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The increasing importance of cross-border trade has brought the topic of allocating cross-border transmission capacity under the limelight. In this paper, we focus on two different transmission capacity calculation methods, namely, Available Transfer Capacity and Flow-based Capacity allocation, in the continuous intraday electricity market. The effect of these two methods is studied on the continuous intraday electricity market through an agent-based model. The model comprises market participants, including renewable producers, consumers, storage, and thermal power producers. The market operator agent is responsible for managing the shared order book and clearing orders while the transmission system operator agent calculates and updates the cross-border transmission capacities in the continuous intraday electricity market. The price-volume decisions for the orders posted by the market participants are determined by two different strategies where one of them adapts to changing market conditions and new information while the other is naive. The model accounts for ramping constraints which enables the analysis of the trading behavior and interaction of agents across multiple delivery products simultaneously. A switch parameter is availed in the trading timeline for the thermal and storage agents to change from a less conservative approach of ignoring the ramping and charging/discharging rate constraints respectively to considering them. An interplay between different switch parameters and cross-border transmission capacity calculation methods is studied. QC 20220830Preprint, submitted to Journal of Applied Energy

Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.

The spatiotemporal patterns of public disturbance acts are investigated in Stockholm, the capital of Sweden.Using crowdsourced data, the number of records is compared 15 months before and after the stay-at-homemeasures of the COVID-19 pandemic, controlling for seasonal trends. Poisson-Gamma-CAR regression modelsare implemented to assess the potential impact of land use on the spatial distribution of public disturbance acts,accounting for the effect of pandemic restrictions and differences in neighborhood context. Findings show that,with the exception of abandoned vehicles, there was a significant increase in records of public disturbance afterthe 2020 stay-at-home pandemic restrictions. Parks, transport hubs and less importantly, schools were significantly associated with public disturbances, controlling for neighborhood characteristics and reporting practices.Recommendations are made for research and practice. QC 20230802

Flexible wearable sensor electronics, combined with advanced software functions, pave the way toward increasingly intelligent healthcare devices. One important application area is limb prosthesis, where printed flexible sensor solutions enable efficient monitoring and assessing of the actual intra-socket dynamic operation conditions in clinical and other more natural environments. However, the data collected by such sensors suffer from variations and errors, leading to difficulty in perceiving the actual operational conditions. This paper proposes a novel method for detecting anomalies in the data that are collected for measuring the intra-socket dynamic operation conditions by printed flexible wearable sensors. A discrete generative model based on Variational AutoEncoder (VAE) is used first to encode the collected multi-variant time-series data in terms of latent states. After that, a clustering method based on the Self-Organizing Map (SOM) is used to acquire discrete and interpretable representations of the VAE encoded latent states. An adaptive Markov chain is utilized to detect anomalies by quantifying state transitions and revealing temporal dependencies. The contributions of the proposed architecture conclude as follows: (1) Using the VAE-SOM hybrid model to regularize the continues data as discrete states, supporting interpreting the operational data to analytic models. (2) Employing adaptive Markov chains to generalize the transitions of these states, allowing to model the complex operational conditions. Compared with benchmark methods, our architecture is validated via two public datasets and achieves the best F1 scores. Moreover, we measure the run-time performance of this lightweight architecture. The results indicate that the proposed method performs low computational complexity, facilitating the applications on real-life productions. QC 20230816

To provide protection against corrosion in harsh environments, high performance anticorrosive coatings are applied on steel structures at all scales. However, to also limit the use of fossil-based ingredients, there is a growing demand to incorporate renewable raw materials in the coating formulations. In this study, to replace pigments and fillers of an epoxy novolac coating, technical Kraft lignin particles were ground and size fractionated (i.e., sieved), and used for formulation work. The effects of sieved and unsieved Kraft lignin, as structure-reinforcing components, on the anticorrosive and mechanical performance of epoxy coatings were subsequently investigated using the following methods: size exclusion chromatography (SEC), phosphorous nuclear magnetic resonance spectroscopy (31P NMR), scanning electron microscopy (SEM), differential scanning calorimeter (DSC), salt spray exposure, pull-off, K¨onig pendulum hardness, and chemical resistance tests.Compared to the unsieved-lignin reference (U-L EN), the coating based on lignin fines (S-L EN) showed about 31 % lower rust creep after 70 days of salt spray exposure. However, no surface defects or chemical degradation were observed for any of the coatings. For the S-L EN coating, excellent adhesion strength (23 MPa) and impact resistance (0.49 N), relative to reference values of 17 and 13 MPa and 0.41 and 0.07 N for commercial and lignin-based diglycidyl ether bisphenol F (L-DGEBF) coatings, respectively, were measured. The addition of lignin particles did not influence the chemical resistance, the hardness, and the glass transition temperature of the epoxy novolac coatings.In summary, chemically unmodified Kraft lignin particles, after grinding and sieving, can be incorporated in epoxy novolac coatings (up to 25 vol%), thereby providing a bio-based alternative to pigments and fillers in heavy duty coatings (primers in particular).

An interturn short-circuit in the stator windings can lead to the breakdown of electrical machines. In the case of induction machines, several fault detection methods and faulted models have been developed in the recent decades. These models differ mainly in how the leakage inductances of the faulted winding are modeled. This work provides a generalized model for interturn short-circuit faults, using different assumptions for the leakage inductances. The model is validated with experimental results for an exhaustive set of fault parameters, and the leakage inductances influence is analyzed. Moreover, the model is used to analyze the drawbacks of the negative-sequence fundamental current as a traditional fault signature. A high-frequency injection method for converter-fed machines is presented to overcome these limits. The proposed fault signature is the negative-sequence current at the injection frequency and it is evaluated experimentally at different operating conditions. The fault severity and its location are proved to be related to the proposed fault signature. Not duplicate with DiVA 1657021QC 20230202

Inexpensive numerical methods are key to enable simulations of systems of a large number of particles of different shapes in Stokes flow. Several approximate methods have been introduced for this purpose. We study the accuracy of the multiblob method for solving the Stokes mobility problem in free space, where the 3D geometry of a particle surface is discretised with spherical blobs and the pair-wise interaction between blobs is described by the RPY-tensor. The paper aims to investigate and improve on the magnitude of the error in the solution velocities of the Stokes mobility problem using a combination of two different techniques: an optimally chosen grid of blobs and a pair-correction inspired by Stokesian dynamics. Optimisation strategies to determine a grid with a certain number of blobs are presented with the aim of matching the hydrodynamic response of a single accurately described ideal particle, alone in the fluid. Small errors in this self-interaction are essential as they determine the basic error level in a system of well-separated particles. With a good match, reasonable accuracy can be obtained even with coarse blob-resolutions of the particle surfaces. The error in the self-interaction is however sensitive to the exact choice of grid parameters and simply hand-picking a suitable blob geometry can lead to errors several orders of magnitude larger in size. The pair-correction is local and cheap to apply, and reduces on the error for more closely interacting particles. Two different types of geometries are considered: spheres and axisymmetric rods with smooth caps. The error in solutions to mobility problems is quantified for particles of varying inter-particle distances for systems containing a few particles, comparing to an accurate solution based on a second kind BIE-formulation where the quadrature error is controlled by employing quadrature by expansion (QBX). 49 pages, 37 figures

We study the impact of data sharing policies on cyber insurance markets. These policies have been proposed to address the scarcity of data about cyber threats, which is essential to manage cyber risks. We propose a Cournot duopoly competition model in which two insurers choose the number of policies they offer (i.e., their production level) and also the resources they invest to ensure the quality of data regarding the cost of claims (i.e., the data quality of their production cost). We find that enacting mandatory data sharing sometimes creates situations in which at most one of the two insurers invests in data quality, whereas both insurers would invest when information sharing is not mandatory. This raises concerns about the merits of making data sharing mandatory. 46 pages, 8 figures, to be published at Computers & Security

Variable phase-pole machines have the potential to extend the operational range to higher speeds through magnetic pole changes. The state-of-the-art vector space decomposition cannot model the transient behavior of the pole change for any possible phase-pole configuration as it creates a discontinuity. The proposed harmonic plane decomposition theory solves this issue by generalizing the vector-space decomposition to the fullest extend by using its discrete Fourier transformation interpretation. The theory for indirect rotor field-oriented control is developed using the harmonic-plane decomposition. A controlled, loaded pole change on a wound independently-controlled stator-coils machine using two transition strategies shows the harmonic-plane decomposition-based controller’s ability to maintain torque in the transition. Additionally, the proposed controller accomplishes real harmonic injection and balanced steady-state operation. QC 20221213