Understanding Inter-Annual Variability of PV Energy Production in the Contiguous United States

2018 ◽  
Author(s):  
Galen Maclaurin ◽  
Wesley Cole ◽  
Anthony Lopez ◽  
Andrew Reimers ◽  
Evan Rosenlieb ◽  
...  
Keyword(s):  
United States ◽  
Energy Production ◽  
Annual Variability

scholarly journals Assessing Renewable Energy Production Capabilities Using DEA Window and Fuzzy TOPSIS Model

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 334 ◽  
Author(s):  
Chia-Nan Wang ◽  
Thanh-Tuan Dang ◽  
Hector Tibo ◽  
Duy-Hung Duong
Keyword(s):  
United States ◽  
Renewable Energy ◽  
Total Energy ◽  
United Arab Emirates ◽  
Energy Production ◽  
Evaluation Method ◽  
Scale Up ◽  
Renewable Energy Resources ◽  
Population Total ◽  
Renewable Energy Production

Climate change and air pollution are among the key drivers of energy transition worldwide. The adoption of renewable resources can act as a peacemaker and give stability regarding the damaging effects of fossil fuels challenging public health as well as the tension made between countries in global prices of oil and gas. Understanding the potential and capabilities to produce renewable energy resources is a crucial pre-requisite for countries to utilize them and to scale up clean and stable sources of electricity generation. This paper presents a hybrid methodology that combines the data envelopment analysis (DEA) Window model, and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) in order to evaluate the capabilities of 42 countries in terms of renewable energy production potential. Based on three inputs (population, total energy consumption, and total renewable energy capacity) and two outputs (gross domestic product and total energy production), DEA window analysis chose the list of potential countries, including Norway, United Kingdom, Kuwait, Australia, Netherlands, United Arab Emirates, United States, Japan, Colombia, and Italy. Following that, the FTOPSIS model pointed out the top three countries (United States, Japan, and Australia) that have the greatest capabilities in producing renewable energies based on five main criteria, which are available resources, energy security, technological infrastructure, economic stability, and social acceptance. This paper aims to offer an evaluation method for countries to understand their potential of renewable energy production in designing stimulus packages for a cleaner energy future, thereby accelerating sustainable development.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Comparison of the incentives used to stimulate energy production in Japan, France, West Germany, and the United States

1981 ◽  
Author(s):  
R.J. Cole ◽  
B.W. Cone ◽  
P. Sommers ◽  
C. Eschbach ◽  
W.J. Sheppard ◽  
...  
Keyword(s):  
United States ◽  
Energy Production ◽  
The United States ◽  
West Germany

Biomass energy production in the United States: an overview

1994 ◽  
Vol 6 (3) ◽  
pp. 161-173 ◽  
Author(s):  
William G. Hohenstein ◽  
Lynn L. Wright
Keyword(s):  
United States ◽  
Energy Production ◽  
The United States ◽  
Biomass Energy

scholarly journals Modelling Natural Gas Energy Production of United States

2021 ◽  
Vol 248 ◽  
pp. 02034
Author(s):  
Yubin Cai ◽  
Yanqiao Deng
Keyword(s):  
United States ◽  
Natural Gas ◽  
Energy Production ◽  
Prediction Method ◽  
Energy System ◽  
The United States ◽  
Gas Production ◽  
Energy Forecasting ◽  
Energy Prediction ◽  
Short Term Forecasting

In the transformation of the energy system, natural gas energy is regarded as a buffer energy. How to make a reasonable energy distribution and effectively predict its production is very significant. In the work of this paper, a grid-optimized fractional-order non-homogeneous grey model is used to predict the natural gas energy production in the United States and obtain reliable results. This paper first introduces the prediction method and prediction mechanism. Then the model is optimized to make the prediction effect more prominent. The natural gas energy prediction results show that this method has high prediction accuracy compared with other methods, which means that the method proposed in this paper can be used as an effective tool for short-term forecasting of natural gas production in the United States and play an auxiliary role in energy forecasting.

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