Cartografia de Orfeu:analogia e assurreição nas Soledades de Luis de Góngora y Argote

O tema desta Dissertação de Mestrado é a análise dos conceitos de analogia e assurreição (objetos formais) na obra Soledades (objeto material) de Luis de Góngora, bem como suas implicações filosóficas e formais. Pretendi fazer uma análise do poema, tendo em vista perseguir a seguinte hipótese: a desmesura e a desproporção que as Soledades apresentam não são fruto de um mero capricho estilístico. Elas nascem de uma intervenção do furor poético, entendido como furor divino. Góngora teve acesso a essas concepções mediante o influxo de ideias herméticas e neoplatônicas na Península Ibérica. Tais influxos são responsáveis por uma amplificação especialmente aguda da doutrina da analogia. O desdobramento máximo dessa ênfase no princípio da analogia é propriamente a assurreição, tal como definida por Claude-Gilbert Dubois. A partir desses elementos, esta Dissertação de Mestrado pretendeu proceder a uma análise de alguns motivos, cenas e trechos do poema narrativo Soledades, de Luis de Góngora y Argote, mostrando como ele elabora a doutrina da analogia de proporcionalidade e em que momento essa analogia aguda produz a assurreição, ou seja, o corte transversal na hierarquia dos corpos institucionais, movimento este em geral entendido em termos teológicos e políticos como heresia. Por outro lado, há a importante relação entre hermetismo, neoplatonismo e literatura na Renascença, bem como a maneira pela qual a doutrina do furor poético se infiltrou nas artes e na poesia espanholas por via italiana nos séculos XVI e XVII. Concentrei-me no conceito de assurreição e na análise do deslocamento metafísico-teológico das analogias de proporcionalidade presentes nas Soledades, cotejando-as com a distribuição dos lugares naturais de cada elemento do cosmos, fixada sobretudo por Santo Tomás de Aquino, a maior autoridade da teologia católica na Península Ibérica do século XVII

O impacto do ativo intangível medido pelo efeito da inovação organizacional em instituição pública multipropósito: o caso do Instituto Nacional de Infectologia Evandro Chagas - INI da Fiocruz

Esta dissertação foi desenvolvida no Instituto Nacional de Infectologia Evandro Chagas (INI) da Fundação Oswaldo Cruz (Fiocruz), uma organização pública multipropósito de saúde com atividades de pesquisa, assistência e ensino, que adotou a estratégia de inovar a sua estrutura organizacional, com vistas a garantir a qualidade dos serviços prestados à população, bem como reforçar a orientação empreendedora das suas ações integradas. Sob o ponto de vista da pesquisa em Ciências Contábeis, a reestruturação do INI constitui um exemplo potencial da acumulação de ativo intangível pela organização e este foi o fato gerador do problema da pesquisa: a aplicação do método DEA para avaliar a melhoria da eficiência dos oito laboratórios de pesquisa multipropósito do INI, está associada ao referencial teórico que justifica a inovação organizacional implantada no Instituto após proposta nos Congressos Internos da Fiocruz? O objetivo geral foi analisar os resultados dos escore-sínteses do Data Envelopment Analysis (DEA), como medida dos ativos intangíveis, associando-os aos efeitos provenientes da implantação de mudanças organizacionais estratégicas, caracterizadas como inovações organizacionais ocorridas nos oito laboratórios de pesquisa clínica multipropósitos, isto é, que contemplam ensino, pesquisa e assistência, no INI da Fiocruz. O método consistiu de quatro etapas. Na primeira, foi realizada a análise da literatura sobre ativos intangíveis; inovação organizacional; estruturas da organização e modelo de análise de eficiência em organizações. Na segunda, foi realizada a coleta dos indicadores qualitativos referentes a mudança da estrutura organizacional de oito laboratórios do INI por meio de análise documental dos Congressos Internos da Fiocruz e uma pesquisa de opinião dos representantes dos laboratórios; quantitativamente, foram levantados dados para calcular o indicador de eficiência de cada um dos laboratórios. Na terceira etapa foi realizada a análise dos dados coletados, do período de 2006-2012, utilizando os indicadores calculados para associar a eficiência do conjunto destas atividades antes e depois da inovação organizacional associada à adoção de uma estrutura inovadora na reestruturação. Finalmente, a quarta etapa apresentou os resultados e as respectivas considerações sobre a pesquisa. Como contribuição, apresenta-se uma associação entre a inovação organizacional, decorrente da reestruturação dos oito laboratórios de pesquisa clínica e os resultados do método empírico que utiliza o DEA.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Electric vehicles in urban areas: test cases using a scenario simulator

Electric vehicles introduction will affect cities environment and urban mobility policies. Network system operators will have to consider the electric vehicles in planning and operation activities due to electric vehicles’ dependency on the electricity grid. The present paper presents test cases using an Electric Vehicle Scenario Simulator (EVeSSi) being developed by the authors. The test cases include two scenarios considering a 33 bus network with up to 2000 electric vehicles in the urban area. The scenarios consider a penetration of 10% of electric vehicles (200 of 2000), 30% (600) and 100% (2000). The first scenario will evaluate network impacts and the second scenario will evaluate CO2 emissions and fuel consumption.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.