ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

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.

D. Juan de e.t.a. Hoffmann e don gionvanni de lorenzo da ponte – análise comparativa e intertextual

Através da análise comparativo/intertextual do conto Don Juan, de E.T.A. Hoffmann e do libreto da ópera Don Giovanni, de Mozart, da autoria de Lorenzo da Ponte, o objectivo deste artigo foi pôr em relevo a relação intrínseca, presente em muitas das obras de E.T.A. Hoffmann, entre a escrita e a música, explicável pela enorme sensibilidade musical deste autor, a que não só dedicou a sua vida, como também produziu várias obras musicais. Tal como na ópera Don Giovanni, de Mozart, a escrita de Hoffmann é dirigida por uma batuta imaginária, apresentando o suspense próprio dos enigmas, cuja revelação é suspensa por um entreacto – neste caso, uma carta escrita a um amigo, em que nos é apresentada uma reformulação do mito de Don Juan – entreacto este que vem prolongar o mistério e nos encaminha para um finale, em que a progressão da melodia e a sequência narrativa se unem mais uma vez. Por seu turno, na análise puramente intertextual, é feita uma tentativa de estabelecer, de forma clara, os paralelismos existentes com o pré-texto, isto é, com o libreto da autoria de Lorenzo da Ponte, bem como com outros pré-textos presentes no texto de forma mais esbatida, mas cujo peso é, também ele, significativo para a criação do novo mito de Don Juan, um homem sempre consciente do absurdo da existência, interpretação esta que abriu caminho para novas abordagens do mito, do herói e da história. Tal como em muitas outras obras, o pós-texto procura, em geral, apropriar-se do texto pressuposto, tentando ultrapassá-lo em mestria, o que acontece com este texto de Hoffmann, literária e semanticamente mais rico que o seu pré-texto.

From Mind to Body: Is Mental Practice Effective on Strength Gains? A Meta-Analysis

Mental practice is an internal reproduction of a motor act (whose intention is to promote learning and improving motor skills). Some studies have shown that other cognitive strategies also increase the strength and muscular resistance in healthy people by the enhancement of the performance during dynamic tasks. Mental training sessions may be primordial to improving muscle strength in different subjects. The aim of this study was to systematically review and meta-analiyze studies that assessed whether mental practice is effective in improving muscular strength. We conducted an electronic-computed search in Pub-Med/Medline and ISI Web of Knowledge, Scielo and manual searchs, searching papers written in English between 1991 and 2014. There were 44 studies in Pub-Med/Medline, 631 in ISI Web of Knowledge, 11 in Scielo and 3 in manual searchs databases. After exclusion of studies for duplicate, unrelated to the topic by title and summary, different samples and methodologies, a meta-analysis of 4 studies was carried out to identify the dose-response relationship. We did not find evidence that mental practice is effective in increasing strength in healthy individuals. There is no evidence that mental practice alone can be effective to induce strength gains or to optimize the training effects.