Improving Predictability of Wind Power Generation
Wind energy plays an important role in decarbonizing the economy and increasingly accounts for a growing share of electricity supply in the United States. However, availability of wind resource is highly dependent on variable factors such as weather and local geographies, making wind power generatio...
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| Format: | Thèse |
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Massachusetts Institute of Technology
2023
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| Accès en ligne: | https://hdl.handle.net/1721.1/150204 |
| _version_ | 1826188558979825664 |
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| author | Zhang, Vivienne Jiao |
| author2 | Knittel, Christopher R. |
| author_facet | Knittel, Christopher R. Zhang, Vivienne Jiao |
| author_sort | Zhang, Vivienne Jiao |
| collection | MIT |
| description | Wind energy plays an important role in decarbonizing the economy and increasingly accounts for a growing share of electricity supply in the United States. However, availability of wind resource is highly dependent on variable factors such as weather and local geographies, making wind power generation forecast a particularly difficult task. This adds to the challenge of grid management, which requires that the supply of electricity equates the demand at all times. Complicating the effort to improve wind power predicitability is a lack of empirical data, since wind power generation data are proprietary and often considered business secrets. To address this lack of empirical study, this thesis uses actual generation data between 2016 to 2021 from seven anonymized wind farms in Midwestern United States that range from 50MW to 235MW in size. The experiments demonstrate how machine learning methods can be used to forecast wind power generation at different time intervals, and how the accuracy of forecasting can be significantly improved when using a combination of newly extracted weather forecast data and weather measurement data. The economic benefits of more accurate forecasting are then studied using a using a simulation with market data from the Midcontinent Independent System Operator and the Southwest Power Pool. The thesis then explores whether predictability of wind power generation can be improved by placing weather stations closer to the wind forecast sites. Implications of these findings can inform investment decisions regarding weather monitoring stations and forecasting models, which can help electricity market participants adapt to a grid with an increasing share of renewable resources. |
| first_indexed | 2024-09-23T08:01:16Z |
| format | Thesis |
| id | mit-1721.1/150204 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:01:16Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1502042023-04-01T04:06:28Z Improving Predictability of Wind Power Generation Zhang, Vivienne Jiao Knittel, Christopher R. Uhler, Caroline Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Institute for Data, Systems, and Society Wind energy plays an important role in decarbonizing the economy and increasingly accounts for a growing share of electricity supply in the United States. However, availability of wind resource is highly dependent on variable factors such as weather and local geographies, making wind power generation forecast a particularly difficult task. This adds to the challenge of grid management, which requires that the supply of electricity equates the demand at all times. Complicating the effort to improve wind power predicitability is a lack of empirical data, since wind power generation data are proprietary and often considered business secrets. To address this lack of empirical study, this thesis uses actual generation data between 2016 to 2021 from seven anonymized wind farms in Midwestern United States that range from 50MW to 235MW in size. The experiments demonstrate how machine learning methods can be used to forecast wind power generation at different time intervals, and how the accuracy of forecasting can be significantly improved when using a combination of newly extracted weather forecast data and weather measurement data. The economic benefits of more accurate forecasting are then studied using a using a simulation with market data from the Midcontinent Independent System Operator and the Southwest Power Pool. The thesis then explores whether predictability of wind power generation can be improved by placing weather stations closer to the wind forecast sites. Implications of these findings can inform investment decisions regarding weather monitoring stations and forecasting models, which can help electricity market participants adapt to a grid with an increasing share of renewable resources. S.M. S.M. 2023-03-31T14:39:25Z 2023-03-31T14:39:25Z 2023-02 2023-03-06T21:53:11.679Z Thesis https://hdl.handle.net/1721.1/150204 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Zhang, Vivienne Jiao Improving Predictability of Wind Power Generation |
| title | Improving Predictability of Wind Power Generation |
| title_full | Improving Predictability of Wind Power Generation |
| title_fullStr | Improving Predictability of Wind Power Generation |
| title_full_unstemmed | Improving Predictability of Wind Power Generation |
| title_short | Improving Predictability of Wind Power Generation |
| title_sort | improving predictability of wind power generation |
| url | https://hdl.handle.net/1721.1/150204 |
| work_keys_str_mv | AT zhangviviennejiao improvingpredictabilityofwindpowergeneration |