Potential Climatic Impacts and Reliability of Very Large-Scale Wind Farms

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Meeting future world energy needs while addressing climate change requires large-scale deployment of low or zero greenhouse gas (GHG) emission technologies such as wind energy. The widespread availability of wind power has fueled legitimate interest in this renewable energy source as one of the needed technologies. For very large-scale utilization of this resource, there are however potential environmental impacts, and also problems arising from its inherent intermittency, in addition to the present need to lower unit costs. To explore some of these issues, we use a threedimensional climate model to simulate the potential climate effects associated with installation of wind-powered generators over vast areas of land or coastal ocean. Using windmills to meet 10% or more of global energy demand in 2100, could cause surface warming exceeding 1oC over land installations. In contrast, surface cooling exceeding 1oC is computed over ocean installations, but the validity of simulating the impacts of windmills by simply increasing the ocean surface drag needs further study. Significant warming or cooling remote from both the land and ocean installations, and alterations of the global distributions of rainfall and clouds also occur. These results are influenced by the competing effects of increases in roughness and decreases in wind speed on near-surface turbulent heat fluxes, the differing nature of land and ocean surface friction, and the dimensions of the installations parallel and perpendicular to the prevailing winds. These results are also dependent on the accuracy of the model used, and the realism of the methods applied to simulate windmills. Additional theory and new field observations will be required for their ultimate validation. Intermittency of wind power on daily, monthly and longer time scales as computed in these simulations and inferred from meteorological observations, poses a demand for one or more options to ensure reliability, including backup generation capacity, very long distance power transmission lines, and onsite energy storage, each with specific economic and/or technological challenges.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Assessing Evapotranspiration Estimates from the Global Soil Wetness Project Phase 2 (GSWP-2) Simulations

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We assess the simulations of global-scale evapotranspiration from the Global Soil Wetness Project Phase 2 (GSWP-2) within a global water-budget framework. The scatter in the GSWP-2 global evapotranspiration estimates from various land surface models can constrain the global, annual water budget fluxes to within ±2.5%, and by using estimates of global precipitation, the residual ocean evaporation estimate falls within the range of other independently derived bulk estimates. However, the GSWP-2 scatter cannot entirely explain the imbalance of the annual fluxes from a modern-era, observationally-based global water budget assessment, and inconsistencies in the magnitude and timing of seasonal variations between the global water budget terms are found. Inter-model inconsistencies in evapotranspiration are largest for high latitude inter-annual variability as well as for inter-seasonal variations in the tropics, and analyses with field-scale data also highlights model disparity at estimating evapotranspiration in high latitude regions. Analyses of the sensitivity simulations that replace uncertain forcings (i.e. radiation, precipitation, and meteorological variables) indicate that global (land) evapotranspiration is slightly more sensitive to precipitation than net radiation perturbations, and the majority of the GSWP-2 models, at a global scale, fall in a marginally moisture-limited evaporative condition. Finally, the range of global evapotranspiration estimates among the models is larger than any bias caused by uncertainties in the GSWP-2 atmospheric forcing, indicating that model structure plays a more important role toward improving global land evaporation estimates (as opposed to improved atmospheric forcing).NASA Energy and Water-cycle Study (NEWS, grant #NNX06AC30A), under the NEWS Science and Integration Team activities

Halo Coronal Mass Ejections during Solar Cycle 24: reconstruction of the global scenario and geoeffectiveness

In this study we present a statistical analysis of 53 fast Earth-directed halo CMEs observed by the SOHO/LASCO instrument during the period Jan. 2009-Sep. 2015, and we use this CME sample to test the capabilities of a Sun-to-Earth prediction scheme for CME geoeffectiveness. First, we investigate the CME association with other solar activity features by means of multi-instrument observations of the solar magnetic and plasma properties. Second, using coronagraphic images to derive the CME kinematical properties at 0.1 AU, we propagate the events to 1 AU by means of the WSA-ENLIL+Cone model. Simulation results at Earth are compared with in-situ observations at L1. By applying the pressure balance condition at the magnetopause and a solar wind-Kp index coupling function, we estimate the expected magnetospheric compression and geomagnetic activity level, and compare them with global data records. The analysis indicates that 82% of the CMEs arrived at Earth in the next 4 days. Almost the totality of them compressed the magnetopause below geosynchronous orbits and triggered a geomagnetic storm. Complex sunspot-rich active regions associated with energetic flares result the most favourable configurations from which geoeffective CMEs originate. The analysis of related SEP events shows that 74% of the CMEs associated with major SEPs were geoeffective. Moreover, the SEP production is enhanced in the case of fast and interacting CMEs. In this work we present a first attempt at applying a Sun-to-Earth geoeffectiveness prediction scheme - based on 3D simulations and solar wind-geomagnetic activity coupling functions - to a statistical set of potentially geoeffective halo CMEs. The results of the prediction scheme are in good agreement with geomagnetic activity data records, although further studies performing a fine-tuning of such scheme are needed.Comment: Accepted for publication in the Journal of Space Weather and Space Climate (SWSC

Avaliação de aplicação de redes neurais artificiais em métodos de medição-correlação-predição

In this study a single artificial neural network (ANN) model was developed to predict the short term mean hourly wind speed and wind direction at target sites using short term mean hourly reference wind data. Standard multi-layered, feed-forward, backpropagation neural networks with single hidden layer architecture was designed using neural network toolbox for MATLAB. The hidden layers and output layer of the network consist of tangent sigmoid transfer function (tansig) and linear transfer function (purelin) as an activation function. Five different sites from Japan, Saudi Arabia, Jordan, France and Russia with different terrain complexity, completely different weather conditions, and different correlation coefficient between reference and target sites were tested. Single model was constructed, and two different approaches were experimented. Approach 1 made use of entire concurrent period dataset, the output values from the model was compared against the three methods: regression, matrix and neural network. Second approach was built on certain period of data and tested on unused data. The purpose behind the fabrication of this approach is to try and understand the neural network model. The results of approach 1 was that the neural network model is able to statistically perform better than other methods and equally well in predicting wind direction sectors. The maximum mean absolute percentage error for NN MATLAB model was found to be 62.5% in Japan to 23.7% in France. The model suffers in predicting the lower wind speeds which explains the distortion in wind frequency distribution and resulting in Power density deviation. The maximum deviation was -18.1% in Jordan and -7.9% in France. The sites in Japan, Saudi Arabia, France and Russia were considered for approach 2. The results were interesting, in case of japan the first month was better than the last month result. Overall the performance of the model was better in case of France followed by Russia site. The maximum deviation of Power density was noticed in case of Japan’s last month scenario -26.6% to minimum of about 3.2% in France and -5.2% was observed in case of Russia. In Saudi Arabia site, the only case where the concurrent period extends over a period of one year, the performance of the model was statistically good but suffers from same problem of previous cases. The deviation in power density was spotted around -21.4%.Neste estudo, foi desenvolvido um modelo de rede neural artificial (RNA) para prever a velocidade média do vento de curto prazo e a direção do vento em locais-alvo, usando dados de vento de referência de curto prazo. Foram projetadas redes neurais padrão multi-camadas, feed-forward, de propagação reversa com arquitetura de camada oculta única usando a caixa de ferramentas de rede neural para o MATLAB. As camadas ocultas e a camada de saída da rede consistem na função de transferência sigmóide tangente (tansig) e na função de transferência linear (purelin) como uma função de ativação. Foram testados cinco locais diferentes, Japão, Arábia Saudita, Jordânia, França e Rússia, com diferentes complexidades de terreno, condições climáticas completamente diferentes e diferentes coeficientes de correlação entre os locais de referência e os de destino. Foram testadas duas abordagens diferentes com o modelo construído. Na abordagem foi usado todo o conjunto de dados do período concorrente e os valores de saída do modelo foram comparados com três métodos em estudo: regressão, matriz e rede neural. A segunda abordagem foi construída usando apenas um determinado período de dados e o modelo foi testado em dados não utilizados. O objetivo desta segunda abordagem foi tentar entender o modelo de rede neural. Os resultados obtidos com a abordagem 1 aplicada aos 5 sítios em estudo permitiram verificar que o modelo de rede neural desenvolvido se apresenta estatisticamente melhor do que os outros métodos testados. Verifica-se que é capaz de prever bem a direção do vento por setores. Foi obtido um erro percentual médio absoluto máximo com o modelo NN MATLAB de 62,5% no Japão e de 23,7% na França. O modelo desenvolvido apresenta uma limitação na previsão das velocidades de vento mais baixas, o que explica a distorção na distribuição da frequência do vento e resulta no desvio da densidade de potência. O desvio máximo obtido para a densidade de potência foi de -18,1% na Jordânia e de -7,9% na França. Na abordagem 2 foram utilizados os dados do Japão, Arábia Saudita, França e Rússia. Os resultados foram interessantes. Verificou-se que no caso do Japão foi possível obter melhores resultados para o primeiro mês do que para o último mês. No geral, o desempenho do modelo foi melhor no caso da França, seguido pela Rússia. O desvio máximo da densidade de potência foi observado no caso do cenário do último mês do Japão -26,6% e foram observados desvios mínimos de cerca de 3,2% na França e -5,2% na Rússia. No site da Arábia Saudita, o único caso em que o período concorrente se estende por um período de um ano, o desempenho do modelo foi estatisticamente bom, verificando-se a mesma dificuldade de previsão de velocidades baixas. O desvio na densidade de potência foi de cerca de -21,4%

A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions

It is expected, and regionally observed, that energy demand will soon be covered by a widespread deployment of renewable energy sources. However, the weather and climate driven energy sources are characterized by a significant spatial and temporal variability. One of the commonly mentioned solutions to overcome the mismatch between demand and supply provided by renewable generation is a hybridization of two or more energy sources in a single power station (like wind-solar, solar-hydro or solar-wind-hydro). The operation of hybrid energy sources is based on the complementary nature of renewable sources. Considering the growing importance of such systems and increasing number of research activities in this area this paper presents a comprehensive review of studies which investigated, analyzed, quantified and utilized the effect of temporal, spatial and spatio-temporal complementarity between renewable energy sources. The review starts with a brief overview of available research papers, formulates detailed definition of major concepts, summarizes current research directions and ends with prospective future research activities. The review provides a chronological and spatial information with regard to the studies on the complementarity concept.Comment: 34 pages 7 figures 3 table