pwc-archive/papers-with-abstracts huggingface dataset:
A Dynamic Equivalent Method for PMSG-WTG Based Wind Farms Considering wind Speeds and Fault Severities
Authors: Dongsheng Li, Chen Shen, Ye Liu, Ying Chen, Shaowei Huang
Date: 2022-11-23 00:00:00
Tasks: Clustering

The dynamic security assessment of power systems needs to scan contingencies in a preselected set through time-domain simulations. With more and more inverter-based-generation, such as wind and solar power generation, integrated into power systems, electro-magnetic transient simulation is adopted. However, the complexity of simulation will increase greatly if inverter-based-generation units are modeled in detail. In order to reduce the complexity of simulation of power systems including large-scale wind farms, it is critical to develop dynamic equivalent methods for wind farms which are applicable to the expected contingency analysis. The dynamic response characteristics of permanent magnet synchronous generator-wind turbine generators (PMSG-WTGs) are not only influenced by their control strategies, but also by the operating wind speeds and the fault severities. Thus, this paper proposes a dynamic equivalent method for PMSG-WTG based wind farms considering the wind speeds and the fault severities. Firstly, this paper analyzes all possible response characteristics of a PMSG-WTG and proposes a clustering method based on the operating wind speed and the terminal voltage of each PMSG-WTG at the end of the fault. Then, a single-machine equivalent method is introduced for each group of PMSG-WTGs. For the group of PMSG-WTGs with active power ramp recovery process, an equivalent model with segmented ramp rate limitation for active current is designed. In order to obtain the clustering indicators, a simulation-based iterative method is put forward to calculate the voltage at point of common connection (PCC) of the wind farm, and a PMSG-WTG terminal voltage calculation method is further presented. Eventually, the efficiency and accuracy of the proposed method are verified by the simulation results.
Entropy Rate Maximization of Markov Decision Processes under Linear Temporal Logic Tasks
Authors: Yu Chen, ShaoYuan Li, Xiang Yin
Date: 2022-11-23 00:00:00
Tasks: Robot Task Planning, Task Planning

We investigate the problem of synthesizing optimal control policies for Markov decision processes (MDPs) with both qualitative and quantitative objectives. Specifically, our goal is to achieve a given linear temporal logic (LTL) task with probability one, while maximizing the \emph{entropy rate} of the system. The notion of entropy rate characterizes the long-run average (un)predictability of a stochastic process. Such an optimal policy is of our interest, in particular, from the security point of view, as it not only ensures the completion of tasks, but also maximizes the unpredictability of the system. However, existing works only focus on maximizing the total entropy which may diverge to infinity for infinite horizon. In this paper, we provide a complete solution to the entropy rate maximization problem under LTL constraints. Specifically, we first present an algorithm for synthesizing entropy rate maximizing policies for communicating MDPs. Then based on a new state classification method, we show the entropy rate maximization problem under LTL task can be effectively solved in polynomial-time. We illustrate the proposed algorithm based on two case studies of robot task planning scenario.
You Don't Know When I Will Arrive: Unpredictable Controller Synthesis for Temporal Logic Tasks
Authors: Yu Chen, Shuo Yang, Rahul Mangharam, Xiang Yin
Date: 2022-11-23 00:00:00
Tasks: Robot Task Planning, Task Planning

In this paper, we investigate the problem of synthesizing controllers for temporal logic specifications under security constraint. We assume that there exists a passive intruder (eavesdropper) that can partially observe the behavior of the system. For the purpose of security, we require that the system's behaviors are unpredictable in the sense that the intruder cannot determine for sure that the system will exactly accomplish the task in $K$ steps ahead. This problem is particularly challenging since future information is involved in the synthesis process. We propose a novel information structure that predicts the effect of control in the future. A sound and complete algorithm is developed to synthesize a controller which ensures both task completion and security guarantee. The proposed approach is illustrated by a case study of robot task planning.
Analysis of Discrete-Time Switched Linear Systems under Logic Dynamic Switchings
Authors: Xiao Zhang, Min Meng, Zhengping Ji
Date: 2022-11-23 00:00:00

The control properties of discrete-time switched linear systems (SLS) with switching signals generated by logical dynamic systems are studied using the semi-tensor product (STP) approach. With the algebraic state space representation (ASSR), the linear modes and the logical generators are aggregated as a hybrid system, leading to the criteria of reachability, controllability, observability, and reconstructibility of the SLSs. Algorithms for checking these properties are given. Then, two kinds of realization problems concerning whether the logical dynamic systems can generate the desired switching signals are investigated, and necessary and sufficient conditions for the realisability of the required switching signals are given with respect to the cases of fixed operating time switching and finite reference signal switching.
Quality Analysis of Battery Degradation Models with Real Battery Aging Experiment Data
Authors: Cunzhi Zhao, Xingpeng Li, Yan Yao
Date: 2022-11-22 00:00:00
Tasks: Scheduling

The installation capacity of energy storage system, especially the battery energy storage system (BESS), has increased significantly in recent years, which is mainly applied to mitigate the fluctuation caused by renewable energy sources (RES) due to the fast response and high round-trip energy efficiency of BESS. The main components of majority of BESSs are lithium-ion batteries, which will degrade during the BESS daily operation. Heuristic battery degradation models are proposed to consider the battery degradation in the operations of energy systems to optimize the scheduling. However, those heuristic models are not evaluated or demonstrated with real battery degradation data. Thus, this paper will perform a quality analysis on the popular heuristic battery degradation models using the real battery aging experiment data to evaluate their performance. A benchmark model is also proposed to represent the real battery degradation value based on the averaged cycle value of the experimental data.
Risk-Aware Stability of Discrete-Time Systems
Authors: Margaret P. Chapman, Dionysios S. Kalogerias
Date: 2022-11-22 00:00:00

We develop a generalized stability framework for stochastic discrete-time systems, where the generality pertains to the ways in which the distribution of the state energy can be characterized. We use tools from finance and operations research called risk functionals (i.e., risk measures) to facilitate diverse distributional characterizations. In contrast, classical stochastic stability notions characterize the state energy on average or in probability, which can obscure the variability of stochastic system behavior. After drawing connections between various risk-aware stability concepts for nonlinear systems, we specialize to linear systems and derive sufficient conditions for the satisfaction of some risk-aware stability properties. These results pertain to real-valued coherent risk functionals and a mean-conditional-variance functional. The results reveal novel noise-to-state stability properties, which assess disturbances in ways that reflect the chosen measure of risk. We illustrate the theory through examples about robustness, parameter choices, and state-feedback controllers.
Electric Autonomous Mobility-on-Demand: Joint Optimization of Routing and Charging Infrastructure Siting
Authors: Fabio Paparella, Karni Chauhan, Theo Hofman, Mauro Salazar
Date: 2022-11-22 00:00:00

The advent of vehicle autonomy, connectivity and electric powertrains is expected to enable the deployment of Autonomous Mobility-on-Demand systems. Crucially, the routing and charging activities of these fleets are impacted by the design of the individual vehicles and the surrounding charging infrastructure which, in turn, should be designed to account for the intended fleet operation. This paper presents a modeling and optimization framework where we optimize the activities of the fleet jointly with the placement of the charging infrastructure. We adopt a mesoscopic planning perspective and devise a time-invariant model of the fleet activities in terms of routes and charging patterns, explicitly capturing the state of charge of the vehicles by resampling the road network as a digraph with iso-energy arcs. Then, we cast the problem as a mixed-integer linear program that guarantees global optimality and can be solved in less than 10 min. Finally, we showcase two case studies with real-world taxi data in Manhattan, NYC: The first one captures the optimal trade-off between charging infrastructure prevalence and the empty-mileage driven by the fleet. We observe that jointly optimizing the infrastructure siting significantly outperforms heuristic placement policies, and that increasing the number of stations is beneficial only up to a certain point. The second case focuses on vehicle design and shows that deploying vehicles equipped with a smaller battery results in the lowest energy consumption: Although necessitating more trips to the charging stations, such fleets require about 12% less energy than the vehicles with a larger battery capacity.
Adaptive Observers for MIMO Discrete-Time LTI Systems
Authors: Anchita Dey, Shubhendu Bhasin
Date: 2022-11-22 00:00:00

In this paper, an adaptive observer is proposed for multi-input multi-output (MIMO) discrete-time linear time-invariant (LTI) systems. Unlike existing MIMO adaptive observer designs, the proposed approach is applicable to LTI systems in their general form. Further, the proposed method uses recursive least square (RLS) with covariance resetting for adaptation that is shown to guarantee that the estimates are bounded, irrespective of any excitation condition, even in the presence of a vanishing perturbation term in the error used for updation in RLS. Detailed analysis for convergence and boundedness has been provided along with simulation results for illustrating the performance of the developed theory.
DiME and AGVis: A Distributed Messaging Environment and Geographical Visualizer for Large-scale Power System Simulation
Authors: Nicholas Parsly, Jinning Wang, Nick West, Qiwei Zhang, Hantao Cui, Fangxing Li
Date: 2022-11-22 00:00:00

This paper introduces the messaging environment and the geographical visualization tool of the CURENT Large-scale Testbed (LTB) that can be used for large-scale power system closed-loop simulation. First, Distributed Messaging Environment (DiME) implements an asynchronous shared workspace to enable high-concurrent data exchange. Second, Another Grid Visualizer (AGVis) is presented as a geovisualization tool that facilitates the visualization of real-time power system simulation. Third, case studies show the use of DiME and AGVis. The results demonstrate that, with the modular structure, the LTB is capable of not only federal use for real-time, large-scale power system simulation, but also independent use for customized power system research.
PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control
Authors: Gargya Gokhale, Bert Claessens, Chris Develder
Date: 2022-11-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Large-scale integration of intermittent renewable energy sources calls for substantial demand side flexibility. Given that the built environment accounts for approximately 40% of total energy consumption in EU, unlocking its flexibility is a key step in the energy transition process. This paper focuses specifically on energy flexibility in residential buildings, leveraging their intrinsic thermal mass. Building on recent developments in the field of data-driven control, we propose PhysQ. As a physics-informed reinforcement learning framework for building control, PhysQ forms a step in bridging the gap between conventional model-based control and data-intensive control based on reinforcement learning. Through our experiments, we show that the proposed PhysQ framework can learn high quality control policies that outperform a business-as-usual, as well as a rudimentary model predictive controller. Our experiments indicate cost savings of about 9% compared to a business-as-usual controller. Further, we show that PhysQ efficiently leverages prior physics knowledge to learn such policies using fewer training samples than conventional reinforcement learning approaches, making PhysQ a scalable alternative for use in residential buildings. Additionally, the PhysQ control policy utilizes building state representations that are intuitive and based on conventional building models, that leads to better interpretation of the learnt policy over other data-driven controllers.
Reconstruction of the temporal correlation network of all-cause mortality fluctuation across Italian regions: the importance of temperature and among-nodes flux
Authors: Guido Gigante, Alessandro Giuliani
Date: 2022-11-22 00:00:00
Tasks: All

All-cause mortality is a very coarse grain, albeit very reliable, index to check the health implications of lifestyle determinants, systemic threats and socio-demographic factors. In this work we adopt a statistical-mechanics approach to the analysis of temporal fluctuations of all-cause mortality, focusing on the correlation structure of this index across different regions of Italy. The correlation network among the 20 Italian regions was reconstructed using temperature oscillations and travellers' flux (as a function of distance and region's attractiveness, based on GDP), allowing for a separation between infective and non-infective death causes. The proposed approach allows monitoring of emerging systemic threats in terms of anomalies of correlation network structure.
Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem
Authors: Alessandro Zunino, Marco Castello, Giuseppe Vicidomini
Date: 2022-11-22 00:00:00
Tasks: Image Deconvolution

Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging. In CLSM, a focused laser beam excites the fluorescence emission from a specific specimen position. Some actuators scan the probed region across the sample and a photodetector collects a single intensity value for each scan point, building a two-dimensional image pixel-by-pixel. Recently, new fast single-photon array detectors have allowed the recording of a full bi-dimensional image of the probed region for each scan point, transforming CLSM into image scanning microscopy (ISM). This latter offers significant improvements over traditional imaging but requires an optimal processing tool to extract a super-resolved image from the four-dimensional dataset. Here we describe the image formation process in ISM from a statistical point of view, and we use the Bayesian framework to formulate a multi-image deconvolution problem. Notably, the single-photon detector suffers exclusively from the photon shot noise, enabling the development of an effective likelihood model. We derive an iterative likelihood maximization algorithm and test it on experimental and simulated data. Furthermore, we demonstrate that the ISM dataset is redundant, enabling the possibility of obtaining reconstruction sampled at twice the scanning step. Our results prove that in ISM, under appropriate conditions, the Nyquist-Shannon sampling criterium is effectively relaxed. This finding can be exploited to speed up the acquisition process by a factor of four, further improving the versatility of ISM systems.
Investigating factors behind the outbreak of the 6th and the 7th waves of COVID-19 in Tokyo
Authors: Yoshihiko Takase
Date: 2022-11-22 00:00:00

The 6th wave of COVID-19 in Tokyo continued for the longest period of infection (about 190 days from late Nov. 2021), and the 7th wave, which occurred in mid-May 2022, was the largest wave ever (cumulative 1.7 million people). In order to elucidate their factors, the infection wave was analyzed by using the Avrami equation. The main component of the 6th wave was formed by the coupling of increased human interaction due to the New Year holidays and the invasion of the new virus variant Omicron BA.1. After that, side waves were formed by the coupling of the invasion of the new virus variant Omicron BA.2 and the human interaction in the consecutive holidays in February, March, and May. These side waves caused the 6th wave not to converge for a long time. The outbreak of the main component of the 7th wave occurred by the coupling of the invasion of the new virus variant Omicron BA.5 and multiple social factors, followed by human interaction during the July holidays. Based on the results that the domain growth rate $K$ and the infection rise time $t_\mathrm{on}$ were almost independent of the initial susceptible $D_\mathrm{s}$, dense nucleation followed by a near growth model was deduced. The quantity $K \cdot t_\mathrm{on}$ was considered to represent the infectivity of the virus.
Macroeconomic Effects of Active Labour Market Policies: A Novel Instrumental Variables Approach
Authors: Ulrike Unterhofer, Conny Wunsch
Date: 2022-11-22 00:00:00

This study evaluates the macroeconomic effects of active labour market policies (ALMP) in Germany over the period 2005 to 2018. We propose a novel identification strategy to overcome the simultaneity of ALMP and labour market outcomes at the regional level. It exploits the imperfect overlap of local labour markets and local employment agencies that decide on the local implementation of policies. Specifically, we instrument for the use of ALMP in a local labour market with the mix of ALMP implemented outside this market but in local employment agencies that partially overlap with this market. We find no effects of short-term activation measures and further vocational training on aggregate labour market outcomes. In contrast, wage subsidies substantially increase the share of workers in unsubsidised employment while lowering long-term unemployment and welfare dependency. Our results suggest that negative externalities of ALMP partially offset the effects for program participants and that some segments of the labour market benefit more than others.
Peer Effects in Labor Market Training
Authors: Ulrike Unterhofer
Date: 2022-11-22 00:00:00

This paper shows that the group composition matters for the effectiveness of labor market training programs for jobseekers. Using rich administrative data from Germany, I document that greater average exposure to highly employable peers leads to increased employment stability after program participation. Peer effects on earnings are positive and long-lasting in classic vocational training and negative but of short duration in retraining, pointing to different mechanisms. Finally, I also find evidence for non-linearities in effects and show that more heterogeneity in the peer group is detrimental.
Asymptotic Properties of the Synthetic Control Method
Authors: Xiaomeng Zhang, Wendun Wang, Xinyu Zhang
Date: 2022-11-22 00:00:00

This paper provides new insights into the asymptotic properties of the synthetic control method (SCM). We show that the synthetic control (SC) weight converges to a limiting weight that minimizes the mean squared prediction risk of the treatment-effect estimator when the number of pretreatment periods goes to infinity, and we also quantify the rate of convergence. Observing the link between the SCM and model averaging, we further establish the asymptotic optimality of the SC estimator under imperfect pretreatment fit, in the sense that it achieves the lowest possible squared prediction error among all possible treatment effect estimators that are based on an average of control units, such as matching, inverse probability weighting and difference-in-differences. The asymptotic optimality holds regardless of whether the number of control units is fixed or divergent. Thus, our results provide justifications for the SCM in a wide range of applications. The theoretical results are verified via simulations.
A Misuse of Specification Tests
Authors: Naoya Sueishi
Date: 2022-11-21 00:00:00
Tasks: valid

Empirical researchers often perform model specification tests, such as the Hausman test and the overidentifying restrictions test, to confirm the validity of estimators rather than the validity of models. This paper examines the effectiveness of specification pretests in finding invalid estimators. We study the local asymptotic properties of test statistics and estimators and show that locally unbiased specification tests cannot determine whether asymptotically efficient estimators are asymptotically biased. The main message of the paper is that correct specification and valid estimation are different issues. Correct specification is neither necessary nor sufficient for asymptotically unbiased estimation under local overidentification.
Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood
Authors: Christian Gourieroux, Joann Jasiak
Date: 2022-11-21 00:00:00

This paper considers nonlinear dynamic models where the main parameter of interest is a nonnegative matrix characterizing the network (contagion) effects. This network matrix is usually constrained either by assuming a limited number of nonzero elements (sparsity), or by considering a reduced rank approach for nonnegative matrix factorization (NMF). We follow the latter approach and develop a new probabilistic NMF method. We introduce a new Identifying Maximum Likelihood (IML) method for consistent estimation of the identified set of admissible NMF's and derive its asymptotic distribution. Moreover, we propose a maximum likelihood estimator of the parameter matrix for a given non-negative rank, derive its asymptotic distribution and the associated efficiency bound.
Interpreting Instrumental Variable Estimands with Unobserved Treatment Heterogeneity: The Effects of College Education
Authors: Clint Harris
Date: 2022-11-23 00:00:00

Many treatment variables used in empirical applications nest multiple unobserved versions of a treatment. I show that instrumental variable (IV) estimands for the effect of a composite treatment are IV-specific weighted averages of effects of unobserved component treatments. Differences between IVs in unobserved component compliance produce differences in IV estimands even without treatment effect heterogeneity. I describe a monotonicity condition under which IV estimands are positively-weighted averages of unobserved component treatment effects. Next, I develop a method that allows instruments that violate this condition to contribute to estimation of treatment effects by allowing them to place nonconvex, outcome-invariant weights on unobserved component treatments across multiple outcomes. Finally, I apply the method to estimate returns to college, finding wage returns that range from 7\% to 30\% over the life cycle. My findings emphasize the importance of leveraging instrumental variables that do not shift individuals between versions of treatment, as well as the importance of policies that encourage students to attend "high-return college" in addition to those that encourage "high-return students" to attend college.