pwc-archive/papers-with-abstracts huggingface dataset:
Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric
Authors: Hamed Haggi, James M. Fenton
Date: 2024-12-01 00:00:00
Tasks: energy management, Management

The decreasing costs of photovoltaic (PV) systems and battery storage, alongside the rapid rise of electric vehicles (EVs), present a unique opportunity to revolutionize energy use in apartment complexes. Generating electricity via PV and batteries is currently cheaper and greener than relying on grid power, which is often expensive. Yet, residents in multi-building apartment complexes typically lack access to fast EV charging infrastructure. To this end, this paper investigates the feasibility and energy management of deploying commercial PV-powered battery storage and EV fast chargers within apartment complexes in Orlando, Florida, operated by complex owners. By modeling the complex as a grid-connected microgrid, it aims to meet residents' energy needs, provide backup power during emergencies, and introduce a profitable business model for property owners. To address PV power generation uncertainty, a distributionally robust chance-constrained optimization method using the Wasserstein metric is employed, ensuring robust and reliable operation. The techno-economic analysis reveals that EVs powered by PV and batteries are more cost-effective and environmentally friendly than gasoline vehicles that EV owners can save up to 100 dollars per month by saving on fuel costs. The results also show that integrating PV and battery systems reduces operational costs, lowers emissions, increases resilience, and supports EV adoption while offering a profitable business model for property owners. These findings highlight a practical and sustainable framework for advancing clean energy use in residential complexes.
Techno-Economic Assessment of Net-Zero Energy Buildings: Financial Projections and Incentives for Achieving Energy Decarbonization Goals
Authors: Hamed Haggi, James M. Fenton
Date: 2024-12-01 00:00:00

Recent advancements, net-zero emission policies, along with declining costs of renewable energy, battery storage, and electric vehicles (EVs), are accelerating the transition toward cleaner, more resilient energy systems. This paper conducts a comprehensive techno-economic analysis of Net-Zero Energy Buildings (NZEBs) within Florida's energy transition by 2050. The analysis focuses on the financial advantages of integrating rooftop photovoltaic (PV) systems, battery storage, and EVs collectively compared to reliance on grid electricity for both existing and newly built homes in Orlando, Florida. By leveraging federal incentives like the Investment Tax Credit and considering energy efficiency improvements, residents can achieve significant savings. Simulation results show that existing homes with a 9.5 kW PV system and 42.2 kWh battery are projected to generate positive returns by 2029, while newly constructed homes meet this threshold as early as 2024. Also, rooftop solar used to charge an EV can save up to 100 dollars per month for residents compared to gasoline. Combining PV and battery storage not only lowers electric bills but also enhances grid independence and resilience against grid outages. Beyond individual savings, NZEBs contribute to grid stability by reducing electricity demand and supporting utility-scale renewable applications. These advancements lower infrastructure costs, help Florida residents and utilities align with national decarbonization goals, retain approximately $23 Billion dollars within the state and foster progress toward a sustainable, low-carbon future.
Exploiting the Benefits of P2P Energy Exchanges in Resilience Enhancement of Distribution Networks
Authors: Hamed Haggi, Wei Sun
Date: 2024-12-01 00:00:00

As the adoption of distributed energy resources grows, power systems are becoming increasingly complex and vulnerable to disruptions, such as natural disasters and cyber-physical threats. Peer-to-peer (P2P) energy markets offer a practical solution to enhance reliability and resilience during power outages while providing monetary and technical benefits to prosumers and consumers. This paper explores the advantages of P2P energy exchanges in active distribution networks using a double auction mechanism, focusing on improving system resilience during outages. Two pricing mechanisms distribution locational marginal price (DLMP) and average price mechanism are used to complement each other in facilitating efficient energy exchange. DLMP serves as a price signal that reflects network conditions and acts as an upper bound for bidding in the P2P market. Meanwhile, prosumers and consumers submit bids in the market and agree on energy transactions based on average transaction prices, ensuring fast matching and fair settlements. Simulation results indicate that during emergency operation modes, DLMP prices increase, leading to higher average transaction prices. Prosumers benefit from increased market clearing prices, while consumers experience uninterrupted electricity supply.
A Control Framework for CUBESAT Rendezvous and Proximity Operations using Electric Propulsion
Authors: Bo-Chuan Lin, Chun-Wei Kong, Simone Semeraro, Jay W. McMahon
Date: 2024-12-01 00:00:00

A control framework is presented to solve the rendezvous and proximity operations (RPO) problem of the EP-Gemini mission. In this mission, a CubeSat chaser is controlled to approach and circumnavigate the other uncooperative CubeSat target. Such a problem is challenging because the chaser operates on a single electric propulsion thruster, for which coupling between attitude control and thrust vector, and charging of the electric propulsion system must be taken into consideration. In addition, the access to relative states in real time is not achievable due to the onboard hardware constraints of the two CubeSats. The developed control framework addresses these limitations by applying four modularized maneuver blocks to correct the chaser's mean orbit elements in sequence. The control framework is based on a relative motion called safety ellipse to ensure a low collision risk. The complete EP-Gemini mission is demonstrated by the implementation of the proposed control framework in a numerical simulation that includes high order perturbations for low Earth orbit. The simulation result shows that a safety ellipse is established after a 41-day RPO maneuver, which consumes 44$\%$ of the total fuel in terms of $\Delta V$. The resulting 3-dimensional safety ellipse circumnavigates the target with an approximate dimension of 14 km $\times$ 27 km $\times$ 8 km.
Bringing Quantum Systems under Control: A Tutorial Invitation to Quantum Computing and Its Relation to Bilinear Control Systems
Authors: Julian Berberich, Robert L. Kosut, Thomas Schulte-Herbrüggen
Date: 2024-12-01 00:00:00

Quantum computing comes with the potential to push computational boundaries in various domains including, e.g., cryptography, simulation, optimization, and machine learning. Exploiting the principles of quantum mechanics, new algorithms can be developed with capabilities that are unprecedented by classical computers. However, the experimental realization of quantum devices is an active field of research with enormous open challenges, including robustness against noise and scalability. While systems and control theory plays a crucial role in tackling these challenges, the principles of quantum physics lead to a (perceived) high entry barrier for entering the field of quantum computing. This tutorial paper aims at lowering the barrier by introducing basic concepts required to understand and solve research problems in quantum systems. First, we introduce fundamentals of quantum algorithms, ranging from basic ingredients such as qubits and quantum logic gates to prominent examples and more advanced concepts, e.g., variational quantum algorithms. Next, we formalize some engineering questions for building quantum devices in the real world, which requires the careful manipulation of microscopic quantities obeying quantum effects. To this end for N-level systems, we introduce basic concepts of (bilinear) quantum systems and control theory including controllability, observability, and optimal control in a unified frame. Finally, we address the problem of noise in real-world quantum systems via robust quantum control, which relies on a set-membership uncertainty description frequently employed in control.
Two-Stage Adaptive Robust Optimization Model for Joint Unit Maintenance and Unit Commitment Considering Source-Load Uncertainty
Authors: Hongrui Lu, Yuxiong Huang, Tong He, Gengfeng Li
Date: 2024-12-01 00:00:00

Unit maintenance and unit commitment are two critical and interrelated aspects of electric power system operation, both of which face the challenge of coordinating efforts to enhance reliability and economic performance. This challenge becomes increasingly pronounced in the context of increased integration of renewable energy and flexible loads, such as wind power and electric vehicles, into the power system, where high uncertainty is prevalent. To tackle this issue, this paper develops a two-stage adaptive robust optimization model for the joint unit maintenance and unit commitment strategy. The first stage focuses on making joint decisions regarding unit maintenance and unit commitment, while the second stage addresses economic dispatch under the worst-case scenarios of wind power and load demand. Then a practical solution methodology is proposed to solve this model efficiently, which combines the inexact column-and-constraint generation algorithm with an outer approximation method. Finally, the economic viability and adaptability of the proposed method is demonstrated based on the RTS-79 test system.
Online Voltage Regulation of Distribution Systems with Disturbance-Action Controllers
Authors: Peng Zhang, Baosen Zhang
Date: 2024-12-01 00:00:00

Inverter-based distributed energy resources facilitate the advanced voltage control algorithms in the online setting with the flexibility in both active and reactive power injections. A key challenge is to continuously track the time-varying global optima with the robustness against dynamics inaccuracy and communication delay. In this paper, we introduce the disturbance-action controller by novelly formulating the voltage drop from loads as the system disturbance. The controller alternatively generates the control input and updates the parameters based on the interactions with grids. Under the linearized power flow model, we provide stability conditions of the control policy and the performance degradation to model inaccuracy. The simulation results on the radial distribution networks show the effectiveness of proposed controller under fluctuating loads and significant improvement on the robustness to these challenges. Furthermore, the ability of incorporating history information and generalization to various loads are demonstrated through extensive experiments on the parameter sensitivity.
Maintaining reliability while navigating unprecedented uncertainty: a synthesis of and guide to advances in electric sector resource adequacy
Authors: Gabriel Mantegna, Ziting Huang, Guillaume Van Caelenberg, Bethany Frew, Muireann Lynch, Mark O'Malley
Date: 2024-11-30 00:00:00
Tasks: Navigate

The reliability of the electric grid has in recent years become a larger concern for regulators, planners, and consumers due to several high-impact outage events, as well as the potential for even more impactful events in the future. These concerns are largely the result of decades-old resource adequacy (RA) planning frameworks being insufficiently adapted to the current types of uncertainty faced by planners, including many sources of deep uncertainty for which probability distributions cannot be defensibly assigned. There are emerging methodologies for dealing with these new types of uncertainty in RA assessment and procurement frameworks, but their adoption has been hindered by the lack of consistent understanding of terminology related to RA and the related concept of resilience, as well as a lack of syntheses of such available methodologies. Here we provide an overview of RA and its relationship to resilience, a summary of available methods for dealing with emerging types of uncertainty faced by RA assessment, and an an overview of procurement methodologies for operationalizing RA in the context of these types of uncertainty. This paper provides a synthesis and guide for both researchers and practitioners seeking to navigate a new, much more uncertain era of power system planning.
Enhancing the Reliability of Closed-Loop Describing Function Analysis for Reset Control Applied to Precision Motion Systems
Authors: Xinxin Zhang, S. Hassan HosseinNia
Date: 2024-11-30 00:00:00

The Sinusoidal Input Describing Function (SIDF) is an effective tool for control system analysis and design, with its reliability directly impacting the performance of the designed control systems. This study enhances the reliability of SIDF analysis and the performance of closed-loop reset feedback control systems, presenting two main contributions. First, it introduces a method to identify frequency ranges where SIDF analysis becomes inaccurate. Second, these identified ranges correlate with high-magnitude, high-order harmonics that can degrade system performance. To address this, a shaped reset control strategy is proposed, which incorporates a shaping filter to tune reset actions and reduce high-order harmonics. Then, a frequency-domain design procedure of a PID shaping filter in a reset control system is outlined as a case study. The PID filter effectively reduces high-order harmonics and resolves limit-cycle issues under step inputs. Finally, simulations and experimental results on a precision motion stage validate the efficacy of the proposed shaped reset control, showing enhanced SIDF analysis accuracy, improved steady-state precision over linear and reset controllers, and elimination of limit cycles under step inputs.
Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees
Authors: Samuel Mallick, Azita Dabiri, Bart De Schutter
Date: 2024-11-30 00:00:00
Tasks: continuous-control, Continuous Control, Model Predictive Control

Online model predictive control (MPC) for piecewise affine (PWA) systems requires the online solution to an optimization problem that implicitly optimizes over the switching sequence of PWA regions, for which the computational burden can be prohibitive. Alternatively, the computation can be moved offline using explicit MPC; however, the online memory requirements and the offline computation can then become excessive. In this work we propose a solution in between online and explicit MPC, addressing the above issues by partially dividing the computation between online and offline. To solve the underlying MPC problem, a policy, learned offline, specifies the sequence of PWA regions that the dynamics must follow, thus reducing the complexity of the remaining optimization problem that solves over only the continuous states and control inputs. We provide a condition, verifiable during learning, that guarantees feasibility of the learned policy's output, such that an optimal continuous control input can always be found online. Furthermore, a method for iteratively generating training data offline allows the feasible policy to be learned efficiently, reducing the offline computational burden. A numerical experiment demonstrates the effectiveness of the method compared to both online and explicit MPC.
Optimal Transmission Switching and Busbar Splitting in Hybrid AC/DC Grids
Authors: Giacomo Bastianel, Marta Vanin, Dirk Van Hertem, Hakan Ergun
Date: 2024-11-29 00:00:00

Driven by global climate goals, an increasing amount of Renewable Energy Sources (RES) is currently being installed worldwide. Especially in the context of offshore wind integration, hybrid AC/DC grids are considered to be the most effective technology to transmit this RES power over long distances. As hybrid AC/DC systems develop, they are expected to become increasingly complex and meshed as the current AC system. Nevertheless, there is still limited literature on how to optimize hybrid AC/DC topologies while minimizing the total power generation cost. For this reason, this paper proposes a methodology to optimize the steady-state switching states of transmission lines and busbar configurations in hybrid AC/DC grids. The proposed optimization model includes optimal transmission switching (OTS) and busbar splitting (BS), which can be applied to both AC and DC parts of hybrid AC/DC grids. To solve the problem, a scalable and exact nonlinear, non-convex model using a big M approach is formulated. In addition, convex relaxations and linear approximations of the model are tested, and their accuracy, feasibility, and optimality are analyzed. The numerical experiments show that a solution to the combined OTS/BS problem can be found in acceptable computation time and that the investigated relaxations and linearisations provide AC feasible results.
Localization Phenomena in Large-Scale Networked Systems: Robustness and Fragility of Dynamics
Authors: Poorva Shukla, Bassam Bamieh
Date: 2024-11-29 00:00:00

We study phenomena where some eigenvectors of a graph Laplacian are largely confined in small subsets of the graph. These localization phenomena are similar to those generally termed Anderson Localization in the Physics literature, and are related to the complexity of the structure of large graphs in still unexplored ways. Using spectral perturbation theory and pseudo-spectrum analysis, we explain how the presence of localized eigenvectors gives rise to fragilities (low robustness margins) to unmodeled node or link dynamics. Our analysis is demonstrated by examples of networks with relatively low complexity, but with features that appear to induce eigenvector localization. The implications of this newly-discovered fragility phenomenon are briefly discussed.
Machine Learning Methods for Automated Interstellar Object Classification with LSST
Authors: Richard Cloete, Peter Vereš, Abraham Loeb
Date: 2024-12-03 00:00:00

The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. We demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.
RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification
Authors: Daniel Kyselica, Marek Šuppa, Jiří Šilha, Roman Ďurikovič
Date: 2024-11-30 00:00:00
Tasks: Classification

Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket body classification based on light curves. The dataset, derived from the Mini Mega Tortora database, includes light curves for six rocket body classes: CZ-3B, Atlas 5 Centaur, Falcon 9, H-2A, Ariane 5, and Delta 4. With 5,676 training and 1,404 test samples, it addresses data inconsistencies using resampling, normalization, and filtering techniques. Several machine learning models were evaluated, including CNN and transformer-based approaches, with Astroconformer reporting the best performance. The dataset establishes a common benchmark for future comparisons and advancements in rocket body classification tasks.
Selfish Evolution: Making Discoveries in Extreme Label Noise with the Help of Overfitting Dynamics
Authors: Nima Sedaghat, Tanawan Chatchadanoraset, Colin Orion Chandler, Ashish Mahabal, Maryam Eslami
Date: 2024-11-26 00:00:00

Motivated by the scarcity of proper labels in an astrophysical application, we have developed a novel technique, called Selfish Evolution, which allows for the detection and correction of corrupted labels in a weakly supervised fashion. Unlike methods based on early stopping, we let the model train on the noisy dataset. Only then do we intervene and allow the model to overfit to individual samples. The ``evolution'' of the model during this process reveals patterns with enough information about the noisiness of the label, as well as its correct version. We train a secondary network on these spatiotemporal ``evolution cubes'' to correct potentially corrupted labels. We incorporate the technique in a closed-loop fashion, allowing for automatic convergence towards a mostly clean dataset, without presumptions about the state of the network in which we intervene. We evaluate on the main task of the Supernova-hunting dataset but also demonstrate efficiency on the more standard MNIST dataset.
Fluctuations and the limit of predictability in protein evolution
Authors: Saverio Rossi, Leonardo Di Bari, Martin Weigt, Francesco Zamponi
Date: 2024-12-02 00:00:00

Protein evolution involves mutations occurring across a wide range of time scales. In analogy with other disordered systems, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct sites and times. To quantify these correlations, we examine the role of various fluctuation sources in protein evolution, simulated using a data-driven epistatic landscape. By applying spatio-temporal correlation functions inspired by statistical physics, we disentangle fluctuations originating from the ancestral protein sequence from those driven by stochastic mutations along independent evolutionary paths. Our analysis shows that, in diverse protein families, fluctuations from the ancestral sequence predominate at shorter time scales. This allows us to identify a time scale over which ancestral sequence information persists, enabling its reconstruction. We link this persistence to the strength of epistatic interactions: ancestral sequences with stronger epistatic signatures impact evolutionary trajectories over extended periods. At longer time scales, however, ancestral influence fades as epistatically constrained sites evolve collectively. To confirm this idea, we apply a standard ancestral sequence reconstruction algorithm and verify that the time-dependent recovery error is influenced by the properties of the ancestor itself.