Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain
Giving the increasing penetration of intermittent wind power in the liberalized electricity market, wind power forecasting (WPF) is a topic of growing importance [Kariniotakis, 2017]. The number of papers on the field WPF evaluating the statistical performance has increased rapidly, while only a pro...
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UiT The Arctic University of Norway
2022
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ftunivtroemsoe:oai:munin.uit.no:10037/25833 2023-05-15T15:03:41+02:00 Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain Svane, Julie Therese 2022-06-01 https://hdl.handle.net/10037/25833 eng eng UiT The Arctic University of Norway UiT Norges arktiske universitet https://hdl.handle.net/10037/25833 Copyright 2022 The Author(s) wind power forecasting day-ahead market trading strategy complex terrain EOM-3901 Master thesis Mastergradsoppgave 2022 ftunivtroemsoe 2022-07-20T22:58:54Z Giving the increasing penetration of intermittent wind power in the liberalized electricity market, wind power forecasting (WPF) is a topic of growing importance [Kariniotakis, 2017]. The number of papers on the field WPF evaluating the statistical performance has increased rapidly, while only a proportion of the former studies focus on the economic benefit of WPF. In this study we have answered how well a set of wind power forecasting (WPF) models works as day-ahead trading strategies for a 54MW wind power park. The performance evaluation is based on both statistic and economic measures. The wind power park is located in Northern Norway in a region with complex terrain and an arctic and coastal climate. The WPF models are applied on weather forecasts provided by two numerical weather prediction (NWP) models i.e., MEPS and AROME Arctic, operated by the Norwegian Meteorological Institute (MET Norway). When applied on MEPS forecasts of wind speed and wind direction, and the statistical performance measures are evaluated over a test period, it is evident that the multilayer perceptron (MLP) model provides the lowest NRMSE of 21.4%. Compared to a current forecasting method of the responsible power trader (ISHK model), the MLP model shows an improvement of 4.0%. Further enhancement of the accuracy of the MLP model is attained by adding air pressure as the third input feature. The resulting NRMSE is 20.9% of installed capacity. This corresponds to a 6.3% improvement compared to the ISHK model, which verify that the MLP model can compete with a current forecasting method of the responsible power trader on statistical measures. When it comes to the economic perspective, given a single-price system of the power market, the naive persistence model surprisingly shows the highest revenue for the power producer. A total revenue of 16.42 MNOK is obtained, where the imbalance revenue accounts for 200 kNOK. However, considering both statistical and economic measures it is evident that the ISHK model is the most effective ... Master Thesis Arctic Northern Norway University of Tromsø: Munin Open Research Archive Arctic Norway |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
wind power forecasting day-ahead market trading strategy complex terrain EOM-3901 |
spellingShingle |
wind power forecasting day-ahead market trading strategy complex terrain EOM-3901 Svane, Julie Therese Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain |
topic_facet |
wind power forecasting day-ahead market trading strategy complex terrain EOM-3901 |
description |
Giving the increasing penetration of intermittent wind power in the liberalized electricity market, wind power forecasting (WPF) is a topic of growing importance [Kariniotakis, 2017]. The number of papers on the field WPF evaluating the statistical performance has increased rapidly, while only a proportion of the former studies focus on the economic benefit of WPF. In this study we have answered how well a set of wind power forecasting (WPF) models works as day-ahead trading strategies for a 54MW wind power park. The performance evaluation is based on both statistic and economic measures. The wind power park is located in Northern Norway in a region with complex terrain and an arctic and coastal climate. The WPF models are applied on weather forecasts provided by two numerical weather prediction (NWP) models i.e., MEPS and AROME Arctic, operated by the Norwegian Meteorological Institute (MET Norway). When applied on MEPS forecasts of wind speed and wind direction, and the statistical performance measures are evaluated over a test period, it is evident that the multilayer perceptron (MLP) model provides the lowest NRMSE of 21.4%. Compared to a current forecasting method of the responsible power trader (ISHK model), the MLP model shows an improvement of 4.0%. Further enhancement of the accuracy of the MLP model is attained by adding air pressure as the third input feature. The resulting NRMSE is 20.9% of installed capacity. This corresponds to a 6.3% improvement compared to the ISHK model, which verify that the MLP model can compete with a current forecasting method of the responsible power trader on statistical measures. When it comes to the economic perspective, given a single-price system of the power market, the naive persistence model surprisingly shows the highest revenue for the power producer. A total revenue of 16.42 MNOK is obtained, where the imbalance revenue accounts for 200 kNOK. However, considering both statistical and economic measures it is evident that the ISHK model is the most effective ... |
format |
Master Thesis |
author |
Svane, Julie Therese |
author_facet |
Svane, Julie Therese |
author_sort |
Svane, Julie Therese |
title |
Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain |
title_short |
Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain |
title_full |
Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain |
title_fullStr |
Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain |
title_full_unstemmed |
Wind power forecasting as input to day-ahead trading strategies for wind in complex terrain |
title_sort |
wind power forecasting as input to day-ahead trading strategies for wind in complex terrain |
publisher |
UiT The Arctic University of Norway |
publishDate |
2022 |
url |
https://hdl.handle.net/10037/25833 |
geographic |
Arctic Norway |
geographic_facet |
Arctic Norway |
genre |
Arctic Northern Norway |
genre_facet |
Arctic Northern Norway |
op_relation |
https://hdl.handle.net/10037/25833 |
op_rights |
Copyright 2022 The Author(s) |
_version_ |
1766335542581002240 |