Over the past few years a number of new mathematical functions have been proposed for wind speed probability density distributions. The most commonly used function that has been cited in literature has been the two-parameter Weibull function. However, in recent years studies have shown that the two-parameter Weibull function might be inadequate in modeling the wind speed probability density distributions or independent of whether the distribution is of unimodal or bimodal nature. For the unimodal distributions, the inadequacy may be due to the intricate behavior of the distribution, which prevents it to be satisfyingly modeled by a two-parameter model. For the bimodal behavior, the two-parameter Weibull function, which produces only a unimodal distribution, is simply inadequate to model it appropriately. Therefore, in recent years, alternative functions have been suggested for both unimodal and bimodal distributions, seeking more involved functions to better model these distributions. This article involves the modeling of observed wind speed probability density distributions using the main body of models found in the literature, namely, Rayleigh, Lognormal, two-parameter Weibull, three-parameter Weibull, and bimodal Weibull probability distribution functions. One of the important steps in the evaluation of different functions is the interpretation of the statistical parameters, namely, slope, R2, mean bias error, and root mean squared error, as are presently used in this article. A novel statistical tool is developed in the present article using these four statistical parameters. The novel tool can be used to evaluate the relative performance of models when more than one model is involved or to determine the overall accuracy of a particular model for a specific site. The calculations are made based on the long term wind speed data collected at 4-s interval at the experimental site at Edinburgh Napier University.

Large wind turbines are usually installed in areas where wind speed distributions have been observed long enough to make sure of their high efficiency. However, micro-wind turbines are mostly used in areas where wind conditions are not necessarily favourable for efficient power production. Therefore, micro-wind turbines require specific designs to work effectively in low and turbulent wind resource areas. However, because this is not the case, more experimental results are being published in recent years that report under-achieving micro-wind turbines. In the present article, a similar under-achievement is reported. The experience gained from the wind energy project undertaken at Napier University is reported, as well as the analysis of the wind speed data collected at the facility and at Edinburgh Airport, some 16 km away from it. Practical applications: A micro-wind energy system can be one of the most promising technological solutions for producing electricity in residential applications for remote consumers as well as in urban areas provided that the problems reported in the literature are successfully tackled. The currently reported research project identifies such problems associated with micro-wind turbines, stemming from their use in urban areas on roofs of buildings. Thus, this will contribute both to the understanding of micro-wind turbines and to their possible improvements in the coming years.

No abstract

The grid is not all the time available and a daily scheduled program of electricity shortage is applied in some developing countries like Lebanon. In such situation renewable energy systems connected to the grid can't be an effective solution. Although net metering or feed forward connectivity is allowed, still the grid outage program prevents its use in an efficient way. For this reason, this research work suggests a new control algorithm for photovoltaic systems connected to a grid with scheduled outage. This solution provides to the consumer the maximum benefit of connectivity.

No abstract