873 resultados para Battery Energy Storage


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The present study approaches the economic and technical evaluation of equivalent carbon dioxide (CO(2) eqv.) capture and storage processes, considered in a proposal case compared to a base case. The base case considers an offshore petroleum production facility, with high CO(2) content (4 vol%) in the composition of the produced gas and both CO(2) and natural gas emissions to the atmosphere, called CO(2) eqv. emissions. The results obtained with this study, by using a Hysys process simulator, showed a CO(2) emission reduction of 65% comparing the proposal case in relation to the base case.

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This work analysed the influence of storage in the quality of forest biomass for energy generation in the region of Lages, Brazil. Logs of Pinus taeda L. and Eucalyptus dunnii Maiden were harvested and piled during the four different seasons: spring, summer, fall and winter. The analyses were performed immediately after harvesting (without being stored), after two, four and six months of storage. The evaluated properties were: moisture content, gross and net calorific value, ash content and solubility in cold water, hot water and sodium hydroxide. The species composition, storage span, harvesting season and storage season influenced the forest biomass characteristics. In general, eucalyptus presented better results than pine, losing moisture faster, having less alteration in the chemical composition and producing greater energetic gain over storage time. For both species, the ideal storage time was four months. Furthermore, spring and summer were the best harvesting seasons. Thus, if the forest biomass is harvested at the end of winter or beginning of spring with subsequent storage during the summer, this biomass will have the best performance for energy production. (C) 2011 Elsevier Ltd. All rights reserved.

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In recent years, the design flows of many dams were re-evaluated, often resulting in discharges larger than the original design. In many cases, the occurrence of the revised flows could result in dam overtopping because of insufficient storage and spillway capacity. An experimental study was conducted herein to gain a better understanding of the flow properties in stepped chutes with slopes typical of embankment dams. The work was based upon a Froude similitude in large-size experimental facilities. A total of 10 configurations were tested including smooth steps, steps equipped with devices to enhance energy dissipation and rough steps. The present results yield a new design procedure. The design method includes some key issues not foreseen in prior studies : e.g., gradually varied flow, type of flow regime, flow resistance. It is believed that the outcomes are valid for a wide range of chute geometry and flow conditions typical of embankment chutes.

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Proteins stored in insect hemolymph may serve (is a source of amino acids and energy for metabolism, and development. The expression of the main storage proteins was assessed in bacterial-challenged honey bees using real-time (RT)-PCH and Western blot.. After ensuring that. the immune system had, been activated by measuring the ensuing expression (, the innate immune response genes, defensin-1 (def-1) and prophenoloxidase (pro PO), we verified the expression of four genes encoding storage proteins. The levels of vitellogenin (vg) mRNA and of the respective protein. were significantly lowered in bees injected with bacteria or water only (injury). An equivalent response was observed in orally-infected bees. The levels of apolipophorin II/I (apoLP-II/I) and hexamerin (hex 70a) mRNAs did not significantly change, but levels of Hex 70a protein subunit showed a substantial decay after bacterial challenge or injury. Infection also caused a strong reduction in the levels of apoLP-III transcripts. Our findings are consistent with a down-regulation, of the express and accumulation of storage proteins as a consequence of activation of the immune system, suggesting that this phenomenon. represents a strategy to redirect resources to combat injury or infection. (C) 2009 Wiley Periodicals, Inc.

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The objective of this study was to evaluate in vitro light activation of the nano-filled resin composite Vita shade A1 and A3 with a halogen lamp (QTH) and argon ion laser by Knoop microhardness profile. Materials and methods: Specimens of nanofilled composite resin (Z350-3 M-ESPE) Vita shade A1 and A3 were prepared with a single increment inserted in 2.0-mm-thick and 3-mm diameter disc-shaped Teflon mold. The light activation was performed with QTH for 20 s (with an intensity of approximately 1,000 mW/cm(2) and 700 mW/cm(2)) and argon ion laser for 10 s (with a power of 150 mW and 200 mW). Knoop microhardness test was performed after 24 h and 6 months. The specimens were divided into the 16 experimental groups (n = 10), according to the factors under study: photoactivation form, resin shade, and storage time. Knoop microhardness data was analyzed by a factorial ANOVA and TukeyA ` s tests at the 0.05 level of significance. Results: Argon ion laser was not able to photo-activate the darker shade of the nanofilled resin composite evaluated but when used with 200 mW it can be as effective as QTH to photo-activate the lighter shade with only 50% of the time exposure. After 6 months storage, an increase in the means of Knoop microhardness values were observed. Conclusions: Light-activation significantly influenced the Knoop microhardness values for the darker nanofilled resin composite.

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We compare the performance of two different low-storage filter diagonalisation (LSFD) strategies in the calculation of complex resonance energies of the HO2, radical. The first is carried out within a complex-symmetric Lanczos subspace representation [H. Zhang, S.C. Smith, Phys. Chem. Chem. Phys. 3 (2001) 2281]. The second involves harmonic inversion of a real autocorrelation function obtained via a damped Chebychev recursion [V.A. Mandelshtam, H.S. Taylor, J. Chem. Phys. 107 (1997) 6756]. We find that while the Chebychev approach has the advantage of utilizing real algebra in the time-consuming process of generating the vector recursion, the Lanczos, method (using complex vectors) requires fewer iterations, especially for low-energy part of the spectrum. The overall efficiency in calculating resonances for these two methods is comparable for this challenging system. (C) 2001 Elsevier Science B.V. All rights reserved.

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We develop a new iterative filter diagonalization (FD) scheme based on Lanczos subspaces and demonstrate its application to the calculation of bound-state and resonance eigenvalues. The new scheme combines the Lanczos three-term vector recursion for the generation of a tridiagonal representation of the Hamiltonian with a three-term scalar recursion to generate filtered states within the Lanczos representation. Eigenstates in the energy windows of interest can then be obtained by solving a small generalized eigenvalue problem in the subspace spanned by the filtered states. The scalar filtering recursion is based on the homogeneous eigenvalue equation of the tridiagonal representation of the Hamiltonian, and is simpler and more efficient than our previous quasi-minimum-residual filter diagonalization (QMRFD) scheme (H. G. Yu and S. C. Smith, Chem. Phys. Lett., 1998, 283, 69), which was based on solving for the action of the Green operator via an inhomogeneous equation. A low-storage method for the construction of Hamiltonian and overlap matrix elements in the filtered-basis representation is devised, in which contributions to the matrix elements are computed simultaneously as the recursion proceeds, allowing coefficients of the filtered states to be discarded once their contribution has been evaluated. Application to the HO2 system shows that the new scheme is highly efficient and can generate eigenvalues with the same numerical accuracy as the basic Lanczos algorithm.

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Reproductive data from southern Queensland indicate that vitellogenesis in female Chelonia mydas takes approximately 8 months and is followed by a migration to a breeding area. At Heron Island, females lay multiple clutches over approximately 3 months. To investigate how females mobilise and store lipid during the breeding season we collected plasma, yolk, and fat tissue samples from females at a variety of stages during the nesting season. In breeding females, concentrations of plasma triglyceride increased seasonally. They reached peak concentrations during vitellogenesis and courtship, remained high throughout the nesting season, and then declined to a nadir after the last clutch. Plasma protein concentration increased throughout the breeding season, peaking following the last clutch for the season. Yolk lipids were highest during courtship and were similar throughout the nesting season, suggesting that uptake of lipid by ovarian follicles is completed prior to the beginning of the nesting season. Plasma triglyceride decreases in females with prolonged periods of unsuccessful nesting, and total lipid levels in adipose tissue and follicle yolks were significantly lower in atretic females. It appears that: (1) endogenous energy reserves can be reduced by stochastic environmental events (such as those reducing nesting success), and (2) a metabolic shift signalling the end of the nesting season is characterised by a drop in plasma triglycerides and slight increase in total plasma protein.

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Lipid homeostasis is controlled by the peroxisome proliferator-activated receptors (PPARalpha, -beta/delta, and -gamma) that function as fatty acid-dependent DNA-binding proteins that regulate lipid metabolism. In vitro and in vivo genetic and pharmacological studies have demonstrated PPARalpha regulates lipid catabolism. In contrast, PPARgamma regulates the conflicting process of lipid storage. However, relatively little is known about PPARbeta/delta in the context of target tissues, target genes, lipid homeostasis, and functional overlap with PPARalpha and -gamma. PPARbeta/delta, a very low-density lipoprotein sensor, is abundantly expressed in skeletal muscle, a major mass peripheral tissue that accounts for approximately 40% of total body weight. Skeletal muscle is a metabolically active tissue, and a primary site of glucose metabolism, fatty acid oxidation, and cholesterol efflux. Consequently, it has a significant role in insulin sensitivity, the blood-lipid profile, and lipid homeostasis. Surprisingly, the role of PPARbeta/delta in skeletal muscle has not been investigated. We utilize selective PPARalpha, -beta/delta, -gamma, and liver X receptor agonists in skeletal muscle cells to understand the functional role of PPARbeta/delta, and the complementary and/or contrasting roles of PPARs in this major mass peripheral tissue. Activation of PPARbeta/delta by GW501516 in skeletal muscle cells induces the expression of genes involved in preferential lipid utilization, beta-oxidation, cholesterol efflux, and energy uncoupling. Furthermore, we show that treatment of muscle cells with GW501516 increases apolipoprotein-A1 specific efflux of intracellular cholesterol, thus identifying this tissue as an important target of PPARbeta/delta agonists. Interestingly, fenofibrate induces genes involved in fructose uptake, and glycogen formation. In contrast, rosiglitazone-mediated activation of PPARgamma induces gene expression associated with glucose uptake, fatty acid synthesis, and lipid storage. Furthermore, we show that the PPAR-dependent reporter in the muscle carnitine palmitoyltransferase-1 promoter is directly regulated by PPARbeta/delta, and not PPARalpha in skeletal muscle cells in a PPARgamma coactivator-1-dependent manner. This study demonstrates that PPARs have distinct roles in skeletal muscle cells with respect to the regulation of lipid, carbohydrate, and energy homeostasis. Moreover, we surmise that PPARgamma/delta agonists would increase fatty acid catabolism, cholesterol efflux, and energy expenditure in muscle, and speculate selective activators of PPARbeta/delta may have therapeutic utility in the treatment of hyperlipidemia, atherosclerosis, and obesity.

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The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

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Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

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The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

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This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

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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.

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The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .