990 resultados para Energy scenario
Resumo:
Nowadays the urgency to address climate change and global warming is growing rapidly: the industry and the energy sector must be decarbonized. Hydrogen can play a key role in the energy transition: it is expected to progressively replace fossil fuels, penetrating economies and gaining interest from the public. However, this new possible energy scenario requires further investigation on safety aspects, which currently represent a challenge. The present study aims at making a little contribution to this field. The focus is on the analysis and modeling of hazardous scenarios concerning liquid hydrogen. The investigation of BLEVEs (Boiling Liquid Expanding Vapor Explosion) consequences lies at the core of this research: among various consequences (overpressure, radiation), the interest is on the generation and projection of fragments. The goal is to investigate whether the models developed for conventional fuels and tanks give good predictions also when handling hydrogen. The experimental data from the SH2IFT - Safe Hydrogen Fuel Handling and Use for Efficient Implementation project are used to validate those models. This project’s objective was to increase competence within safety of hydrogen technology, especially focusing on consequences of handling large amounts of this substance.
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The development in power systems and the introduction of decentralized generation and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which considers not only the generation, but also the management of loads through demand response programs, energy storage units, EVs and other players in a liberalized electricity markets environment. This paper proposes a methodology to be used by Virtual Power Players (VPPs), concerning the energy resource scheduling in smart grids, considering day-ahead, hour-ahead and real-time scheduling. The case study considers a 33-bus distribution network with high penetration of distributed energy resources. The wind generation profile is based on a real Portuguese wind farm. Four scenarios are presented taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour-ahead and realtime scheduling.
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The power is still today an issue in wearable computing applications. The aim of the present paper is to raise awareness of the power consumption of wearable computing devices in specific scenarios to be able in the future to design energy efficient wireless sensors for context recognition in wearable computing applications. The approach is based on a hardware study. The objective of this paper is to analyze and compare the total power consumption of three representative wearable computing devices in realistic scenarios such as Display, Speaker, Camera and microphone, Transfer by Wi-Fi, Monitoring outdoor physical activity and Pedometer. A scenario based energy model is also developed. The Samsung Galaxy Nexus I9250 smartphone, the Vuzix M100 Smart Glasses and the SimValley Smartwatch AW-420.RX are the three devices representative of their form factors. The power consumption is measured using PowerTutor, an android energy profiler application with logging option and using unknown parameters so it is adjusted with the USB meter. The result shows that the screen size is the main parameter influencing the power consumption. The power consumption for an identical scenario varies depending on the wearable devices meaning that others components, parameters or processes might impact on the power consumption and further study is needed to explain these variations. This paper also shows that different inputs (touchscreen is more efficient than buttons controls) and outputs (speaker sensor is more efficient than display sensor) impact the energy consumption in different way. This paper gives recommendations to reduce the energy consumption in healthcare wearable computing application using the energy model.
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We propose an alternative, nonsingular, cosmic scenario based on gravitationally induced particle production. The model is an attempt to evade the coincidence and cosmological constant problems of the standard model (Lambda CDM) and also to connect the early and late time accelerating stages of the Universe. Our space-time emerges from a pure initial de Sitter stage thereby providing a natural solution to the horizon problem. Subsequently, due to an instability provoked by the production of massless particles, the Universe evolves smoothly to the standard radiation dominated era thereby ending the production of radiation as required by the conformal invariance. Next, the radiation becomes subdominant with the Universe entering in the cold dark matter dominated era. Finally, the negative pressure associated with the creation of cold dark matter (CCDM model) particles accelerates the expansion and drives the Universe to a final de Sitter stage. The late time cosmic expansion history of the CCDM model is exactly like in the standard Lambda CDM model; however, there is no dark energy. The model evolves between two limiting (early and late time) de Sitter regimes. All the stages are also discussed in terms of a scalar field description. This complete scenario is fully determined by two extreme energy densities, or equivalently, the associated de Sitter Hubble scales connected by rho(I)/rho(f) = (H-I/H-f)(2) similar to 10(122), a result that has no correlation with the cosmological constant problem. We also study the linear growth of matter perturbations at the final accelerating stage. It is found that the CCDM growth index can be written as a function of the Lambda growth index, gamma(Lambda) similar or equal to 6/11. In this framework, we also compare the observed growth rate of clustering with that predicted by the current CCDM model. Performing a chi(2) statistical test we show that the CCDM model provides growth rates that match sufficiently well with the observed growth rate of structure.
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Ultra-high-energy cosmic rays (UHECRs), with energies above similar to 6 x 10(19) eV, seem to show a weak correlation with the distribution of matter relatively near to us in the universe. It has earlier been proposed that UHECRs could be accelerated in either the nucleus or the outer lobes of the nearby radio galaxy Cen A. We show that UHECR production at a spatially intermediate location about 15 kpc northeast from the nucleus, where the jet emerging from the nucleus is observed to strike a large star-forming shell of gas, is a plausible alternative. A relativistic jet is capable of accelerating lower energy heavy seed cosmic rays (CRs) to UHECRs on timescales comparable to the time it takes the jet to pierce the large gaseous cloud. In this model, many CRs arising from a starburst, with a composition enhanced in heavy elements near the knee region around PeV, are boosted to ultra-high energies by the relativistic shock of a newly oriented jet. This model matches the overall spectrum shown by the Auger data and also makes a prediction for the chemical composition as a function of particle energy. We thus predict an observable anisotropy in the composition at high energy in the sense that lighter nuclei should preferentially be seen toward the general direction of Cen A. Taking into consideration the magnetic field models for the Galactic disk and a Galactic magnetic wind, this scenario may resolve the discrepancy between HiRes and Auger results concerning the chemical composition of UHECRs.
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Portugal has the largest LPG (Liquefied Petroleum Gas) share of primary energy demand in the EU (about 5%). Due to the increasing international cost of LPG in the last years and the high price sensitivity of the consumers the preference for substitute energy sources in new and existing consumers has been increasing. To select the kind of energy, some consumer estimate and compare the total costs while others follow agents (equipment sellers) recommendations. It takes time to build agents perception about the most advantageous source of energy, which is seen as an important resource that drives client resource accumulation and retention. Marketing strategies have to take into consideration some market dynamic effects derived from the accumulation and depletion of these resources. A simple system dynamics model was built, combined with Economic Value Added framework, to evaluate some pricing strategies under different scenarios of LPG international cost.
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Portugal has the largest LPG (Liquefied Petroleum Gas) share of primary energy demand in the EU (about 5%). Due to the increasing international cost of LPG in the last years and the high price sensitivity of the consumers the preference for substitute energy sources in new and existing consumers has been increasing. To select the kind of energy, some consumer estimate and compare the total costs while others follow agents (equipment sellers) recommendations. It takes time to build agents perception about the most advantageous source of energy, which is seen as an important resource that drives client resource accumulation and retention. Marketing strategies have to take into consideration some market dynamic effects derived from the accumulation and depletion of these resources. A simple system dynamics model was built, combined with Economic Value Added framework, to evaluate some pricing strategies under different scenarios of LPG international cost.
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In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.
Resumo:
Energy resources management can play a very relevant role in future power systems in a SmartGrid context, with intensive penetration of distributed generation and storage systems. This paper deals with the importance of resource management in incident situations. The paper presents DemSi, an energy resources management simulator that has been developed by the authors to simulate electrical distribution networks with high distributed generation penetration, storage in network points and customers with demand response contracts. DemSi is used to undertake simulations for an incident scenario, evidencing the advantages of adequately using flexible contracts, storage, and reserve in order to limit incident consequences.
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This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.
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The purpose of this article is to analyse and evaluate the economical, energetic and environmental impacts of the increasing penetration of renewable energies and electrical vehicles in isolated systems, such as Terceira Island in Azores and Madeira Island. Given the fact that the islands are extremely dependent on the importation of fossil fuels - not only for the production of energy, but also for the transportation’s sector – it’s intended to analyse how it is possible to reduce that dependency and determine the resultant reduction of pollutant gas emissions. Different settings have been analysed - with and without the penetration of EVs. The Terceira Island is an interesting case study, where EVs charging during off-peak hours could allow an increase in geothermal power, limited by the valley of power demand. The percentage of renewable energy in the electric power mix could reach the 74% in 2030 while at the same time, it is possible to reduce the emissions of pollutant gases in 45% and the purchase of fossil fuels in 44%. In Madeira, apart from wind, solar and small hydro power, there are not so many endogenous resources and the Island’s emission factor cannot be so reduced as in Terceira. Although, it is possible to reduce fossil fuels imports and emissions in 1.8% in 2030 when compared with a BAU scenario with a 14% of the LD fleet composed by EVs.
Resumo:
The purpose of this article is to analyse and evaluate the economical, energetic and environmental impacts of the increasing penetration of renewable energies and electrical vehicles in isolated systems, such as Terceira Island in Azores and Madeira Island. Given the fact that the islands are extremely dependent on the importation of fossil fuels - not only for the production of energy, but also for the transportation’s sector – it’s intended to analyse how it is possible to reduce that dependency and determine the resultant reduction of pollutant gas emissions. Different settings have been analysed - with and without the penetration of EVs. The Terceira Island is an interesting case study, where EVs charging during off-peak hours could allow an increase in geothermal power, limited by the valley of power demand. The percentage of renewable energy in the electric power mix could reach the 74% in 2030 while at the same time, it is possible to reduce the emissions of pollutant gases in 45% and the purchase of fossil fuels in 44%. In Madeira, apart from wind, solar and small hydro power, there are not so many endogenous resources and the Island’s emission factor cannot be so reduced as in Terceira. Although, it is possible to reduce fossil fuels imports and emissions in 1.8% in 2030 when compared with a BAU scenario with a 14% of the LD fleet composed by EVs.
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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas Ambientais
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Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia
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Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), which obtain their fuel from the grid by charging a battery, are set to be introduced into the mass market and expected to contribute to oil consumption reduction. This research is concerned with studying the potential impacts on the electric utilities of large-scale adoption of plug-in electric vehicles from the perspective of electricity demand, fossil fuels use, CO2 emissions and energy costs. Simulations were applied to the Portuguese case study in order to analyze what would be the optimal recharge profile and EV penetration in an energy-oriented, an emissions-oriented and a cost-oriented objective. The objectives considered were: The leveling of load profiles, minimization of daily emissions and minimization of daily wholesale costs. Almost all solutions point to an off-peak recharge and a 50% reduction in daily wholesale costs can be verified from a peak recharge scenario to an off-peak recharge for a 2 million EVs in 2020. A 15% improvement in the daily total wholesale costs can be verified in the costs minimization objective when compared with the off-peak scenario result.