pwc-archive/papers-with-abstracts huggingface dataset:
High-Spatial Resolution Transmission and Storage Expansion Planning for High Renewable Grids: A Case Study
Authors: Kevin Wu, Rabab Haider, Pascal Van Hentenryck
Date: 2024-12-05 00:00:00

Transmission Expansion Planning (TEP) is the process of optimizing the development and upgrade of the power grid to ensure reliable, efficient, and cost-effective electricity delivery while addressing grid constraints. To support growing demand and renewable energy integration, energy storage is emerging as a pivotal asset that provides temporal flexibility and alleviates congestion. This paper presents a TEP model that incorporates the sizing and siting of short-duration storage. With a focus on high spatial resolution, the model is applied to a 2,000-bus synthetic Texas power system, offering detailed insights into geographic investment and operational patterns. To maintain computational feasibility, a simple yet effective storage candidates (SC) method is introduced, significantly reducing the search space. Results highlight that transmission investments are primarily driven by renewable energy expansion, while storage investments are shaped by renewable curtailment and load-shedding events, with their primary function being peak load shaving. The findings underscore the importance of co-optimizing transmission and storage to minimize costs and enhance grid reliability. However, limitations in the ability of the SC method to identify optimal storage locations to meet long-term needs suggest opportunities for future research, including dynamic candidate selection and hybrid optimization techniques.
CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor
Authors: Taehyeun Kim, Anouck Girard, Ilya Kolmanovsky
Date: 2024-12-04 00:00:00
Tasks: Kolmogorov-Arnold Networks

The paper considers a Constrained-Informed Neural Network (CINN) approximation for the Time Shift Governor (TSG), which is an add-on scheme to the nominal closed-loop system used to enforce constraints by time-shifting the reference trajectory in spacecraft rendezvous applications. We incorporate Kolmogorov-Arnold Networks (KANs), an emerging architecture in the AI community, as a fundamental component of CINN and propose a Constrained-Informed Kolmogorov-Arnold Network (CIKAN)-based approximation for TSG. We demonstrate the effectiveness of the CIKAN-based TSG through simulations of constrained spacecraft rendezvous missions on highly elliptic orbits and present comparisons between CIKANs, MLP-based CINNs, and the conventional TSG.
Rectified Control Barrier Functions for High-Order Safety Constraints
Authors: Pio Ong, Max H. Cohen, Tamas G. Molnar, Aaron D. Ames
Date: 2024-12-04 00:00:00

This paper presents a novel approach for synthesizing control barrier functions (CBFs) from high relative degree safety constraints: Rectified CBFs (ReCBFs). We begin by discussing the limitations of existing High-Order CBF approaches and how these can be overcome by incorporating an activation function into the CBF construction. We then provide a comparative analysis of our approach with related methods, such as CBF backstepping. Our results are presented first for safety constraints with relative degree two, then for mixed-input relative degree constraints, and finally for higher relative degrees. The theoretical developments are illustrated through simple running examples and an aircraft control problem.
Asymptotics of Linear Regression with Linearly Dependent Data
Authors: Behrad Moniri, Hamed Hassani
Date: 2024-12-04 00:00:00
Tasks: regression

In this paper we study the asymptotics of linear regression in settings with non-Gaussian covariates where the covariates exhibit a linear dependency structure, departing from the standard assumption of independence. We model the covariates using stochastic processes with spatio-temporal covariance and analyze the performance of ridge regression in the high-dimensional proportional regime, where the number of samples and feature dimensions grow proportionally. A Gaussian universality theorem is proven, demonstrating that the asymptotics are invariant under replacing the non-Gaussian covariates with Gaussian vectors preserving mean and covariance, for which tools from random matrix theory can be used to derive precise characterizations of the estimation error. The estimation error is characterized by a fixed-point equation involving the spectral properties of the spatio-temporal covariance matrices, enabling efficient computation. We then study optimal regularization, overparameterization, and the double descent phenomenon in the context of dependent data. Simulations validate our theoretical predictions, shedding light on how dependencies influence estimation error and the choice of regularization parameters.
Data-Driven Approach for Accelerating Selective Harmonic Elimination Algorithm in Parallel Power Converters
Authors: E. Karimi, S. Shahnooshi, E. Meshkati, T. Dragičević, F. Blaabjerg
Date: 2024-12-04 00:00:00

Current ripple minimization is one of the challenges in parallel converters to increase the capacitor lifetime in various applications. In this paper, a deep neural network-based phase-shifting (PS) technique is proposed for parallel-connected buck converters to minimize the amplitude of a selective harmonic component and facilitate a classic optimum PS at the same time. The proposed method identifies the global optimum point in real time, without the need for complicated computations. The common-link current, common-link voltage, and the duty ratios are selected as the inputs of the neural network to provide the proper phase shifts for the switching signals. To accumulate the required dataset, a Different Start-Same Step (DSSS) technique is also introduced to generate the training data and test/validation data in a separate way. The effect of the number of hidden layers on the network output error is investigated, and a proper number of hidden layers is designed based on a compromise between accuracy and computation efficiency (and execution time). Experimental results prove that the proposed artificial neural network-based PS method preserves the performance of classic optimum PS and minimizing the implementation time significantly.
A Proportional-Integral Model for Fractional Voltage Tripping of Distributed Energy Resources
Authors: Milos Katanic, Gregor Verbic, John Lygeros, Gabriela Hug
Date: 2024-12-04 00:00:00

In regions with high shares of distributed energy resources (DERs), massive disconnection of small-scale DERs in low-voltage distribution grids during disturbances poses a serious threat to power system security. However, modeling this effect in a computationally efficient way remains challenging. This paper proposes a novel proportional-integral aggregate model for predicting the fraction of tripped DERs based on the voltage at the substation connection point. The model effectively captures the cumulative behavior of the system, is simple to implement, and includes seven parameters for undervoltage tripping and seven for overvoltage tripping behavior, each with a distinct physical meaning. We further propose an optimization-based approach to tune the model parameters. Simulation results show significantly more accurate predictions compared to the DER\_A model -- a standard dynamic model for aggregate DER behavior -- even when the latter is optimized, with only a minor increase in model complexity.
Robust Model Predictive Control for Constrained Uncertain Systems Based on Concentric Container and Varying Tube
Authors: Shibo Han, Yuhao Zhang, Xiaotong Shi, Xingwei Zhao
Date: 2024-12-04 00:00:00
Tasks: Model Predictive Control

This paper proposes a novel robust model predictive control (RMPC) method for the stabilization of constrained systems subject to additive disturbance (AD) and multiplicative disturbance (MD). Concentric containers are introduced to facilitate the characterization of MD, and varying tubes are constructed to bound reachable states. By restricting states and the corresponding inputs in containers with free sizes and a fixed shape, feasible MDs, which are the products of model uncertainty with states and inputs, are restricted into polytopes with free sizes. Then, tubes with different centers and shapes are constructed based on the nominal dynamics and the knowledge of AD and MD. The free sizes of containers allow for a more accurate characterization of MD, while the fixed shape reduces online computational burden, making the proposed method less conservative and computationally efficient. Moreover, the shape of containers is optimized to further reduce conservativeness. Compared to the RMPC method using homothetic tubes, the proposed method has a larger region of attraction while involving fewer decision variables and constraints in the online optimization problem.
The effects of four-wheel steering on the path-tracking control of automated vehicles
Authors: Sungjin Lim, Illés Vörös, Yongseob Lim, Gábor Orosz
Date: 2024-12-04 00:00:00

In this study, we analyze the stability of a path-tracking controller designed for a four-wheel steering vehicle, incorporating the effects of the reference path curvature. By employing a simplified kinematic model of the vehicle with steerable front and rear wheels, we derive analytical expressions for the stability regions and optimal control gains specific to different four-wheel steering strategies. To simplify our calculations, we keep the rear steering angle $\delta_r$ proportional to the front steering angle $\delta_f$ by using the constant parameter $a$, i.e., $\delta_r = a\delta_f$, where $\delta_f$ is calculated from a control law having both feedforward and feedback terms. Our findings, supported by stability charts and numerical simulations, indicate that for high velocities and paths of small curvatures, the appropriately tuned four-wheel steering controller significantly reduces lateral acceleration and enhances path-tracking performance when compared to using only front-wheel steering. Furthermore, for low velocities and large curvatures, the using negative a values (i.e., steering the rear wheels in the opposite direction than the front wheels) allows for a reduced turning radius, increasing the vehicle's capability to perform sharp turns in confined spaces like in parking lots or on narrow roads.
Modularized Neural Network Incorporating Physical Priors for Smart Building Control, Accuracy or Consistency?
Authors: Zixin Jiang, Bing Dong
Date: 2024-12-04 00:00:00
Tasks: Model Predictive Control

Model predictive control can achieve significant energy savings, offer grid flexibility, and mitigate carbon emissions. However, the challenge of identifying individual control-oriented building dynamic models limits large-scale real-world applications. To address this issue, this study proposed a Modularized Neural Network Incorporating Physical Priors (ModNN), capable of establishing a control-oriented and physical-consistent building dynamic model within minutes without substantial modeling effort. This is also the first study to evaluate the physical consistency of a given data-driven model both qualitatively and quantitively. We compared the physical consistency of a classical Long Short-Term Memory (LSTM) model and our ModNN. The ModNN strictly satisfies physical constraints, whereas the LSTM model learned contradictory system dynamics. Additionally, we compared their control performance on an EnergyPlus virtual testbed. While the LSTM model demonstrated slightly better prediction accuracy in dynamic modeling, it failed in control optimization, resulting in an 89{\deg}C-h temperature violation, whereas the ModNN showed only a 0.57{\deg}C-h violation and achieved up to a 78% peak load reduction. Our findings highlight the importance of incorporating physics priors into data-driven models and provide a promising solution for future smart building control optimization. Furthermore, the proposed evaluation framework defines two physical consistency indicators, providing guidelines for selecting and testing control-oriented, data-driven building dynamic models.
Nutzung von Massespeichern zur Flexibilisierung des Energieverbrauchs: Kosteneffizienter Anlagenbetrieb durch Anpassung an Marktpreise
Authors: Lukas Peter Wagner, Lasse Matthias Reinpold, Maximilian Kilthau, Felix Gehlhoff, Christian Derksen, Nils Loose, Julian Jepsen, Alexander Fay
Date: 2024-11-27 00:00:00

The increasing share of renewable energy sources and necessitate new concepts for energy flexible operation of industrial production resources. In this paper, we demonstrate the potential of mass storage to increase energy flexibility in industrial operations through the application of optimized operational planning based on market prices. A wastewater treatment plant equipped with decanters and storages is examined to optimally utilize its energy-flexibility. An MILP model was created for energy-flexible production facilities. The resulting operation plan was transmitted to the plant operators and executed over a period of more than 24 hours. The model shows good agreement with real measurements (average errors between 3% to 7%). The results demonstrate significant potential for cost savings of roughly 56% for the investigated time horizon. -- Der zunehmende Anteil erneuerbarer Energien erfordert neue Konzepte zur energieflexiblen Steuerung industrieller Produktionsanlagen. In diesem Beitrag wird demonstriert, wie das Potenzial von Massespeichern zur Steigerung der Energieflexibilit\"at in industriellen Prozessen durch die Anwendung optimierter Betriebsplanung basierend auf Marktpreisen, genutzt werden kann. Es wird eine Abwasseraufbereitungsanlage mit Dekantern und zugeh\"origen Massespeichersystemen betrachtet, um deren Energieflexibilit\"at optimiert zu nutzen. Der resultierende Betriebsplan wurde in einem Assistenzsystem an die Anlagenbetreiber \"ubermittelt und \"uber einen Zeitraum von \"uber 24 Stunden ausgef\"uhrt. Das Modell zeigt eine gute \"Ubereinstimmung mit den realen Messwerten (Fehlern zwischen 3% und 7%). Alle von den Betreibern gesetzten Betriebsziele wurden erreicht, was die Anwendbarkeit des Modells in industriellen Umgebungen best\"atigt. Die Ergebnisse zeigen ein erhebliches Einsparpotenzial von etwa 56% f\"ur den untersuchten Zeitraum.
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
Authors: Haoyang Li, Marko Stamenkovic, Alexander Shmakov, Michael Fenton, Darius Shih-Chieh Chao, Kaitlyn Maiya White, Caden Mikkelsen, Jovan Mitic, Cristina Mantilla Suarez, Melissa Quinnan, Greg Landsberg, Harvey Newman, Pierre Baldi, Daniel Whiteson, Javier Duarte
Date: 2024-12-05 00:00:00

The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to beyond the standard model effects that can enhance production at large transverse momentum $p_{\mathrm{T}}$. The largest event fraction arises from the fully hadronic final state in which every Higgs boson decays to a bottom quark-antiquark pair ($b\bar{b}$). This introduces a combinatorial challenge known as the \emph{jet assignment problem}: assigning jets to sets representing Higgs boson candidates. Symmetry-preserving attention networks (SPA-Nets) have been been developed to address this challenge. However, the complexity of jet assignment increases when simultaneously considering both $H\rightarrow b\bar{b}$ reconstruction possibilities, i.e., two "resolved" small-radius jets each containing a shower initiated by a $b$-quark or one "boosted" large-radius jet containing a merged shower initiated by a $b\bar{b}$ pair. The latter improves the reconstruction efficiency at high $p_{\mathrm{T}}$. In this work, we introduce a generalization to the SPA-Net approach to simultaneously consider both boosted and resolved reconstruction possibilities and unambiguously interpret an event as "fully resolved'', "fully boosted", or in between. We report the performance of baseline methods, the original SPA-Net approach, and our generalized version on nonresonant $HH$ and $HHH$ production at the LHC. Considering both boosted and resolved topologies, our SPA-Net approach increases the Higgs boson reconstruction purity by 57--62\% and the efficiency by 23--38\% compared to the baseline method depending on the final state.
Interpreting Transformers for Jet Tagging
Authors: Aaron Wang, Abhijith Gandrakota, Jennifer Ngadiuba, Vivekanand Sahu, Priyansh Bhatnagar, Elham E Khoda, Javier Duarte
Date: 2024-12-04 00:00:00
Tasks: Jet Tagging

Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging tasks, which are critical for identifying particles resulting from proton collisions. This study focuses on interpreting ParT by analyzing attention heat maps and particle-pair correlations on the $\eta$-$\phi$ plane, revealing a binary attention pattern where each particle attends to at most one other particle. At the same time, we observe that ParT shows varying focus on important particles and subjets depending on decay, indicating that the model learns traditional jet substructure observables. These insights enhance our understanding of the model's internal workings and learning process, offering potential avenues for improving the efficiency of transformer architectures in future high-energy physics applications.
Online Physics-Informed Dynamic Mode Decomposition: Theory and Applications
Authors: Biqi Chen, Ying Wang
Date: 2024-12-04 00:00:00
Tasks: Computational Efficiency

Dynamic Mode Decomposition (DMD) has received increasing research attention due to its capability to analyze and model complex dynamical systems. However, it faces challenges in computational efficiency, noise sensitivity, and difficulty adhering to physical laws, which negatively affect its performance. Addressing these issues, we present Online Physics-informed DMD (OPIDMD), a novel adaptation of DMD into a convex optimization framework. This approach not only ensures convergence to a unique global optimum, but also enhances the efficiency and accuracy of modeling dynamical systems in an online setting. Leveraging the Bayesian DMD framework, we propose a probabilistic interpretation of Physics-informed DMD (piDMD), examining the impact of physical constraints on the DMD linear operator. Further, we implement online proximal gradient descent and formulate specific algorithms to tackle problems with different physical constraints, enabling real-time solutions across various scenarios. Compared with existing algorithms such as Exact DMD, Online DMD, and piDMD, OPIDMD achieves the best prediction performance in short-term forecasting, e.g. an $R^2$ value of 0.991 for noisy Lorenz system. The proposed method employs a time-varying linear operator, offering a promising solution for the real-time simulation and control of complex dynamical systems.
Samudra: An AI Global Ocean Emulator for Climate
Authors: Surya Dheeshjith, Adam Subel, Alistair Adcroft, Julius Busecke, Carlos Fernandez-Granda, Shubham Gupta, Laure Zanna
Date: 2024-12-05 00:00:00

AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi-depth levels of ocean data. We show that the ocean emulator - Samudra - which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work.
A Hybrid Deep-Learning Model for El Niño Southern Oscillation in the Low-Data Regime
Authors: Jakob Schloer, Matthew Newman, Jannik Thuemmel, Antonietta Capotondi, Bedartha Goswami
Date: 2024-12-04 00:00:00
Tasks: Deep Learning

While deep-learning models have demonstrated skillful El Ni\~no Southern Oscillation (ENSO) forecasts up to one year in advance, they are predominantly trained on climate model simulations that provide thousands of years of training data at the expense of introducing climate model biases. Simpler Linear Inverse Models (LIMs) trained on the much shorter observational record also make skillful ENSO predictions but do not capture predictable nonlinear processes. This motivates a hybrid approach, combining the LIMs modest data needs with a deep-learning non-Markovian correction of the LIM. For O(100 yr) datasets, our resulting Hybrid model is more skillful than the LIM while also exceeding the skill of a full deep-learning model. Additionally, while the most predictable ENSO events are still identified in advance by the LIM, they are better predicted by the Hybrid model, especially in the western tropical Pacific for leads beyond about 9 months, by capturing the subsequent asymmetric (warm versus cold phases) evolution of ENSO.
A large language model-type architecture for high-dimensional molecular potential energy surfaces
Authors: Xiao Zhu, Srinivasan S. Iyengar
Date: 2024-12-05 00:00:00
Tasks: Computational chemistry, Language Modeling, Language Modelling, Large Language Model

Computing high dimensional potential surfaces for molecular and materials systems is considered to be a great challenge in computational chemistry with potential impact in a range of areas including fundamental prediction of reaction rates. In this paper we design and discuss an algorithm that has similarities to large language models in generative AI and natural language processing. Specifically, we represent a molecular system as a graph which contains a set of nodes, edges, faces etc. Interactions between these sets, which represent molecular subsystems in our case, are used to construct the potential energy surface for a reasonably sized chemical system with 51 dimensions. Essentially a family of neural networks that pertain to the graph-based subsystems, get the job done for this 51 dimensional system. We then ask if this same family of lower-dimensional neural networks can be transformed to provide accurate predictions for a 186 dimensional potential surface. We find that our algorithm does provide reasonably accurate results for this larger dimensional problem with sub-kcal/mol accuracy for the higher dimensional potential surface problem.