Application of the Maximum Entropy Method for Determining a Sensitive Distribution in the Renewable Energy Systems

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Gholamhossein Yari ◽  
Zahra Amini Farsani
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Maximum Entropy ◽  
Energy Systems ◽  
Rayleigh Distribution ◽  
Entropy Method ◽  
Climatic Data ◽  
Data Set ◽  
Renewable Energy Systems

In the field of the wind energy conversion, a precise determination of the probability distribution of wind speed guarantees an efficient use of the wind energy and enhances the position of wind energy against other forms of energy. The present study thus proposes utilizing an accurate numerical-probabilistic algorithm which is the combination of the Newton’s technique and the maximum entropy (ME) method to determine an important distribution in the renewable energy systems, namely the hyper Rayleigh distribution (HRD) which belongs to the family of Weibull distribution. The HRD is mainly used to model the wind speed and the variations of the solar irradiance level with a negligible error. The purpose of this research is to find the unique solution to an optimization problem which occurs when maximizing Shannon’s entropy. To confirm the accuracy and efficiency of our algorithm, we used the long-term data for the average daily wind speed in Toyokawa for 12 yr to examine the Rayleigh distribution (RD). This data set was obtained from the National Climatic Data Center (NCDC) in Japan. It seems that the RD is more closely fitted to the data. In addition, we presented different simulation studies to check the reliability of the proposed algorithm.

Economical Design of Wind-Solar Hybrid Renewable Energy Systems

2009 ◽  
Author(s):  
Susan W. Stewart ◽  
Lucas T. Witmer
Keyword(s):  
Renewable Energy ◽  
Cost Savings ◽  
Energy Systems ◽  
Pv System ◽  
Data Set ◽  
Renewable Energy Systems ◽  
Hybrid Renewable Energy Systems ◽  
Wind Turbine System ◽  
Penn State ◽  
Hybrid Renewable Energy

Every location on Earth has its own unique set of natural resources to draw upon for sustainable energy production. As these resources are generally of an intermittent nature, hybrid systems will be necessary in many situations to achieve economical energy independence while meeting our inconsistent demands for electricity with minimal or no energy storage. Wind and solar resources often have complimentary attributes that combined can more closely match energy load requirements. This match can be customized for optimum economy by adjusting the orientation and design of the PV system as well as the rotor length and generator size of the wind turbine system. Different load requirements and electricity rate structures require a different design approach in order to achieve optimum cost savings. Using the Penn State SURFRAD wind speed and solar radiation data set the design process for solar-wind hybrid renewable energy systems is explored for the case of a grid-tied residential scale application with a time of use electricity rate structure.

scholarly journals Improving mid-altitude mesoscale wind speed forecasts using LiDAR-based observation nudging for Airborne Wind Energy Systems

2019 ◽  
Author(s):  
Markus Sommerfeld ◽  
Curran Crawford ◽  
Gerald Steinfeld ◽  
Martin Dörenkämper
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Atmospheric Stability ◽  
Energy Systems ◽  
Data Set ◽  
Diurnal Variability ◽  
Advantages And Disadvantages ◽  
Lidar Measurements ◽  
Wind Energy Systems ◽  
The Impact

Abstract. Airborne wind energy systems (AWES) aim to operate at altitudes above conventional wind turbines where reliable high resolution wind data is scarce. Wind LiDAR measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This article investigates whether assimilating measurements into the mesoscale WRF model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the LiDAR measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long term statistics. Finally, wind speed data is used to estimate optimal traction power and operating altitudes of AWES. Observation nudging improves the overall accuracy of WRF. Close to the surface the impact of nudging is limited as effects of the air-surface interaction dominate, but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed probability distribution shows a multi-modality caused by changing atmospheric stability conditions. Based on a simplified AWES model the most probable optimal altitude will be around 400 m. Such systems will benefit from dynamically adjusting their operating altitude.

scholarly journals Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

2019 ◽  
Vol 4 (4) ◽  
pp. 563-580
Author(s):  
Markus Sommerfeld ◽  
Martin Dörenkämper ◽  
Gerald Steinfeld ◽  
Curran Crawford
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Systems ◽  
Stability Conditions ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Lidar Measurements ◽  
Wind Energy Systems ◽  
The Impact

Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air–surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude.

scholarly journals Hybrid renewable energy systems, Solar energy, Standalone micro-grid, wind energy

2010 ◽  
Vol 15 (4) ◽  
pp. 313-322
Author(s):  
Luiz Antonio de Souza Ribeiro ◽  
Osvaldo Ronald Saavedra ◽  
José Gomes de Matos ◽  
Shigeaki Leite Lima ◽  
Guilherme Bonan ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Wind Energy ◽  
Energy Systems ◽  
Renewable Energy Systems ◽  
Hybrid Renewable Energy Systems ◽  
Micro Grid ◽  
Hybrid Renewable Energy

scholarly journals Evaluation of wind energy investment with artificial neural networks

2019 ◽  
Vol 9 (2) ◽  
pp. 142-147
Author(s):  
Hasan Huseyin Yildirim ◽  
Mehmet Yavuz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Energy Investment ◽  
Data Set

Countries aiming for sustainability in economic growth and development ensure the reliability of energy supplies. For countries to provide their energy needs uninterruptedly, it is important for domestic and renewable energy sources to be utilised. For this reason, the supply of reliable and sustainable energy has become an important issue that concerns and occupies mankind. Of the renewable energy sources, wind energy is a clean, reliable and inexhaustible source of energy with low operating costs. Turkey is a rich nation in terms of wind energy potential. Forecasting of investment efficiency is an important issue before and during the investment period in wind energy investment process because of high investment costs. It is aimed to forecast the wind energy products monthly with multilayer neural network approach in this study. For this aim a feed forward back propagation neural network model has been established. As a set of data, wind speed values 48 months (January 2012-December 2015) have been used. The training data set occurs from 36 monthly wind speed values (January 2012-December 2014) and the test data set occurs from other values (January-December 2015). Analysis findings show that the trained Artificial Neural Networks (ANNs) have the ability of accurate prediction for the samples that are not used at training phase. The prediction errors for the wind energy plantation values are ranged between 0.00494-0.015035. Also the overall mean prediction error for this prediction is calculated as 0.004818 (0.48%). In general, we can say that ANNs be able to estimate the aspect of wind energy plant productions.

Sign in / Sign up

Export Citation Format

Share Document