pwc-archive/papers-with-abstracts huggingface dataset:
On the Data-Driven Modeling of Price-Responsive Flexible Loads: Formulation and Algorithm
Authors: Mingji Chen, Shuai Lu, Wei Gu, ZhaoYang Dong, Yijun Xu, Jiayi Ding
Date: 2025-03-05 00:00:00
Tasks: Bayesian Optimization, parameter estimation

The flexible loads in power systems, such as interruptible and transferable loads, are critical flexibility resources for mitigating power imbalances. Despite their potential, accurate modeling of these loads is a challenging work and has not received enough attention, limiting their integration into operational frameworks. To bridge this gap, this paper develops a data-driven identification theory and algorithm for price-responsive flexible loads (PRFLs). First, we introduce PRFL models that capture both static and dynamic decision mechanisms governing their response to electricity price variations. Second, We develop a data-driven identification framework that explicitly incorporates forecast and measurement errors. Particularly, we give a theoretical analysis to quantify the statistical impact of such noise on parameter estimation. Third, leveraging the bilevel structure of the identification problem, we propose a Bayesian optimization-based algorithm that features the scalability to large sample sizes and the ability to offer posterior differentiability certificates as byproducts. Numerical tests demonstrate the effectiveness and superiority of the proposed approach.
The Untapped Potential of Smart Charging: How EV Owners Can Save Money and Reduce Emissions Without Behavioral Change
Authors: Yash Gupta, William Vreeland, Andrew Peterman, Coley Girouard, Brian Wang
Date: 2025-03-05 00:00:00

The transportation sector is the single largest contributor to US emissions and the second largest globally. Electric vehicles (EVs) are expected to represent half of global car sales by 2035, emerging as a pivotal solution to reduce emissions and enhance grid flexibility. The electrification of buildings, manufacturing, and transportation is expected to grow electricity demand substantially over the next decade. Without effectively managed EV charging, EVs could strain energy grid infrastructure and increase electricity costs. Drawing on de-identified 2023 EV telematics data from Rivian Automotive, this study found that 72% of home charging commenced after the customer plugged in their vehicle regardless of utility time of use (TOU) tariffs or managed charging programs. In fewer than 26% of charging sessions in the sample, EV owners actively scheduled charging times to align or participate in utility tariffs or programs. With a majority of drivers concurrently plugged in during optimal charging periods yet not actively charging, the study identified an opportunity to reduce individual EV owner costs and carbon emissions through smarter charging habits without significant behavioral modifications or sacrifice in user preferences. By optimizing home charging schedules within existing plug-in and plug-out windows, the study suggests that EV owners can save an average of $140 annually and reduce the associated carbon emissions of charging their EV by as much as 28%.
Determinantal Learning for Subset Selection in Wireless Networks
Authors: Xiangliu Tu, Chiranjib Saha, Harpreet S. Dhillon
Date: 2025-03-05 00:00:00
Tasks: Scheduling

Subset selection is central to many wireless communication problems, including link scheduling, power allocation, and spectrum management. However, these problems are often NP-complete, because of which heuristic algorithms applied to solve these problems struggle with scalability in large-scale settings. To address this, we propose a determinantal point process-based learning (DPPL) framework for efficiently solving general subset selection problems in massive networks. The key idea is to model the optimal subset as a realization of a determinantal point process (DPP), which balances the trade-off between quality (signal strength) and similarity (mutual interference) by enforcing negative correlation in the selection of {\em similar} links (those that create significant mutual interference). However, conventional methods for constructing similarity matrices in DPP impose decomposability and symmetry constraints that often do not hold in practice. To overcome this, we introduce a new method based on the Gershgorin Circle Theorem for constructing valid similarity matrices. The effectiveness of the proposed approach is demonstrated by applying it to two canonical wireless network settings: an ad hoc network in 2D and a cellular network serving drones in 3D. Simulation results show that DPPL selects near-optimal subsets that maximize network sum-rate while significantly reducing computational complexity compared to traditional optimization methods, demonstrating its scalability for large-scale networks.
Regularization for Covariance Parameterization of Direct Data-Driven LQR Control
Authors: Feiran Zhao, Alessandro Chiuso, Florian Dörfler
Date: 2025-03-04 00:00:00

As the benchmark of data-driven control methods, the linear quadratic regulator (LQR) problem has gained significant attention. A growing trend is direct LQR design, which finds the optimal LQR gain directly from raw data and bypassing system identification. To achieve this, our previous work develops a direct LQR formulation parameterized by sample covariance. In this paper, we propose a regularization method for the covariance-parameterized LQR. We show that the regularizer accounts for the uncertainty in both the steady-state covariance matrix corresponding to closed-loop stability, and the LQR cost function corresponding to averaged control performance. With a positive or negative coefficient, the regularizer can be interpreted as promoting either exploitation or exploration, which are well-known trade-offs in reinforcement learning. In simulations, we observe that our covariance-parameterized LQR with regularization can significantly outperform the certainty-equivalence LQR in terms of both the optimality gap and the robust closed-loop stability.
Robust time series generation via Schrödinger Bridge: a comprehensive evaluation
Authors: Alexandre Alouadi, Baptiste Barreau, Laurent Carlier, Huyên Pham
Date: 2025-03-04 00:00:00
Tasks: Image Generation, Time Series, Time Series Generation

We investigate the generative capabilities of the Schr\"odinger Bridge (SB) approach for time series. The SB framework formulates time series synthesis as an entropic optimal interpolation transport problem between a reference probability measure on path space and a target joint distribution. This results in a stochastic differential equation over a finite horizon that accurately captures the temporal dynamics of the target time series. While the SB approach has been largely explored in fields like image generation, there is a scarcity of studies for its application to time series. In this work, we bridge this gap by conducting a comprehensive evaluation of the SB method's robustness and generative performance. We benchmark it against state-of-the-art (SOTA) time series generation methods across diverse datasets, assessing its strengths, limitations, and capacity to model complex temporal dependencies. Our results offer valuable insights into the SB framework's potential as a versatile and robust tool for time series generation.
Frequency domain identification for multivariable motion control systems: Applied to a prototype wafer stage
Authors: M. van der Hulst, R. A. González, K. Classens, P. Tacx, N. Dirkx, J. van de Wijdeven, T. Oomen
Date: 2025-03-04 00:00:00

Multivariable parametric models are essential for optimizing the performance of high-tech systems. The main objective of this paper is to develop an identification strategy that provides accurate parametric models for complex multivariable systems. To achieve this, an additive model structure is adopted, offering advantages over traditional black-box model structures when considering physical systems. The introduced method minimizes a weighted least-squares criterion and uses an iterative linear regression algorithm to solve the estimation problem, achieving local optimality upon convergence. Experimental validation is conducted on a prototype wafer-stage system, featuring a large number of spatially distributed actuators and sensors and exhibiting complex flexible dynamic behavior, to evaluate performance and demonstrate the effectiveness of the proposed method.
Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference
Authors: Amir Farakhor, Iman Askari, Di wu, Yebin Wang, Huazhen Fang
Date: 2025-03-04 00:00:00
Tasks: Bayesian Inference, Management, Model Predictive Control, parameter estimation

Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliability and resilience. However, their optimal power management poses significant challenges: the underlying high-dimensional nonlinear nonconvex optimization lacks computational tractability in real-world implementation, and the uncertainty of the exogenous power demand makes exact optimization difficult. This paper presents a new solution framework to address these bottlenecks. The solution pivots on introducing power-sharing ratios to specify each cell's power quota from the output power demand. To find the optimal power-sharing ratios, we formulate a nonlinear model predictive control (NMPC) problem to achieve power-loss-minimizing BESS operation while complying with safety, cell balancing, and power supply-demand constraints. We then propose a parameterized control policy for the power-sharing ratios, which utilizes only three parameters, to reduce the computational demand in solving the NMPC problem. This policy parameterization allows us to translate the NMPC problem into a Bayesian inference problem for the sake of 1) computational tractability, and 2) overcoming the nonconvexity of the optimization problem. We leverage the ensemble Kalman inversion technique to solve the parameter estimation problem. Concurrently, a low-level control loop is developed to seamlessly integrate our proposed approach with the BESS to ensure practical implementation. This low-level controller receives the optimal power-sharing ratios, generates output power references for the cells, and maintains a balance between power supply and demand despite uncertainty in output power. We conduct extensive simulations and experiments on a 20-cell prototype to validate the proposed approach.
Efficient Sampling and Sensitivity Analysis of Rare Transient Instability Events via Subset Simulation
Authors: Jingyu Liu, Xiaoting Wang, Xiaozhe Wang
Date: 2025-03-04 00:00:00
Tasks: Sensitivity

Assessing the risk of low-probability high-impact transient instability (TI) events is crucial for ensuring robust and stable power system operation under high uncertainty. However, direct Monte Carlo (DMC) simulation for rare TI event sampling is computationally intensive. This paper proposes a subset simulation-based method for efficient small TI probability estimation, rare TI events sampling, and subsequent sensitivity analysis. Numerical studies on the modified WSCC 9-bus system demonstrate the efficiency of the proposed method over DMC. Additionally, targeted stability enhancement strategies are designed to eliminate rare TI events and enhance the system's robustness to specific transient faults.
Closed-loop model-predictive wind farm flow control under time-varying inflow using FLORIDyn
Authors: Marcus Becker, Maarten J. van den Broek, Dries Allaerts, Jan-Willem van Wingerden
Date: 2025-03-04 00:00:00

Wind farm flow control has been a key research focus in recent years, driven by the idea that a collectively operating wind farm can outperform individually controlled turbines. Control strategies are predominantly applied in an open-loop manner, where the current flow conditions are used to look up precomputed steady-state set points. Closed-loop control approaches, on the other hand, take measurements from the farm into account and optimize their set points online, which makes them more flexible and resilient. This paper introduces a closed-loop model-predictive wind farm controller using the dynamic engineering model FLORIDyn to maximize the energy generated by a ten-turbine wind farm. The framework consists of an Ensemble Kalman Filter to continuously correct the flow field estimate, as well as a novel optimization strategy. To this end the paper discusses two dynamic ways to maximize the farm energy and compares this to the current look-up table industry standard. The framework relies solely on turbine measurements without using a flow field preview. In a 3-hour case study with time-varying conditions, the derived controllers achieve an overall energy gain of 3 to 4.4 % with noise-free wind direction measurements. If disturbed and biased measurements are used, this performance decreases to 1.9 to 3 % over the greedy control baseline with the same measurements. The comparison to look-up table controllers shows that the closed-loop framework performance is more robust to disturbed measurements but can only match the performance in noise-free conditions.
Graph-Based Dynamics and Network Control of a Single Articulated Robotic System
Authors: Jonathan Lane, Nak-seung Patrick Hyun
Date: 2025-03-03 00:00:00

Extensive research on graph-based dynamics and control of multi-agent systems has successfully demonstrated control of robotic swarms, where each robot is perceived as an independent agent virtually connected by a network topology. The strong advantage of the network control structure lies in the decentralized nature of the control action, which only requires the knowledge of virtually connected agents. In this paper, we seek to expand the ideas of virtual network constraints to physical constraints on a class of tree-structured robots which we denote as single articulated robotic (SAR) systems. In our proposed framework, each link can be viewed as an agent, and each holonomic constraint connecting links serves as an edge. By following the first principles of Lagrangian dynamics, we derive a consensus-like matrix-differential equation with weighted graph and edge Laplacians for the dynamics of a SAR system. The sufficient condition for the holonomic constraint forces becoming independent to the control inputs is derived. This condition leads to a decentralized leader-follower network control framework for regulating the relative configuration of the robot. Simulation results demonstrate the effectiveness of the proposed control method.
Goal-Oriented Interference Coordination in 6G In-Factory Subnetworks
Authors: Daniel Abode, Pedro Maia de Sant Ana, Ramoni Adeogun, Alexander Artemenko, Gilberto Berardinelli
Date: 2025-03-04 00:00:00

Subnetworks are expected to enhance wireless pervasiveness for critical applications such as wireless control of plants, however, they are interference-limited due to their extreme density. This paper proposes a goal-oriented joint power and multiple sub-bands allocation policy for interference coordination in 6G in-factory subnetworks. Current methods for interference coordination in subnetworks only focus on optimizing communication metrics, such as the block error rate, without considering the goal of the controlled plants. This oversight often leads to inefficient allocation of the limited radio resources. To address this, we devise a novel decentralized inter-subnetwork interference coordination policy optimized using a Bayesian framework to ensure the long-term stability of the subnetwork-controlled plants. Our results show that the proposed decentralized method can support more than twice the density of subnetwork-controlled plants compared to centralized schemes that aim to minimize the block error rate while reducing execution complexity significantly.
Linear Convergence of Distributed Compressed Optimization with Equality Constraints
Authors: Zihao Ren, Lei Wang, Zhengguang Wu, Guodong Shi
Date: 2025-03-04 00:00:00

In this paper, the distributed strongly convex optimization problem is studied with spatio-temporal compressed communication and equality constraints. For the case where each agent holds an distributed local equality constraint, a distributed saddle-point algorithm is proposed by employing distributed filters to derive errors of the transmitted states for spatio-temporal compression purposes. It is shown that the resulting distributed compressed algorithm achieves linear convergence. Furthermore, the algorithm is generalized to the case where each agent holds a portion of the global equality constraint, i.e., the constraints across agents are coupled. By introducing an additional design freedom, the global equality constraint is shown to be equivalent to the one where each agent holds an equality constraint, for which the proposed distributed compressed saddle-point algorithm can be adapted to achieve linear convergence. Numerical simulations are adopted to validate the effectiveness of the proposed algorithms.
Optimizing Parameter Estimation for Electrochemical Battery Model: A Comparative Analysis of Operating Profiles on Computational Efficiency and Accuracy
Authors: Feng Guo, Luis D. Couto, Khiem Trad, Grietus Mulder, Keivan Haghverdi, Guillaume Thenaisie
Date: 2025-03-01 00:00:00
Tasks: Computational Efficiency, parameter estimation

Parameter estimation in electrochemical models remains a significant challenge in their application. This study investigates the impact of different operating profiles on electrochemical model parameter estimation to identify the optimal conditions. In particular, the present study is focused on Nickel Manganese Cobalt Oxide(NMC) lithium-ion batteries. Based on five fundamental current profiles (C/5, C/2, 1C, Pulse, DST), 31 combinations of conditions were generated and used for parameter estimation and validation, resulting in 961 evaluation outcomes. The Particle Swarm Optimization is employed for parameter identification in electrochemical models, specifically using the Single Particle Model (SPM). The analysis considered three dimensions: model voltage output error, parameter estimation error, and time cost. Results show that using all five profiles (C/5, C/2, 1C, Pulse, DST) minimizes voltage output error, while {C/5, C/2, Pulse, DST} minimizes parameter estimation error. The shortest time cost is achieved with {1C}. When considering both model voltage output and parameter errors, {C/5, C/2, 1C, DST} is optimal. For minimizing model voltage output error and time cost, {C/2, 1C} is best, while {1C} is ideal for parameter error and time cost. The comprehensive optimal condition is {C/5, C/2, 1C, DST}. These findings provide guidance for selecting current conditions tailored to specific needs.
Multi-Partite Output Regulation of Multi-Agent Systems
Authors: Kürşad Metehan Gül, Selahattin Burak Sarsilmaz
Date: 2025-03-04 00:00:00

This article proposes a simple, graph-independent perspective on partitioning the node set of a graph and provides multi-agent systems (MASs) with objectives beyond cooperation and bipartition. Specifically, we first introduce the notion of $k$-partition transformation to achieve any desired partition of the nodes. Then, we use this notion to formulate the multi-partite output regulation problem (MORP) of heterogeneous linear MASs, which comprises the existing cooperative output regulation problem (CORP) and bipartite output regulation problem (BORP) as subcases. The goal of the MORP is to design a distributed control law such that each follower that belongs to the same set in the partition asymptotically tracks a scalar multiple of the reference while ensuring the internal stability of the closed-loop system. It is shown that the necessary and sufficient conditions for the solvability of the MORP with a feedforward-based distributed control law follow from the CORP and lead to the first design strategy for the control parameters. However, it has a drawback in terms of scalability due to a partition-dependent condition. We prove that this condition is implied by its partition-independent version under a mild structural condition. This implication yields the second design strategy that is much more scalable than the first one. Finally, an experiment is conducted to demonstrate the MORP's flexibility, and two numerical examples are provided to illustrate its generality and compare both design strategies regarding scalability.
Optimal Control for Remote Patient Monitoring with Multidimensional Health States
Authors: Siddharth Chandak, Isha Thapa, Nicholas Bambos, David Scheinker
Date: 2025-03-04 00:00:00

Selecting the right monitoring level in Remote Patient Monitoring (RPM) systems for e-healthcare is crucial for balancing patient outcomes, various resources, and patient's quality of life. A prior work has used one-dimensional health representations, but patient health is inherently multidimensional and typically consists of many measurable physiological factors. In this paper, we introduce a multidimensional health state model within the RPM framework and use dynamic programming to study optimal monitoring strategies. Our analysis reveals that the optimal control is characterized by switching curves (for two-dimensional health states) or switching hyper-surfaces (in general): patients switch to intensive monitoring when health measurements cross a specific multidimensional surface. We further study how the optimal switching curve varies for different medical conditions and model parameters. This finding of the optimal control structure provides actionable insights for clinicians and aids in resource planning. The tunable modeling framework enhances the applicability and effectiveness of RPM services across various medical conditions.
Rethinking Static Line Rating for Economic and Efficient Power Operation in South Korea
Authors: Junseon Park, Junhyun Lee, Hyeongon Park
Date: 2025-03-04 00:00:00

In South Korea, power grid is currently operated based on the static line rating (SLR) method, where the transmission line capacity is determined based on extreme weather conditions. However, with global warming, there is a concern that the temperatures during summer may exceed the SLR criteria, posing safety risks. On the other hand, the conservative estimates used for winter conditions limit the utilization of renewable energy. Proposals to install new lines face significant financial and environmental hurdles, complicating efforts to adapt to these changing conditions. Dynamic Line Rating (DLR) offers a real-time solution but requires extensive weather monitoring and complex integration. This paper proposes a novel method that improves on SLR by analyzing historical data to refine line rating criteria on a monthly, seasonal, and semi-annual basis. Through simulations, we show our approach significantly enhances cost effectiveness and reliability of the power system, achieving efficiencies close to DLR with existing infrastructure. This method offers a practical alternative to overcome the limitations of SLR and the implementation challenges of DLR.