911 resultados para 660502 Renewable energy


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Nowadays the electricity consumption in the residential sector attracts policy and research efforts, in order to propose saving strategies and to attain a better balance between production and consumption, by integrating renewable energy production and proposing suitable demand side management methods. To achieve these objectives it is essential to have real information about household electricity demand profiles in dwellings, highly correlated, among other aspects, with the active occupancy of the homes and to the personal activities carried out in homes by their occupants. Due to the limited information related to these aspects, in this paper, behavioral factors of the Spanish household residents, related to the electricity consumption, have been determined and analyzed, based on data from the Spanish Time Use Surveys, differentiating among the Autonomous Communities and the size of municipalities, or the type of days, weekdays or weekends. Activities involving a larger number of houses are those related to Personal Care, Food Preparation and Washing Dishes. The activity of greater realization at homes is Watching TV, which together with Using PC, results in a high energy demand in an aggregate level. Results obtained enable identify prospective targets for load control and for efficiency energy reduction recommendations to residential consumers.

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The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/

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Integrating renewable energy into built environments requires additional attention to the balancing of supply and demand due to their intermittent nature. Demand Side Response (DSR) has the potential to make money for organisations as well as support the System Operator as the generation mix changes. There is an opportunity to increase the use of existing technologies in order to manage demand. Company-owned standby generators are a rarely used resource; their maintenance schedule often accounts for a majority of their running hours. DSR encompasses a range of technologies and organisations; Sustainability First (2012) suggest that the System Operator (SO), energy supply companies, Distribution Network Operators (DNOs), Aggregators and Customers all stand to benefit from DSR. It is therefore important to consider impact of DSR measures to each of these stakeholders. This paper assesses the financial implications of organisations using existing standby generation equipment for DSR in order to avoid peak electricity charges. It concludes that under the current GB electricity pricing structure, there are several regions where running diesel generators at peak times is financially beneficial to organisations. Issues such as fuel costs, Carbon Reduction Commitment (CRC) charges, maintenance costs and electricity prices are discussed.

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This paper aims to address the characteristics of urban microclimates that affect the building energy performance and implementation of the renewable energy technologies. An experimental campaign was designed to investigate the microclimate parameters including air and surface temperature, direct and diffuse solar irradiation levels on both horizontal and vertical surfaces, wind speed and direction in a dense urban area in London. The outcomes of this research reveal that the climatic parameters are significantly influenced by the attributes of urban textures, which highlight the need for both providing the microclimatic information and using them in buildings design stages. This research provides a valuable set of microclimatic information for a dense urban area in London. According to the outcomes of this research, the feasibility study for implementation of renewable energy technologies and the thermal/ energy performance assessment of buildings need to be conducted using the microclimatic information rather than the meteorological weather data mostly collected from non-urban environments.

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With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics. Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling. A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter. An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/.

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The techno-economic performance of a small wind turbine is very sensitive to the available wind resource. However, due to financial and practical constraints installers rely on low resolution wind speed databases to assess a potential site. This study investigates whether the two site assessment tools currently used in the UK, NOABL or the Energy Saving Trust wind speed estimator, are accurate enough to estimate the techno-economic performance of a small wind turbine. Both the tools tend to overestimate the wind speed, with a mean error of 23% and 18% for the NOABL and Energy Saving Trust tool respectively. A techno-economic assessment of 33 small wind turbines at each site has shown that these errors can have a significant impact on the estimated load factor of an installation. Consequently, site/turbine combinations which are not economically viable can be predicted to be viable. Furthermore, both models tend to underestimate the wind resource at relatively high wind speed sites, this can lead to missed opportunities as economically viable turbine/site combinations are predicted to be non-viable. These results show that a better understanding of the local wind resource is a required to make small wind turbines a viable technology in the UK.

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Dynamic electricity pricing can produce efficiency gains in the electricity sector and help achieve energy policy goals such as increasing electric system reliability and supporting renewable energy deployment. Retail electric companies can offer dynamic pricing to residential electricity customers via smart meter-enabled tariffs that proxy the cost to procure electricity on the wholesale market. Current investments in the smart metering necessary to implement dynamic tariffs show policy makers’ resolve for enabling responsive demand and realizing its benefits. However, despite these benefits and the potential bill savings these tariffs can offer, adoption among residential customers remains at low levels. Using a choice experiment approach, this paper seeks to determine whether disclosing the environmental and system benefits of dynamic tariffs to residential customers can increase adoption. Although sampling and design issues preclude wide generalization, we found that our environmentally conscious respondents reduced their required discount to switch to dynamic tariffs around 10% in response to higher awareness of environmental and system benefits. The perception that shifting usage is easy to do also had a significant impact, indicating the potential importance of enabling technology. Perhaps the targeted communication strategy employed by this study is one way to increase adoption and achieve policy goals.

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Washing machine and dishwasher appliance use accounts for approximately 10% of electricity demand in EU households. The majority of this demand is due to the operation of electric heating elements inside appliances. This paper investigates the potential benefits that can be realised by adding a hot fill connection to washing appliances, with respect to carbon emissions, demand side management and renewable energy integration. Initial laboratory testing of new hot and cold fill appliances has resulted in modifications to optimise hot fill intake, and a novel numerical model presents a method of characterising appliance electricity use in different configurations. In order to validate model findings and test the use of new hot fill appliances in situ, a pilot study has recorded appliances’ resource consumption at one-minute resolution in fourteen households. The addition of hot fill reduced the total dishwasher and washing machine electricity consumption by 38% and 67% respectively. Depending on how hot water is supplied to appliances it is estimated that hot fill use results in an annual household carbon saving of up to 147 kgCO2. Further to direct electricity reduction, hot fill appliances can offer a method of time shifting demand away from peak periods without inconveniencing occupants’ lifestyles.

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The increased concern for the impacts of climate change on the environment, along with the growing industry of renewable energy sources, and especially wind power, has made the valuation of environmental services and goods of great significance. Offshore wind energy is being exploited exponentially and its importance for renewable energy generation is increasing. We apply a double-bound dichotomous Contingent Valuation Method analysis in order to both a) estimating the Willingness to Pay (WTP) of Greek residents for green electricity produced by offshore wind farm located between the islands of Tinos and Andros and b) identifying factors behind respondents’ WTP including individual’s behaviour toward environment and individual’s views on climate change and renewable energy. A total of 141 respondents participated in the questionnaire. Results show that the respondents are willing to pay on average 20€ every two months through their electricity bill in return for carbon-free electricity and water saving from the wind farm. Respondents’ environmental consciousness and their perception towards climate change and renewable energy have a positive effect on their WTP.

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Small-scale anaerobic digester installation has been a development objective of the Indian government to provide rural households clean fuel. Anaerobic digester installation is heavily subsidised. Depending on caste, the rate of subsidy offered for the smallest system available (1m3) varies between 32.35% and 41.18% of the total installation price. Yet, there are gaps in knowledge regarding the usefulness of such subsidies from a sustainability perspective. A cost-benefit analysis was conducted to evaluate the circumstances required for digester sustainability. The analysis used household data collected from 115 cattle owning households in Odisha, India to evaluate profitability at three levels of subsidy (none, General caste subsidy, and Schedule Caste/Schedule Tribe subsidy). Additional analyses considered the effect of; taking a loan, replacing electric lighting with biogas lighting, and the wealth level of the household. The results indicated that access to subsidy improved profitability. Yet, profitability could be achieved without the use of subsidy. The level of benefit accrued by households was similar independent of wealth. However, the provision of subsidy was essential for ensuring profitability for those households required to take a loan to meet the expense of installation. Such findings highlight the importance of subsidy as a means of including the poor.

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The relationships between the four radiant fluxes are analyzed based on a 4 year data archive of hourly and daily global ultraviolet (I(UV)), photosynthetically active-PAR (I(PAR)), near infrared (I(NIR)) and broadband global solar radiation (I(G)) collected at Botucatu, Brazil. These data are used to establish both the fractions of spectral components to global solar radiation and the proposed linear regression models. Verification results indicated that the proposed regression models predict accurately the spectral radiant fluxes at least for the Brazilian environment. Finally, results obtained in this analysis agreed well with most published results in the literature. (c) 2010 Elsevier Ltd. All rights reserved.

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Genetic algorithm has been widely used in different areas of optimization problems. Ithas been combined with renewable energy domain, photovoltaic system, in this thesis.To participate and win the solar boat race, a control program is needed and C++ hasbeen chosen for programming. To implement the program, the mathematic model hasbeen built. Besides, the approaches to calculate the boundaries related to conditionhave been explained. Afterward, the processing of the prediction and real time controlfunction are offered. The program has been simulated and the results proved thatgenetic algorithm is helpful to get the good results but it does not improve the resultstoo much since the particularity of the solar driven boat project such as the limitationof energy production

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The present paper examines building integrated solar collectors with absorbers of polymeric materials. Efficiency measurements of façade-integrated collectors with non-selective black and spectrally selective coloured absorbers are carried out. The performance of the polymeric absorber was compared with solar glass and polycarbonate twin-wall sheets as collector cover. Simulations demonstrate a high solar fraction for a solar combisystem with façade collectors for a well-insulated house in a Nordic climate. Two examples of house concepts with façade collectors are presented which address a new type of customer than the solar enthusiasts with special interest in renewable energy

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PV-Wind-Hybrid systems for stand-alone applications have the potential to be more cost efficient compared to PV-alone systems. The two energy sources can, to some extent, compensate each others minima. The combination of solar and wind should be especially favorable for locations at high latitudes such as Sweden with a very uneven distribution of solar radiation during the year. In this article PV-Wind-Hybrid systems have been studied for 11 locations in Sweden. These systems supply the household electricity for single family houses. The aim was to evaluate the system costs, the cost of energy generated by the PV-Wind-Hybrid systems, the effect of the load size and to what extent the combination of these two energy sources can reduce the costs compared to a PV-alone system. The study has been performed with the simulation tool HOMER developed by the National Renewable Energy Laboratory (NREL) for techno-economical feasibility studies of hybrid systems. The results from HOMER show that the net present costs (NPC) for a hybrid system designed for an annual load of 6000 kWh with a capacity shortage of 10% will vary between $48,000 and $87,000. Sizing the system for a load of 1800 kWh/year will give a NPC of $17,000 for the best and $33,000 for the worst location. PV-Wind-Hybrid systems are for all locations more cost effective compared to PV-alone systems. Using a Hybrid system is reducing the NPC for Borlänge by 36% and for Lund by 64%. The cost per kWh electricity varies between $1.4 for the worst location and $0.9 for the best location if a PV-Wind-Hybrid system is used.