O artigo propõe metodologia de avaliação ex-ante de projetos de P&D&I do setor elétrico, incorporada a uma ferramenta de análise multicritério, com foco em risco. A P&D do setor elétrico é regulada pela Agência Nacional de Energia Elétrica (ANEEL) e obriga as empresas a investirem parte de sua Receita Operacional Líquida (ROL) em P&D&I. A imposição de P&D&I ao setor levou suas empresas a buscarem formas de alocar recursos em projetos que tentam harmonizar o contexto regulatório com seus direcionadores de mercado, forçando-as a estruturarem em suas rotinas uma gestão da inovação que mitigasse riscos regulatórios. Uma forma de se viabilizar essa mitigação é o uso de metodologias de seleção de projetos capazes de ponderar múltiplos elementos, de certo modo, antagônicos. A PiTech, Prioritization in Technology, é uma ferramenta de análise multivariada paramétrica, flexível e propícia a essa ponderação. Neste artigo, aplicaram-se critérios operacionais, mercadológicos e regulatórios, em múltiplas combinações, para 19 projetos de um grupo empresarial de energia elétrica. A ferramenta mostrou-se útil ao permitir aos tomadores de decisão verificarem a pertinência de investimento em 16 projetos, totalizando R$ 61 milhões em 2010, com riscos de glosa minimizados e concomitante aderência a interesses empresariais.
Artificial Neural Networks (ANNs) have been successfully applied to the problem of forecasting future load values, especially in the short term framework (a few minutes to a few hours ahead). Traditional analytical models have shown difficulties when dealing with (i) the highly variable demand curve shapes, (ii) some independent variables that exhibit random behaviour, and (iii) the identification of variables that could explain relevant load variations, such as weather variables. Current available ANN applications to this problem are by far aimed at a systemwide level, where the load behaviour is more regular than at substation or even primary feeder levels.
Recognizing that to survive in 21 th century, utilities must take advantages of smart platforms, IEC has provided a homogeneous IT landscape based on CIM/XLM standardized data format for source data. It allows utilities to vitally combine their large number of autonomous IT systems, with great potential for optimizing their core processes. But this landscape itself will not be enough, unless utility actually and smartly connects IEDs and systems at that surviving critical level. This article presents an approach that tries to make easier the utility improve core processes, based on substation "smartizing", by means of creating in smart substations, key-value operating and functional data, information and knowledge, in a continuous upstream add-value process, making them suited to each IED, System and decision maker at every utility level. "Smartizing" architecture is fully IEC compliant. The approach is being applied in a 25 MVA distribution substation in Brazil, in a 10 GW demand peak utility group.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.