Assessing the Norwegian Offshore Wind Resources: Climatology, Power Variability and Wind Farm Siting

De norske havvindressursene er fantastiske. Likevel er det ingen operative vindparker i de norske havvområdene. Tatt i betraktning både utslippsmål og forventet økning i elektrisitetsforbruk har regjerningen nå begynt å vise interesse for kraftproduksjon til havs. Å utnyttelse de norske havvområdene til vindkraftproduksjon krever en storskala kartlegging og økt kunnskap om vindressursene. Denne doktorgradsavhandlingen tar for seg kartlegging av vindressursene, tilhørende vindkraftestimat, reduksjon av uønskede hendelser i vindkraftproduksjonen, og optimal plassering av fremtidige havvindparker. Observasjoner og data fra "the 3 km Norwegian Reanalysis (NORA3)'' danner datagrunnlaget for resultatene i avhandlingen. Resultatene er fordelt på fire forskningsartikler. Den første artikkelen ser på hvordan vi kan utnytte den naturlige variasjonen i værsystemene til fordel for vindkraftproduksjonen. Artikkelen svarer på mye vi kan forvente å redusere vindkraftvariabilitet ved å koble sammen vindparker i Nordsjøen og Norskehavet. Ved å koble sammen produksjonssteder vil uønskede vindkrafthendelser, som variabilitet og null-hendelser, reduseres drastisk. I tillegg har vi sett på hvilke værsystemer som er forbundet med langvarige null-hendelser. Typisk vil langvarige null-hendelser, hvor vinden er for svak til å generere vindkraft, sammenfalle med et høytrykk lokalisert over de sammenkoblede vindparkene. På den andre siden, langvarige null-hendelser forårsaket av veldig høy vind vil typisk være forbundet med et lavtrykk nord for de sammenkoblede vindparkene. Den andre artikkelen tar for seg hvorvidt NORA3 kan egne seg som et vindressursgrunnlag i planleggingsfasen av nye havvindprosjekter. Ved hjelp av statistiske metoder gjennomfører vi en grundig validering av vinddataene fra NORA3 i typiske vindturbinhøyder. Konklusjonen er at NORA3 er godt egnet for vindkraftestimering, men at datasettet tenderer mot å gi konservative estimat. For eksempel, modellen underestimerer den observerte vindkraftproduksjonen fordi modellen overestimerer antall hendelser med vind under en typisk nominell vindhastighet (u 11-13 $\mathrm{ ms^{-1}}$). I artikkel tre presenterer vi et nytt vindkraftrelatert datasett: "NORA3-WP: A high-resolution offshore wind power dataset for the Baltic, North, Norwegian, and Barents Seas''. Datasettet er basert på NORA3, og dekker områdene Nordsjøen, Østersjøen og deler av Norskehavet og Barentshavet. NORA3-WP er åpent tilgjengelig for nedlastning, og er generert for å tilrettelegge for at forskere, politikere og besluttningstakere enkelt skal ha tilgang til vindressurser og vindkraftrelatert data i planleggingsfasen av nye havvindprosjekter. NORA3-WP er det første vindkraftrelaterte datasettet som dekker hele den norske økonomiske sonen (NØS). I den siste artikkelen tar vi i bruk relevante datasett (bl.a NORA3-WP) for å presentere den første kartleggingen av hvor egnet de norske havområdene er for vindkraftutbygging. Vi bruker en metode kalt "multi-criteria decision analysis (MCDA)'' i tillegg til "analytical hierarchical process (AHP)'' hvor vi inkluderer kriterier innenfor vindressurser, teknoøkonomiske aspekt, sosial aksept, miljøhensyn og maritime begrensninger knyttet til vind- og bølgeforhold. Resultatet genereres gjennom en baseaktør. Denne aktøren har ikke sterke preferanser for ett sett av kriterier, men ser derimot viktigheten av en et prosjekt med økonomisk lønnsomhet, samt lav forutsetning for potensielle arealkonflikter. Hvor robuste resultatene er blir testet ved å opprette andre aktører med mer distinkte kriteriepreferanser for en fremtidig havvindpark: "investoren'', "miljøaktivisten'' og "fiskeren''. Resultatene viser at den sørlige delen av NØS er relativt sett den best egnede og mest motstandsdyktige regionen for havvindutbygging. Den norske delen av Barentshavet og langs kysten av Midt-Norge er også områder som er godt egnet for havvindproduksjon, men her er resultatene mindre motstandsdyktige mot endringer i hvilke kriterier som er viktige.The Norwegian offshore wind resources are outstanding. Yet, no wind farms are commissioned in the Norwegian waters. Considering emission reduction targets and predicted increase in electricity demand, the Norwegian government has in the recent years started to look towards the marine environment for energy extraction. Exploiting the offshore area for wind power deployment requires large-scale mapping and improved understanding of the Norwegian offshore wind resource characteristics. This thesis deals with wind resource assessment and related wind power estimates, mitigation of unwanted wind power production events, and wind farm siting considering the Norwegian offshore area. Observations and data from "the 3 km Norwegian Reanalysis (NORA3)'' form the basis for the results in this thesis. The results from this thesis are divided into four research papers. The first paper deals with mitigation of wind power intermittency through interconnection of allocated wind farms in the North and Norwegian Seas using observations. By interconnecting production sites unwanted power events, like variability and zero-production events, were drastically reduced. In this paper we also investigate the main atmospheric circulation associated with long-lasting zero-events. The average atmospheric pattern resulting in too low winds for power production is a associated with a high-pressure system located over the connected sites. Whereas, the average atmospheric situation associated with too strong winds is a low-pressure systems located to the north of the connected sites. The second paper investigates whether NORA3 can serve as a wind resource dataset in the planning phase of new wind farm projects. We carry out an in-depth near-hub-height validation of the wind resources in NORA3 towards offshore wind power using different statistical measures. We conclude that NORA3 is well suited for wind power estimates, but gives slightly conservative estimates on the offshore wind metrics. For example, the model output is biased towards lower wind power estimates due to an overestimation of the wind speed events below typical rated wind speed limits (u 11-13 $\mathrm{ ms^{-1}}$). In the third paper we present a new high resolution wind power related dataset named "NORA3-WP: A high-resolution offshore wind power dataset for the Baltic, North, Norwegian, and Barents Seas''. The dataset is based on NORA3 and covers the North Sea, the Baltic Sea and parts of the Norwegian and Barents Seas. NORA3-WP is an open access dataset intended for use in research, governmental management and for stakeholders to attain relevant wind resource and wind power information in the planning phase of a new wind farm project. NORA3-WP is the first wind power related dataset covering the entire Norwegian economic zone (NEZ). In the fourth paper we assembly multidisciplinary datasets (NORA3-WP, among others) presenting the first mapping of wind power suitability scores (WPSS) for the entire Norwegian offshore area. The method used to generate the WPSS is a Multi-criteria decision analysis (MCDA) framework including an analytical hierarchical process (AHP) approach considering wind resources, techno-economic aspects, social acceptance, environmental considerations, and met-ocean constraints. Results are obtained through a baseline scenario representing a decision-maker that does not prioritize one set of criteria strongly, but realizes the importance of selecting areas that are economically sound as well as having a low potential for social conflicts. We test the robustness of the results obtained in the baseline scenario by including three different actors with distinct preferences for siting of a wind farm: "the investor'', "the environmentalist'', and "the fisherman''. The results show that the southern part of NEZ is the region that is most suitable and robust for offshore wind power deployment. Offshore areas in the Norwegian part of the Barents Sea and the near-coastal areas outside mid-Norway are also suited, but these regions are rather sensitive to tuning of the criteria importance.Doktorgradsavhandlin

NORA3-WP: A high-resolution offshore wind power dataset for the Baltic, North, Norwegian, and Barents Seas

We present a new high resolution wind resource and wind power dataset named NORA3-WP. The dataset covers the North Sea, the Baltic Sea and parts of the Norwegian and Barents Seas. The 3-km Norwegian reanalysis (NORA3) forms the basis for the new dataset. NORA3-WP is an open access dataset intended for use in research, governmental management and for stakeholders to attain relevant wind resource and wind power information in the planning phase of a new wind farm project. The variables are available as monthly data, and provides a climatological overview of 25 wind resource and wind power related variables for three selected turbines for the ocean areas surrounding Norway. In addition, the underlying hourly wind speed data and hourly wind power generation for three selected turbines are also available for higher frequency analysis and case-studies.publishedVersio

The 3 km Norwegian reanalysis (NORA3) – a validation of offshore wind resources in the North Sea and the Norwegian Sea

We validate a new high-resolution (3 km) numerical mesoscale weather simulation for offshore wind power purposes for the time period 2004–2016 for the North Sea and the Norwegian Sea. The 3 km Norwegian reanalysis (NORA3) is a dynamically downscaled data set, forced with state-of-the-art atmospheric reanalysis as boundary conditions. We conduct an in-depth validation of the simulated wind climatology towards the observed wind climatology to determine whether NORA3 can serve as a wind resource data set in the planning phase of future offshore wind power installations. We place special emphasis on evaluating offshore wind-power-related metrics and the impact of simulated wind speed deviations on the estimated wind power and the related variability. We conclude that the NORA3 data are well suited for wind power estimates but give slightly conservative estimates of the offshore wind metrics. In other words, wind speeds in NORA3 are typically 5 % (0.5 m s−1) lower than observed wind speeds, giving an underestimation of offshore wind power of 10 %–20 % (equivalent to an underestimation of 3 percentage points in the capacity factor) for a selected turbine type and hub height. The model is biased towards lower wind power estimates due to overestimation of the wind speed events below typical wind speed limits of rated wind power (u11–13 m s−1). The hourly wind speed and wind power variability are slightly underestimated in NORA3. However, the number of hours with zero power production caused by the wind conditions (around 12 % of the time) is well captured, while the duration of each of these events is slightly overestimated, leading to 25-year return values for zero-power duration being too high for the majority of the sites. The model performs well in capturing spatial co-variability in hourly wind power production, with only small deviations in the spatial correlation coefficients among the sites. We estimate the observation-based decorrelation length to be 425.3 km, whereas the model-based length is 19 % longer.publishedVersio

A one-year comparison of new wind atlases over the North Sea

The New European Wind Atlas (NEWA) and the Norwegian hindcast archive (NORA3) database have become publicly available since the end of 2019 and mid-2021, respectively. They aim to model the long-term wind climatology with a spatial resolution of ca. 3 km and a temporal resolution of 1 h (NORA3) or 30 min (NEWA). Both products have a high potential for wind energy applications. Although their geographical coverages partly overlap, an inter-comparison of the NEWA and NORA3 databases in an offshore environment is still lacking. The paper compares the hourly mean wind speed and wind direction recorded in 2009 at the FINO1 platform (North Sea) with hindcast data from the NEWA and the NORA3 database. Both products were found to provide reliable estimates of the mean wind speed at 101 m above sea level. However, NORA3 shows slightly better performances than NEWA for the mean wind speed in terms of root-mean-square error, bias, earth mover's distance (EMD) and Pearson correlation coefficient. For the mean wind direction, a larger circular EMD than previously documented is found, which could be due to a directional bias in the wind vane data. Finally, the Brunt-Väisälä frequency is computed using sea-surface temperature analyses and the air temperature from NORA3 and NEWA at 101 m above sea level. The encouraging description of the static atmospheric stability by the wind atlases opens the possibility to study in more detail thermally-induced wind events for wind resource assessment or wind turbine design.publishedVersio

Topographic Effects on Strong Winds in Southern Norway; A Case Study of the Storm Dagmar

During the 25th of December 2011 a strong low pressure system struck Soutwestern Norway. This rapidly evolving system got the name "Dagmar". The aim of this thesis was to find out how the complex topography in Southern Norway affected the strong winds caused by Dagmar. An observational analysis was carried out, together with high resolution numerical simulations performed by the the Weather Research and Forcasting model (WRF). A linear wave model \citep{Barstad2005} was used to address the result form the WRF model and the observational analysis. In addition, the linear wave model was used to investigate how changes in key parameters like wind speed, wind direction, mountain height, and atmospheric stability could have influenced the WRF model results. The highest observed 10-m wind speed was measured at Kråkenes lighthouse, \SI{43.8}{\meter \per \second}, while the strongest wind gust was measured at Juvvasshø, \SI{64.7}{\meter \per \second}. The WRF model was set to simulate Dagmar with a realistic topography in Southern Norway. In addition, two other simulations were carried out: One where the topography of Southern Norway was smoothed out, and one run where the topography was completely removed. This was done to see the effect of Norway's complex topography on the strong winds caused by Dagmar. The flat topography simulation showed that the sting jet (strong winds to the south of Dagmar's core), which did not reach the coast in the topography-runs, reached inland when the mountain was removed. The reason for this retardation of the flow upstream of the mountain in the topography-runs was due to the presence of the mountain, and the corresponding high pressure region created on the windward side. The strength of this upstream wind shadow varied when the upstream wind direction changed. Another feature, present only in the mountain- runs, was a "left side jet", e.g., an region of accelerated air along the northwest coast. The linear wave model showed that the wind pattern was sensitive to changes in the key parameters, and varying the upstream wind direction from westerly to southwesterly was crucial for the exsistence of the left side jet

Assessing the Norwegian Offshore Wind Resources: Climatology, Power Variability and Wind Farm Siting

De norske havvindressursene er fantastiske. Likevel er det ingen operative vindparker i de norske havvområdene. Tatt i betraktning både utslippsmål og forventet økning i elektrisitetsforbruk har regjerningen nå begynt å vise interesse for kraftproduksjon til havs. Å utnyttelse de norske havvområdene til vindkraftproduksjon krever en storskala kartlegging og økt kunnskap om vindressursene. Denne doktorgradsavhandlingen tar for seg kartlegging av vindressursene, tilhørende vindkraftestimat, reduksjon av uønskede hendelser i vindkraftproduksjonen, og optimal plassering av fremtidige havvindparker. Observasjoner og data fra "the 3 km Norwegian Reanalysis (NORA3)'' danner datagrunnlaget for resultatene i avhandlingen. Resultatene er fordelt på fire forskningsartikler. Den første artikkelen ser på hvordan vi kan utnytte den naturlige variasjonen i værsystemene til fordel for vindkraftproduksjonen. Artikkelen svarer på mye vi kan forvente å redusere vindkraftvariabilitet ved å koble sammen vindparker i Nordsjøen og Norskehavet. Ved å koble sammen produksjonssteder vil uønskede vindkrafthendelser, som variabilitet og null-hendelser, reduseres drastisk. I tillegg har vi sett på hvilke værsystemer som er forbundet med langvarige null-hendelser. Typisk vil langvarige null-hendelser, hvor vinden er for svak til å generere vindkraft, sammenfalle med et høytrykk lokalisert over de sammenkoblede vindparkene. På den andre siden, langvarige null-hendelser forårsaket av veldig høy vind vil typisk være forbundet med et lavtrykk nord for de sammenkoblede vindparkene. Den andre artikkelen tar for seg hvorvidt NORA3 kan egne seg som et vindressursgrunnlag i planleggingsfasen av nye havvindprosjekter. Ved hjelp av statistiske metoder gjennomfører vi en grundig validering av vinddataene fra NORA3 i typiske vindturbinhøyder. Konklusjonen er at NORA3 er godt egnet for vindkraftestimering, men at datasettet tenderer mot å gi konservative estimat. For eksempel, modellen underestimerer den observerte vindkraftproduksjonen fordi modellen overestimerer antall hendelser med vind under en typisk nominell vindhastighet (u 11-13 $\mathrm{ ms^{-1}}$). I artikkel tre presenterer vi et nytt vindkraftrelatert datasett: "NORA3-WP: A high-resolution offshore wind power dataset for the Baltic, North, Norwegian, and Barents Seas''. Datasettet er basert på NORA3, og dekker områdene Nordsjøen, Østersjøen og deler av Norskehavet og Barentshavet. NORA3-WP er åpent tilgjengelig for nedlastning, og er generert for å tilrettelegge for at forskere, politikere og besluttningstakere enkelt skal ha tilgang til vindressurser og vindkraftrelatert data i planleggingsfasen av nye havvindprosjekter. NORA3-WP er det første vindkraftrelaterte datasettet som dekker hele den norske økonomiske sonen (NØS). I den siste artikkelen tar vi i bruk relevante datasett (bl.a NORA3-WP) for å presentere den første kartleggingen av hvor egnet de norske havområdene er for vindkraftutbygging. Vi bruker en metode kalt "multi-criteria decision analysis (MCDA)'' i tillegg til "analytical hierarchical process (AHP)'' hvor vi inkluderer kriterier innenfor vindressurser, teknoøkonomiske aspekt, sosial aksept, miljøhensyn og maritime begrensninger knyttet til vind- og bølgeforhold. Resultatet genereres gjennom en baseaktør. Denne aktøren har ikke sterke preferanser for ett sett av kriterier, men ser derimot viktigheten av en et prosjekt med økonomisk lønnsomhet, samt lav forutsetning for potensielle arealkonflikter. Hvor robuste resultatene er blir testet ved å opprette andre aktører med mer distinkte kriteriepreferanser for en fremtidig havvindpark: "investoren'', "miljøaktivisten'' og "fiskeren''. Resultatene viser at den sørlige delen av NØS er relativt sett den best egnede og mest motstandsdyktige regionen for havvindutbygging. Den norske delen av Barentshavet og langs kysten av Midt-Norge er også områder som er godt egnet for havvindproduksjon, men her er resultatene mindre motstandsdyktige mot endringer i hvilke kriterier som er viktige

Norwegian offshore wind power—Spatial planning using multi-criteria decision analysis

The Norwegian government recently agreed on the goal 30by40, which involves opening Norwegian offshore areas to host 30 GW of installed wind power by 2040. We address this goal by presenting a first mapping of wind power suitability scores (WPSS) for the entire Norwegian economic zone (NEZ) using a multi-criteria decision analysis framework (MCDA), namely, the analytical hierarchical process (AHP) approach. We obtain WPSS considering relevant criteria like wind resources, techno-economic aspects, social acceptance, environmental considerations, and met-ocean constraints such as wind and wave conditions. The results starts with a baseline scenario, where the criterion importance is pairwise compared in the context of balancing economic incentives and conflicting interests. Additionally, to reveal regions that are robust to changes in criterion importance, we carry out a sensitivity analysis by introducing three additional scenarios. These scenarios represent stereotypical actors with distinct preferences for siting of wind farms: the investor, the environmentalist, and the fisherman. The results show that the southern part of the NEZ is the most suitable and robust region for offshore wind power deployment. This region receives the highest suitability category (“very high” suitability for wind power application) throughout all the scenarios. Areas in the Norwegian part of the Barents Sea and the near-coastal areas outside mid-Norway are also well suited regions, but these are more sensitive to the choice of criterion importance. The use of AHP within the framework of MCDA is shown to be a promising tool for pinpointing the best Norwegian offshore areas for wind power application.publishedVersio

The 3 km Norwegian reanalysis (NORA3) – a validation of offshore wind resources in the North Sea and the Norwegian Sea

We validate a new high-resolution (3 km) numerical mesoscale weather simulation for offshore wind power purposes for the time period 2004–2016 for the North Sea and the Norwegian Sea. The 3 km Norwegian reanalysis (NORA3) is a dynamically downscaled data set, forced with state-of-the-art atmospheric reanalysis as boundary conditions. We conduct an in-depth validation of the simulated wind climatology towards the observed wind climatology to determine whether NORA3 can serve as a wind resource data set in the planning phase of future offshore wind power installations. We place special emphasis on evaluating offshore wind-power-related metrics and the impact of simulated wind speed deviations on the estimated wind power and the related variability. We conclude that the NORA3 data are well suited for wind power estimates but give slightly conservative estimates of the offshore wind metrics. In other words, wind speeds in NORA3 are typically 5 % (0.5 m s−1) lower than observed wind speeds, giving an underestimation of offshore wind power of 10 %–20 % (equivalent to an underestimation of 3 percentage points in the capacity factor) for a selected turbine type and hub height. The model is biased towards lower wind power estimates due to overestimation of the wind speed events below typical wind speed limits of rated wind power (u11–13 m s−1). The hourly wind speed and wind power variability are slightly underestimated in NORA3. However, the number of hours with zero power production caused by the wind conditions (around 12 % of the time) is well captured, while the duration of each of these events is slightly overestimated, leading to 25-year return values for zero-power duration being too high for the majority of the sites. The model performs well in capturing spatial co-variability in hourly wind power production, with only small deviations in the spatial correlation coefficients among the sites. We estimate the observation-based decorrelation length to be 425.3 km, whereas the model-based length is 19 % longer

Mitigation of offshore wind power intermittency by interconnection of production sites

This study uses a unique set of hourly wind speed data observed over a period of 16 years to quantify the potential of collective offshore wind power production. We address the well-known intermittency problem of wind power for five locations along the Norwegian continental shelf. Mitigation of wind power intermittency is investigated using a hypothetical electricity grid. The degree of mitigation is examined by connecting different configurations of the sites. Along with the wind power smoothing effect, we explore the risk probability of the occurrence and duration of wind power shutdown due to too low or high winds. Typical large-scale atmospheric situations resulting in long term shutdown periods are identified. We find that both the wind power variability and the risk of not producing any wind power decrease significantly with an increasing array of connected sites. The risk of no wind power production for a given hour is reduced from the interval 8.0 %–11.2 % for a single site to under 4 % for two sites. Increasing the array size further reduces the risk, but to a lesser extent. The average atmospheric weather pattern resulting in wind speed that is too low (too high) to produce wind power is associated with a high-pressure (low-pressure) system near the production sites