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For material modeling of microstructured media, an accurate characterization of the underlying microstructure is indispensable. Mathematically speaking, the overall goal of microstructure characterization is to find simple functionals which describe the geometric shape as well as the composition of the microstructures under consideration, and enable distinguishing microstructures with distinct effective material behavior. For this purpose, we propose using Minkowski tensors, in general, and the quadratic normal tensor, in particular, and introduce a computational algorithm applicable to voxel-based microstructure representations. Rooted in the mathematical field of integral geometry, Minkowski tensors associate a tensor to rather general geometric shapes, which make them suitable for a wide range of microstructured material classes. Furthermore, they satisfy additivity and continuity properties, which makes them suitable and robust for large-scale applications. We present a modular algorithm for computing the quadratic normal tensor of digital microstructures. We demonstrate multigrid convergence for selected numerical examples and apply our approach to a variety of microstructures. Strikingly, the presented algorithm remains unaffected by inaccurate computation of the interface area. The quadratic normal tensor may be used for engineering purposes, such as mean-field homogenization or as target value for generating synthetic microstructures.

Materials hosting magnetic skyrmions at room temperature could enable new computing architectures as well as compact and energetically efficient magnetic storage such as racetrack memories. In a racetrack device, information is coded by the presence/absence of magnetic skyrmions forming a chain that is moved through the device. The skyrmion Hall effect that would eventually lead to an annihilation of the skyrmions at the edges of the racetrack can be suppressed for example by anti-ferromagnetically-coupled skyrmions. However, avoiding modifications of the inter-skyrmion distances in the racetrack remains challenging. As a solution to this issue, a chain of bits could also be encoded by two different solitons such as a skyrmion and a chiral bobber. The major limitation of this approach is that it has solely been realized in B20-type single crystalline material systems that support skyrmions only at low temperatures, thus hindering the efficacy for future applications. Here we demonstrate that a hybrid ferro/ferri/ferromagnetic multilayer system can host two distinct skyrmion phases at room temperature. By matching quantitative magnetic force microscopy data with micromagnetic simulations, we reveal that the two phases represent tubular skyrmions and partial skyrmions (similar to skyrmion bobbers). Furthermore, the tubular skyrmion can be converted into a partial skyrmion. Such multilayer systems may thus serve as a platform for designing skyrmion memory applications using distinct types of skyrmions and potentially for storing information using the vertical dimension in a thin film device.

The Large Hadron electron Collider (LHeC) is designed to move the field of deep inelastic scattering (DIS) to the energy and intensity frontier of particle physics. Exploiting energy recovery technology, it collides a novel, intense electron beam with a proton or ion beam from the High Luminosity--Large Hadron Collider (HL-LHC). The accelerator and interaction region are designed for concurrent electron-proton and proton-proton operation. This report represents an update of the Conceptual Design Report (CDR) of the LHeC, published in 2012. It comprises new results on parton structure of the proton and heavier nuclei, QCD dynamics, electroweak and top-quark physics. It is shown how the LHeC will open a new chapter of nuclear particle physics in extending the accessible kinematic range in lepton-nucleus scattering by several orders of magnitude. Due to enhanced luminosity, large energy and the cleanliness of the hadronic final states, the LHeC has a strong Higgs physics programme and its own discovery potential for new physics. Building on the 2012 CDR, the report represents a detailed updated design of the energy recovery electron linac (ERL) including new lattice, magnet, superconducting radio frequency technology and further components. Challenges of energy recovery are described and the lower energy, high current, 3-turn ERL facility, PERLE at Orsay, is presented which uses the LHeC characteristics serving as a development facility for the design and operation of the LHeC. An updated detector design is presented corresponding to the acceptance, resolution and calibration goals which arise from the Higgs and parton density function physics programmes. The paper also presents novel results on the Future Circular Collider in electron-hadron mode, FCC-eh, which utilises the same ERL technology to further extend the reach of DIS to even higher centre-of-mass energies., Comment: 373 pages, many figures, to be published by J. Phys. G

The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system.

When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unscheduled islanding events may trigger cascading disconnections of Distributed Energy Resources (DERs) inside the MG due to the transient response, leading to a partial or full loss of load. In this paper, a min-max-min, hybrid, stochastic-robust investment planning model is proposed to obtain a resilient MG considering both High-Impact-Low-Frequency (HILF) and Low-Impact-High-Frequency (LIHF) uncertainties. The HILF uncertainty pertains to the unscheduled islanding of the MG after a disastrous event, and the LIHF uncertainty relates to correlated loads and DER generation, characterized by a set of scenarios. The MG resilience under both types of uncertainty is ensured by incorporating static and transient islanding constraints into the proposed investment model. The inclusion of transient response constraints leads to a min-max-min problem with a non-linear dynamic frequency response model that cannot be solved directly by available optimization tools. Thus, in this paper, a three-stage solution approach is proposed to find the optimal investment plan. The performance of the proposed algorithm is tested on the CIGRE 18-node distribution network.

In this study, the magnetic reversal process of exchange-coupled bilayer systems, consisting of a ferrimagnetic TbFeCo alloy layer and a ferromagnetic [Co/Ni/Pt]N multilayer, was investigated. In particular, minor loop studies, probing solely the reversal characteristics of the softer ferromagnetic layer, reveal two distinct reversal mechanisms, which depend strongly on the thickness of the ferromagnetic layer. For thick layers, irreversible switching of the macroscopic minor loop is observed. The underlying microscopic origin of this reversal process was studied in detail by high-resolution magnetic force microscopy, showing that the reversal is triggered by in-plane domain walls propagating through the ferromagnetic layer. In contrast, thin ferromagnetic layers show a hysteresis-free reversal, which is nucleation-dominated due to grain-to-grain variations in magnetic anisotropy of the Co/Ni/Pt multilayer and an inhomogeneous exchange coupling with the magnetically hard TbFeCo layer, as confirmed by micromagnetic simulations., Comment: 8 pages, 7 figures

The increasing installation of controllable Distributed Energy Resources (DERs) in Distribution Networks (DNs) opens up new opportunities for the provision of ancillary services to the Transmission Network (TN) level. As the penetration of smart meter devices and communication infrastructure in DNs increases, they become more observable and controllable with centralized optimization-based control schemes becoming efficient and practical. In this paper, we propose a centralized tractable Optimal Power Flow (OPF)-based control scheme that optimizes the real-time operation of active DNs, while also considering the provision of voltage support as an ancillary service to the TN. We embed in the form of various constraints the current voltage support requirements of Switzerland and investigate the potential benefit of ancillary services provision assuming different operational modes of the DER inverters. We demonstrate the performance of the proposed scheme using a combined HV-MV-LV test system., Comment: 10 pages

In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This paper demonstrates the use of Variable-Speed Refrigeration (VSR) technology, based on brushless DC motors, for fast-frequency response. First, we derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, we consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived though transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full- and suitable reduced-order models, allowing the application of simplified models in large-scale system studies.

Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.

We study the magneto-conductance of a $1.4~\mathrm{\mu m}$-wide quantum dot in the fractional quantum Hall regime. For a filling factor $\approx 2/3$ and $\gtrsim 1/3$ in the quantum dot the observed Coulomb resonances show a periodic modulation in magnetic field. This indicates a non-trivial reconstruction of the 2/3 fractional quantum Hall state in the quantum dot. We present a model for the charge stability diagram of the system assuming two compressible regions separated by an incompressible stripe of filling factor $2/3$ and $1/3$, respectively. From the dependence of the magnetic field period on total magnetic field we construct the zero-field charge density distribution in the quantum dot. The tunneling between the two compressible regions exhibits fractional Coulomb blockade. For both filling factor regions, we extract a fractional charge $e^*/e = 0.32 \pm 0.03$ by comparing to measurements at filling factor 2. With their close relation to quantum Hall Fabry-P\'{e}rot interferometers, our investigations on quantum dots in the fractional quantum Hall regime extend and complement interference experiments investigating the nature of anyonic fractional quantum Hall quasiparticles., Comment: 7 pages, 4 figures

An increasing penetration of renewable generation has led to reduced levels of rotational inertia and damping in the system. The consequences are higher vulnerability to disturbances and deterioration of the dynamic response of the system. To overcome these challenges, novel converter control schemes that provide virtual inertia and damping have been introduced, which raises the question of optimal distribution of such devices throughout the network. This paper presents a framework for performance-based allocation of virtual inertia and damping to the converter-interfaced generators in a low-inertia system. This is achieved through an iterative, eigensensitivity-based optimization algorithm that determines the optimal controller gains. Two conceptually different problem formulations are presented and validated on a 3-area, 12-bus test system.

This paper studies the procurement and pricing of inertial response using a frequency-constrained unit commitment formulation, which co-optimizes the provision of energy and inertia services while accounting for their complementary properties. The proposed approach builds on a two-step process that allows to differentiate between the units being online for energy purposes and the ones committed additionally solely for inertia provision. Subsequently, three novel pricing and payment schemes that reimburse inertia providers in a transparent and fair manner according to their individual participation are proposed. The analysis considers both synchronous and converter-based generators and provides insights regarding the impact of each pricing scheme on total system cost, as well as on the investment signals for technologies capable of offering affordable inertial response. The results show that all three methods will have a beneficial impact on frequency stability and aid the operator in ensuring system reliability, whereas the policy implications to different inertia providing units will vary between the payment schemes.

The early stage of the reactive interdiffusion in the Cu-Al system was investigated at 350 {\textdegree}C and 300 {\textdegree}C thanks to in-situ transmission electron microscopy. A special care was given to find conditions where the electron beam and the sample free surface do not affect significantly the reaction. A special emphasis was then given on the influence of grain boundaries that are fast diffusion paths, and on nanoscaled particles that may interact with the transformation front. It was found that there is a transient state followed by a steady state where the mean growth rates of intermetallic compounds follow a parabolic law indicating that the kinetics is diffusion controlled. Thanks to the in-situ observations at the nanoscale, it was also possible to track the local velocity of interfaces between the different phases. Strong fluctuations were exhibited within length scales smaller than 100 nm and they are partly attributed to interface pinning by nanoscaled particles. Last, considering thermodynamic and kinetic arguments, it is shown that it is mainly an indirect effect induced by a local change of solute fluxes and of concentration gradients.

The large shares of wind power generation in electricity markets motivate higher levels of operating reserves. However, current reserve sizing practices fail to account for important topological aspects that might hinder their deployment, thus resulting in high operating costs. Zonal reserve procurement mitigates such inefficiencies, however, the way the zones are defined is still open to interpretation. This paper challenges the efficiency of predetermined zonal setups that neglect the location of stochastic power production in the system, as well as the availability, cost and accessibility of flexible generating units. To this end, we propose a novel reserve procurement approach, formulated as a two-stage stochastic bilevel model, in which the upper level identifies a number of contiguous reserve zones using dynamic grid partitioning and sets zonal requirements based on the total expected operating costs. Using two standard IEEE reliability test cases, we show how the efficient partitioning of reserve zones can reduce expected system cost and promote the integration of stochastic renewables., Comment: Submitted to IEEE Transactions on Power Systems on the 20th of March 2020

We consider an even probability distribution on the $d$-dimensional Euclidean space with the property that it assigns measure zero to any hyperplane through the origin. Given $N$ independent random vectors with this distribution, under the condition that they do not positively span the whole space, the positive hull of these vectors is a random polyhedral cone (and its intersection with the unit sphere is a random spherical polytope). It was first studied by Cover and Efron. We consider the expected face numbers of these random cones and describe a threshold phenomenon when the dimension $d$ and the number $N$ of random vectors tend to infinity. In a similar way, we treat the solid angle, and more generally the Grassmann angles. We further consider the expected numbers of $k$-faces and of Grassmann angles of index $d-k$ when also $k$ tends to infinity.

Probabilistic load flow (PLF) allows to evaluate uncertainties introduced by renewable energy sources on system operation. Ideally, the PLF calculation is implemented for an entire grid requiring all the parameters of the transmission lines and node load/generation to be available. However, in a multi-regional interconnected grid, the independent system operators (ISOs) across regions may not share the parameters of their respective areas with other ISOs. Consequently, the challenge is how to identify the functional relationship between the flows in the regional grid and the uncertain power injections of renewable generation sources across regions without full information about the entire grid. To overcome this challenge, we first propose a privacy-preserving distributed accelerated projection-based consensus algorithm for each ISO to calculate the corresponding coefficient matrix of the desired functional relationship. Then, we leverage a privacy-preserving accelerated average consensus algorithm to allow each ISO to obtain the corresponding constant vector of the same relationship. Using the two algorithms, we finally derive a privacy-preserving distributed PLF method for each ISO to analytically obtain its regional joint PLF in a fully distributed manner without revealing its parameters to other ISOs. The correctness, effectiveness, and efficiency of the proposed method are verified through a case study on the IEEE 118-bus system.

Exchange-coupled structures consisting of ferromagnetic and ferrimagnetic layers become technologically more and more important. We show experimentally the occurrence of completely reversible, hysteresis-free minor loops of [Co(0.2 nm)/Ni(0.4 nm)/Pt(0.6 nm)]$_N$ multilayers exchange-coupled to a 20 nm thick ferrimagnetic Tb$_{28}$Co$_{14}$Fe$_{58}$ layer, acting as hard magnetic pinning layer. Furthermore, we present detailed theoretical investigations by means of micromagnetic simulations and most important a purely analytical derivation for the condition of the occurrence of full reversibility in magnetization reversal. Hysteresis-free loops always occur if a domain wall is formed during the reversal of the ferromagnetic layer and generates an intrinsic hard-axis bias field that overcomes the magnetic anisotropy field of the ferromagnetic layer. The derived condition further reveals that the magnetic anisotropy and the bulk exchange of both layers, as well as the exchange coupling strength and the thickness of the ferromagnetic layer play an important role for its reversibility., Comment: 9 pages, 7 figures

A rapid deployment of renewable generation has led to significant reduction in the rotational system inertia and damping, thus making frequency control in power systems more challenging. This paper proposes a novel control scheme based on Model Predictive Control (MPC) for converter-interfaced generators operating in a grid-forming mode, with the goal of exploiting their fast response capabilities to provide fast frequency control service to the system. The controller manipulates converter power injections to limit the frequency nadir and rate-of-change-of-frequency after a disturbance. Both centralized and decentralized MPC approaches are considered and compared in terms of performance and practical implementation. Special attention is given to the decentralized controller by generating an explicit MPC solution to enhance computational efficiency and reduce hardware requirements. Simulation results obtained from a high-fidelity differential-algebraic equation model of the IEEE 39-bus system demonstrate the effectiveness of the proposed control schemes.

The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks. In order to enable a more informative assembly of a data basis, an approach for determining a dataset representation in terms of a small set of distinguishable prototypical sub-sequences is proposed. The approach employs a sequence alignment followed by a learning vector quantization (LVQ) stage. A first proof of concept on synthetically generated and real-world datasets shows the viability of the approach., Comment: Accepted at LHMP2020 Workshop (ICRA 2020)

Electrical load profiling supports retailers and distribution network operators in having a better understanding of the consumption behavior of consumers. However, traditional clustering methods for load profiling are centralized and require access to all the smart meter data, thus causing privacy issues for consumers and retailers. To tackle this issue, we propose a privacy-preserving distributed clustering framework for load profiling by developing a privacy-preserving accelerated average consensus (PP-AAC) algorithm with proven convergence. Using the proposed framework, we modify several commonly used clustering methods, including k-means, fuzzy C-means, and Gaussian mixture model, to provide privacy-preserving distributed clustering methods. In this way, load profiling can be performed only by local calculations and information sharing between neighboring data owners without sacrificing privacy. Meanwhile, compared to traditional centralized clustering methods, the computational time consumed by each data owner is significantly reduced. The privacy and complexity of the proposed privacy-preserving distributed clustering framework are analyzed. The correctness, efficiency, effectiveness, and privacy-preserving feature of the proposed framework and the proposed PP-AAC algorithm are verified using a real-world Irish residential dataset.

The increasing adoption of smart meters introduces growing concerns about consumer privacy risks stemming from high resolution metering data. To counter these risks, there have been various works in actively shaping the grid-visible energy consumption profile using controllable loads such as energy storage systems (ESSs) and flexible consumer loads. In this paper, we compare the use of flexible thermal-based consumer loads (FTLs) against ESSs for consumer privacy protection. By first assuming ideal conditions, and subsequently bringing them closer to reality, the limitations of using FTLs for privacy protection are identified. Through theoretical analyses and realistic simulations, it is shown that, due to the limitations in the operation of FTLs, without significant over-sizing of systems and sacrifices in consumer comfort, FTLs of much higher equivalent energy storage capacity are required to afford the same level of protection as ESSs. Nonetheless, given their increasing ubiquity, controllable FTLs should be considered for use in consumer privacy protection., Comment: This is the accepted version of the article published in Applied Energy, and is shared under the Creative Commons CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

In this paper, a novel linear algorithm is proposed for state estimation including bad data detection of power systems that are monitored both by conventional and synchrophasor measurements. Both types of data are treated simultaneously and the states are estimated in rectangular coordinates. The proposed estimator is based on the linear weighted least square method. To enable the derivation of linear measurement functions, the network is modelled in terms of voltages and currents in rectangular form and pseudo-measurements are used to represent conventional measurements. Furthermore, the largest normalized residual test is used to detect bad data. To validate the accuracy and robustness of the proposed algorithm, several test cases of different sizes are solved and the results are presented and discussed.

Barthe proved that the regular simplex maximizes the mean width of convex bodies whose John ellipsoid (maximal volume ellipsoid contained in the body) is the Euclidean unit ball; or equivalently, the regular simplex maximizes the $\ell$-norm of convex bodies whose L\"owner ellipsoid (minimal volume ellipsoid containing the body) is the Euclidean unit ball. Schmuckenschl\"ager verified the reverse statement; namely, the regular simplex minimizes the mean width of convex bodies whose L\"owner ellipsoid is the Euclidean unit ball. Here we prove stronger stability versions of these results. We also consider related stability results for the mean width and the $\ell$-norm of the convex hull of the support of centered isotropic measures on the unit sphere.

The establishment of a single European day-ahead market has accomplished the integration of the regional day-ahead markets. However, the reserves provision and activation remain an exclusive responsibility of regional operators. This limited spatial coordination and the sequential market structure hinder the efficient utilization of flexible generation and transmission, since their capacities have to be ex-ante allocated between energy and reserves. To promote reserve exchange, recent work has proposed a preemptive model that defines the optimal inter-area transmission capacities for energy and reserves reducing the expected system cost. This decision-support tool, formulated as a stochastic bilevel program, respects the current architecture but does not suggest area-specific costs that guarantee sufficient incentives for all areas to accept the proposed solution. To this end, we formulate a preemptive model in a framework that allows the application of coalitional game theory methods to obtain a stable benefit allocation, i.e., an outcome immune to coalitional deviations ensuring willingness of all areas to coordinate. We show that benefit allocation mechanisms can be formulated either at the day-ahead or the real-time stages, in order to distribute the expected or the scenario-specific benefits, respectively. For both games, the proposed benefits achieve minimal stability violation, while allowing for a tractable computation with limited queries to the bilevel program. Our case studies, based on an illustrative and a more realistic test case, compare our method with well-studied benefit allocations, namely, the Shapley value and the nucleolus. We show that our method performs better in stability, tractability, and fairness, which would potentially be dictated by a criterion chosen by the regulator.