pwc-archive/papers-with-abstracts huggingface dataset:
Saturation-Informed Current-Limiting Control for Grid-Forming Converters
Authors: Maitraya Avadhut Desai, Xiuqiang He, Linbin Huang, Florian Dörfler
Date: 2024-04-11 00:00:00

In this paper, we investigate the transient stability of a state-of-the-art grid-forming complex-droop control (i.e., dispatchable virtual oscillator control, dVOC) under current saturation. We quantify the saturation level of a converter by introducing the concept of degree of saturation (DoS), and we propose a provably stable current-limiting control with saturation-informed feedback, which feeds the degree of saturation back to the inner voltage-control loop and the outer grid-forming loop. As a result, although the output current is saturated, the voltage phase angle can still be generated from an internal virtual voltage-source node that is governed by an equivalent complex-droop control. We prove that the proposed control achieves transient stability during current saturation under grid faults. We also provide parametric stability conditions for multi-converter systems under grid-connected and islanded scenarios. The stability performance of the current-limiting control is validated with various case studies.
Dynamic modeling and simulation of a flash clay calciner
Authors: Nicola Cantisani, Jan Lorenz Svensen, Ole Fink Hansen, John Bagterp Jørgensen
Date: 2024-04-11 00:00:00

We present a novel dynamic model of a flash clay calciner. The model consists of thermophysical properties, reaction kinetics and stoichiometry, transport, mass and energy balances, and algebraic constraints. This gives rise to a system of partial differential-algebraic equations (PDAE). Spatial discretization is performed to convert the PDAEs into a system of differential-algebraic equations (DAE). The model can be used, for example, to perform dynamic simulations with changing inputs, and process design and optimization. Moreover, it can be used to develop model-based control, which is relevant for flexible operation of a clay calcination plant for green cement production.
Leveraging Eclipse MOSAIC for Modeling and Analyzing Ride-Hailing Services
Authors: Karl Schrab, Moritz Schweppenhäuser, Robert Protzmann, Kay Massow, Ilja Radusch
Date: 2024-04-11 00:00:00

Ride-hailing services enjoy a large popularity in the sector of individualized mobility. Due to broad availability, ease of use, and competitive pricing strategies, these services have established themselves throughout the last decades. With the increased popularity, ride-hailing providers aimed to consistently improve the efficiency of their services, leading to the inception of novel research questions. Many of which can be effectively tackled using simulation. In this paper, we present such a simulation-based approach using Eclipse MOSAIC in-hand with a large-scale traffic scenario of Berlin. We analyze real-world logbook data including detailed shifts of drivers and discuss how to integrate them with the simulation scenario. Moreover, we present extensions to MOSAIC required for the modeling of the ride-hailing services, utilizing the powerful Application Simulator. Accordingly, as the primary result of this paper, we managed to extend the Eclipse MOSAIC framework to be able to answer research questions in the domain of ride-hailing and ride-sharing. Additionally, in an initial exemplary study, we analyze the traffic and environmental impacts of different, yet basic, rebalancing strategies, finding non-negligible differences in mileages and pollutant emissions. We, furthermore, applied our findings to the entire ride-hailing fleet in the city of Berlin for one year, showcasing the impacts different rebalancing strategies could have on environment and general traffic. To our knowledge, the consideration of environmental factors on a city-wide scale is a novel contribution of this paper, not addressed in previous research.
An advanced 1D physics-based model for PEM hydrogen fuel cells with enhanced overvoltage prediction
Authors: Raphaël Gass, Zhongliang Li, Rachid Outbib, Samir Jemei, Daniel Hissel
Date: 2024-04-11 00:00:00

A one-dimensional, dynamic, two-phase, isothermal and finite-difference model of proton exchange membrane fuel cell (PEMFC) systems has been developed. It is distinct from most existing models which are either fast but imprecise, such as lumped-parameter models, or detailed but computationally intensive, such as computational fluid dynamics models. This model, partially validated using experimental polarisation curves, provides a comprehensive description of cell internal states while maintaining a low computational burden. Additionally, a new physical quantity, named the limit liquid water saturation coefficient ($s_{lim}$), is introduced in the overvoltage calculation equation. This quantity replaces the limit current density coefficient ($i_{lim}$) and establishes a connection between the voltage drop at high current densities, the amount of liquid water present in the catalyst layers of the cell, and the operating conditions. At high current densities, a significant amount of liquid water is generated, which limits the accessibility of reactants to certain triple point zones within the catalyst layers by covering them. This, in turn, increases overpotential. It has also been observed that $s_{lim}$ is influenced, at minimum, by the gas pressure imposed by the operator.
Grouping of $N-1$ Contingencies for Controller Synthesis: A Study for Power Line Failures
Authors: Neelay Junnarkar, Emily Jensen, Xiaofan Wu, Suat Gumussoy, Murat Arcak
Date: 2024-04-11 00:00:00

The problem of maintaining power system stability and performance after the failure of any single line in a power system (an "N-1 contingency") is investigated. Due to the large number of possible N-1 contingencies for a power network, it is impractical to optimize controller parameters for each possible contingency a priori. A method to partition a set of contingencies into groups of contingencies that are similar to each other from a control perspective is presented. Design of a single controller for each group, rather than for each contingency, provides a computationally tractable method for maintaining stability and performance after element failures. The choice of number of groups tunes a trade-off between computation time and controller performance for a given set of contingencies. Results are simulated on the IEEE 39-bus and 68-bus systems, illustrating that, with controllers designed for a relatively small number of groups, power system stability may be significantly improved after an N-1 contingency compared to continued use of the nominal controller. Furthermore, performance is comparable to that of controllers designed for each contingency individually.
Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
Authors: Omkar Sudhir Patil, Duc M. Le, Emily J. Griffis, Warren E. Dixon
Date: 2024-04-10 00:00:00

Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the adaptive control literature provides a Lyapunov-based approach to derive weight adaptation laws for each layer of a fully-connected feedforward DNN-based adaptive controller. However, deriving weight adaptation laws from a Lyapunov-based analysis remains an open problem for deep residual neural networks (ResNets). This paper provides the first result on Lyapunov-derived weight adaptation for a ResNet-based adaptive controller. A nonsmooth Lyapunov-based analysis is provided to guarantee asymptotic tracking error convergence. Comparative Monte Carlo simulations are provided to demonstrate the performance of the developed ResNet-based adaptive controller. The ResNet-based adaptive controller shows a 64% improvement in the tracking and function approximation performance, in comparison to a fully-connected DNN-based adaptive controller.
Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees
Authors: Neelay Junnarkar, Murat Arcak, Peter Seiler
Date: 2024-04-10 00:00:00

In this paper, a method is presented to synthesize neural network controllers such that the feedback system of plant and controller is dissipative, certifying performance requirements such as L2 gain bounds. The class of plants considered is that of linear time-invariant (LTI) systems interconnected with an uncertainty, including nonlinearities treated as an uncertainty for convenience of analysis. The uncertainty of the plant and the nonlinearities of the neural network are both described using integral quadratic constraints (IQCs). First, a dissipativity condition is derived for uncertain LTI systems. Second, this condition is used to construct a linear matrix inequality (LMI) which can be used to synthesize neural network controllers. Finally, this convex condition is used in a projection-based training method to synthesize neural network controllers with dissipativity guarantees. Numerical examples on an inverted pendulum and a flexible rod on a cart are provided to demonstrate the effectiveness of this approach.
Inferring nonlinear dynamics of cell migration
Authors: Pedrom Zadeh, Brian A. Camley
Date: 2024-04-10 00:00:00

The motility of eukaryotic cells is strongly influenced by their environment, with confined cells often developing qualitatively different motility patterns from those migrating on simple two-dimensional substrates. Recent experiments, coupled with data-driven methods to extract a cell's equation of motion, showed that cancerous MDA-MB-231 cells persistently hop in a limit cycle when placed on two-state adhesive micropatterns (two large squares connected by a narrow bridge), while they remain stationary on average in rectangular confinements. In contrast, healthy MCF10A cells migrating on the two-state micropattern are bistable, i.e., they settle into either basin on average with only noise-induced hops between the two states. We can capture all these behaviors with a single computational phase field model of a crawling cell, under the assumption that contact with non-adhesive substrate inhibits the cell front. Our model predicts that larger and softer cells are more likely to persistently hop, while smaller and stiffer cells are more likely to be bistable. Other key factors controlling cell migration are the frequency of protrusions and their magnitude of noise. Our results show that relatively simple assumptions about how cells sense their geometry can explain a wide variety of different cell behaviors, and show the power of data-driven approaches to characterize both experiment and simulation.
Actionable forecasting as a determinant of function in noisy biological systems
Authors: Jose M. G. Vilar, Leonor Saiz
Date: 2024-04-11 00:00:00

Continuous adaptation to variable environments is crucial for the survival of living organisms. Here, we analyze how adaptation, forecasting, and resource mobilization towards a target state, termed actionability, interact to determine biological function. We develop a general theory and show that it is possible for organisms to continuously track their optimal state in a dynamic environment by adapting towards an actionable target that incorporates just current information on the optimal state and its rate of change. If the environmental information is precise and readily actionable, it is possible to implement perfect tracking without anticipatory mechanisms, irrespective of the adaptation rate. In contrast, predictive functions, like those of circadian rhythms, are beneficial if sensing the environment is slow or unreliable, as they allow better adaptation with fewer resources. To explore potential actionable forecasting mechanisms, we develop a general approach that implements the adaptation dynamics with forecasting through a dynamics-informed neural network.
Compositional Growth Models
Authors: José Moran, Massimo Riccaboni
Date: 2024-04-11 00:00:00

We review models of compositional growth, which were introduced to explain the growth statistics of various quantities ranging from firm sizes to GDP. In these models, entities are decomposed into units that grow independently. Thus, the growth rate of the entity is the addition of the growth rates of the composing units, with possibly heterogeneous weights. We review such models and show that they can be understood through a unifying theoretical framework, explaining the resulting growth rate distributions using mixtures of Gaussians.
A Modified Depolarization Approach for Efficient Quantum Machine Learning
Authors: Bikram Khanal, Pablo Rivas
Date: 2024-04-10 00:00:00
Tasks: Quantum Machine Learning

Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite the progress, challenges persist due to system noise, errors, and decoherence that complicate the simulation of quantum systems. The depolarization channel is a standard tool for simulating a quantum system's noise. However, modeling such noise for practical applications is computationally expensive when we have limited hardware resources, as is the case in the NISQ era. We propose a modified representation for a single-qubit depolarization channel with two Kraus operators based only on X and Z Pauli matrices. Our approach reduces the computational complexity from six to four matrix multiplications per execution of a channel. Experiments on a Quantum Machine Learning (QML) model on the Iris dataset across various circuit depths and depolarization rates validate that our approach maintains the model's accuracy while improving efficiency. This simplified noise model enables more scalable simulations of quantum circuits under depolarization, advancing capabilities in the NISQ era.
Merger Analysis with Latent Price
Authors: Paul S. Koh
Date: 2024-04-11 00:00:00

Standard empirical tools for merger analysis assume price data, which are often unavailable. I characterize sufficient conditions for identifying the unilateral effects of mergers without price data using the first-order approach and merger simulation. Data on merging firms' revenues, margins, and revenue diversion ratios are sufficient to identify their gross upward pricing pressure indices and compensating marginal cost reductions. Standard discrete-continuous demand assumptions facilitate the identification of revenue diversion ratios as well as the feasibility of merger simulation in terms of percentage change in price. I apply the framework to the Albertsons/Safeway (2015) and Staples/Office Depot (2016) mergers.
Liquidity Jump, Liquidity Diffusion, and Treatment on Wash Trading of Crypto Assets
Authors: Qi Deng, Zhong-guo Zhou
Date: 2024-03-24 00:00:00

We propose that the liquidity of an asset includes two components: liquidity jump and liquidity diffusion. We show that liquidity diffusion has a higher correlation with crypto wash trading than liquidity jump and demonstrate that treatment on wash trading significantly reduces the level of liquidity diffusion, but only marginally reduces that of liquidity jump. We confirm that the autoregressive models are highly effective in modeling the liquidity-adjusted return with and without the treatment on wash trading. We argue that treatment on wash trading is unnecessary in modeling established crypto assets that trade in unregulated but mainstream exchanges.
The Broken Rung: Gender and the Leadership Gap
Authors: Ingrid Haegele
Date: 2024-04-11 00:00:00

Addressing female underrepresentation in leadership positions has become a key policy objective. However, little is known about the extent to which leadership appeals differently to women. Collecting new data from a large firm, I document that women are substantially less likely to apply for early-career promotions. Realized application patterns and large-scale surveys reveal the role of an understudied feature of promotions -- having to assume responsibility over a team -- which is less appealing to women. This gender difference is not accounted for by standard explanations, such as success likelihood or confidence, but is rather a product of common design features of leadership positions.
Impacto Distributivo Potencial de Reformas na Tributacao Indireta no Brasil: Simulacoes Baseadas na PEC 45/2019
Authors: Rozane Bezerra dde Siqueira, Jose Ricardo Bezerra Nogueira, Carlos Feitosa Luna
Date: 2024-04-11 00:00:00

This paper analyzes the redistributive impacts of indirect taxation reforms in Brazil inspired by PEC 45/2019, particularly in the version that led to EC 132/2023. Comparisons are made between the current system and the simulated reforms, considering the distribution of the tax burden among families in different income classes, as well as the impact on poverty and inequality indicators. The simulations are conducted based on the combination of effective tax rates that apply to goods and services consumed by households, using nationally representative microdata from family budgets.
A Theory of Investors' Disclosure
Authors: Jinzhi Lu, Pingyang Gao
Date: 2024-04-11 00:00:00
Tasks: Position

We investigate the voluntary disclosure decision of investors under uncertainty about information endowment (Dye 1985). In our model, an investor first uncovers initial evidence about the target firm and then seeks additional information to help interpret the initial evidence. The investor takes a position in the firm's stock, then voluntarily discloses some or all of their findings, and finally closes their position after the disclosure. We present two main findings. First, the investor will always disclose the initial evidence, even though the market is uncertain about whether the investor possesses such evidence. Second, the investor's disclosure strategy of the additional information increases stock price volatility: they disclose extreme news and withhold moderate news.
International environmental treaties: An honest or a misguided effort
Authors: Reza Hafezi, David A. Wood, Firouzeh Rosa Taghikhah
Date: 2024-04-11 00:00:00
Tasks: Decision Making

Climate change and environmental concerns represent a global crisis accompanied by significant economic challenges. Regular international conferences held to address these issues, such as in the UK (2021) and Egypt (2022), spark debate about the effectiveness and practicality of international commitments. This study examines international treaties from a different perspective, emphasizing the need to understand the power dynamics and stakeholder interests that delay logical actions to mitigate anthropogenic contributions to climate change and their impacts. Environmental and social concerns tend to increase within nations as their economies develop, where they fight to keep acceptable standards of living while reducing emissions volume. So, nations play disproportionate roles in global decision-making based on the size of their economies. Addressing climate change requires a paradigm shift to emphasize acknowledging and adhering to global commitments through civil pressure, rather than relying on traditional yet biased systems of international political diplomacy. Here, climate-friendly actions are evaluated and ideas to promote such activities are proposed. We introduce a "transition regime" as a solution to this metastasis challenge which gradually infects all nations.
Financial climate risk: a review of recent advances and key challenges
Authors: Victor Cardenas
Date: 2024-04-10 00:00:00
Tasks: Management

The document provides an overview of financial climate risks. It delves into how climate change impacts the global financial system, distinguishing between physical risks (such as extreme weather events) and transition risks (stemming from policy changes and economic transitions towards low carbon technologies). The paper underlines the complexity of accurately defining financial climate risk, citing the integration of climate science with financial risk analysis as a significant challenge. The paper highlights the pivotal role of microfinance institutions (MFIs) in addressing financial climate risk, especially for populations vulnerable to climate change. The document emphasizes the importance of updating risk management practices within MFIs to explicitly include climate risk assessments and suggests leveraging technology to improve these practices.
Re-Interpreting the Step-Response Probability Curve to Extract Fundamental Physical Parameters of Event-based Vision Sensors
Authors: Brian Mcreynolds, Rui Graca, Lucas Kulesza, Peter McMahon-Crabtree
Date: 2024-04-11 00:00:00
Tasks: Event-based vision

Biologically inspired event-based vision sensors (EVS) are growing in popularity due to performance benefits including ultra-low power consumption, high dynamic range, data sparsity, and fast temporal response. They efficiently encode dynamic information from a visual scene through pixels that respond autonomously and asynchronously when the per-pixel illumination level changes by a user-selectable contrast threshold ratio, $\theta$. Due to their unique sensing paradigm and complex analog pixel circuitry, characterizing Event-based Vision Sensor (EVS) is non-trivial. The step-response probability curve (S-curve) is a key measurement technique that has emerged as the standard for measuring $\theta$. In this work, we detail the method for generating accurate S-curves by applying an appropriate stimulus and sensor configuration to decouple 2nd-order effects from the parameter being studied. We use an EVS pixel simulation to demonstrate how noise and other physical constraints can lead to error in the measurement, and develop two techniques that are robust enough to obtain accurate estimates. We then apply best practices derived from our simulation to generate S-curves for the latest generation Sony IMX636 and interpret the resulting family of curves to correct the apparent anomalous result of previous reports suggesting that $\theta$ changes with illumination. Further, we demonstrate that with correct interpretation, fundamental physical parameters such as dark current and RMS noise can be accurately inferred from a collection of S-curves, leading to more accurate parameterization for high-fidelity EVS simulations.