pwc-archive/papers-with-abstracts huggingface dataset:
Learn to Bid as a Price-Maker Wind Power Producer
Authors: Shobhit Singhal, Marta Fochesato, Liviu Aolaritei, Florian Dörfler
Date: 2025-03-20 00:00:00
Tasks: Bilevel Optimization

Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a large enough market share to influence prices with their bidding decisions, existing optimal bidding methods rarely account for this aspect. Price-maker approaches typically model bidding as a bilevel optimization problem, but these methods require complex market models, estimating other participants' actions, and are computationally demanding. To address these challenges, we propose an online learning algorithm that leverages contextual information to optimize WPP bids in the price-maker setting. We formulate the strategic bidding problem as a contextual multi-armed bandit, ensuring provable regret minimization. The algorithm's performance is evaluated against various benchmark strategies using a numerical simulation of the German day-ahead and real-time markets.
Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach
Authors: Burak Dindar, Can Berk Saner, Hüseyin K. Çakmak, Veit Hagenmeyer
Date: 2025-03-20 00:00:00
Tasks: Privacy Preserving

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs), in our case, distributed generators (DGs), in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on non-sensitive data, the transfer of sensitive information between stakeholders is prevented.
A Unified Stability Analysis of Safety-Critical Control using Multiple Control Barrier Functions
Authors: Matheus F. Reis, José P. Carvalho, A. Pedro Aguiar
Date: 2025-03-20 00:00:00

Ensuring liveness and safety of autonomous and cyber-physical systems remains a fundamental challenge, particularly when multiple safety constraints are present. This letter advances the theoretical foundations of safety-filter Quadratic Programs (QP) and Control Lyapunov Function (CLF)-Control Barrier Function (CBF) controllers by establishing a unified analytical framework for studying their stability properties. We derive sufficient feasibility conditions for QPs with multiple CBFs and formally characterize the conditions leading to undesirable equilibrium points at possible intersecting safe set boundaries. Additionally, we introduce a stability criterion for equilibrium points, providing a systematic approach to identifying conditions under which they can be destabilized or eliminated. Our analysis extends prior theoretical results, deepening the understanding of the conditions of feasibility and stability of CBF-based safety filters and the CLF-CBF QP framework.
Disturbance Observers for Robust Backup Control Barrier Functions
Authors: David E. J. van Wijk, Ersin Das, Anil Alan, Samuel Coogan, Tamas G. Molnar, Joel W. Burdick, Manoranjan Majji, Kerianne L. Hobbs
Date: 2025-03-19 00:00:00

Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the construction of safe controllers online by considering the flow of the system under a backup controller. However, in the presence of model uncertainties, the flow cannot be accurately computed, making this method insufficient for safety assurance. To tackle this shortcoming, we integrate backup control barrier functions with a disturbance observer and estimate the flow under a reconstruction of the disturbance while refining this estimate over time. We prove that the controllers resulting from the proposed Disturbance Observer Backup Control Barrier Function (DO-bCBF) approach guarantee safety, are robust to unknown disturbances, and satisfy input constraints.
Pervasive Sensing for Livestock Health and Activity Monitoring: Current Methods and Techniques
Authors: Jeffrey D Shulkin, Abhipol Vibhatasilpin, Vedant Adhana
Date: 2025-03-19 00:00:00

Pervasive sensing is transforming health and activity monitoring by enabling continuous and automated data collection through advanced sensing modalities. While extensive research has been conducted on human subjects, its application in livestock remains underexplored. In large-scale agriculture, real-time monitoring of biological signals and behavioral patterns can facilitate early disease detection, optimize feeding and breeding strategies, and ensure compliance with welfare standards. This survey examines key sensing technologies -- including structural vibration, radio frequency (RF), computer vision, and wearables -- highlighting their benefits and challenges in livestock monitoring. By comparing these approaches, we provide insights into their effectiveness, limitations, and potential for integration into modern smart farming systems. Finally, we discuss research gaps and future directions to advance pervasive sensing in livestock health and activity monitoring.
The value of hedging against energy storage uncertainties when designing energy parks
Authors: Max Langtry, Ruchi Choudhary
Date: 2025-03-19 00:00:00

Energy storage is needed to match renewable generation to industrial loads in energy parks. However, the future performance of bulk storage technologies is currently highly uncertain. Due to the urgency of decarbonization targets, energy park projects must be designed and begun now. But, as uncertainty in storage performance reduces, a different technology than identified during initial design may turn out cheaper. Enabling flexibility so that design adaptations can be made as better information becomes available would lower the cost of decarbonizing industry. But having this flexibility is itself costly. This raises the question, "Is it worth it?" This study quantifies the benefit of retaining flexibility to adapt energy park designs and optionality over storage technology choice as uncertainty reduces, to determine whether it is economically worthwhile. It applies the Value of Information analysis framework to the sizing of wind, solar, and storage in an illustrative energy park model based on a real-world proposal near Rotterdam, considering uncertainty in storage efficiency, lifetime, and capital cost. Updating asset sizings after storage uncertainty reduced is found to reduce total costs by 18% on average. Having the option to switch storage technology choice as well reduces costs by a further 13%, which is substantially greater than the cost of providing storage optionality. Using two storage technologies in the energy park reduces costs by 14%, and in this case storage optionality is not worthwhile. These results are robust to the level of uncertainty reduction in storage performance, and the risk aversion of the system designer.
Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles
Authors: Lukas Theiner, Sebastian Hirt, Alexander Steinke, Rolf Findeisen
Date: 2025-03-19 00:00:00
Tasks: Bayesian Optimization, Model Predictive Control, Trajectory Planning

Trajectory planning for automated vehicles commonly employs optimization over a moving horizon - Model Predictive Control - where the cost function critically influences the resulting driving style. However, finding a suitable cost function that results in a driving style preferred by passengers remains an ongoing challenge. We employ preferential Bayesian optimization to learn the cost function by iteratively querying a passenger's preference. Due to increasing dimensionality of the parameter space, preference learning approaches might struggle to find a suitable optimum with a limited number of experiments and expose the passenger to discomfort when exploring the parameter space. We address these challenges by incorporating prior knowledge into the preferential Bayesian optimization framework. Our method constructs a virtual decision maker from real-world human driving data to guide parameter sampling. In a simulation experiment, we achieve faster convergence of the prior-knowledge-informed learning procedure compared to existing preferential Bayesian optimization approaches and reduce the number of inadequate driving styles sampled.
Advancing MG Energy Management: A Rolling Horizon Optimization Framework for Three-Phase Unbalanced Networks Integrating Convex Formulations
Authors: Pablo Cortés, Alejandra Tabares, Fredy Franco
Date: 2025-03-19 00:00:00
Tasks: energy management, Management, Scheduling

Real-world three-phase microgrids face two interconnected challenges: 1. time-varying uncertainty from renewable generation and demand, and 2. persistent phase imbalances caused by uneven distributed energy resources DERs, load asymmetries, and grid faults. Conventional energy management systems fail to address these challenges holistically and static optimization methods lack adaptability to real-time fluctuations, while balanced three-phase models ignore critical asymmetries that degrade voltage stability and efficiency. This work introduces a dynamic rolling horizon optimization framework specifically designed for unbalanced three-phase microgrids. Unlike traditional two-stage stochastic approaches that fix decisions for the entire horizon, the rolling horizon algorithm iteratively updates decisions in response to real-time data. By solving a sequence of shorter optimization windows, each incorporating the latest system state and forecasts, the method achieves three key advantages: Adaptive Uncertainty Handling by continuously re plans operations to mitigate forecast errors. Phase Imbalance Correction by dynamically adjusts power flows across phases to minimize voltage deviations and losses caused by asymmetries, and computational Tractability, i.e., shorter optimization windows, combined with the mathematical mhodel, enable better decision making holding accuracy. For comparison purposes, we derive three optimization models: a nonlinear nonconvex model for high-fidelity offline planning, a convex quadratic approximation for day-ahead scheduling, and a linearized model to important for theoretical reasons such as decomposition algorithms.
Energy-efficient Merging of Connected and Automated Vehicles using Control Barrier Functions
Authors: Shreshta Rajakumar Deshpande, Mrdjan Jankovic
Date: 2025-03-19 00:00:00
Tasks: Collision Avoidance

Highway merges present difficulties for human drivers and automated vehicles due to incomplete situational awareness and a need for a structured (precedence, order) environment, respectively. In this paper, an unstructured merge algorithm is presented for connected and automated vehicles. There is neither precedence nor established passing order through the merge point. The algorithm relies on Control Barrier Functions for safety (collision avoidance) and for coordination that arises from exponential instability of stall-equilibria in the inter-agent space. A Monte Carlo simulation comparison to a first-in-first-out approach shows improvement in traffic flow and a significant energy efficiency benefit.
Probabilistic Flexibility Aggregation of DERs for Ancillary Services Provision
Authors: Matthieu Jacobs, Mario Paolone
Date: 2025-03-19 00:00:00

This paper presents a grid-aware probabilistic approach to compute the aggregated flexibility at the grid connection point (GCP) of active distribution networks (ADNs) to allow the participation of DERs in ancillary services (AS) markets. Specifically an optimal power flow (OPF) method using a linear network model is used to compute the aggregated capability for the provision of multiple AS. We start from the method proposed in [1] and extend it to allow for optimizing the provision of multiple services simultaneously, ensure cost-effectiveness of the used DERs and handle uncertainties in a probabilistic way. The allocation of individual DERs power flexibilities accounts for the operational costs associated to the provision of different services and ensures cost-effectiveness while maximizing the value of the advertised aggregated flexibility, assuming known service prices. Empirical uncertainty sets are obtained to achieve a predefined coverage of the probability distribution in line with recent developments in the Nordic AS markets. Finally, a feeder-decomposition approach is proposed to ensure the methods applicability to realistic distribution networks with a large number of buses. Different case studies show the effectiveness of the method, highlight the importance of accounting for network constraints and illustrate its applicability to realistic distribution systems.
Priority-driven Constraints Softening in Safe MPC for Perturbed Systems
Authors: Ying Shuai Quan, Mohammad Jeddi, Francesco Prignoli, Paolo Falcone
Date: 2025-03-19 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

This paper presents a safe model predictive control (SMPC) framework designed to ensure the satisfaction of hard constraints for systems perturbed by an external disturbance. Such safety guarantees are ensured, despite the disturbance, by online softening a subset of adjustable constraints defined by the designer. The selection of the constraints to be softened is made online based on a predefined priority assigned to each adjustable constraint. The design of a learning-based algorithm enables real-time computation while preserving the original safety properties. Simulations results, obtained from an automated driving application, show that the proposed approach provides guarantees of collision-avoidance hard constraints despite the unpredicted behaviors of the surrounding environment.
Lyapunov-Based Graph Neural Networks for Adaptive Control of Multi-Agent Systems
Authors: Brandon C. Fallin, Cristian F. Nino, Omkar Sudhir Patil, Zachary I. Bell, Warren E. Dixon
Date: 2025-03-19 00:00:00
Tasks: Graph Attention

Graph neural networks (GNNs) have a message-passing framework in which vector messages are exchanged between graph nodes and updated using feedforward layers. The inclusion of distributed message-passing in the GNN architecture makes them ideally suited for distributed control and coordination tasks. Existing results develop GNN-based controllers to address a variety of multi-agent control problems while compensating for modeling uncertainties in the systems. However, these results use GNNs that are pre-trained offline. This paper provides the first result on GNNs with stability-driven online weight updates to address the multi-agent target tracking problem. Specifically, new Lyapunov-based distributed GNN and graph attention network (GAT)-based controllers are developed to adaptively estimate unknown target dynamics and address the second-order target tracking problem. A Lyapunov-based stability analysis is provided to guarantee exponential convergence of the target state estimates and agent states to a neighborhood of the target state. Numerical simulations show a 20.8% and 48.1% position tracking error performance improvement by the GNN and GAT architectures over a baseline DNN architecture, respectively.
Experimental Validation of Distributed Dispatching of Multiple Active Distribution Networks Using the ADMM
Authors: Matthieu Jacobs, Hanmin Cai, Mario Paolone
Date: 2025-03-19 00:00:00

This paper presents the experimental validation of a framework for the coordinated dispatch and control of multiple active distribution networks (ADNs) hosting distributed energy resource (DER). We show that the presented method, which builds further on work done in [1], effectively allows to control multiple ADNs in a distributed way to ensure they achieve a common objective without revealing information on their DERs capabilities or grid model. This experimental validation is carried out using demonstrators at the DESL of EPFL and the NEST site at Empa, both in Switzerland. The coordination of the systems to share the flexibility of their controllable assets is demonstrated through a set of 24h experiments. Finally, the limitations of the method are discussed and future extensions proposed.
Automated Functional Decomposition for Hybrid Zonotope Over-approximations with Application to LSTM Networks
Authors: Jonah J. Glunt, Jacob A. Siefert, Andrew F. Thompson, Justin Ruths, Herschel C. Pangborn
Date: 2025-03-19 00:00:00

Functional decomposition is a powerful tool for systems analysis because it can reduce a function of arbitrary input dimensions to the sum and superposition of functions of a single variable, thereby mitigating (or potentially avoiding) the exponential scaling often associated with analyses over high-dimensional spaces. This paper presents automated methods for constructing functional decompositions used to form set-based over-approximations of nonlinear functions, with particular focus on the hybrid zonotope set representation. To demonstrate these methods, we construct a hybrid zonotope set that over-approximates the input-output graph of a long short-term memory neural network, and use functional decomposition to represent a discrete hybrid automaton via a hybrid zonotope.
Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety Assessment
Authors: Songqiao Hu, Zeyi Liu, Xiao He
Date: 2025-03-19 00:00:00
Tasks: Ensemble Learning, Multi-Armed Bandits

Ensemble learning plays a crucial role in practical applications of online learning due to its enhanced classification performance and adaptable adjustment mechanisms. However, most weight allocation strategies in ensemble learning are heuristic, making it challenging to theoretically guarantee that the ensemble classifier outperforms its base classifiers. To address this issue, a performance-bounded online ensemble learning method based on multi-armed bandits, named PB-OEL, is proposed in this paper. Specifically, multi-armed bandit with expert advice is incorporated into online ensemble learning, aiming to update the weights of base classifiers and make predictions. A theoretical framework is established to bound the performance of the ensemble classifier relative to base classifiers. By setting expert advice of bandits, the bound exceeds the performance of any base classifier when the length of data stream is sufficiently large. Additionally, performance bounds for scenarios with limited annotations are also derived. Numerous experiments on benchmark datasets and a dataset of real-time safety assessment tasks are conducted. The experimental results validate the theoretical bound to a certain extent and demonstrate that the proposed method outperforms existing state-of-the-art methods.
Diffusion-Based Forecasting for Uncertainty-Aware Model Predictive Control
Authors: Stelios Zarifis, Ioannis Kordonis, Petros Maragos
Date: 2025-03-19 00:00:00
Tasks: Decision Making, Model Predictive Control, Time Series, Time Series Forecasting

We propose Diffusion-Informed Model Predictive Control (D-I MPC), a generic framework for uncertainty-aware prediction and decision-making in partially observable stochastic systems by integrating diffusion-based time series forecasting models in Model Predictive Control algorithms. In our approach, a diffusion-based time series forecasting model is used to probabilistically estimate the evolution of the system's stochastic components. These forecasts are then incorporated into MPC algorithms to estimate future trajectories and optimize action selection under the uncertainty of the future. We evaluate the framework on the task of energy arbitrage, where a Battery Energy Storage System participates in the day-ahead electricity market of the New York state. Experimental results indicate that our model-based approach with a diffusion-based forecaster significantly outperforms both implementations with classical forecasting methods and model-free reinforcement learning baselines.
Enhancing Reset Control Phase with Lead Shaping Filters: Applications to Precision Motion Systems
Authors: Xinxin Zhang, S. Hassan HosseinNia
Date: 2025-03-19 00:00:00

This study presents a shaped reset feedback control strategy to enhance the performance of precision motion systems. The approach utilizes a phase-lead compensator as a shaping filter to tune the phase of reset instants, thereby shaping the nonlinearity in the first-order reset control. {The design achieves either an increased phase margin while maintaining gain properties or improved gain without sacrificing phase margin, compared to reset control without the shaping filter.} Then, frequency-domain design procedures are provided for both Clegg Integrator (CI)-based and First-Order Reset Element (FORE)-based reset control systems. Finally, the effectiveness of the proposed strategy is demonstrated through two experimental case studies on a precision motion stage. In the first case, the shaped reset control leverages phase-lead benefits to achieve zero overshoot in the transient response. In the second case, the shaped reset control strategy enhances the gain advantages of the previous reset element, resulting in improved steady-state performance, including better tracking precision and disturbance rejection, while reducing overshoot for an improved transient response.
Semi-Gradient SARSA Routing with Theoretical Guarantee on Traffic Stability and Weight Convergence
Authors: Yidan Wu, Yu Yu, Jianan Zhang, Li Jin
Date: 2025-03-19 00:00:00
Tasks: Learning Theory

We consider the traffic control problem of dynamic routing over parallel servers, which arises in a variety of engineering systems such as transportation and data transmission. We propose a semi-gradient, on-policy algorithm that learns an approximate optimal routing policy. The algorithm uses generic basis functions with flexible weights to approximate the value function across the unbounded state space. Consequently, the training process lacks Lipschitz continuity of the gradient, boundedness of the temporal-difference error, and a prior guarantee on ergodicity, which are the standard prerequisites in existing literature on reinforcement learning theory. To address this, we combine a Lyapunov approach and an ordinary differential equation-based method to jointly characterize the behavior of traffic state and approximation weights. Our theoretical analysis proves that the training scheme guarantees traffic state stability and ensures almost surely convergence of the weights to the approximate optimum. We also demonstrate via simulations that our algorithm attains significantly faster convergence than neural network-based methods with an insignificant approximation error.