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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Main Author: Svane, Julie Therese
Format: Master Thesis
Language:English
Published: UiT The Arctic University of Norway 2022
Subjects:
Online Access:https://hdl.handle.net/10037/25833
id ftunivtroemsoe:oai:munin.uit.no:10037/25833
record_format openpolar
spelling 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)
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