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Platoon control of connected vehicles with heterogeneous model structuresconsidering external disturbances

Green Energy and Intelligent Transportation 2022年第3期1卷 94-102页

作者： Yongfu Li Zongyu Qin Hao Zhu Srinivas Peeta Xinbo Gao Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and TelecommunicationsChongqing400065China Schools of Civil and Environmental Engineering and Industrial and Systems Engineering Georgia Institute of TechnologyAtlantaGA30332United States of America

This article studies the distributed cooperative control problem with the heterogeneous model structures and external disturbances for the connected vehicle(CV)*** propose a hierarchical framework to separate information flow topology from local dynamics control,which aims to deal with the heterogeneous model structures of CV *** hierarchical framework splits the control scheme into two layers,which include an observer in the upper-level layer and an integral sliding mode(ISM)controller in the lower-level ***,the conditions for the asymptotic stability of the CV platoon are derived and the effectiveness of the ISM controller is demonstrated through the Lyapunov *** research shows that compared with the traditional methods,the hierarchical framework does not need to specify the topology structure as a commonly used ***,numerical simulation results are performed to test the effectiveness and superiority of the developed controller.

关键词： Connected vehicle Platoon control Heterogeneity Disturbances Lyapunov method

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Mitigating Risks in Renewable Energy Power systems: Predicting Transient Voltage Angles with Physics-Informed Neural Networks

Mitigating Risks in Renewable Energy Power Systems: Predicti...

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International Conference on System Reliability and Science (ICSRS)

作者： Ruoqing Yin Liz Varga Gemma Cremen Department of Civil Environmental and Geomatic Engineering Infrastructure Systems Institute University College London London UK

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

As the penetration of renewable energy in global power systems continues to increase, new challenges to the stability and reliability of these systems have emerged. This study focuses on the dynamic impact of wind power generation's uncertainty and variability on power systems under gusty conditions characterized by significant short-term wind speed fluctuations. This study employs Physics-Informed Neural Networks (PINNs) to predict transient voltage angle variations in power systems subjected to wind power generation uncertainty. Using the IEEE 9 BUS system as a case study, voltage angle variations are generated through the swing equation and an ODE solver, serving as training data for the PINNs. The results demonstrate that PINNs effectively predicts transient voltage angles in unseen wind power datasets with only 20 training wind scenarios dataset. Overall, this paper illustrates the potential of PINNs in handling the complex dynamics of renewable energy-integrated power systems, particularly in forecasting and managing grid fluctuations caused by wind power generation.

关键词： Renewable energy sources Fluctuations Uncertainty Power system dynamics Neural networks Power system stability Wind power generation Mathematical models Data models Transient analysis

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Operational Uncertainty and the Missing Money Problem

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SSRN 2025年

作者： Shu, Han Visvesvaran, Siva Mays, Jacob Systems Engineering Cornell University IthacaNY14853 United States Civil and Environmental Engineering Cornell University IthacaNY14853 United States

This study investigates how algorithmic choices in power system operations affect the total compensation available to different technologies participating in wholesale electricity markets. We focus on systems that utilize capacity payments. The need for capacity payments arises due to the ``missing money'' problem, i.e., that prices in short-term markets for energy and ancillary services are suppressed below the level required to support resource adequacy. Capacity payments to different technologies are calibrated under the assumption that price suppression occurs only in instances of scarcity, such that market operators can restore theoretically efficient remuneration by tying capacity payments to estimates of production during scarcity events. However, the complexity of power system operations creates challenges with short-term price formation that can lead to price suppression in non-scarcity hours. Compensating for this form of price suppression through capacity payments results in inefficient remuneration overall. In simulations using data from the New York Independent System Operator, we show that current configurations can result in elevated payments to peaking power plants relative to technologies whose production is spread over more hours of the year (e.g., nuclear, wind, and hydropower). The results reinforce the case for price formation reforms that shift the composition of overall revenue in wholesale electricity markets away from capacity toward energy and ancillary services. © 2025, The Authors. All rights reserved.

关键词： Electric utilities

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Capturing Driver Status Using Naturalistic Driving Study Data: Implications for Assessing Health and Safety 66th

Capturing Driver Status Using Naturalistic Driving Study Dat...

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66th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2022

作者： Binjolkar, Mayuree Boyle, Linda Ng Dept. of Civil and Environmental Engineering University of Washington SeattleWA United States Dept. of Industrial and Systems Engineering University of Washington SeattleWA United States

The design of advanced driver-assistance systems (ADASs) and driving monitoring systems can be improved with a better understanding of on-road driving behavior. Information on traffic environment, health, engagement in secondary tasks, and corresponding eye glance locations can provide important insights into driver’s in-vehicle movements and status. This study examines the in-vehicle head movement of the driver and the factors affecting it using data from a naturalistic driving study. Linear mixed models were used to examine head position at a given point in time, and hierarchical clustering with dynamic time warping (DTW) was used to explore the trajectories of the head position. The findings show that some health conditions (e.g., limited flexibility, cancer) were correlated to restrictive head position during driving, and vehicle acceleration and traffic environment factors contributed to the variation in head positions. © 2022 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Advanced driver assistance systems

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Dymola-Enabled Reinforcement Learning for Real-time Generator Set-point Optimization

Dymola-Enabled Reinforcement Learning for Real-time Generato...

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2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2022

作者： Pigott, Aisling Baker, Kyri Dorado-Rojas, Sergio A. Vanfretti, Luigi University of Colorado Boulder Civil Environmental and Architectural Engineering BoulderCO United States Rensselaer Polytechnic Institute Electrical Computer and Systems Engineering TroyNY United States

ISBN: (纸本)9781665437752

This paper introduces a reinforcement learning framework which determines real-time generator set-points in a dynamically changing environment in order to optimize a chosen objective. This is performed within the high-fidelity simulation environment Dymola, which utilizes the Modelica programming language to model complex systems. A case study is created using the OpenIPSL IEEE 9-bus dynamic model, with the objective of minimizing voltage deviations across the network. The reinforcement learning agent shows improvement in minimizing voltage deviations versus the default droop controlled governors without any explicit knowledge of the topology of the system or relative location of the controllers. The results indicate that reinforcement learning may be a useful tool for applications in model-free, real-time power systems dynamics and control. An open-source Python package is provided for the proposed framework with the present case-study as an example. © 2022 IEEE.

关键词： Reinforcement learning

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COVID-19 Impact on Household Water-Energy-Food Accessibility Concerning Achieving the Sustainable Development Goals in Cities and Societies of Northern Nigeria

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Journal of the Knowledge Economy 2025年 1-28页

作者： Sani, Yahaya Scholz, Miklas Division of Water Resources Engineering Department of Building and Environmental Technology Faculty of Engineering Lund University Lund Sweden Department of Environmental Resources Management Faculty of Earth and Environmental Sciences Federal University Dutsinma Dutsinma Katsina State Nigeria Department of Civil Engineering Science School of Civil Engineering and the Built Environment University of Johannesburg Kingsway Campus Auckland Park Johannesburg South Africa Directorate of Engineering School of Science Engineering and Environment The University of Salford Greater Manchester UK Department of Town Planning Engineering Networks and Systems South Ural State University Chelyabinsk Russia

The recent COVID-19 pandemic has considerably impacted negatively on many aspects of human life such as access to basic necessities including water, energy and food. This article aims to investigate the impact of the COVID-19 lockdown on the water, energy and food accessibility in Katsina state, Nigeria. The survey involved the administration of a questionnaire to a total of 900 households out of which 66.7% representing 600 urban households and 33.3% are linked to 300 households of the rural sampled households. The null hypothesis (H0) was that there was no significant influence of the COVID-19 pandemic on the water, energy and food security concerning households. A paired t-test analysed the statistical mean of the water-energy-food accessibility under two scenarios (i.e. pre-COVID-19 and post-COVID-19 lockdown). The investigation found that the lockdown measures introduced to control the spread of COVID-19 had a significant influence on the accessibility of water, energy and food. Results revealed that admission to safely managed water provisions dropped from 30.0 to 24.6% across the scenarios. The electricity index access has also reduced from 1.9 to 1.5. Additionally, households that experience severe food insecurity have significantly increased from 4.9 to 38.6% during the pre- and post-COVID-19 lockdown, respectively. Concerning the investigated households for both the urban and rural strata, the investigation highlights the need for policy intervention to ensure that vulnerable households are not disproportionately affected during crises such as a pandemic. The findings will be useful to policymaking and will advance awareness towards achieving the sustainable development goals (SDG).

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Project Management in Hospital Environments: A 5-Year Systematic Literature Review 29th

Project Management in Hospital Environments: A 5-Year System...

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29th International Joint Conference on Industrial engineering and Operations Management, IJCIEOM 2023

作者： Lopes, Erik T. Lopes, Derek C. Souza, Thiago A. Lima, Rui M. Souza, André L. A. C. Algoritmi Research Centre / LASI Department of Production and Systems School of Engineering of University of Minho Guimarães Portugal Medical School School of Health Sciences Brasília Brazil Department of Civil and Environmental Engineering University of Brasília Brasília Brazil

ISBN: (纸本)9783031470578

Health systems have countless examples of adaptations of existing project management approaches to improve hospital operations and deliver value to the patient. This article aims to review the project management publications of the last 5 years, to establish findings in relation to what has been published, to allow the visualization of knowledge gaps and future research to be carried out. Thus, 50 articles and reviews of the last 5 years were initially considered and after the exclusion criteria, 26 articles were listed for review. The analysis allowed identifying publications, sources, universities, countries, and authors that stand out in the theme, in addition to the domains of performance, artefacts used and type of projects. The results indicate publications mainly in two large groups: improvement in service quality and increase in hospital efficiency. Based on this initial study, the authors suggest that further research may be carried out covering project management in the entire health ecosystem, as well as an analysis of the types of health projects and the practices adopted in their management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Effectiveness Healthcare management Hospital operations management Project management

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FT-AED: benchmark dataset for early freeway traffic anomalous event detection 24

FT-AED: benchmark dataset for early freeway traffic anomalou...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Austin Coursey Junyi Ji Marcos Quinones-Grueiro William Barbour Yuhang Zhang Tyler Derr Gautam Biswas Daniel B. Work Institute for Software Integrated Systems and Department of Computer Science Vanderbilt University Institute for Software Integrated Systems and Department of Civil and Environmental Engineering Vanderbilt University Institute for Software Integrated Systems Department of Computer Science Vanderbilt University Institute for Software Integrated Systems and Department of Computer Science and Department of Civil and Environmental Engineering Vanderbilt University

ISBN: (纸本)9798331314385

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and mistakes from manual crash reporting records make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Tennessee Department of Transportation Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://***/ft-aed/ to facilitate future research.

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Improving the Representation of Climate Risks in Long-Term Electricity systems Planning: a Critical Review

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Current Sustainable/Renewable Energy Reports 2023年第4期10卷 206-217页

作者： Doss-Gollin, James Amonkar, Yash Schmeltzer, Katlyn Cohan, Daniel Department of Civil and Environmental Engineering Rice University 6100 Main Street HoustonTX77005 United States Center on Financial Risk in Environmental Systems UNC Institute for the Environment University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Chemistry Rice University 6100 Main Street HoustonTX77005 United States

Purpose of Review: Electricity systems face substantial climate risks which are escalating due to electrification, renewable energy intermittency, population changes, and the intensifying impacts of climate change such as extreme temperatures and weather-induced infrastructure damage. This critical review investigates climate risks to the electricity sector and scrutinizes the methodologies used to represent climate risk in long-term electricity system planning studies. Recent Findings: Climate risks to electricity systems are driven by extreme weather, average weather, technology, and other social and technological factors. All are expected to evolve in the future. Future climate risks to electricity systems depend on interactions between each, and thus assessing future climate risks to electricity systems requires exploring a wide range of possible futures. Summary: Many studies rely on weather data and socio-economic scenarios that are inadequate to fully characterize climate risks to present and future electricity systems. We advocate for more holistic assessments that incorporate comprehensive weather data, acknowledge dynamic multi-sector interactions, and employ adaptive and robust methodologies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Climate change

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Platoon Trajectory Reconstruction with Conflict Resolution Using Semidefinite Relaxation

Platoon Trajectory Reconstruction with Conflict Resolution U...

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International Conference on Intelligent Transportation

作者： Junyi Ji Yanbing Wang William Barbour Daniel B. Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University

The quality of trajectory data plays a crucial role in the investigation of microscopic traffic flow. However, the challenge of effectively and automatically eliminating false positive collisions from the measured data still remains an unsolved problem. Firstly, this paper formulates the problem of reconstructing multi-vehicle trajectories as a nonconvex quadratic constraint quadratic programming (QCQP) problem. Subsequently, it derives the equivalent semidefinite programming (SDP) problem and develops a semidefinite relaxation technique to e·fficiently obtain the global minimum of the relaxed problem. We then apply a randomization method to obtain a feasible near-optimal solution to the original problem. The proposed approach is validated through experiments conducted on the NGSIM I–80 camera 6 lane 5 data, and the results of these experiments demonstrate that the unreasonable vehicle dynamics are removed and the platoon inconsistency decreased from 0.622% to 0.268% in the experimental data.

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