74 resultados para RENEWABLE ENERGY SOURCES


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Under the European Union Renewable Energy Directive each Member State is mandated to ensure that 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020. The Irish Government intends to achieve this target with a number of policies including ensuring that 10% of all vehicles in the transport fleet are powered by electricity by 2020. This paper investigates the impact of the 10% electric vehicle target in Ireland in 2020 using a dynamic programming based long term generation expansion planning model. The model developed optimizes power dispatch using hourly electricity demand curves up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. Two distinct scenarios are analysed based on a peak and off-peak charging regimes in order to simulate the effects of the electric vehicles charging in 2020. The importance and influence of the charging regimes on the amount of energy used and tailgate emissions displaced is then determined.

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The future European power system will have a hierarchical structure created by layers of system control from a Supergrid via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the context of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called 'back-up generation' needed to support an 80% renewable energy portfolio in Europe by 2050. © 2013 IEEE.

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Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

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This paper proposes a hierarchical energy management system for multi-source multi-product (MSMP) microgrids. Traditional energy hub based scheduling method is combined with a hierarchical control structure to incorporate transient characteristics of natural gas flow and dynamics of energy converters in microgrids. The hierarchical EMS includes a supervisory control layer, an optimizing control layer, and an execution control layer. In order to efficiently accommodate the systems multi time-scale characteristics, the optimizing control layer is decomposed into three sub-layers: slow, medium and fast. Thermal, gas and electrical management systems are integrated into the slow, medium, and fast control layer, respectively. Compared with wind energy, solar energy is easier to integrate and more suitable for the microgrid environment, therefore, potential impacts of the hierarchical EMS on MSMP microgrids is investigated based on a building energy system integrating photovoltaic and microturbines. Numerical studies indicate that by using a hierarchical EMS, MSMP microgrids can be economically operated. Also, interactions among thermal, gas, and electrical system can be effectively managed.

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With the increasing utilization of combined heat and power plants (CHP), electrical, gas, and thermal systems are becoming tightly integrated in the urban energy system (UES). However, the three systems are usually planned and operated separately, ignoring their interactions and coordination. To address this issue, the coupling point of different systems in the UES is described by the energy hub model. With this model, an integrated load curtailment method is proposed for the UES. Then a Monte Carlo simulation based approach is developed to assess the reliability of coordinated energy supply systems. Based on this approach, a reliability-optimal energy hub planning method is proposed to accommodate higher renewable energy penetration. Numerical studies indicate that the proposed approach is able to quantify the UES reliability with different structures. Also, optimal energy hub planning scheme can be determined to ensure the reliability of the UES with high renewable penetration.

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In this paper, niobium doping is evaluated as a means of enhancing the electrochemical performance of a Sr2Fe1.5Mo0.5O6-δ (SFM) perovskite structure cathode material for intermediate temperature solid oxide fuel cells (IT-SOFCs) applications. As the radius of Nb approximates that of Mo and exhibits +4/+5 mixed valences, its substitution is expected to improve material performance. A series of Sr2Fe1.5Mo0.5-xNbxO6-δ (x = 0.05, 0.10, 0.15, 0.20) cathode materials are prepared and the phase structure, chemical compatibility, microstructure, electrical conductivity, polarization resistance and power generation are systematically characterized. Among the series of samples, Sr2Fe1.5Mo0.4Nb0.10O6-δ (SFMNb0.10) exhibits the highest conductivity value of 30 S cm-1 at 550°C, and the lowest area specific resistance of 0.068 Ω cm2 at 800°C. Furthermore, an anode-supported single cell incorporating a SFMNb0.10 cathode presents a maximum power density of 1102 mW cm-2 at 800°C. Furthermore no obvious performance degradation is observed over 15 h at 750°C with wet H2(3% H2O) as fuel and ambient air as the oxidant. These results demonstrate that SFMNb shows great promise as a novel cathode material for IT-SOFCs.

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In this work, an economical route based on hydrothermal and layer-by-layer (LBL) self-assembly processes has been developed to synthesize unique Al 2O3-modified LiV3O8 nanosheets, comprising a core of LiV3O8 nanosheets and a thin Al 2O3 nanolayer. The thickness of the Al2O 3 nanolayer can be tuned by altering the LBL cycles. When evaluated for their lithium-storage properties, the 1 LBL Al2O 3-modified LiV3O8 nanosheets exhibit a high discharge capacity of 191 mA h g-1 at 300 mA g-1 (1C) over 200 cycles and excellent rate capability, demonstrating that enhanced physical and/or chemical properties can be achieved through proper surface modification. © 2014 Elsevier B.V. All rights reserved.

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This paper outlines a forensic method for analysing the energy, environmental and comfort performance of a building. The method has been applied to a recently developed event space in an Irish public building, which was evaluated using on-site field studies, data analysis, building simulation and occupant surveying. The method allows for consideration of both the technological and anthropological aspects of the building in use and for the identification of unsustainable operational practice and emerging problems. The forensic analysis identified energy savings of up to 50%, enabling a more sustainable, lower-energy operational future for the building. The building forensic analysis method presented in this paper is now planned for use in other public and commercial buildings.

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This paper presents a model for availability analysis of standalone hybrid microgrid. The microgrid used in the study consists of wind, solar storage and diesel generator. Boolean driven Markov process is used to develop the availability of the system in the proposed method. By modifying the developed model, the relationship between the availability of the system with the fine (normal) weather and disturbed (stormy) weather durations are analyzed. Effects of different converter technologies on the availability of standalone microgrid were investigated and the results have shown that the availability of microgrid increased by 5.80 % when a storage system is added. On the other hand, the availability of standalone microgrid could be overestimated by 3.56 % when weather factor is neglected. In the same way 200, 500 and 1000 hours of disturbed weather durations reduced the availability of the system by 5.36%, 9.73% and 13.05 %, respectively. In addition, the hybrid energy storage cascade topology with a capacitor in the middle maximized the system availability.

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Algae biodiesel is a promising but expensive alternative fuel to petro-diesel. To overcome cost barriers, detailed cost analyses are needed. A decade-old cost analysis by the U.S. National Renewable Energy Laboratory indicated that the costs of algae biodiesel were in the range of $0.53–0.85/L (2012 USD values). However, the cost of land and transesterification were just roughly estimated. In this study, an updated comprehensive techno-economic analysis was conducted with optimized processes and improved cost estimations. Latest process improvement, quotes from vendors, government databases, and other relevant data sources were used to calculate the updated algal biodiesel costs, and the final costs of biodiesel are in the range of $0.42–0.97/L. Additional improvements on cost-effective biodiesel production around the globe to cultivate algae was also recommended. Overall, the calculated costs seem promising, suggesting that a single step biodiesel production process is close to commercial reality.

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In this work Cu1.4Mn1.6O4 (CMO) spinel oxide is prepared and evaluated as a novel cobalt-free cathode for intermediate temperature solid oxide fuel cells (IT-SOFCs). Single phase CMO powder with cubic structure is identified using XRD. XPS results confirm that mixed Cu+/Cu2+ and Mn3+/Mn4+ couples exist in the CMO sample, and a maximum conductivity of 78 S cm−1 is achieved at 800 °C. Meanwhile, CMO oxide shows good thermal and chemical compatibility with a 10 mol% Sc2O3 stabilized ZrO2 (ScSZ) electrolyte material. Impedance spectroscopy measurements reveals that CMO exhibits a low polarization resistance of 0.143 Ω cm2 at 800 °C. Furthermore, a Ni-ScSZ/ScSZ/CMO single cell demonstrates a maximum power density of 1076 mW cm−2 at 800 °C under H2 (3% H2O) as the fuel and ambient air as the oxidant. These results indicate that Cu1.4Mn1.6O4 is a superior and promising cathode material for IT-SOFCs.

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The transition to a “low carbon, climate resilient and environmentally sustainable economy by the end of the 
year 2050” has been conceptualised as the “national transition objective” in the Irish Climate Action and Low Carbon Development Bill, passed in late 2015. This has raised a myriad of questions over how this can be operationalised and resourced and whether it can maintain political momentum. This paper assesses the utility of framings informed by the transitions (MLP) and technological innovation systems perspectives in contributing to transformative societal processes, by examining their application in an Irish case study on policy and technology. Through a qualitative exploration of the broader societal and policy context of the energy sector and a more detailed examination of the innovation systems of selected niche technologies (bioenergy and electric vehicles), the Irish case study sought to identify potential catalysts for a sustainability transition in the energy sector in Ireland: where these exist, how these are being built or enabled, and barriers to change. Following a discussion on the theoretical approaches used, a description will be given of how these were applied in the conducting of the research on transition in Ireland case study and the key findings which emerged. A critical reflection will then be made on the utility of these perspectives (as applied) to contribute to broader processes of societal transformation in Ireland.

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This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

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This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.