Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data

Ethiopia has huge wind energy potential. In order to be able to simulate the power system operation, hourly time series of wind power is needed. These can be obtained from ERA5 data but first a realistic model is needed. Therefore, in this paper ERA5 reanalysis data were used to model wind power pro...

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Main Authors: Kena Likassa Nefabas, Lennart Söder, Mengesha Mamo, Jon Olauson
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/14/9/2573
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spelling doaj-d1108ee45ac74563bfff1047a58cb1a12021-04-30T23:00:53ZengMDPI AGEnergies1996-10732021-04-01142573257310.3390/en14092573Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis DataKena Likassa Nefabas0Lennart Söder1Mengesha Mamo2Jon Olauson3School of Electrical and Computer Engineering, AAU Addis Ababa University, Addis Ababa 3614, EthiopiaDepartment of Electric Power & Energy Systems, KTH Royal Institute of Technology, 10044 Stockholm, SwedenSchool of Electrical and Computer Engineering, AAU Addis Ababa University, Addis Ababa 3614, EthiopiaDepartment of Electric Power & Energy Systems, KTH Royal Institute of Technology, 10044 Stockholm, SwedenEthiopia has huge wind energy potential. In order to be able to simulate the power system operation, hourly time series of wind power is needed. These can be obtained from ERA5 data but first a realistic model is needed. Therefore, in this paper ERA5 reanalysis data were used to model wind power production at two topographically different and distant regions of Ethiopian wind farms—Adama II and Ashegoda. Wind speed was extracted from the ERA5 nearest grid point, bi-linearly interpolated to farms location and statistically down-scaled to increase its resolution at the site. Finally, the speed is extrapolated to hub-height of turbine and converted to power through farm specific power curve to compare with actual data for validation. The results from the model and historical data of wind farms are compared using performance error metrics like hourly mean absolute error (MAE) and hourly root mean square error (RMSE). When comparing with data from Ethiopian Electric Power (EEP), we found hourly MAE and RMSE of 2.5% and 4.54% for Adama II and 2.32% and 5.29% for Ashegoda wind farms respectively, demonstrating a good correlation between the measured and our simulation model result. Thus, this model can be extended to other parts of the country to forecast future wind power production, as well as to indicate simulation of wind power production potential for planning and policy applications using ERA5 reanalysis data. To the best of our knowledge, such modeling of wind power production using reanalysis data has not yet been tried and no researcher has validated generation output against measurement in the country.https://www.mdpi.com/1996-1073/14/9/2573ERA5measurementmodelingreanalysiswind speed
collection DOAJ
language English
format Article
sources DOAJ
author Kena Likassa Nefabas
Lennart Söder
Mengesha Mamo
Jon Olauson
spellingShingle Kena Likassa Nefabas
Lennart Söder
Mengesha Mamo
Jon Olauson
Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data
Energies
ERA5
measurement
modeling
reanalysis
wind speed
author_facet Kena Likassa Nefabas
Lennart Söder
Mengesha Mamo
Jon Olauson
author_sort Kena Likassa Nefabas
title Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data
title_short Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data
title_full Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data
title_fullStr Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data
title_full_unstemmed Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data
title_sort modeling of ethiopian wind power production using era5 reanalysis data
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2021-04-01
description Ethiopia has huge wind energy potential. In order to be able to simulate the power system operation, hourly time series of wind power is needed. These can be obtained from ERA5 data but first a realistic model is needed. Therefore, in this paper ERA5 reanalysis data were used to model wind power production at two topographically different and distant regions of Ethiopian wind farms—Adama II and Ashegoda. Wind speed was extracted from the ERA5 nearest grid point, bi-linearly interpolated to farms location and statistically down-scaled to increase its resolution at the site. Finally, the speed is extrapolated to hub-height of turbine and converted to power through farm specific power curve to compare with actual data for validation. The results from the model and historical data of wind farms are compared using performance error metrics like hourly mean absolute error (MAE) and hourly root mean square error (RMSE). When comparing with data from Ethiopian Electric Power (EEP), we found hourly MAE and RMSE of 2.5% and 4.54% for Adama II and 2.32% and 5.29% for Ashegoda wind farms respectively, demonstrating a good correlation between the measured and our simulation model result. Thus, this model can be extended to other parts of the country to forecast future wind power production, as well as to indicate simulation of wind power production potential for planning and policy applications using ERA5 reanalysis data. To the best of our knowledge, such modeling of wind power production using reanalysis data has not yet been tried and no researcher has validated generation output against measurement in the country.
topic ERA5
measurement
modeling
reanalysis
wind speed
url https://www.mdpi.com/1996-1073/14/9/2573
work_keys_str_mv AT kenalikassanefabas modelingofethiopianwindpowerproductionusingera5reanalysisdata
AT lennartsoder modelingofethiopianwindpowerproductionusingera5reanalysisdata
AT mengeshamamo modelingofethiopianwindpowerproductionusingera5reanalysisdata
AT jonolauson modelingofethiopianwindpowerproductionusingera5reanalysisdata
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