Multidisciplinary Design Optimization for Space Applications

Multidisciplinary Design Optimization (MDO) has been increasingly studied in aerospace engineering with the main purpose of reducing monetary and schedule costs. The traditional design approach of optimizing each discipline separately and manually iterating to achieve good solutions is substituted by exploiting the interactions between the disciplines and concurrently optimizing every subsystem. The target of the research was the development of a flexible software suite capable of concurrently optimizing the design of a rocket propellant launch vehicle for multiple objectives. The possibility of combining the advantages of global and local searches have been exploited in both the MDO architecture and in the selected and self developed optimization methodologies. Those have been compared according to computational efficiency and performance criteria. Results have been critically analyzed to identify the most suitable optimization approach for the targeted MDO problem

A research survey: review of flexible job shop scheduling techniques

In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results. A total of 404 distinct publications were found addressing the FJSSP. Some of the research papers presented more than one technique/algorithm to solve the problem that is categorized into 410 different applications. Selected time period of these research papers is between 1990 and February 2014. Articles were searched mainly on major databases such as SpringerLink, Science Direct, IEEE Xplore, Scopus, EBSCO, etc. and other web sources. All databases were searched for “flexible job shop” and “scheduling” in the title an

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Estudo e implementação de um esquema de self-healing em sistemas modernos de distribuição de energia elétrica

Orientador: Marcos Julio Rider FloresTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O objetivo deste trabalho é projetar e implementar um software eficiente de self-healing em sistemas de distribuição de energia elétrica trifásicos, usando as leituras dos medidores inteligentes instalados na rede e considerando a operação dos geradores distribuídos (GD). Self-healing é a capacidade de um sistema de distribuição para se restaurar automaticamente após a identificação e isolamento de uma falta permanente na rede. Em função dos parâmetros do sistema e das medidas, o esquema de self-healing proposto deve: a) estimar as demandas dos nós, no estado de pré e pós-falta, através de um estimador de estado trifásico e um modelo de previsão da demanda ao curto prazo, b) identificar a zona da rede onde existe uma falta permanente, e c) gerar a sequência de operações das chaves instaladas ao longo do sistema para isolar a zona com falta e restaurar o serviço de energia do maior número de usuários desenergizados, no menor tempo possível, e com mínima intervenção humana. Foi desenvolvida uma ferramenta computacional com um entorno gráfico amigável capaz de ler e processar os dados elétricos e geográficos das redes trifásicas, os parâmetros das fontes de GD, as leituras dos medidores inteligentes, e o estado de operação das chaves remotamente controláveis. Um algoritmo de estimação de estado trifásico determinará continuamente as injeções de potência nos nós em função das medidas registradas pelos medidores. Em caso de falta permanente, um modelo de localização de faltas, baseado nas leituras dos medidores e dos indicadores de falta instalados na rede, fornecerá o local aproximado da falta. Após localizar a falta e, segundo o valor das demandas estabelecidas pelo estimador de estado, o esquema de restauração fornecerá a sequência de operações das chaves para levar o sistema até um estado restaurativo eficiente. Modelos de otimização matemática serão desenvolvidos para representar o estimador de estado e o problema de restauração trifásica, respectivamente. O método de localização de falta utilizado será uma versão melhorada do método basedo em medida esparsas e a matriz de impedância das barras. A meta-heurística Tabu Search será utilizada para resolver os modelos de otimização propostos. O modelo ARIMA será utilizado para a previsão da demanda. O software de self-healing será testado utilizando sistemas reaisAbstract: The main objective of this theses is to design and to implement a centralized self-healing software for unbalanced three-phase electrical distribution systems (EDS), using the information provided by smart meters and considering distributed generation (DG). Self-healing is the ability of the EDS to automatically restore themselves in case of a permanent fault. According to the data gathered by smart meters and the EDS's parameters, the proposed self-healing software is able to: a) estimate the nodal demands during the pre and post-fault status, using a three-phase state estimator and a short-term load forecasting method, b) identify the zone wherein a permanent fault is located, and c) generate the sequence of operations that must be deployed by the remote-controlled switches installed along the system. Ultimately, the self-healing scheme will isolate the faulty section of the network and restore the service of as many customers as possible, in the least amount of time and with minimal human intervention. The proposed self-healing software will have a friendly graphical user interface to simplify the data acquisition process and to present the results, considering geographic data, dispatchable DG units, smart meters and remote-controlled switching devices. A three-phase state estimator will continuously calculate the power demands at the nodes. In case of a permanent fault, the fault location algorithm will use the smart meters' data and the fault indicators signals to establish the zone where a permanent fault is most probably located. After finding the faulty section of the system and the estimated post-fault demands, an optimal service restoration will be deployed in order to determine the sequence of switch operations. Mathematical optimization models will be used to represent the three-phase state estimator and the service restoration process. An enhanced bus-impedance-matrix-based fault-location method will be also implemented. The methodology used to solve the optimization models will be the metaheuristic \emph{Tabu {Search}}. The short-term load forecasting method will be an adaptation of the seasonal ARIMA models. The proposed self-healing scheme will be tested using real EDSDoutoradoEnergia EletricaDoutor em Engenharia Elétrica2015/12564-1FAPES

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimization tools in net zero energy buildings (NZEB) design. The paper reviews current trends in simulation-based building performance optimization (BPO) and outlines major criteria for optimization tools selection and evaluation. This is based on analyzing user's needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimization experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB design. The findings indicate a breakthrough in using evolutionary algorithms in solving highly constrained envelope, HVAC and renewable optimization problems. Simple genetic algorithm solved many design and operation problems and allowed measuring the improvement in the optimality of a solution against a base case. Evolutionary algorithms are also easily adapted to enable them to solve a particular optimization problem more effectively. However, existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented.Peer reviewe

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG

Layout optimization and Sustainable development of waste water networks with the use of heuristic algorithms: The Luxemburgish case

Fresh water tends to increasingly comprise a scarcity today both in arid or demographically boosted regions of the world such as large and smaller cities. On this basis, research is directed towards minimization of fresh water supply into a Waste Water Network Topology (WWNT) and maximizing water re-use. This might be composed of a cluster of agents which have certain demands for fresh water as well as waste water dependent on their daily uses and living profiles. This work is divided into two parts. In the first part, different waste water flows within a reference building unit i.e. a typical household of four (4) occupants is simulated. This type of building represents a major part of the total building stock in Luxembourg. In its first part the present study attempts to examine the optimized fresh and waste water flow pathways between water using units of the building. Between water flows two domestic treatment units are adopted. The simulation of above mentioned system is attempted by adopting different algorithm methods such as the Sequential Quadratic Programming (SQP), the interior point and meta-heuristic optimization algorithms such as the Genetic Algorithms (GA’s).Suitable computational platform tools such as MATLAB and GAMS are incorporated. A comparison study on the most efficient approach is then realized on the single household unit by developing four (4) different mathematical model formulation versions. The second part of this study comprises simulation and development of the Waste Water Network Grid (WWNG) in the upscale level, such as the neighborhood level within or outside the urban context. This model encompasses all possible land uses and different kinds of buildings of different use envelopes thus demands. This range of units includes mainly building stock, agricultural and infrastructure of the tertiary sector. Integration of above mentioned model to the existing WWNG will enhance attempts to more closely reach the optimum points. The use of appropriate mathematical programming methods for the upscale level, will take place. Increased uncertainties within the built model will be attempted to be tackled by developing linear programming techniques and suitable assumptions without distorting initial condition largely. Assumptions are then drawn on the efficiency of the adopted method an additional essential task is the minimization of the overall infrastructure and network cost, which may in turn give rise to corresponding reduced waste effluents discharge off the proposed network. The case study comprises selected rural and semi-rural areas zone districts of similar living profiles outside the City of Luxembourg. Therefore a clustering of end users of similar demand will be attempted. Possible redesign of an optimized WWNG comprises a vital need within the context of large scale demographic growth of urban environments today.Open Acces

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés