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Summary: This paper is concerned with innovative approaches to renewable energy sources computation methodologies, which provide more refined results than the classical alternatives. Such refinements provide additional improvements especially for replacement of fossil energy usages that emit greenhouse gas (GHG) into the atmosphere leading to climate change impact. Current knowledge gap among each renewable energy source calculation is rather missing fundamentals of plausible, rational, and logical explanations for the interpretation of results. In the literature, there are rather complicated and mechanically applicable methodologies, which require input and output measurement data match with missing physical explanations. The view taken in this review paper is to concentrate on quite plausible, logical, rational, and effectively applicable innovative energy calculation methodologies with simplistic fundamentals. For this purpose, a set of renewable energy methodological approaches is revisited with their innovative structures concerning solar, wind, hydro, current, and geothermal energy resources. With the increase in the renewable energy utilizations to combat the undesirable impacts of global warming and climate change, there is a need for better models that will include physical environmental conditions and data properties in the probabilistic, statistical, stochastic, logical, and rational senses leading to refined and more reliable estimations with application examples in the text. Finally, new research directions are also recommended for more refined innovative energy system calculations. [ABSTRACT FROM AUTHOR]

Several theoretical studies have been reported in recent years which have indicated that large-scale wind farms can have an impact on local and regional meteorology, possibly on climate. Now evidence of it based on field observations has also begun to emerge. The present paper traces the evolution of this knowledge. It discusses the theoretical studies on the possibility of wind turbines effecting climate change, and summarizes the emerging confirmation of those predictions. The concluding part of the paper assesses the implications of these findings in the context of the world׳s past experience on global warming and its present thrust to meet substantial portions of its energy needs with renewables. [ABSTRACT FROM AUTHOR]

Solar energy harvesting using Photovoltaic (PV) systems is one of the most popular sources of renewable energy, however the main drawback of PV systems is their low conversion efficiency. An optimal system operation requires an efficient tracking of the Maximum Power Point (MPP), which represents the maximum energy that can be extracted from the PV panel. This paper presents a novel control approach for the Maximum Power Point Tracking (MPPT) based on the differential flatness property of the Boost converter, which is one of the most used converters in PV systems. The underlying idea of the proposed control approach is to use the classical flatness-based trajectory tracking control where a reference voltage will be defined in terms of the maximum power provided by the PV panel. The effectiveness of the proposed controller is assessed through numerical simulations and experimental tests. The results show that the controller based on differential flatness is capable of converging in less than 0.15 s and, compared with other MPPT techniques, such as Incremental Conductance and Perturb and Observe, it improves the response against sudden changes in load or weather conditions, reducing the ringing in the output of the system. Based on the results, it can be inferred that the new flatness-based controller represents an alternative to improve the MPPT in PV systems, especially when they are subject to sudden load or weather changes. [ABSTRACT FROM AUTHOR]