Offshore wind farms (OWFs) are important infrastructure which provide an alternative and clean means of energy production worldwide. The offshore wind industry has been continuously growing. Over the years, however, it has become evident that OWFs are facing a variety of safety and security challenges. If not addressed, these issues may hinder their progress. Based on these safety and security goals and on a Bayesian network model, this work presents a methodological approach for structuring and organizing expert knowledge and turning it into a probabilistic model to assess the safety and security of OWFs. This graphical probabilistic model allowed us to create a high-level representation of the safety and security state of a generic OWF. By studying the interrelations between the different functions of the model, and by proposing different scenarios, we determined the impacts that a failing function may have on other functions in this complex system. Finally, this model helped us define the performance requirements of such infrastructure, which should be beneficial for optimizing operation and maintenance.

An anticipated challenge for the offshore wind industry is the legally standardized decommissioning of offshore wind infrastructure after the expiration of the respective approval period. To meet the energy and climate targets set by, e.g., the German Federal Government, this challenge must be mastered in the context of sustainability. Potential concepts are (i) the deconstruction of offshore infrastructure without replacement, (ii) the continued operation of the plants, (iii) partially or even completely replacing them with newer, modernized plants (re-powering). Re-powering could also be a combination of existing infrastructures with other innovative technologies, such as hydrogen. In this work, the three concepts are analyzed along with their risks and additional factors, such as feasibility, cost-effectiveness, predictability of technological progress, and, planning security, are discussed. A quantitative risk and resilience analysis is conceptually demonstrated for the specific risk of extreme weather and wave conditions caused by climate change. Synthetic wave height data are generated and the corresponding load changes are applied to example offshore wind farms. The three end-of-life options are compared using resilience indicators that serve as exemplary measures for the energy output, which serves as the key performance indicator.

Offshore wind energy (OWE) production is a crucial element for increasing the amount of renewable energy. Consequently, one can observe a strong and constant rise of the OWE industry, turning it to an important contributor of national energy provision. This trend, however, is accompanied by increasing pressure on the reliability, safety, and security of the OWE infrastructure. Related security threats are characterized by high uncertainty regarding impact and probability leading to considerable complication of the risk assessment. On the other hand, the resilience concept emphasizes the consideration of the system’s response to such threats, and thus, offers a solution for dealing with the high uncertainty. In this work, we present an approach for combining the strengths of risk and resilience management to provide a solution for handling security threats in OWE infrastructures. Within this context, we introduce a quality assessment enabling the quantification of the trustworthiness of obtained results.

The effective protection of critical infrastructure against cyber and physical security threats involves many different steps from initially the identification of risks to finally the implementation of counter measures in the infrastructure. To derive counter measures and to come to intelligent decisions facing the identified risks, the impact calculation plays a central role. The impact of a specific threat can propagate through the systems of the infrastructure and thus needs to be analysed carefully. In this paper, the role of impact propagation of cyber-physical threats for infrastructure protection is discussed, exemplified for airport systems. In the ongoing EU-H2020 project SATIE (Security of Air Transport Infrastructure of Europe) a toolkit is developed containing two tools for impact propagation, namely the Business Impact Assessment (BIA) and the Impact Propagation Simulation (IPS). Both tools are described and for a small test case the propagation of a cyber threat and the transformation into a physical threat is demonstrated in a network representation as well as an agent-based model of the airport’s systems employing the IPS.

The identification and characterization of fractures is an important objective in many areas of earth and environmental sciences. Amplitude variation with offset and azimuth (AVOAz) analysis of seismic reflection data is a key method for achieving these tasks. Theoretical and experimental studies have shown that the presence of pore fluids together with the strong mechanical contrast between the fractures and their embedding background give rise to wave-induced fluid flow (WIFF) effects. This implies that the effective stiffness tensor of a fluid-saturated fractured rock defining its seismic response becomes viscoelastic and frequency-dependent. In spite of this, AVOAz analysis typically relies on end-member-type elastic stiffness models that either assume a relaxed (i.e., equilibrated) or unrelaxed (i.e., unequilibrated) state of the wave-induced fluid pressure in the rock. In general, however, neither the appropriateness of the chosen model nor the associated errors in the inversion process are known. To investigate this topic, we have considered a poroelastic medium containing parallel vertical fractures and generate synthetic seismic AVOAz data using the classic Rüger approximations for PP-wave reflection coefficients in horizontally transversely isotropic media. A Markov chain Monte Carlo method is used to perform a Bayesian inversion of the synthetic seismic AVOAz data. We quantify the influence of WIFF effects on the AVOAz inversion results when elastic relaxed and unrelaxed models are used as forward solvers of inversion schemes to estimate the fracture volume fraction, the elastic moduli, and the porosity of the background rock, as well as the overall weakness of the medium due to the presence of fractures. Our results indicate that, when dealing with single-frequency data, relaxed elastic models provide biased but overall better inversion results than unrelaxed ones, for which some fracture parameters cannot be resolved. Improved inversion performance is achieved when using frequency-dependent data, which illustrates the importance of accounting for poroelastic effects.