18 research outputs found
A Framework for Synthetic Power System Dynamics
Information on power grids is confidential and thus real data is often
inaccessible. This necessitates the use of synthetic power grid models in
research. So far the models used, for example, in machine learning had to be
very simple and homogeneous to produce large ensembles of robust grids. We
present a modular framework to generate synthetic power grids that considers
the heterogeneity of real power grid dynamics but remains simple and tractable.
This enables the generation of large sets of synthetic grids for a wide range
of applications. We also include the major drivers of fluctuations on
short-time scales. The synthetic grids generated are robust and show good
synchronization under all evaluated scenarios, as should be expected for
realistic power grids. This opens the door to future research that studies
grids under severe stress due to extreme events which could lead to
destabilization and black-outs. A software package that includes an efficient
Julia implementation of the framework is released as a companion to the paper
Spike Spectra for Recurrences
In recurrence analysis, the τ-recurrence rate encodes the periods of the cycles of the underlying high-dimensional time series. It, thus, plays a similar role to the autocorrelation for scalar time-series in encoding temporal correlations. However, its Fourier decomposition does not have a clean interpretation. Thus, there is no satisfactory analogue to the power spectrum in recurrence analysis. We introduce a novel method to decompose the τ-recurrence rate using an over-complete basis of Dirac combs together with sparsity regularization. We show that this decomposition, the inter-spike spectrum, naturally provides an analogue to the power spectrum for recurrence analysis in the sense that it reveals the dominant periodicities of the underlying time series. We show that the inter-spike spectrum correctly identifies patterns and transitions in the underlying system in a wide variety of examples and is robust to measurement noise.German Research FoundationPeer Reviewe
Data-driven load profiles and the dynamics of residential electricity consumption
The dynamics of power consumption constitutes an essential building block for planning and operating sustainable energy systems. Whereas variations in the dynamics of renewable energy generation are reasonably well studied, a deeper understanding of the variations in consumption dynamics is still missing. Here, we analyse highly resolved residential electricity consumption data of Austrian, German and UK households and propose a generally applicable data-driven load model. Specifically, we disentangle the average demand profiles from the demand fluctuations based purely on time series data. We introduce a stochastic model to quantitatively capture the highly intermittent demand fluctuations. Thereby, we offer a better understanding of demand dynamics, in particular its fluctuations, and provide general tools for disentangling mean demand and fluctuations for any given system, going beyond the standard load profile (SLP). Our insights on the demand dynamics may support planning and operating future-compliant (micro) grids in maintaining supply-demand balance
Non-standard power grid frequency statistics in Asia, Australia, and Europe
The power-grid frequency reflects the balance between electricity supply and
demand. Measuring the frequency and its variations allows monitoring of the
power balance in the system and, thus, the grid stability. In addition, gaining
insight into the characteristics of frequency variations and defining precise
evaluation metrics for these variations enables accurate assessment of the
performance of forecasts and synthetic models of the power-grid frequency.
Previous work was limited to a few geographical regions and did not quantify
the observed effects. In this contribution, we analyze and quantify the
statistical and stochastic properties of self-recorded power-grid frequency
data from various synchronous areas in Asia, Australia, and Europe at a
resolution of one second. Revealing non-standard statistics of both empirical
and synthetic frequency data, we effectively constrain the space of possible
(stochastic) power-grid frequency models and share a range of analysis tools to
benchmark any model or characterize empirical data. Furthermore, we emphasize
the need to analyze data from a large range of synchronous areas to obtain
generally applicable models.Comment: 7 pages; 7 figure
Perspectives on adaptive dynamical systems
Adaptivity is a dynamical feature that is omnipresent in nature,
socio-economics, and technology. For example, adaptive couplings appear in
various real-world systems like the power grid, social, and neural networks,
and they form the backbone of closed-loop control strategies and machine
learning algorithms. In this article, we provide an interdisciplinary
perspective on adaptive systems. We reflect on the notion and terminology of
adaptivity in different disciplines and discuss which role adaptivity plays for
various fields. We highlight common open challenges, and give perspectives on
future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure
Perspectives on adaptive dynamical systems
Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches
Moving the epidemic tipping point through topologically targeted social distancing
The epidemic threshold of a social system is the ratio of infection and
recovery rate above which a disease spreading in it becomes an epidemic. In the
absence of pharmaceutical interventions (i.e. vaccines), the only way to
control a given disease is to move this threshold by non-pharmaceutical
interventions like social distancing, past the epidemic threshold corresponding
to the disease, thereby tipping the system from epidemic into a non-epidemic
regime. Modeling the disease as a spreading process on a social graph, social
distancing can be modeled by removing some of the graphs links. It has been
conjectured that the largest eigenvalue of the adjacency matrix of the
resulting graph corresponds to the systems epidemic threshold. Here we use a
Markov chain Monte Carlo (MCMC) method to study those link removals that do
well at reducing the largest eigenvalue of the adjacency matrix. The MCMC
method generates samples from the relative canonical network ensemble with a
defined expectation value of . We call this the
"well-controlling network ensemble" (WCNE) and compare its structure to
randomly thinned networks with the same link density. We observe that networks
in the WCNE tend to be more homogeneous in the degree distribution and use this
insight to define two ad-hoc removal strategies, which also substantially
reduce the largest eigenvalue. A targeted removal of 80\% of links can be as
effective as a random removal of 90\%, leaving individuals with twice as many
contacts
Vibrational lifetimes of hydrated phospholipids
Large-scale ab initio molecular-dynamics simulations have been carried out to compute, at human-body temperature, the vibrational modes and lifetimes of pure and hydrated dipalmitoylphosphatidylcholine (DPPC) lipids. The projected atomic vibrations calculated from the spectral energy density are used to compute the vibrational modes and the lifetimes. All the normal modes of the pure and hydrated DPPC and their frequencies are identified. The computed lifetimes incorporate the full anharmonicity of the atomic interactions. The vibrational modes of the water molecules close to the head group of DPPC are active (possess large projected spectrum amplitudes) in the frequency range 0.5–55 THz, with a peak at 2.80 THz in the energy spectrum. The computed lifetimes for the high-frequency modes agree well with the recent data measured at room temperature where high-order phonon scattering is not negligible. The computed lifetimes of the low-frequency modes can be tested using the current experimental capabilities. Moreover, the approach may be applied to other lipids and biomolecules, in order to predict their vibrational dispersion relations, and to study the dynamics of vibrational energy transfer