pwc-archive/papers-with-abstracts huggingface dataset:
Actors in Multi-Sector Transitions -- Discourse Analysis on Hydrogen in Germany
Authors: Nils Ohlendorf, Meike Löhr, Jochen Markard
Date: 2023-11-19 00:00:00
Tasks: Articles

With net-zero emission goals, low-carbon transitions enter a new phase of development, leading to new challenges for policymaking and research. Multiple transitions unfold in parallel across different sectors. This involves a broad range of technologies, while actors engage in increasingly complex discourses. Here, we study the discourses on hydrogen in Germany. Based on the analysis of 179 newspaper articles from 2016 to 2020, we find that a diverse set of actors, including many industry incumbents, speak favorably about hydrogen, emphasizing economic opportunities and its relevance for the energy transition, whereas skeptics highlight its low energy efficiency and expected scarcity. With the help of discourse network analysis, we identify three emerging conflicts around the use, production, and import of hydrogen. We explain these conflicts and the widespread support of incumbents with a conceptual framework that captures the complex interplay of sectoral contexts, specific technologies and actor interests.
Assessment of Transmission-level Fault Impacts on 3-phase and 1-phase Distribution IBR Operation
Authors: Qi Xiao, Jongha Woo, Lidong Song, Bei Xu, David Lubkeman, Ning Lu, Abdul Shafae Mohammed, Johan Enslin, Cara De Coste Chacko, Kat Sico, Steven G. Whisenant
Date: 2023-11-19 00:00:00

The widespread deployment of inverter-based resources (IBRs) renders distribution systems susceptible to transmission-level faults. This paper presents a comprehensive analysis of the impact of transmission-level faults on 3-phase and 1-phase distribution IBR operation. To evaluate distributed IBR tripping across various phases and locations on a distribution feeder, we conduct simulations of both symmetrical and unsymmetrical transmission faults at progressively greater electrical distances on a real-time transmission and distribution (T&D) co-simulation platform. The IBR power-to-load ratios (PLRs) at 50%, 100%, and 300% are considered to emulate low, medium, and high IBR conditions. Our results indicate that, while 1-phase and 2-phase faults typically trigger fewer IBR trips when compared to 3-phase faults, a significant power imbalance arises from the tripping of 1-phase IBRs on the affected phases. The imbalance can result in significant power quality problems and unintended equipment tripping. It may be necessary to design fault-ride-through mechanisms specifically tailored to 1-phase IBRs to help mitigate the power imbalances caused by unbalanced faults.
H2 suboptimal containment control of homogeneous and heterogeneous multi-agent systems
Authors: Yuan Gao, Junjie Jiao, Zhongkui Li, Sandra Hirche
Date: 2023-11-19 00:00:00

This paper deals with the H2 suboptimal state containment control problem for homogeneous linear multi-agent systems and the H2 suboptimal output containment control problem for heterogeneous linear multi-agent systems. For both problems, given multiple autonomous leaders and a number of followers, we introduce suitable performance outputs and an associated H2 cost functional, respectively. The aim is to design a distributed protocol by dynamic output feedback that achieves state/output containment control while the associated H2 cost is smaller than an a priori given upper bound. To this end, we first show that the H2 suboptimal state/output containment control problem can be equivalently transformed into H2 suboptimal control problems for a set of independent systems. Based on this, design methods are then provided to compute such distributed dynamic output feedback protocols. Simulation examples are provided to illustrate the performance of our proposed protocols.
Threshold-Based Algorithms for an Online Rolling Horizon Framework Under Uncertainty -- With an Application to Energy Management
Authors: Jens Hönen, Johann L. Hurink, Bert Zwart
Date: 2023-11-19 00:00:00
Tasks: energy management, Management, Scheduling

Decision problems encountered in practice often possess a highly dynamic and uncertain nature. In particular fast changing forecasts for parameters (e.g., photovoltaic generation forecasts in the context of energy management) pose large challenges for the classical rolling horizon framework. Within this work, we propose an online scheduling algorithm for a rolling horizon framework, which directly uses short-term forecasts and observations of the uncertainty. The online scheduling algorithm is based on insights and results from combinatorial online optimization problems and makes use of key properties of robust optimization. Applied within a robust energy management approach, we show that the online scheduling algorithm is able to reduce the total electricity costs within a local microgrid by more than 85% compared to a classical rolling horizon framework and by more than 50% compared to a tailor-made dynamic, yet still offline rolling horizon framework. A detailed analysis provides insights into the working of the online scheduling algorithm under different underlying forecast error distributions.
Robust Control of Unknown Switched Linear Systems from Noisy Data
Authors: Wenjie Liu, Yifei Li, Jian Sun, Gang Wang, Jie Chen
Date: 2023-11-19 00:00:00

This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics. In the absence of noise, a data-based state feedback stabilizing controller can be obtained by solving a semi-definite program (SDP) on-the-fly, which automatically adapts to the changes of switching dynamics. However, when noise is present, the persistency of excitation condition based on the closed-loop data may be undermined, rendering the SDP infeasible. To address this issue, an auxiliary function-based switching control law is proposed, which only requires intermittent SDP solutions when its feasibility is guaranteed. By analyzing the relationship between the controller and the system switching times, it is shown that the proposed controller guarantees input-to-state practical stability (ISpS) of the closed-loop switched linear system, provided that the noise is bounded and the dynamics switches slowly enough. Two numerical examples are presented to verify the effectiveness of the proposed controller.
Grid-Aware Real-Time Control and Balancing Between Microgrids
Authors: Jens Hönen, Johann L. Hurink, Bert Zwart
Date: 2023-11-19 00:00:00
Tasks: energy management, energy trading

Due to the energy transition, lots of research has been conducted within the last decade on the topics of energy management systems or local energy trading approaches, often on the day-ahead or intraday level. A large majority of these approaches focuses on 15 or 60-minute time intervals for their operation, however, the question of how the planned solutions are realized within these time intervals is often left unanswered. Within this work, we aim to close this gap and propose a real-time balancing and control approach for a set of microgrids, which implements the day-ahead solutions. The approach is based on a three-step framework, in which the first step consists of ensuring the feasibility of devices within the microgrids. The second step focuses on the grid constraints of the connecting medium voltage grid using the DC power flow formulation due to the running time requirements of a real-time approach. The last step is to propagate the solution into the individual microgrids, where the allocated power needs to be distributed among the devices and households. Within a case study, we show that the proposed real-time control approach works as intended and is comparable to an optimal offline algorithm under some mild assumptions.
From Optimization to Control: Quasi Policy Iteration
Authors: Mohammad Amin Sharifi Kolarijani, Peyman Mohajerin Esfahani
Date: 2023-11-18 00:00:00

Recent control algorithms for Markov decision processes (MDPs) have been designed using an implicit analogy with well-established optimization algorithms. In this paper, we review this analogy across four problem classes with a unified solution characterization allowing for a systematic transformation of algorithms from one domain to the other. In particular, we identify equivalent optimization and control algorithms that have already been pointed out in the existing literature, but mostly in a scattered way. With this unifying framework in mind, we adopt the quasi-Newton method from convex optimization to introduce a novel control algorithm coined as quasi-policy iteration (QPI). In particular, QPI is based on a novel approximation of the "Hessian" matrix in the policy iteration algorithm by exploiting two linear structural constraints specific to MDPs and by allowing for the incorporation of prior information on the transition probability kernel. While the proposed algorithm has the same computational complexity as value iteration, it interestingly exhibits an empirical convergence behavior similar to policy iteration with a very low sensitivity to the discount factor.
Economic Viability of the Energy-Water-Hydrogen Nexus for Power System Decarbonization
Authors: Mostafa Goodarzi, QiFeng Li
Date: 2023-11-18 00:00:00

This paper aims to evaluate the economic viability of the energy-water-hydrogen (EWH) nexus as a new solution for reducing carbon emissions from power systems. The urgency around climate change emphasizes the pressing need to mitigate carbon emissions, especially from the electricity sector, which accounts for a significant portion of total emissions in the US. In response, incorporating more renewable energy sources (RESs) and green hydrogen, created through water electrolysis and RES, stands out as a crucial strategy to combat climate challenges. We delve into various aspects of the EWH nexus, including carbon emissions from different power plants, capturing these emissions, and potential options for their reuse or storage. This paper involves modeling different sections of the EWH nexus and conducting an economic analysis across scenarios in power plants to determine optimal water supply methods, suitable chemical products for carbon reuse, and an appropriate carbon emission penalty to encourage emission reduction through the EWH nexus. The results indicate that reusing captured carbon emissions emerges as the most beneficial option across all power plant types. This finding underscores the potential of carbon reuse as a pivotal strategy within the EWH nexus framework for addressing carbon emissions.
6G Fresnel Spot Beamfocusing using Large-Scale Metasurfaces: A Distributed DRL-Based Approach
Authors: Mehdi Monemi, Mohammad Amir Fallah, Mehdi Rasti, Matti Latva-aho
Date: 2023-11-18 00:00:00
Tasks: Deep Reinforcement Learning

In this paper, we introduce the concept of spot beamfocusing (SBF) in the Fresnel zone through extremely large-scale programmable metasurfaces (ELPMs) as a key enabling technology for 6G networks. A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including efficient wireless power transfer (WPT), interference mitigation, reduced RF pollution, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by proposing an adaptive novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structures benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.
Estimation Strategies for the Condition Monitoring of a Battery Systemin a Hybrid Electric Vehicle
Authors: Stephen Andrew Gadsden, Mohammad Al-Shabi, Saeid Reza Habibi
Date: 2023-11-18 00:00:00
Tasks: Management, parameter estimation

This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.
Virtual trajectories for I-24 MOTION: data and tools
Authors: Junyi Ji, Yanbing Wang, Derek Gloudemans, Gergely Zachár, William Barbour, Daniel B. Work
Date: 2023-11-17 00:00:00

This article introduces a new virtual trajectory dataset derived from the I-24 MOTION INCEPTION v1.0.0 dataset to address challenges in analyzing large but noisy trajectory datasets. Building on the concept of virtual trajectories, we provide a Python implementation to generate virtual trajectories from large raw datasets that are typically challenging to process due to their size. We demonstrate the practical utility of these trajectories in assessing speed variability and travel times across different lanes within the INCEPTION dataset. The virtual trajectory dataset opens future research on traffic waves and their impact on energy.
Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate - blood pressure coupling quantified by entropy-based indices
Authors: Paweł Pilarczyk, Grzegorz Graff, José M. Amigó, Katarzyna Tessmer, Krzysztof Narkiewicz, Beata Graff
Date: 2023-11-04 00:00:00
Tasks: Classification

We introduce an entropy-based classification method for pairs of sequences (ECPS) for quantifying mutual dependencies in heart rate and beat-to-beat blood pressure recordings. The purpose of the method is to build a classifier for data in which each item consists of the two intertwined data series taken for each subject. The method is based on ordinal patterns, and uses entropy-like indices. Machine learning is used to select a subset of indices most suitable for our classification problem in order to build an optimal yet simple model for distinguishing between patients suffering from obstructive sleep apnea and a control group.
Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in Kolkata, India: A Machine Learning Approach
Authors: Rahul Jain, Anoushka Saha, Gourav Daga, Durba Bhattacharya, Madhura Das Gupta, Sourav Chowdhury, Suparna Roychowdhury
Date: 2023-10-15 00:00:00

Type 2 diabetes mellitus represents a prevalent and widespread global health concern, necessitating a comprehensive assessment of its risk factors. This study aimed towards learning whether there is any differential impact of age, Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes mellitus in males and females in Kolkata, West Bengal, India based on a sample observed from the out-patient consultation department of Belle Vue Clinic in Kolkata. Various machine learning models like Logistic Regression, Random Forest, and Support Vector Classifier, were used to predict the risk of diabetes, and performance was compared based on different predictors. Our findings indicate a significant age-related increase in risk of diabetes for both males and females. Although exercising and BMI was found to have significant impact on the risk of Type 2 diabetes in males, in females both turned out to be statistically insignificant. For both males and females, predictive models based on WhtR demonstrated superior performance in risk assessment compared to those based on BMI. This study sheds light on the gender-specific differences in the risk factors for Type 2 diabetes, offering valuable insights that can be used towards more targeted healthcare interventions and public health strategies.
Forecast U.S. Covid-19 Numbers by Open SIR Model with Testing
Authors: Bo Deng
Date: 2023-11-15 00:00:00

The U.S. Covid-19 data exhibit a high-frequency oscillation along a low-frequency wave for outbreaks. There is no model to account for it. A modified SIR model is proposed to explain this spiking phenomenon. It is also used to best-fit the data and to make forecast. For the simulated duration of 590 days, the model is capable of achieving a 0.5 percent mean squared relative error (MSRE) fit to the seven-day average of the daily case number. The outright 28-day prediction by the model generates a 20 percent MSRE for the cumulative case total due to a persistent underestimation of the data by the model. With the proposed correction to the aberration, the model is able to keep the 28-day cumulative case total forecast within 10 percent MSRE of the data.
Theory coherent shrinkage of Time-Varying Parameters in VARs
Authors: Andrea Renzetti
Date: 2023-11-20 00:00:00

This paper introduces a novel theory-coherent shrinkage prior for Time-Varying Parameter VARs (TVP-VARs). The prior centers the time-varying parameters on a path implied a priori by an underlying economic theory, chosen to describe the dynamics of the macroeconomic variables in the system. Leveraging information from conventional economic theory using this prior significantly improves inference precision and forecast accuracy compared to the standard TVP-VAR. In an application, I use this prior to incorporate information from a New Keynesian model that includes both the Zero Lower Bound (ZLB) and forward guidance into a medium-scale TVP-VAR model. This approach leads to more precise estimates of the impulse response functions, revealing a distinct propagation of risk premium shocks inside and outside the ZLB in US data.
Would Monetary Incentives to COVID-19 vaccination reduce motivation?
Authors: Eiji Yamamura, Yoshiro Tsutsui, Fumio Ohtake
Date: 2023-11-20 00:00:00

Some people did not get the COVID-19 vaccine even though it was offered at no cost. A monetary incentive might lead people to vaccinate, although existing studies have provided different findings about this effect. We investigate how monetary incentives differ according to individual characteristics. Using panel data with online experiments, we found that (1) subsidies reduced vaccine intention but increased it after controlling heterogeneity; (2) the stronger the social image against the vaccination, the lower the monetary incentive; and (3) persistently unvaccinated people would intend to be vaccinated only if a large subsidy was provided.
Workforce pDEI: Productivity Coupled with DEI
Authors: Lanqing Du, Jinwook Lee
Date: 2023-11-19 00:00:00
Tasks: Diversity, Fairness

Ranking pertaining to the human-centered tasks -- underscoring their paramount significance in these domains such as evaluation and hiring process -- exhibits widespread prevalence across various industries. Consequently, decision-makers are taking proactive measurements to promote diversity, underscore equity, and advance inclusion. Their unwavering commitment to these ideals emanates from the following convictions: (i) Diversity encompasses a broad spectrum of differences; (ii) Equity involves the assurance of equitable opportunities; and (iii) Inclusion revolves around the cultivation of a sense of value and impartiality, concurrently empowering individuals. Data-driven AI tools have been used for screening and ranking processes. However, there is a growing concern that the presence of pre-existing biases in databases may be exacerbated, particularly in the context of imbalanced datasets or the black-box-schema. In this research, we propose a model-driven recruitment decision support tool that addresses fairness together with equity in the screening phase. We introduce the term ``pDEI" to represent the output-input oriented production efficiency adjusted by socioeconomic disparity. Taking into account various aspects of interpreting socioeconomic disparity, our goals are (i) maximizing the relative efficiency of underrepresented groups and (ii) understanding how socioeconomic disparity affects the cultivation of a DEI-positive workplace.
Research on the Dynamic Evolution and Influencing Factors of Energy Resilience in China
Authors: Tie Wei, Youqi Chen, Zhicheng Duan
Date: 2023-11-18 00:00:00
Tasks: Density Estimation

Energy security is the guarantee for achieving the goal of carbon peaking and carbon neutrality, and exploring energy resilience is one of the important ways to promote energy security transition and adapt to changes in international and domestic energy markets. This paper applies the combined dynamic evaluation method to measure China's energy resilience level from 2004-2021, analyses the spatio-temporal dynamic evolution of China's energy resilience through the center of gravity-standard deviation ellipse and kernel density estimation, and employs geo-detectors to detect the main influencing factors and interactions of China's energy resilience. The study finds that:(1)China's energy resilience level generally shows a zigzagging forward development trend, and the spatial imbalance characteristic of China's energy resilience is more obvious.(2)The spatial dynamics of China's energy resilience level evolves in a northeast-southwest direction, and the whole moves towards the southwest, with an overall counterclockwise trend of constant offset.(3)When the energy resilience level of neighboring provinces is too low or too high, it has little effect on the improvement of the energy resilience level of the province; when the energy resilience level of neighboring provinces is 1-1.4, it has a positive spatial correlation with the energy resilience level of the province, and the synergistic development of the provinces can improve the energy resilience level together.(4)GDP, the number of employees, the number of employees enrolled in basic pension and medical insurance, and the number of patent applications in high-tech industries have a more significant impact on China's energy resilience, while China's energy resilience is affected by the interaction of multiple factors.