pwc-archive/papers-with-abstracts huggingface dataset:
Strategic Electric Distribution Network Sensing via Spectral Bandits
Authors: Samuel Talkington, Rahul Gupta, Richard Asiamah, Paprapee Buason, Daniel K. Molzahn
Date: 2024-10-28 00:00:00

Despite their wide-scale deployment and ability to make accurate high-frequency voltage measurements, communication network limitations have largely precluded the use of smart meters for real-time monitoring purposes in electric distribution systems. Although smart meter communication networks have limited bandwidth available per meter, they also have the ability to dedicate higher bandwidth to varying subsets of meters. Using this capability to enable real-time monitoring from smart meters, this paper proposes an online bandwidth-constrained sensor sampling algorithm that takes advantage of the graphical structure inherent in the power flow equations. The key idea is to use a spectral bandit framework where the estimated parameters are the graph Fourier transform coefficients of the nodal voltages. The structure provided by this framework promotes a sampling policy that strategically accounts for electrical distance. Maxima of sub-Gaussian random variables model the policy rewards, which relaxes distributional assumptions common in prior work. The scheme is implemented on a synthetic electrical network to dynamically identify meters exposing violations of voltage magnitude limits, illustrating the effectiveness of the proposed method.
Quantum Reinforcement Learning-Based Two-Stage Unit Commitment Framework for Enhanced Power Systems Robustness
Authors: Xiang Wei, Ziqing Zhu, Linghua Zhu, Ze Hu, Xian Zhang, Guibin Wang, Siqi Bu, Ka Wing Chan
Date: 2024-10-28 00:00:00
Tasks: Computational Efficiency, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Scheduling, Sequential Decision Making

Unit commitment (UC) optimizes the start-up and shutdown schedules of generating units to meet load demand while minimizing costs. However, the increasing integration of renewable energy introduces uncertainties for real-time scheduling. Existing solutions face limitations both in modeling and algorithmic design. At the modeling level, they fail to incorporate widely adopted virtual power plants (VPPs) as flexibility resources, missing the opportunity to proactively mitigate potential real-time imbalances or ramping constraints through foresight-seeing decision-making. At the algorithmic level, existing probabilistic optimization, multi-stage approaches, and machine learning, face challenges in computational complexity and adaptability. To address these challenges, this study proposes a novel two-stage UC framework that incorporates foresight-seeing sequential decision-making in both day-ahead and real-time scheduling, leveraging VPPs as flexibility resources to proactively reserve capacity and ramping flexibility for upcoming renewable energy uncertainties over several hours. In particular, we develop quantum reinforcement learning (QRL) algorithms that integrate the foresight-seeing sequential decision-making and scalable computation advantages of deep reinforcement learning (DRL) with the parallel and high-efficiency search capabilities of quantum computing. Experimental results demonstrate that the proposed QRL-based approach outperforms in computational efficiency, real-time responsiveness, and solution quality.
Data-Efficient System Identification via Lipschitz Neural Networks
Authors: Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2024-10-28 00:00:00

Extracting dynamic models from data is of enormous importance in understanding the properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of neural networks with a prescribed upper bound on their Lipschitz constant, to address the problem of data-efficient nonlinear system identification. Under the (fairly weak) assumption that the unknown system is Lipschitz continuous, we propose a method to estimate the approximation error bound of the trained network and the bound on the difference between the simulated trajectories by the trained models and the true system. Empirical results show that our method outperforms classic fully connected neural networks and Lipschitz regularized networks through simulation studies on three dynamical systems, and the advantage of our method is more noticeable when less data is used for training.
Systemic Decarbonization of Road Freight Transport: A Comprehensive Total Cost of Ownership Model
Authors: Ruixiao Sun, Vivek A. Sujan, Gurneesh Jatana
Date: 2024-10-28 00:00:00

The decarbonization of road freight transport is crucial for reducing greenhouse gas emissions (GHG) and achieving climate neutrality goals. This study develops a comprehensive Total Cost of Ownership (TCO) model to evaluate the economic viability and strategic pathways for decarbonizing road freight transport. The model integrates vehicles with infrastructures, encompassing costs associated with acquisition, operation, maintenance, energy consumption, environmental impacts, and end-of-life considerations. Our analysis covers medium- and heavy-duty vehicles across eight powertrain types, with variants on battery sizes and fuel cell powers, incorporating key financial parameters, technological advancements, and policy incentives. Data sources include real-world fleet data and costs gathered from expert interviews, cross-referenced with multiple public resources. Findings indicate that zero-emission and near-zero-emission vehicles, though currently more expensive, will become cost-competitive with diesel vehicles by leveraging advancements in battery, fuel cell, and hydrogen technologies.
Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning
Authors: Shuyang Zhu, Ziqing Zhu
Date: 2024-10-28 00:00:00
Tasks: Bilevel Optimization, Reinforcement Learning (RL)

In response to the increasing complexity of electricity markets due to low-carbon requirements and the integration of sustainable energy sources, this paper proposes a dynamic quantum computing enhanced bilevel optimization model for electricity market operations. The upper level focuses on market mechanism optimization using Reinforcement Learning (RL), specifically Proximal Policy Optimization (PPO), while the lower level models the bidding strategies of Generating Companies (GENCOs) using a Multi-Agent Deep Q-Network (MADQN) enhanced with quantum computing through a Variational Quantum Circuit (VQC). The three main contributions of this work are: (1) establishing a dynamic bilevel model with timely feedback between the upper and lower levels; (2) parameterizing and optimizing market mechanisms to derive the most effective designs; and (3) introducing quantum computing into the context of electricity markets to more realistically simulate market operations. The proposed model is tested on the IEEE 30-bus system with six GENCOs, demonstrating its effectiveness in capturing the complexities of modern electricity markets.
Combining Deep Reinforcement Learning with a Jerk-Bounded Trajectory Generator for Kinematically Constrained Motion Planning
Authors: Seyed Adel Alizadeh Kolagar, Mehdi Heydari Shahna, Jouni Mattila
Date: 2024-10-28 00:00:00
Tasks: Deep Reinforcement Learning, Motion Planning

Deep reinforcement learning (DRL) is emerging as a promising method for adaptive robotic motion and complex task automation, effectively addressing the limitations of traditional control methods. However, ensuring safety throughout both the learning process and policy deployment remains a key challenge due to the risky exploration inherent in DRL, as well as the discrete nature of actions taken at intervals. These discontinuities, despite being part of a continuous action space, can lead to abrupt changes between successive actions, causing instability and unsafe intermediate states. To address these challenges, this paper proposes an integrated framework that combines DRL with a jerk-bounded trajectory generator (JBTG) and a robust low-level control strategy, significantly enhancing the safety, stability, and reliability of robotic manipulators. The low-level controller ensures the precise execution of DRL-generated commands, while the JBTG refines these motions to produce smooth, continuous trajectories that prevent abrupt or unsafe actions. The framework also includes pre-calculated safe velocity zones for smooth braking, preventing joint limit violations and ensuring compliance with kinematic constraints. This approach not only guarantees the robustness and safety of the robotic system but also optimizes motion control, making it suitable for practical applications. The effectiveness of the proposed framework is demonstrated through its application to a highly complex heavy-duty manipulator.
Provisioning for Solar-Powered Base Stations Driven by Conditional LSTM Networks
Authors: Yawen Guo, Sonia Naderi, Colleen Josephson
Date: 2024-10-28 00:00:00

Solar-powered base stations are a promising approach to sustainable telecommunications infrastructure. However, the successful deployment of solar-powered base stations requires precise prediction of the energy harvested by photovoltaic (PV) panels vs. anticipated energy expenditure in order to achieve affordable yet reliable deployment and operation. This paper introduces an innovative approach to predict energy harvesting by utilizing a novel conditional Long Short-Term Memory (Cond-LSTM) neural network architecture. Compared with LSTM and Transformer models, the Cond-LSTM model reduced the normalized root mean square error (nRMSE) by 69.6% and 42.7%, respectively. We also demonstrate the generalizability of our model across different scenarios. The proposed approach would not only facilitate an accurate cost-optimal PV-battery configuration that meets the outage probability requirements, but also help with site design in regions that lack historical solar energy data.
Observability of Linear Time-Invariant Systems with Relative Measurements: A Geometric Approach
Authors: Ioannis Raptis
Date: 2024-10-27 00:00:00
Tasks: State Estimation

This paper explores the observability and estimation capability of dynamical systems using predominantly relative measurements of the system's state-space variables, with minimal to no reliance on absolute measurements of these variables. We concentrate on linear time-invariant systems, in which the observation matrix serves as the algebraic representation of a graph object. This graph object encapsulates the availability of relative measurements. Utilizing algebraic graph theory and abstract linear algebra (geometric) tools, we establish a link between the structure of the graph of relative measurements and the system-theoretic observability subspace of linear systems. Special emphasis is given to multi-agent networked systems whose dynamics are governed by the linear consensus protocol. We demonstrate the importance of absolute information and its placement to the system's dynamics in achieving full-state estimation. Finally, the analysis shifts to the synthesis of a distributed observer with relative measurements for single integrator dynamics, exemplifying the relevance of the preceding analytical findings. We support our theoretical analysis with numerical simulations.
Data-driven Analysis of T-Product-based Dynamical Systems
Authors: Xin Mao, Anqi Dong, Ziqin He, Yidan Mei, Can Chen
Date: 2024-10-27 00:00:00

A wide variety of data can be represented using third-order tensors, spanning applications in chemometrics, psychometrics, and image processing. However, traditional data-driven frameworks are not naturally equipped to process tensors without first unfolding or flattening the data, which can result in a loss of crucial higher-order structural information. In this article, we introduce a novel framework for the data-driven analysis of T-product-based dynamical systems (TPDSs), where the system evolution is governed by the T-product between a third-order dynamic tensor and a third-order state tensor. In particular, we examine the data informativity of TPDSs concerning system identification, stability, controllability, and stabilizability and illustrate significant computational improvements over traditional approaches by leveraging the unique properties of the T-product. The effectiveness of our framework is demonstrated through numerical examples.
Wireless Localization with Space-Time Coded Reconfigurable Intelligent Surfaces
Authors: Mehdi Gholami, Soheil Khajavi, Mohammad Neshat, Simon Tewes, Aydin Sezgin
Date: 2024-10-27 00:00:00

In this paper, a novel approach for wireless localization is proposed and experimentally validated that leverages space-time coded reconfigurable intelligent surfaces (RIS). It is demonstrated that applying proper single-bit codes to each RIS element, enables accurate determination of the direction of arrival (AOA) at the receiver. Moreover, we introduce different scenarios that such technique can be used for localization. By incorporating RIS, a passive component, the method significantly reduces the complexity found in previous localization techniques. Additionally, the use of 1-bit codes minimizes hardware requirements, offering a reliable, low-cost solution for localization in advanced telecommunications networks.
A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties
Authors: Longyan Li, Chao Ning, Guangsheng Pan, Leiqi Zhang, Wei Gu, Liang Zhao, Wenli Du, Mohammad Shahidehpour
Date: 2024-10-27 00:00:00

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods.
Optimal Hardening Strategy for Electricity-Hydrogen Networks with Hydrogen Leakage Risk Control against Extreme Weather
Authors: Sicheng Liu, Bo Yang, Xin Li, Xu Yang, Zhaojian Wang, Dafeng Zhu, Xinping Guan
Date: 2024-10-27 00:00:00
Tasks: Computational Efficiency

Defense hardening can effectively enhance the resilience of distribution networks against extreme weather disasters. Currently, most existing hardening strategies focus on reducing load shedding. However, for electricity-hydrogen distribution networks (EHDNs), the leakage risk of hydrogen should be controlled to avoid severe incidents such as explosions. To this end, this paper proposes an optimal hardening strategy for EHDNs under extreme weather, aiming to minimize load shedding while limiting the leakage risk of hydrogen pipelines. Specifically, modified failure uncertainty models for power lines and hydrogen pipelines are developed. These models characterize not only the effect of hardening, referred to as decision-dependent uncertainties (DDUs), but also the influence of disaster intensity correlations on failure probability distributions. Subsequently, a hardening decision framework is established, based on the two-stage distributionally robust optimization incorporating a hydrogen leakage chance constraint (HLCC). To enhance the computational efficiency of HLCC under discrete DDUs, an efficient second-order-cone transformation is introduced. Moreover, to address the intractable inverse of the second-order moment under DDUs, lifted variables are adopted to refine the main-cross moments. These reformulate the hardening problem as a two-stage mixed-integer second-order-cone programming, and finally solved by the column-and-constraint generation algorithm. Case studies demonstrate the effectiveness and superiority of the proposed method.
Optimal Bayesian Persuasion for Containing SIS Epidemics
Authors: Urmee Maitra, Ashish R. Hota, Philip E. Paré
Date: 2024-10-27 00:00:00

We consider a susceptible-infected-susceptible (SIS) epidemic model in which a large group of individuals decide whether to adopt partially effective protection without being aware of their individual infection status. Each individual receives a signal which conveys noisy information about its infection state, and then decides its action to maximize its expected utility computed using its posterior probability of being infected conditioned on the received signal. We first derive the static signal which minimizes the infection level at the stationary Nash equilibrium under suitable assumptions. We then formulate an optimal control problem to determine the optimal dynamic signal that minimizes the aggregate infection level along the solution trajectory. We compare the performance of the dynamic signaling scheme with the optimal static signaling scheme, and illustrate the advantage of the former through numerical simulations.
Who is Responsible? Explaining Safety Violations in Multi-Agent Cyber-Physical Systems
Authors: Luyao Niu, Hongchao Zhang, Dinuka Sahabandu, Bhaskar Ramasubramanian, Andrew Clark, Radha Poovendran
Date: 2024-10-26 00:00:00
Tasks: counterfactual, Counterfactual Reasoning

Multi-agent cyber-physical systems are present in a variety of applications. Agent decision-making can be affected due to errors induced by uncertain, dynamic operating environments or due to incorrect actions taken by an agent. When an erroneous decision that leads to a violation of safety is identified, assigning responsibility to individual agents is a key step toward preventing future accidents. Current approaches to carrying out such investigations require human labor or high degree of familiarity with operating environments. Automated strategies to assign responsibility can achieve a significant reduction in human effort and associated cognitive burden. In this paper, we develop an automated procedure to assign responsibility for safety violations to actions of any single agent in a principled manner. We base our approach on reasoning about safety violations in road safety. Given a safety violation, we use counterfactual reasoning to create alternative scenarios, showing how different outcomes could have occurred if certain actions had been replaced by others. We introduce the degree of responsibility (DoR) metric for each agent. The DoR, using the Shapley value, quantifies each agent's contribution to the safety violation, providing a basis to explain and justify decisions. We also develop heuristic techniques and methods based on agent interaction structures to improve scalability as agent numbers grow. We examine three safety violation cases from the National Highway Traffic Safety Administration (NHTSA). We run experiments using CARLA urban driving simulator. Results show the DoR improves the explainability of decisions and accountability for agent actions and their consequences.
Advancing Hybrid Quantum Neural Network for Alternative Current Optimal Power Flow
Authors: Ze Hu, Ziqing Zhu, Linghua Zhu, Xiang Wei, Siqi Bu, Ka Wing Chan
Date: 2024-10-26 00:00:00
Tasks: Deep Learning, Physics-informed machine learning

Alternative Current Optimal Power Flow (AC-OPF) is essential for efficient power system planning and real-time operation but remains an NP-hard and non-convex optimization problem with significant computational challenges. This paper proposes a novel hybrid classical-quantum deep learning framework for AC-OPF problem, integrating parameterized quantum circuits (PQCs) for feature extraction with classical deep learning for data encoding and decoding. The proposed framework integrates two types of residual connection structures to mitigate the ``barren plateau" problem in quantum circuits, enhancing training stability and convergence. Furthermore, a physics-informed neural network (PINN) module is incorporated to guarantee tolerable constraint violation, improving the physical consistency and reliability of AC-OPF solutions. Experimental evaluations on multiple IEEE test systems demonstrate that the proposed approach achieves superior accuracy, generalization, and robustness to quantum noise while requiring minimal quantum resources.
Multi-input Multi-output Loewner Framework for Vibration-based Damage Detection on a Trainer Jet
Authors: Gabriele Dessena, Marco Civera, Andrés Marcos, Bernardino Chiaia
Date: 2024-10-26 00:00:00
Tasks: Benchmarking, Cantilever Beam, Structural Health Monitoring

Structural health monitoring of aerostructures often faces challenges identifying damage, especially in complex systems. Multi-input multi-output modal parameter identification methods are known to offer enhanced insight compared to single-input multi-output testing, as they allow for the identification of additional out-of-plane modes. The improved Loewner Framework presents a computationally efficient approach to extracting these modal parameters, focusing on natural frequencies and mode shapes as indicators of structural health. To address the challenges of damage detection, a numerical case study involving a cantilever beam with variable cross-sections is used to simulate various damage scenarios. Additionally, a full-scale experimental dataset from the BAE Hawk T1A trainer jet aircraft is employed for SHM for the first time. The modified total modal assurance criterion (MTMAC) is proposed as a standalone metric for assessing damage severity, while the coordinate modal assurance criterion (COMAC) is applied for localising damage. Benchmarking against methods such as least-squares complex exponential (LSCE) and stochastic subspace identification with the canonical variate analysis (SSI-CVA) demonstrates the effectiveness of the improved Loewner Framework in accurately identifying even small changes in modal parameters. The MTMAC and COMAC are shown to be valuable tools for, respectively, damage quantification and localisation.