pwc-archive/papers-with-abstracts huggingface dataset:
Energy-carbon comprehensive efficiency evaluation of hydrogen metallurgy system considering low-temperature waste heat recovery
Authors: Qiang Ji, Lin Cheng, Zeng Liang, Yingrui Zhuang, Fashun Shi, Jianliang Zhang, Kejiang Li
Date: 2025-02-27 00:00:00

To address the lack of energy-carbon efficiency evaluation and the underutilization of low-temperature waste heat in traditional direct reduction iron (DRI) production, this paper proposes a novel zero-carbon hydrogen metallurgy system that integrates the recovery and utilization of low-temperature and high-temperature waste heat, internal energy, and cold energy during hydrogen production, storage, reaction and circulation. Firstly, the detailed mathematical models are developed to describe energy and exergy characteristics of the operational components in the proposed zero-carbon hydrogen metallurgy system. Additionally, energy efficiency, exergy efficiency, and energy-carbon efficiency indices are introduced from a full life-cycle perspective of energy flow, avoiding the overlaps in energy inputs and outputs. Subsequently, the efficiency metrics of the proposed zero-carbon hydrogen metallurgy system are then compared with those of traditional DRI production systems with H$_2$/CO ratios of 6:4 and 8:2. The comparative results demonstrate the superiority and advancement of the proposed zero-carbon hydrogen metallurgy system. Finally, sensitivity analysis reveals that the overall electricity energy generated by incorporating the ORC and expander equipments exceeds the heat energy recovered from the furnace top gas, highlighting the energy potential of waste energy utilization.
Offline Reinforcement Learning via Inverse Optimization
Authors: Ioannis Dimanidis, Tolga Ok, Peyman Mohajerin Esfahani
Date: 2025-02-27 00:00:00
Tasks: Model Predictive Control, MuJoCo, reinforcement-learning, Reinforcement Learning

Inspired by the recent successes of Inverse Optimization (IO) across various application domains, we propose a novel offline Reinforcement Learning (ORL) algorithm for continuous state and action spaces, leveraging the convex loss function called ``sub-optimality loss" from the IO literature. To mitigate the distribution shift commonly observed in ORL problems, we further employ a robust and non-causal Model Predictive Control (MPC) expert steering a nominal model of the dynamics using in-hindsight information stemming from the model mismatch. Unlike the existing literature, our robust MPC expert enjoys an exact and tractable convex reformulation. In the second part of this study, we show that the IO hypothesis class, trained by the proposed convex loss function, enjoys ample expressiveness and achieves competitive performance comparing with the state-of-the-art (SOTA) methods in the low-data regime of the MuJoCo benchmark while utilizing three orders of magnitude fewer parameters, thereby requiring significantly fewer computational resources. To facilitate the reproducibility of our results, we provide an open-source package implementing the proposed algorithms and the experiments.
Robust Mean Field Social Control: A Unified Reinforcement Learning Framework
Authors: Zhenhui Xu, Jiayu Chen, Bing-Chang Wang, Yuhu Wu, Tielong Shen
Date: 2025-02-27 00:00:00

This paper studies linear quadratic Gaussian robust mean field social control problems in the presence of multiplicative noise. We aim to compute asymptotic decentralized strategies without requiring full prior knowledge of agents' dynamics. The primary challenges lie in solving an indefinite stochastic algebraic Riccati equation for feedback gains, and an indefinite algebraic Riccati equation for feedforward gains. To overcome these challenges, we first propose a unified dual-loop iterative framework that simultaneously handles both indefinite Riccati-type equations, and provide rigorous convergence proofs for both outer-loop and inner-loop iterations. Second, recognizing that biases may arise in iterative processes due to estimation and modeling errors, we analyze the robustness of the proposed algorithm by using the small-disturbance input-to-state stability technique. This ensures convergence to a neighborhood of the optimal solution, even in the existence of disturbances. Finally, to address the limitation of requiring precise knowledge of agents' dynamics, we employ the integral reinforcement learning technique to develop a data-driven method within the dual-loop iterative framework. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm.
Dynamic Energy Flow Analysis of Integrated Electricity and Gas Systems: A Semi-Analytical Approach
Authors: Zhikai Huang, Shuai Lu, Wei Gu, Ruizhi Yu, Suhan Zhang, Yijun Xu, Yuan Li
Date: 2025-02-27 00:00:00
Tasks: Computational Efficiency

Ensuring the safe and reliable operation of integrated electricity and gas systems (IEGS) requires dynamic energy flow (DEF) simulation tools that achieve high accuracy and computational efficiency. However, the inherent strong nonlinearity of gas dynamics and its bidirectional coupling with power grids impose significant challenges on conventional numerical algorithms, particularly in computational efficiency and accuracy. Considering this, we propose a novel non-iterative semi-analytical algorithm based on differential transformation (DT) for DEF simulation of IEGS. First, we introduce a semi-discrete difference method to convert the partial differential algebraic equations of the DEF model into ordinary differential algebraic equations to resort to the DT. Particularly, by employing spatial central difference and numerical boundary extrapolation, we effectively avoid the singularity issue of the DT coefficient matrix. Second, we propose a DT-based semi-analytical solution method, which can yield the solution of the DEF model by recursion. Finally, simulation results demonstrate the superiority of the proposed method.
Data-Driven Model Identification of Unbalanced Induction Motor Dynamics and Forces using SINDYc
Authors: Emma Vancayseele, Philip Desenfans, Zifeng Gong, Dries Vanoost, Herbert De Gersem, Davy Pissoort
Date: 2025-02-27 00:00:00
Tasks: Time Series

This paper identifies the stator currents, torque and unbalanced magnetic pull (UMP) of an unbalanced induction motor by the System Identification of Nonlinear Dynamics with Control (SINDYc) method from time-series data of measurable quantities. The SINDYc model has been trained on data coming from a nonlinear magnetic equivalent circuit model for three rotor eccentricity configurations. When evaluating the SINDYc model for static eccentricity, torques and UMPs with excellent accuracies, i.e., 8.8 mNm and 4.87 N of mean absolute error, respectively, are found. When compared with a reference torque equation, this amounts to a 65% error reduction. For dynamic eccentricity, the estimation is more difficult. The SINDYc model is fast enough to be embedded in a control procedure.
Data-Driven Mean Field Equilibrium Computation in Large-Population LQG Games
Authors: Zhenhui Xu, Jiayu Chen, Bing-Chang Wang, Tielong Shen
Date: 2025-02-27 00:00:00

This paper presents a novel data-driven approach for approximating the $\varepsilon$-Nash equilibrium in continuous-time linear quadratic Gaussian (LQG) games, where multiple agents interact with each other through their dynamics and infinite horizon discounted costs. The core of our method involves solving two algebraic Riccati equations (AREs) and an ordinary differential equation (ODE) using state and input samples collected from agents, eliminating the need for a priori knowledge of their dynamical models. The standard ARE is addressed through an integral reinforcement learning (IRL) technique, while the nonsymmetric ARE and the ODE are resolved by identifying the drift coefficients of the agents' dynamics under general conditions. Moreover, by imposing specific conditions on models, we extend the IRL-based approach to approximately solve the nonsymmetric ARE. Numerical examples are given to demonstrate the effectiveness of the proposed algorithms.
Convergence Guarantees of Model-free Policy Gradient Methods for LQR with Stochastic Data
Authors: Bowen Song, Andrea Iannelli
Date: 2025-02-27 00:00:00
Tasks: Policy Gradient Methods

Policy gradient (PG) methods are the backbone of many reinforcement learning algorithms due to their good performance in policy optimization problems. As a gradient-based approach, PG methods typically rely on knowledge of the system dynamics. However, this information is not always available, and in such cases, trajectory data can be utilized to approximate first-order information. When the data are noisy, gradient estimates become inaccurate and a formal investigation that encompasses uncertainty estimation and the analysis of its propagation through the algorithm is currently missing. To address this, our work focuses on the Linear Quadratic Regulator (LQR) problem for systems subject to additive stochastic noise. After briefly summarizing the state of the art for cases with a known model, we focus on scenarios where the system dynamics are unknown, and approximate gradient information is obtained using zeroth-order optimization techniques. We analyze the theoretical properties by computing the error in the estimated gradient and examining how this error affects the convergence of PG algorithms. Additionally, we provide global convergence guarantees for various versions of PG methods, including those employing adaptive step sizes and variance reduction techniques, which help increase the convergence rate and reduce sample complexity. One contribution of this work is the study of the robustness of model-free PG methods, aiming to identify their limitations in the presence of noise and propose improvements to enhance their applicability. Numerical simulations show that these theoretical analyses provide valuable guidance in tuning the algorithm parameters, thereby making these methods more reliable in practically relevant scenarios.
Model-Free Privacy Preserving Power Flow Analysis in Distribution Networks
Authors: Dong Liu, Juan S. Giraldo, Peter Palensky, Pedro P. Vergara
Date: 2025-02-27 00:00:00
Tasks: Incremental Learning, Privacy Preserving

Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
From Data to Sliding Mode Control of Uncertain Large-Scale Networks with Unknown Dynamics
Authors: Behrad Samari, Gian Paolo Incremona, Antonella Ferrara, Abolfazl Lavaei
Date: 2025-02-27 00:00:00

Large-scale interconnected networks, composed of multiple low-dimensional subsystems, serve as a crucial framework for modeling a wide range of real-world applications. Despite offering computational scalability, the inherent interdependence among subsystems poses significant challenges to the effective control of such networks. This complexity is further exacerbated in the presence of external perturbations and when the dynamics of individual subsystems, and accordingly the overall network, are unknown-scenarios frequently encountered in modern practical applications. In this paper, we develop a compositional data-driven approach to ensure the global asymptotic stability (GAS) of large-scale nonlinear networks with unknown mathematical models, subjected to external perturbations. To achieve this, we first gather two sets of data from each unknown nominal subsystem without perturbation, which we refer to as two input-state trajectories. The collected data from each subsystem is then utilized to design an input-to-state stable (ISS) Lyapunov function and its corresponding controller for each nominal subsystem, rendering them ISS. To cancel the effect of external perturbations on the dynamic of each subsystem, and accordingly the whole network, we then design a local integral sliding mode (ISM) controller for each subsystem using the collected data. Under a small-gain compositional condition, we employ data-driven ISS Lyapunov functions designed for subsystems and construct a control Lyapunov function for the network, rendering the assurance of GAS property over the nominal network. We then extend this compositional result to network perturbed models, demonstrating that the synthesized ISM controllers ensure the GAS property even in the presence of perturbations.
Transient Stability Analysis and Fault Clearing Angle Estimation of VSG Based on Domain of Attraction Estimated by Trajectory Reversing Method
Authors: Jiayue Lyu, Tianzhi Fang, Zhiheng Lin, Jingxue Han, Yantao Zhu
Date: 2025-02-27 00:00:00

The virtual synchronous generator (VSG), with the analogous nonlinear power-angle relationship to the synchronous generator (SG), has attracted much attention as a promising solution for converter-based power systems. In this paper, a large signal model of the grid-connected VSG is first established. The trajectory reversing method (TRM) is then introduced to estimate the domain of attraction (DOA) of VSG. Subsequently, the transient instability mechanism is revealed in detail based on the estimated DOA boundary. The impacts of system parameters on the DOA range are further investigated. It is found that loss of synchronization (LOS) occurs if the system trajectory lies outside the post-fault DOA range. In scenarios where no equilibrium points exist after a grid fault, system stability can be reestablished only when the fault clearing angle (FCA) does not exceed the critical clearing angle (CCA). Finally, the CCA derived from the DOA and that from the conventional equal area criteria (EAC) are compared. The results show that CCA obtained by our solution has a higher accuracy. Time-domain simulations are performed to verify the effectiveness of the proposed transient stability analysis method of grid-connected VSG.
Dynamics on Lie groups with applications to attitude estimation
Authors: T. Forrest Kieffer, Michael Wall
Date: 2025-02-27 00:00:00
Tasks: Bayesian Inference

The problem of filtering - propagation of states through stochastic differential equations (SDEs) and association of measurement data using Bayesian inference - in a state space which forms a Lie group is considered. Particular emphasis is given to concentrated Gaussians (CGs) as a parametric family of probability distributions to capture the uncertainty associated with an estimated state. The so-called group-affine property of the state evolution is shown to be necessary and sufficient for linearity of the dynamics on the associated Lie algebra, in turn implying CGs are invariant under such evolution. A putative SDE on the group is then reformulated as an SDE on the associated Lie algebra. The vector space structure of the Lie algebra together with the notion of a CG enables the leveraging of techniques from conventional Gaussian-based Kalman filtering in an approach called the tangent space filter (TSF). We provide example calculations for several Lie groups that arise in the problem of estimating position, velocity, and orientation of a rigid body from a noisy, potentially biased inertial measurement unit (IMU). For the specific problem of attitude estimation, numerical experiments demonstrate that TSF-based approaches are more accurate and robust than another widely used attitude filtering technique.
Assessing the Performance and Impact of PV Technologies on Storage in Hybrid Renewable Systems
Authors: Sharaf K. Magableh, Oraib Dawaghreh, Xuesong Wang, Caisheng Wang
Date: 2025-02-27 00:00:00
Tasks: Management

Traditional monofacial photovoltaic (mPV) systems are commonly adopted and well-documented because of their lower upfront costs in comparison to bifacial photovoltaic (bPV) systems. This study investigates how PV technologies impact energy storage in grid-scale hybrid renewable systems, focusing on optimizing and assessing the performance of mPV and bPV technologies integrated with pumped storage hydropower. Using Ludington City, Michigan as a case study and analyzing realworld data such as solar irradiance, ambient temperature, and utility-scale load profiles, the research highlights the operational and economic benefits of bPV systems. The results reveal that bPV systems can pump approximately 10.38% more water annually to the upper reservoir while achieving a lower levelized cost of energy ($0.0578/kWh for bPV vs. $0.0672/kWh for mPV). This study underscores the outstanding potential of bPV systems in enhancing energy storage and management strategies, contributing to a more sustainable and resilient renewable energy future.
Electromagnetically Reconfigurable Fluid Antenna System for Wireless Communications: Design, Modeling, Algorithm, Fabrication, and Experiment
Authors: Ruiqi Wang, Pinjun Zheng, Vijith Varma Kotte, Sakandar Rauf, Yiming Yang, Muhammad Mahboob Ur Rahman, Tareq Y. Al-Naffouri, Atif Shamim
Date: 2025-02-27 00:00:00

This paper presents the concept, design, channel modeling, beamforming algorithm, prototype fabrication, and experimental measurement of an electromagnetically reconfigurable fluid antenna system (ER-FAS), in which each FAS array element features electromagnetic (EM) reconfigurability. Unlike most existing FAS works that investigate spatial reconfigurability, the proposed ER-FAS enables direct control over the EM characteristics of each element, allowing for dynamic radiation pattern reconfigurability. Specifically, a novel ER-FAS architecture leveraging software-controlled fluidics is proposed, and corresponding wireless channel models are established. A low-complexity greedy beamforming algorithm is developed to jointly optimize the analog phase shift and the radiation state of each array element. The accuracy of the ER-FAS channel model and the effectiveness of the beamforming algorithm are validated through (i) full-wave EM simulations and (ii) numerical spectral efficiency evaluations. Simulation results confirm that the proposed ER-FAS significantly enhances spectral efficiency compared to conventional antenna arrays. To further validate this design, we fabricate hardware prototypes for both the ER-FAS element and array, using Galinstan liquid metal alloy, fluid silver paste, and software-controlled fluidic channels. The simulation results are experimentally verified through prototype measurements conducted in an anechoic chamber. Additionally, indoor communication trials are conducted via a pair of software-defined radios which demonstrate superior received power and bit error rate performance of the ER-FAS prototype. This work presents the first demonstration of a liquid-based ER-FAS in array configuration for enhancing communication systems.
Battery State of Health Estimation and Incremental Capacity Analysis under General Charging Profiles Using Neural Networks
Authors: Qinan Zhou, Gabrielle Vuylsteke, R. Dyche Anderson, Jing Sun
Date: 2025-02-26 00:00:00

Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are two effective approaches for battery degradation monitoring. One limiting factor for their real-world application is that they require constant-current charging profiles. This research removes this limitation and proposes an approach that enables ICA/DVA-based degradation monitoring under general charging profiles. A novel concept of virtual incremental capacity (VIC) and virtual differential voltage (VDV) is proposed. Then, two related convolutional neural networks (CNNs), called U-Net and Conv-Net, are proposed to construct VIC/VDV curves and estimate the state of health (SOH) from general charging profiles across any state-of-charge (SOC) ranges that satisfy some constraints. Finally, for onboard implementations, two CNNs called Mobile U-Net and Mobile-Net are proposed as replacements for the U-Net and Conv-Net, respectively, to reduce the computational footprint and memory requirements. Using an extensive experimental dataset of battery modules, the proposed CNNs are demonstrated to provide accurate VIC/VDV curves and enable ICA/DVA-based battery degradation monitoring under various fast-charging protocols and different SOC ranges.
Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning
Authors: Jan-Hendrik Ewers, David Anderson, Douglas Thomson
Date: 2025-02-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Wilderness search and rescue operations are often carried out over vast landscapes. The search efforts, however, must be undertaken in minimum time to maximize the chance of survival of the victim. Whilst the advent of cheap multicopters in recent years has changed the way search operations are handled, it has not solved the challenges of the massive areas at hand. The problem therefore is not one of complete coverage, but one of maximizing the information gathered in the limited time available. In this work we propose that a combination of a recurrent autoencoder and deep reinforcement learning is a more efficient solution to the search problem than previous pure deep reinforcement learning or optimisation approaches. The autoencoder training paradigm efficiently maximizes the information throughput of the encoder into its latent space representation which deep reinforcement learning is primed to leverage. Without the overhead of independently solving the problem that the recurrent autoencoder is designed for, it is more efficient in learning the control task. We further implement three additional architectures for a comprehensive comparison of the main proposed architecture. Similarly, we apply both soft actor-critic and proximal policy optimisation to provide an insight into the performance of both in a highly non-linear and complex application with a large observation Results show that the proposed architecture is vastly superior to the benchmarks, with soft actor-critic achieving the best performance. This model further outperformed work from the literature whilst having below a fifth of the total learnable parameters and training in a quarter of the time.