Machine learning of power grid frequency dynamics and control: prediction, explanation and stochastic modelling

A reliable supply of electric power is not a matter of course. Power grids enable the transport of power from generators to consumers, but their stable operation constantly requires corrective measures and a careful supervision. In particular, power generation and demand have to be balanced at all times. A large power imbalance threatens the reliability of the power supply and can, in extreme cases, lead to a large-scale blackout. Therefore, the power imbalance is constantly corrected through distinct control schemes. The power grid frequency measures the balance of power generation and demand. To guarantee frequency stability, and thereby a balance of generation and demand, load-frequency control constantly counteracts large frequency deviations. However, the transition of the energy system to renewable energy sources challenges frequency stability and control. Wind and solar power do not provide intrinsic inertia, which leads to increasingly fast frequency dynamics. Different economic sectors become strongly coupled to the power system, as, for example, the adoption of electric vehicles will interconnect the transport sector and the power system. Finally, wind and solar power are weather-dependent, which increases the variability of power generation. All in all, this gives rise to diverse, interdependent and stochastic impact factors, that drive the balance of power demand and generation, and thus the grid frequency. How can we predict, explain and model frequency dynamics given its strong non-autonomous and stochastic character? In this thesis, I use machine learning to disentangle the effects of external drivers on grid frequency dynamics and control. First, I propose a prediction model that only uses historic frequency data, but fails in representing external impacts. Therefore, I include time series of techno-economic drivers and model their impact on grid frequency data using explainable machine learning methods. These methods reveal the dependencies between external drivers and frequency deviations, such as the important impact of forecast errors in the Scandinavian grid or the varying effects of different generation types. Finally, I integrate these drivers into a stochastic dynamical model of the grid frequency, which both represents short-term dynamics and long-term trends due to techno-economic impacts. My work complements traditional simulation-based approaches through validation and modelling inspiration. It offers flexible modelling and prediction tools for power system dynamics, which are profitable for systems with diverse impact factors but noisy and insufficient data

Secondary control activation analysed and predicted with explainable AI

The transition to a renewable energy system poses challenges for power grid operation and stability. Secondary control is key in restoring the power system to its reference following a disturbance. Underestimating the necessary control capacity may require emergency measures, such as load shedding. Hence, a solid understanding of the emerging risks and the driving factors of control is needed. In this contribution, we establish an explainable machine learning model for the activation of secondary control power in Germany. Training gradient boosted trees, we obtain an accurate description of control activation. Using SHapely Additive exPlanation (SHAP) values, we investigate the dependency between control activation and external features such as the generation mix, forecasting errors, and electricity market data. Thereby, our analysis reveals drivers that lead to high reserve requirements in the German power system. Our transparent approach, utilizing open data and making machine learning models interpretable, opens new scientific discovery avenues.Comment: 8 pages, 6 figure

Comparison of laboratory and immediate diagnosis of coagulation for patients under oral anticoagulation therapy before dental surgery

BACKGROUND: Dental surgery can be carried out on patients under oral anticoagulation therapy by using haemostyptic measures. The aim of the study was a comparative analysis of coagulation by laboratory methods and immediate patient diagnosis on the day of the planned procedure. METHODS: On the planned day of treatment, diagnoses were carried out on 298 patients for Prothrombin Time (PT), the International Normalised Ratio (INR), and Partial Thromboplastin Time (PTT). The decision to proceed with treatment was made with an INR < 4.0 according to laboratory results. RESULTS: Planned treatment did not go ahead in 2.7% of cases. Postoperatively, 14.8% resulted in secondary bleeding, but were able to be treated as out-patients. 1.7% had to be treated as in-patients. The average error between the immediate diagnosis and the laboratory method: 95% confidence interval was -5.8 ± 15.2% for PT, -2.7 ± 17.9 s for PTT and 0.23 ± 0.80 for INR. The limits for concordance were 9.4 and -21.1% for PT, 15.2 and -20.5 s for PTT, and 1.03 and -0.57 for INR. CONCLUSION: This study showed a clinically acceptable concordance between laboratory and immediate diagnosis for INR. Concordance for PT and PTT did not meet clinical requirements. For patients under oral anticoagulation therapy, patient INR diagnosis enabled optimisation of the treatment procedure when planning dental surgery

Physics-inspired machine learning for power grid frequency modelling

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-inspired machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in Continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveals their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasises the importance of modelling power system dynamics as a stochastic non-autonomous system with both intrinsic dynamics and external drivers.Comment: 21 pages, 5 figure

Assessment of productivity and profitability of sole and double-cropping for agricultural biomass production

Zweifruchtsysteme werden in Deutschland als alterna­tive Anbausysteme für die landwirtschaftliche Biomasse­produktion erwogen. In dieser Untersuchung wurden die Produktivität und Wirtschaftlichkeit von Zweifruchtnutzung und Hauptfruchtanbau in den Jahren 2007 bis 2009 an drei klimatisch unterschiedlichen Standorten in Deutschland verglichen. Die wärmeliebenden Kulturen Mais (Zea mays L.), Futterhirse [Sorghum bicolor (L.) Moench] und Sudangras [S.&nbsp;bicolor (L.) Moench × S. sudanense (Piper) Stapf] wurden entweder allein als Hauptfrüchte oder als Zweitfrüchte nach Winterroggen (Secale cereale L.) angebaut. Bei Zweifruchtnutzung wurde der Winterroggen entweder zwischen Anfang und Mitte Mai (früh) oder Anfang Juni (spät) geerntet. Während der Winterroggen kein Zusatzwasser erhielt, wurden Mais, Futterhirse und Sudangras sowohl mit als auch ohne künstliche Bewässerung angebaut. Der Winterroggen lieferte einen oberirdischen Trockenmasseertrag von 5,2 t ha–1 bei früher Ernte und von 9,0 t ha–1 bei später Ernte. Die ertragreichste Zweifruchtnutzung (Roggen-Mais) war der produktivsten Hauptfrucht (Mais) ohne Zusatzbewässerung um 3,6 t ha–1 (23%) und mit Zusatzbewässerung um 5,2 t ha–1 (24%) überlegen. Durch die Zusatzbewässerung erhöht sich der Trockenmasseertrag bei den Hauptfrüchten um 5,3 t ha–1 (37%), bei den früh gesäten Zweitfrüchten um 5,6 t ha–1 (43%) und bei den spät gesäten Zweitfrüchten um 6,8 t ha–1 (77%). Ohne Zusatzbewässerung wurden die, im Vergleich zum Hauptfruchtanbau, höheren Produktionskosten bei der Zwei­frucht­nutzung nicht durch entsprechend höhere Trocken­masseerträge kompensiert. Mit Zusatzbewässerung hingegen erzielte das Zweifruchtsystem Roggen-Mais an zwei von drei Versuchsstandorten höhere Deckungsbeiträge als der Hauptfruchtanbau von Mais. &nbsp; &nbsp;Double-crop (DC) systems are receiving serious consideration as cropping alternative for agricultural biomass production in Germany. In this study the productivity and economics of DC and sole-crop (SC) systems were compared from 2007 to 2009 at three climatically diverse sites of Germany. The warm season crops maize (Zea mays L.), forage sorghum [Sorghum bicolor (L.) Moench] and sorghum-sudangrass [S. bicolor (L.) Moench × S. sudanense (Piper) Stapf] were either grown as sole crops or as second crop following winter rye (Secale cereale L.). The winter rye first crop was harvested premature at early-to-mid May (early) or early June (late). While the winter rye was grown under rainfed conditions, maize, forage sorghum, and sorghum-sudangrass were grown with or without irrigation. Winter rye produced an aboveground dry matter yield (DMY) of 5.2&nbsp;t ha–1 at early harvest and 9.0&nbsp;t ha–1 at late harvest. The highest yielding DC system (rye-maize) out-yielded the most productive SC system (maize) by 3.6&nbsp;t ha–1 (23%) under rainfed conditions and by 5.2&nbsp;t ha–1 (24%) with irrigation. Irrigation increased DMY of sole crops by 5.3&nbsp;t&nbsp;ha–1&nbsp;(37%), of early sown second crops by 5.6&nbsp;t ha–1&nbsp;(43%), and of late sown second crops by 6.8&nbsp;t&nbsp;ha–1&nbsp;(77%). Under rainfed conditions, the higher DMY of the DC as compared with the SC systems did not compensate the higher production costs. With irrigation, however, the rye-maize DC achieved higher contribution margins than SC maize at two of the three experimental sites. &nbsp; &nbsp

Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence

A stable supply of electrical energy is essential for the functioning of our society. Therefore, the electrical power grid's operation and energy and balancing markets are subject to strict regulations. As the external technical, economic, or social influences on the power grid change, these regulations must also be constantly adapted. However, whether these regulatory changes lead to the intended results is not easy to assess. Could eXplainable Artificial Intelligence (XAI) models distinguish regulatory settings and support the understanding of the effects of these changes? In this article, we explore two examples of regulatory changes in the German energy markets for bulk electricity and for reserve power. We explore the splitting of the German-Austrian bidding zone and changes in the pricing schemes of the German balancing energy market. We find that boosted tree models and feedforward neural networks before and after a regulatory change differ in their respective parametrizations. Using Shapley additive explanations, we reveal model differences, e.g. in terms of feature importances, and identify key features of these distinct models. With this study, we demonstrate how XAI can be applied to investigate system changes in power systems.Comment: 7 pages, 3 figure

Advanced control strategies for the continuous production of monoclonal antibodies

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Measuring personality functioning with the 12-item version of the OPD-Structure Questionnaire (OPD-SQS): reliability, factor structure, validity, and measurement invariance in the general population

BackgroundThe assessment of personality functioning is at the core of current dimensional models of personality disorders. A variety of measures from different clinical and research traditions aim to assess basic psychological capacities regarding the self and others. While some instruments have shown reliability and validity in clinical or other selected samples, much less is known about their performance in the general population.MethodsIn three samples representative of the German adult population with a total of 7,256 participants, levels of personality functioning were measured with the short 12-item version of the Operationalized Psychodynamic Diagnosis – Structure Questionnaire (OPD-SQS). We addressed questions of factor structure, reliability, validity, factorial invariance, and provide norm values.ResultsConfirmatory factor analysis indicated a satisfactory to good model fit. OPD-SQS models were mostly unaffected by variables such as gender, age, or measurement time. As expected, personality functioning was associated with general psychopathology as well as indices of occupational functioning.ConclusionThe OPD-SQS is a viable measure to assess personality functioning in the general population