pwc-archive/papers-with-abstracts huggingface dataset:
Design Considerations of a Coordinative Demand Charge Mitigation Strategy
Authors: Rongxing Hu, Kai Ye, Hyeonjin Kim, Hanpyo Lee, Ning Lu, Di wu, PJ Rehm
Date: 2022-12-16 00:00:00
Tasks: energy management, Management

This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods. Available DCM resources include batteries, diesel generators, controllable loads, and conservation voltage reduction. All resources are directly controlled by load serving entities. A mixed integer linear programming based energy management algorithm is developed to optimally coordinate of DCM resources considering the load payback effect. To better capture system peak periods, two different kinds of load forecast are used: the day-ahead load forecast and the peak-hour probability forecast. Five DCM strategies are compared for reconciling the discrepancy between the two forecasting results. The DCM strategies are tested using actual utility data. Simulation results show that the proposed algorithm can effectively mitigate the demand charge while preventing the system peak from being shifted to the payback hours. We also identify the diminishing return effect, which can help load serving entities optimize the size of their DCM resources.
Adaptive Tracking Control of Uncertain Euler-Lagrange Systems with State and Input Constraints
Authors: Poulomee Ghosh, Shubhendu Bhasin
Date: 2022-12-16 00:00:00

This paper proposes a novel control architecture for state and input constrained Euler-Lagrange (E-L) systems with parametric uncertainties. A simple saturated controller is strategically coupled with a Barrier Lyapunov Function (BLF) based controller to ensure state and input constraint satisfaction. To the best of the authors' knowledge, this is the first result for E-L systems that guarantee asymptotic tracking with user-specified state and input constraints. The proposed controller also ensures that all the closed-loop signals remain bounded. The efficacy of the proposed controller in terms of constraint satisfaction and tracking performance is verified using simulation on a robot manipulator system.
Comparing Approaches to Distributed Control of Fluid Systems based on Multi-Agent Systems
Authors: Kevin T. Logan, J. Marius Stürmer, Tim M. Müller, Peter F. Pelz
Date: 2022-12-16 00:00:00
Tasks: Deep Reinforcement Learning, Model Predictive Control

Conventional control of fluid systems does not consider system-wide knowledge for optimising energy efficient operation. Distributed control of fluid systems combines reliable local control of components while using system-wide cooperation to ensure energy efficient operation. The presented work compares three approaches to distributed control based on multi-agent systems, distributed model predictive control (DMPC), multi-agent deep reinforcement learning (MADRL) and market mechanism design. These approaches were applied to a generic fluid system and evaluated with regard to functionality, energy efficient operation, modeling effort, reliability in the face of disruptions, and transparency of control decisions. All approaches were shown to fulfil the functionality, though a trade-off between functional quality and energy efficiency was identified. Increased modeling effort was shown to improve the performance slightly while a strong interdependence of information caused by excessive information sharing has proven to be disadvantageous. DMPC and partially observable MADRL were less sensitive to disruptions than market mechanism. In conclusion, agent-based control of fluid systems achieves greater energy efficiency than conventional methods, with values similar to centralized optimal control and thus represent a viable design approach of fluid system control.
Methods and Tools for the Management of Renewable Energy Communities: the ComER project
Authors: Anna Rita Di Fazio, Arturo Losi, Mario Russo, Filippo Cacace, Francesco Conte, Giulio Iannello, Gianluca Natrella, Matteo Saviozzi
Date: 2022-12-16 00:00:00
Tasks: Management

Renewable Energy Communities (RECs) have been officially introduced into the European legislation through the Clean Energy for all Europeans package. A REC is defined as an association of citizens, commercial activities, enterprises, and local authorities that own small-scale power plants based on Renewable Energy Sources (RESs). The community has the objective of maximizing the share of renewable energy, i.e. the self-consumption of the energy generated by the community RES power plants and to generally optimize the use of electrical energy. This paper describes the ComER project, developed by the University of Cassino and the Campus Bio-Medico University of Rome. The project focuses on the main technical problems to face for the realization of a REC. The principal objective is to develop methods and tools necessary for the management and control of RECs. In particular, this paper describes the rules established for RECs in the Italian legislations, the organization of the ComER project, the adopted solutions and the first obtained results.
Exponentially Stable Adaptive Observation for Systems Parameterized by Unknown Physical Parameters
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2022-12-16 00:00:00

The method to design exponentially stable adaptive observers is proposed for linear time-invariant systems parameterized by unknown physical parameters. Unlike existing adaptive solutions, the system state-space matrices A, B are not restricted to be represented in the observer canonical form to implement the observer. The original system description is used instead, and, consequently, the original state vector is obtained. The class of systems for which the method is applicable is identified via three assumptions related to: (i) the boundedness of a control signal and all system trajectories, (ii) the identifiability of the physical parameters of A and B from the numerator and denominator polynomials of a system input/output transfer function and (iii) the complete observability of system states. In case they are met and the regressor is finitely exciting, the proposed adaptive observer, which is based on the known GPEBO and DREM procedures, ensures exponential convergence of both system parameters and states estimates to their true values. Detailed analysis for stability and convergence has been provided along with simulation results to validate the developed theory.
Temporal Variation Measure Analysis: An Improved Second-Order Difference Plot
Authors: Chen Diao, Ning Cai
Date: 2022-12-16 00:00:00
Tasks: Descriptive, Heart Rate Variability

In this study, an improved second-order difference plot is proposed to analyze the variability of heart rate variability. Although the variation of physiological status of cardiovascular system can be shown graphically by the second-order difference plot, the descriptive ability of existing indicators for this plot is insufficient. As a result, the physiological information contained in the second-order difference plot cannot be extracted adequately. Addressing the problem, the temporal variation measure analysis is presented to describe distribution patterns of scatter points in the second-order difference plot quantitatively and extract the acceleration information for variation of heart rate variability. Experiment results demonstrate the effectiveness of the temporal variation measure analysis. As a quantitative indicator, the temporal variation entropy is properly designed and successfully applied in the recognition and classification of the physiological statuses of the heart.
Structure preserving discontinuous Galerkin approximation of one-dimensional port-Hamiltonian systems
Authors: Tobias Thoma, Paul Kotyczka
Date: 2022-12-16 00:00:00

In this article, we present the structure-preserving discretization of linear one-dimensional port-Hamiltonian (PH) systems of two conservation laws using discontinuous Galerkin (DG) methods. We recall the DG discretization procedure which is based on a subdivision of the computational domain, an elementwise weak formulation with up to two integration by parts, and the interconnection of the elements using several numerical fluxes. We present the interconnection of the element models, which is power preserving in the case of conservative (unstabilized) numerical fluxes, and we set up the resulting global PH state space model. We discuss the properties of the obtained models, including the effect of the flux stabilization parameter on the spectrum. Finally, we show simulations with different parameters for a boundary controlled linear hyperbolic system.
Relative Error-based Time-limited H2 Model Order Reduction via Oblique Projection
Authors: Umair Zulfiqar, Xin Du, Qiuyan Song, Zhi-Hua Xiao, Victor Sreeram
Date: 2022-12-16 00:00:00

In time-limited model order reduction, a reduced-order approximation of the original high-order model is obtained that accurately approximates the original model within the desired limited time interval. Accuracy outside that time interval is not that important. The error incurred when a reduced-order model is used as a surrogate for the original model can be quantified in absolute or relative terms to access the performance of the model reduction algorithm. The relative error is generally more meaningful than an absolute error because if the original and reduced systems' responses are of small magnitude, the absolute error is small in magnitude as well. However, this does not necessarily mean that the reduced model is accurate. The relative error in such scenarios is useful and meaningful as it quantifies percentage error irrespective of the magnitude of the system's response. In this paper, the necessary conditions for a local optimum of the time-limited H2 norm of the relative error system are derived. Inspired by these conditions, an oblique projection algorithm is proposed that ensures small H2-norm relative error within the desired time interval. Unlike the existing relative error-based model reduction algorithms, the proposed algorithm does not require solutions of large-scale Lyapunov and Riccati equations. The proposed algorithm is compared with time-limited balanced truncation, time-limited balanced stochastic truncation, and time-limited iterative Rational Krylov algorithm. Numerical results confirm the superiority of the proposed algorithm over these existing algorithms.
Small-Signal Stability Impacts of Load and Network Dynamics on Grid-Forming Inverters
Authors: Rodrigo Henriquez-Auba, Jose Daniel Lara, Duncan S. Callaway
Date: 2022-12-15 00:00:00

This paper presents several stability analyses for grid-forming inverters and synchronous generators considering the dynamics of transmission lines and different load models. Load models are usually of secondary importance compared to generation source models, but as the results show, they play a crucial role in stability studies with the introduction of inverter-based resources. Given inverter control time scales, the implications of considering or neglecting electromagnetic transients of the network are very relevant in the stability assessments. In this paper, we perform eigenvalue analyses for inverter-based resources and synchronous machines connected to a load and explore the effects of multiples models under different network representations. We explore maximum loadability of inverter-based resources and synchronous machines, while analyzing the effects of load and network dynamic models on small-signal stability. The results show that the network representation plays a fundamental role in the stability of the system of different load models. The resulting stability regions are significantly different depending on the source and load model considered.
Distributed Maneuver Planning with Connected and Automated Vehicles for Boosting Traffic Efficiency
Authors: Nathan Goulet, Beshah Ayalew
Date: 2022-12-15 00:00:00

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. The framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.
A Multi-Modal Machine Learning Approach to Detect Extreme Rainfall Events in Sicily
Authors: Eleonora Vitanza, Giovanna Maria Dimitri, Chiara Mocenni
Date: 2022-12-14 00:00:00

In 2021 300 mm of rain, nearly half the average annual rainfall, fell near Catania (Sicily island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. This is the reason why, detecting extreme rainfall events is a crucial prerequisite for planning actions able to reverse possibly intensified dramatic future scenarios. In this paper, the Affinity Propagation algorithm, a clustering algorithm grounded on machine learning, was applied, to the best of our knowledge, for the first time, to identify excess rain events in Sicily. This was possible by using a high-frequency, large dataset we collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily Extreme dataset). Weather indicators were then been employed to validate the results, thus confirming the presence of recent anomalous rainfall events in eastern Sicily. We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate changes.
Moate Simulation of Stochastic Processes
Authors: Michael E. Mura
Date: 2022-12-16 00:00:00

A novel approach called Moate Simulation is presented to provide an accurate numerical evolution of probability distribution functions represented on grids arising from stochastic differential processes where initial conditions are specified. Where the variables of stochastic differential equations may be transformed via It\^o-Doeblin calculus into stochastic differentials with a constant diffusion term, the probability distribution function for these variables can be simulated in discrete time steps. The drift is applied directly to a volume element of the distribution while the stochastic diffusion term is applied through the use of convolution techniques such as Fast or Discrete Fourier Transforms. This allows for highly accurate distributions to be efficiently simulated to a given time horizon and may be employed in one, two or higher dimensional expectation integrals, e.g. for pricing of financial derivatives. The Moate Simulation approach forms a more accurate and considerably faster alternative to Monte Carlo Simulation for many applications while retaining the opportunity to alter the distribution in mid-simulation.
The Long-Term Effects of Teachers' Gender Stereotypes
Authors: Joan Martinez
Date: 2022-12-16 00:00:00
Tasks: Math

This paper studies the effects of teachers' stereotypical assessments of boys and girls on students' long-term outcomes, including high school graduation, college attendance, and formal sector employment. I measure teachers' gender stereotypical grading based on preconceptions about boys' aptitude in math and science and girls' aptitude in communicating and verbal skills by analyzing novel data on teachers' responses to the Implicit Association Test (IAT) and differences in gender gaps between teacher-assigned and blindly graded tests. To collect IAT scores on a national scale, I developed a large-scale educational program accessible to teachers and students in Peruvian public schools. This analysis provides evidence that teachers' gender stereotypes are reflected in student evaluations: math teachers with stronger stereotypes associating boys with scientific disciplines award male students with higher test scores compared to blindly-graded test scores. In contrast, language arts teachers who stereotypically associate females with humanities-based disciplines give female students higher grades. Using graduation, college enrollment, and matched employer-employee data on 1.7 million public high school students expected to graduate between 2015 and 2019, I find that female students who are placed with teachers who use more stereotypical grading practices are less likely to graduate from high school and apply to college than male students. In comparison to their male counterparts, female high school graduates whose teachers employ more stereotypical grading practices are less likely to be employed in the formal sector and have fewer paid working hours. Furthermore, exposure to teachers with more stereotypical grading practices reduces women's monthly earnings, thereby widening the gender pay gap.
Economic impacts of AI-augmented R&D
Authors: Tamay Besiroglu, Nicholas Emery-Xu, Neil Thompson
Date: 2022-12-15 00:00:00
Tasks: Deep Learning, Drug Discovery, Protein Folding

Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction. As more scientists and engineers adopt deep learning, it is important to consider what effect widespread deployment would have on scientific progress and, ultimately, economic growth. We assess this impact by estimating the idea production function for AI in two computer vision tasks that are considered key test-beds for deep learning and show that AI idea production is notably more capital-intensive than traditional R&D. Because increasing the capital-intensity of R&D accelerates the investments that make scientists and engineers more productive, our work suggests that AI-augmented R&D has the potential to speed up technological change and economic growth.
Cap or No Cap? What Can Governments Do to Promote EV Sales?
Authors: Zunian Luo
Date: 2022-10-26 00:00:00
Tasks: regression, Time Series, Time Series Analysis

This paper examines the effect of the federal EV income tax subsidy on EV sales. I find that reduction of the federal subsidy caused sales to decline by $43.2 \%$. To arrive at this result, I employ historical time series data from the Department of Energy Alternative Fuels Data Center. Using the fact that the subsidy is available only for firms with fewer than 200,000 cumulative EV sales, I separate EV models into two groups. The treatment group consists of models receiving the full subsidy, and the control group consists of models receiving the reduced subsidy. This allows for a difference in differences (DiD) model structure. To examine the robustness of the results, I conduct regression analyses. Due to a relatively small sample size, the regression coefficients lack statistical significance. Above all, my results suggest that federal incentives designed to promote EV consumption are successful in their objectives.
Rationally Inattentive Statistical Discrimination: Arrow Meets Phelps
Authors: Federico Echenique, Anqi Li
Date: 2022-12-16 00:00:00

When information acquisition is costly but flexible, a principal may rationally acquire information that favors one group over another. The former group faces incentives to invest in becoming productive, while the latter is discouraged from such investments. The principal, in turn, ignores the productivity difference between groups unless the underinvested group surprises him with a genuinely outstanding outcome. We give conditions under which the discriminatory equilibrium is most preferred by the principal, despite all groups being ex-ante identical. Our results inform the discussion of affirmative action, implicit bias, and occupational segregation and stereotypes.
Semantic Sensing and Communications for Ultimate Extended Reality
Authors: BoWen Zhang, Zhijin Qin, Yiyu Guo, Geoffrey Ye Li
Date: 2022-12-16 00:00:00

As a key technology in metaversa, wireless ultimate extended reality (XR) has attracted extensive attentions from both industry and academia. However, the stringent latency and ultra-high data rates requirements have hindered the development of wireless ultimate XR. Instead of transmitting the original source data bit-by-bit, semantic communications focus on the successful delivery of semantic information contained in the source, which have shown great potentials in reducing the data traffic of wireless systems. Inspired by semantic communications, this article develops a joint semantic sensing, rendering, and communication framework for wireless ultimate XR. In particular, semantic sensing is used to improve the sensing efficiency by exploring the spatial-temporal distributions of semantic information. Semantic rendering is designed to reduce the costs on semantically-redundant pixels. Next, semantic communications are adopted for high data transmission efficiency in wireless ultimate XR. Then, two case studies are provided to demonstrate the effectiveness of the proposed framework. Finally, potential research directions are identified to boost the development of semantic-aware wireless ultimate XR.
Quaternion Tensor Completion with Sparseness for Color Video Recovery
Authors: Liqiao Yang, Kit Ian Kou, Jifei Miao, Yang Liu, Maggie Pui Man Hoi
Date: 2022-12-16 00:00:00

A novel low-rank completion algorithm based on the quaternion tensor is proposed in this paper. This approach uses the TQt-rank of quaternion tensor to maintain the structure of RGB channels throughout the entire process. In more detail, the pixels in each frame are encoded on three imaginary parts of a quaternion as an element in a quaternion matrix. Each quaternion matrix is then stacked into a quaternion tensor. A logarithmic function and truncated nuclear norm are employed to characterize the rank of the quaternion tensor in order to promote the low rankness of the tensor. Moreover, by introducing a newly defined quaternion tensor discrete cosine transform-based (QTDCT) regularization to the low-rank approximation framework, the optimized recovery results can be obtained in the local details of color videos. In particular, the sparsity of the quaternion tensor is reasonably characterized by l1 norm in the QDCT domain. This strategy is optimized via the two-step alternating direction method of multipliers (ADMM) framework. Numerical experimental results for recovering color videos show the obvious advantage of the proposed method over other potential competing approaches.