregat, l’Escola d’Enginyeria de Telecomunicació i Aerospacial de Castelldefels (EETAC) i l’Escola Superior d’Agricultura de Barcelona (ESAB) durant el 2014.Postprint (author’s final draft

Hierarchical outranking methods for multi-criteria decision aiding

Els mètodes d’Ajut a la Decisió Multi-Criteri assisteixen en la pressa de decisions implicant múltiples criteris conflictius. Existeixen dos enfocaments principals per resoldre aquest tipus de problemes: els mètodes basats en utilitat i d’outranking, cadascun amb les seves fortaleses i debilitats. Els mètodes outranking estan basats en models d’elecció social combinats amb tècniques d’intel·ligència artificial (com gestió de dades categòriques o d’incertesa). Son eines per una avaluació i comparació realista d’alternatives, basant-se en les necessitats i coneixements del prenedor de la decisió. Una de les debilitats dels mètodes outranking és la no consideració de jerarquies de criteris, que permeten una organització natural del problema, distingint diferents nivells de generalitat que modelen les relacions taxonòmiques implícites entre criteris. En aquesta tesi ens enfoquem en el desenvolupament d’eines d’outranking jeràrquiques i la seva aplicació en casos d’estudi reals per problemes de classificació i rànquing.Los métodos de Ayuda a la Decisión Multi-Criterio asisten en la toma de decisiones involucrando múltiples criterios conflictivos. Existen dos enfoques principales para resolver éste tipo de problemas: los métodos basados en utilidad y de outranking, cada uno con sus fortalezas y debilidades. Los métodos outranking están basados en modelos de elección social combinados con técnicas de Inteligencia Artificial (como gestión de datos categóricos o de incertidumbre). Son herramientas para una evaluación y comparación realista de alternativas, basándose en las necesidades y conocimientos del tomador de decisión. Una de las debilidades de los métodos outranking es la no consideración de jerarquías de criterios, que permiten una organización natural del problema, distinguiendo distintos niveles de generalidad que modelan las relaciones taxonómicas implícitas entre criterios. En ésta tesis nos enfocamos en el desarrollo de herramientas de outranking jerárquicas y su aplicación en casos de estudio reales para problemas de clasificación y ranking.Multi-Criteria Decision Aiding (MCDA) methods support complex decision making involving multiple and conflictive criteria. MCDA distinguishes two main approaches to deal with this type of problems: utility-based and outranking methods, each with its own strengths and weaknesses. Outranking methods are based on social choice models combined with Artificial Intelligence techniques (such as the management of categorical data or uncertainty). They are recognized as providing tools for a realistic assessment and comparison of a set of alternatives, based on the decision maker’s knowledge and needs. One of the main weaknesses of the outranking methods is the lack of consideration of hierarchies of criteria, which enables the decision maker to naturally organize the problem, distinguishing different levels of generality that model the implicit taxonomical relations between the criteria. In this thesis we focus on developing hierarchical outranking tools and their application to real-world case studies for ranking and sorting problems

Projecting Future Locations for Commercial Wind Energy Development in the Conterminous United States using a Logistic Regression-Cellular Automata Model

Pressures to decarbonize the United States’ electricity production, reduce dependence on foreign energy imports, and the declining levelized cost of renewable electricity is making wind energy an increasingly appealing means of meeting electricity demand in the United States. However, the installation of new commercial wind farms to meet this demand requires knowledge of the most suitable locations for their installation, which depends on a combination of environmental, technical, economic, political, and social characteristics. Wind Farm Site Suitability (WiFSS) models are frequently enlisted to assist in this decision-making process in countries around the world for both onshore and offshore wind farm siting decisions. However, existing WiFSS models serve to assess present-day wind farm siting potential, rather than project specific locations for future wind energy development. Taking cues from Socio-Environmental Systems (SES) models of urban growth, this dissertation presents a Logistic Regression-Cellular Automata (LRCA) model, henceforth referred to as WiFSS-LRCA, conceived to produce maps that identify scenarios of potential future locations and timing of future commercial wind farms across the Conterminous United States (CONUS) between now and the year 2050. Following a review of existing WiFSS modeling approaches, and of common practices by which WiFSS modeling studies select and represent their predictors, the niche that WiFSS-LRCA serves to fill was consequently identified. The majority of WiFSS studies take a Geographic Information Systems-based Multi-Criteria Decision Analysis (GIS-MCDA) approach that combines spatial data layers corresponding to selected predictors to construct a composite suitability surface. Other common approaches include Non-GIS-MCDA models that rank discrete potential wind farm sites to prioritize their order of development, Bayesian Network (BN) models that construct and convey probabilistic relationships between predictors, and Logistic Regression (LR) models that perform either spatial or non-spatial assessment of a wind farm’s suitability of presence based on the log-odds of a linear combination of predictors. The common limitation of these modeling approaches is their lack of a temporal component, meaning that they can assess WiFSS only at a single point in time. WiFSS-LRCA fills this niche by combining an LR equation with the decision rules of Cellular Automata (CA) to iteratively advance the computed probabilities of each grid cell, based on areas constrained from development and neighboring grid cells that already contain wind farms. WiFSS-LRCA enlists a large set of predictors ranging from wind speed to legislation in effect in order for the model to represent the influence that environmental, technical, economic, political, and social predictors have on wind farm siting decisions. Data were aggregated at 20 different grid cell resolutions, collated in four different predictor configurations, and adjustments to the model’s constraint, neighborhood effect, and equation-based scenario transition rules were incorporated into the model’s construction, facilitating WiFSS-LRCA’s sensitivity and scenario analysis of model outputs by end-users. WiFSS-LRCA incorporates both calibration of its LR equation’s predictors and validation of the model’s performance to determine its ability to correctly identify the observed locations of present-day wind farms. Subsequently, the model constructs a WiFSS map whose interpretation and predictive accuracy are informed by the calibration and validation process. Construction of scenarios that modify WiFSS-LRCA’s predictors allow for the model to consider the impacts of changes in these predictors on the locations of future wind energy development (e.g., new transmission line construction, opinions of wind energy improving with time, increasing temperatures due to climate change). The ability of WiFSS-LRCA to produce suitability surfaces with verifiable accuracy is greatest under the following conditions: when running the model over an individual U.S. state rather than the CONUS, when using a smaller grid cell size, when using a more complete (Full configuration) or more refined (Reduced configuration) set of predictors, and when the selected study area contains a larger number of present-day commercial wind farms. Across most study areas, however, WiFSS-LRCA is typically able to correctly identify 75-85% of grid cells that do and do not contain commercial wind farms, with these classifications most often associated with high wind speed, proximity to transmission lines, legislation that supports wind energy development, and large tracts of undeveloped land. CONUS-level model runs indicate five regions as being the most suitable for present wind energy development: Southern California, the Pacific Northwest, the Central Plains, the Great Lakes, and the Northeastern United States. CONUS-level model runs have a tendency to over(under)-estimate grid cell probabilities within (outside) the Central Plains and Great Lakes, which makes state-level model runs useful for revealing smaller-scale differences in the probabilities computed within these five broad regions. Subsequent iterations of WiFSS-LRCA out to the year 2050 show projected wind energy development to remain concentrated within these same regions. Many of the grid cells initially classified as false positive in the model’s first iteration are those that gain wind farms in subsequent iterations, particularly false positive grid cells that were part of high-probability hotspots identified by Getis-Ord statistics. Running WiFSS-LRCA over states outside of these five regions projects wind energy development potential in low-probability areas (as shown in this dissertation for Florida and Kentucky) with projected wind farms in these states concentrated closer to existing infrastructure and away from protected natural areas. The Odds Ratios (ORs) computed during WiFSS-LRCA’s initial calibration provide geographical insight into its projections, with grid cells characterized by high wind speed, undeveloped land, and ambitious Renewable Portfolio Standards (RPS) being the most likely to gain wind farms in future decades. The model’s projections are, however, shown to be sensitive to end-user definitions of parameters, with neighborhood effect and constraint definitions greatly affecting the location and timing of projected wind farm locations. The scenario setup, by contrast, is shown to mostly influence the timing of these projections, with grid cell size moderately affecting both. Multiple limitations exist in the application and interpretation of WiFSS-LRCA. Firstly, the lack of existing LRCA approaches to assessing wind farm siting potential meant few standards existed to guide this model’s development, such as the setting of default constraints and establishing cutoff statistics for refining the model’s enlisted predictors. Secondly, the use of an LR equation to construct suitability surfaces in the model’s first iteration means that both classes of the dependent variable must be filled, requiring a study area to contain at least two commercial wind farms, compromising the model’s reliability in runs over the Southeastern United States. Finally, the lack of spatial stratification during WiFSS-LRCA’s calibration and validation means that the model is trained to recognize predictors associated with wind energy development in regions where many wind farms exist, namely the Central Plains and Great Lakes, hence the greater number of Type 2 errors in CONUS-level model runs outside of these regions. Selecting stratified samples of grid cells that contain wind farms from different parts of the CONUS could be incorporated into WiFSS-LRCA to address this bias. Other directions for future work with WiFSS-LRCA include the following: optimization to assess offshore wind energy development potential by training the model with proposed offshore wind farm sites surrounding the CONUS; adapting WiFSS-LRCA to run over multiple states simultaneously to identify predictors that influence wind farm siting decisions at regional spatial scales; and performing projections of other types decentralized land-use change, such as solar energy development given similarities in the required model predictors

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

Climate Change, Security Risks, and Violent Conflicts

Research on security-related aspects of climate change is an important element of climate change impact assessments. Hamburg has become a globally recognized center of pertinent analysis of the climate-conflict-nexus. The essays in this collection present a sample of the research conducted from 2009 to 2018 within an interdisciplinary cooperation of experts from Universität Hamburg and other institutions in Hamburg related to the research group “Climate Change and Security” (CLISEC). This collection of critical assessments covers a broad understanding of security, ranging from the question of climate change as a cause of violent conflict to conditions of human security in the Anthropocene. The in-depth analyses utilize a wide array of methodological approaches, from agent-based modeling to discourse analysis