Renewable Energy 

About: Renewable Energy is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Renewable energy & Wind power. It has an ISSN identifier of 0960-1481. Over the lifetime, 18756 publications have been published receiving 764594 citations.

Renewable Power-to-Gas: A technological and economic review

Abstract: The Power-to-Gas (PtG) process chain could play a significant role in the future energy system. Renewable electric energy can be transformed into storable methane via electrolysis and subsequent methanation. This article compares the available electrolysis and methanation technologies with respect to the stringent requirements of the PtG chain such as low CAPEX, high efficiency, and high flexibility. Three water electrolysis technologies are considered: alkaline electrolysis, PEM electrolysis, and solid oxide electrolysis. Alkaline electrolysis is currently the cheapest technology; however, in the future PEM electrolysis could be better suited for the PtG process chain. Solid oxide electrolysis could also be an option in future, especially if heat sources are available. Several different reactor concepts can be used for the methanation reaction. For catalytic methanation, typically fixed-bed reactors are used; however, novel reactor concepts such as three-phase methanation and micro reactors are currently under development. Another approach is the biochemical conversion. The bioprocess takes place in aqueous solutions and close to ambient temperatures. Finally, the whole process chain is discussed. Critical aspects of the PtG process are the availability of CO 2 sources, the dynamic behaviour of the individual process steps, and especially the economics as well as the efficiency.

Bioethanol production from agricultural wastes: An overview

Abstract: Due to rapid growth in population and industrialization, worldwide ethanol demand is increasing continuously. Conventional crops such as corn and sugarcane are unable to meet the global demand of bioethanol production due to their primary value of food and feed. Therefore, lignocellulosic substances such as agricultural wastes are attractive feedstocks for bioethanol production. Agricultural wastes are cost effective, renewable and abundant. Bioethanol from agricultural waste could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to ethanol needs some new fermentation technologies, to make the whole process cost effective. In this review, available technologies for bioethanol production from agricultural wastes are discussed.

Biodiesel production from oleaginous microorganisms

Abstract: High energy prices, energy and environment security, concerns about petroleum supplies are drawing considerable attention to find a renewable biofuels. Biodiesel, a mixture of fatty acid methyl esters (FAMEs) derived from animal fats or vegetable oils, is rapidly moving towards the mainstream as an alternative source of energy. However, biodiesel derived from conventional petrol or from oilseeds or animal fat cannot meet realistic need, and can only be used for a small fraction of existing demand for transport fuels. In addition, expensive large acreages for sufficient production of oilseed crops or cost to feed animals are needed for raw oil production. Therefore, oleaginous microorganisms are available for substituting conventional oil in biodiesel production. Most of the oleaginous microorganisms like microalgae, bacillus, fungi and yeast are all available for biodiesel production. Regulation mechanism of oil accumulation in microorganism and approach of making microbial diesel economically competitive with petrodiesel are discussed in this review.

Machine learning methods for solar radiation forecasting: A review

Abstract: Forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach.

Current methods and advances in forecasting of wind power generation

Abstract: Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.