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To mitigate climate change and meet increasing electricity demand, the global installed capacity of renewable energy sources like wind power is rapidly increasing. Further, external threats against the power system are expected to increase. The threats can arise from extreme weather events driven by climate change, or from antagonistic attacks like sabotage or military conflicts. The power grid is extensive and difficult to oversee due to its complexity and size and is therefore sensitive to such threats. Transmission lines are particularly vulnerable due to their exposure to harsh weather and the difficulty in monitoring them, making them susceptible to sabotage. With more wind power and external threats, it is crucial to study how wind farm integration affects power system resilience to severe line outages. In the present study, this scenario is investigated by simulating 29 different grid topologies 1 000 times each though Monte Carlo simulations. Each topology represents a case where conventional generators are replaced by wind farms. Severe line outages are triggered, and the effect is quantified in terms of disconnected load. The results show that replacing conventional generation by wind power can enhance the power system resilience. The outcome is determined by underlaying factors such as the installed wind power capacity, the electrical distance between wind farms, and the electrical distance between wind farms and loads. The finding that deliberate integration of wind power can enhance the resilience of a power system to severe line outages is important knowledge for transmission system operators and policymakers.

The expansion of offshore wind power as part of a transition to low-carbon electricity generation presents challenges for the resilience of power grids. One strategy to address this challenge is to rely on imports from neighboring power systems with excess production capacity. However, constraints due to grid topography and internal generation may limit the import capacity. This study uses a quasi-dynamic power flow simulation based on the IEEE 39-bus system to assess the limits of increasing offshore wind power penetration on the grid’s ability to meet demand. The model is a synthetic grid based on the topography of the New England power grid, loads are held constant, and between one and five conventional generators are replaced with wind farms while the remaining generators are constrained by ramp rates. To model wind power production, we have constructed a composite wind farm power curve by smoothing the power curve of a single wind turbine generator. Wind speed data are retrieved from single locations in the WIND Toolkit Long-Term Ensemble Dataset with five-minute resolution. We find that high levels of wind power penetration increase the risk of unmet demand due to physical and operational power flow constraints in certain wind conditions. We also identify critical network elements and imported power limits. These findings both expand and challenge the findings of previous work by shedding light on underlying constraints while considering future wind power technology.

In this study, the impact of data time resolution on long-term voltage stability assessment of a power grid with high penetration of wind-solar hybrid power plants is investigated. Historical and synthetic wind data as well as solar irradiance are used to calculate power output from hypothetical offshore wind-solar hybrid power plants, geographically located off the coast of Massachusetts, USA. The results show that using hourly input data can overestimate the long-term voltage stability, compared with using minute data. However, the relative difference in terms of voltage mean value and standard deviation is marginal whilst the most significant difference is the intensity of the voltage fluctuations. The main drawback of using high-resolution data is the execution time, increasing proportionally with the number of time steps. Thus, it is argued that the choice of da ta time resolution should be based on the aspects of long-term voltage stability and the size of the power grid to be studied.

In the next decades, the dependencies on power production from renewable energy sources are expected to increase dramatically. A transition towards large-scale offshore wind farms together with an increased electrification of the industry and transportation sectors introduces new vulnerabilities to society. Further, extreme weather events are expected to increase in intensity and frequency, driven by climate change. However, there are significant knowledge gaps concerning the impacts of severe weather conditions on the resilience of power systems with large dependencies on offshore wind. In the present study, a comparison between two different power systems’ resilience to historical extreme storm conditions has been conducted. The power systems are the IEEE39-bus New England model and the Great Britain model. The results show significant differences between the two power systems, which underlying reasons are analysed and explained. With an offshore wind penetration level of 30 %, the New England model stays intact in terms of connected load. When increasing the penetration level to 40 %, about 10 % of the total connected load gets disconnected, whereas about 33 % of the load gets disconnected with a penetration level of 50 %. The Great Britain model stays intact in terms of connected load with a penetration level of at least 49 %.

To reduce the vulnerability of power grids to high impact low probability (HILP) events, analysis methods can be applied to quantify the criticality of the nodes in the grid. The method implemented in this article is one originating from complex network theory. It is used to quantify the structural vulnerability of an open-source transmission grid model representing the Nordic transmission grid. The analytical measures used are clustering coefficient and betweenness, closeness, degree, and combined centrality, which are weighted with respect to the estimated values of the transmission lines’ series reactance. The results, which are presented in the form of geographic and network representations, show substantial differences in terms of criticality between the nodes. The most critical ones are highlighted in geographic representations and are further compared with an open-source system analysis performed by the Swedish transmission system operator (TSO). The outcome from this study is that the weighted and combined centrality measure performed the best in terms of identifying critical nodes in the Nordic transmission grid. Thus, the method can be used as a tool for assessing the structural vulnerability of a real transmission grid, even with limited access to electrical grid data. However, the results from this method should not be considered conclusive.

The ongoing transition towards large installations of offshore wind and the electrification of the transport sector and other critical infrastructures introduce new vulnerabilities to the society. Large dependencies of power production from offshore wind are expected in the next decades, but there are large knowledge gaps regarding the power production reliability under severe weather conditions. Simultaneously, weather extremes may increase in frequency and intensity, driven by climate change. In this paper we investigate the resilience of a power system subject to a hurricane event. The power system is based on the IEEE39-bus New England system but with different scenarios for increasing penetration of offshore wind. We find that an offshore wind penetration level of 30% or less results in a power system resilient to hurricane events, with no need for load disconnection. However, when increased to 40% offshore wind penetration, 650 MW corresponding to 10% of the total load demand gets disconnected during the storm peak. With a penetration of 50% offshore wind, the disconnected load ranges from 2.2 GW of load corresponding to 1/3 of the total load demand, to a total power system blackout.

In a future energy system based on renewable energy sources, wave energy will most likely play a role due to its high energy potential and low intermittency. The power production from parks of wave energy converters of point absorber type has been extensively studied. This is also the case for the wave energy resource at many coastal areas around the globe. Wave energy has not yet reached a commercial level, and a large variety of technologies exist; therefore, an established method to calculate the technical potential for wave energy has still not been established. To estimate the technical potential of wave energy conversion, some approximations inevitably need to be taken due to the systems high complexity. In this study, a detailed mapping of the wave climate and simulation of large arrays of hydrodynamically cross‐coupled wave energy converters are combined to calculate the technical potential for wave energy conversion in the Swedish exclusive economic zone. A 16‐year wave data set distributed in a 1.1 km × 1.1 km grid is used to calculate the absorbed energy from a park of 200 generic point absorbers. The areas with best potential have an average annual energy absorption of 16 GWh for the selected wave energy park adapted to 1 km2 when using a constant damping, while the theoretical upper bound is 63 GWh for the same area.

In wind integration studies, sub-hourly, load synchronous wind data are often preferable. These datasets can be generatedby a hybrid approach, combining hourly measurements or output from meteorological models with a stochastic simulationof the high-frequency fluctuations. This paper presents a method for simulating aggregated intra-hourly wind power fluc-tuations for a power system, taking into account the time-varying volatility seen in measurements. Some key elements inthe modelling were transformations to stationarity, the use of frequency domain techniques including a search for appropri-ate phase angles and an adjustment of the resulting time series in order to get correct hourly means. Generation data fromDenmark and Germany with 5 and 15 min temporal resolution were used for training models. It is shown that the distribu-tion and non-stationarity of simulated deviations from hourly means closely follow those of measurements. Power spectraldensities and step change distributions agree well. Of particular importance is that the results are good also when the train-ing and objective power systems are not the same. The computational cost is low in comparison with other approaches forgenerating high-frequency data.

When performing wind integration studies, synthetic wind power forecasts are key elements. Historically, data from operational forecasting systems have been used sparsely, likely due to the high costs involved. Purely statistical methods for simulating wind power forecasts are more common,but have problems mimicking all relevant aspects of actual forecasts. Consequently, a new approach to obtain wind power forecasts for integration studies is proposed, relying on long time series of freely and globally available reforecasts. In order to produce synthetic forecasts with similar properties as operational ditto, some processing (noise addition and error reduction) is necessary. Validations with measurements from Belgium and Sweden show that the method is adequate; and distributions, correlations, autocorrelations and power spectral densities of forecast errors correspond well. Furthermore, abrupt changes when forecasts are updated and the existence of level and phase errors are reproduced. The influence from terrain complexity on error magnitude is promising, but more data is necessary for a proper validation.