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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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 |
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