A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

Fatores influenciadores da gestão da qualidade e independência do auditor: um estudo de caso

Dissertação apresentada ao Instituto Superior de Contabilidade para obtenção do Grau de Mestre em Auditoria Orientada por: Doutora Alcina Dias

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Seller, buyer and VPP agents behavior on a simulated electricity market

Competitive electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is an electricity market simulator able to model market players and simulate their operation in the market. As market players are complex entities, having their characteristics and objectives, making their decisions and interacting with other players, a multi-agent architecture is used and proved to be adequate. MASCEM players have learning capabilities and different risk preferences. They are able to refine their strategies according to their past experience (both real and simulated) and considering other agents’ behavior. Agents’ behavior is also subject to its risk preferences.

Auction of financial transmission rights in electricity market environment

An auction model is used to increase the individual profits for market players with products they do not use. A Financial Transmission Rights Auction has the goal of trade transmission rights between Bidders and helps them raise their own profits. The ISO plays a major rule on keep the system in technical limits without interfere on the auctions offers. In some auction models the ISO decide want bids are implemented on the network, always with the objective maximize the individual profits for all bidders in the auction. This paper proposes a methodology for a Financial Transmission Rights Auction and an informatics application. The application receives offers from the purchase and sale side and considers bilateral contracts as Base Case. This goal is maximize the individual profits within the system in their technical limits. The paper includes a case study for the 30 bus IEEE test case.

Conhecimentos sobre VIH sida nos clientes de um hospital de Lakes State, Sudão do Sul

Enquadramento: O VIH/Sida exige uma ação direcionada na vertente da prevenção, cujo suporte integra a transmissão de conhecimentos promotores da adoção e manutenção de comportamentos seguros, em conformidade com as características sociais e culturais dos indivíduos. Objetivos: Validar, para a população do Sudão do Sul, a Escala de Conhecimentos sobre VIH/Sida, The HIV Knowledge Questionnaire: HIV-KQ-45, de Carey et al. (1997); analisar de que forma as variáveis sociodemográficas influenciam os conhecimentos sobre VIH/Sida, dos cidadãos de Mapuordit Sudão do Sul; verificar se a frequência de formação sobre VIH/Sida influencia o seu nível de conhecimentos. Metodologia: Estudo quantitativo, descritivo-analítico e transversal, com 232 clientes do Mary Immaculate de Mapuordit Hospital. Foi utilizado um Questionário de caracterização sociodemográfica e do contexto de formação sobre o VIH/Sida, e o HIV Knowledge Questionnaire (HVI-K-Q) de Carey, Morrison-Beedy e Johnson (1997). Resultados: Amostra é maioritariamente masculina (74.6%), com uma média de idade 22,83 (±5.793 anos). A análise fatorial confirmatória do HIV-K-Q permitiu apurar 5 fatores, cujos valores médios mais significativos foram nos fatores preconceitos/medos (média=80.60%), conhecimentos sobre os comportamentos de risco (média=76.58%) e vias de transmissão (média=70.36%). Os sudaneses pontuaram maioritariamente com razoáveis conhecimentos sobre a Sida (média=68.08%). As mulheres, os participantes mais velhos, com companheiro(a), mais escolarizados, profissionalmente ativos, a distar do hospital =<20 Km, deslocando-se num veículo não motorizado e com diagnóstico de VIH relataram mais conhecimentos sobre a Sida. Os participantes com informação sobre a prevenção do VIH/Sida e frequência em workshop na área demonstraram melhores conhecimentos. Revelaram-se preditivas dos conhecimentos acerca da doença as habilitações literárias (β=0.32) e o diagnóstico de VIH/Sida (β=0.14) revelou-se preditor dos conhecimentos sobre os comportamentos de risco. Conclusão: As casuísticas significativas do VIH/Sida justificam considerar as habilitações literárias e a presença de diagnóstico VIH/Sida como variáveis a avaliar previamente ao planeamento estratégico das ações de educação para a prevenção do VIH/Sida no Sudão do Sul. Palavras-chave: Conhecimentos; VIH/Sida; Sudão do Sul.

Automatização da E.T.A.R. e Plano de Manutenção para a Adega Cooperativa de Mangualde

Visto que o tratamento das águas residuais é um tema muito importante para a saúde pública, é necessário criar vários processos para o tratamento de efluentes. Após a finalização de todos os processos, o efluente tratado terá de cumprir requisitos de qualidade químicos e biológicos para a sua descarga no meio ambiente. A utilização de equipamentos e técnicas, caraterizados por elevados níveis de precisão (como a tecnologia), é muito importante, pois é possível tornar o sistema mais autónomo, monitorizar processos e controlá-los de forma eficiente e acessível para o utilizador. Assim, neste tipo de sistemas terá que ser utilizado um elemento central de controlo e vários elementos auxiliares, nomeadamente vários tipos de sensores. Também terá que se usar uma interface que permita ao utilizador comunicar com o sistema de controlo, de forma a poder manipular e ajustar determinados parâmetros que influenciam os processos de tratamento. Neste sentido, a presente dissertação apresenta um estudo/projeto, na Adega Cooperativa de Mangualde, onde se pretende automatizar, controlar e monitorizar a Estação de Tratamento de Águas Residuais (ETAR) da mesma. Como, cada vez mais, os lucros das empresas dependem do bom funcionamento, não só dos trabalhadores mas também dos seus equipamentos, é necessário que estes estejam em ótimas condições de trabalho, de forma a evitar avarias e paragens na produção, o que vem trazer, por consequência, prejuízos para as empresas. Assim, realizar-se-á, também, um plano de manutenção para garantir o bom desempenho dos equipamentos desta unidade fabril.

Azorean Agriculture Efficiency by PAR

Copyright © 2013 Springer Netherlands.

Acrescentar valor à organização com a auditoria interna

Dissertação de Mestrado Apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Auditoria, sob orientação de Doutora Alcina Portugal Dias

Ethical dilemmas in scientific publication: pitfalls and solutions for editors

Editors of scientific journals need to be conversant with the mechanisms by which scientific misconduct is amplified by publication practices. This paper provides definitions, ways to document the extent of the problem, and examples of editorial attempts to counter fraud. Fabrication, falsification, duplication, ghost authorship, gift authorship, lack of ethics approval, non-disclosure, 'salami' publication, conflicts of interest, auto-citation, duplicate submission, duplicate publications, and plagiarism are common problems. Editorial misconduct includes failure to observe due process, undue delay in reaching decisions and communicating these to authors, inappropriate review procedures, and confounding a journal's content with its advertising or promotional potential. Editors also can be admonished by their peers for failure to investigate suspected misconduct, failure to retract when indicated, and failure to abide voluntarily by the six main sources of relevant international guidelines on research, its reporting and editorial practice. Editors are in a good position to promulgate reasonable standards of practice, and can start by using consensus guidelines on publication ethics to state explicitly how their journals function. Reviewers, editors, authors and readers all then have a better chance to understand, and abide by, the rules of publishing.