Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets
Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/13/3427 |
| _version_ | 1797563429840486400 |
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| author | Geovanny Marulanda Antonio Bello Jenny Cifuentes Javier Reneses |
| author_facet | Geovanny Marulanda Antonio Bello Jenny Cifuentes Javier Reneses |
| author_sort | Geovanny Marulanda |
| collection | DOAJ |
| description | Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France. |
| first_indexed | 2024-03-10T18:42:29Z |
| format | Article |
| id | doaj.art-603622fcc7b243b08e01bf6e9f958f57 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:42:29Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-603622fcc7b243b08e01bf6e9f958f572023-11-20T05:43:01ZengMDPI AGEnergies1996-10732020-07-011313342710.3390/en13133427Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity MarketsGeovanny Marulanda0Antonio Bello1Jenny Cifuentes2Javier Reneses3Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, SpainInstitute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, SpainSantander Big Data Institute, Universidad Carlos III de Madrid, 28903 Getafe, SpainInstitute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, SpainWind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.https://www.mdpi.com/1996-1073/13/13/3427ARIMAlong-term forecastingmulti-area electricity marketsSARIMAwind power forecasting |
| spellingShingle | Geovanny Marulanda Antonio Bello Jenny Cifuentes Javier Reneses Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets Energies ARIMA long-term forecasting multi-area electricity markets SARIMA wind power forecasting |
| title | Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets |
| title_full | Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets |
| title_fullStr | Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets |
| title_full_unstemmed | Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets |
| title_short | Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets |
| title_sort | wind power long term scenario generation considering spatial temporal dependencies in coupled electricity markets |
| topic | ARIMA long-term forecasting multi-area electricity markets SARIMA wind power forecasting |
| url | https://www.mdpi.com/1996-1073/13/13/3427 |
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