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.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Lisboa e a Invasão de Junot: população, periódicos e panfletos (1807-1808)

No termo das comemorações dos duzentos anos da Guerra Peninsular (1808-1814), este trabalho propõe-se trazer uma reflexão sobre a ocupação de Lisboa pelo exército francês, comandado pelo general Jean-Andoche Junot, e a luta do povo da cidade contra as forças napoleónicas, durante um período de nove meses, entre 30 de Novembro de 1807 e 30 de Agosto de 1808. A cidade de Lisboa foi personagem principal e testemunha dos acontecimentos que marcaram a ocupação militar francesa, cujos participantes foram, em primeiro lugar a população de Lisboa, com maior relevo para o povo simples, mas também outros estratos da população que, em menor ou maior grau, sofreram igualmente as difíceis condições criadas pela presença militar estrangeira. A importância do papel que Lisboa viria a desempenhar nestas difíceis circunstâncias, justifica o relevo que foi dado ao período da sua ocupação pelo exército francês, através das diversas formas de que se revestia a vida na cidade, nos seus aspectos sociais e culturais, incluindo, além da sua morfologia urbana, a vida social e cultural, os hábitos e tradições, as condições de vida, os entretenimentos e as instituições que identificavam a cidade. Em seguida, estabelecemos as circunstâncias em que a cidade se encontrava nesse último mês de Dezembro de 1807, com a retirada para o Brasil do Príncipe Regente D. João, acompanhado pela família real, a corte e a maioria da primeira nobreza do país, coincidindo com a entrada das tropas francesas em Lisboa. Finalmente, abordámos as consequências destes acontecimentos para a população, cuja manifestação se evidenciou no sentimento de perda e na fraqueza de ânimo por ela sentidos. Por último, sublinha-se o papel desempenhado pela imprensa portuguesa da época que, embora pouco representativa em número, conseguiu um efeito mobilizador junto de largas camadas da população, transformando-se num dos principais veículos da sustentação da luta contra o ocupante francês, através não apenas da imprensa periódica mas, igualmente, dos panfletos anti-napoleónicos que se imprimiram e distribuíram às centenas.

Duas novas espécies de Belostoma Latreille, 1807 (Hemiptera: Heteroptera: Belostomatidae) do grupo plebejum sensu Nieser, 1975

Representantes do grupo plebejum são encontrados de Honduras até o sul da América do Sul e compreendem sete espécies de pequeno porte. Como resultado de um estudo de revisão do grupo, são descritas duas espécies novas: Belostoma estevezae Ribeiro & Alecrim, sp. nov., do Estado do Mato Grosso, Brasil, similar a B. nicaeum Estévez & Polhemus, 2007, em termos de carena prosternal, e B. nessimiani Ribeiro & Alecrim, sp. nov., do Estado do Amazonas, Brasil, sendo bastante similar a B. parvum Estévez & Polhemus, 2007, em termos de genitália masculina. Uma chave de identificação para as espécies do grupo plebejum com as espécies novas incluídas é fornecida.

A multivariate study of the family molossidae (mammalia, chiroptera) : morphology, ecology, evolution / Patricia Waring Freeman.

n.s. no.7(1981)

Avenços en l'estudi de la litiasi renal: mètodes de chemical imaging i intel·ligencia artificial (ANN) per la caracterització de càlculs renals

La litiasi urinària és un trastorn que implica la formació de precipitats en qualsevol part del tracte urinari. Aquest és un desordre comú que afecta aproximadament a una desena part de la població de la Unió Europea al llarg de la seva vida. A més, durant els cinc anys posteriors a un episodi litiàsic el percentatge de recurrència dels pacients és del 45 al 75%. Aquest trastorn urinari està influït per una gran quantitat de variables, d’origen fisiològic, psicològic i ambiental. Els episodis litiàsics, es poden solucionar espontàniament, amb l’expulsió del càlcul renal, o bé a través de diverses intervencions mèdiques. Els tractaments mèdics derivats de la litiasi urinària; és a dir, la fragmentació del càlcul, intervencions quirúrgiques i tractaments posteriors generen unes grans despeses als sistemes mèdics. Pels motius exposats, la identificació del desordre que ha originat l’episodi litiàsic és de radical importància, per tal de minimitzar el risc de reincidència. Els mètodes més usuals per determinar les causes que desencadenen la formació del càlcul renal són les anàlisis d’orina i l’estudi del càlcul generat. La correcta descripció de la composició i, especialment, l’estructura del càlcul renal pot aportar informació clau sobre les possibles causes de la seva formació, tant de l’inici de nucleació del càlcul com de les successives etapes de creixement cristal·lí. Tenint en compte aquest darrer aspecte, el present estudi s’ha dirigit a la caracterització de càlculs urinaris mitjançant l’aplicació de metodologies d’imatge química (Hyperspectral Imaging), el que va contribuir a determinar les principals característiques espectrals de cada compost majoritari als càlculs renals. D’altra banda, la utilització de mostres de composició coneguda ha possibilitat la creació d’un model amb Xarxes Neuronals Artificials, que permet la classificació de noves mostres de composició desconeguda, de manera més ràpida que el procediment actual. Aquest treball constitueix una nova contribució a la comprensió de l’estructura de les pedres de ronyó, així com les condicions de la seva formació. Els resultats obtinguts destaquen les possibilitats que presenten les tècniques emprades al camp de la litiasi renal, que permeten complementar els coneixements existents enfocats a millorar la qualitat de vida dels pacients.