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.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Dgenco market balancing - cost repartition under light regulation markets

In this paper we present a new methodology, based in game theory, to obtain the market balancing between Distribution Generation Companies (DGENCO), in liberalized electricity markets. The new contribution of this methodology is the verification of the participation rate of each agent based in Nucléolo Balancing and in Shapley Value. To validate the results we use the Zaragoza Distribution Network with 42 Bus and 5 DGENCO.

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.

Modelação e simulação de sistemas fotovoltaicos

Os sistemas fotovoltaicos produzem energia eléctrica limpa, e inesgotável na nossa escala temporal. A Agência Internacional de Energia encara a tecnologia fotovoltaica como uma das mais promissoras, esperando nas suas previsões mais optimistas, que em 2050 possa representar 20% da produção eléctrica mundial, o equivalente a 18000 TWh. No entanto, e apesar do desenvolvimento notável nas últimas décadas, a principal condicionante a uma maior proliferação destes sistemas é o ainda elevado custo, aliado ao seu fraco desempenho global. Apesar do custo e ineficiência dos módulos fotovoltaicos ter vindo a diminuir, o rendimento dos sistemas contínua dependente de factores externos sujeitos a grande variabilidade, como a temperatura e a irradiância, e às limitações tecnológicas e falta de sinergia dos seus equipamentos constituintes. Neste sentido procurou-se como objectivo na elaboração desta dissertação, avaliar o potencial de optimização dos sistemas fotovoltaicos recorrendo a técnicas de modelação e simulação. Para o efeito, em primeiro lugar foram identificados os principais factores que condicionam o desempenho destes sistemas. Em segundo lugar, e como caso prático de estudo, procedeu-se à modelação de algumas configurações de sistemas fotovoltaicos, e respectivos componentes em ambiente MatlabTM/SimulinkTM. Em seguida procedeu-se à análise das principais vantagens e desvantagens da utilização de diversas ferramentas de modelação na optimização destes sistemas, assim como da incorporação de técnicas de inteligência artificial para responder aos novos desafios que esta tecnologia enfrentará no futuro. Através deste estudo, conclui-se que a modelação é não só um instrumento útil para a optimização dos actuais sistemas PV, como será, certamente uma ferramenta imprescindível para responder aos desafios das novas aplicações desta tecnologia. Neste último ponto as técnicas de modelação com recurso a inteligência artificial (IA) terão seguramente um papel preponderante. O caso prático de modelação realizado permitiu concluir que esta é igualmente uma ferramenta útil no apoio ao ensino e investigação. Contudo, convém não esquecer que um modelo é apenas uma aproximação à realidade, devendo recorrer-se sempre ao sentido crítico na interpretação dos seus resultados.

Modern cancer epidemiological research: genetic polymorphisms and environment

Individual cancer susceptibility seems to be related to factors such as changes in oncogenes and tumor suppressor genes expression, and differences in the action of metabolic enzymes and DNA repair regulated by specific genes. Epidemiological studies on genetic polymorphisms of human xenobiotics metabolizing enzymes and cancer have revealed low relative risks. Research considering genetic polymorphisms prevalence jointly with environmental exposures could be relevant for a better understanding of cancer etiology and the mechanisms of carcinogenesis and also for new insights on cancer prognosis. This study reviews the approaches of molecular epidemiology in cancer research, stressing case-control and cohort designs involving genetic polymorphisms, and factors that could introduce bias and confounding in these studies. Similarly to classical epidemiological research, genetic polymorphisms requires considering aspects of precision and accuracy in the study design.

Avaliação da rentabilidade económica dos sistemas de microgeração fotovoltaica em Portugal (1990-2010)

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica - ramo de Energia

Cost of schizophrenia: direct costs and use of resources in the State of São Paulo

OBJECTIVE: To estimate the direct costs of schizophrenia for the public sector. METHODS: A study was carried out in the state of São Paulo, Brazil, during 1998. Data from the medical literature and governmental research bodies were gathered for estimating the total number of schizophrenia patients covered by the Brazilian Unified Health System. A decision tree was built based on an estimated distribution of patients under different types of psychiatric care. Medical charts from public hospitals and outpatient services were used to estimate the resources used over a one-year period. Direct costs were calculated by attributing monetary values for each resource used. RESULTS: Of all patients, 81.5% were covered by the public sector and distributed as follows: 6.0% in psychiatric hospital admissions, 23.0% in outpatient care, and 71.0% without regular treatment. The total direct cost of schizophrenia was US$191,781,327 (2.2% of the total health care expenditure in the state). Of this total, 11.0% was spent on outpatient care and 79.2% went for inpatient care. CONCLUSIONS: Most schizophrenia patients in the state of São Paulo receive no regular treatment. The study findings point out to the importance of investing in research aimed at improving the resource allocation for the treatment of mental disorders in Brazil.

O impacto da Lei nº 37/2007 (Lei do Tabaco) no consumo de tabaco em ambiente profissional na ilha Terceira e a sua influência na produtividade e absentismo laboral

Dissertação de Mestrado, Ambiente, Saúde e Segurança, 19 de Julho de 2013, Universidade dos Açores.

Videovigilância, compressão e criptografia

Mestrado em Engenharia Electrotécnica e de Computadores

Distributed Model Predictive Control for Housing with Hourly Auction of Available Energy

This paper presents a distributed model predictive control (DMPC) for indoor thermal comfort that simultaneously optimizes the consumption of a limited shared energy resource. The control objective of each subsystem is to minimize the heating/cooling energy cost while maintaining the indoor temperature and used power inside bounds. In a distributed coordinated environment, the control uses multiple dynamically decoupled agents (one for each subsystem/house) aiming to achieve satisfaction of coupling constraints. According to the hourly power demand profile, each house assigns a priority level that indicates how much is willing to bid in auction for consume the limited clean resource. This procedure allows the bidding value vary hourly and consequently, the agents order to access to the clean energy also varies. Despite of power constraints, all houses have also thermal comfort constraints that must be fulfilled. The system is simulated with several houses in a distributed environment.

Estudo de esmalte estufa aquoso definitivo

Mestrado em Engenharia Química

Android as a cloud ticket validator

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Informática e de Computadores

Potential distribution and cost estimation of the damage caused by Cryptotermes brevis (Isoptera: Kalotermitidae) in the Azores

Copyright © 2014 Entomological Society of America.

Avaliação energética de uma escola

Mestrado em Engenharia Química