Search

Reliable and efficient power modeling from meteorological wind data is vital for optimal implementation and monitoring of wind energy, and it is important for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea that similar wind conditions lead to similar wind powers; this paper constructs a modeling scheme that orderly integrates three types of ensemble learning algorithms—bagging, boosting, and stacking—and clustering approaches to achieve wind power modeling from multiple wind-based meteorological factors in a wind farm. The paper also investigates the applications of different clustering algorithms and methodologies to determine cluster numbers in the modeling. The results reveal that all ensemble models with clustering exploit the intrinsic information in wind data and thus outperform models without clustering by approximately 15% on average in modeling wind power. The model with the best-performing Farthest First clustering is computationally rapid and with an improvement of around 30% compared with the baselines. Given the diversity introduced by clustering algorithms, the power modeling performance is further boosted by about 5% by introducing stacking that fuses ensembles with varying clusters. The proposed modeling framework thus demonstrates promise by delivering efficient and robust performance on the targeted problem. • Systematic demonstration of wind power modeling from weather data. • Construction and comparison of various clustering methods for classifying wind data. • Combination of bagging, boosting, and stacking for modeling. • Multiple wind meteorological characteristics are considered in the modeling. [ABSTRACT FROM AUTHOR]

Research on climate change impacts on renewable energy is becoming increasingly relevant due to the vulnerability of the sector and to the continual development of methodologies and availability of data. Public and private decision-making needs specific research. However, many gaps still exist in certain geographical regions and technologies. Providing economic estimates with a value chain perspective are also missing from most papers. This paper addresses the most relevant studies that project quantitative estimates of climate change impacts on solar, wind, hydro and other renewable generation technologies. Summary tables of impacts and projections are provided so that researchers, governments and the private sector may have an accurate view of the state-of-the-art on this topic. • The impacts of climate change on renewables make up a growing area of research. • Despite uncertainties, climate models are the most well-trusted method. • Comparing projections is complex due to scopes, methodologies and variables. • Hydro and solar are more frequently analysed than other technologies. • Main gaps are lack of economics assessments and projections in developing countries. [ABSTRACT FROM AUTHOR]

The goal of carbon neutrality provides a new opportunity for the development of renewable energy, especially for wind power. In this paper, 11 regional climate models (RCMs) including CORDEX-EA and PRECIS at two different resolutions are used to evaluate the performance in simulating the spatio-temporal characteristics for wind speed and energy potential from 1981 to 2005. The results show that most models can reproduce the spatio-temporal patterns, and the simulation of the eastern region is better than that of the western region. Besides, most of the higher-resolution models had better simulation performance for spatial-temporal patterns and inter-annual variation. Then, the weighted multi-model ensemble is used to project the changes of the wind speed and the wind power density over China's mainland during the mid-21st century under the RCP8.5 scenario. In future, a decrease in wind speed is projected by most RCMs over China, although some regions, such as the southern parts of China, are projected to have more wind energy potential. The percentage variation of annual mean wind speed will remain in the range of ±4%. The wind power density will decrease in the north and the largest decrease will be found in the northwest. However, the annual mean wind power density in the southeast China will have an increase of 2.21% in the middle of this century. The inter-annual variation of the wind power density in most regions will increase by more than 20%. However, intra-annual variation in the wind power density is likely to decrease in most regions of China, ranging from about −20% to −50%. • A weighted multi-model ensemble was used to project the changes of wind power density over China. • The changes of annual mean wind power density vary in different sub-regions of China, ranging from −3.21% to 2.21%. • The inter-annual variation of the WPD in most regions will increase by more than 20%. [ABSTRACT FROM AUTHOR]

Energy-economy models play an increasingly important role in informing climate policy decisions, yet their results lack consistency in terms of projected greenhouse gas emissions and economic impacts. The paper employs a scoping review of the key improvements, knowledge gaps, and critiques of energy-economy models in the academic, public, private, and not-for-profit sectors in Canada over the past ten years. The three objectives are to (1) identify the key energy-economy modelling assessment criteria, (2) develop an inventory of energy-economy models assessed against the criteria in objective 1, and (3) discuss implications for modelling climate policies. Four criteria for assessing the ability of energy-economy models to evaluate climate policy impacts are identified: treatment of technology, microeconomic realism, macroeconomic realism, and policy representation. The assessment of 21 models against these criteria reveals similarities and differences across models. Models that share similarities in over-arching methodological approaches are also similar in the way they represent technologies, market heterogeneity, trade effects, different policy types, and energy equilibrium. Conversely, there are quite diverse approaches used in the representation of technological change, non-financial decision factors, financial or monetary features, and non-energy equilibrium. Model documentation does not often address how policy interaction is taken into account. Based on this assessment, implications for climate policy modelling and future research are discussed. • Climate policy projections vary across energy-economy models. • A scoping literature review compares energy-economy models in Canada's public and private sectors. • Four key criteria are identified for evaluating energy-economy models. • The review reveals similarities and differences across models. • Implications for climate policy modelling are identified. [ABSTRACT FROM AUTHOR]