Spectral and dynamical invariants in a complete clustered network

The main result of this work is a new criterion for the formation of good clusters in a graph. This criterion uses a new dynamical invariant, the performance of a clustering, that characterizes the quality of the formation of clusters. We prove that the growth of the dynamical invariant, the network topological entropy, has the effect of worsening the quality of a clustering, in a process of cluster formation by the successive removal of edges. Several examples of clustering on the same network are presented to compare the behavior of other parameters such as network topological entropy, conductance, coefficient of clustering and performance of a clustering with the number of edges in a process of clustering by successive removal.

Transporte marítimo de curta distância na União Europeia e em Portugal

O transporte marítimo sempre assumiu uma importância primordial no transporte, quer de pessoas como de mercadorias. O uso das vias marítimas para o transporte de mercadorias é uma prática utilizada desde os tempos antigos. O transporte marítimo na Europa sempre foi uma forma eficaz e de baixo custo de transportar passageiros e mercadorias de um local para outro. Atualmente, todos os modos de transporte necessitam de melhorar a sua eficiência e reduzir o montante de poluentes que libertam para o meio-ambiente. O transporte marítimo de curta distância (TMCD) é parte essencial no sistema de transporte multimodal Europeu representando cerca de 37% das transações intracomunitárias (tkm). Os resultados alcançados advêm desde que, em 2001, a European Shortsea Network (ESN) em parceria com os Shortsea Promotion Centres (SPC) de cada Estado-Membro da União Europeia (EU) conseguiram efetuar progressos significativos na promoção e desenvolvimento do transporte por esta via. Esta tese de mestrado pretende caraterizar o TMCD na EU e em Portugal e a sua articulação com os outros meios de transporte de mercadorias. Será também analisada a situação Portuguesa ao nível do transporte marítimo de mercadorias fazendo especial relevância à mercadoria contentorizada, às infraestruturas atualmente existentes em Portugal assim como às perspetivas futuras para o setor. A metodologia utilizada inclui a revisão de literatura da investigação efetuada sobre o setor, a recolha de informação primária através do instrumento inquérito e a recolha de informação estatística a partir de fontes secundárias, permitindo a caraterização do setor. O inquérito foi desenvolvido e partilhado com mais de um milhar de empresas a operar em Portugal, nas que se incluem as empresas transportadoras, operadores logísticos, agentes de navegação e as principais empresas exportadoras/importadoras a operar em Portugal, tendo-se obtido 106 respostas válidas. A nível nacional, o inquérito realizado permitiu confirmar que os principais portos logísticos para a movimentação internacional de mercadorias são os portos de Leixões, Lisboa, Sines e Setúbal, estando a maioria das empresas inquiridas localizadas no hinterland destes portos. O inquérito permitiu também concluir que o facto de o transporte rodoviário ser um meio de transporte rápido, de baixo custo para curtas e médias distâncias, de elevada frequência de transporte, de oferecer elevada capacidade e diversos tipos de carga transportada, de oferecer serviços Porta-a-Porta, da elevada flexibilidade e mobilidade oferecida, assim como a rapidez das operações de carga e descarga continuam a contribuir para a elevada competitividade deste modo de transporte de mercadorias, sendo as razões pelo qual este modo é escolhido, pese embora, ser o modo mais poluente por tonelada transportada. Os modos de transporte TMCD e ferroviário apenas são alvo de escolha, como modo de transporte de mercadorias, por parte das empresas mais responsáveis socialmente e ambiental. O inquérito permitiu ainda concluir que, para haver uma melhor articulação entre o TMCD com outros meios de transporte, é necessário que sejam disponibilizados novos serviços, portos e terminais logísticos com serviços privatizados, assim como a redução das tarifas aplicáveis TMCD.

GAIC: Um sistema inteligente e flexível para simulação e apoio à participação de pequenos e médios consumidores na gestão ativa de cargas no âmbito de smart grids

A liberalização dos mercados de energia e a utilização intensiva de produção distribuída tem vindo a provocar uma alteração no paradigma de operação das redes de distribuição de energia elétrica. A continuidade da fiabilidade das redes de distribuição no contexto destes novos paradigmas requer alterações estruturais e funcionais. O conceito de Smart Grid vem permitir a adaptação das redes de distribuição ao novo contexto. Numa Smart Grid os pequenos e médios consumidores são chamados ao plano ativo das participações. Este processo é conseguido através da aplicação de programas de demand response e da existência de players agregadores. O uso de programas de demand response para alcançar benefícios para a rede encontra-se atualmente a ser estudado no meio científico. Porém, existe a necessidade de estudos que procurem benefícios para os pequenos e médios consumidores. O alcance dos benefícios para os pequenos e médios consumidores não é apenas vantajoso para o consumidor, como também o é para a rede elétrica de distribuição. A participação, dos pequenos e médios consumidores, em programas de demand response acontece significativamente através da redução de consumos energéticos. De modo a evitar os impactos negativos que podem provir dessas reduções, o trabalho aqui proposto faz uso de otimizações que recorrem a técnicas de aprendizagem através da utilização redes neuronais artificiais. Para poder efetuar um melhor enquadramento do trabalho com as Smart Grids, será desenvolvido um sistema multiagente capaz de simular os principais players de uma Smart Grid. O foco deste sistema multiagente será o agente responsável pela simulação do pequeno e médio consumidor. Este agente terá não só que replicar um pequeno e médio consumidor, como terá ainda que possibilitar a integração de cargas reais e virtuais. Como meio de interação com o pequeno e médio consumidor, foi desenvolvida no âmbito desta dissertação um sistema móvel. No final do trabalho obteve-se um sistema multiagente capaz de simular uma Smart Grid e a execução de programas de demand response, sSendo o agente representante do pequeno e médio consumidor capaz de tomar ações e reações de modo a poder responder autonomamente aos programas de demand response lançados na rede. O desenvolvimento do sistema permite: o estudo e análise da integração dos pequenos e médios consumidores nas Smart Grids por meio de programas de demand response; a comparação entre múltiplos algoritmos de otimização; e a integração de métodos de aprendizagem. De modo a demonstrar e viabilizar as capacidades de todo o sistema, a dissertação inclui casos de estudo para as várias vertentes que podem ser exploradas com o sistema desenvolvido.

Smart Grid Market Using Joint Energy and Ancillary Services Bids

The power systems operation in the smart grid context increases significantly the complexity of their management. New approaches for ancillary services procurement are essential to ensure the operation of electric power systems with appropriate levels of stability, safety, quality, equity and competitiveness. These approaches should include market mechanisms which allow the participation of small and medium distributed energy resources players in a competitive market environment. In this paper, an energy and ancillary services joint market model used by an aggregator is proposed, considering bids of several types of distributed energy resources. In order to improve economic efficiency in the market, ancillary services cascading market mechanism is also considered in the model. The proposed model is included in MASCEM – a multi-agent system electricity market simulator. A case study considering a distribution network with high penetration of distributed energy resources is presented.

Contextual Intelligent Load Management Considering Real-Time Pricing in a Smart Grid Environment

The use of demand response programs enables the adequate use of resources of small and medium players, bringing high benefits to the smart grid, and increasing its efficiency. One of the difficulties to proceed with this paradigm is the lack of intelligence in the management of small and medium size players. In order to make demand response programs a feasible solution, it is essential that small and medium players have an efficient energy management and a fair optimization mechanism to decrease the consumption without heavy loss of comfort, making it acceptable for the users. This paper addresses the application of real-time pricing in a house that uses an intelligent optimization module involving artificial neural networks.

Reactive power management strategies in future smart grids

The reactive power management in distribution network with large penetration of distributed energy resources is an important task in future power systems. The control of reactive power allows the inclusion of more distributed recourses and a more efficient operation of distributed network. Currently, the reactive power is only controlled in large power plants and in high and very high voltage substations. In this paper, several reactive power control strategies considering a smart grids paradigm are proposed. In this context, the management of distributed energy resources and of the distribution network by an aggregator, namely Virtual Power Player (VPP), is proposed and implemented in a MAS simulation tool. The proposed methods have been computationally implemented and tested using a 32-bus distribution network with intensive use of distributed resources, mainly the distributed generation based on renewable resources. Results concerning the evaluation of the reactive power management algorithms are also presented and compared.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Demand Response Design and Use Based on Network Locational Marginal Prices

Power systems have been experiencing huge changes mainly due to the substantial increase of distributed generation (DG) and the operation in competitive environments. Virtual Power Players (VPP) can aggregate several players, namely a diversity of energy resources, including distributed generation (DG) based on several technologies, electric storage systems (ESS) and demand response (DR). Energy resources management gains an increasing relevance in this competitive context. This makes the DR use more interesting and flexible, giving place to a wide range of new opportunities. This paper proposes a methodology to support VPPs in the DR programs’ management, considering all the existing energy resources (generation and storage units) and the distribution network. The proposed method is based on locational marginal prices (LMP) values. The evaluation of the impact of using DR specific programs in the LMP values supports the manager decision concerning the DR use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 33-bus network with intensive use of DG.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

Dynamic Approach and Testbed for Small and Medium Players Simulation in Smart Grid Environments

The Smart Grid environment allows the integration of resources of small and medium players through the use of Demand Response programs. Despite the clear advantages for the grid, the integration of consumers must be carefully done. This paper proposes a system which simulates small and medium players. The system is essential to produce tests and studies about the active participation of small and medium players in the Smart Grid environment. When comparing to similar systems, the advantages comprise the capability to deal with three types of loads – virtual, contextual and real. It can have several loads optimization modules and it can run in real time. The use of modules and the dynamic configuration of the player results in a system which can represent different players in an easy and independent way. This paper describes the system and all its capabilities.

Real-Time Simulation of Real-Time Pricing Demand Response to Meet Wind Variations

Recent changes of paradigm in power systems opened the opportunity to the active participation of new players. The small and medium players gain new opportunities while participating in demand response programs. This paper explores the optimal resources scheduling in two distinct levels. First, the network operator facing large wind power variations makes use of real time pricing to induce consumers to meet wind power variations. Then, at the consumer level, each load is managed according to the consumer preferences. The two-level resources schedule has been implemented in a real-time simulation platform, which uses hardware for consumer’ loads control. The illustrative example includes a situation of large lack of wind power and focuses on a consumer with 18 loads.

Chlorella sp. coagulation‐flocculation by inducing a modification on the pH broth medium

Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Bioenergia

Models and tools for value systems analysis in collaborative environments

Dissertation to obtain the degree of Doctor in Electrical and Computer Engineering, specialization of Collaborative Networks

Use of selective medium for Burkholderia cepacia isolation in respiratory samples from cystic fibrosis patients

Burkholderia cepacia colonizes cystic fibrosis (CF) patients. We evaluated the impact of the use of a selective medium in the rate of B. cepacia recovery from respiratory samples of CF patients. During a 6-month period, respiratory samples were collected from 106 CF patients and cultivated on selective media including a B. cepacia selective medium. Confirmation of the identity of B. cepacia isolates was carried out by species specific PCR and determination of genomovar status performed by a sequential PCR approach. Results of B. cepacia isolation during this period were compared to the preceding two years, when the sample processing was identical except for the lack of the B. cepacia selective medium. B. cepacia was isolated in 11/257 (4.2%) of the samples using the selective medium, in contrast with the preceding two years, when it was isolated in 6/1029 samples (0.58%), p < 0.0001. Identity of all 11 isolates was confirmed by PCR and genomovar determination was accomplished in all but one isolate. These results suggest that the use of a selective medium increases recovery rate of B. cepacia from respiratory samples.