pwc-archive/papers-with-abstracts huggingface dataset:
Implicit Constraint-Aware Off-Policy Correction for Offline Reinforcement Learning
Authors: Ali Baheri
Date: 2025-06-16 00:00:00
Tasks: Q-Learning

Offline reinforcement learning promises policy improvement from logged interaction data alone, yet state-of-the-art algorithms remain vulnerable to value over-estimation and to violations of domain knowledge such as monotonicity or smoothness. We introduce implicit constraint-aware off-policy correction, a framework that embeds structural priors directly inside every Bellman update. The key idea is to compose the optimal Bellman operator with a proximal projection on a convex constraint set, which produces a new operator that (i) remains a $\gamma$-contraction, (ii) possesses a unique fixed point, and (iii) enforces the prescribed structure exactly. A differentiable optimization layer solves the projection; implicit differentiation supplies gradients for deep function approximators at a cost comparable to implicit Q-learning. On a synthetic Bid-Click auction -- where the true value is provably monotone in the bid -- our method eliminates all monotonicity violations and outperforms conservative Q-learning and implicit Q-learning in return, regret, and sample efficiency.
Observer Switching Strategy for Enhanced State Estimation in CSTR Networks
Authors: Lisbel Bárzaga-Martell, Francisco Ibáñez, Angel L. Cedeño, Maria Coronel, Francisco Concha, Norelys Aguila-Camacho, José Ricardo Pérez-Correa
Date: 2025-06-16 00:00:00
Tasks: Fault Detection, State Estimation

Accurate state estimation is essential for monitoring and controlling nonlinear chemical reactors, such as continuous stirred-tank reactors (CSTRs), where limited sensor coverage and process uncertainties hinder real-time observability. This paper introduces a novel multi-observer switching framework that combines several advanced estimators: Extended Luenberger Observer (ELO), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Quadrature Kalman Filter (QKF), and Particle Filter (PF), operating in parallel. At each sampling instant, a cost function based on the $L_1$ norm and Kullback-Leibler divergence selects the observer, yielding the best agreement with available measurements. The proposed architecture is validated through simulations of both linearized and fully nonlinear CSTR models with up to three reactors in series. Results show that the switching strategy significantly reduces estimation errors compared to single-observer approaches, especially under partial observability and parametric uncertainty. Despite its modular design, the framework remains computationally tractable, making it suitable for real-time industrial applications such as fault detection and model-based predictive control.
BattBee: Equivalent Circuit Modeling and Early Detection of Thermal Runaway Triggered by Internal Short Circuits for Lithium-Ion Batteries
Authors: Sangwon Kang, Hao Tu, Huazhen Fang
Date: 2025-06-16 00:00:00
Tasks: Fault Detection

Lithium-ion batteries are the enabling power source for transportation electrification. However, in real-world applications, they remain vulnerable to internal short circuits (ISCs) and the consequential risk of thermal runaway (TR). Toward addressing the challenge of ISCs and TR, we undertake a systematic study that extends from dynamic modeling to fault detection in this paper. First, we develop {\em BattBee}, the first equivalent circuit model to specifically describe the onset of ISCs and the evolution of subsequently induced TR. Drawing upon electrochemical modeling, the model can simulate ISCs at different severity levels and predict their impact on the initiation and progression of TR events. With the physics-inspired design, this model offers strong physical interpretability and predictive accuracy, while maintaining structural simplicity to allow fast computation. Then, building upon the BattBee model, we develop fault detection observers and derive detection criteria together with decision-making logics to identify the occurrence and emergence of ISC and TR events. This detection approach is principled in design and fast in computation, lending itself to practical applications. Validation based on simulations and experimental data demonstrates the effectiveness of both the BattBee model and the ISC/TR detection approach. The research outcomes underscore this study's potential for real-world battery safety risk management.
A Model-Free Detection Method for Internal Short Circuits in Single Lithium-ion Cells Using Pseudo Open-Circuit Voltage Difference
Authors: Yangyang Xu, Chenglin Liao
Date: 2025-06-16 00:00:00
Tasks: Diagnostic, Management

This letter proposes a lightweight, model-free online diagnostic framework for detecting internal short circuits (ISC) in single lithium-ion cells under dynamic operating conditions. The core of the method lies in computing the first-order difference of pseudo open-circuit voltage ($\boldsymbol{\mathrm{OCV}_{\text{pseudo}}}$) to extract high-frequency deviations caused by ISC events from low-frequency polarization variations. The method relies solely on terminal voltage, current measurements, and an offline $R_0$--SOC look-up table, thereby eliminating the need for electrochemical or equivalent-circuit observers. Validated on ten real and one false fault scenarios, the proposed approach achieves a 100\% detection success rate with no missed or false alarms. In addition, the proposed method exhibits extremely low computational and memory requirements, making it highly suitable for real-time deployment in battery management systems (BMS).
Voltage Stability of Inverter-Based Systems: Impact of Parameters and Irrelevance of Line Dynamics
Authors: Sushobhan Chatterjee, Sijia Geng
Date: 2025-06-16 00:00:00

This paper investigates voltage stability in inverter-based power systems concerning fold and saddle-node bifurcations. An analytical expression is derived for the sensitivity of the stability margin using the normal vector to the bifurcation hypersurface. Such information enables efficient identification of effective control parameters in mitigating voltage instability. Comprehensive analysis reveals that reactive loading setpoint and current controller's feedforward gain are the most influential parameters for enhancing voltage stability in a grid-following (GFL) inverter system, while the voltage controller's feedforward gain plays a dominant role in a grid-forming (GFM) inverter. Notably, both theoretical and numerical results demonstrate that transmission line dynamics have no impact on fold/saddle-node bifurcations in these systems. Results in this paper provide insights for efficient analysis and control in future inverter-dominated power systems through reductions in parameter space and model complexity.
Aggregating Inverter-Based Resources for Fast Frequency Response: A Nash Bargaining Game-Based Approach
Authors: Xiang Zhu, Hua Geng, Hongyang Qing, Xin Zou
Date: 2025-06-16 00:00:00

This paper proposes a multi-objective optimization (MOO) approach for grid-level frequency regulation by aggregating inverter-based resources (IBRs). Virtual power plants (VPPs), acting as aggregators, can efficiently respond to dynamic response requirements from the grid. Through parametric modeling, grid-level frequency regulation requirements are accurately quantified and translated into a feasible parameter region defined by device-level parameters. Based on this feasible region, an MOO model is developed to address the conflicting demands of IBRs during frequency response. A Nash bargaining game-based approach is then employed to optimally allocate regulation requirements within the VPP, balancing the various demands of the IBRs. Numerical experiments demonstrate the effectiveness of the proposed method in enhancing frequency stability and improving coordination among IBRs.
EPC Framework for BESS Projects
Authors: Zeenat Hameed, Chresten Træholt
Date: 2025-06-16 00:00:00

Battery Energy Storage Systems (BESS) are critical for modern power networks, supporting grid services such as frequency regulation, peak shaving, and black start. Delivering a BESS under an Engineering, Procurement, and Construction (EPC) model requires a concise methodology that balances regulatory compliance, technical details, and schedule efficiency. This paper presents a streamlined, five step EPC framework covering feasibility assessment, permitting, procurement, construction, and commissioning. A Danish demonstration (the BOSS project on Bornholm) serves as a case study.
Stability and Performance of Online Feedback Optimization for Distribution Grid Flexibility
Authors: Florian Klein-Helmkamp, Tina Möllemann, Irina Zettl, Andreas Ulbig
Date: 2025-06-16 00:00:00
Tasks: Management

The integration of distributed energy resources (DERs) into sub-transmission systems has enabled new opportunities for flexibility provision in ancillary services such as frequency and voltage support, as well as congestion management. This paper investigates the stability and performance of Online Feedback Optimization (OFO) controllers in ensuring reliable flexibility provision. A hierarchical control architecture is proposed, emphasizing safe transitions between system states within the Feasible Operating Region (FOR). We evaluate the controller's stability and performance through simulations of transitions to the vertices of the FOR, analyzing the impact of tuning parameters. The study demonstrates that controller stability is sensitive to parameter tuning, particularly gain and sensitivity approximations. Results demonstrate that improper tuning can lead to oscillatory or unstable behavior, highlighting the need for systematic parameter selection to ensure reliable operation across the full flexibility range.
Evaluation methodology of Model Predictive Controllers for building's energy systems
Authors: Ali Chouman, Peter Riederer, Frédéric Wurtz
Date: 2025-06-16 00:00:00
Tasks: energy management

Climate change poses a serious threat to the Earth's ecosystems, fueled primarily by escalating greenhouse gas emissions. Among the main contributors, the building sector stands out due to its significant energy demand. Addressing this challenge requires innovative techniques in the control of energy systems in buildings. This paper deals with the formulation of a methodology designed to evaluate the performance of these controllers. The evaluation process involves the establishment of a comprehensive test protocol and a diverse set of scenarios to evaluate the controllers. Key performance indicators are used to quantify their effectiveness based on the test results. A practical case study is presented as an application to introduce this methodology, focusing on the integration of Model Predictive Controllers (MPCs) with the Dimosim thermal simulation platform. The digital twin of the Greener building in Grenoble is used as a model for emulation. The paper demonstrates the ability of the proposed methodology to test and rank MPCs in different test scenarios, providing valuable feedback on their performance capabilities. The paper highlights the importance of the developed approach in systematically evaluating and ranking MPCs for optimized building energy management.
Online-Optimized Gated Radial Basis Function Neural Network-Based Adaptive Control
Authors: Mingcong Li
Date: 2025-06-16 00:00:00

Real-time adaptive control of nonlinear systems with unknown dynamics and time-varying disturbances demands precise modeling and robust parameter adaptation. While existing neural network-based strategies struggle with computational inefficiency or inadequate temporal dependencies, this study proposes a hybrid control framework integrating a Temporal-Gated Radial Basis Function (TGRBF) network with a nonlinear robust controller. The TGRBF synergizes radial basis function neural networks (RBFNNs) and gated recurrent units (GRUs) through dynamic gating, enabling efficient offline system identification and online temporal modeling with minimal parameter overhead (14.5% increase vs. RBFNNs). During control execution, an event-triggered optimization mechanism activates momentum-explicit gradient descent to refine network parameters, leveraging historical data to suppress overfitting while maintaining real-time feasibility. Concurrently, the nonlinear controller adaptively tunes its gains via Jacobian-driven rules derived from the TGRBF model, ensuring rapid error convergence and disturbance rejection. Lyapunov-based analysis rigorously guarantees uniform ultimate boundedness of both tracking errors and adaptive parameters. Simulations on a nonlinear benchmark system demonstrate the framework's superiority: compared to PID and fixed-gain robust controllers, the proposed method reduces settling time by 14.2%, limits overshoot to 10%, and achieves 48.4% lower integral time-weighted absolute error under dynamic disturbances. By unifying data-driven adaptability with stability-guaranteed control, this work advances real-time performance in partially observable, time-varying industrial systems.
Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss
Authors: Emil Marcus Buchberg, Kent Vugs Nielsen
Date: 2025-06-16 00:00:00
Tasks: Anomaly Detection, feature selection

Wind energy significantly contributes to the global shift towards renewable energy, yet operational challenges, such as Leading-Edge Erosion on wind turbine blades, notably reduce energy output. This study introduces an advanced, scalable machine learning framework for condition monitoring of wind turbines, specifically targeting improved detection of anomalies using Supervisory Control and Data Acquisition data. The framework effectively isolates normal turbine behavior through rigorous preprocessing, incorporating domain-specific rules and anomaly detection filters, including Gaussian Mixture Models and a predictive power score. The data cleaning and feature selection process enables identification of deviations indicative of performance degradation, facilitating estimates of annual energy production losses. The data preprocessing methods resulted in significant data reduction, retaining on average 31% of the original SCADA data per wind farm. Notably, 24 out of 35 turbines exhibited clear performance declines. At the same time, seven improved, and four showed no significant changes when employing the power curve feature set, which consisted of wind speed and ambient temperature. Models such as Random Forest, XGBoost, and KNN consistently captured subtle but persistent declines in turbine performance. The developed framework provides a novel approach to existing condition monitoring methodologies by isolating normal operational data and estimating annual energy loss, which can be a key part in reducing maintenance expenditures and mitigating economic impacts from turbine downtime.
KCLNet: Physics-Informed Power Flow Prediction via Constraints Projections
Authors: Pantelis Dogoulis, Karim TIT, Maxime Cordy
Date: 2025-06-15 00:00:00
Tasks: Graph Neural Network, Prediction

In the modern context of power systems, rapid, scalable, and physically plausible power flow predictions are essential for ensuring the grid's safe and efficient operation. While traditional numerical methods have proven robust, they require extensive computation to maintain physical fidelity under dynamic or contingency conditions. In contrast, recent advancements in artificial intelligence (AI) have significantly improved computational speed; however, they often fail to enforce fundamental physical laws during real-world contingencies, resulting in physically implausible predictions. In this work, we introduce KCLNet, a physics-informed graph neural network that incorporates Kirchhoff's Current Law as a hard constraint via hyperplane projections. KCLNet attains competitive prediction accuracy while ensuring zero KCL violations, thereby delivering reliable and physically consistent power flow predictions critical to secure the operation of modern smart grids.
Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Authors: Jan C. Schulze, Alexander Mitsos
Date: 2025-06-15 00:00:00
Tasks: Chemical Process

Computationally cheap yet accurate enough dynamical models are vital for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order simplified model can enable such real-time applications. Herein, we review state-of-the-art nonlinear model order reduction methods and provide a theoretical comparison of method properties. Additionally, we discuss both general-purpose methods and tailored approaches for (chemical) process systems and we identify similarities and differences between these methods. As manifold-Galerkin approaches currently do not account for inputs in the construction of the reduced state subspace, we extend these methods to dynamical systems with inputs. In a comparative case study, we apply eight established model order reduction methods to an air separation process model: POD-Galerkin, nonlinear-POD-Galerkin, manifold-Galerkin, dynamic mode decomposition, Koopman theory, manifold learning with latent predictor, compartment modeling, and model aggregation. Herein, we do not investigate hyperreduction (reduction of FLOPS). Based on our findings, we discuss strengths and weaknesses of the model order reduction methods.
Behavioral Generative Agents for Energy Operations
Authors: Cong Chen, Omer Karaduman, Xu Kuang
Date: 2025-06-14 00:00:00
Tasks: Decision Making, energy management, Management

Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This paper introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. We demonstrate that these agents behave more optimally and rationally in simpler market scenarios, while their performance becomes more variable and suboptimal as task complexity rises. Furthermore, the agents exhibit heterogeneous customer preferences, consistently maintaining distinct, persona-driven reasoning patterns. Our findings highlight the potential value of integrating generative agents into energy management simulations to improve the design and effectiveness of energy policies and incentive programs.
A Theoretical Framework for Virtual Power Plant Integration with Gigawatt-Scale AI Data Centers: Multi-Timescale Control and Stability Analysis
Authors: Ali Peivandizadeh
Date: 2025-06-14 00:00:00

The explosive growth of artificial intelligence has created gigawatt-scale data centers that fundamentally challenge power system operation, exhibiting power fluctuations exceeding 500 MW within seconds and millisecond-scale variations of 50-75% of thermal design power. This paper presents a comprehensive theoretical framework that reconceptualizes Virtual Power Plants (VPPs) to accommodate these extreme dynamics through a four-layer hierarchical control architecture operating across timescales from 100 microseconds to 24 hours. We develop control mechanisms and stability criteria specifically tailored to converter-dominated systems with pulsing megawatt-scale loads. We prove that traditional VPP architectures, designed for aggregating distributed resources with response times of seconds to minutes, cannot maintain stability when confronted with AI data center dynamics exhibiting slew rates exceeding 1,000 MW/s at gigawatt scale. Our framework introduces: (1) a sub-millisecond control layer that interfaces with data center power electronics to actively dampen power oscillations; (2) new stability criteria incorporating protection system dynamics, demonstrating that critical clearing times reduce from 150 ms to 83 ms for gigawatt-scale pulsing loads; and (3) quantified flexibility characterization showing that workload deferability enables 30% peak reduction while maintaining AI service availability above 99.95%. This work establishes the mathematical foundations necessary for the stable integration of AI infrastructure that will constitute 50-70% of data center electricity consumption by 2030.
ECLIP: Energy-efficient and Practical Co-Location of ML Inference on Spatially Partitioned GPUs
Authors: Ryan Quach, Yidi Wang, Ali Jahanshahi, Daniel Wong, Hyoseung Kim
Date: 2025-06-14 00:00:00
Tasks: GPU

As AI inference becomes mainstream, research has begun to focus on improving the energy consumption of inference servers. Inference kernels commonly underutilize a GPU's compute resources and waste power from idling components. To improve utilization and energy efficiency, multiple models can co-locate and share the GPU. However, typical GPU spatial partitioning techniques often experience significant overheads when reconfiguring spatial partitions, which can waste additional energy through repartitioning overheads or non-optimal partition configurations. In this paper, we present ECLIP, a framework to enable low-overhead energy-efficient kernel-wise resource partitioning between co-located inference kernels. ECLIP minimizes repartitioning overheads by pre-allocating pools of CU masked streams and assigns optimal CU assignments to groups of kernels through our resource allocation optimizer. Overall, ECLIP achieves an average of 13% improvement to throughput and 25% improvement to energy efficiency.
Experimental Verification of a Time-Domain Load Identification Method for Single-Phase Circuits
Authors: Francisco M. Arrabal-Campos, Francisco G. Montoya, Jorge Ventura, Santiago Sánchez-Acevedo, Raymundo E. Torres-Olguin, Francisco de León
Date: 2025-06-14 00:00:00

This paper presents experimental validation of a time-domain load parameter determination method for single-phase circuits. The verification is performed in a state-of-the-art smart grid laboratory equipped with power hardware and real-time emulators. The proposed method enables the identification of circuit parameters using only instantaneous voltage and current measurements at the point of common coupling. The experimental setup includes a range of test cases covering linear and non-sinusoidal single-phase conditions. Voltage and current waveforms are acquired, preprocessed, and used to calculate the relevant circuit parameters. The experimental results demonstrate a high degree of accuracy and robustness, with minimal percentage errors across all test cases. The identified parameters show excellent agreement with the theoretical expectations, confirming the validity and applicability of the proposed method to identify the load of single-phase systems. This validation highlights the potential of the method for improved monitoring, control, and protection of smart grids, paving the way for future extensions to three-phase systems and real-time implementations.