arXiv subjects

Manuel Baumann

Publications and source records attributed to Manuel Baumann.

4 recordsLinked to original sources

arXiv2021

Environmental assessment of a new generation battery: The magnesium-sulfur system

As environmental concerns mostly drive the electrification of our economy and the corresponding increase in demand for battery storage systems, information about the potential environmental impacts of the different battery systems is required. However, this kind of information is scarce for emerging post-lithium systems such as the magnesium-sulfur (MgS) battery. Therefore, we use life cycle assessment following a cradle-to-gate perspective to quantify the cumulative energy demand and potential environmental impacts per Wh of the storage capacity of a hypothetical MgS battery (46 Wh/kg). Furthermore, we also estimate global warming potential (0.33 kg CO2 eq/Wh) , fossil depletion potential (0.09 kg oil eq / Wh), ozone depletion potential (2.5E-08 kg CFC-11/Wh) and metal depletion potential (0.044 kg Fe eq/Wh), associated with the MgS battery production. The battery is modelled based on an existing prototype MgS pouch cell and hypothetically optimised according to the current state of the art in lithium-ion batteries (LIB), exploring future improvement potentials. It turns out that the initial (non-optimised) prototype cell cannot compete with current LIB in terms of energy density or environmental performance, mainly due to the high share of non-active components, decreasing its performance substantially. Therefore, if the assumed evolutions of the MgS cell composition are achieved to overcome current design hurdles and reach a comparable lifespan, efficiency, cost and safety levels to that of existing LIB; then the MgS battery has significant potential to outperform both existing LIB, and lithium-sulfur batteries.

econ.GN↗

arXiv2020

Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation

In this work, we present a nonlinear model reduction approach for reducing two commonly used nonlinear dynamical models of power grids: the effective network (EN) model and the synchronous motor (SM) model. Such models are essential in real-time security assessments of power grids. However, as power grids are often large-scale, it is necessary to reduce the models in order to utilize them in real-time. We reformulate the nonlinear power grid models as quadratic systems and reduce them using balanced truncation based on approximations of the reachability and observability Gramians. Finally, we present examples involving numerical simulation of reduced EN and SM models of the IEEE 57 bus and IEEE 118 bus systems.

eess.SY↗

arXiv2019

Surrogate Models in Bidirectional Optimization of Coupled Microgrids

The energy transition entails a rapid uptake of renewable energy sources. Besides physical changes within the grid infrastructure, energy storage devices and their smart operation are key measures to master the resulting challenges like, e.g., a highly fluctuating power generation. For the latter, optimization based control has demonstrated its potential on a microgrid level. However, if a network of coupled microgrids is considered, iterative optimization schemes including several communication rounds are typically used. Here, we propose to replace the optimization on the microgrid level by using surrogate models either derived from radial basis functions or neural networks to avoid this iterative procedure. We prove well-posedness of our approach and demonstrate its efficiency by numerical simulations based on real data provided by an Australian grid operator.

math.OC↗

arXiv2016

Space-time Galerkin POD with application in optimal control of semi-linear parabolic partial differential equations

In the context of Galerkin discretizations of a partial differential equation (PDE), the modes of the classical method of Proper Orthogonal Decomposition (POD) can be interpreted as the ansatz and trial functions of a low-dimensional Galerkin scheme. If one also considers a Galerkin method for the time integration, one can similarly define a POD reduction of the temporal component. This has been described earlier but not expanded upon -- probably because the reduced time discretization globalizes time which is computationally inefficient. However, in finite-time optimal control systems, time \textit{is} a global variable and there is no disadvantage from using a POD reduced Galerkin scheme in time. In this paper, we provide a newly developed generalized theory for space-time Galerkin POD, prove its optimality in the relevant function spaces, show its application for the optimal control of nonlinear PDEs, and, by means of a numerical example with Burgers' equation, discuss the competitiveness by comparing to standard approaches.

math.OC↗