arXiv subjects

Pavol Bauer

Publications and source records attributed to Pavol Bauer.

10 recordsLinked to original sources

arXiv2025

Sequential Operation of Residential Energy Hubs using Physics-Based Economic Nonlinear MPC

The operation of residential energy hubs with multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to different carrier dynamics, hybrid storage coordination and high-dimensional action-spaces. Energy management systems oversee their operation, deciding the set points of the primary control layer. This paper presents a novel 2-stage economic model predictive controller for electrified buildings including physics-based models of the battery degradation and thermal systems. The hierarchical control operates in the Dutch sequential energy markets. In particular common assumptions regarding intra-day markets (auction and continuous-time) are discussed as well as the coupling of the different storage systems. The best control policy it is best to follow continuous time intra-day in the summer and the intra-day auction in the winter. This sequential operation comes at the expense of increased battery degradation. Lastly, under our controller, the realized short-term flexibility of the thermal energy storage is marginal compared to the flexibility delivered by stationary battery pack and electric vehicles with bidirectional charging.

eess.SY↗

arXiv2025

Ageing-aware Energy Management for Residential Multi-Carrier Energy Systems

In the context of building electrification, the operation of distributed energy resources integrating multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to the nonlinear device dynamics, uncertainty, and computational issues. As such, energy management systems seek to decide the power dispatch in the best way possible. The objective is to minimize and balance operative costs (energy bills or asset degradation) with user requirements (mobility, heating, etc.). Current energy management uses empirical battery ageing models outside of their specific fitting conditions, resulting in inaccuracies and poor performance. Moreover, the link to thermal systems is also overlooked. This paper presents an ageing-aware nonlinear economic model predictive controller for electrified buildings that incorporates physics-based battery ageing models. The models distinguish between energy storage systems (chemistry, ageing state, etc.) and make explicit the trade-off between grid cost and battery degradation. The proposed algorithm can either cut down on grid costs or extend battery lifetime (electric vehicle or stationary battery packs). Additionally, substituting NMC cells with LFP chemistries optimizes grid performance during the summer, yielding a 10% grid cost reduction and a 20% decrease in degradation. Finally, the grid cost and degradation of the presented MPC when using aged batteries are improved with respect to the state of the art by 10% and 5% respectively, in periods with high solar generation and low thermal loads like summer.

eess.SY↗

arXiv2021

Evaluation of deep lift pose models for 3D rodent pose estimation based on geometrically triangulated data

The assessment of laboratory animal behavior is of central interest in modern neuroscience research. Behavior is typically studied in terms of pose changes, which are ideally captured in three dimensions. This requires triangulation over a multi-camera system which view the animal from different angles. However, this is challenging in realistic laboratory setups due to occlusions and other technical constrains. Here we propose the usage of lift-pose models that allow for robust 3D pose estimation of freely moving rodents from a single view camera view. To obtain high-quality training data for the pose-lifting, we first perform geometric calibration in a camera setup involving bottom as well as side views of the behaving animal. We then evaluate the performance of two previously proposed model architectures under given inference perspectives and conclude that reliable 3D pose inference can be obtained using temporal convolutions. With this work we would like to contribute to a more robust and diverse behavior tracking of freely moving rodents for a wide range of experiments and setups in the neuroscience community.

cs.CV↗

arXiv2018

Boundaries of Operation for Refurbished Parallel AC-DC Reconfigurable Links in Distribution Grids

Parallel ac-dc reconfigurable link technology can find interesting applications in medium voltage power distribution. A given system can operate in different configurations while maintaining equivalent capacity during (n-1) contingencies. It is proved that within the defined operating boundaries, a parallel ac-dc configuration has higher efficiency as compared to pure ac or pure dc power delivery. Using sensitivity analysis, the variations in these efficiency boundaries with power demand, power factor, grid voltages, link lengths, conductor areas and converter efficiency is described. It is shown that parallel ac-dc system can have smaller payback time as compared to a purely dc power transmission for the same capacity due to lower investment cost in converter station and superior efficiency. As compared to a purely ac system, the payback of a refurbished parallel acdc configuration can be less than 5 years for a 10 km, 10 kV distribution link within the specified assumptions and operating conditions.

eess.SP↗

arXiv2018

SimInf: An R package for Data-driven Stochastic Disease Spread Simulations

We present the R package SimInf which provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and OpenMP to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goal was to make SimInf extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. In this paper, we provide a technical description of the framework and demonstrate its use on some basic examples. We also discuss how to specify and extend the framework with user-defined models.

q-bio.PE↗

arXiv2017

Multiscale modeling via split-step methods in neural firing

Neuronal models based on the Hodgkin-Huxley equation form a fundamental framework in the field of computational neuroscience. While the neuronal state is often modeled deterministically, experimental recordings show stochastic fluctuations, presumably driven by molecular noise from the underlying microphysical conditions. In turn, the firing of individual neurons gives rise to an electric field n extracellular space, also thought to affect the firing pattern of nearby neurons. We develop a multiscale model which combines a stochastic ion channel gating process taking place on the neuronal membrane, together with the propagation of an action potential along the neuronal structure. We also devise a numerical method relying on a split-step strategy which effectively couples these two processes and we experimentally test the feasibility of this approach. We finally also explain how the approach can be extended with Maxwell's equations to allow the potential to be propagated in extracellular space.

q-bio.NC↗

arXiv2017

Integrated PV Charging of EV Fleet Based on Dynamic Energy Prices and Offer of Reserves

Workplace charging of electric vehicles (EV) from photovoltaic (PV) panels installed on an office building can provide several benefits. This includes the local production and use of PV energy for charging the EV and making use of dynamic tariffs from the grid to schedule the energy exchange with the grid. The long parking time of EV at the workplace provide the chance for the EV to support the grid via vehicle-to-grid technology, the use of a single EV charger for charging several EV by multiplexing and the offer of ancillary services to the grid for up and down regulation. Further, distribution network constraints can be considered to limit the power and prevent the overloading of the grid. A single MILP formulation that considers all the above applications has been proposed in this paper for a charging a fleet of EVs from PV. The MILP is implemented as a receding-horizon model predictive energy management system. Numerical simulation based on market and PV data in Austin, Texas have shown 31% to 650% reduction in the cost of EV charging when compared to immediate and average rate charging policies.

math.OC↗

arXiv2016

Fast event-based epidemiological simulations on national scales

We present a computational modeling framework for data-driven simulations and analysis of infectious disease spread in large populations. For the purpose of efficient simulations, we devise a parallel solution algorithm targeting multi-socket shared memory architectures. The model integrates infectious dynamics as continuous-time Markov chains and available data such as animal movements or aging are incorporated as externally defined events. To bring out parallelism and accelerate the computations, we decompose the spatial domain and optimize cross-boundary communication using dependency-aware task scheduling. Using registered livestock data at a high spatio-temporal resolution, we demonstrate that our approach not only is resilient to varying model configurations, but also scales on all physical cores at realistic work loads. Finally, we show that these very features enable the solution of inverse problems on national scales.

q-bio.PE↗

arXiv2015

Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks

Nature presents multiple intriguing examples of processes which proceed at high precision and regularity. This remarkable stability is frequently counter to modelers' experience with the inherent stochasticity of chemical reactions in the regime of low copy numbers. Moreover, the effects of noise and nonlinearities can lead to "counter-intuitive" behavior, as demonstrated for a basic enzymatic reaction scheme that can display stochastic focusing (SF). Under the assumption of rapid signal fluctuations, SF has been shown to convert a graded response into a threshold mechanism, thus attenuating the detrimental effects of signal noise. However, when the rapid fluctuation assumption is violated, this gain in sensitivity is generally obtained at the cost of very large product variance, and this unpredictable behavior may be one possible explanation of why, more than a decade after its introduction, SF has still not been observed in real biochemical systems. In this work we explore the noise properties of a simple enzymatic reaction mechanism with a small and fluctuating number of active enzymes that behaves as a high-gain, noisy amplifier due to SF caused by slow enzyme fluctuations. We then show that the inclusion of a plausible negative feedback mechanism turns the system from a noisy signal detector to a strong homeostatic mechanism by exchanging high gain with strong attenuation in output noise and robustness to parameter variations. Moreover, we observe that the discrepancy between deterministic and stochastic descriptions of stochastically focused systems in the evolution of the means almost completely disappears, despite very low molecule counts and the additional nonlinearity due to feedback. The reaction mechanism considered here can provide a possible resolution to the apparent conflict between intrinsic noise and high precision in critical intracellular processes.

q-bio.MN↗

arXiv2013

Finite element analysis of neuronal electric fields: the effect of heterogeneous resistivity

Simulation of extracellular fields is one of the substantial methods used in the area of computational neuroscience. Its most common usage is validation of experimental methods as EEG and extracellular spike recordings or modeling of physiological phenomena which can not be easily determined empirically. Continuous experimental work has been re-raising the importance of polarization effects between neuronal structures to neuronal communication. As this effects relies on very small potential changes, better modeling methods are necessary to quantify the weak electrical fields in the microscopic scale in a more realistic way. An important factor of influence on local field effects in the hippocampal formation is the heterogeneous resistivity of extracellular tissue. The vast majority of modeling studies consider the extracellular space to be homogeneous while experimentally, it has been shown that the stratum pyramidale has two times higher resistivity then other hippocampal layers. Common simulation methods for extracellular electrical fields based on the point source approximation are bound to describe the resistance of the space with a single, linear factor. We propose that models should be based on the space- and time-dependent Maxwell equations in order to account for heterogeneous properties of the extracellular space and specific arrangements of neurons in dense hippocampal layers. To demonstrate the influence of heterogeneous extracellular resistivity and neuronal spatial orientation on modeling results, we combine solutions of classical compartment models with spatiotemporal PDEs solved by the FEM. With the help of these methods, we show that the inclusion of heterogeneous resistivity has a substantial impact on voltages in close proximity to emitting neurons, increasing the extracellular potentials substantially compared to the homogeneous variant.

q-bio.NC↗