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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 (Regjeringen, 2022).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), including an 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 criteria importance are pair-vise compared in the context of balancing economic incentives and conflicting interests. Additionally, to reveal regions that are robust to changes in criteria importance we carry out a sensitivity analysis by introducing three additional scenarios. These scenarios represent 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 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 criteria 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.

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 (u–13 m s−1) and underestimation of high-wind-speed events (u>11–13 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.

A new high-resolution (3 km) numerical mesoscale weather simulation spanning the period 2004–2018 is validated for offshore wind power purposes for the North Sea and Norwegian Sea. The NORwegian hindcast Archive (NORA3) was created by dynamical downscaling, forced with state-of-the-art hourly atmospheric reanalysis as boundary conditions. A validation of the simulated wind climatology has been carried out to determine the ability of NORA3 to act as a tool for planning future offshore wind power installations. Special emphasis is placed on evaluating offshore wind power-related metrics and the impact of simulated wind speed deviations on the estimated wind power and the related variability. The general conclusion of the validation is that the NORA3 data is rather well suited for wind power estimates, but gives slightly conservative estimates on the offshore wind metrics. Wind speeds are typically 5 % (0.5 ms−1) lower than observed wind speeds, giving an underestimation of offshore wind power of 10 %–20 % (equivalent to an underestimation of 3 percentage point in the capacity factor), for a selected turbine type and hub height. The model is biased towards lower wind power estimates because of overestimation of the frequency of low-speed wind events (< 10 ms−1) and underestimation of high-speed wind events (> 10 ms−1). The hourly wind speed and wind power variability are slightly underestimated in NORA3. However, the number of hours with zero power production (around 12 % of the time) is fairly 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 four of the six sites. The model is relatively good at capturing spatial co-variability in hourly wind power production among the sites. However, the observed decorrelation length was estimated to be 432 km, whereas the model-based length was 19 % longer.

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 shut-down. Typical large-scale atmospheric situations resulting in long term shut-down 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 10 % for a single site to under 4 % for two sites. Increasing the array-size further reduces the risk, but to a lesser extend. The average atmospheric weather pattern resulting in wind speed that is too low (too high) to produce wind power is associated with a high- (low-) pressure system near the production sites.

This paper presents a thorough review of 75 modelling tools currently used for analysing energy and electricity systems. Increased activity within model development in recent years has led to several new models and modelling capabilities, partly motivated by the need to better represent the integration of variable renewables. The purpose of this paper is to give an updated overview of currently available modelling tools, their capabilities and to serve as an aid for modellers in their process of identifying and choosing an appropriate model. A broad spectrum of modelling tools, ranging from small-scale power system analysis tools to global long-term energy models, has been assessed. Key information regarding the general logic, spatiotemporal resolution as well as the technological and economic features of the models is presented in three comprehensive tables. This information has been validated and updated by model developers or affiliated contact persons, and is state-of-the-art as of the submission date. With the available suite of modelling tools, most challenges of today's electricity system can be assessed. For a future with an increasing share of variable renewables and increasing electrification of the energy system, there are some challenges such as how to represent short-term variability in long-term studies, incorporate the effect of climate change and ensure openness and transparency in modelling studies.

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.

Den norske regjeringen har som mål å tildele områder med potensial på 30 GW havvind innen 2040. På grunn av den stokastiske naturen til vindkraft som en væravhengig energikilde, er en mer helhetlig tilnærming til energiplanlegging nødvendig. Avstanden på tvers av Norges økonomiske sone (NEZ) er stor på grunn av Norges langstrakte form. Dermed opplever NEZ mange typer vær samtidig. Dette kan føre til utfordringer for det norske kraftsystemet, ettersom vindkraft kan svinge raskt. Denne avhandlingen undersøker egenskapene til åtte utvalgte områder for havvind i NEZ. Data samles inn fra et høyoppløselig offshore vindkraftdatasett kalt NORA3-WP, som er utledet fra det meteorologiske datasettet NORA3. Moderne porteføljeteori (MPT) blir brukt på alle åtte områdene for å bestemme den optimale fordelingen av havvind i to forskjellige scenarier. De åtte havvindområdene er optimalisert for gjennomsnitt-varians og Sharpe-forhold porteføljene for begge scenariene. The Norwegian government aims to allocate areas with a potential of 30 GW offshore wind within 2040. Because of the stochastic nature of wind power as a weather-dependent energy source, a more holistic approach to energy planning is needed. The distance across the Norwegian economic zone (NEZ) are vast due to Norway's elongated shape. Thus, NEZ experience many types of weather at the same time. This can cause challenges for the Norwegian power system as wind power can fluctuate quickly. This thesis investigates the characteristics of eight selected offshore sites across NEZ. Data is gathered from a high-resolution offshore wind power dataset called NORA3-WP, which is derived from the meteorological hindcast dataset NORA3. Modern portfolio theory (MPT) is also utilized on all eight sites to determine the optimal spatial distribution of offshore wind in two different scenarios. The eight sites are optimised for the mean-variance and Sharpe-ratio portfolio in both scenarios.