Plataforma de gestão de guias de programação de TV digital

Com o consumismo de mais variedade e qualidade de informação, assim como, produtos interativos, surgiu a necessidade de apresentar mais conteúdos, para além da programação de televisão comum. Com os avanços tecnológicos ligados à indústria da televisão e sua distribuição nos lares portugueses pelos operadores de TV, a quantidade de oferta de canais deixou de ser um foco, passando a ser prioritário a melhoria da experiência do cliente. Com a introdução de novas funcionalidades nas caixas recetoras de sinais de transmissão de canais, como por exemplo, a capacidade de apresentar informações adicionais sobre os programas, desde da sua apresentação em modo trailer até ao elenco detalhado que o compõe, os clientes podem ter uma nova experiência de interação com os serviços de TV. A funcionalidade de gravação agendada de programas levou ao próximo ponto de melhoria de experiência do cliente. As gravações que resultavam em programas indevidamente cortados, quer no seu início quer no seu fim, foi um dos motivos que levou os operadores de TV a procurarem um melhor serviço de gestão de guias de programação digitais. A InfoPortugal, entidade detentora do seguinte projeto e EPG Provider de algumas operadoras de TV nacionais, viu-se obrigada a atualizar os seu sistemas de distribuição de conteúdos, para responder à evolução dos requisitos dos seus clientes.

Optimal scheduling of aircrafts’ engines repair process

We address a real world scheduling problem concerning the repair process of aircrafts’ engines by TAP - Maintenance & Engineering (TAP-ME). TAP-ME is the maintenance, repair and overhaul organization of TAP Portugal, Portugal’s leading airline, which employs about 4000 persons to provide maintenance and engineering services in aircraft, engines and components. TAP-ME is aiming to optimize its maintenance services, focusing on the reduction of the engines repair turnaround time.

Scheduling aircraft’s engines repair process: a mathematical model

In this talk, we discuss a scheduling problem that originated at TAP - Maintenance & Engineering - the maintenance, repair and overhaul organization of Portugal’s leading airline. In the repair process of aircrafts’ engines, the operations to be scheduled may be executed on a certain workstation by any processor of a given set, and the objective is to minimize the total weighted tardiness. A mixed integer linear programming formulation, based on the flexible job shop scheduling, is presented here, along with computational experiment on a real instance, provided by TAP-ME, from a regular working week. The model was also tested using benchmarking instances available in literature.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Constrained Consumption Shifting Management in the Distributed Energy Resources Scheduling Considering Demand Response

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

Distributed energy resources scheduling considering real-time resources forecast

Energy resource scheduling is becoming increasingly important, such as the use of more distributed generators and electric vehicles connected to the distribution network. This paper proposes a methodology to be used by Virtual Power Players (VPPs), regarding the energy resource scheduling in smart grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used.

Real-time Energy Resource Scheduling considering a Real Portuguese Scenario

The development in power systems and the introduction of decentralized generation and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which considers not only the generation, but also the management of loads through demand response programs, energy storage units, EVs and other players in a liberalized electricity markets environment. This paper proposes a methodology to be used by Virtual Power Players (VPPs), concerning the energy resource scheduling in smart grids, considering day-ahead, hour-ahead and real-time scheduling. The case study considers a 33-bus distribution network with high penetration of distributed energy resources. The wind generation profile is based on a real Portuguese wind farm. Four scenarios are presented taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour-ahead and realtime scheduling.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

A New Heuristic Providing an Effective Initial Solution for a Simulated Annealing approach to Energy Resource Scheduling in Smart Grids

An intensive use of dispersed energy resources is expected for future power systems, including distributed generation, especially based on renewable sources, and electric vehicles. The system operation methods and tool must be adapted to the increased complexity, especially the optimal resource scheduling problem. Therefore, the use of metaheuristics is required to obtain good solutions in a reasonable amount of time. This paper proposes two new heuristics, called naive electric vehicles charge and discharge allocation and generation tournament based on cost, developed to obtain an initial solution to be used in the energy resource scheduling methodology based on simulated annealing previously developed by the authors. The case study considers two scenarios with 1000 and 2000 electric vehicles connected in a distribution network. The proposed heuristics are compared with a deterministic approach and presenting a very small error concerning the objective function with a low execution time for the scenario with 2000 vehicles.

Resource Scheduling in Residential Microgrids Considering Energy Selling to External Players

The operation of distribution networks has been facing changes with the implementation of smart grids and microgrids, and the increasing use of distributed generation. The specific case of distribution networks that accommodate residential buildings, small commerce, and distributed generation as the case of storage and PV generation lead to the concept of microgrids, in the cases that the network is able to operate in islanding mode. The microgrid operator in this context is able to manage the consumption and generation resources, also including demand response programs, obtaining profits from selling electricity to the main network. The present paper proposes a methodology for the energy resource scheduling considering power flow issues and the energy buying and selling from/to the main network in each bus of the microgrid. The case study uses a real distribution network with 25 bus, residential and commercial consumers, PV generation, and storage.

Environmental/Regatta Buoy: Sensing System

Um dos principais objetivos da ciência é perceber a natureza, i.e., descobrir e explicar o funcionamento do mundo que nos rodeia. Para tal, os cientistas precisam de coligir dados e monitorar o meio ambiente. Em particular, considerando que cerca de 70% da Terra é coberta por água, a coleta de parâmetros de caracterização da água de grandes superfícies é uma prioridade. A monitorização das condições da água é feita principalmente através de bóias. No entanto, as bóias disponíveis no mercado não satisfazem as necessidades existentes. Esta é uma das principais razões que levaram o Laboratório de Sistemas Autónomos (LSA) do Instituto Superior de Engenharia do Porto a lançarem um projeto para o desenvolvimento de uma bóia reconfigurável e com dois modos de funcionamento: monitorização ambiental e baliza ativa de regata. O segundo modo é destinado a regatas de veleiros autónomos. O projeto começou há um ano com um projeto do European Project Project [1] (EPS), realizado por quatro estudantes internacionais, destinado à construção da estrutura da bóia e à selecção dos componentes mais adequados para o sistema de medição e controlo. A arquitetura que foi definida para este sistema é do tipo mestre-escravo e é composta por uma unidade de controlo mestre para a telemetria e configuração e uma unidade de controlo escrava para a medição e armazenamento de dados. O desenvolvimento do projeto continuou com dois estudantes belgas que trabalharam na comunicação e no armazenamento de dados. Este projeto, que prossegue com o desenvolvimento da medição e do armazenamento de dados do lado da unidade de controlo escrava, tem os seguintes objetivos: (i ) implementar o protocolo de comunicação na unidade de controlo escrava; (ii ) coligir e armazenar os dados dos sensores no cartão SD em tempo real; (iii ) fornecer dados em tempo útil; e (iv) recuperar dados do cartão SD em tempo diferido. As contribuições anteriores foram estudadas e foi feito um levantamento dos projetos congéneres existentes. O desenvolvimento do projeto atual começou com o protocolo de comunicação. Este protocolo, que foi projetado pelos alunos anteriores, foi um bom ponto de partida. No entanto, o protocolo foi atualizado e melhorado com novas funcionalidades. Esta última componente foi um trabalho conjunto com Laurens Allart, que esteve a trabalhar no subsistema de telemetria e de configuração durante este semestre. O protocolo foi implementado do lado da unidade de controlo escrava através de uma estrutura de múltiplas actividades paralelas (multithreaded). Esta estrutura recebe as mensagens da unidade mestre, executa as ações solicitadas e envia de volta o resultado. A bóia é um dispositivo reconfigurável multimodo que pode ser expandido com novos modos de operação no futuro. Infelizmente, sofre de algumas limitações: suporta uma carga máxima de 40 kg e tem uma área de implantação limitada pela distância máxima à estacão base.

MicroRNA-375 plays a dual role in prostate carcinogenesis

Background: Prostate cancer (PCa), a highly incident and heterogeneous malignancy, mostly affects men from developed countries. Increased knowledge of the biological mechanisms underlying PCa onset and progression are critical for improved clinical management. MicroRNAs (miRNAs) deregulation is common in human cancers, and understanding how it impacts in PCa is of major importance. MiRNAs are mostly downregulated in cancer, although some are overexpressed, playing a critical role in tumor initiation and progression. We aimed to identify miRNAs overexpressed in PCa and subsequently determine its impact in tumorigenesis. Results: MicroRNA expression profiling in primary PCa and morphological normal prostate (MNPT) tissues identified 17 miRNAs significantly overexpressed in PCa. Expression of three miRNAs, not previously associated with PCa, was subsequently assessed in large independent sets of primary tumors, in which miR-182 and miR-375 were validated, but not miR-32. Significantly higher expression levels of miR-375 were depicted in patients with higher Gleason score and more advanced pathological stage, as well as with regional lymph nodes metastases. Forced expression of miR-375 in PC-3 cells, which display the lowest miR-375 levels among PCa cell lines, increased apoptosis and reduced invasion ability and cell viability. Intriguingly, in 22Rv1 cells, which displayed the highest miR-375 expression, knockdown experiments also attenuated the malignant phenotype. Gene ontology analysis implicated miR-375 in several key pathways deregulated in PCa, including cell cycle and cell differentiation. Moreover, CCND2 was identified as putative miR-375 target in PCa, confirmed by luciferase assay. Conclusions: A dual role for miR-375 in prostate cancer progression is suggested, highlighting the importance of cellular context on microRNA targeting.

Selection Constructive based Hyper-heuristic for Dynamic Scheduling

A função de escalonamento desempenha um papel importante nos sistemas de produção. Os sistemas de escalonamento têm como objetivo gerar um plano de escalonamento que permite gerir de uma forma eficiente um conjunto de tarefas que necessitam de ser executadas no mesmo período de tempo pelos mesmos recursos. Contudo, adaptação dinâmica e otimização é uma necessidade crítica em sistemas de escalonamento, uma vez que as organizações de produção têm uma natureza dinâmica. Nestas organizações ocorrem distúrbios nas condições requisitos de trabalho regularmente e de forma inesperada. Alguns exemplos destes distúrbios são: surgimento de uma nova tarefa, cancelamento de uma tarefa, alteração na data de entrega, entre outros. Estes eventos dinâmicos devem ser tidos em conta, uma vez que podem influenciar o plano criado, tornando-o ineficiente. Portanto, ambientes de produção necessitam de resposta imediata para estes eventos, usando um método de reescalonamento em tempo real, para minimizar o efeito destes eventos dinâmicos no sistema de produção. Deste modo, os sistemas de escalonamento devem de uma forma automática e inteligente, ser capazes de adaptar o plano de escalonamento que a organização está a seguir aos eventos inesperados em tempo real. Esta dissertação aborda o problema de incorporar novas tarefas num plano de escalonamento já existente. Deste modo, é proposta uma abordagem de otimização – Hiper-heurística baseada em Seleção Construtiva para Escalonamento Dinâmico- para lidar com eventos dinâmicos que podem ocorrer num ambiente de produção, a fim de manter o plano de escalonamento, o mais robusto possível. Esta abordagem é inspirada em computação evolutiva e hiper-heurísticas. Do estudo computacional realizado foi possível concluir que o uso da hiper-heurística de seleção construtiva pode ser vantajoso na resolução de problemas de otimização de adaptação dinâmica.