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Coordinated inauthentic behavior is used as a tool on social media to shape public opinion by elevating or suppressing topics using systematic engagements -- e.g. through *likes* or similar reactions. In an honest world, reactions may be informative to users when selecting on what to spend their attention: through the wisdom of crowds, summed reactions may help identifying relevant and high-quality content. This is nullified by coordinated inauthentic liking. To restore wisdom-of-crowds effects, it is therefore desirable to separate the inauthentic agents from the wise crowd, and use only the latter as a voting *jury* on the relevance of a post. To this end, we design two *jury selection procedures* (JSPs) that discard agents classified as inauthentic. Using machine learning techniques, both cluster on binary vote data -- one using a Gaussian Mixture Model (GMM JSP), one the k-means algorithm (KM JSP) -- and label agents by logistic regression. We evaluate the jury selection procedures with an agent-based model, and show that the GMM JSP detects more inauthentic agents, but both JSPs select juries with vastly increased correctness of vote by majority. This proof of concept provides an argument for the release of reactions data from social media platforms through a direct use-case in the fight against online misinformation.

This paper presents a novel estimator for a non-standard restriction to both symmetry and low rank in the context of high dimensional cointegrated processes. Furthermore, we discuss rank estimation for high dimensional cointegrated processes by restricted bootstrapping of the Gaussian innovations. We demonstrate that the classical rank test for cointegrated systems is prone to underestimate the true rank and demonstrate this effect in a 100 dimensional system. We also discuss the implications of this underestimation for such high dimensional systems in general. Also, we define a linearized Kuramoto system and present a simulation study, where we infer the cointegration rank of the unrestricted $p\times p$ system and successively the underlying clustered network structure based on a graphical approach and a symmetrized low rank estimator of the couplings derived from a reparametrization of the likelihood under this unusual restriction., Comment: 20 pages, 7 figures

Quantum computing has the potential to solve many computational problems exponentially faster than classical computers. The high shares of renewables and the wide deployment of converter-interfaced resources require new tools that shall drastically accelerate power system computations, including optimization and security assessment, which can benefit from quantum computing. To the best of our knowledge, this is the first paper that goes beyond quantum computing simulations and performs an experimental application of Quantum Computing for power systems on a real quantum computer. We use five different quantum computers, apply the HHL quantum algorithm, and examine the impact of current noisy quantum hardware on the accuracy and speed of an AC power flow algorithm. We perform the same studies on a 3-bus and a 5-bus system with real quantum computers to identify challenges and open research questions related with the scalability of these algorithms., Comment: 8 pages, 6 figures, 4 tables, In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canada

Antigen test kits have been used extensively as a screening tool during the worldwide pandemic of coronavirus (SARS-CoV-2). While it is generally expected that taking samples for analysis with PCR testing gives more reliable results than using antigen test kits, the overall sensitivity and specificity of the two protocols in the field have not yet been estimated without assuming that the PCR test constitutes a gold standard. We use latent class models to estimate the in situ performance of both PCR and antigen testing, using data from the Danish national registries. The results are based on 240,000 paired tests results sub-selected from the 55 million test results that were obtained in Denmark during the period from February 2021 until June 2021. We found that the specificity of both tests is very high in our data sample (>99.7%), while the sensitivity of PCR sampling was estimated to be 95.7% (95% CI: 92.8-98.4%) and that of the antigen test kits used in Denmark over the study period was estimated at 53.8% (95% CI: 49.8-57.9%). Our findings can be used as supplementary information for consideration when implementing serial testing strategies that employ a confirmatory PCR sample following a positive result from an antigen test kit, such as the policy used in Denmark. We note that while this strategy reduces the number of false positives associated with antigen test screening, it also increases the false negatives. We demonstrate that the balance of trading false positives for false negatives only favours the use of serial testing when the expected true prevalence is low. Our results contain substantial uncertainty in the estimates for sensitivity due to the relatively small number of positive test results over this period: validation of our findings in a population with higher prevalence would therefore be highly relevant for future work.

Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihood-based estimators and tests. As an alternative, and to improve finite sample performance, this paper considers bootstrap-based inference for interval estimation and testing. Specifically, for a wide class of point process models we consider a novel bootstrap scheme labeled 'fixed intensity bootstrap' (FIB), where the conditional intensity is kept fixed across bootstrap repetitions. The FIB, which is very simple to implement and fast in practice, extends previous ideas from the bootstrap literature on time series in discrete time, where the so-called 'fixed design' and 'fixed volatility' bootstrap schemes have shown to be particularly useful and effective. We compare the FIB with the classic recursive bootstrap, which is here labeled 'recursive intensity bootstrap' (RIB). In RIB algorithms, the intensity is stochastic in the bootstrap world and implementation of the bootstrap is more involved, due to its sequential structure. For both bootstrap schemes, we provide new bootstrap (asymptotic) theory which allows to assess bootstrap validity, and propose a 'non-parametric' approach based on resampling time-changed transformations of the original waiting times. We also establish the link between the proposed bootstraps for point process models and the related autoregressive conditional duration (ACD) models. Lastly, we show effectiveness of the different bootstrap schemes in finite samples through a set of detailed Monte Carlo experiments, and provide applications to both financial data and social media data to illustrate the proposed methodology., Comment: 51 pages, 4 figures

Massive investments in offshore wind power generate significant challenges on how this electricity will be integrated into the incumbent energy systems. In this context, green hydrogen produced by offshore wind emerges as a promising solution to remove barriers towards a carbon-free economy in Europe and beyond. Motivated by the recent developments in Denmark with the decision to construct the world's first artificial Offshore Energy Hub, this paper investigates how the lowest cost for green hydrogen can be achieved. A model proposing an integrated design of the hydrogen and offshore electric power infrastructure, determining the levelised costs of both hydrogen and electricity, is proposed. The economic feasibility of hydrogen production from Offshore Wind Power Hubs is evaluated considering the combination of different electrolyser placements, technologies and modes of operations. The results show that costs down to 2.4 EUR per kg can be achieved for green hydrogen production offshore, competitive with the hydrogen costs currently produced by natural gas. Moreover, a reduction of up to 13 pct. of the cost of wind electricity is registered when an electrolyser is installed offshore shaving the peak loads., Comment: Submitted to "Renewable and Sustainable Energy Transition" on May 20, 2021 - Accepted on August 24, 2021

Taking concrete steps towards a carbon-free society, the Danish Parliament has recently made an agreement on the establishment of the world's first two offshore energy hubs, one on the island of Bornholm and one on an artificial island in the North Sea. Being the two first-of-their-kind projects, several aspects related to the inclusion of these "energy islands" in the current market setup are still under discussion. To this end, this paper presents the first large-scale impact analysis of offshore hubs on the whole European power system and electricity market. The detailed models used for such analysis are publicly released with the paper. Our study shows that energy hubs in the North Sea have a positive impact, and overall increase economic welfare in EU. However, when considering the impact on each country, benefits are not shared equally. In order to help the development of such projects, we focus on the identification of market challenges and system needs arising from the hubs. From a market perspective, we show how exporting countries are negatively affected by the lower electricity prices and we point at potential strategic behaviors induced by the large amount of new transmission capacity installed in the North Sea. From a system point of view, we show how the large amount of wind energy stresses conventional generators, which are required to become more flexible, and national grids, which cannot always accommodate large imports from the hubs., Comment: Submitted to Energy Policy on March 31, 2021 - Revised on February 7, 2022

Online discussion threads are important means for individual decision-making and for aggregating collective judgments, e.g. the `wisdom of crowds'. Empirical investigations of the wisdom of crowds are currently ambivalent about the role played by social information. While some findings suggest that social information undermines crowd accuracy due to correlated judgment errors, others show that accuracy improves. We investigate experimentally the accuracy of threads in which participants make magnitude estimates of varying difficulty while seeing a varying number of previous estimates. We demonstrate that, for difficult tasks, seeing preceding estimates aids the wisdom of crowds. If, however, participants only see extreme estimates, wisdom quickly turns into folly. Using a Gaussian Mixture Model, we assign a persuadability score to each participant and show that persuadability increases with task difficulty and with the amount of social information provided. In filtered threads, we see an increasing gap between highly persuadable participants and skeptics., Comment: improved method of analysis using quantile regression + change in authorship

In 2017, Energinet and TenneT, the Danish and Dutch Transmission System Operators (TSOs), have announced the North Sea Wind Power Hub (NSWPH) project. The project aims at increasing by 36 GW the North Sea offshore wind capacity, with an artificial island collecting all the power produced by wind turbines and several HVDC links transmitting this power to the onshore grids. This project brings together new opportunities and new challenges, both from a technical and economic point of view. In this regard, this paper presents three analyses regarding the design and operation of such an offshore system. First, we perform a techno-economic assessment of different grid configurations for the collection of the power produced by wind farms and its transmission to the hub. In this analysis, two frequencies and two voltage levels for the operation of the offshore grid are investigated. Our findings show that the nominal-frequency high-voltage option is the more suitable, as low-frequency does not bring any advantage and low-voltage would results in higher costs. The second analysis is related to the differences in operating the system with low- or zero-inertia; different dynamic studies are performed for each configuration to identify proper control actions and their stability properties. Comparing the outcomes of the simulations, we observed that voltage and frequency oscillations are better damped in the zero-inertia system; however, the risk of propagating offshore faults in the connected onshore grids is mitigated with the inclusion of the synchronous condensers. Lastly, a comparison of ElectroMagnetic Transient (EMT) and phasor-mode (also known as RMS) models is presented, in order to understand their appropriateness of simulating low- and zero- inertia systems. The results show that phasor approximation modelling can be used, as long as eigen-frequencies in power network are well damped., Comment: Submitted to "CIGRE Technical Exhibition 2020 - Session 48" on January 3, 2020 - Revised on February 15, 2020 - Accepted on June 4, 2020

In the Nordic region, many interconnectors are formed by HVDC links, as Scandinavia, Continental Europe and the Baltic region are non-synchronous AC systems. This paper presents two cost benefit analyses on the utilization of HVDC interconnectors in the Nordic countries: in the first we investigate the utilization of HVDC interconnectors for reserve procurement and, in the second, we assess the implementation of implicit grid losses on HVDC interconnectors in the day-ahead market. The first analysis is motivated by real events in 2018 where the inertia of the Nordic system dropped below a critical level and the most critical generating unit, a nuclear power plant in Sweden, was redispatched to guarantee the security of the system. In order to guarantee system security while reducing the costs of preventive actions, in summer 2020 new frequency products were introduced in the Nordic system: the Fast Frequency Reserves (FFR). HVDC lines, however, can perform similar tasks at lower costs. In our analysis, we are, thus, investigating the cost savings of using HVDC lines for frequency support using their Emergency Power Control (EPC) functionality, instead of redispatching or FFR. The second analysis is based on the proposition of Nordic Transmission System Operators (TSOs) to introduce linear HVDC loss factors in the market clearing. With our analysis, we show that linear loss factors can unfairly penalize one HVDC line over the other, and this can reduce social benefits and jeopardize revenues of merchant HVDC lines. In this regard, we propose piecewise-linear loss factors: a simple-to-implement but highly-effective solution. Moreover, we demonstrate how the introduction of HVDC loss factors is a partial solution, since it disproportionally increases the AC losses. Our results show that the additional inclusion of AC loss factors can eliminate this problem., Comment: Submitted to "CIGRE Centennial Exhibition 2021" on December 15, 2020. arXiv admin note: text overlap with arXiv:1910.05607, arXiv:2001.00664

This paper proposes a novel wind power smoothing control paradigm in context of performance-based regulation service. Conventional methods aim at adjusting wind power output using hard-coded filtering algorithms that can result in visually smoothed power output with unmeasurable impacts on system generation-demand balance. Distinguished from conventional methods, the newly proposed control method smooths wind power output from a power system perspective by using the regulation mileage as a key performance indicator. To simultaneously address the system needs and maximize wind energy harvesting, a mileage-responsive framework is developed to enable wind farms to optimally generate smoothing power. The effectiveness of the proposed method is well demonstrated through case studies, of which the simulation results shows a great potential for practical applications., Comment: Submitted to IEEE Power Engineering Letters