pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models
Authors: Paul Scharnhorst, Baptiste Schubnel, Rafael E. Carrillo, Pierre-Jean Alet, Colin N. Jones
Date: 2022-10-07 00:00:00

Buildings are a promising source of flexibility for the application of demand response. In this work, we introduce a novel battery model formulation to capture the state evolution of a single building. Being fully data-driven, the battery model identification requires one dataset from a period of nominal controller operation, and one from a period with flexibility requests, without making any assumptions on the underlying controller structure. We consider parameter uncertainty in the model formulation and show how to use risk measures to encode risk preferences of the user in robust uncertainty sets. Finally, we demonstrate the uncertainty-aware prediction of flexibility envelopes for a building simulation model from the Python library Energym.
Tracing, Ranking and Valuation of Aggregated DER Flexibility in Active Distribution Networks
Authors: Andrey Churkin, Wangwei Kong, Jose N. Melchor Gutierrez, Eduardo A. Martínez Ceseña, Pierluigi Mancarella
Date: 2022-10-07 00:00:00

The integration of distributed energy resources (DER) makes active distribution networks (ADNs) natural providers of flexibility services. However, the optimal operation of flexible units in ADNs is highly complex, which poses challenges for distribution system operators (DSOs) in aggregating DER flexibility. For example, to maximise the provision of services, flexible units must be strongly coordinated to manage network constraints, e.g., perform power swaps. Furthermore, due to the nonlinearities of aggregated DER flexibility provision, some units may need to rapidly change their outputs to enable the services. To address these challenges, this paper brings together exact AC optimal power flow (OPF) models and a cooperative game formulation and presents a new framework for tracing, ranking, and valuation of aggregated DER flexibility in ADNs. Extensive tests and simulations performed for the 33-bus radial distribution network demonstrate that the framework enables translating complex DER interactions into useful information for DSOs by ranking the criticality of flexible units and performing flexibility valuation based on its cost or economic surplus. Additionally, the framework proposes no-swap constraints and a nonlinearity metric which can be used by DSOs to identify unreliable operating regions with power swaps or rapid changes in flexible unit dispatch.
Variability in electricity consumption by category of consumer: the impact on electricity load profiles
Authors: Philipp Andreas Gunkel, Henrik Klinge Jacobsen, Claire-Marie Bergaentzlé, Fabian Scheller, Frits Møller Andersen
Date: 2022-10-07 00:00:00

Residential electrification of transport and heat is changing consumption and its characteristics significantly. Previous studies have demonstrated the impact of socio-techno-economic determinants on residential consumption. However, they fail to capture the distributional characteristics of such consumer groups, which impact network planning and flexibility assessment. Using actual residential electricity consumption profile data for 720,000 households in Denmark, we demonstrate that heat pumps are more likely to influence aggregated peak consumption than electric vehicles. At the same time, other socio-economic factors, such as occupancy, dwelling area and income, show little impact. Comparing the extrapolation of a comprehensive rollout of heat pumps or electric vehicles indicates that the most common consumer category deploying heat pumps has 14% more maximum consumption during peak load hours, 46% more average consumption and twice the higher median compared to households owning an electric vehicle. Electric vehicle show already flexibility with coincidence factors that ranges between 5-15% with a maximum of 17% whereas heat pumps are mostly baseload. The detailed and holistic outcomes of this study support flexibility assessment and grid planning in future studies but also the operation of flexible technologies.
Total stability and integral action for discrete-time nonlinear systems
Authors: Samuele Zoboli, Daniele Astolfi, Vincent Andrieu
Date: 2022-10-07 00:00:00

Robustness guarantees are important properties to be looked for during control design. They ensure stability of closed-loop systems in face of uncertainties, unmodeled effects and bounded disturbances. While the theory on robust stability is well established in the continuous-time nonlinear framework, the same cannot be stated for its discrete-time counterpart. In this paper, we propose the discrete-time parallel of total stability results for continuous-time nonlinear system. This enables the analysis of robustness properties via simple model difference in the discrete-time context. First, we study how existence of equilibria for a nominal model transfers to sufficiently similar ones. Then, we provide results on the
Kalman-Bucy-Informed Neural Network for System Identification
Authors: Tobias Nagel, Marco F. Huber
Date: 2022-10-07 00:00:00

Identifying parameters in a system of nonlinear, ordinary differential equations is vital for designing a robust controller. However, if the system is stochastic in its nature or if only noisy measurements are available, standard optimization algorithms for system identification usually fail. We present a new approach that combines the recent advances in physics-informed neural networks and the well-known achievements of Kalman filters in order to find parameters in a continuous-time system with noisy measurements. In doing so, our approach allows estimating the parameters together with the mean value and covariance matrix of the system's state vector. We show that the method works for complex systems by identifying the parameters of a double pendulum.
Optimal Thermal Management and Charging of Battery Electric Vehicles over Long Trips
Authors: Ahad Hamednia, Victor Hanson, Jiaming Zhao, Nikolce Murgovski, Jimmy Forsman, Mitra Pourabdollah, Viktor Larsson, Jonas Fredriksson
Date: 2022-10-07 00:00:00
Tasks: Management

This paper studies optimal thermal management and charging of a battery electric vehicle driving over long distance trips. The focus is on the potential benefits of including a heat pump in the thermal management system for waste heat recovery, and charging point planning, in a way to achieve optimality in time, energy, or their trade-off. An optimal control problem is formulated, in which the objective function includes the energy delivered by the charger(s), and the total charging time including the actual charging time and the detour time to and from the charging stop. To reduce the computational complexity, the formulated problem is then transformed into a hybrid dynamical system, where charging dynamics are modelled in the domain of normalized charging time. Driving dynamics can be modelled in either of the trip time or travel distance domains, as the vehicle speed is assumed to be known a priori, and the vehicle is only stopping at charging locations. Within the hybrid dynamical system, a binary variable is introduced for each charging location, in order to decide to use or skip a charger. This problem is solved numerically, and simulations are performed to evaluate the performance in terms of energy efficiency and time. The simulation results indicate that the time required for charging and total energy consumption are reduced up to 30.6% and 19.4%, respectively, by applying the proposed algorithm.
Decentralized event-triggered estimation of nonlinear systems
Authors: E. Petri, R. Postoyan, D. Astolfi, D. Nešić, W. P. M. H. Heemels
Date: 2022-10-07 00:00:00

We investigate the scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. The sensors are grouped into N nodes and each of these nodes decides when its measured data is transmitted over the network independently. The objective is to design both the observer and the local transmission policies in order to obtain accurate state estimates, while only sporadically using the communication network. In particular, given a general nonlinear observer designed in continuous-time satisfying an input-to-state stability property, we explain how to systematically design a dynamic event-triggering rule for each sensor node that avoids the use of a copy of the observer, thereby keeping local calculation simple. We prove the practical convergence property of the estimation error to the origin and we show that there exists a uniform strictly positive minimum inter-event time for each local triggering rule under mild conditions on the plant. The efficiency of the proposed techniques is illustrated on a numerical case study of a flexible robotic arm.
On the Resilience of Traffic Networks under Non-Equilibrium Learning
Authors: Yunian Pan, Tao Li, Quanyan Zhu
Date: 2022-10-06 00:00:00

We investigate the resilience of learning-based \textit{Intelligent Navigation Systems} (INS) to informational flow attacks, which exploit the vulnerabilities of IT infrastructure and manipulate traffic condition data. To this end, we propose the notion of \textit{Wardrop Non-Equilibrium Solution} (WANES), which captures the finite-time behavior of dynamic traffic flow adaptation under a learning process. The proposed non-equilibrium solution, characterized by target sets and measurement functions, evaluates the outcome of learning under a bounded number of rounds of interactions, and it pertains to and generalizes the concept of approximate equilibrium. Leveraging finite-time analysis methods, we discover that under the mirror descent (MD) online-learning framework, the traffic flow trajectory is capable of restoring to the Wardrop non-equilibrium solution after a bounded INS attack. The resulting performance loss is of order $\tilde{\mathcal{O}}(T^{\beta})$ ($-\frac{1}{2} \leq \beta < 0 )$), with a constant dependent on the size of the traffic network, indicating the resilience of the MD-based INS. We corroborate the results using an evacuation case study on a Sioux-Fall transportation network.
Small Coupling Expansion for Multiple Sequence Alignment
Authors: Louise Budzynski, Andrea Pagnani
Date: 2022-10-07 00:00:00
Tasks: Multiple Sequence Alignment

The alignment of biological sequences such as DNA, RNA, and proteins, is one of the basic tools that allow to detect evolutionary patterns, as well as functional/structural characterizations between homologous sequences in different organisms. Typically, state-of-the-art bioinformatics tools are based on profile models that assume the statistical independence of the different sites of the sequences. Over the last years, it has become increasingly clear that homologous sequences show complex patterns of long-range correlations over the primary sequence as a consequence of the natural evolution process that selects genetic variants under the constraint of preserving the functional/structural determinants of the sequence. Here, we present a new alignment algorithm based on message passing techniques that overcomes the limitations of profile models. Our method is based on a new perturbative small-coupling expansion of the free energy of the model that assumes a linear chain approximation as the $0^\mathrm{th}$-order of the expansion. We test the potentiality of the algorithm against standard competing strategies on several biological sequences.
Biofilms as self-shaping growing nematics
Authors: Japinder Nijjer, Mrityunjay Kothari, Changhao Li, Thomas Henzel, Qiuting Zhang, Jung-Shen B. Tai, Shuang Zhou, Sulin Zhang, Tal Cohen, Jing Yan
Date: 2022-10-07 00:00:00
Tasks: Friction

Active nematics are the nonequilibrium analog of passive liquid crystals in which anisotropic units consume free energy to drive emergent behavior. Similar to liquid crystal (LC) molecules in displays, ordering and dynamics in active nematics are sensitive to boundary conditions; however, unlike passive liquid crystals, active nematics, such as those composed of living matter, have the potential to regulate their boundaries through self-generated stresses. Here, using bacterial biofilms confined by a hydrogel as a model system, we show how a three-dimensional, living nematic can actively shape itself and its boundary in order to regulate its internal architecture through growth-induced stresses. We show that biofilms exhibit a sharp transition in shape from domes to lenses upon changing environmental stiffness or cell-substrate friction, which is explained by a theoretical model considering the competition between confinement and interfacial forces. The growth mode defines the progression of the boundary, which in turn determines the trajectories and spatial distribution of cell lineages. We further demonstrate that the evolving boundary defines the orientational ordering of cells and the emergence of topological defects in the interior of the biofilm. Our findings reveal novel self-organization phenomena in confined active matter and provide strategies for guiding the development of programmed microbial consortia with emergent material properties.
Order Statistics Approaches to Unobserved Heterogeneity in Auctions
Authors: Yao Luo, Peijun Sang, Ruli Xiao
Date: 2022-10-07 00:00:00

We establish nonparametric identification of auction models with continuous and nonseparable unobserved heterogeneity using three consecutive order statistics of bids. We then propose sieve maximum likelihood estimators for the joint distribution of unobserved heterogeneity and the private value, as well as their conditional and marginal distributions. Lastly, we apply our methodology to a novel dataset from judicial auctions in China. Our estimates suggest substantial gains from accounting for unobserved heterogeneity when setting reserve prices. We propose a simple scheme that achieves nearly optimal revenue by using the appraisal value as the reserve price.
Power in the Pipeline
Authors: Quentin Gallea, Massimo Morelli, Dominic Rohner
Date: 2022-10-07 00:00:00

This paper provides the first comprehensive empirical analysis of the role of natural gas for the domestic and international distribution of power. The crucial role of pipelines for the trade of natural gas determines a set of network effects that are absent for other natural resources such as oil and minerals. Gas rents are not limited to producers but also accrue to key players occupying central nodes in the gas network. Drawing on our new gas pipeline data, this paper shows that gas betweenness-centrality of a country increases substantially the ruler's grip on power as measured by leader turnover. A main mechanism at work is the reluctance of connected gas trade partners to impose sanctions, meaning that bad behavior of gas-central leaders is tolerated for longer before being sanctioned. Overall, this reinforces the notion that fossil fuels are not just poison for the environment but also for political pluralism and healthy regime turnover.
Grid tariff designs coping with the challenges of electrification and their socio-economic impacts
Authors: Philipp Andreas Gunkel, Claire-Marie Bergaentzlé, Dogan Keles, Fabian Scheller, Henrik Klinge Jacobsen
Date: 2022-10-07 00:00:00

This paper investigates volumetric grid tariff designs under consideration of different pricing mechanisms and resulting cost allocation across socio-techno-economic consumer categories. In a case study of 1.56 million Danish households divided into 90 socio-techno-economic categories, we compare three alternative grid tariffs and investigate their impact on annual electricity bills. The results of our design consisting of a time-dependent threshold penalizing individual peak consumption and a system peak tariff show (a) a range of different allocations that distribute the burden of additional grid costs across both technologies and (b) strong positive outcomes, including reduced expenses for lower-income groups and smaller households.
Agenda manipulation-proofness, stalemates, and redundant elicitation in preference aggregation. Exposing the bright side of Arrow's theorem
Authors: Stefano Vannucci
Date: 2022-10-06 00:00:00

This paper provides a general framework to explore the possibility of agenda manipulation-proof and proper consensus-based preference aggregation rules, so powerfully called in doubt by a disputable if widely shared understanding of Arrow's `general possibility theorem'. We consider two alternative versions of agenda manipulation-proofness for social welfare functions, that are distinguished by `parallel' vs. `sequential' execution of agenda formation and preference elicitation, respectively. Under the `parallel' version, it is shown that a large class of anonymous and idempotent social welfare functions that satisfy both agenda manipulation-proofness and strategy-proofness on a natural domain of single-peaked `meta-preferences' induced by arbitrary total preference preorders are indeed available. It is only under the second, `sequential' version that agenda manipulation-proofness on the same natural domain of single-peaked `meta-preferences' is in fact shown to be tightly related to the classic Arrowian `independence of irrelevant alternatives' (IIA) for social welfare functions. In particular, it is shown that using IIA to secure such `sequential' version of agenda manipulation-proofness and combining it with a very minimal requirement of distributed responsiveness results in a characterization of the `global stalemate' social welfare function, the constant function which invariably selects universal social indifference. It is also argued that, altogether, the foregoing results provide new significant insights concerning the actual content and the constructive implications of Arrow's `general possibility theorem' from a mechanism-design perspective.
Joint Communication and Sensing in RIS-enabled mmWave Networks
Authors: Lu Wang, Luis F. Abanto-Leon, Arash Asadi
Date: 2022-10-07 00:00:00

Empowering cellular networks with augmented sensing capabilities is one of the key research areas in 6G communication systems. Recently, we have witnessed a plethora of efforts to devise solutions that integrate sensing capabilities into communication systems, i.e., joint communication and sensing (JCAS). However, most prior works do not consider the impact of reconfigurable intelligent surfaces (RISs) on JCAS systems, especially at millimeter-wave (mmWave) bands. Given that RISs are expected to become an integral part of cellular systems, it is important to investigate their potential in cellular networks beyond communication goals. In this paper, we study mmWave orthogonal frequency-division multiplexing (OFDM) JCAS systems in the presence of RISs. Specifically, we jointly design the hybrid beamforming and RIS phase shifts to guarantee the sensing functionalities via minimizing a chordal-distance metric, subject to signal-to-interference-plus-noise (SINR) and power constraints. The non-convexity of the investigated problem poses a challenge which we address by proposing a solution based on the penalty method and manifold-based alternating direction method of multipliers (ADMM). Simulation results demonstrate that under various settings both sensing and communication experience improved performance when the RIS is adequately designed. In addition, we discuss the tradeoff between sensing and communication.
Learning for Perturbation-Based Fiber Nonlinearity Compensation
Authors: Shenghang Luo, Sunish Kumar Orappanpara Soman, Lutz Lampe, Jeebak Mitra, Chuandong Li
Date: 2022-10-07 00:00:00

Several machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed benefits of those over non-learned methods. Numerical results suggest that learned linear processing of perturbation triplets of PB-NLC is preferable over feedforward neural-network solutions.
The Trajectory PHD Filter for Coexisting Point and Extended Target Tracking
Authors: Shaoxiu Wei, Ángel F. García-Fernández, Wei Yi
Date: 2022-10-07 00:00:00

This paper develops a general trajectory probability hypothesis density (TPHD) filter, which uses a general density for target-generated measurements and is able to estimate trajectories of coexisting point and extended targets. First, we provide a derivation of this general TPHD filter based on finding the best Poisson posterior approximation by minimizing the Kullback-Leibler divergence, without using probability generating functionals. Second, we adopt an efficient implementation of this filter, where Gaussian densities correspond to point targets and Gamma Gaussian Inverse Wishart densities for extended targets. The L-scan approximation is also proposed as a simplified version to mitigate the huge computational cost. Simulation and experimental results show that the proposed filter is able to classify targets correctly and obtain accurate trajectory estimation.
Ray-Optics Simulations of Outdoor-to-Indoor Multipath Channels at 4 and 14 GHz
Authors: Pasi Koivumäki, Aki Karttunen, Katsuyuki Haneda
Date: 2022-10-06 00:00:00

Radio wave propagation simulations based on the ray-optical approximation have been widely adopted in coverage analysis for a range of situations, including the outdoor-to-indoor scenario. This work presents O2I ray-tracing simulations utilizing a complete office building floor plan in the form of a laser-scanned point cloud. The simulated radio channels are compared to their measured counterparts at 4 and 14 GHz in terms of path loss and delay and angular spreads. Validation of channel simulations for the O2I case is rare, and so far non-existent for above-$6$~GHz bands. This work reveals the importance of a floor plan model in accurately simulating the channel; it is confirmed that path loss can be replicated with a simple interior path loss model in place of a detailed building interior model, but neglecting to model the interior results in high delay and angular spread errors. By modeling the interior, the ray-tracing simulations achieve relative mean error of under 10% for delay and angular spreads. Finally, effects of multi-layer insulating window on propagation simulations are reported. Noticeable variation of the penetration loss on a small change of the incident angle of a propagation path causes large changes in estimated coverage.
Functional Connectome of the Human Brain with Total Correlation
Authors: Qiang Li, Greg Ver Steeg, Shujian Yu, Jesus Malo
Date: 2022-10-06 00:00:00
Tasks: Functional Connectivity

Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such as correlation or mutual information. In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of total correlation and clustering results are trustable compared to ground truth values. Second, the inferred large scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies but, interestingly, can estimate additional relations beyond pair-wise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.