A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

Accessing complexity from genome information

This paper studies the information content of the chromosomes of 24 species. In a first phase, a scheme inspired in dynamical system state space representation is developed. For each chromosome the state space dynamical evolution is shed into a two dimensional chart. The plots are then analyzed and characterized in the perspective of fractal dimension. This information is integrated in two measures of the species’ complexity addressing its average and variability. The results are in close accordance with phylogenetics pointing quantitative aspects of the species’ genomic complexity.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

Study of Load Curves Concerning the Influence of Socioeconomic and Cultural Issues

The increase of electricity demand in Brazil, the lack of the next major hydroelectric reservoirs implementation, and the growth of environmental concerns lead utilities to seek an improved system planning to meet these energy needs. The great diversity of economic, social, climatic, and cultural conditions in the country have been causing a more difficult planning of the power system. The work presented in this paper concerns the development of an algorithm that aims studying the influence of the issues mentioned in load curves. Focus is given to residential consumers. The consumption device with highest influence in the load curve is also identified. The methodology developed gains increasing importance in the system planning and operation, namely in the smart grids context.

Contextual and Environmental Awareness Laboratory for Energy Consumption Management

The recent changes on power systems paradigm requires the active participation of small and medium players in energy management. With an electricity price fluctuation these players must manage the consumption. Lowering costs and ensuring adequate user comfort levels. Demand response can improve the power system management and bring benefits for the small and medium players. The work presented in this paper, which is developed aiming the smart grid context, can also be used in the current power system paradigm. The proposed system is the combination of several fields of research, namely multi-agent systems and artificial neural networks. This system is physically implemented in our laboratories and it is used daily by researchers. The physical implementation gives the system an improvement in the proof of concept, distancing itself from the conventional systems. This paper presents a case study illustrating the simulation of real-time pricing in a laboratory.

Dimensionamento do Sistema Híbrido para Alimentação de Energia Elétrica da Escola Rural de Nangade (Cabo Delgado)

Os combustíveis fósseis, como o carvão, o petróleo e o gás, constituem fontes de energia que em breve se esgotarão e que são demasiado caras para serem desperdiçadas pelas centrais elétricas na produção de electricidade. Para além desse facto, existem outros argumentos (sobretudo económicos) que inviabilizam a utilização destas fontes de energia em algumas regiões, abrindo caminho a fontes de energia alternativas (e.g. solar, eólica, biomassa, mini-hídricas, geotérmicas, etc) e preferencialmente com contornos locais. No caso particular de Moçambique, tem-se verificado um interesse crescente por parte do governo e de várias ONGs na promoção do uso de energias alternativas para as zonas onde a energia convencional não chega e não chegará, devido aos custos muito elevados que esse processo acarretaria. Esta dissertação apresenta um estudo aprofundado do dimensionamento dum sistema híbrido de geração de energia elétrica envolvendo gerador FV e grupo eletrogéneo de emergência para a Escola Rural da Nangade, situada no Distrito de Nangade, na Província do Cabo Delgado. São também descritos os diversos componentes e as tecnologias associadas a um sistema deste género, com a inclusão de sistemas inteligentes de controlo de energia com a utilização de inversores bidireccionais (inversores de bateria e carregadores) para sistemas isolados. Os resultados são apresentados de forma a facilitar a aplicação e montagem deste tipo de sistemas in loco. Espera-se que esta dissertação possa servir de base no futuro próximo, para a implementação deste tipo de sistemas para permitir a melhoria da qualidade de ensino através de melhores infraestruturas, democratizando desta forma o acesso à educação para as crianças das zonas rurais das várias províncias de Moçambique. Como as energias renováveis são parte integrante do Sistema Elétrico Nacional, apresenta-se resumidamente, no anexo 17, o “Plano de Desenvolvimento na Área de Energia de Moçambique”.

Formation Control Driven by Cooperative Object Tracking

In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles.

Le totalitarisme en mouvement : propagande, politique eugénique et la création d’un « homme nouveau » dans le Troisième Reich

Le but de ce mémoire est de poser un regard comparatiste sur les conséquences éventuelles de la politique eugénique totalitaire du Troisième Reich, et ce, dans l’optique où ce régime aurait eu la chance de poursuivre ses ambitions à ce niveau. En portant respectivement notre attention sur la structure organisationnelle du NSDAP, de l’État et de l’autorité, sur les étapes spécifiques de l’établissement du totalitarisme hitlérien, sur les diverses techniques de propagande et d’endoctrinement utilisées par les nazis pour accomplir l’unification du peuple allemand, ainsi que sur l’application pratique et le discours relatif à la politique eugénique dans le Reich et sur les territoires occupés, nous comprendrons que le mouvement propre au totalitarisme hitlérien, en changeant constamment sa définition respective de l’« élite » et de l’être « dépravé », n’aurait jamais mis fin à la purge raciale de la population sous son joug. Par conséquent, la place de l’« allemand moyen » aurait été quasi inexistante. Le Troisième Reich, par élimination et élevage social constant, aurait donc créé un « homme nouveau », basé sur l’idéologie arbitraire et instable du régime et pigé dans les peuples occupés à divers degré. Au bout de plusieurs générations, cet être nouveau aurait constitué le « noyau racial » de la population d’une nouvelle Europe aryanisée, construite sur le cadavre de la plus grande partie des anciens peuples du continent, incluant le peuple allemand.

Analysis of Some Stochastic Inventory Models with Pooling/ Retrial of Customers

In this thesis we have presented several inventory models of utility. Of these inventory with retrial of unsatisfied demands and inventory with postponed work are quite recently introduced concepts, the latt~~ being introduced for the first time. Inventory with service time is relatively new with a handful of research work reported. The di lficuity encoLlntered in inventory with service, unlike the queueing process, is that even the simplest case needs a 2-dimensional process for its description. Only in certain specific cases we can introduce generating function • to solve for the system state distribution. However numerical procedures can be developed for solving these problem.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Erneuerbare Energien im Verbund mit Hochleistungs-Bleiakkumulatoren zur Bereitstellung von Spitzenleistung - am Beispiel von Windkraftwerken im 20 kV-Versorgungsnetz der CEGEDEL / Luxemburg -

Die wachsende Weltbevölkerung bedingt einen höheren Energiebedarf, dies jedoch unter der Beachtung der nachhaltigen Entwicklung. Die derzeitige zentrale Versorgung mit elektrischer Energie wird durch wenige Erzeugungsanlagen auf der Basis von fossilen Primärenergieträgern und Kernenergie bestimmt, die die räumlich verteilten Verbraucher zuverlässig und wirtschaftlich über ein strukturiertes Versorgungssystem beliefert. In den Elektrizitätsversorgungsnetzen sind keine nennenswerten Speicherkapazitäten vorhanden, deshalb muss die von den Verbrauchern angeforderte Energie resp. Leistung jederzeit von den Kraftwerken gedeckt werden. Bedingt durch die Liberalisierung der Energiemärkte und die geforderte Verringerung der Energieabhängigkeit Luxemburgs, unterliegt die Versorgung einem Wandel hin zu mehr Energieeffizienz und erhöhter Nutzung der dargebotsabhängigen Energiequellen. Die Speicherung der aus der Windkraft erzeugten elektrischen Energie, wird in den Hochleistungs-Bleiakkumulatoren, errichtet im ländlichen Raum in der Nähe der Windkraftwerke, eingespeichert. Die zeitversetzte Einspeisung dieser gespeicherten elektrischen Energie in Form von veredelter elektrischer Leistung während den Lastspitzen in das 20 kV-Versorgungsnetz der CEGEDEL stellt die Innovation in der luxemburgischen Elektrizitätsversorgung dar. Die Betrachtungen beschränken sich somit auf die regionale, relativ kleinräumige Einbindung der Windkraft in die elektrische Energieversorgung des Großherzogtums Luxemburg. Die Integration der Windkraft im Regionalbereich wird in den Vordergrund der Untersuchung gerückt. Überregionale Ausgleichseffekte durch Hochspannungsleitungen der 230/400 kV-Systeme werden außer Acht gelassen. Durch die verbrauchernahe Bereitstellung von elektrischer Spitzenleistung vermindern sich ebenfalls die Übertragungskosten aus den entfernten Spitzenlastkraftwerken, der Ausbau von Kraftwerkskapazitäten kann in die Zukunft verschoben werden. Die Emission von Treibhausgasen in thermischen Kraftwerken wird zum Teil reduziert. Die Berechnungen der Wirtschaftlichkeit von Hybridanlagen, zusammengesetzt aus den Windkraftwerken und den Hochleistungs-Bleiakkumulatoren bringen weitere Informationen zum Einsatz dieser dezentralen Speichern, als Partner der nachhaltigen Energieversorgung im ländlichen Raum. Die untersuchte Einspeisung von erneuerbarer Spitzenleistung lässt sich auch in die Entwicklungsländer übertragen, welche nicht über zentrale Kraftwerkskapazitäten und Verteilungsnetze verfügen.