Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Electric vehicle scenario simulator tool for smart grid operators

This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operator´s planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

Intelligent behavior in a Cyber-Ambient training system for control center operators

This paper deals with the application of an intelligent tutoring approach to delivery training in diagnosis procedures of a Power System. In particular, the mechanisms implemented by the training tool to support the trainees are detailed. This tool is part of an architecture conceived to integrate Power Systems tools in a Power System Control Centre, based on an Ambient Intelligent paradigm. The present work is integrated in the CITOPSY project which main goal is to achieve a better integration between operators and control room applications, considering the needs of people, customizing requirements and forecasting behaviors.

Using an ambient intelligent architecture for developing an intelligent tutoring system for training operators of power system control centres

This paper describes an architecture conceived to integrate Power Sys-tems tools in a Power System Control Centre, based on an Ambient Intelligent (AmI) paradigm. This architecture is an instantiation of the generic architecture proposed in [1] for developing systems that interact with AmI environments. This architecture has been proposed as a consequence of a methodology for the inclu-sion of Artificial Intelligence in AmI environments (ISyRAmI - Intelligent Sys-tems Research for Ambient Intelligence). The architecture presented in the paper will be able to integrate two applications in the control room of a power system transmission network. The first is SPARSE expert system, used to get diagnosis of incidents and to support power restoration. The second application is an Intelligent Tutoring System (ITS) incorporating two training tools. The first tutoring tool is used to train operators to get the diagnosis of incidents. The second one is another tutoring tool used to train operators to perform restoration procedures.

Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

An intelligent tutoring system for operators’ training in power system control centres

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, taking into account context awareness and the unobtrusive integration in the working environment.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

Structural characterization and immunogenicity in wild-type and immune tolerant mice of degraded recombinant human interferon Alpha2b

Purpose: This study was conducted to study the influence of protein structure on the immunogenicity in wild-type and immune tolerant mice of well-characterized degradation products of recombinant human interferon alpha2b (rhIFNα2b). Methods: RhIFNα2b was degraded by metal-catalyzed oxidation (M), cross-linking with glutaraldehyde (G), oxidation with hydrogen peroxide (H), and incubation in a boiling water bath (B). The products were characterized with UV absorption, circular dichroism and fluorescence spectroscopy, gel permeation chromatography, reverse-phase high-pressure liquid chromatography, sodium dodecyl sulfate polyacrylamide gel electrophoresis, Western blotting, and mass spectrometry. The immunogenicity of the products was evaluated in wild-type mice and in transgenic mice immune tolerant for hIFNα2. Serum antibodies were detected by enzyme-linked immunosorbent assay or surface plasmon resonance. Results: M-rhIFNα2b contained covalently aggregated rhIFNα2b with three methionines partly oxidized to methionine sulfoxides. G-rhIFNα2b contained covalent aggregates and did not show changes in secondary structure. H-rhIFNα2b was only chemically changed with four partly oxidized methionines. B-rhIFNα2b was largely unfolded and heavily aggregated. Nontreated (N) rhIFNα2b was immunogenic in the wild-type mice but not in the transgenic mice, showing that the latter were immune tolerant for rhIFNα2b. The anti-rhIFNα2b antibody levels in the wild-type mice depended on the degradation product: M-rhIFNα2b > H-rhIFNα2b ∼ N-rhIFNα2b ≫ B-rhIFNα2b; G-rhIFNα2b did not induce anti-rhIFNα2b antibodies. In the transgenic mice, only M-rhIFNα2b could break the immune tolerance. Conclusions: RhIFNα2b immunogenicity is related to its structural integrity. Moreover, the immunogenicity of aggregated rhIFNα2b depends on the structure and orientation of the constituent protein molecules and/or on the aggregate size.

Synthesis and antioxidant activity of new xanthone-type compounds

3rd Portuguese Meeting on Medicinal Chemistry and 1st Portuguese-Spanish-Brazilian Meeting on Medicinal Chemistry, Aveiro, 28-30 Novembro 2012.

Sistema multi-agente para gestão de tráfego em centros urbanos

Mestrado em Engenharia Informática

Influence of ventilation type in volatile organic compounds exposure: poultry case

Agricultural workers especially poultry farmers are at increased risk of occupational respiratory diseases. Epidemiological studies showed increased prevalence of respiratory symptoms and adverse changes in pulmonary function parameters in poultry workers. In poultry production volatile organic compounds (VOCs) presence can be due to some compounds produced by molds that are volatile and are released directly into the air. These are known as microbial volatile organic compounds (MVOCs). Because these compounds often have strong and/or unpleasant odors, they can be the source of odors associated with molds. MVOC's are products of the microorganisms primary and secondary metabolism and are composed of low molecular weight alcohols, aldehydes, amines, ketones, terpenes, aromatic and chlorinated hydrocarbons, and sulfur-based compounds, all of which are variations of carbon-based molecules.

Invasion success and development of benthic assemblages: effect of timing, duration of submersion and substrate type

Copyright © 2013 Elsevier Ltd. All rights reserved.

Flavored asymmetries for type II seesaw leptogenesis

A novel contribution to the leptonic CP asymmetries in type II seesaw leptogenesis scenarios is obtained for the cases in which flavor effects are relevant for the dynamics of leptogenesis. In the so-called flavored leptogenesis regime, the interference between the tree-level amplitude of the scalar triplet decaying into two leptons and the one-loop wave function correction with leptons in the loop, leads to a new nonvanishing CP asymmetry contribution. The latter conserves total lepton number but violates lepton flavor. Cases in which this novel contribution may be dominant in the generation of the baryon asymmetry are briefly discussed.

Observador de mobilidade pessoal

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores