Gasification technology assessment of sterile coal to clean electrical power generation.

Sterile coal is a low-value residue associated to the coal extraction and mining activity. According to the type and origin of the coal bed configuration, sterile coal production can mainly vary on quantity, calorific value and presence of sulphur compounds. In addition, the potential availability of sterile coal within Spain is apparently high and its contribution to the local power generation would be of interest playing a significant role. The proposed study evaluates the availability and deployment of gasification technologies to drive clean electricity generation from waste coal and sterile rock coal, incorporating greenhouse gas emission mitigation systems, like CO2, H2S and NOx removal systems. It establishes the target facility and its conceptual basic design proposal. The syngas obtained after the gasification of sterile coal is processed through specific conditioning units before entering into the combustion chamber of a gas turbine. Flue gas leaving the gas turbine is ducted to a heat recovery steam generation boiler; the steam produced within the boilerdrives a steam turbine. The target facility resembles a singular Integrated Gasification in Combined Cycle (IGCC) power station. The evaluation of the conceptual basic design according to the power output set for a maximum sterile contribution, established that rates over 95% H2S and 90% CO2 removal can be achieved. Noticeable decrease of NOx compounds can be also achieved by the use of commercial technology. A techno-economic approach of the conceptual basic design is made evaluating the integration of potential unitsand their implementation within the target facility aiming toachieve clean power generation. The criterion to be compliant with the most restrictive regulation regarding environmental emissions is setting to carry out this analysis.

Gobernar la complejidad urbana desde la sostenibilidad. Evaluación del gobierno de la gestión visible utilizando indicadores de gobernanza (Venezuela)

Se evalúa con indicadores de gobernanza urbana la sostenibilidad de las formas de hacer ciudad hibrida compleja del gobierno de la gestión visible (GGV). Argumenta que el GGV hace ciudad para legitimarse por desempeño y fortalecer la gobernanza local, en un contexto de mutaciones múltiples y radicales que tienden a diluir y centralizar el poder local y fractalizar la ciudad, profundizando la segregación sociopolítica-territorial y la ingobernabilidad genética de la ciudad hibrida, poniendo en riesgo el Estado federal descentralizado, el derecho a la ciudad, al gobierno local y la gobernanza urbana y multinivel (hipótesis). La estrategia de evaluación de gobernanza innovadora (EEG+i) diseñada para evaluar la relación entre las formas de hacer ciudad hibrida (variables espaciales) y gobernanza (variable a-espacial) es transversal, multidimensional y se construye desde la complejidad, el análisis de escenarios, formulación de constructos, modelos e indicadores de gobernanza, entretejiendo tres campos de conocimiento, gobierno, ciudad y sostenibilidad, en cuatro fases. La Fase 1, contextualiza la gobernanza en la dramática del siglo XXI. La Fase 2, desarrolla la fundamentación teórico-práctica, nuevos conceptos y un abordaje analítico propio ‘genética territorial’, para analizar y comprehender la complejidad de la ciudad hibrida de países en desarrollo, tejiendo ontogenética territorial y el carácter autopoiético del gen informal. En la Fase 3, se caracterizan las formas de hacer ciudad desde la genética del territorio, se formulan modelos e indicadores de gobernanza con los que se evalúan, aplicando un delphi y cuestionarios, los genes tipológicos-formas de hacer ciudad y validan las conclusiones. En la Fase 4, se correlacionan los resultados de los instrumentos aplicados con la praxis urbana del GGV, durante cuatro periodos de gobierno (1996-2010). Concluyendo que, la estrategia de evaluación comprobó las hipótesis y demostró la correlación transversal y multinivel existente entre, las mutaciones en curso que contradicen el modelo de gobernanza constitucional, el paisaje de gobernanza latinoamericano y venezolano, la praxis de los regímenes híbridos ricos en recursos naturales, las perspectivas de desarrollo globales y se expresa sociopolíticamente en déficit de gobernanza, Estado de derecho y cohesión-capital social y, espaciolocalmente, en la ciudad hibrida dispersa y diluida (compleja) y en el gobierno del poder diluido centralizado. La confrontación de flujos de poder centrípetos y centrífugos en la ciudad profundiza la fragmentación socioespacial y política y el deterioro de la calidad de vida, incrementando las protestas ciudadanas e ingobernabilidad que obstaculiza la superación de la pobreza y gobernanza urbana y multinivel. La evaluación de la praxis urbana del GGV evidenció que la correlación entre gobernanza, la producción de genes formales y la ciudad por iniciativa privada tiende a ser positiva y entre gobernanza, genes y producción de ciudad informal negativa, por el carácter autopoiético-autogobernable del gen informal y de los nuevos gobiernos sublocales que dificulta gobernar en gobernanza. La praxis del GGV es contraria al modelo de gobernanza formulado y la disolución centralizada del gobierno local y de la ciudad hibrida-dispersa es socio-espacial y políticamente insostenible. Se proponen estrategias y tácticas de gobernanza multinivel para recuperar la cohesión social y de planificación de la gestión innovadora (EG [PG] +i) para orquestar, desde el Consejo Local de Gobernanza (CLG) y con la participación de los espacios y gobiernos sublocales, un proyecto de ciudad compartido y sostenible. ABSTRACT The sustainability of the forms of making the hybrid-complex city by the visible management government (VMG) is evaluated using urban governance indicators. Argues that the VMG builds city to legitimate itself by performance and to strengthen local governance in a context of multiple and radical mutations that tend to dilute and centralize local power and fractalize the city, deepening the socio-spatial and political segregation, the genetic ingovernability of the hybrid city and placing the decentralized federal State, the right to city, local government and urban governance at risk (hypothesis). The innovative governance evaluation strategy (GES+i) designed to assess the relationship between the forms of making the hybrid city (spatial variables) and governance (a-spatial variable) is transversal, multidimensional; is constructed from complexity, scenario analysis, the formulation of concepts, models and governance indicators, weaving three fields of knowledge, government, city and sustainability in four phases. Phase 1, contextualizes governance in the dramatic of the twenty-first century. Phase 2, develops the theoretical and practical foundations, new concepts and a proper analytical approach to comprehend the complexity of the hybrid city from developing countries, weaving territorial ontogenetic with the autopiethic character of the informal city gen. In Phase 3, the ways of making city are characterized from the genetics of territory; governance indicators and models are formulated to evaluate, using delphi and questionnaires, the ways of making city and validate the conclusions. In Phase 4, the results of the instruments applied are correlated with the urban praxis of the VMG during the four periods of government analyzed (1996-2010). Concluding that, the evaluation strategy proved the hypothesis and showed the transversal and multilevel correlation between, mutations that contradict the constitutional governance model, the governance landscape of Latinamerica and the country, the praxis of the hybrid regimes rich in natural resources, the perspectives of the glocal economy and expresses socio-politically the governance and rule of law and social capital-cohesion deficit and spatial-temporarily the hybrid disperse and diluted city (complex) and the diluted-centralized local government. The confrontation of flows of power centripetal and centrifugal in the city deepens the socio-spatial and political fragmentation and deterioration of the quality of life, increasing citizens' protests and ingovernability which hinders poverty eradication and, multilevel and urban governance. The evaluation of the VMG urban praxis showed the correlation between governance, the production of formal genes and city by private initiative tended to be positive and, between informal genes-city production and governance negative, due to its autopiethic-self governable character that hinders governance. The urban praxis of the VMG contradicts the formulated governance model and thecentralized dissolution of the local government and hybrid city are socio-spatial and politically unsustainable. Multiscale governance strategies are proposed to recreate social cohesion and a management planning innovative method (EG [PG] + i) to orchestrate, from the Local Governance Council (LGC) and with the participation of sublocal governments and spaces, a shared and sustainable city project.

Electricity price forecasting accounting for renewable energies: optimal combined forecasts

Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al(2007 and 2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models(according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combi nation of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to Decem ber 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.

Influence of local wind speed and direction on wind power dynamics - Application to offshore very short-term forecasting

Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some of these effects by means of statistical models. To this end, a benchmarking between two different families of varying-coefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power transformation process can be introduced by considering the local wind speed. In both cases, conditional parametric models showed a better performance than the one achieved by the regime-switching strategy. The results attained reinforce the idea that each explanatory variable allows the modelling of different underlying effects in the dynamics of wind power time series.

Experience with PV-diesel hybrid village power systems in southern Morocco

In October 2002, under the auspices of Spanish Cooperation, a pilot electrification project put into operation two centralised PV-diesel hybrid systems in two different Moroccan villages. These systems currently provide a full-time energy service and supply electricity to more than a hundred of families, six community buildings, street lighting and one running water system. The appearance of the electricity service is very similar to an urban one: one phase AC supply (230V/50Hz) distributed up to each dwelling using a low-voltage mini-grid, which has been designed to be fully compatible with a future arrival of the utility grid. The management of this electricity service is based on a “fee-for-service” scheme agreed between a local NGO, partner of the project, and electricity associations created in each village, which are in charge of, among other tasks, recording the daily energy production of systems and the monthly energy consumption of each house. This register of data allows a systematic evaluation of both the system performance and the energy consumption of users. Now, after four years of operation, this paper presents the experience of this pilot electrification project and draws lessons that can be useful for designing, managing and sizing this type of small village PV-hybrid system

Efficient local unfolding with ancestor stacks

The most successful unfolding rules used nowadays in the partial evaluation of logic programs are based on well quasi orders (wqo) applied over (covering) ancestors, i.e., a subsequence of the atoms selected during a derivation. Ancestor (sub)sequences are used to increase the specialization power of unfolding while still guaranteeing termination and also to reduce the number of atoms for which the wqo has to be checked. Unfortunately, maintaining the structure of the ancestor relation during unfolding introduces significant overhead. We propose an efficient, practical local unfolding rule based on the notion of covering ancestors which can be used in combination with a wqo and allows a stack-based implementation without losing any opportunities for specialization. Using our technique, certain non-leftmost unfoldings are allowed as long as local unfolding is performed, i.e., we cover depth-first strategies. To deal with practical programs, we propose assertion-based techniques which allow our approach to treat programs that include (Prolog) built-ins and external predicates in a very extensible manner, for the case of leftmost unfolding. Finally, we report on our mplementation of these techniques embedded in a practical partial evaluator, which shows that our techniques, in addition to dealing with practical programs, are also significantly more efficient in time and somewhat more efficient in memory than traditional tree-based implementations. To appear in Theory and Practice of Logic Programming (TPLP).

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

3D simulation of complex shading affecting PV systems taking benefit from the power of graphics cards developed for the video game industry

Shading reduces the power output of a photovoltaic (PV) system. The design engineering of PV systems requires modeling and evaluating shading losses. Some PV systems are affected by complex shading scenes whose resulting PV energy losses are very difficult to evaluate with current modeling tools. Several specialized PV design and simulation software include the possibility to evaluate shading losses. They generally possess a Graphical User Interface (GUI) through which the user can draw a 3D shading scene, and then evaluate its corresponding PV energy losses. The complexity of the objects that these tools can handle is relatively limited. We have created a software solution, 3DPV, which allows evaluating the energy losses induced by complex 3D scenes on PV generators. The 3D objects can be imported from specialized 3D modeling software or from a 3D object library. The shadows cast by this 3D scene on the PV generator are then directly evaluated from the Graphics Processing Unit (GPU). Thanks to the recent development of GPUs for the video game industry, the shadows can be evaluated with a very high spatial resolution that reaches well beyond the PV cell level, in very short calculation times. A PV simulation model then translates the geometrical shading into PV energy output losses. 3DPV has been implemented using WebGL, which allows it to run directly from a Web browser, without requiring any local installation from the user. This also allows taken full benefits from the information already available from Internet, such as the 3D object libraries. This contribution describes, step by step, the method that allows 3DPV to evaluate the PV energy losses caused by complex shading. We then illustrate the results of this methodology to several application cases that are encountered in the world of PV systems design. Keywords: 3D, modeling, simulation, GPU, shading, losses, shadow mapping, solar, photovoltaic, PV, WebGL

Damage identification from power spectrum density transmissibility

Damage identification under real operating conditions of the structure during its daily use would be suitable and attractive to civil engineers due to the difficulty and problems of carrying out controlled forced excitation tests on this kind of structures. In this case, output-only response measurements would be available, and an output-only damage identification procedure should be implemented. Transmissibility, defined on an output-to-output relationship, is getting increased attention in damage detection applications because of its dependence with output-only data and its sensitivity to local structural changes. In this paper, a method based on the power spectrum density transmissibility (PSDT) is proposed to detect structural damage.