pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Distribution System Restoration via Tractable Modeling of Decision-Dependent Interruption Cost and Cold Load Pickup
Authors: Wei Wang, Minwu Chen, Hongbin Wang, Gaoqiang Peng, Hongzhou Chen
Date: 2024-11-19 00:00:00

Developing optimized restoration strategies for power distribution systems (PDSs) is critical to enhancing resilience. Prior knowledge of customer interruption cost (CIC) and load restoration behaviors, particularly cold load pickup (CLPU), is essential for effective decision-making. However, both CIC and CLPU are reciprocally influenced by the realized customer interruption duration (CID), making them decision-dependent and challenging to model, especially given the limited understanding of their underlying physical mechanisms. This paper proposes a novel and tractable modeling approach to capture the varying patterns of CIC and CLPU with CID - patterns derived from data that reflect observable surface - level correlations rather than underlying mechanisms - thereby enabling practical surrogate modeling of these decision-dependent factors. Specifically, quadratic functions are employed to model the increasing rate of CIC with respect to CID according to data fitting results. For CLPU, several defining characteristics are extracted and modeled in a piecewise linear form relative to CID, and the actual restored load accounting for CLPU is subsequently reconstructed. Building on these models, a PDS restoration optimization framework is developed, incorporating mobile energy storage systems (MESSs) and network reconfiguration strategies. Case studies validate the effectiveness of the proposed approach and highlight MESS's unique potential in accelerating CLPU-related restoration.
The Soft-PVTOL: modeling and control
Authors: Gerardo Flores, Mark W. Spong
Date: 2024-11-19 00:00:00

This paper presents, for the first time, the soft planar vertical take-off and landing (Soft-PVTOL) aircraft. This concept captures the soft aerial vehicle's fundamental dynamics with a minimum number of states and inputs but retains the main features to consider when designing control laws. Unlike conventional PVTOL and multi-rotors, where altering position inevitably impacts orientation due to their underactuated design, the Soft-PVTOL offers the unique advantage of separating these dynamics, opening doors to unparalleled maneuverability and precision. We demonstrate that the Soft-PVTOL can be modeled using the Euler-Lagrange equations by assuming a constant curvature model in the aerial robot's arms. Such a mathematical model is presented in detail and can be extended to several constant curvature segments in each Soft-PVTOL arm. Moreover, we design a passivity-based control law that exploits the flexibility of the robot's arms. We solve the tracking control problem, proving that the error equilibrium globally exponentially converges to zero. The controller is tested in numerical simulations, demonstrating robust performance and ensuring the efficacy of the closed-loop system.
Development of a Comprehensive Physics-Based Battery Model and Its Multidimensional Comparison with an Equivalent-Circuit Model: Accuracy, Complexity, and Real-World Performance under Varying Conditions
Authors: Guodong Fan, Boru Zhou, Chengwen Meng, Tengwei Pang, Xi Zhang, Mingshu Du, Wei Zhao
Date: 2024-11-19 00:00:00

This paper develops a comprehensive physics-based model (PBM) that spans a wide operational range, including varying temperatures, charge/discharge conditions, and real-world field data cycles. The PBM incorporates key factors such as hysteresis effects, concentration-dependent diffusivity, and the Arrhenius law to provide a realistic depiction of battery behavior. Additionally, the paper presents an in-depth analysis comparing the PBM with an equivalent-circuit model (ECM) for accurately capturing the dynamics of lithium-ion batteries under diverse operating conditions. To ensure a fair comparison, both the PBM and ECM are rigorously calibrated and validated through parameter identification and testing across 55 different operating conditions. To the best of the authors' knowledge, this represents the most comprehensive model calibration and validation effort for PBM and ECM in the literature to date, encompassing large temperature variations (-20 to 40{\deg}C), various charging/discharging C-rates, and real-world driving cycles. Comparative analysis between the PBM and ECM highlights key differences in accuracy, computational complexity, parameterization requirements, and performance under varying temperature conditions. appropriate models for battery management applications.
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection
Authors: Kejun Chen, Truc Nguyen, Malik Hassanaly
Date: 2024-11-19 00:00:00
Tasks: continuous-control, Continuous Control, Multi-agent Reinforcement Learning, Transfer Learning

Smart inverters are instrumental in the integration of renewable and distributed energy resources (DERs) into the electric grid. Such inverters rely on communication layers for continuous control and monitoring, potentially exposing them to cyber-physical attacks such as false data injection attacks (FDIAs). We propose to construct a defense strategy against a priori unknown FDIAs with a multi-agent reinforcement learning (MARL) framework. The first agent is an adversary that simulates and discovers various FDIA strategies, while the second agent is a defender in charge of detecting and localizing FDIAs. This approach enables the defender to be trained against new FDIAs continuously generated by the adversary. The numerical results demonstrate that the proposed MARL defender outperforms a supervised offline defender. Additionally, we show that the detection skills of an MARL defender can be combined with that of an offline defender through a transfer learning approach.
Is Locational Marginal Price All You Need for Locational Marginal Emission?
Authors: Xuan He, Danny H. K. Tsang, Yize Chen
Date: 2024-11-18 00:00:00
Tasks: All

Growing concerns over climate change call for improved techniques for estimating and quantifying the greenhouse gas emissions associated with electricity generation and transmission. Among the emission metrics designated for power grids, locational marginal emission (LME) can provide system operators and electricity market participants with valuable information on the emissions associated with electricity usage at various locations in the power network. In this paper, by investigating the operating patterns and physical interpretations of marginal emissions and costs in the security-constrained economic dispatch (SCED) problem, we identify and draw the exact connection between locational marginal price (LMP) and LME. Such interpretation helps instantly derive LME given nodal demand vectors or LMP, and also reveals the interplay between network congestion and nodal emission pattern. Our proposed approach helps reduce the computation time of LME by an order of magnitude compared to analytical approaches, while it can also serve as a plug-and-play module accompanied by an off-the-shelf market clearing and LMP calculation process.
A Robust Solver for Phasor-Domain Short-Circuit Analysis with Inverter-Based Resources
Authors: Aboutaleb Haddadi, Evangelos Farantatos, Ilhan Kocar
Date: 2024-11-18 00:00:00

The integration of Inverter-Based Resource (IBR) model into phasor-domain short circuit (SC) solvers challenges their numerical stability. To address the challenge, this paper proposes a solver that improves numerical stability by employing the Newton-Raphson iterative method. The solver can integrate the latest implementation of IBR SC model in industry-standard fault analysis programs including the voltage controlled current source tabular model as well as vendor-specific black-box and white-box equation-based models. The superior numerical stability of the proposed solver has been mathematically demonstrated, with identified convergence conditions. An algorithm for the implementation of the proposed solver in fault analysis programs has been developed. The objective is to improve the capability of the industry to accurately represent IBRs in SC studies and ensure system protection reliability in an IBR-dominated future.
Uncertainty Propagation and Minimization for Channel Estimation in UAV-mounted RIS Systems
Authors: Kevin Weinberger, David Müller, Martin Mönnigmann, Aydin Sezgin
Date: 2024-11-18 00:00:00

Reconfigurable Intelligent Surfaces (RIS) are emerging as a key technology for sixth-generation (6G) wireless networks, leveraging adjustable reflecting elements to dynamically control electromagnetic wave propagation and optimize wireless connectivity. By positioning the RIS on an unmanned aerial vehicle (UAV), it can maintain line-of-sight and proximity to both the transmitter and receiver, critical factors that mitigate path loss and enhance signal strength. The lightweight, power-efficient nature of RIS makes UAV integration feasible, yet the setup faces significant disturbances from UAV motion, which can degrade RIS alignment and link performance. In this study, we address these challenges using both experimental measurements and analytical methods. Using an extended Kalman filter (EKF), we estimate the UAV's orientation in real time during experimental flights to capture real disturbance effects. The resulting orientation uncertainty is then propagated to the RIS's channel estimates by applying the Guide to the Expression of Uncertainty in Measurement (GUM) framework as well as complex-valued propagation techniques to accurately assess and minimize the impact of UAV orientation uncertainties on RIS performance. This method enables us to systematically trace and quantify how orientation uncertainties affect channel gain and phase stability in real-time. Through numerical simulations, we find that the uncertainty of the RIS channel link is influenced by the RIS's configuration. Furthermore, our results demonstrate that the uncertainty area is most accurately represented by an annular section, enabling a 58% reduction in the uncertainty area while maintaining a 95% coverage probability.
During and after COVID-19: What happened to the home advantage in Germany's first football division?
Authors: Thorsten Schank, Vivien Voigt, Christian Orthey
Date: 2024-11-19 00:00:00

It is well-established that the home advantage (HA), the phenomenon that on average the local team performs better than the visiting team, exists in many sports. In response to the COVID-19 outbreak, spectators were banned from football stadiums, which we leverage as a natural experiment to examine the impact of stadium spectators on HA. Using data from the first division of the German Bundesliga for seasons 2016/17 to 2023/24, we are the first to focus on a longer time horizon and consider not only the first but all three seasons subject to spectator regulations as well as two subsequent seasons without. We confirm previous studies regarding the disappearance of the HA in the last nine matches of season 2019/20. This drop materialised almost entirely through a reduction of home goals. The HA in season 2020/21 (with spectator ban during most matches) was very close to the pre-COVID-19 season 2018/19, indicating that teams became accustomed to the absence of spectators. For season 2021/22, with varying spectator regulations, we detect a U-shaped relationship between HA and the stadium utilisation rate, where HA increases considerably for matches with medium stadium utilisation which is associated with a larger difference in running distance between the home and away teams.
Clustering with Potential Multidimensionality: Inference and Practice
Authors: Ruonan Xu, Luther Yap
Date: 2024-11-20 00:00:00
Tasks: Clustering, valid

We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their estimands and inference procedures.
Revealed Information
Authors: Laura Doval, Ran Eilat, Tianhao Liu, Yangfan Zhou
Date: 2024-11-20 00:00:00

An analyst observes the frequency with which a decision maker (DM) takes actions, but not the frequency conditional on payoff-relevant states. We ask when the analyst can rationalize the DM's choices as if the DM first learns something about the state before acting. We provide a support-function characterization of the triples of utility functions, prior beliefs, and (marginal) distributions over actions such that the DM's action distribution is consistent with information given the DM's prior and utility function. Assumptions on the cardinality of the state space and the utility function allow us to refine this characterization, obtaining a sharp system of finitely many inequalities the utility function, prior, and action distribution must satisfy. We apply our characterization to study comparative statics and to identify conditions under which a single information structure rationalizes choices across multiple decision problems. We characterize the set of distributions over posterior beliefs that are consistent with the DM's choices. We extend our results to settings with a continuum of actions and states assuming the first-order approach applies, and to simple multi-agent settings.
Trade, Trees, and Lives
Authors: Xinming Du, Lei LI, Eric Zou
Date: 2024-11-20 00:00:00

This paper shows a cascading mechanism through which international trade-induced deforestation results in a decline of health outcomes in cities distant from where trade activities occur. We examine Brazil, which has ramped up agricultural export over the last two decades to meet rising global demand. Using a shift-share research design, we first show that export shocks cause substantial local agricultural expansion and a virtual one-for-one decline in forest cover. We then construct a dynamic area-of-effect model that predicts where atmospheric changes should be felt - due to loss of forests that would otherwise serve to filter out and absorb air pollutants as they travel - downwind of the deforestation areas. Leveraging quasi-random variation in these atmospheric connections, we establish a causal link between deforestation upstream and subsequent rises in air pollution and premature deaths downstream, with the mortality effects predominantly driven by cardiovascular and respiratory causes. Our estimates reveal a large telecoupled health externality of trade deforestation: over 700,000 premature deaths in Brazil over the past two decades. This equates to $0.18 loss in statistical life value per $1 agricultural exports over the study period.
Price Setting Rules, Rounding Tax, and Inattention Penalty
Authors: Doron Sayag, Avichai Snir, Daniel Levy
Date: 2024-11-20 00:00:00

We study the price rounding regulation in Israel, which outlawed non-0-ending prices, forcing retailers to round 9 ending prices, which in many stores, comprised more than 60 percent of all prices. The goal of the regulation was to eliminate the rounding tax, the extra amount consumers paid because of price rounding, which was necessitated by the abolition of low denomination coins, and the inattention tax, the extra amount consumers paid the retailers because of their inattention to the prices rightmost digits. Using 4 different datasets, we assess the success of the government in achieving these goals, focusing on fast moving consumer goods, a category of products strongly affected by the price rounding regulation. We focus on the response of the retailers to the price rounding regulation and find that although the government succeeded in eliminating the rounding tax, the bottom line is that shoppers end up paying more, not less, because of the regulation, underscoring, once again, the warning of Milton Friedman that policies should be judged by their results, not by their intentions.
Underlying Core Inflation with Multiple Regimes
Authors: Gabriel Rodriguez-Rondon
Date: 2024-11-19 00:00:00

This paper introduces a new approach for estimating core inflation indicators based on common factors across a broad range of price indices. Specifically, by utilizing procedures for detecting multiple regimes in high-dimensional factor models, we propose two types of core inflation indicators: one incorporating multiple structural breaks and another based on Markov switching. The structural breaks approach can eliminate revisions for past regimes, though it functions as an offline indicator, as real-time detection of breaks is not feasible with this method. On the other hand, the Markov switching approach can reduce revisions while being useful in real time, making it a simple and robust core inflation indicator suitable for real-time monitoring and as a short-term guide for monetary policy. Additionally, this approach allows us to estimate the probability of being in different inflationary regimes. To demonstrate the effectiveness of these indicators, we apply them to Canadian price data. To compare the real-time performance of the Markov switching approach to the benchmark model without regime-switching, we assess their abilities to forecast headline inflation and minimize revisions. We find that the Markov switching model delivers superior predictive accuracy and significantly reduces revisions during periods of substantial inflation changes. Hence, our findings suggest that accounting for time-varying factors and parameters enhances inflation signal accuracy and reduces data requirements, especially following sudden economic shifts.
The Distributional Effects of Economic Uncertainty
Authors: Florian Huber, Massimiliano Marcellino, Tommaso Tornese
Date: 2024-11-19 00:00:00

We study the distributional implications of uncertainty shocks by developing a model that links macroeconomic aggregates to the US distribution of earnings and consumption. We find that: initially, the fraction of low-earning workers decreases, while the share of households reporting low consumption increases; at longer horizons, the fraction of low-income workers increases, but the consumption distribution reverts to its pre-shock shape. While the first phase reduces income inequality and increases consumption inequality, in the second stage income inequality rises, while the effects on consumption inequality dissipate. Finally, we introduce Functional Local Projections and show that they yield similar results.
Procompetitive effects of vertical takeovers. Evidence from the European Union
Authors: Chiara Bellucci, Armando Rungi
Date: 2024-11-19 00:00:00

This study investigates the causal impact of takeovers on firm-level financial accounts on a sample of 4,482 targets in the European Union in the period 2007- 2021. Findings suggest that horizontal integrations do not have a statistically significant impact, while vertical takeovers bring about a lower markup (0.7%), a larger market share (2.5%), a higher profitability (2.3%), and a lower capital intensity (7.2%). The impact of vertical integrations grows over time, and it is higher when the corporate perimeter of the acquirer is bigger. Our results point to strategies aimed at eliminating double profit margins along supply chains. Finally, we reconnect with the debate initiated by the U.S. Vertical Merger Guidelines in 2020 and 2023, where the presumption of harm after vertical deals has been softened, thus considering procompetitive effects, but the discussion of potential foreclosure risks has been expanded.
Impacto Redistributivo da Reforma da Tributacao do Consumo no Brasil: Simulacoes Baseadas no PLP68/2024
Authors: Rozane Bezerra de Siqueira, Jose Ricardo Bezerra Nogueira, Carlos Feitosa Luna
Date: 2024-11-18 00:00:00

The aim of this study is to estimate the implications for the reference rate and for the distribution of the tax burden among households from the consumption tax reform as outlined in the version of the Projeto de Lei Complementar (PLP) 68 approved by the Brazilian Chamber of Deputies in July 2024.
Matching Design with Algorithms and Applications to Foster Care
Authors: Terence Highsmith Ii
Date: 2024-11-19 00:00:00

We study the problem of an organization that matches agents to objects where agents have preference rankings over objects and the organization uses algorithms to construct a ranking over objects on behalf of each agent. Our new framework carries the interpretation that the organization and its agents may be misaligned in pursuing some underlying matching goal. We design matching mechanisms that integrate agent decision-making and the algorithm by avoiding matches that are unanimously disagreeable between the two parties. Our mechanisms also satisfy restricted efficiency properties. Subsequently, we prove that no unanimous mechanism is strategy-proof but that ours can be non-obviously manipulable. We generalize our framework to allow for any preference aggregation rules and extend the famed Gibbard-Satterthwaite Theorem to our setting. We apply our framework to place foster children in foster homes to maximize welfare. Using a machine learning model that predicts child welfare in placements and a (planned) novel lab-in-the-field eliciting real caseworkers' preferences, we empirically demonstrate that there are important match-specific welfare gains that our mechanisms extract that are not realized under the status quo.
Scoring and Favoritism in Optimal Procurement Design
Authors: Pasha Andreyanov, Ilia Krasikov, Alex Suzdaltsev
Date: 2024-11-19 00:00:00

We study buyer-optimal procurement mechanisms when quality is contractible. When some costs are borne by every participant of a procurement auction regardless of winning, the classic analysis should be amended. We show that an optimal symmetric mechanism is a scoring auction with a score function that may be either flatter or steeper than classically. This depends on the relative degrees of information asymmetry over the all-pay and winner-pay costs. However, the symmetry of the optimal mechanism is not granted due to the presence of all-pay costs. When ex-post efficiency is less important than the duplication of costs, favoritism becomes optimal. We show that, depending on the degree of convexity of costs, the solution takes one of two novel formats with a partially asymmetric treatment of firms, which we call a score floor and a score ceiling auction. Interestingly, these auctions feature side payments from or to the buyer, which has nothing to do with corruption.
Quasi-stability notions in two-sided matching models
Authors: Nadia Guiñazú, Noelia Juarez, Pablo Neme, Jorge Oviedo
Date: 2024-11-19 00:00:00

This paper presents weakened notions of corewise stability and setwise stability for matching markets where agents have substitutable choice functions. We introduce the concepts of worker-quasi-core, firm-quasi-core, and worker-quasisetwise stability. We also examine their relationship to established notions in the literature, such as worker-quasi and firm-quasi stability in both many-to-one and many-to-many markets.