pwc-archive/papers-with-abstracts huggingface dataset:
Grid-Forming Control of Modular Dynamic Virtual Power Plants
Authors: Xiuqiang He, Josué Duarte, Verena Häberle, Florian Dörfler
Date: 2024-10-18 00:00:00

This article explores a flexible and coordinated control design for an aggregation of heterogeneous distributed energy resources (DERs) in a dynamic virtual power plant (DVPP). The control design aims to provide a desired aggregate grid-forming (GFM) response based on the coordination of power contributions between different DERs. Compared to existing DVPP designs with an AC-coupled AC-output configuration, a more generic modular DVPP design is proposed in this article, which comprises four types of basic DVPP modules, involving AC- or DC-coupling and AC- or DC-output, adequately accommodating diverse DER integration setups, such as AC, DC, AC/DC hybrid microgrids and renewable power plants. The control design is first developed for the four basic modules by the aggregation of DERs and the disaggregation of the control objectives, and then extended to modular DVPPs through a systematic top-down approach. The control performance is comprehensively validated through simulation. The modular DVPP design offers scalable and standardizable advanced grid interfaces (AGIs) for building and operating AC/DC hybrid power grids.
Differential Predictive Control of Residential Building HVACs for Maximizing Renewable Local Consumption and Supporting Fast Voltage Control
Authors: Patrick Salter, Celina Wilkerson, Qiuhua Huang, Paulo Cesar Tabares-Velasco, Dongbo Zhao, Dmitry Ishchenko
Date: 2024-10-18 00:00:00
Tasks: energy management

High penetration of distributed energy resources in distribution systems, such as rooftop solar PVs, has caused voltage fluctuations which are much faster than typical voltage control devices can react to, leading to increased operation cost and reduced equipment life. Residential buildings consume about 35% of the electricity in U.S. and are co-located with rooftop solar PV. Thus, they present an opportunity to mitigate these fluctuations locally, while benefiting both the grid and building owners. Previous works on DER-aware localized building energy management mostly focus on commercial buildings and analyzing impacts either on buildings or the grid. To fill the gaps, this paper proposes a distributed, differential predictive control scheme for residential HVAC systems for maximizing renewable local consumption. In addition, a detailed controller-building-grid co-simulation platform is developed and utilized for analyzing the potential impacts of the proposed control scheme on both the buildings and distribution system. Our studies show that the proposed method can provide benefits to both the buildings' owners and the distribution system by reducing energy draw from the grid by 12%, voltage violations and fast fluctuations by 20%, and the number of tap changes in voltage regulators by 14%.
Frequency Control and Disturbance Containment Using Grid-Forming Embedded Storage Networks
Authors: Kaustav Chatterjee, Ramij Raja Hossain, Sai Pushpak Nandanoori, Soumya Kundu, Diane Baldwin, Ronald Melton
Date: 2024-10-18 00:00:00

The paper discusses fast frequency control in bulk power systems using embedded networks of grid-forming energy storage resources. Differing from their traditional roles of regulating reserves, the storage resources in this work operate as fast-acting grid assets shaping transient dynamics. The storage resources in the network are autonomously controlled using local measurements for distributed frequency support during disturbance events. Further, the grid-forming inverter systems interfacing with the storage resources, are augmented with fast-acting safety controls designed to contain frequency transients within a prescribed tolerance band. The control action, derived from the storage network, improves the frequency nadirs in the system and prevents the severity of a disturbance from propagating far from the source. The paper also presents sensitivity studies to evaluate the impacts of storage capacity and inverter controller parameters on the dynamic performance of frequency control and disturbance localization. The performance of the safety-constrained grid-forming control is also compared with the more common grid-following control. The results are illustrated through case studies on an IEEE test system.
Coordinated Frequency Regulation in Grid-Forming Storage Network via Safety-Consensus
Authors: Ramij Raja Hossain, Kaustav Chatterjee, Sai Pushpak Nandanoori, Soumya Kundu, Laurentiu Marinovici, Karan Kalsi, Diane Baldwin
Date: 2024-10-18 00:00:00

Inverter-based storages are poised to play a prominent role in future power grids with massive renewable generation. Grid-forming inverters (GFMs) are emerging as a dominant technology with synchronous generators (SG)-like characteristics through primary control loops. Advanced secondary control schemes, e.g., consensus algorithms, allow GFM-interfaced storage units to participate in frequency regulations and restore nominal frequency following grid disturbances. However, it is imperative to ensure transient frequency excursions do not violate critical safety limits while the grid transitions from pre- to post-disturbance operating point. This paper presents a hierarchical safety-enforced consensus method -- combining a device-layer (decentralized) transient safety filter with a secondary-layer (distributed) consensus coordination -- to achieve three distinct objectives: limiting transient frequency excursions to safe limits, minimizing frequency deviations from nominal, and ensuring coordinated power sharing among GFM-storage units. The proposed hierarchical (two-layered) safety-consensus technique is illustrated using a GFM-interfaced storage network on an IEEE 68-bus system under multiple grid transient scenarios.
IoT-Based Water Quality Monitoring System in Philippine Off-Grid Communities
Authors: Jenny Vi Abrajano, Khavee Agustus Botangen, Jovith Nabua, Jenalyn Apanay, Chezalea Fay Peña
Date: 2024-10-18 00:00:00

Contaminated and polluted water poses significant threats to human health, necessitating vigilant monitoring of water sources for potential contamination. This paper introduces a low-cost Internet of Things (IoT)-based water quality monitoring system designed to address water quality challenges in rural communities, as demonstrated through a case study conducted in the Philippines. The system consists of two core components. The hardware component of the system, built on Arduino technology and featuring real-time data transmission, focuses on monitoring pH levels, turbidity, and temperature via sensors. The system is equipped to transmit data to a cloud database and send informative messages to mobile numbers, updating users on the status of water supplies. The application component acts as a user interface for accessing and managing data collected by the sensors. The successful deployment of this Water Quality Monitoring (WQM) system not only helps community leaders and health workers monitor water sources but also underscores its potential to empower communities in safeguarding their water sources, thereby contributing to the advancement of clean and safe water access.
Parametric Digital Twins for Preserving Historic Buildings: A Case Study at Löfstad Castle in Östergötland, Sweden
Authors: Zhongjun Ni, Jelrik Hupkes, Petra Eriksson, Gustaf Leijonhufvud, Magnus Karlsson, Shaofang Gong
Date: 2024-10-18 00:00:00

This study showcases the digitalization of L\"ofstad Castle in Sweden to contribute to preserving its heritage values. The castle and its collections are deteriorating due to an inappropriate indoor climate. To address this, thirteen cloud-connected sensor boxes, equipped with 84 sensors, were installed throughout the main building, from the basement to the attic, to continuously monitor various indoor environmental parameters. The collected extensive multi-parametric data form the basis for creating a parametric digital twin of the building. The digital twin and detailed data analytics offer a deeper understanding of indoor climate and guide the adoption of appropriate heating and ventilation strategies. The results revealed the need to address high humidity problems in the basement and on the ground floor, such as installing vapor barriers. Opportunities for adopting energy-efficient heating and ventilation strategies on the upper floors were also highlighted. The digitalization solution and findings are not only applicable to L\"ofstad Castle but also provide valuable guidance for the conservation of other historic buildings facing similar challenges.
Robustness to Model Approximation, Model Learning From Data, and Sample Complexity in Wasserstein Regular MDPs
Authors: Yichen Zhou, Yanglei Song, Serdar Yüksel
Date: 2024-10-18 00:00:00
Tasks: model

The paper studies the robustness properties of discrete-time stochastic optimal control under Wasserstein model approximation for both discounted cost and average cost criteria. Specifically, we study the performance loss when applying an optimal policy designed for an approximate model to the true dynamics compared with the optimal cost for the true model under the sup-norm-induced metric, and relate it to the Wasserstein-1 distance between the approximate and true transition kernels. A primary motivation of this analysis is empirical model learning, as well as empirical noise distribution learning, where Wasserstein convergence holds under mild conditions but stronger convergence criteria, such as total variation, may not. We discuss applications of the results to the disturbance estimation problem, where sample complexity bounds are given, and also to a general empirical model learning approach, obtained under either Markov or i.i.d. learning settings.
Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer
Authors: Sida Huang, Yuanting Wu, Dinh Hoa Nguyen
Date: 2024-10-18 00:00:00
Tasks: Deep Learning

Optical wireless power transfer (OWPT) is a technology that wirelessly transmit light energy from an optical transmitter to an optical receiver, usually a solar cell. In order to achieve the highest transmission efficiency, the solar cell receiver should be accurately aligned with the optical transmitter. Hitherto, only a few works have been existed for solar cell recognition in presence of complex backgrounds. In this paper, we employ a deep learning approach based on Yolov5-Lite for the solar cell recognition purpose, due to its lightweight, fast and easy to deploy on hardware characteristics. Our tests show a high accuracy of the employed deep learning model with the highest F1 score of 91% and mAP of 94.8%. Therefore, this deep learning model is highly promising for use in OWPT systems to precisely align optical transmitters and solar cell receivers.
Inverter Output Impedance Estimation in Power Networks: A Variable Direction Forgetting Recursive-Least-Square Algorithm Based Approach
Authors: Jaesang Park, Alireza Askarian, Srinivasa Salapaka
Date: 2024-10-17 00:00:00

As inverter-based loads and energy sources become increasingly prevalent, accurate estimation of line impedance between inverters and the grid is essential for optimizing performance and enhancing control strategies. This paper presents a non-invasive method for estimating output-line impedance using measurements local to the inverter. It provides a specific method for signal conditioning of signals measured at the inverter, which makes the measured data better suited to estimation algorithms. An algorithm based on the Variable Direction Forgetting Recursive Least Squares (VDF-RLS) method is introduced, which leverages these conditioned signals for precise impedance estimation. The signal conditioning process transforms measurements into the direct-quadrature (dq) coordinate frame, where the rotating frame frequency is determined to facilitate a simpler and more accurate estimation. This frequency is implemented using a secondary Phase-Locked Loop (PLL) to attenuate grid voltage measurement variations. By isolating the variation-sensitive q-axis and relying solely on the less sensitive d-axis, the method further minimizes the impact of variations. The VDF-RLS estimation method achieves rapid adaptation while ensuring stability in the absence of persistent excitation by selectively discarding outdated data during updates. Proposed conditioning and estimation methods are non-invasive; estimations are solely done using measured outputs, and no signal is injected into the power network. Simulation results demonstrate a significant improvement in impedance estimation stability, particularly in low-excitation conditions, where the VDF-RLS method achieves more than three time lower error compared to existing approaches such as constant forgetting RLS and the Kalman filter.
Finite-volume method and observability analysis for core-shell enhanced single particle model for lithium iron phosphate batteries
Authors: Le Xu, Simone Fasolato, Simona Onori
Date: 2024-10-17 00:00:00

The increasing adoption of Lithium Iron Phosphate (LFP) batteries in Electric Vehicles is driven by their affordability, abundant material supply, and safety advantages. However, challenges arise in controlling/estimating unmeasurable LFP states such as state of charge (SOC), due to its flat open circuit voltage, hysteresis, and path dependence dynamics during intercalation and de-intercalation processes. The Core Shell Average Enhanced Single Particle Model (CSa-ESPM) effectively captures the electrochemical dynamics and phase transition behavior of LFP batteries by means of Partial Differential-Algebraic Equations (PDAEs). These governing PDAEs, including a moving boundary Ordinary Differential Equation (ODE), require a fine-grained spatial grid for accurate and stable solutions when employing the Finite Difference Method (FDM). This, in turn, leads to a computationally expensive system intractable for the design of real-time battery management system algorithms. In this study, we demonstrate that the Finite Volume Method (FVM) effectively discretizes the CSa-ESPM and provides accurate solutions with fewer than 4 control volumes while ensuring mass conservation across multi ple operational cycles. The resulting control-oriented reduced order FVM-based CSa-ESPM is experimentally validated using various C-rate load profiles and its observability is assessed through nonlinear observability analysis. Our results reveal that different current inputs and discrete equation numbers influence model observability, with non-observable regions identified where solid-phase concentration gradients are negligible.
Resilience-Oriented DG Siting and Sizing Considering Energy Equity Constraint
Authors: Chenchen Li, Fangxing Li, Sufan Jiang, Jin Zhao, Shiyuan Fan, Leon M. Tolbert
Date: 2024-10-17 00:00:00

Extreme weather events can cause widespread power outages and huge economic losses. Low-income customers are more vulnerable to power outages because they live in areas with poorly equipped distribution systems. However, existing approaches to improve grid resilience focus on the overall condition of the system and ignore the outage experiences of low-income customers, which leads to significant energy inequities in resilience. Therefore, this paper explores a new resilience-oriented planning method for distributed generator (DG) siting and sizing, by embedding an additional energy equity constraint (EEC). First, the expected load shedding index (ELSI) is defined as the ratio of the load shedding to the original load, which quantifies the resilience-oriented energy equity. Then, the DG siting and sizing problem is formulated as a two-stage stochastic programming with the EEC. The first stage determines the optimal sites and sizes of DG units under investment constraints and EECs, while the second stage optimizes expected costs of unserved load. A subsidiary variable is introduced to ensure the model's solvability. Finally, numerical studies are performed on the IEEE 33-bus and 123-bus systems to verify the effectiveness of the proposed DG planning model in achieving energy equity. Three observations are presented as future guidelines for resilience-oriented DG planning.
A Physics-Based Context-Aware Approach for Anomaly Detection in Teleoperated Driving Operations Under False Data Injection Attacks
Authors: Subhadip Ghosh, Aydin Zaboli, Junho Hong, Jaerock Kwon
Date: 2024-10-17 00:00:00
Tasks: Anomaly Detection, Steering Control

Teleoperated driving (ToD) systems are a special type of cyber-physical system (CPS) where the operator remotely controls the steering, acceleration, and braking actions of the vehicle. Malicious actors may inject false data into communication channels to manipulate the teleoperator's driving commands to cause harm. Hence, protection of this communication is necessary for a safe operation of the target vehicle. However, according to the National Institute of Standards and Technology (NIST) cybersecurity framework, protection is not enough, and detecting an attack is necessary. Moreover, UN R155 mandates that vehicle fleets detect and log security incidents. Thus, the cyber-physical threats of ToD are modeled using the attack-centric approach in this paper. Then, an attack model with false data injection (FDI) on the steering control command is created from real vehicle data. A risk of this attack model is assessed for a last-mile delivery (LMD) application. Finally, a physics-based context-aware anomaly detection system (PCADS) is proposed to detect such false injection attacks, and preliminary experimental results are presented to validate the model.
Islanding Detection for Active Distribution Networks Using WaveNet+UNet Classifier
Authors: Amirhosein Alizadeh, Seyed Fariborz Zarei, Mohammadhadi Shateri
Date: 2024-10-17 00:00:00
Tasks: Denoising

This paper proposes an AI-based scheme for islanding detection in active distribution networks. By reviewing existing studies, it is clear that there are several gaps in the field to ensure reliable islanding detection, including (i) model complexity and stability concerns, (ii) limited accuracy under noisy conditions, and (iii) limited applicability to systems with different types of resources. Accordingly, this paper proposes a WaveNet classifier reinforced by a denoising U-Net model to address these shortcomings. The proposed scheme has a simple structure due to the use of 1D convolutional layers and incorporates residual connections that significantly enhance the model's generalization. Additionally, the proposed scheme is robust against noisy conditions by incorporating a denoising U-Net model. Furthermore, the model is sufficiently fast using a sliding window time series of 10 milliseconds for detection. Utilizing positive/negative/zero sequence components of voltages, superimposed waveforms, and the rate of change of frequency provides the necessary features to precisely detect the islanding condition. In order to assess the effectiveness of the suggested scheme, over 3k islanding/non-islanding cases were tested, considering different load active/reactive powers values, load switching transients, capacitor bank switching, fault conditions in the main grid, different load quality factors, signal-to-noise ratio levels, and both types of conventional and inverter-based sources.
Machine Learning Aided Modeling of Granular Materials: A Review
Authors: Mengqi Wang, Krishna Kumar, Y. T. Feng, Tongming Qu, Min Wang
Date: 2024-10-18 00:00:00

Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.
Stylized facts in money markets: an empirical analysis of the eurozone data
Authors: Victor Le Coz, Nolwenn Allaire, Michael Benzaquen, Damien Challet
Date: 2024-10-21 00:00:00

Using the secured transactions recorded within the Money Markets Statistical Reporting database of the European Central Bank, we test several stylized facts regarding interbank market of the 47 largest banks in the eurozone. We observe that the surge in the volume of traded evergreen repurchase agreements followed the introduction of the LCR regulation and we measure a rate of collateral re-use consistent with the literature. Regarding the topology of the interbank network, we confirm the high level of network stability but observe a higher density and a higher in- and out-degree symmetry than what is reported for unsecured markets.
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks
Authors: Vasileios Vatellis
Date: 2024-10-18 00:00:00
Tasks: Binary Classification, Computational Efficiency

In an era increasingly focused on green computing and explainable AI, revisiting traditional approaches in theoretical and phenomenological particle physics is paramount. This project evaluates various machine learning (ML) algorithms-including Nearest Neighbors, Decision Trees, Random Forest, AdaBoost, Naive Bayes, Quadratic Discriminant Analysis (QDA), and XGBoost-alongside standard neural networks and a novel Physics-Informed Neural Network (PINN) for physics data analysis. We apply these techniques to a binary classification task that distinguishes the experimental viability of simulated scenarios based on Higgs observables and essential parameters. Through this comprehensive analysis, we aim to showcase the capabilities and computational efficiency of each model in binary classification tasks, thereby contributing to the ongoing discourse on integrating ML and Deep Neural Networks (DNNs) into physics research. In this study, XGBoost emerged as the preferred choice among the evaluated machine learning algorithms for its speed and effectiveness, especially in the initial stages of computation with limited datasets. However, while standard Neural Networks and Physics-Informed Neural Networks (PINNs) demonstrated superior performance in terms of accuracy and adherence to physical laws, they require more computational time. These findings underscore the trade-offs between computational efficiency and model sophistication.